High-Performance Global Account Management Teams: Design Dimensions, Processes and Outcomes

DISSERTATION of the University of St. Gallen,

Graduate School of Business Administration, Economics, Law and Social Sciences (HSG)

to obtain the title of Doctor of Business Administration

submitted by

Yana Atanasova from

Bulgaria

Approved on the application of

Prof. Winfried Ruigrok, PhD and

Dr. Christoph Senn

Dissertation no. 3324

Gutenberg AG, Schaan 2007

The University of St. Gallen, Graduate School of Business Administration,

Economics, Law and Social Sciences (HSG) hereby consents to the printing of

the present dissertation, without hereby expressing any opinion on the views

herein expressed.

St. Gallen, May 21, 2007

The President:

Prof. Ernst Mohr, PhD

Acknowledgements

This dissertation has been a very challenging and interesting endeavor, which would have been impossible without the contributions of a number of persons. Therefore, I would like to thank all the people who supported me throughout this ambitious project. I am particularly grateful to all managers who participated in the empirical part of the study and gave me the opportunity to learn more about their companies. Sincere thanks to all interviewees who took time to discuss the topics with me as well as to all survey sponsors who showed interest in my work and helped me to make it a success – Andrew Payne from Halcrow, Heinz Felber and Michael Merz from Hilti, Richard Willems from Honeywell, Steve Richard from Marriott, Bart Logghe from Philips, and Teri Walker from Xerox. Thank you also to Elisabeth Cornell from the Strategic Account Management Association who provided me with valuable contacts and study materials. I would like to thank my supervisors Dr. Christoph Senn and Prof. Winfried Ruigrok. Throughout the last years, I had the opportunity to work on a lot of interesting projects in the area of global account management with Dr. Christoph Senn and my colleague and fellow doctoral student Axel Thoma and I am very grateful to them for the many enriching discussions. This dissertation profited greatly also from the statistical support of Dr. Jürg Schwarz from the University of Zurich whose guidance taught me a lot about analytical methods and resulted in very interesting research findings in the study. Furthermore, I benefited from the financial support of the Research Commission of the University of St. Gallen that awarded me the Central Europe Scholarship to finance the final stages of my research. Last but not least I would like to thank all my friends and loved ones. Many thanks to Yanko Djinkov, Sabina Tacheva and Mladen Rikanovic for always being there for me to offer feedback and encouragement. Warm thanks to Claudio Castagnetti for the many wonderful moments that made my “life outside the dissertation” a truly happy one. Undoubtedly, my family has been the greatest source of strength throughout my years of research and education. My parents Mariana and Cvetan Atanasovi and my brother Philip have always believed in me and have been a pillar of reassurance and comfort for me. So it is with great gratitude and love that I dedicate this work to them. Zurich, July 2007 Yana Atanasova

Abstract

Designing and implementing global account management (GAM) teams represents a

key task for suppliers that are expanding the scope of their relationships with global

customers. However, research has not provided an explanation of how these teams

function and what determines their performance. Extending concepts from several

research streams regarding teams in an organization to the complex GAM context, this

dissertation develops and empirically tests a framework of GAM team design and

performance. The results indicate that team performance is directly influenced by three

team processes: communication and collaboration, conflict management, and

proactiveness. Team design in terms of goal and role definition, customer coverage,

empowerment, heterogeneity, skills adequacy and leadership has an indirect influence

on performance, mediated by the three team processes. In addition, three factors from

the organizational environment – top management support, rewards and incentives,

and training – have similar indirect effects. From a theoretical perspective, this study

makes a contribution by examining this new and emerging organizational form and

extending existing research in the area of supplier-customer relationships and

organizational behavior. From a practical point of view, it identifies key success

factors of GAM teams, and thus, aims to help companies achieve better performance

with their strategic customers.

i

Table of Contents

List of figures................................................................................................................. iii List of tables................................................................................................................... iv List of abbreviations ....................................................................................................... v

1. Introduction............................................................................................................... 1 1.1. Background and relevance ................................................................................... 1 1.2. Objective and research questions ......................................................................... 4 1.3. Study outline ........................................................................................................ 6

2. Literature review ...................................................................................................... 8 2.1. Global account management ................................................................................ 8

2.1.2. GAM literature overview ............................................................................ 10 2.1.3. Reasons for implementing GAM ................................................................ 11 2.1.4. GAM relationship development .................................................................. 14 2.1.5. Strategy........................................................................................................ 17 2.1.6. GAM/KAM configuration........................................................................... 18 2.1.7. GAM performance....................................................................................... 26 2.1.8. Limitations................................................................................................... 31

2.2. Small groups in the organization........................................................................ 33 2.2.1. Definitions ................................................................................................... 33 2.2.2. Small group effectiveness models and frameworks .................................... 36 2.2.3. Limitations................................................................................................... 42

2.3. Cross-functional teams....................................................................................... 44 2.3.1. Functional diversity ..................................................................................... 44 2.3.2. External activity........................................................................................... 48 2.3.3. Limitations................................................................................................... 51

2.4. Sales teams ......................................................................................................... 53 2.4.1. Team selling definition................................................................................ 53 2.4.2. Functional interdependence......................................................................... 54 2.4.3. Selling center composition .......................................................................... 56 2.4.4. Roles of selling center members ................................................................. 57 2.4.5. Selling team frameworks............................................................................. 58 2.4.6. Limitations................................................................................................... 62

2.5. Summary of research gaps ................................................................................. 62 3. Conceptual framework........................................................................................... 63

3.1. Framework development.................................................................................... 63 3.1.1. Overview ..................................................................................................... 63 3.1.2. Qualitative study description....................................................................... 64 3.1.3. Summary findings from the qualitative study ............................................. 66

3.2. Conceptual framework ....................................................................................... 69 3.2.1. GAM team design........................................................................................ 70 3.2.2. Organizational context................................................................................. 83 3.2.3. Team processes............................................................................................ 89 3.2.4. GAM team performance.............................................................................. 98

ii

4. Empirical study ..................................................................................................... 101 4.1. Method ............................................................................................................. 102 4.2. Sample.............................................................................................................. 103 4.3. Questionnaire ................................................................................................... 106 4.4. Variable operationalization .............................................................................. 107

4.4.1. Dependent variables .................................................................................. 108 4.4.2. Independent variables................................................................................ 109 4.4.3. Control variables ....................................................................................... 112

4.5. Data collection procedures ............................................................................... 113 4.6. Data analysis procedures.................................................................................. 114

5. Results .................................................................................................................... 116 5.1. Sample description ........................................................................................... 116 5.2. Examination for potential biases...................................................................... 120

5.2.1. Nonresponse bias....................................................................................... 120 5.2.2. Common method bias................................................................................ 122

5.3. Examination of the variables............................................................................ 122 5.4. Reliability and item analysis ............................................................................ 124 5.5. Results of the exploratory factor analysis ........................................................ 127 5.6. Results of the confirmatory factor analysis...................................................... 134 5.7. Test of the structural path model...................................................................... 141

5.7.1. Results of the model test............................................................................ 141 5.7.2. Effect sizes................................................................................................. 145

5.8. Comparison among performance groups ......................................................... 147

6. Discussion and conclusions .................................................................................. 150 6.1 Key findings: What are the key elements of GAM team design?..................... 151 6.2 Key findings: How does GAM team design influence team performance? ..... 154 6.3 Key findings: What other factors have an influence on GAM team performance?........................................................................................................... 157 6.4 Contributions to theory ..................................................................................... 158 6.5 Managerial implications.................................................................................... 160 6.6 Limitations and suggestions for further research.............................................. 163 6.7 Concluding remarks .......................................................................................... 165

Bibliography ............................................................................................................... 166 Appendix A: Account Management Literature .......................................................... 190 Appendix B: Organizational Team Models ................................................................ 198 Appendix C: Selling Team Models ............................................................................ 201 Appendix D: List of Interviews .................................................................................. 203 Appendix E: Interview Questionnaire ........................................................................ 205 Appendix F: Survey questionnaire ............................................................................. 206 Appendix G: Means, standard deviations and correlations ........................................ 211 Appendix H: CFA results for the first-order constructs ............................................. 213 Appendix I: Full structural path model....................................................................... 215

iii

List of figures

Figure 1: Interactions of traditional selling and GAM…………………………………2

Figure 2: Study outline………………………………………..………………………..6

Figure 3: A typology of global vendor-customer relationships……………….…........13

Figure 4: Development stages in the GAM relationship……………………..……….15

Figure 5: Conceptual framework………………………………………………..…….69

Figure 6: Summary of the sequence of data analysis……………………….……….115

Figure 7: Respondents by company………………………………………….……...116

Figure 8: Respondents by country…………………………………………..……….117

Figure 9: Respondents’ position……………………………………………………..117

Figure 10: Respondents’ team tenure………………………………………..………118

Figure 11: Respondents’ organizational tenure……………………………………...119

Figure 12: Structural path model 1…………………………………………..………142

Figure 13: Structural path model 2………………………………………………..…143

iv

List of tables

Table 1: Overview of study companies………………………………………………104

Table 2: Assessment of nonresponse bias………………………………….………...121

Table 3: Variables measures and construct reliability estimates…………….……....125

Table 4: Results of exploratory factor analysis of the team design domain………....129

Table 5: Results of exploratory factor analysis of the organizational context

domain…………………………………………………………………….…………131

Table 6: Results of exploratory factor analysis of the team processes domain……...132

Table 7: Results of exploratory factor analysis of the team performance domain…..133

Table 8: Means, standard deviations and correlations of the constructs…………….134

Table 9: Results of the second-order confirmatory factor analysis……….….……...137

Table 10: Results of the discriminant validity tests………………………….……...140

Table 11: Effects of the individual dimensions on their dependent domains……….146

Table 12: Comparison among performance groups based on individual scores….....148

Table 13: Comparison among performance groups based on team scores……….....149

v

List of abbreviations

ANOVA Analysis of variance

B2B Business-to-business

CFA Confirmatory factor analysis

CFI Comparative fit index

CRM Customer relationship management

EFA Exploratory factor analysis

e.g. Exempli gratia (for example)

EMEA Europe, Middle East and Africa

etc. Et cetera (and so forth)

GAM Global account management

HR Human resources

i.e. Id est (that is)

KAM Key account management

M Mean

MNC Multinational corporation

NFI Normed fit index

P&L Profit and loss

SAMA Strategic Account Management Association

SD Standard deviation

SEM Structural equation modeling

RMR Root-mean-square residual

RMSEA Root-mean-square error of approximation

UK United Kingdom

USA United States of America

1

1. Introduction

1.1. Background and relevance

Driven by the search for both new business opportunities and competitive advantage,

companies in business-to-business (B2B) markets increasingly have moved away from

a transactional form of exchange (Dyer, 1997) to look for closer, more collaborative

relationships with their customers (Cannon and Perreault, 1999; Heide and John, 1990;

Narayandas and Rangan, 2004). A common approach to exploiting the potential of

long-term supplier–customer relationships (Anderson and Weitz, 1992) has been to

adopt various customer management techniques, such as relationship management

(Subramani and Venkatraman, 2003), relationship marketing (Grönroos, 1994;

Morgan and Hunt, 1994; Webster, 1992), and national and key account management

(McDonald et al., 1997; Shapiro and Moriarty, 1984; Weilbaker and Weeks, 1997).

However, these approaches often become more challenging as customers become

more global and powerful. With their global expansions, customers establish a direct

presence in a growing number of countries and expect the supplier to provide

consistent, coordinated services worldwide, which entails moving away from

traditional relationships with local subsidiaries toward uniform prices, terms, and

services in markets in which the supplier has no operations (Montgomery and Yip,

2000). Furthermore, these customers recognize the strategic value-adding potential of

global procurement (Cohen and Huchzermeier, 1999; Ellram and Carr, 1994) and

therefore adopt integrated, centralized purchasing practices (Olsen and Ellram, 1997;

Sheth and Sharma, 1997) and reduce their supplier base (Capon, 2001). Examples of

this practice are abundant, including IBM’s decision to replace the 40 advertising

agencies with which it had been working worldwide with a single agency that could

provide global services (Montgomery and Yip, 2000) or Motorola’s decision to reduce

its suppliers from 10,000 to 3,000 in only a few years (Senn, 1999).

The resulting shift of power to the customer further increases through industry

consolidation (Birkinshaw et al., 2001) and advances in information and

communication technologies, which enable customers to track the supplier’s quality

and prices globally (Narayandas et al., 2000). These factors have heightened the

2

challenges facing suppliers and made international approaches such as global account

management (GAM) (Birkinshaw et al., 2001; Harvey et al., 2003a; Shi et al., 2004,

2005) the new frontier in customer management (Yip and Madsen, 1996).

Defined as “an organizational form and process in multinational companies by which

the worldwide activities serving one or more multinational customers are coordinated

centrally by one person or team within the supplier company” (Montgomery and Yip,

2000: 24), GAM represents a key organizational design issue for suppliers (Homburg

et al., 2002). Unlike traditional sales and relationship marketing, GAM involves a

complex network of interactions (Figure 1) that demands coordinated cross-functional

effort, including the establishment of a customer dimension that crosses existing

product, country, or functional units and mobilizes organization-wide resources to

deliver on customer expectations (Galbraith, 2001).

Figure 1: Interactions of traditional selling and GAM

An interaction between a customer organization and traditional sales force

An interaction between a customer organization and global account management

Source: Adapted from Sharma (1997: 29).

Finance

Purchasing

Engi- neering

Sales- person

Finance

Engi- neering

Finance

Purchasing

Engi- neering

Account Manager

Finance

Engi- neering

3

In its simplest form, the customer dimension might be a specialized global account

manager who plays a pivotal boundary-spanning role in managing external and

internal relationships and coordinating dispersed value-adding activities (Millman,

1996). As the relationship with the customer develops, however, the number of

contacts between the two companies and the need for dedicated resources and

relationship-specific adaptations increases (Dwyer et al., 1987; Millman and Wilson,

1994), as does the number of people involved in the relationship. A dedicated team—

which can be composed of sales representatives from various product lines and

countries or take a cross-functional composition to include manufacturing,

distribution, finance, R&D, and other functional units—works to present a unified face

to the customer (Galbraith, 2001). Ultimately, in an overall GAM organization, all

account managers and teams integrate to reconcile the customer’s external alignment

requirements with the organization’s existing configuration of activities.

Consequently, three main levels of analysis emerge in the context of GAM

organizational solutions: the individual global account manager, the GAM team, and

the overall GAM program. Whereas prior work has addressed the role of global

account managers (Harvey et al., 2003b; Millman, 1996; Wilson and Millman, 2003)

and some structural aspects at the program level (Birkinshaw et al., 2001; Homburg et

al., 2002; Kempeners and van der Hart, 1999; Shapiro and Moriarty, 1984), no

research has considered the design and functioning of the team in detail. As a result, a

lack of understanding remains about when teams should be formed, how they should

be structured and managed, and, most important, what determines their performance.

Following the call of Workman et al. (2003) for more research that examines how

team types influence effectiveness, this dissertation develops and tests a framework of

GAM team design and performance that draws on relevant literature from the fields of

GAM and team selling (Moon and Armstrong, 1994; Moon and Gupta, 1997; Smith

and Barclay, 1993), as well as organizational behavior studies of small groups within

the organization (e.g., Ancona and Caldwell, 1992a; Cohen and Bailey, 1997;

Gladstein, 1984).

4

1.2. Objective and research questions

This research aims to fill the gap in the existing literature with regard to GAM team

characteristics and their effects on performance. Its main objective is to contribute by

identifying the key determinants of GAM performance and integrating them in a

comprehensive framework of GAM team dimensions, processes, and outcomes.

Toward that end, it answers a general question—what determines team

performance?—by breaking it down into three more specific research questions:

Q1: What are the key elements of GAM team design?

This question helps to conceptualize and delineate the specific aspects of team

structure and composition that have impacts on team performance.

Q2: How does GAM team design affect GAM team performance?

Furthermore, the study seeks to assess the relationship between team design and

performance and investigate the mechanisms through which they may be linked. The

literature suggests that this link rarely is straightforward and may be mediated by team

members’ interactions and behaviors (Lawrence, 1997). Therefore, this dissertation

aims to identify key team processes that may mediate the relationship or have their

own influences on team outcomes.

Q3: What other factors have an influence on GAM team performance?

Finally, this third question investigates whether additional factors in the team

environment, external to the team structure, composition, and processes, may influence

team performance.

By answering these questions, this study offers several theoretical and practical

contributions. From a theoretical perspective, the dissertation extends previous

contributions in the field of global and key account management and the more general

area of customer management by narrowing the traditional focus on the overall GAM

approach to concentrate on one of its less researched building blocks. Moreover, it

complements prior suggestions regarding the key decisions involved in designing an

account team (Kempeners and van der Hart, 1999) by proposing additional design

dimensions and linking them to outcomes. Beyond its GAM implications, this study

5

contributes to organizational behavior research by extending key concepts from

research on small groups (Gladstein, 1984; Hackman, 1987) and cross-functional

teams (Ancona and Caldwell, 1992a, b; Denison et al., 1996) to the more complex,

boundary-transcending context of GAM. From this perspective, the novelty of this

study lies in its focus on teams with higher degrees of complexity, characterized by

blurred boundaries and multifaceted vertical and horizontal involvements.

From a practical point of view, this research helps managers to better understand the

drivers of their GAM team performance. By identifying key elements of the GAM

team structure, composition, and organizational environment that uniquely

characterize high-performance teams, the study highlights critical success factors of

effective, collaborative teamwork and provides managers with ideas about how to

improve their overall performance with global customers. Best practice examples

obtained in interviews with GAM experts from leading multinational companies

further strengthen these contributions.

6

1.3. Study outline

The study is organized into six chapters which follow the logical flow of the main

research steps. Figure 2 presents the organization of the study.

Figure 2: Study outline

Chapter 1 introduces the research context, elaborates on the relevance of the topic, and

outlines the three research questions that guide this dissertation. Chapter 2 establishes

the theoretical foundations of the study through a review of extant literature in four

relevant research streams: global and key account management, small groups in the

organization, cross-functional teams, and selling teams. In a first step, Chapter 3

describes the qualitative study and explains how it helped to construct the conceptual

framework, and then in a second step, consolidates the findings from the literature

review and the exploratory interviews into an integrated framework that defines the

constructs and leads to specific hypotheses. Chapter 4 describes the empirical study by

Introduction

Literature review Qualitative study

Conceptual framework

Empirical study

Analysis and results

Discussion and conclusions

1

2

3.2

4

5

6

3.1

7

outlining the sample, questionnaire, measures, and procedures employed in the data

collection and analysis. Chapter 5 provides the results of the quantitative analysis and

elaborates on the chosen analytical approach and methods. Finally, Chapter 6

discusses the implications of the empirical results for both theory and practice. The

dissertation concludes with a discussion of the limitations of this study and

suggestions for further research.

8

2. Literature review

This chapter reviews relevant literature in several research streams. The resultant

detailed discussion of key constructs, models, and frameworks helps to indicate

implications for this study. In addition, in this chapter, I outline the limitations of each

literature stream for the study of GAM teams.

2.1. Global account management

2.1.1. Definitions Theory and practice use different terms to describe managing global customers: global

account management, international account management, parent account management,

and so forth. However, they usually refer to the same concept. The most commonly

cited definition of global account management, which is also employed in this study,

is “an organizational form and process in multinational companies by which the

worldwide activities serving a given multinational customer are coordinated centrally

by one person or team within the supplying company” (Montgomery and Yip, 2000:

2).

This definition can be extended to include the element of long-term relationship

building, as identified by Kempeners and van den Hart (1999: 311): “Account

management is the process of building and maintaining relationships over an extended

period, which cuts across multiple levels, functions, and operating units in both the

selling organization and in carefully selected customers (accounts) that contribute to

the company’s objectives now or in the future.” Adding a dependency dimension from

the relational contracting theory, Harvey et al. (2003b: 564) define GAM as “a

dependency agreement between the customer and supplying organizations (or their

parts) that are interrelated through both formal and informal ties at multiple levels

across national borders.” Finally, recognizing the role of collaboration, Shi et al.

(2004: 539) provide the following definition: “GAM is a collaborative process

between a multinational customer and a multinational supplier by which the

9

worldwide buying-selling activities are centrally coordinated between the two

organizations.”

A closely related term is key account management (KAM), defined by McDonald et al.

(1997: 737) as “the approach adopted by selling companies aimed at building a

portfolio of loyal key accounts by offering them, on a continuing basis, a

product/service package tailored to their individual needs.” The literature on KAM

developed prior to that on GAM and usually refers to managing strategic accounts

with a narrower geographic scope (also referred to as national account management).

However, more recent studies (e.g., Homburg et al., 2002) use the term KAM to

incorporate all approaches to managing the most important customers described by

different terms such as key account selling, national account management, strategic

account management, major account management, and global account management.

Furthermore, the term “national account management” has become limiting because

companies with important customers increasingly conduct business internationally

(Colletti and Tubridy, 1987). Consequently, this study focuses on the more global side

of customer management and uses the term GAM.

Global key accounts are those multinational customers with growing expectations of

being supplied and serviced worldwide in a consistent and coordinated way (Millman,

1996). Wilson and Croom (1999: 26) define global strategic accounts as those that

“coordinate and integrate their operations internationally, are of major strategic

importance to the supplier and demand a coordinated account management process

worldwide.” This definition matches Wilson et al.’s (2000), who state that

geographical reach itself does not constitute a global account. Three additional criteria

must also be fulfilled: strategic importance, demand for and acceptance of global

solutions, and a centrally coordinated purchasing process.

Academic literature does not provide a precise definition of global account

management teams. Through deduction, however, such a team comprises all the

persons involved in developing and maintaining relationships with one or several

related key customers on a global basis; its responsibilities include developing a

10

customer strategy and account plan, creating innovative solutions, and coordinating

various networks (Galbraith, 2001). Its distinguishing characteristics include:

– Geographic dispersion and multinational composition (Montgomery and Yip,

2000).

– Cross-functional involvement (Galbraith, 2001).

– The involvement of several hierarchical levels (Harvey et al., 2003b).

– Full- and part-time membership (Kempeners and van den Hart, 1999).

– Parallel existence with other organizational forms (Arnold et al., 1999).

– A boundary-spanning role (Harvey et al., 2003a).

– A continuous, long-term task (Wilson et al., 2000).

2.1.2. GAM literature overview

A summary overview of the existing literature in the fields of GAM and KAM,

including major findings and contributions, appears in Appendix A. A brief analysis of

the reviewed studies indicates that the majority of research is descriptive and

conceptual, which demonstrates that it remains in an early stage of development

(Arnold et al. 2001; Birkinshaw and Terjesen 2002; Harvey et al. 2003a, b; Millman

1996, 1999; Wilson 1999; Yip and Madsen 1996). Most studies are based on

interviews or surveys in a relatively small number of firms (Birkinshaw et al. 1999;

Dishman and Nitse, 1998; Narayandas et al. 2000; Senn and Arnold 1999; Shi et al.,

2004, 2005; Stevenson and Page, 1979). This type of survey research thus has resulted

in descriptive data about the concept, the situations in which it might be used, and

some aspects of GAM/KAM programs.

Some more recent studies attempt to overcome these limitations by conducting surveys

of a greater number of companies, testing a more extensive set of hypotheses, and

embarking on more advanced theoretical development (Birkinshaw et al., 2001;

Homburg et al., 2002; Montgomery et al., 1998a, b, 2001, 2002; Senn, 1999;

Workman et al., 2003).

11

The next sections focus on the major aspects of GAM that have been discussed in the

literature to provide a broad overview of the recurring topics and significant findings,

as well as elaborate on the research gaps addressed in this dissertation. The groups of

discussed topics follow the logical sequence of events, starting with reasons to

establish a GAM program, the major elements of a GAM program, GAM

implementation, and performance.

2.1.3. Reasons for implementing GAM

The most frequently discussed dimension of the collaboration between suppliers and

global customers pertains to the reasons and general internal and external conditions

for entering such a relationship.

On the customer side, one of the major driving forces for GAM has been supply chain

management practices. Economies of scale in purchasing offer good opportunities for

cost savings and have been growing due to the increased internationalization, merger

and acquisition activity, and communications advances (Wilson et al., 2000). Evidence

shows that many corporations perceive supply chain management as being of high

strategic importance and a source of competitive advantage (Cohen and Huchzermeier,

1999). The increasingly centralized purchasing functions of the buyer thus start posing

demands for the aggregation of the supplier’s selling operations.

That is, both the customer and globalization drivers in the supplier’s industry have

been major triggers of GAM implementation. Yip and Madsen (1996) outline the

underlying market, cost, and other industry conditions that create the potential for a

worldwide business to achieve the benefits of GAM relationship, among which they

rank the global client as the first and most important. However, global/regional

channels of distribution and middlemen can also force suppliers to rationalize their

international pricing and terms of trade because they have the capability to exploit

differences in pricing through arbitrage. In addition, multinational corporations

(MNCs) may be encouraged to adopt GAM practices to benefit from transferable

marketing elements, such as brand names and advertising, which require only minimal

adaptation and simultaneously facilitate customer relationship management on an

12

international basis. Moreover, GAM can be initiated as a tool for knowledge creation

through access to so-called “lead countries,” where innovation in products or processes

is concentrated. For example, most global accounts of high-tech companies, such as

Hewlett-Packard, are located in the United States, Japan, and a few Western European

countries whose use of IT is greatest. Other factors for the development of GAM

include high product development costs, fast changing technology, and global

competitors.

These arguments are in line with and can be supported by the theory of

internationalization of the firm, which focuses on the industry or nature of the business

as the key determinants of globalization potential. The industry globalization

conditions can be summarized as market, cost, government, and competition (Yip,

1989). In terms of GAM, market globalization factors refer to the possibility of

offering global products, which would encourage the customer to buy on a global basis

and therefore demand standardized pricing, service, terms, and conditions. Cost

globalization drivers are associated with the possibility of exploiting scale economies

and the extent to which global shipment of the product becomes possible through

logistics and other factors. Thus, a company that can benefit from scale-related cost

reductions in product development would be willing to integrate development

activities for a given international customer. The extent to which a supplier can serve

customers on a global basis also depends on existing regulations imposed by

governments, such as tariffs and other barriers, investment restrictions, and so forth.

Finally, the competitive drivers with regard to GAM mainly refer to transferable

competitive advantages. Customers in industries with transferable advantages, such as

technology in the IT industry, are more likely to employ global procurement practices.

Although customer demands are a strong driver for suppliers to provide GAM

services, many authors emphasize the potential benefit of being proactive in

establishing GAM relationships and its positive impact on the balance of power

(Arnold et al., 1999). Using the bargaining framework of relational contracting theory,

Harvey et al. (2003a) identify two dimensions of supplier GAM motivation: relational

behavior (ranging from negative/reactive to positive/proactive) and supplier

dependence (reflecting an orientation toward the short- or long-term), and argue that

13

the potential for increasing efficiency and effectiveness through global customer

relationships is greatest when the supplier has a proactive, long-term perspective.

Other factors that require assessment to determine the appropriate type of relationship

with the customer include the degree of globalization and international coordination of

the two companies. Arnold et al. (1999, 2001) (Figure 3) and Montgomery and Yip

(2000) develop frameworks comparing these two dimensions and present four

situations with differing degrees of supplier–customer fit.

Figure 3: A typology of global vendor–customer relationships

Source: Arnold et al. (1999: 16).

The combination of these dimensions determines the capacity of both the buyer and

the supplier to implement a global agreement, which affects the power balance in the

relationship. Any imbalance in capabilities likely lowers the potential for developing a

profitable and real GAM partnership that will benefit both sides.

The main conclusion is that though the development of GAM relationships is often

driven by the customer or other external factors, the supplier’s success depends on its

ability to take a proactive approach and assess the situational factors. The environment

and degree of globalization of the industries and the specific companies determine the

Avoid: Costs likely tooutweigh benefits Divide and Conquer

Supplier Squeeze Develop Partnership

Low High

Cu

stom

er’s

In

tern

atio

nal

Coo

rdin

atio

n

Vendor’s International Coordination

Low

Hig

h

14

type of relationship, the behavior of the two parties, and the design of the GAM

organization. Consequently, these factors should play key roles in the formation of

GAM cross-functional teams by determining their composition and geographic reach.

2.1.4. GAM relationship development

As the previous section indicated, the readiness and motives of the customer and the

supplier to enter a GAM agreement likely determine the degree of collaboration,

which in turn influences GAM performance. As it moves from discrete market

transactions to full integration, the customer–supplier relationship becomes

progressively more collaborative. Lambe and Spekman (1997) outline a continuum of

national account management relationships ranging from repeated transactions to long-

term relationships to buyer–seller alliances. Movement along this spectrum is

associated with increasing relationship-specific investments, higher switching costs for

both sides, and higher degrees of collaboration, which include joint planning, joint

coordination, information sharing, and long-term commitments. In addition, the

emphasis on selling and concern about price decrease.

These findings mirror the notion of business relation evolution, which serves as a basis

for Dwyer et al’s (1987) model, in which they distinguish among several types of

relationships on the basis of the degrees of the buyer’s and seller’s motivational

investment in the collaboration according to five development phases: awareness,

exploration, expansion, commitment, and dissolution. This evolution moves

companies from discrete spot market transactions to relational exchanges, which helps

both partners benefit from reduced uncertainty, well-managed dependence, greater

efficiencies, and social gains. Increasing the depth of the relationship is associated

with increases in trust, largely based on consecutive rounds of specific investments.

Morgan and Hunt (1994) build on this research and consider commitment and trust the

key mediating variables of a relationship, as well as crucial for long-term partnerships.

In a more specific KAM context, Millman and Wilson (1994) provide similar insights

in the Key Account Relational Development model (Figure 4). These authors suggest

that KAM goes through several stages as the relationship changes from transactional to

15

collaborative and involvement with customers moves from simple to more complex.

The main stages are pre-KAM, early KAM, mid-KAM, partnership KAM, and

synergistic KAM.

Figure 4: Development stages in the GAM relationship

Source: Wilson et al. (2002: 52).

McDonald et al. (1997) suggest that the potential for collaborative KAM also depends

on two other dimensions: the complexity of the processes between buyer and seller

and the complexity of the product and technology supplied by the seller. Studying the

case of the key account unit of a telecom company, Pardo et al. (1995) also find

Relational distance

Increasing organizational complexity and cultural diversity

Time

Pre-GAM

Early-GAM

Mid-GAM

Partnership GAM

Synergistic GAM

Buyer

Seller

Buyer‘s stategic drivers

Seller‘s stategic drivers

Relational distance

Increasing organizational complexity and cultural diversity

Time

Pre-GAM

Early-GAM

Mid-GAM

Partnership GAM

Synergistic GAM

Buyer

Seller

Buyer‘s stategic drivers

Seller‘s stategic drivers

16

evidence that KAM is a process of adaptation in the organization that aims to manage

relationships with important customers. They call this process “the key accountization

of the firm.”

This adaptation is influenced by the GAM strategy and configuration and therefore is

associated with the GAM team. In the earlier stages (pre-GAM and early GAM), the

supplier likely conducts business with the customer at a national or regional level.

Therefore, the team is likely to be smaller and less international; the focal relationship

is that between the global account manager and the global purchasing officer or a

small team at the global customer headquarters. Although there might be centers of

interaction with the customer at various locations around the world, they are localized

and uncoordinated. Links may exist between individual managers within both the

supplier and the customer organization, but an integrated network of global cross-

functional interaction is not in place. As a result, neither an established GAM team for

the account nor a small core team whose main tasks include establishing a network of

relationships with key support functions of the supplier and key players of the client

(i.e., expanding and strengthening the team and its influence) exist.

The movement to mid-GAM is associated with greater relationship complexity, more

cross-boundary contacts, and increasing needs for dedicated resources and

relationship-specific adaptations. Therefore, the number of people involved in the

relationship increases, and a dedicated, cross-functional, likely interorganizational

team develops. However, though this team might have a global reach, it is not yet

considered of strategic importance to either party, and its activities focus on

operational issues.

Partnership GAM implies that the two parties perceive each other as strategic partners,

share risks, and explore joint opportunities for value creation. There are strong ties

between the two companies at many levels around the world. Therefore, the

composition and size of the team depend on the various interdependencies between the

buyer and the seller. The team tasks become more complex, and the structure must

resolve the implications for individual country and functional managers. Finally, in the

synergistic stage, the two companies stop perceiving themselves as two separate

17

organizations but rather as parts of a larger entity, creating synergies. Thus, between

the two organizations emerges a third one: the GAM team, which includes members

from both sides who work as one team to integrate their skills and create joint value.

2.1.5. Strategy

Another key dimension of the supplier–customer relationship is strategy, one of the

four building blocks of the Key Account Congruence Model of Capon (2001) that

encompasses the degree of commitment to key accounts and the selection of such

accounts. Wilson et al. (2000) categorize GAM strategies into three hierarchical

groups to reflect the culture of the supplier and the operationalization of their global

capabilities. The economic GAM strategy focuses on sales volume and supply chain

efficiencies; the relationship is based on adversarial negotiations. The innovative

strategy demonstrates a higher degree of collaboration and focuses on problem

resolution and value creation. Finally, companies with an entrepreneurial strategy aim

to exploit joint value creation and wider business opportunities and build a relationship

based on strategic alignment, integration, and coordination of core competencies.

Dishman and Nitse (1998) identify a similar range of strategic approaches, including

protective, technology-based, and needs-based. The protective strategy is most reactive

and might be a short-term solution because it does not provide a high level of

customization. At the other end, the needs-based strategy entails the highest

commitment to key accounts and requires additional resources and support be

dedicated to their management. The implication is that more complex and

collaborative strategies place higher demands on the GAM team and therefore require

a larger and more diverse cross-functional commitment.

Customer assessment and selection is a major part of the strategy. Having assessed the

balance of power, suppliers must define clear and objective selection criteria. Wilson

et al. (2000) and Millman (1999) divide the various selection criteria into two large

groups—operational/financial and strategic—and stress the importance of both.

McDonald et al. (1997) find that the most common factors for the selection of key

accounts are volume-related factors, potential for profit, and status-related factors. On

18

the buyer’s side, the most common selection factors include ease of doing business,

quality of the product/service, and people factors, such as the personality and skills of

the selling company contacts.

In addition, it is crucial to assess the customer’s receptiveness to global partnering and

the possibility of establishing a relationship on an equal footing. Relationships that

have been initiated by the supplier for strategic reasons are usually more successful

than those based entirely on operational and financial rationales, because they manage

to shift the negotiation’s focus away from price and discounts. Arnold et al. (2001)

argue that this strategic potential refers to three different levels—overall strategic

importance, marketing and sales strategy, and top executive support—and that the

relationship is unlikely to be successful if any of them is missing.

2.1.6. GAM/KAM configuration

Depending on the GAM strategy selected, firms must allocate specific resources and

configure their activities. Research contributions in this vein refer to the building

blocks of a GAM program, which has great relevance for this study, because most

researched elements have implications for the formation of teams and can influence

performance. The literature review table in Appendix A indicates that much of the

research has addressed only one or a few relevant factors. One of the few empirical

studies that attempt to encompass all key dimensions of KAM is that by Homburg and

his coauthors (Homburg et al., 2002; Workman et al., 2003). They aim to develop an

integrative conceptualization of KAM approaches and define key constructs in four

areas: activities, actors, resources, and approach formalization.

Activities: Products/services, pricing, information

Key account activities are those not offered to average customers, including special

pricing, special service and distribution arrangements, customization of

products/services, and information sharing. The two most important dimensions of this

19

construct are activity intensity and activity proactiveness, which have significant

positive impacts on KAM effectiveness (Workman et al., 2003).

Other researchers emphasize the importance of offering a consistent value proposition

worldwide and global pricing. Millman (1996) discusses systems selling in global key

account management, that is, offering and delivering a comprehensive “package” of

products and services, including both standardized and customized components, to

selected customers. Arnold et al. (2001) find that GAM leads to downward pressure on

prices, which requires close attention to this aspect and relationships that go beyond

pricing agreements. Narayandas et al. (2000) provide a comprehensive analysis of the

benefits and risks of entering into a global pricing agreement and argue that these

agreements could be crucial for the company’s performance and so must be

approached carefully.

In addition, key customers increase the information processing and sharing needs of

the supplier, which makes information a major dimension of the customer–supplier

collaboration and demands the establishment of appropriate systems and procedures.

Using information processing theory, Birkinshaw et al. (2001) find that the scope of

relationships between two organizations, the frequency of communication between the

global account manager and other individuals in the two companies, and the use of

extensive internal support systems lead to better information management and hence to

improved efficiency and sales growth.

The key account manager

The key account manager and others involved in the relationship are key elements of

the buyer–seller dyad. There seems to be consensus that the global account manager is

a special position that requires high-caliber personnel with varied skills and

knowledge. Due to his or her boundary-spanning role, the manager must be able to

perform a wide range of tasks: coordination, communication, account planning,

external and internal relationship management, sales and profit responsibility,

selling/negotiation, and multicultural teamwork, to name just a few (Colletti and

Tubridy, 1987; McDonald et al., 1997; Millman, 1996). Therefore, he or she also

20

needs strong skills in the areas of finance (analytic and bargaining skills related to

pricing, revenue improvement, contract negotiations), sales (problem-solving and

presentation skills related to account performance, company and product capabilities),

human relations (interpersonal and intercultural relationship skills), and marketing

(market research and account profiling skills, information collection, ability to

communicate customer needs internally) (Colletti and Tubridy, 1987). In addition, the

skills involved in the role of a key account manager include integrity, product/service

knowledge, and a solid understanding of the buying company’s business and business

environment (McDonald et al., 1997).

Very often, the traditional role of the supply manager must be adapted and new

competencies developed to meet the demands of global customers (Harvey et al.,

2003b). Therefore, this position is associated with a specific job analysis, the creation

of specific job profiles, and stringent hiring practices (Wotruba and Castleberry, 1993).

Furthermore, these managers need special compensation arrangements (Colletti and

Tubridy, 1987) and skills development through training programs (Weeks and Stevens,

1997). Finally, a very important actor in this scene is top management, because top

executive involvement consistently has been identified as a critical success factor

(Arnold et al., 2001; Homburg et al., 2002; Millman and Wilson, 1999a; Napolitano,

1997; Toulan et al., 2002).

Organizational structures

The structure of GAM/KAM activities is of major importance because global account

management is, above all, an organizational challenge (McDonald et el., 1997). As

Shapiro and Moriarty (1984: 1) note, “formal organization structure is perhaps the

most interesting and controversial part of national account management.” Because the

issue of GAM teams represents part of the larger GAM organizational context, the

major findings in this area require a more in-depth analysis to assess their applicability

to the more specific context of teams.

One of the most extensive discussions of the diverse structural forms employed in

GAM appears in Shapiro and Moriarty (1984). These authors use as a foundation the

21

concepts of differentiation and integration by Lawrence and Lorsch (1967: 11), who

define differentiation as “the difference in cognitive and emotional orientation among

managers in different functional departments” and integration as “the quality of the

state of collaboration that exists among departments that are required to achieve unity

of effort by the demands of the environment.” Balancing the requirements of the

environment and the customer means that a trade-off must always occur between

differentiation and integration, on the one hand, and among different forms of

integration (i.e., with the customer, with nonmarketing and nonsales functions in the

supplier company, within the account management organization) on the other. On this

basis, Shapiro and Moriarty (1984) identify six options for structuring the GAM

organization:

– No KAM program.

– Part-time KAM program.

– Operating unit program at the group level.

– Operating unit program at the division level.

– Corporate-level program (centralized).

– National account division.

The no-KAM option has no implications for the topic being researched herein, because

teams likely form only at more advanced KAM development stages. The part-time

KAM may have relevance if it serves as a bridge from no program to a full-time

program. In this case, the development of teams would begin and likely has

consequences for the future development of the program. As McDonald et al. (1997)

report, the first step in building an effective GAM organization is the “dotted-line” or

semi-formal team whose members tend to have different line managers but meet

regularly with the account manager. A key success factor for this organizational

decision is the establishment of shared incentives and rewards. Team members tend to

have objectives related to key accounts, such as the percentage of time they should be

devoting to their key account. Moreover, strong performance on the account reportedly

leads to improved career development and job security for the team members.

22

When moving to a more formalized and structured approach, companies must choose

among programs at the operating unit or corporate level or a separate account division.

The choice between the group-level and the division-level largely depends on three

factors: (1) the amount of overlap among the largest customers and prospects of each

division, (2) the advisability of joint sales efforts among divisions, and (3) the

organization of the general sales force.

The benefits of a group-level program are greatest when the various divisions in the

group have the same customers and the group has a pooled or shared sales force in

which the KAM program can be embedded. The rationale for establishing a corporate-

level program is similar, namely, a substantial overlap in the customer bases of the

operating units. This type of organizational structure has two important advantages.

First, the account managers have greater authority and the opportunity to use corporate

power to solve problems and exploit business opportunities. As a result, the program

has greater leverage both internally and externally. Second, a corporate-level program

contributes to increasing customer orientation in the company and making the voice of

the customer heard. This conclusion is supported by McDonald et al. (1997), who find

that decision-making authority is a key element of GAM design. The customer expects

that the account managers can get things done and have sufficient resources at their

disposal. The main indication of authority is the global account manager’s reporting

line, which implies that the GAM organization has a greater chance of success if it is

positioned at a high organizational level and has sufficient support from top

management. In contrast, corporate programs face the disadvantages of greater

complexity and lower responsiveness to the needs of individual operating units.

The ultimate form of a KAM organization is a separate operating unit that serves all

key accounts. Such a division incorporates some other functions such as

manufacturing, engineering, product development, and other sales and marketing

functions. Consequently, it is the most likely form to employ cross-functional KAM

teams on a full-time basis.

Although Shapiro and Moriarty’s (1984) work provides valuable insights into overall

KAM organizational structures and some of the options for structuring a KAM team, it

23

is limited in three primary ways. First, it is mostly descriptive and offers a summary of

the most common forms used in practice but does not validate the motivations for

choosing one option over another and all the advantages and disadvantages related to

it. Second, it focuses on national account programs and does not account for the

increased complexity of GAM. Third, the relationship between the choice of an

organizational form and performance is not discussed, which leaves open the question

of whether some forms are superior to others.

Kempeners and van der Hart (1999) build on Shapiro and Moriarty’s (1984) work and

advance their findings by matching them with case study field research. The result is

an identification of 15 decision topics in the process of designing an account

management organization. Some of the major decisions include the organization level

at which the GAM program should be positioned (group, division, business unit), the

levels of account managers (single vs. multiple), the number of account managers per

level and accounts per account manager, the use of teams, the functions included in the

teams, the location of the account managers, and the reporting lines.

Homburg et al. (2002) attempt to overcome the limitations of previous studies and

develop a taxonomy of eight prototypical approaches to organizing KAM. On the basis

of the key constructs in the four areas of activities, actors, resources, and approach

formalization, their study distinguishes between top-management KAM, middle-

management GAM, operating-level GAM, cross-functional dominant KAM,

unstructured KAM, isolated KAM, country-club KAM, and no KAM. Thus, it

empirically confirms Shapiro and Moriarty’s (1984) types of KAM but also finds two

additional approaches.

The results further indicate that the approaches with the highest values for most of the

variables are cross-functional dominant KAM, top management KAM, and operating-

level KAM. That is, companies with such programs do more for their global customers

than do companies with other forms of KAM. It is important to note that these three

approaches also make more extensive use of teams. Therefore, full-time formalized

GAM teams should be found in companies with fairly developed GAM activities and a

greater focus on customers, which results in structured and formalized programs

24

located relatively high in the organization. In such companies, the account managers

have authority and the ability to access resources from other parts of the organization

(i.e., marketing and sales as well as nonmarketing and nonsales), which facilitates the

work of the team.

GAM teams

Although teams facilitate the complex task of coordinating the efforts of individuals

across functional, product, and geographic units to serve customer needs (Shapiro and

Moriarty, 1984) and form an integral part of many GAM programs (Homburg et al.,

2002), the literature does not provide a precise definition of what constitutes a GAM

team.

Shapiro and Moriarty (1984) make a first attempt to identify the ways a KAM team can

be organized. They note the importance of integrating two groups of support

functions—sales and other—into the KAM structure. The option of developing account

teams, which include field salespeople, appears preferable to using existing,

independent field sales forces, because doing so provides greater integration and more

responsiveness to the key account managers, who have direct line authority over the

local representatives. The suggested organizational choices are (1) a set of field

representatives dedicated to the accounts of each global account manager, (2) account

managers sharing the services of the field representatives on a predetermined basis and

the field representatives reporting to the account managers for whom they work, or (3)

a pooled field sales force directed by a field manager who reports to the head of

account management (Shapiro and Moriarty, 1984).

In addition, the account team must integrate other functions that are relevant in

managing key customers. The options are similar to those for the sales force, namely, a

general function shared by account management and other sales functions, functional

specialists dedicated to one account and reporting to the account manager, functional

specialists shared by a few accounts and account managers, and a pooled force of

functional specialists managed by a functional specialist manager who reports to the

head of account management.

25

Recognizing that account teams often cross unit, division, and region borders,

Kempeners and van der Hart (1999) elaborate on the various ways to organize such

complex structures. They distinguish between a full-time core team and various other

functions (e.g., technical support) that can participate as part-time members. Another

identified possibility is the “ad hoc team,” whose members take part only when a

certain problem arises. Such teams are disbanded when the account problem is solved

(Sengupta et al., 1997).

Kempeners and van den Hart’s (1999) results show that in two of the seven cases they

study, the companies used no teams, and the account managers received no support at

all. In another case, the account manager worked at a highly centralized level and was

supported extensively, whereas in the three other cases, the account teams were less

formalized. These observations suggest a list of decision topics:

1. Account team or no account team.

2. Participating functions in the team.

3. Full- and/or part-time members.

4. Hierarchical relationships: members report to account managers and/or other

managers.

5. When reporting to account managers, account managers have dedicated or shared

teams.

6. Location of account teams/managers.

These decisions, combined with Shapiro and Moriarty’s (1984) options for sales and

nonsales support integration, can lead to three different types of teams:

– Own account team—Dedicated members report directly to the account

manager. Appropriate for large and concentrated account teams; otherwise, the

costs exceed revenues.

– Shared account team—Members work in small account management systems

or on naturally overlapping accounts. These teams can create ambiguity in

26

terms of control and resource allocation. To avoid conflicts, teams must share

members on a predetermined basis.

– Shared account team with own manager—Appropriate for large account

organizations involving intensive coverage of many customer locations.

Empirical evidence shows that companies tend to avoid this option to avoid

creating an extra management function. Instead, the support staff reports

directly to the account managers.

An additional possibility is a team formed of lower-level account managers who

provide various support tasks. Two of Kempeners and Van der Hart’s (1999) case

study subjects employed international account managers who coordinated a group of

other account managers globally. This manager was located close to the headquarters

of the customer, while the lower-level account managers supported him in other

countries and regions.

Although this research offers some clarity to the issue of organizing GAM teams, it

does not explain if these options lead to different outcomes. Therefore, the next section

analyzes the literature on overall GAM performance and identifies such factors in a

more general GAM context.

2.1.7. GAM performance

The models related to GAM performance may be divided into three major groups, as

determined by their content and scope. The first group includes models that study the

environment–GAM configuration–performance link. The second group focuses on the

link between interorganizational capabilities and performance. Finally, the third group

addresses only internal factors and their effects on outcomes.

Environment, GAM configuration, and performance

Montgomery and his coauthors (Montgomery and Yip, 2000; Montgomery et al.,

2002) build a model based on Yip and Madsen’s (1996) framework and test it

empirically to identify a link between the use of GAM and performance. Linking

27

various globalization factors in both the suppliers’ and the customers’ industries to the

implementation of GAM, they find some support that the supplier’s industry

globalization drivers and customer’s demand for GAM lead to the development of

GAM programs in response, and this GAM adoption in turn has an impact on overall

performance. However, this relationship is quite moderate in magnitude.

Whereas their research contributes by finding a positive correlation between GAM

efforts and performance, it is not complete because it fails to identify specific GAM

elements, particularly team-related factors. To a lesser degree, this limitation also

appears in a similar study (Townsend et al., 2004), which tests the links among

globalization drivers, marketing programs, and outcomes. From a broader marketing

perspective, Townsend et al. (2004) find support for the hypothesis that the main

global marketing dimensions include global product standardization, global marketing

structure, and global product processes and that the development of the first two

capabilities is triggered by both external (global consumer convergence) and internal

(leadership’s global orientation) factors. Global product standardization and global

marketing structure in turn drive global product processes. However, the study fails to

find a significant relationship with marketing performance.

Using a similar line of thinking but narrowing the focus to global marketing strategy,

Zou and Cavusgil (2002) find that global marketing strategy is influenced by

international experience, global orientation, and external globalizing conditions.

Furthermore, it has a positive impact on global strategic performance in terms of

global market share, competitive position, and global financial performance regarding

cost position, sales growth, and profitability. The implication in a GAM context is that

global strategy is likely to improve performance, but the findings are incomplete

because the model does not consider other important aspects of GAM, such as

relationship management capabilities.

To conclude, the studies in this group provide support for the link among

environmental drivers, use of GAM or global customer strategy, and performance. In

particular, the two main factors triggering the development of GAM capabilities are

industry globalization drivers and firm orientation. However, the models allow for two

28

major critiques. First, most fail to identify clearly the critical success factors for GAM.

Instead, they present a general conceptualization of GAM antecedents and

consequences and omit the organizational competencies required for GAM

implementation. Second, though they find a positive relation with performance, in

most cases, it is relatively weak.

Interorganizational capabilities and performance

Shi et al. (2004) develop a framework of GAM capabilities in which they

conceptualize what constitutes competence and its impact on performance. Using the

resource-based view (RBV), they argue that the sources of GAM competitive

advantage are interorganizational capabilities, which could be defined as bundles of

knowledge and skills “deeply embedded in inter-organizational routines and processes

and deployed through the joint idiosyncratic contributions of the specific GAM

partners” (Shi et al., 2004: 541). Those capabilities may be divided into three

categories: collaborative orientation, GAM strategic fit (including standardization fit,

participation fit, and coordination fit), and GAM configuration—and have a positive

effect on the GAM strategic outcomes, specifically, joint profit performance and

dyadic competitive advantage. The antecedents of these GAM-related capabilities are

the extent to which both supplier and customer have a common set of strategic goals

(goal congruence) and each of the companies can provide complementary knowledge

and assets (resource complementarity). A major contribution of Shi et al. (2004) is

their extension of prior studies (Homburg et al., 2002; Toulan et al., 2002) by

combining them into a broader framework that considers both sides of the buyer–seller

dyad and links GAM capabilities to the joint performance of the two parties.

Similarly, the concept of GAM interorganizational fit was studied by Toulan et al.

(2002), who define it according to four dimensions: fit regarding the strategic

importance of the relationship, product/marketing standardization, the configuration of

activities, and senior executive involvement. The results indicate that strategic,

configuration, and executive involvement fit improve efficiency and sales growth;

simultaneously, standardization and executive involvement fit affect the extent to

29

which partnerships with the customer get established.

At first, the finding that a match between the configuration of activities at the

supplier–customer interface relates positively to performance seems to contradict the

results of a prior study by the same authors. Birkinshaw et al. (2001) use information

processing theory and resource dependency theory to test the effect of GAM

capabilities on efficiency/sales growth and partnerships with customers. Their

evidence supports the two hypotheses of resource dependency theory: (1) the higher

the buyer’s dependence on the supplier, the better the supplier’s performance, and (2)

the more centralized the sales activities of the supplier and the more decentralized the

buyer, the better the supplier’s performance. However, Toulan et al. (2002) find that

the second hypothesis is valid only when the relationship is unbalanced, because fit

remains the first best solution.

With regard to the hypotheses from information processing theory, statistical evidence

indicates account performance is predicated on the scope of relationships and the

frequency of communication. As relationships get established at multiple levels, not

just through the global account manager, and as the global account manager begins to

communicate more frequently both with the customer and within his or her own

organization on matters related to the account, account performance improves.

Therefore, managerial competencies and internal support systems represent key

capabilities that lead to successful GAM.

The common finding of this research group is that GAM performance is determined by

the degree of collaboration and the match of strategies and structures between the

supplier and the customer. The most important areas demanding coordination are

product and marketing standardization and activities configuration. As compared with

studies in the previous subsection, this group defines GAM capabilities more precisely

and tests their individual and joint effects on performance. In addition, it recognizes

customers and their role in the GAM relationship in a more dynamic, active, and

participative manner, unlike the previous group, which limits the analysis to the

customer’s pressure for GAM implementation.

30

Internal capabilities and performance

Taking an internal supplier perspective, Homburg et al. (2002) show significant

performance differences among their eight configuration designs, such that cross-

functional dominant KAM provides the highest level for almost all variables. In a

subsequent paper, Workman et al. (2003) test the impact of the four key dimensions

(activities, actors, resources, and approach formalization) on KAM effectiveness and

firm profitability directly. As predicted, activity intensity and proactiveness, top

management involvement, KAM esprit de corps, and control over marketing and sales

resources relate positively to KAM effectiveness. The only unexpected results of the

model are that the use of teams has no significant relation with KAM effectiveness and

that approach formalization has a negative impact. However, this research identifies

and refines specific KAM capabilities that lead to effectiveness through a large-scale,

cross-national empirical study.

Shi et al. (2005) provide a conceptual framework of GAM capability and performance,

in which they posit that GAM capability comprises three distinct processes—

intelligence acquisition, coordination, and reconfiguration—that have positive impacts

on GAM market performance and profitability. However, they do not test this

framework empirically.

Another study, which develops a detailed model and identifies various GAM success

drivers, is that by Senn and Arnold (1999). Using customer satisfaction as a proxy for

success, the authors identify nine core fields that determine performance with global

accounts. These fields are positioned at three levels—strategic, operational, and

tactical—and stress the importance of both a holistic (across all process levels) and an

integrative (across functions of the organization) view. On the strategic level,

companies are concerned with selecting global accounts, developing long-term

relationships, and leveraging corporate knowledge. On the operational level, the focus

is on creating consistent product and service packages, synchronizing business

processes, and establishing appropriate systems. On the tactical level, companies select

and train the right people, put in place the right structures, and collect relevant

information. The results show that each of the nine fields relates positively to customer

31

satisfaction and that seven (excluding task and information management) have

significant impacts. The Congruence Model of Capon (2001) confirms the relevance of

some of these success drivers as it divides the elements of the KAM process into four

interconnected building blocks, including strategy, organization, systems and

processes, and human resources. However, Capon (2001) does not test the

performance effect or interactions among them.

2.1.8. Limitations

This extensive GAM literature review reveals various factors that are important for

GAM, including global strategy (Capon, 2001; Yip and Madsen, 1996; Zou and

Cavusgil, 2002), organizational structures (Capon, 2001; Homburg et al., 2002; Shi et

al., 2004; Townsend et al., 2004, Yip and Madsen, 1996), processes (Senn and Arnold,

1999; Shi et al., 2004, 2005; Townsend et al., 2004), and human resources

(Birkinshaw et al., 2001; Capon, 2001; Harvey et al., 2003b; Senn and Arnold, 1999;

Yip and Madsen, 1996). However, despite the importance of the topic, research in the

area of GAM teams remains sparse.

The main limitation of this research stream is that virtually no study focuses on the

factors that facilitate GAM teamwork and impact team performance. The only

empirical attempt to test the relationship between the use of teams and key account

management effectiveness finds no significant impact (Workman et al., 2003);

however, its lack of delineation between different types of teams and failure to identify

specific team characteristics limits the usefulness of its results. In contrast, studies that

have touched on more specific design issues discuss only the structural options

available from a practical perspective and stop short of identifying the determinants

and performance effects of various team dimensions (Harvey et al., 2003 a, b;

Kempeners and van der Hart, 1999). Recent research indicates that the development of

specific supplier and interorganizational capabilities is a more important predictor of

GAM performance than pure structural solutions (Shi et al., 2004, 2005), indicating

that GAM team research should move beyond these issues to encompass team

32

competencies and processes. This literature review therefore highlights the need for a

comprehensive model that integrates all relevant elements and links them to outcomes.

The reviewed GAM studies contain many limitations from a methodological

perspective as well. First, the existing models are either conceptual and descriptive

(Capon, 2001; Shi et al., 2004; Yip and Madsen, 1996) or use data from a small

number of companies or a single country (cf. Homburg et al., 2002). This point

indicates the need for more empirical studies that use larger samples and more

advanced research techniques. Second, the discussed academic work examines GAM

mainly from the supplier’s perspective and thus lacks a means to perform comparisons

with the customer point of view. Such research risks getting lost in internal issues,

which may be of little relevance for the buyer. Third, the performance measures are

based on self-assessments, which may be related to certain biases, and do not present

objective valuations, such as sales and profit.

In summary, this review of GAM/KAM contributions outlines the growing importance

of the GAM team as an organizational form. Furthermore, it indicates the positive

correlation between GAM efforts and performance, which further confirms the

relevance of the topic researched herein. However, the review provides very limited

insights about this dissertation’s research questions because of the lack of studies in

this specific area. Therefore, in the next sections, I consider studies in the broader

context of organizational teams and try to identify useful implications.

33

2.2. Small groups in the organization

The importance of teams for organizational success has been widely recognized in

practice and in academic research. The key benefit of using teams in organizations lies

in their potential to increase both productivity and satisfaction among employees

simultaneously (Gladstein, 1984; Goodman et al., 1988), thus helping resolve the

productivity–satisfaction trade-off previously presumed to be inherent in work design

(Campion et al., 1993). However, some negative effects also have been associated with

teamwork: poor decisions (Janis, 1972), conflict (Alderfer, 1977), and member

frustration or disappointment (Katzenbach, 1987). As a result, researchers have

attempted to understand how teams work and what determines their effectiveness. The

wide range of team effectiveness models developed in recent years encompasses

different disciplines such as social psychology (McGrath, 1964; Steiner, 1972), socio-

technical theory (Cummings, 1978; Pasmore et al., 1982), industrial engineering

(Davis and Wacker, 1987), and organizational psychology (Gladstein, 1984; Guzzo

and Shea, 1992; Hackman, 1987; Sundstrom et al., 1990). This section reviews the

major models and frameworks of teamwork effectiveness and summarizes the main

findings.

2.2.1. Definitions

Team

Before analyzing research contributions pertaining to team effectiveness, it is

important to define the concept of a team in an organizational setting. Numerous

definitions of organizational groups and teams can be found in academic studies, and

whereas popular management literature has embraced the term “team,” academic

contributions tend to use the term “group,” as in group dynamics, group processes, and

group effectiveness. Katzenbach and Smith (1993: 85) explicitly distinguish between a

work group and a team, noting that

unlike teams, working groups rely on the sum of ‘individual bests’ for their

performance. They pursue no collective work products requiring joint

34

effort. By choosing the team path instead of the working group, people

commit to take the risks of conflict, joint work-products, and collective

action necessary to build a common purpose, set of goals, approach, and

mutual accountability.

Although this delineation is not widely shared and most authors use the two terms

interchangeably, it is clear that groups differ in their degree of “groupness,” with some

being more integrated and interdependent than others (Cohen and Bailey, 1997). The

main determinants of the degree of the groupness appear to be the size,

interdependence, and temporal patterns of the group (McGrath, 1984:3).

Teams have been defined as “a small number of people with complementary skills

who are committed to a common purpose, performance goals, and approach for which

they hold themselves mutually accountable” (Katzenbach and Smith, 1993: 45). Cohen

and Bailey (1997: 241) use a slightly different definition: “a collection of individuals

who are interdependent in their tasks, who share responsibility for outcomes, who see

themselves and who are seen by others as an intact social entity embedded in one or

more larger social systems (for example, business unit or the corporation), and who

manage their relationships across organizational boundaries.” In a more recent study of

team processes, teams have been defined as “multitasking units that perform multiple

processes simultaneously and sequentially to orchestrate goal-directed taskwork”

(Marks et al., 2001: 356). The attributes that are stressed in all definitions are

interdependence, common goals, and identity, as well as interactions among team

members.

Different team typologies have been presented in literature, but the categories often

overlap. Sundstrom et al. (1990) distinguish between advice and involvement teams,

production and service teams, project and development teams, and action and

negotiation teams. These four pairs of categories correspond notably to the four types

of teams suggested by Cohen and Bailey (1997): parallel, work, project, and

management teams. The most common type is the work team, which differs from other

forms in that it is a continuous work unit responsible for producing goods or providing

services, characterized by stable, full-time, and well-defined membership. Recently,

35

such teams have been increasingly organized as self-managing or empowered entities

to reduce costs or improve productivity (Pearson, 1992; Cohen and Ledford, 1994). In

contrast, project teams exist for a limited duration and usually are nonrepetitive.

Because their tasks are related to the development or introduction of new concepts

(e.g., products, information systems, plants), they commonly draw team members from

various functions to create a pool with specialized expertise. Parallel teams also attract

members from different work units, but their goal is to perform functions that the

regular organization is not prepared to do well. Typical examples include quality

circles or quality and employee improvement teams. These teams exist in parallel with

the established organization and have limited authority.

Team process

Another important construct used in all teamwork studies is the team process.

Gladstein (1984: 500) refers to group processes as “the intragroup and intergroup

actions that transform resources into a product” and differentiates between two forms

of process behavior: maintenance behaviors that build, strengthen, and regulate group

life and task behaviors that help the group achieve the common goal. Similarly, Cohen

and Bailey (1997: 244) highlight the central role of internal and external interaction

when they refer to the team process as “interactions such as communication and

conflict that occur among group members and external others.” Team process is also

regarded as “members’ interdependent acts that convert inputs to outcomes through

cognitive, verbal, and behavioral activities directed toward organizing taskwork to

achieve collective goals” (Marks et al., 2001: 357). In essence, this construct describes

how teams interact with one another and their environment to complete their tasks.

Team effectiveness

Probably the variable with the most crucial importance is team effectiveness. Most

models use different conceptualizations of this aspect, but the majority of effectiveness

variables are based on three fundamental dimensions: team performance, ability of the

36

team to work together over time, and satisfaction of team members’ needs (Hackman

and Morris, 1975). Cohen and Bailey (1997) also categorize effectiveness in three

dimensions, two of which refer to performance and member attitudes, such as

satisfaction, commitment, and trust. In addition, they highlight another set of

variables—behavioral outcomes such as absenteeism, turnover, and safety.

2.2.2. Small group effectiveness models and frameworks

The models discussed in this section and illustrated in Appendix B emerge from the

large body of small group research as the most influential and inclusive. Some have

served as a basis or frame of reference for numerous subsequent studies (McGrath,

1964; Gladstein, 1984; Hackman, 1987) and thus have shaped scientific thought on

group functioning. Others are based on extensive reviews of research conducted over

the years (Cohen and Bailey, 1997; Gist et al., 1987) and therefore integrate the most

relevant findings from those periods. As a result, these models may represent existing

conceptualizations of groups in organizations.

One of the classic models of team effectiveness, which originates from the social

psychology research stream, is that of McGrath (1964). Based on a typical input–

process–output view of teamwork, the model distinguishes among three categories of

inputs and outputs: those associated with individual group members; those that

describe the group as a whole; and those that refer to the physical, sociocultural, and

technological environment in which the group functions. A fundamental assumption of

this model is that input variables affect group outputs only through the interaction that

takes place in the group. This assumption, as well as the overall logic of the model, has

served as a basis for much subsequent research on team functioning.

Following an organizational psychology approach, Gladstein (1984) develops a

revised version of McGrath’s (1964) model that distinguishes between inputs at the

group and organizational levels. Furthermore, the inputs at group level are divided into

composition (adequate skills, heterogeneity, organizational tenure, job tenure) and

structure (role and goal clarity, specific work norms, task control, size, formal

leadership). The organizational variables considered important are the resources

37

available (training and technical consultation, markets served) and organizational

structure (rewards for group performance and supervisory control). The model predicts

that these inputs influence the team process, which in turn leads to effectiveness. The

key maintenance components of the team process include open communication,

supportiveness, and conflict, whereas the task components are discussion of strategy,

weighting individual inputs according to knowledge and skill, and boundary

management. According to the model, the group process–effectiveness link varies with

the nature of the task to be performed. For example, flexible communication patterns

likely lead to better performance only when task uncertainty is high. Consequently,

Gladstein (1984) suggests that the nature of the task has a moderating effect on the

link between process and performance and identifies three critical task dimensions

(task complexity, environmental uncertainty, and interdependence) derived from the

information processing approach (Galbraith, 1977; Lawrence and Lorsch, 1967). The

empirical study of 100 sales teams in a single organization provides support for some

of the hypothesized relationships. However, the group process scales form two

clusters, intragroup processes and boundary management, which indicates that sales

team members regarded behaviors they needed to interact with the external

environment as distinctly different from those required for internal interactions. The

link between group processes and performance, as measured by sales revenue, is not

supported, though Gladstein finds evidence of a link between intragroup processes and

subjectively rated effectiveness measures, such as team and customer satisfaction.

Hackman (1987) takes a more action-oriented approach and proposes a model that

defines group effectiveness as team performance, members’ satisfaction, and the

ability of the group to exist over time. Similarly, the input variables are divided into

organizational context and group design. However, this model suggests new variables,

namely, information systems as an important factor in the organizational context and

group norms pertaining to performance processes. The assumption is that group

members usually agree on approaches to performing a task during the early stages, but

not all actions and behaviors needed to achieve the group goals can be specified in

advance. Therefore, the development and execution of appropriate performance

strategies depends on established group norms. Hackman (1987) argues that the norms

38

that lead to higher performance are those that support self-regulation, situation

scanning, and strategy planning.

This author also challenges the traditional view of team functioning, in which input

variables influence performance exclusively through their effect on how team

members interact. Instead, he sees the interaction process both as an indicator of how

(and how well) the group is doing its work and as a point of intervention for improving

group performance. Therefore, he suggests focusing on those aspects of interaction

that relate directly to the team’s work on its task—the effort group members exert

collectively, the amount of knowledge and skills brought by members to the team, and

the appropriateness of the task performance strategies. These aspects are useful in

identifying the strengths and weaknesses of the group and directing improvement

interventions. The key proposition of this model is that overall team effectiveness is a

joint function of these three variables, which Hackman (1987) refers to as process

criteria of effectiveness. Moreover, group interaction offers a potential source of group

synergy that moderates the impact of design and contextual factors, because it helps

reduce process losses or create process gains.

In addition, a few models have been developed on the basis of summaries of empirical

studies. Such heuristic models have not been tested in practice and do not constitute an

exhaustive or definitive description of the variables and relationships impacting team

effectiveness. Nevertheless, they represent a useful point of reference because of their

broad theoretical basis. Reviewing articles from 27 journals in the period 1982–1987,

Gist et al. (1987) develop a heuristic model, in which design input variables have both

direct and indirect influences on team outcomes. The factors identified as key inputs

include group structure (size, ability, personality, gender, race), group strategies,

leadership, and reward allocation. Additional input variables appear in the reviewed

literature but were not included in the model, specifically, social comparison (Seta,

1982) and time limits and task types (Kelly and McGrath, 1985). Borrowing from

Hackman and Walton (1985), the outcome dimensions include group performance

(quantity, quality, and timeliness of group output), the degree to which group

processes increase members’ capability to work independently in the future, and the

extent to which the group contributes to the growth and personal well-being of team

39

members. The intervening role of these processes is represented by three groups of

factors: influence of members on one another, group development and member

integration, and decision making. The influence of other members should have

positive consequences as a result of facilitation and social impact, as well as negative

effects related to social loafing. These authors also analyze the effects of the decision-

making process on outcomes through variables such as participative decision making,

alternative/information generation, alternative evaluation, and consensus building.

Reviewing 54 studies from the period 1990–1996, Cohen and Bailey (1997) develop a

similar heuristic framework but focus on studies in organizational settings, in which

the dependent variables relate to various dimensions of effectiveness. As noted

previously, they differentiate among four types of teams—work, parallel, project, and

management teams—and consider the most important dimensions for each type. Their

model thus depicts team effectiveness as a function of task, group, and organization

design dimensions; environmental factors; internal and external processes; and group

psychosocial traits. Effectiveness combines performance outcomes (work quality and

productivity), attitudinal outcomes (job satisfaction and trust), and behavioral

outcomes (turnover and absenteeism). The model departs from the pure input–

process–output tradition by suggesting a richer set of relationships, such that design

factors can influence effectiveness both directly and indirectly. Furthermore, linkages

between the two types of processes suggest they should not be studied in isolation.

Finally, these authors predict that environmental factors affect only the design

variables, unlike other researchers, who believe they either have a direct influence on

team processes (McGrath, 1964) or moderate the relationship between process and

outcome (Gladstein, 1984). Moreover, novel in this framework is the separation of

group psychosocial traits from group processes. Psychosocial traits such as norms,

cohesiveness, and shared mental models can impact effectiveness directly, but more

important, they shape the internal and external processes of teamwork.

Another distinctive feature of this review is its recognition that critical interactions

occur both inside and outside the group, and hence, internal and external processes

determine performance. This recognition indicates a newer development in the

understanding of team functioning, not present in earlier studies. As Gist et al. (1987:

40

237) remark in their review of team research in the 1980s, “the limited empirical

literature on intergroup relations led to a decision to exclude this topic from review.”

The importance of external interaction, however, seems to vary across types of teams.

It appears mostly in a project team context, where groups are formed for R&D or

product development purposes, which requires cooperation across functional and

divisional borders in the organization (Ancona, 1990; Ancona and Caldwell, 1992 a, b;

Hansen, 1995; Kim and Lee, 1995).

Finally, much of the value of this research contribution lies in its selection of studies of

real organizational teams. Most group research has been conducted with laboratory

experiments, in which small groups of people (usually students) come together to work

for a limited period of time, with the assumption that the results from such

experiments can be generalized to real organizational settings. Gist et al. (1987) adopt

the same assumption and include in their review studies conducted in both the lab and

the field. However, experimental studies are often criticized for their lack of practical

relevance (Hackman, 1987; McGrath, 1964; McGrath and Kravitz, 1982; Smith and

Barclay, 1993). As Guzzo and Shea (1992) note, it is not certain if this methodology

allows inferences about natural groups; in laboratory settings, some of the variables

(e.g., external factors such organizational structure, reward systems, task

characteristics) that may powerfully affect outcomes remain constant, which makes it

impossible to determine their effects. This shortcoming is particularly obvious in a

GAM context, in which teams operate in a complex network of internal and external

relations, with loosely structured tasks and responsibilities, and in which team

members often receive direction from different managers and reward systems.

Therefore, the literature review herein excludes studies of this type and focuses

explicitly on empirical research in real organizational settings. In this respect, the

study on work teams by Campion et al. (1993) is worth discussing because it develops

and tests a full multivariate model of team effectiveness.

Campion et al. (1993) adopt a work design perspective to groups and examine the

relationships between design characteristics and various outcomes. Their literature

review led to the identification of five common themes of work group design,

containing 19 individual characteristics: job design (self-management, participation,

41

task variety, task significance, task identity), interdependence (task interdependence,

goal interdependence, interdependent feedback, rewards), composition (heterogeneity,

flexibility, relative size, preference for group work), context (training, managerial

support, communication/cooperation between groups), and process (potency, social

support, workload sharing, communication/cooperation within groups). Consistent

with prior studies (Gladstein, 1984; Hackman, 1987; Sundstrom et al, 1990; Wall et

al., 1986), their definition of effectiveness includes productivity and employee

satisfaction. In addition, managers’ judgments of effectiveness serve as an

effectiveness criterion. The model predicts a direct link between these five sets of

work group characteristics and outcomes but provides no indication about any

potential interrelations among these five factors or any moderating/mediating

influences in the design–outcome relationship. The empirical tests support the model,

and job design and process characteristics have slightly higher predictive power than

the other three themes. The managers’ judgments construct was most predictable,

followed by productivity and then satisfaction.

However, these discussed models display some differences in their conceptualizations.

Some focus on team and organizational design as the main determinants of

performance, whereas others examine the role of processes for team outcomes.

Although the focal point of most studies is the final effectiveness criteria, Hackman

(1987) distinguishes between intermediate and final criteria. In all cases, however,

aspects such as task characteristics, group composition, and organizational factors play

important roles. Furthermore, many of the constructs and the underlying logic are

similar in nature.

Although these models represent the state of the art in understanding team behavior,

they have been criticized for several reasons. First, they remain predominantly

heuristic and therefore offer a good conceptual overview of importance variables but

fail to identify the critical ones and their interrelations. That is, relationships appear in

terms of linear directions rather than functional forms or weights among variables

(Goodman et al., 1986). Second, the suggested lists of variables are usually very large

and constructs are broadly defined, which makes it difficult to assess the models’

validity. Further refinement of the variables and simplification of the models could

42

help uncover some less obvious relationships. Third, a major concern results from the

role of time for team interactions and performance, in that none of the models

explicitly considers the dynamic nature of team functioning. In response, some

research has looked more closely at the influence of time (Gersick, 1988, 1989; Kelly

and McGrath, 1985; Kozlowski et al., 1999; McGrath, 1991). For example, McGrath

(1991) suggests that teams usually engage in multiple activities over time, which

simultaneously creates complex sequences of interdependent tasks that cannot be

presented within a single, static model. Kozlowski et al. (1999) argue that team tasks

follow a cyclical path that leads to the creation of learning skills at different stages of

development. Thus, “team compilation is a sequence of modal phases and transition

points” (Kozlowski et al., 1999: 248). The notion of time as an environmental factor

linked to goal achievement in an episodic framework is the fundamental assumption of

the temporally based framework of team processes offered by Marks et al. (2001), who

argue that team performance is best depicted by a series of related input–process–

output episodes in which outcomes from earlier episodes serve as inputs for the later

cycles. As a result, teams engage in two main types of interactions, which follow each

other sequentially: action processes that contribute directly to accomplishing goals and

transition processes that pertain to the evaluation and/or planning activities to guide

team goal accomplishment. Both action and transition phases are accompanied by

interpersonal team processes, such as conflict management, motivation and confidence

building, and affect management.

2.2.3. Limitations

In addition to the methodological limitations, such as the use of laboratory

experiments, this group of studies offers limited applicability to studying GAM teams

because of their low generalizability, specifically, their varying explanatory power and

predictability across different types of groups. Although many of the constructs and

findings can be useful for understanding the way GAM teams interact and perform,

some major differences remain between small groups in organizations and this form of

teamwork. First, the responsibility of GAM teams is to manage relationships with the

43

largest and most important customers, which involves complex interactions with

groups external to the organization (customers, competitors), whereas most small

group research uses more bounded or isolated groups as the unit of analysis. Second,

GAM teams are usually cross-functional and geographically dispersed, so they must

manage complex relationships both within the team and with other parts of the

organization. These challenges are not evident in studies of smaller organizational

teams. Third, the general models of team effectiveness usually assume that teams form

to perform well-defined tasks and have clear goals. In the dynamic GAM context,

however, tasks are likely to be unstructured, customer-specific, and evolving over the

course of the customer relationship. Furthermore, the involvement of people from

different functions and country organizations can lead to a complex matrix in which

GAM members report to different managers and have blurred tasks and

responsibilities (Birkinshaw et al., 2001). All these aspects necessitate the examination

of team research in areas more closely related to GAM. Consequently, the next section

reviews literature on cross-functional teams.

44

2.3. Cross-functional teams

Cross-functional teams are widely used in companies, particularly those that want to

cut cycle times for new product development (Ancona and Caldwell, 1992c; Hitt et al.,

1993). The most typical form of cross-functional teams is a working group that links

with multiple subunits of the organization and overlays existing functional structures.

Such teams are designed to integrate the expertise of different functions and may

include planning, quality, process improvement, product development, or various ad

hoc project teams. Cross-functional teams differ from conventional teams in several

aspects. First, they include members from various parts of the organization who

possess competing social identities and perform tasks for other subunits (Alderfer,

1987). Second, the team’s lifespan is usually short, because such teams form

specifically for temporary tasks. Third, the performance expectations of cross-

functional teams usually differ, in that they are concerned with more subtle and

intangible outputs such as innovation and knowledge dissemination (Denison et al.,

1996).

In searching for the determinants of effective new product development, studies in this

area identify two groups of factors: team diversity in terms of the functional expertise

and skills represented by members and the ability to span organizational borders to

integrate various inputs (Fruin, 1996). Due to the importance of these two factors, the

next sections contain more detailed examinations of them.

2.3.1. Functional diversity

Empirical research on functional diversity in teams has shown both positive and

negative links to performance. In a thorough review of team diversity literature,

Williams and O’Reilly (1998) find that functionally diverse teams perform at a higher

level than teams composed of members from similar functions. For example, cross-

functional teams produce more successful administrative innovations (Bantel and

Jackson, 1989), developing clearer strategies (Bantel, 1993), react more appropriately

to environmental change (Keck and Tushman, 1993), respond more effectively to

competitive threats (Hambrick et al., 1996), and take less time to implement

45

organizational changes (Williams et al., 1995). From an internal perspective, such

teams tend to possess a wider range of viewpoints, which leads to more extensive and

creative information exchange within the team (Glick et al., 1993). Externally, they

also are likely to communicate more often and more successfully, which ensures more

external resources for the team (Ancona and Caldwell, 1992a).

At the same time, functional diversity can have some negative consequences due to the

inherent differences in opinions and perspectives. Such consequences can include

increased conflict, reduced group cohesion, and communication difficulties within the

team (Knight et al., 1999; Pelled et al., 1999; Wagner et al., 1984). In some cases,

diversity in function even compromises performance (Murray, 1989; Simons et al.,

1999). As a result, studies that review the extensive body of group diversity literature

(Milliken and Martins, 1996; Williams and O’Reilly, 1998) conclude that functional

diversity is a double-edged sword that has both positive and negative effects,

depending on the context and the process or performance variables being investigated.

Bunderson and Sutcliffe (2002) challenge these findings by arguing that the difference

in effects could be a function of not only the examined context or dependent variables

but also the way in which functional diversity has been conceptualized and measured.

They imagine the construct in two ways: dominant and intrapersonal functional

diversity. Dominant function diversity refers to the extent to which team members

differ in the functional areas in which they have gained the most experience during

their careers. The larger the number of functions represented in the team in this way,

the broader the knowledge base of the team should be. Intrapersonal functional

diversity, in contrast, refers to the diversity represented in the functional backgrounds

of individual team members, that is, the extent to which each member is a narrow

functional specialist with experience in a limited number of functions or a broad

generalist who has worked in various functional areas. The two conceptualizations are

fundamentally different; dominant function diversity pertains to the breadth of

functional experiences at a team level, whereas intrapersonal functional diversity

concerns functional diversity at an individual team member level. Bunderson and

Sutcliffe (2002) find empirical evidence that intrapersonal functional diversity relates

positively to performance, though the relationship is mediated by the information

46

sharing process. However, the results for dominant function diversity suggest a

negative influence on performance, only partially mediated by information sharing,

which indicates it may lead to further negative consequences, such as increased

conflict, slower decision making, and coordination difficulties. Thus, teams composed

of functionally broad members likely perform better, because these members are more

motivated to share information and understand one another’s positions; they also are

less susceptible to stereotypes and biases about other functions, which could impede

their successful collaboration.

With a few exceptions (Ancona and Caldwell, 1992b; Pelled et al., 1999), research on

functional diversity exclusively addresses top management teams, which differ from

more conventional cross-functional teams. To expand its scope, this literature review

also considers studies of other organizational groups, such as product development

teams. In their model of new product team performance, Ancona and Caldwell (1992b)

examine the direct and indirect effects of group heterogeneity on team performance

and emphasize the intervening role of internal and external group processes. The

critically important elements of team diversity in such teams are organizational tenure

and functional diversity. Those who join the organization at a similar time tend to have

more opportunities for interaction, develop similar experiences and perspectives, and,

as a result, create more cohesive work groups. Similarly, it may prove difficult for

people from different functional areas to develop an effective work process because of

the different perspectives they bring to the team. Consequently, Ancona and Caldwell

(1992b) predict that tenure and functional diversity have negative influences on

internal task processes but relate positively to external communications. They reason

that diverse team members have access to a wide set of external networks they can use

to accomplish group goals. In addition to the indirect link with performance through

the processes, the two demographic variables may be directly and positively related to

performance (both team- and managerial-rated). Although the authors expect them to

have similar effects, the empirical results indicate that each variable has its own

distinct effects. Thus, heterogeneity in terms of tenure has a positive influence on

internal task processes, such as goal and priority clarification, which in turn lead to

higher team ratings of performance. In contrast, the diversity of represented functions

47

enhances external communication, which is associated with higher managerial ratings

of performance. However, the direct impact of both independent variables on team

performance is negative and significant, which indicates that the link between diversity

and team performance may not be straightforward. A possible explanation for these

conflicting results could be a missing mediating variable, such as conflict, that

impedes performance. Thus, for example, cross-functional teams may have strong

creative potential but poor implementation because of their weaker teamwork

capabilities or because they lack social cohesion, which prevents their effective use of

the information and resources obtained from the outside.

Building on Ancona and Caldwell’s (1992b) suggestion that further research should

assess the mediating effect of conflict, Pelled et al. (1999) develop a model of the

relationships using diversity, conflict, and performance and test it with cross-

functional teams responsible for new product and process development. In this model,

tenure and functional diversity represent job-related types of diversity, and three other

heterogeneity variables are added, namely, age, gender, and race. Task conflict,

defined as disagreement about task issues such as goals, procedures, and courses of

action, is mostly driven by job-related diversity and enhances cognitive task

performance. They argue that differences in viewpoints encourage teams to gather

more information, analyze issues more extensively, and reach alternative solutions. In

contrast, the three other diversity constructs lead to emotional conflict, which

decreases performance due to interpersonal clashes. Similar to Ancona and Caldwell

(1992b), these authors conclude that the performance of cross-functional teams can be

influenced in both positive and negative directions by various team composition

characteristics and that different types of team diversity lead to different results. Here,

the benefits of functional heterogeneity may be counterbalanced by other

dissimilarities among team members, which increase conflict and diminish

performance. The novelty of this research is that it introduces conflict as a key

intervening variable and delineates two types of conflict.

48

2.3.2. External activity

The other factor likely to determine the performance of cross-functional teams is the

interaction between the team and its context. Although organizational context and an

organization’s impact on teams appear among the variables of the group models

discussed previously (Gladstein, 1984; Hackman, 1987), these studies consider context

as a given and depict teams as without much control over it. Thus, groups get treated

as closed systems, in which the main issue of interest is the interaction among team

members. From a resource-dependence perspective however, groups actively manage

their relations with external parties to secure access to vital resources and information

(Pfeffer, 1986). This activity is particularly prevalent among teams, which depend

extensively on inputs from other units of the organization and therefore need to gain

support from those entities or senior management. Consequently, Ancona and

Caldwell (2000) argue that interactions between teams and external environments

should be studied in a more action-oriented manner and that managers should

configure teams specifically to maximize external boundary-spanning activities. As

this description often holds true for GAM teams, some implications for the study of

GAM teams can be drawn from this limited body of literature.

A few studies have addressed different aspects of the interaction between a team and

its context. For some, the focal point is the amount of information exchanged with

external entities; they argue mostly that teams should match their information-

processing capability to the information-processing capability of the context (Nadler

and Tushman, 1988; Gresov, 1989). Others examine more specific types of external

interaction, such as boundary spanning and the transfer of technical information in an

R&D context, and identify various communication roles, such as stars, gatekeepers,

and liaisons (Allen, 1984; Tushman, 1977, 1979; Tushman and Katz, 1980). For

example, Tushman and Katz (1980) suggest that certain boundary-spanning persons,

whom they label gatekeepers, can fulfill an important linking function between project

teams and outsiders: They develop links to external domains and facilitate

communication. Empirical findings support the hypothesis that teams with gatekeepers

perform better than teams without such mediators, which implies that external

communication is positively associated with team performance.

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Although these researchers demonstrate the importance of external communication

and identify some key dimensions, they analyze only a small number of specific

aspects. A more extensive, “external” perspective of teams mostly is associated with

the work of Ancona and Caldwell (Ancona, 1987, 1990; Ancona and Caldwell, 1988,

1992a, b, c, 2000). For example, Ancona (1990) describes the role orientations of

consulting teams in an educational organization and reveals three strategies that teams

adopt toward their environment: informing, parading, and probing. Informing teams

remain concentrated on internal processes and relatively isolated from the outside

world, whereas parading teams take a more balanced approach of simultaneously

building internal cohesion and achieving external visibility for the team’s work and

importance. However, this latter group exhibits high levels of passive observation of

the environment. In contrast, probing teams stress external processes and require their

members to engage outsiders actively in the teamwork to understand their needs and

test possible new solutions. This final type of team achieves the highest performance,

though they rank poorly in terms of cohesiveness and member satisfaction in the short

run. That is, external activities are better predictors of performance. However, this

study examines teams with a high degree of external dependence in a new and

demanding environment, so these results might not be valid in contexts with lower

complexity.

Ancona and Caldwell (1992a) extend this research and propose a taxonomy of external

activities and strategies based on an empirical investigation of new product teams. The

most common activities include ambassador (buffering and representation), task

coordinator (technical coordination), scout (information gathering and scanning), and

guard (protecting internal information). Teams use different strategies according to the

combinations of their activities. Some are specialized (e.g., ambassadorial or technical

scouting teams). Others prefer a generalist approach and engage simultaneously in

ambassadorial and technical scouting activities. Yet another group chooses an

isolationist strategy with low levels of external interaction. These four strategies, as

well as their relationship to performance and internal processes, are similar to those

identified by Ancona (1990). In contrast to propositions from information processing

theory, the type of external activities rather than the amount and frequency of external

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communication determines performance. Specifically, according to the empirical

evidence, technical scouting and isolationist strategies suffer poor performance,

whereas the ambassadorial strategy performs well in the short run and comprehensive

teams are the best performers. These findings imply that managing the power structure

and persuading others to support the team has positive consequences in the short term.

However, only teams that successfully manage both the power structure and the

workflow structure (i.e., both external and internal activities) can maintain their

performance over time. Such comprehensive teams usually create positive cycles of

external activities and internal processes, which enables them to meet budget and

schedule demands in the short run and develop innovative products in the longer term.

Similar results are reported by Zirger and Maidique (1990) in their study of new

product development processes in high-tech companies. This finding has a very

important implication for GAM teams; namely, their performance depends on how

they manage to balance the needs for external interactions and intrateam collaboration.

Although literature on cross-functional teams has generated some valuable insights

into the way such teams function, it has not provided a more comprehensive

framework for understanding team effectiveness. Aiming to overcome this limitation,

Denison et al. (1996) develop and validate a diagnostic model that focuses on three

domains: organizational context, internal process, and outcome measures. The key

variables defining the context in which cross-functional teams operate include

coordination with other teams, autonomy and power, linkages to functions, resources,

mission and direction, and reward for team performance. Thus, context is depicted in a

broader way to include both external process dimensions, such as coordination with

other teams and functions, and organizational factors, which are less likely to be

determined by the team itself. The primary contribution of these findings is that they

identify hierarchical, lateral, and function-specific links, missing from previous

research (Ancona and Caldwell, 1992a, b). The process dimension encompasses

aspects of intragroup interaction such as norms, the importance of work, effort,

efficiency, creative strategy, and breadth. This conceptualization is based on

Hackman’s (1987) process criteria of effectiveness but extends to include measures

such as breadth of perspectives and efficiency. Another contribution is its

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identification of a broader set of outcome measures than those in previous models. The

range of performance indicators includes factors such as information creation, time

compression, image expansion, learning, growth satisfaction, capability development,

and overall effectiveness. These results imply that both short- and long-term measures

should be applied to assess performance appropriately. Although Denison et al. (1996)

provide a broad definition of concepts and a useful framework for studying cross-

functional teams, they do not reveal the interactions among their lists of variables. As a

result, it remains unclear how different aspects of context and process affect

performance.

2.3.3. Limitations

Research on cross-functional teams helps forward the understanding of GAM teams,

because it shifts the focus from small and relatively isolated groups to more complex

teams, which must manage relations with various stakeholders in the organization. It

also offers some useful implications, such as the important roles of external activity

and context. In addition, it uses more empirical research methods to investigate real

organizational teams.

However, several limitations are common to cross-functional team literature. First, the

effect of different structural designs on team performance has not been extensively

researched. Apart from the impact of functional diversity and tenure (Ancona and

Caldwell, 1992b), very little is known about other dimensions of team composition,

such as the role of the team manager, team size, and skills other than functional

expertise. Second, most studies focus on task-focused, limited-duration teams that

form for a specific project and are dismantled after its completion. In contrast, GAM

teams create and maintain long-term relationships with global customers. In turn, their

long-term, relation-oriented goal often clashes with the short-term, profit-driven

objectives of some team members or units that must support the team. This conflict

heightens the importance of factors such as role clarification, empowerment, rewards,

and top management support, which do not appear in cross-functional team literature.

Moreover, the short-term nature of cross-functional teams stresses a different skill set

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compared with that required by GAM teams. Although cross-functional team

performance depends on the team’s ability to develop a collaborative team process

during early stages to avoid conflicts (Ford and Randolph, 1992), their emphasis is

primarily on team members’ product or functional expertise rather than their

relationship-building and communication skills. In contrast, GAM emphasizes

intercultural, social, managerial, and strategic skills, which are stronger predictors of

people’s ability to work together over time, handle less structured tasks, and achieve

long-term goals. As a result, to integrate literature on teams whose natures are more

similar to GAM teams, the next section reviews research contributions on team selling.

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2.4. Sales teams

Although much more complicated and far-reaching than traditional selling, GAM can

be subsumed within the wider context of sales and marketing management.

Consequently, the body of research on team selling may be a fruitful source of ideas

for the conceptualization of GAM teams.

Functional groups other than sales play increasing roles in servicing key customers

(Hutt et al., 1985). As a result, the literature in this field distinguishes between core

selling teams, which have a permanent responsibility for specific accounts, and the

larger selling center, which includes members of other functional units on a part-time

or ad hoc basis. Before models of team processes and performance can be analyzed, a

clear understanding of the composition of these teams is necessary, which implies (1) a

definition of the selling team and (2) a delineation of functions and roles involved in

the sales process.

2.4.1. Team selling definition

The terms selling team and selling center are often used interchangeably and

inconsistently in academic literature; some authors use one term to describe groups

that are relatively permanent and customer-oriented, whereas others use the same term

for groups of a rather temporary and transaction-oriented nature. Hutt et al. (1985: 38)

refer to a selling center as a rather permanent formation and note that “a national

account program might be considered a formalized selling center.” In contrast, Moon

and Armstrong (1994) define the selling team as a relatively permanent, customer-

focused group whose primary objective is to establish and maintain strong customer

relationships. Membership in such teams is relatively stable and determined by

assignment to a specific buying organization. The selling center, however, refers to a

transaction-focused group with the objective of successfully completing a specific

sales task. Its membership is very fluid and determined by involvement in a sales

transaction for a particular good or service. In practice, it might be difficult to

distinguish where the team ends and the selling center begins, so it may be helpful to

think of these forms not as mutually exclusive but as endpoints on a continuum (Smith

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and Barclay, 1990). On the basis of this conclusion, this literature review involves

studies that explore groups at the higher end of the continuum in terms of long-term

customer focus. Although both selling team and selling center terminology may be

employed, in neither case does this review refer to less stable, transaction-oriented

formations.

Selling center literature has emerged from the observation that in certain conditions,

the supplier–customer interaction involves a large number of people on both sides.

Therefore, the selling center concept is an extension of the buying center idea,

originally developed by Robinson et al. (1967) and later extended by various authors

(e.g., Bunn, 1993; Johnston and Bonoma, 1981; Lau et al., 1999; Spekman and Stern,

1979; Wilson et al., 1991).

Similar to the buying center, which consists of “those members of an organization who

become involved in the buying process for a particular product or service” (Johnston

and Bonoma, 1981: 143), the selling center includes persons from the selling company

who are involved in the selling process to a particular customer. Hutt et al. (1985: 33)

describe them as “the organizational members who are involved in initiating and

maintaining exchange relationships with industrial customers.” This definition

recognizes that the special requirements of the buyer necessitate the involvement of

functional areas such as physical distribution, R&D, manufacturing, and technical

services in the selling process. Therefore, the selling center can be depicted as an

interfunctional decision unit with representatives from both inside and outside the

selling organization. In turn, one of the key dimensions of selling teams is functional

interdependence.

2.4.2. Functional interdependence

Spekman and Johnston (1986) stress that all parts of a company are linked inextricably

to the success of the marketing and selling plan and that value to the customer is often

a result of joint activities that converge to satisfy the customer’s purchasing criteria.

This convergence requires the management of interdependencies among functional

units, which is ultimately driven by the corporate plan and therefore should involve

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both senior management and sales and marketing management. The first step in

building an effective selling center is the diagnosis of the degrees of functional

interdependence that exist in the organization and their effect on competitive

advantage. This type of analysis can enhance understanding of the responsibilities to

be undertaken by different functions to serve the customer and can help identify

deficiencies in critical areas that must be assessed and eliminated.

The selling center consists of people from different functional units who are

responsible for different tasks; the key salespeople must initiate and manage

communications with the functional specialists. Facilitating and coordinating

cooperation among the functional units is crucial because each department is likely to

have different objectives and priorities, as well as different views of the customer

(Spekman and Johnston, 1986).

On the basis of social systems theory and resource dependence models, Ruekert and

Walker (1987) develop a framework that clarifies the interaction across different

functional areas and different types of marketing positions. Using the system-structural

perspective (Van de Ven, 1976), it explores the interrelations among three groups of

dimensions: situational, structural and process, and outcome. The centerpiece of the

framework is the interaction process between marketing and other functional areas,

which is divided into transactions, communication flows, and coordination patterns.

The authors propose that transactions between marketing and other functions take the

form of exchanges of resources, work, and technical assistance. At the same time,

these interactions require information flow exchanges, which are determined by the

amount of communication, the difficulty of communication, and the degree of

formalization. Furthermore, interaction must be coordinated through formal rules and

procedures, informal influence, and/or conflict-resolution mechanisms. Each of these

three process dimensions is influenced by the context within which the interaction

takes place, so the framework distinguishes between two groups of contextual

dimensions: internal environmental conditions including resource dependence, domain

similarity, and strategic imperatives and external environmental conditions, such as

complexity and turbulence. Finally, the interaction process determines the nature of

the performance outcomes, explicitly divided into functional and psychosocial

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outcomes. Beyond the functional result of achieving the goals desired by marketing

and the other functions, psychosocial outcomes, such as the perceived effectiveness of

the relationship and conflicts, determine the willingness of members to participate in

similar forms of cooperation in the future.

This framework is generally supported by the preliminary empirical tests, but though

the relations between situational and process variables earn strong support from both

marketing and nonmarketing respondents, the impact of the interaction process on

outcomes produces mixed results. The perceptions of the effectiveness of the

relationship are inconsistent across functions, which suggests that marketing’s

relations with other areas might depend on the kind of resources being exchanged.

2.4.3. Selling center composition

The second step in understanding the selling center involves an examination of its

composition, coordination, and control mechanisms. Johnston and Bonoma (1981) use

five dimensions to describe the composition of the buying center: (1) vertical

involvement, or the number of hierarchical levels of the organization represented in

the center; (2) horizontal involvement, or the number of different departments

involved; (3) extensivity, or the number of members belonging to the center; (4)

connectedness, or the degree of direct communication links between members; and (5)

member centrality, or the number of communication links between a member and

other center participants divided by the total number of possible links.

Moon and Armstrong (1994) hypothesize these five dimensions also can apply to

selling center composition, according to several factors. First, if the purchase situation

contains a higher degree of novelty for the buyer, the selling center will achieve

greater vertical and horizontal involvement, as well as greater extensivity and

connectedness. Novel situations require more extensive information flows to the buyer

and make customer needs more uncertain, which necessitates more complex

communication patterns and the involvement of more functions and management

levels. The same effects are likely when the sales situation is more novel to the

supplier. This hypothesis matches Hutt et al.’s (1985) finding that new selling

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situations demand more information and problem solving by the selling organization.

In addition, such situations require more substantial management involvement and

hence result in higher levels of vertical integration. Novel selling situations also have

important implications for the coordination and control strategies adopted by the

selling firm. Because such situations are characterized by higher interdependence and

task uncertainty, less formal coordination strategies, such as spontaneous contacts

between managers and greater horizontal information flows, likely will be more

appropriate (McCann and Galbraith, 1981).

Second, the greater importance of the sales opportunity to the customer and the

supplier may make the selling center larger, wider, and more connected. The logical

explanation is that situations of strategic importance tend to require greater resource

commitments to meet objectives.

Third, Moon and Armstrong (1994) find that selling center composition is influenced

by the experience of members. Low levels of experience relate to greater centrality of

the member due to his or her need to obtain more information and guidance from other

members. Lack of experience also leads to greater vertical integration, because it

necessitates more extensive management involvement and monitoring.

2.4.4. Roles of selling center members

Buying center literature distinguishes among the different roles of buying center

members. For example, Robinson et al. (1967) identify deciders, influencers,

gatekeepers, users, and buyers. Selling center members likely also play distinct roles.

Moon and Armstrong (1994) define five such roles: initiator, coordinator, resource,

approver, and implementer. As the name suggests, the initiator first recognizes a

business opportunity and seeks support within the supplier company to realize that

opportunity. The coordinator’s role is to ensure that all members work together

effectively, while the approver reviews their work and suggests improvements. The

resource and implementer provide expertise and carry out the main tasks.

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Hutt et al. (1985) outline five roles as well, namely, responsible, approve, consult,

inform, and implement. The responsible role, similar to the initiator role of Moon and

Armstrong (1994), takes the initiative to analyze customer needs, develops

alternatives, and makes an initial recommendation. The approval and implementation

roles are also analogous, whereas the consulting reflects slightly different content.

2.4.5. Selling team frameworks

A summary of the most relevant selling team frameworks appears in Appendix C.

Despite its potential managerial implications, research on the link between selling

team composition and team performance remains scarce. Using the contributions of

Ruekert and Walker (1987), Moon and Armstrong (1994), and Souder (1987), Moon

and Gupta (1997) develop an input–process–output model that links the situational,

structural, and outcome dimensions of selling center formations. Whereas Ruekert and

Walker (1987) include both internal and external environmental conditions as

determinants of team structure and process, this framework proposes that only the

internal environment has a direct influence on these dimensions. Furthermore, it

suggests different variables characterize the internal company environment—market

orientation and information infrastructure. The influence of external environmental

complexity is limited to its moderating effect on the link between the size and shape of

the selling center and functional outcomes. This conceptualization of selling center

structure meets Johnston and Bonoma’s (1981) dimensions, as adapted by Moon and

Armstrong (1994). However, in this case, the connectedness dimension represents a

process rather than structural variable and therefore, together with difficulty of

communication, describes the communication processes among selling center

participants. Adapted from Ruekert and Walker’s (1987) model, the suggested

outcomes are based on both functional and psychosocial variables. This research

identifies customer responsiveness as an additional functional outcome, and by

reflecting the nature of the selling center better, it emphasizes the crucial task of

satisfying customer requests to remain successful during both current sales

opportunities and future interactions (Wilson, 1995). The framework enhances

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understanding of the structure, functions, and performance effects of selling centers, as

well as the factors that can enhance or inhibit an organization’s ability to seize and

deliver on selling opportunities. However, its lack of empirical tests of the

hypothesized interrelations constitutes a major limitation that has not been addressed

in subsequent studies.

Another framework of team selling effectiveness developed by Smith and Barclay

(1993) represents a concerted effort to understand team selling and draws on

constructs from the multidisciplinary small group research stream to create an

integrative conceptual framework. Based on McGrath’s (1964) input–process–output

view of team functioning, the framework includes a long list of variables at the

individual, group, organizational, and task/environmental levels. Its major contribution

is the broad array of identified factors and relationships involved in team selling

effectiveness and their delineation at four distinct levels.

In a next step, Smith and Barclay (1993) refine the framework by eliminating the

variables with relatively lower theoretical and managerial relevance for a team selling

context. The resulting team selling model identifies key constructs at the individual,

group, and organizational levels and hypothesizes that they influence perceived task

performance either directly or indirectly through their effect on two group process

factors: selling center cohesiveness and boundary management. Thus, the effectiveness

measure with the highest relevance is the subjective evaluation of the degree to which

a selling center achieves its goals and objectives. This approach suggests that group-

level outcomes are more appropriate performance measures than individual and

organizational outcomes in studies of selling centers. Furthermore, it improves on

objective performance outcomes, which are difficult to measure in a team selling

context (Gladstein, 1984). However, it still allows for a strong evaluation basis,

because it can be measured by the team, its management, and/or customers. According

to this revised framework, selling center cohesiveness and boundary management,

which reflect both the internal processes of belonging to and actively participating in a

team and the external processes of communication and resource acquisition, are

influenced by five constructs at three levels: (1) individual level, with participative

leader behavior and supportive leader behavior; (2) group level, including member

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interdependence and potency; and (3) organizational level, or market orientation.

Although the individual and organizational level factors should influence effectiveness

only indirectly through the process variables, the group-level inputs influence

perceived task performance both directly and indirectly. The idea that the selling task

is influenced by both internal and external interaction processes is similar to the

concept at the heart of Ruekert and Walker’s (1987) and Moon and Gupta’s (1997)

models. However, Smith and Barclay’s (1993) input dimensions present a different set

of variables and, most important, introduce the leader behavior and potency constructs.

This incorporation of leadership aspects responds to the suggestion of Perry et al.

(1999) that leadership and empowerment are major determinants of selling team

outcomes. Because sales managers ultimately have less time and opportunity to

micromanage sales teams as their span of control increases, Perry et al. (1999) argue

that empowerment and shared leadership can enhance team effectiveness. They

explain this claim by arguing that given sufficient power and authority to lead

themselves, teams develop greater levels of collaboration and coordination and make

better decisions that are based on the unique skills of all members. Katzenbach and

Smith’s (1993) study of teams also provides evidence that high-performance teams

engage in shared leadership much more often than do other teams.

In their model of empowered selling teams, Perry et al. (1999) identify five basic types

of leadership behavior (transactional, transformational, directive, empowering, and

social supportive) that team members might share. Although empowered teams are

responsible for making decisions on their own, they cannot function completely

independently; therefore, the model assumes that vertical leadership also plays a role

by influencing team characteristics and shared leadership. However, the vertical

manager’s role differs from that portrayed in traditional models of vertical leadership,

because the responsibilities include team design, boundary management, contingent

support, and facilitating empowerment within the team. The inputs to the model reflect

two aspects: team and task characteristics. Perry et al. (1999) identify five key

composition characteristics: proximity, ability, maturity, diversity, and team size.

Whereas higher ability and maturity likely lead to shared leadership, the effect of the

other three variables is less clear. As teams become larger, more dispersed, and more

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diverse, the increasing difficulty of communication and coordination make shared

leadership less likely. However, the process of shared authority and decision making in

such conditions can provide a better mechanism for team interaction than would a

single vertical leader. Finally, the model distinguishes between two types of outcomes:

intermediate, represented by affective/cognitive and behavioral responses to the shared

leadership process, and ultimate, reflected in team effectiveness. Arguing that selling

team effectiveness should not be measured solely by sales volume at a single point in

time because of the long-term nature of developing customer relationships, the authors

suggest both qualitative and quantitative effectiveness measures. The qualitative

criteria include ratings by the team, its management, and the customer, whereas the

qualitative measures focus on sales volume and profitability.

Although the reviewed models exhibit a wide variety of relevant variables and

hypothesized relationships, several common themes emerge. First, all models are of

the input–process–output type, which indicates that team processes play important

intervening roles in the team characteristics–performance relationship. Although some

input variables also may have direct effects on performance, the influence usually

works through the internal and external interactions of the team, which then shape the

outcomes. Second, team composition and process are rarely considered in isolation;

rather, the models consider the influence of environmental factors and the selling task

characteristics, though in different ways. For example, the nature of the selling task

and environmental complexity appear as independent variables in some models (Perry

et al, 1999; Ruekert and Walker, 1987) but as moderators of the link between team

composition and performance in others (Moon and Gupta, 1997). Third, this literature

appears to accept that outcomes have two components, functional and psychological.

Furthermore, the studies recognize that quantitative indicators such as sales volume

and profitability are likely to be affected by many factors external to the team and the

company. Consequently, such measures are usually avoided, and team performance is

conceptualized as perceived task performance or the accomplishment of specified

goals.

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2.4.6. Limitations

Selling team research can offer some insights into GAM teams because it involves

tasks that are more similar to those of GAM teams. However, its implications also are

limited because team selling literature does not capture the difficulty of managing

multinational teams that cross geographic and business unit boundaries. Furthermore,

the focus is primarily on selling, whereas the role of GAM teams goes far beyond sales

and includes developing innovative solutions for the customer and coordinating

processes along the entire value chain. The key limitation of team selling research,

however, is the lack of empirical validation of the proposed models. All are conceptual

and unsupported by empirical research.

2.5. Summary of research gaps

In summary, no clear conceptualization of what constitutes a GAM team exists, which

highlights the need for an integrated framework to address this research gap. The

review of organizational behavior studies indicates a wide range of team constructs

and relationships, but most findings are context specific with low degrees of

generalizability, making it difficult to compare results and preventing the direct

transfer of frameworks and concepts to a GAM context. For example, GAM teams

differ from (1) pure sales teams in that they reflect the increased organizational

complexity, cultural diversity, and complexity of solutions associated with global

customers; (2) cross-functional teams in that they have a long-term, continuous task;

and (3) other small groups in that they have a much higher degree of external

interactions, blurred boundaries and responsibilities, and less structured tasks.

Consequently, as a distinct type of group, the GAM team requires more profound

investigation to identify which of these constructs are most relevant for team

performance in a global customer management context.

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3. Conceptual framework

3.1. Framework development

3.1.1. Overview

The findings from the literature, together with the research objective and questions,

determine the research design that led to the development and validation of the

conceptual framework. On the one hand, the general phenomenon of teamwork is not

new, and prior studies have developed a variety of concepts to explain it, which favors

a more quantitative approach. On the other hand, the specific context of teamwork in a

GAM setting remains underresearched and requires further elucidation of the relevant

concepts, which makes qualitative research methods more appropriate. To address

both sides, this research follows a two-stage approach. The first phase, exploratory and

qualitative in nature, aims to deepen understanding of GAM teamwork by determining

which of the wide array of variables proposed in the literature have the greatest

relevance in a GAM context and exploring whether any additional factors determine

team formation, interaction, and performance. This phase ends with the development

of the framework. The second stage is quantitative and builds on the first by

empirically testing the framework that emerges from the investigation.

In the beginning of this research, the scope of the phenomenon and the related

concepts were relatively unclear, which required a flexible and unrestrictive research

design. Therefore, a qualitative approach appeared the best way to start the research

process. The qualitative approach provides a deeper understanding through less

structured research techniques and more open-ended questions. In turn, it provides

more flexibility and greater possibility to explore various directions. Furthermore,

qualitative data offer rich descriptions of processes in a local context and preserve

chronological flows, which enables researchers to determine which events lead to

specific consequences (Miles and Huberman, 1994). They are also useful for

explaining why emergent relationships hold and providing an understanding of the

dynamics of a phenomenon (Eisenhardt, 1989). Thus, data provided through

qualitative research represent the best strategy for exploring a new area and developing

hypotheses.

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An evaluation of the major phenomenological, or qualitative, methodologies—action

research, case studies, ethnography, grounded theory, hermeneutics, and participative

enquiry (Collis and Hussey, 2003)—reveals that an approach based on grounded

theory would be the most appropriate for framework development. Originating from

Glaser and Strauss’s (1967) work, this methodology entails a systematic set of

procedures that develop a theoretical framework through an iterative process of

alternating inductive and deductive methods. Theory development follows a

progression in which the researcher collects information, develops proposals or

hypotheses on the basis of the collected data, and then goes back to the field to test the

hypotheses or fill in missing information. This cycling back and forth between theory

construction and data examination results in continuous improvements and a solid

grounding for the emerging theory (Glaser and Strauss, 1967).

3.1.2. Qualitative study description

The qualitative study that assisted the framework development is based on 13

exploratory in-depth interviews with GAM experts from 12 companies conducted

during May–July 2006. The list of interview partners appears in Appendix D. In

selecting interviewees, I followed the principles of theoretical sampling, which

requires the sampling to develop the researcher’s theory, not represent a population, as

is common in interview research (Strauss and Corbin, 1990). The criteria for selecting

the companies were as follows:

- Large multinational companies with an established GAM program.

- Extensive use of GAM teams.

- Different extent of GAM development.

- Representative of different industries.

First, I identified a sample of companies that meet these criteria, according to

knowledge gleaned from various practical projects and research into practice-oriented

literature (primarily publications of the Strategic Account Management Association),

which provides examples of the GAM practices of different multinational companies.

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Second, I identified the most appropriate informants in these companies, namely, the

person responsible for managing GAM teams. At this stage of the research process, it

was crucial to obtain an overview of GAM team–related issues across the entire

company, so the informant had to have a broader perspective and objective

observations of the determinants of team functioning and performance. Thus, the

interviewees were all senior managers either in charge of the entire GAM program or

who led a GAM team.

All interviews were face-to-face and semi-structured. The semi-structured approach

guarantees that a predetermined set of questions guides the interview but still leaves

enough flexibility to explore new and unanticipated issues. Interviewees received the

initial list of questions (see Appendix E), together with a description of the research

project, in advance to make them more comfortable about participating in the

interview and ensure their better preparation.

The first part of each interview centered on general GAM team issues, such as the

number of teams and team members, responsibilities and tasks performed by the

teams, positioning within the organization, and reporting lines. The second part

concentrated on key topical areas: context, design dimensions, team processes, and

performance criteria. The questions pertained to the most important factors in each

area and how they influenced other areas. Using both the laddering approach (Durgee,

1986) and the narrative approach (Mishler, 1986), which are common for qualitative

research of this type, follow-up questions served to explore these issues in greater

detail.

Eleven of the interviews were audio-recorded; however, in two cases, the respondents

did not feel comfortable with recording, so only hand-written notes are available. In all

cases, the interviews were transcribed immediately after the meeting to capture any

information that may be potentially useful for the analysis. The data collection process

overlapped with the data analysis, which means the data collection process could be

adjusted as necessary, a key advantage of qualitative research (Eisenhardt, 1989).

Therefore, after each interview, the data were analyzed and the findings compared

with existing research. Some preliminary conclusions emerged after each analysis,

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which then provided an expanded basis for the subsequent interviews. For example,

when earlier interviews did not provide sufficient responses to some questions and the

analysis revealed the need for more information or a different direction, subsequent

interviews could address these gaps. This approach also resulted in some questions

being omitted or added during the interviews. Because the goal was to understand the

phenomenon with as much depth as possible, altering the question set is considered

completely legitimate (Eisenhardt, 1989).

The analysis of the collected qualitative data aimed to find integrative themes that

could capture the entire range of issues represented in the data. This process followed

the coding methodology described by Charmaz (2002). In the first step, initial or open

coding of each interview, detailed codes constructed for each interview transcript

indicated categories that simultaneously describe and dissect the data. The codes

capture the participants’ implied and explicit meanings and, at the same time, preserve

the flow of events or ideas expressed by the participant. In the second step, selective or

focused coding served to identify the codes that appear most frequently to sort and

synthesize the data into a comprehensive framework. These focused codes are more

general and yet more analytically incisive than the initial codes, because they cut

across multiple interviews and categorize recurrent themes more precisely (Charmaz,

2002). A reanalysis of relevant literature supported my decisions about which focused

codes to adopt and include in the conceptual framework. Thus, the emerging

conceptual framework was grounded in both theory and empirical findings. The next

section describes the framework and elaborates on each construct by discussing its

theoretical underpinnings and the related results from the qualitative analysis.

3.1.3. Summary findings from the qualitative study

This section explains how the interviews guided the research process and highlights

their contributions to the development of a relevant conceptual framework. The major

findings of the interview research are further described in the next section, including

specific examples from the experience of the study companies.

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The significance of the qualitative study results from several large areas of

contributions. First, the interviews provided a solid confirmation of the relevance of

the research topic and the need for more research in the area due to its novelty and the

fast growing use of GAM teams in practice. On average, the interviewed companies

have had a structured GAM program for 11 years and all managers stated that it has

played a vital role for the growth and overall success of their companies. The tendency

has been for these GAM programs to start from a relatively small pilot program with a

few selected customers and to develop over time to involve more customers and

account management personnel. As a result, many of the responsibilities that were

initially carried out by individual account managers are nowadays entrusted to entire

GAM teams who in many cases manage more extensive relationships and satisfy the

requests of ever larger and more demanding customers. With this comes increased

complexity in the organization and need for special attention on GAM team issues.

One of the interviewees expresses well the importance of developing a clear

understanding of all GAM team issues:

You don’t build something like this [GAM team] easily because you

basically change the structure of how decisions are taken and this creates

different areas of tension. Therefore, you need to balance things out and

above all to know all key elements that drive the behavior in the team and

the organization.

Identifying those crucial performance-shaping elements was the key objective of the

interviews. Therefore, their second key contribution is that in the process of creating

grounded theory the interview findings helped to determine which concepts from the

pool identified in literature are most relevant in a GAM context. Thus, the qualitative

research helped to both eliminate constructs and relationships that turned to be of little

importance and to refine others whose content and definition needed adaptation to the

GAM context. As a result, some concepts that have been traditionally employed in

sales teams context – vertical involvement and horizontal involvement – were

disregarded although there was some theoretical support for including them in the

framework. The interviews indicated that the number of people from various

departments involved (horizontal involvement) is important but only to the extent that

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it brings people from different functions and backgrounds that can share their

knowledge to create superior solutions for the customer. Therefore, the pure number

has significantly lower predictive power than variables such as the scope of skills and

diversity in expertise represented on the team. Similarly, the number of hierarchical

levels involved (vertical involvement) did not receive strong support in the interviews.

Instead, the interviewed managers singled out one particular level – top management –

whose involvement has proved critical for the GAM programs in all interviewed

companies.

In addition, the interviews provided strong support for several concepts from literature

– goal and role definition, structure, heterogeneity, adequate skills, leadership, rewards

and incentives, communication and conflict management – which led to the integration

of this constructs in the framework. Apart from confirming their relevance, the

qualitative study helped to crystallize their specific content in a GAM context. Thus,

out of the large number of heterogeneity aspects it helped to identify skills diversity as

the most relevant type of heterogeneity in this context, which resulted in a more

focused subsequent investigation of the concept. Furthermore, due to the nature of

GAM teams’ work, the interviews pointed to the need to study some of these

constructs in relation to the customer. Thus, the team structure variable gained a

different meaning from the one usually attached to it in team studies. The adapted

construct reflects the need for the team to both reflect the customer organization and fit

in the internal supplier organization. Similarly, the importance of aligning team roles

and objectives with those of the customer emerged from the interview research

findings.

The third key contribution of the interview research is that it helped to identify

concepts that are relatively new or underresearched in a team context. These include

empowerment, top management support, training and proactiveness. Although they

may appear in extant literature, they are not strongly emphasized as performance

determinants and would likely have been omitted from the framework were it not for

the findings from the interviews. The explanation is that these factors gain increasing

importance for GAM teams due to the higher complexity, the more dynamic

environment and the blurred boundaries in which teams operate. Furthermore, the

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interviews played a crucial role in identifying a novel relationship among the

constructs that was included in the framework. Whereas previous research traditionally

does not view relational and financial performance as interlinked, all interviewed

managers considered relational performance as a step towards improving financial

performance and emphasized the causal relationship between the two. Thus, this first

qualitative research stage helped to significantly enhance the understanding of GAM

teams gained through the literature review and to develop a more precise and relevant

framework, which increased the precision of the subsequent empirical study.

3.2. Conceptual framework

The conceptual framework presented in Figure 5 is based on the four key domains

identified through the literature review and exploratory interviews: team design,

organizational context, team processes, and team performance indicators. Although the

main focus is on team structure and composition, as well as their direct and indirect

effects on team performance, various factors related to the organizational context of

the team also emerged in the analysis and therefore appear in the framework.

Figure 5: Conceptual framework

Team relational performance

GAM team design

• Goal and role definition• Structure• Empowerment• Size adequacy• Heterogeneity • Adequate skills• Leadership

Organizational context

• Top management support• Rewards and incentives• Training

Team processes

• External communication/ collaboration

• Communication/ collaboration within team

• Conflict management• Proactiveness Team financial

performance

H1

H2

H3

H4

H5

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3.2.1. GAM team design

A GAM team must operate within multi-actor structures with different tasks and levels

of authority, and its success depends on its ability to secure continuous support and

manage the various technical, legal, economic, and political dimensions of the

relationship (Harvey et al., 2003b). In these circumstances, the design of the team

becomes a critical managerial issue because it determines the operating format and

effectiveness of the GAM effort (Harvey et al., 2003a). The key variables that

characterize GAM team design can be mapped along two main dimensions: team

organization and team composition (Gladstein, 1984; Smith and Barclay, 1993). In this

context, the organization refers to the relatively stable arrangements pertaining to the

division of work and the coordination and control mechanisms. Because it is a rather

broad concept, several organizational subdimensions also emerge from the analysis,

including goal and role definition, structure, size adequacy, and empowerment.

Composition, in contrast, here reflects the distribution of team member characteristics,

captured in the framework by the heterogeneity of represented skills, the adequacy of

skills and competencies that team members possess, and the leadership qualities of the

team manager.

Team organization

Goal and role definition

A key problem in forming groups is that team members might have mixed motives in

terms of cooperating and sharing information, which creates a dilemma in which group

and individual goals conflict (Bettenhausen, 1991). In a review of cross-functional

team studies, Holland et al. (2000) find that some of the most common obstacles to

team effectiveness include conflicting organizational or personal goals, overlapping

responsibilities, and a lack of clear direction or priorities. Similar results appear in a

GAM context, in which team members often have multiple, organizationally defined

roles and identities (Arnold et al., 2001). That is, they are members not only of

functional areas (e.g., sales, marketing, R&D, production), business units, and country

organizations but also of various other project groups. The result of these multiple

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memberships is their mixed objectives regarding their clients, which increases the

likelihood of conflict with authority and responsibility. This problem is further

exacerbated when GAM is not a full-time responsibility for all team members. In the

majority of the interviewed companies, only 2–5 core team members were full time;

the rest were responsible for sales to smaller customers or for other specialized

activities in addition to their involvement in the GAM team. Consequently, many of

the interviewees highlighted that without clear delineations of the role and mission of

the GAM team, conflicts would likely arise and the support of operating managers for

the GAM team would be minimal. The first step most of the researched companies

undertook was to communicate clearly to all related parties who had been assigned to

the team and what their responsibilities were. In Clariant, Wacker, and SAP, for

example, this communication took the form of posting the organizational chart and the

description of members’ responsibilities on the companies’ intranets.

The interview research indicates that role and goal setting should be aligned along

several dimensions. First, the team goals and responsibilities should be aligned with

the customer’s if the company is to provide the required value. Following this

principle, many of the interview companies organize joint planning and strategy

development sessions between the GAM team and its customer. Second, the goals of

the GAM team must be closely aligned with the overall strategic goals of the

company. As Cespedes et al. (1989: 48) mention, “a strategy that does not imply

certain behavior by the sales force is often no strategy; it is merely an interesting idea.”

Linking team objectives to corporate strategy gives GAM team members information

about the company’s goals in the marketplace, the nature of its potential competitive

advantage, and their expected role in achieving those goals. Third, group goals often

coexist with individual performance goals, and when they conflict, dysfunctions within

the team are inevitable. As a result, team goals should be compatible with the

objectives of individual members.

The literature offers extensive support for the link between such goal and role

alignment and performance. For example, specifying relationships between various

organizational roles and clarifying their responsibilities and dependencies enhances

interfunctional collaboration when marketing and R&D collaborate to develop new

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products (Moenaert and Souder, 1990). In a similar setting, Ruekert and Walker

(1987) argue that formalization through prescribed policies and norms increases

effectiveness, whereas Brown and Eisenhardt (1995) draw attention to more flexible

and fluid job descriptions and novel routines. In both cases, however, the goal is to

clarify team members’ roles and responsibilities to reduce confusion and wasted time.

Empirical studies of small groups also show that setting compatible individual and

group goals leads to significantly greater performance improvements than do situations

with no goals, individual goals only, or group goals only (Gowen, 1986; Matsui et al.,

1987). Weldon and Weingart (1993) argue that this effect occurs indirectly through the

mediating function of processes such as cooperation, communication, and member

effort. Tjosvold (1988) supports this argument by observing that employees from

different groups within a company who believe that their goals are cooperative, rather

than competitive or independent, exchange more information and resources and are

more willing to collaborate.

In summary, teams with well-defined and aligned goals and responsibilities are more

likely to communicate efficiently, collaborate, and experience lower levels of conflict.

Furthermore, they are more likely to act proactively in search of opportunities to fulfill

their objectives.

Team structure

As Shi et al. (2005) propose, one of the key GAM capabilities that influences market

performance and profitability is a process to ensure integrated efforts, both across

hierarchical levels in the supplier and customer organizations (i.e., from top executives

to sales representatives) and across all the markets in which the customer conducts

business. This integration requires that the GAM team be structured to facilitate

coordination. Team structure describes the nature and strength of the relationships

among individual members of the team and often is described by three dimensions:

vertical differentiation, horizontal differentiation, and connectedness (O’Reilly and

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Roberts, 1977; Pearce and David, 1983). As discussed in Chapter 2.4, though these

dimensions have been considered in a GAM or team selling context, no strong

empirical evidence exists regarding their impact on performance. Conceivably, group

structural properties alone may be less significant predictors of team performance than

the degree of fit between the team’s structure and environment (Bettenhausen, 1991),

as is supported empirically (e.g., Gresov et al., 1989). This contingency perspective

has its roots in the principle of requisite variety, which contends that organizational

adaptability increases when the organizational structure reflects the complexity of the

environment (Ashby, 1952), as well as the more general concept of fit, which has been

applied widely to various elements of organizational strategy, structure, and

environment (Egelhoff, 1982; Lawrence and Lorsch, 1967; Meyer et al., 1993; Van de

Ven and Drazin, 1985) and previously employed in GAM literature (Shi et al., 2004;

Toulan et al., 2002).

The GAM team has the boundary-spanning role of absorbing and interpreting the

complexity of the environment (Galbraith, 1973). Its task thus is to interact with

diverse parts of the customer organization, bring together the multiple demands of

those parts, develop appropriate strategies and solutions, and organize additional

elements of the supplier firm to deliver on those demands (Birkinshaw and Terjesen,

2002). This complex responsibility requires, on the one hand, a structure that fits with

the customer structure and, on the other, a structure that is well integrated and fits with

the existing supplier organization. Structural fit between the supplier and the customer

can improve supplier performance (Toulan et al., 2002), as well as joint supplier–

customer outcomes in terms of dyadic competitive advantage and joint profit

performance (Shi et al., 2004). In addition, establishing a team structure that is aligned

with the two organizations leads to performance improvements, because it allows the

supplier to increase its information-processing capability and bargaining power vis-à-

vis its global account (Birkinshaw et al., 2001).

Interviewees reached a consensus about the importance of structural fit and

accentuated the importance of adapting the GAM team structure to the customer’s

requirements. When Clariant created its first teams, it approached the customer to

discuss how it could adjust its structure to provide optimal customer coverage. In most

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researched companies, the core members of the GAM team are based close to the

customer headquarters, with extended team members spread around the world to cover

the various locations where the customer operated. In addition, product and functional

specialists often come from different parts of the supplier company to provide

additional needed coverage. For example, when a DHL account team develops a

proposal, whether proactively or in response to a customer request, it pools members

from various units: logistics consultants, IT consultants, transportation and operations

specialists, customer specialists, pricing experts, and even financial specialists for

billing and tax advice. Many of these people are virtual members based in different

locations worldwide and may be part of the GAM organization or pulled from

different business units. An interviewee from Citigroup describes the rationale for

adopting such a flexible and yet complex structure best:

What we are trying to do by combining these three dimensions—

geography, product, and functional expertise—is to deliver the industry

knowledge, the local presence, and the in-depth understanding of the client

in a superior way than our competitors.

To conclude, a team structure based on a fit with the internal supplier organization and

with the customer should facilitate both internal and external communication and

collaboration, reduce conflicts, and, ultimately, improve team performance.

Empowerment

During the interviews, GAM experts highlighted GAM team empowerment as one of

the most common success factors. Providing the team with sufficient authority tells the

customer that account managers have the power and resources to “get things done” in

a timely and effective manner (Homburg et al., 2002; McDonald et al., 1997). In a

more general aspect, team empowerment represents a powerful mechanism for

enabling organizations to be more flexible and responsive to environmental changes

and customer needs (Kirkman and Rosen, 1999; Kirkman et al., 2004).

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The organizational behavior literature contains two conceptualizations of

empowerment: structural and psychological. Structural empowerment refers to the set

of practices and work arrangements in a company that delegate authority and

responsibilities (Kirkman and Shapiro, 2001; Mills and Ungson, 2003). The

psychological approach considers empowerment the psychological state that team

members possess in terms of their perceived authority to control how their work is

conducted and responsibility for their work outcomes. This responsibility may include

the freedom to choose how to perform tasks, a sense that the team’s work is important,

and the belief that the team influences the effectiveness of the larger systems within

which it operates (Kirkman and Rosen, 1999; Spreitzer, 1995, 1996). These two

approaches are not antithetical but rather complementary and thereby provide a

comprehensive perspective of empowerment (Menon, 2001).

The interview results point to two distinct aspects of empowerment in a GAM context:

decision-making authority and sufficient resources. As Perry et al. (1999) note, a fully

empowered selling team possesses the authority to make decisions for which,

collectively, the team is responsible. Providing teams with this opportunity is

particularly appropriate when they need to take advantage of the unique skills and

talents of individual employees across the organization, because authority facilitates

the team’s access to these persons and ensures that the team’s demands and needs are

met. Consequently, Perry et al. (1999) argue that empowered selling teams result in

greater collaboration, coordination, and cooperation, as well as novel and innovative

solutions to customer problems. This claim is supported by empirical research that

indicates empowerment influences team processes and is a significant moderator of the

relationship between organizational context and team performance (Mathieu et al.,

2006). Of equal importance, previous research has identified a positive relationship

between empowerment and proactiveness (Kirkman and Rosen, 1999), as well as with

important customer-related outcomes such as customer service (Kirkman and Rosen,

1999), customer satisfaction, and process improvement (Kirkman et al., 2004).

The experiences of IBM and Citigroup confirm these claimed benefits. The GAM

teams at these companies receive full profit and loss (P&L) responsibility for their

customers. Although the country general managers at IBM are responsible for sales,

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they do not oversee revenues and are not allowed to make any significant decisions

about the customer without consultation with the GAM team. Providing the teams with

this level of responsibility offers multiple advantages: It ensures the team can respond

proactively and quickly to customer needs and guarantees the decisions it makes will

not be blocked at the country level by managers with different interests and their own

agendas. These factors facilitate communication and reduce the risk of conflicts.

In addition, resources are key to empowerment (Mohrman et al., 1995) and important

predictors of team performance (Cohen et al., 1996; Denison et al., 1996; Gladstein,

1984). Because global customers are crucial to the success of the company, the GAM

team must have sufficient resources to innovate and make a distinctive value

proposition based on customer needs and preferences. In turn, this creates value for the

customer and therefore leads to sustainable competitive advantage for the supplier

(Gosselin and Heene, 2005).

Team size adequacy

The size of the team also can be an important predictor of how well it carries out its

activities. In general, teams should be large enough to accomplish their goals but not

too large, because too many members can lead to overwhelming coordination needs or

reduced involvement (Gladstein, 1984; O’Reilly and Roberts, 1977). Therefore, some

authors argue that teams should take the smallest size needed to do the work

(Goodman et al., 1986; Sundstrom et al., 1990). Whereas early research suggests an

inverted U-shape or curvilinear relationship between size and performance (Steiner,

1972), subsequent empirical studies identify various performance improvements

associated with size. For example, Campion et al. (1993) find a positive impact of

group size on productivity, manager judgments of effectiveness, and employee

satisfaction. In employee involvement teams, Magjuka and Baldwin (1991) also find a

significant positive relationship with performance. In contrast, Vinokur-Kaplan’s

(1995) study, which applies Hackman’s (1987) model in practice, suggests that size is

a negative predictor of performance. These varied research findings clearly indicate

that size has a differential impact depending on the setting and team design. In a GAM

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team context, smaller team size should facilitate internal communication and

coordination and result in relatively lower conflict levels. However, smaller teams

might have difficulty obtaining sufficient resources and communicating the

significance of their work, as a result of their lower visibility, which may suggest that

size increases external communication.

The interview results confirm Cohen et al.’s (1996) suggestions that the absolute

number of people on the team is a poor indicator of its potential, because the “ideal”

size depends largely on the context. The teams in the interviewed companies ranged

from 4–5 to 50–60 members on a global basis, but in most cases, the interviewees

stated they still experienced capacity problems and would happily increase the number

of people on their GAM teams. In line with these arguments, the concept of team size

adequacy, or whether the size of the team is sufficient to perform its tasks, emerges.

When confronted with an insufficient number of people, the team experiences

increased workloads, time constraints, and reduced job satisfaction, the results of

which include decreased communication and collaboration, more tensions and

disputes, and a lower degree of proactivity. In other words, insufficient team size

likely has a negative impact on all team processes and performance.

Team composition

Team composition pertains to what members bring to the group in terms of skill,

ability, and disposition (Hollenbeck et al., 1998). In a recent meta-analytic review of

the effects of team design on performance, Stewart (2006) argues that the skill and

capability composition of a team can be viewed from two perspectives: (1) individual

characteristics that aggregate in a linear fashion, such that more is always better, or (2)

individual characteristics that do not aggregate in this manner and whose desirability

therefore depends on the traits of other team members, such that the heterogeneous

characteristics require fit among members. Following this classification, the proposed

framework incorporates both skill heterogeneity and adequate skills, as well as a third

distinct type of skill—team leadership.

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Heterogeneity

The extent to which team members are similar or bring unique qualities to the team

can influence group processes and outcomes, so significant research has studied team

diversity according to various attributes, such as race or ethnic background, age,

gender, nationality, skills and knowledge, values, and tenure (Milliken and Martins,

1996). In a GAM context, the most relevant diversity variables should be diversity in

skills and nationality, because they represent proxies for the team’s ability to develop

creative solutions to complex problems and manage the cultural diversity inherent in

its environment. In addition, many of the interviewees discussed these two types of

diversity extensively. Overall, the results of the qualitative analysis indicate that

nationality diversity should be taken into account but usually is a given because of the

multinational nature of the teams. Therefore, it is not a strong predictor of performance

by itself. Executives typically noted that it is more important to ensure the right mix of

skills on the team and recruit members who are able to work in a diverse, multicultural

environment than focus on a specific mix of nationalities. Consequently, I drop the

nationality diversity variable from further analysis and focus on skills heterogeneity.

Heterogeneity in terms of skills and expertise usually refers to task-related abilities or

professional experience, with the basic premise that when people with relevant areas

of expertise come together, team decisions and actions likely include the entire range

of perspectives and skills, which then leads to success (Van der Vegt and Bunderson,

2005). However, reviews by Milliken and Martins (1996), Webber and Donahue

(2001), and Williams and O’Reilly (1998) suggest that empirical evidence about the

performance benefits of diversity in skills is equivocal and indicates both positive and

negative relationships. Some studies (e.g., Watson et al., 1993) find that skill diversity

can both enhance and diminish performance on cognitive tasks, whereas others (e.g.,

Bantel and Jackson, 1989) indicate a positive link between diversity and cognitive task

performance, that is, tasks that involve generating creative ideas, solving problems, or

making decisions. The proposed explanations for the positive relationship between

skill diversity and team performance range from a better use of member knowledge to

better communication and cooperation with external groups (Goodman et al., 1986;

Magjuka and Baldwin, 1991). More recent research has tried to explain the skill

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diversity–performance link by opening the “black box” of team processes (Lawrence,

1997) and identifying relevant moderating and mediating factors. Process variables

such as intrateam conflict (Jehn et al., 1999; Pelled et al., 1999; Simons et al., 1999),

external communication (Ancona and Caldwell, 1992; Keller, 2001), information

sharing (Bantel and Jackson, 1989; Bunderson and Sutcliffe, 2002), and the ability to

acquire, refine, and combine knowledge (i.e., learning orientation, Van der Vegt and

Bunderson, 2005) represent the key mechanisms by which diversity promotes or

hinders performance.

An important finding that can provide insights into the effects of diversity in GAM

teams is that skill diversity has more positive performance effects on nonroutine,

diverse tasks that require a wide range of competencies (Hambrick et al., 1996;

Murray, 1989, Wall et al., 1986), which is a good description of GAM team

responsibilities. Following the resource-based view (Barney, 1991; Teece et al., 1997),

Harvey et al. (2003a) argue that individual and collective organizational knowledge as

a resource enables the GAM team to develop unique competencies valuable to global

customers. Similarly, the interviewee from Hewlett-Packard compared its GAM teams

to a lens that brings into focus the supplier’s resources, including everything from

R&D and product development to distribution and customer service, for the global

customer. Therefore, incorporating a variety of backgrounds, areas of expertise, and

experience in the team is a prerequisite if the team is to develop superior knowledge

bases to coordinate and combine its system-wide resources and capabilities in unique

ways that provide value to the customer.

Adequate skills

As opposed to skill heterogeneity, this construct focuses on individual characteristics

that combine in such a way that more is always better for a team. Several

organizational behavior studies assess member skills and ability levels (LePine et al.,

1997), personality traits (Barrick et al., 1998; Neumann and Wright, 1999), and

background and experience (Bantel and Jackson, 1989), but their results tend to

depend on the context and type of team.

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This construct should have a very strong impact on GAM teams, because it is among

the top three success factors reported during the expert interviews. Team members

need a broad range of skills to manage the complex networks that exist both at the

external interface between the supplier firm and the global account and at the internal

interface between the GAM team and other organizational units. The role of the global

account manager therefore can be compared instructively to that of a political

entrepreneur (Wilson and Millman, 2003) who must overcome cultural barriers and

navigate the sensitive political aspects of multiple interfaces while generating

continuous business opportunities with the customer. One of the interviewees, based in

Austria, shared the way he often describes the essence of the job to his account

managers:

You are entrepreneurs. You have your account, your P&L, and you are

managing a [client] company which is bigger that many of the Austrian

companies. You have to think as entrepreneurs, how can you save costs,

how can you develop revenue, how can you build relationships.

Similarly, Eastman Chemical Company’s chairman and chief executive officer Earnest

Deavenport (1999: 15) describes the competencies of a global account manager as

follows:

The mission of the GAM is not so much to sell products and services as

much as it is to manage relationships…. As GAMs, they need to know

everything about key customers in every side of business that customer has.

These demands require competencies that go beyond a traditional sales role (Harvey et

al., 2003b) and include both business and multicultural social skills. Consequently, the

required skills of the core members of the GAM team may be more closely aligned

with those of a general manager than a senior salesperson. Not only are GAM team

members expected to have the capability to build and maintain trusted relationships

with senior executives across divergent cultures and economies, but they also must be

able to obtain extensive knowledge about the customer and use that information to

create value. These demands also require strong communication and selling skills,

leadership and management skills, problem-solving capabilities, business and financial

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acumen, and strategic vision and planning capabilities (Millman and Wilson, 1999b).

One of the executives described what he looks for in an account manager:

First of all, we look for a high achiever, top performance type of a person.

You do not want to deal with mediocrity in this area. These are people with

deep business skills and savvy business understanding. They are team

players with strong social skills. They are strong leaders who can be

compared to the conductor of an orchestra because they have to manage by

influence an organization that does not report directly to them. They are

people who can build trust and credibility.

This example further highlights the importance of the people skills factor and supports

the proposition that a team whose members possess this broad skill set will be

characterized by more collaborative and efficient team processes and superior

performance.

Leadership

Leader characteristics, behaviors, and expectations all are vital to performance

outcomes and therefore appear in various research contexts. McGrath (1984) identifies

two leadership functions, task and interpersonal, whereas other studies (Cohen et al.,

1996) examine the effect of different leadership styles on team performance. In a sales

setting, George (1995), building on George and Bettenhausen (1990), finds that a sales

manager’s positive mood positively predicts customer service behavior among 53

retail sales groups. Gladstein (1984) similarly finds a significant positive relationship

between leadership and team self-reported effectiveness, as well as between leadership

and intragroup processes, when leadership represents the leader’s task behavior,

maintenance (interpersonal) behavior, and influence in the organization. A recent

study of the effects of leadership style on performance and innovation in functionally

heterogeneous teams indicates that leadership style is an important construct that can

enhance constructive team processes, which in turn promote team outcomes (Somech,

2006). According to its results, in functionally diverse teams, participative leadership

based on joint decision making can improve innovation by encouraging team

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reflection, which prioritizes a collective debate and analysis of the team’s objectives,

strategies, and the best way to use relevant knowledge. Such leaders reduce the

barriers that exist between diverse professionals in heterogeneous teams by opening

communication channels inside and outside the team and facilitating the exchange of

ideas and perspectives (Lewis et al., 2002). However, many of these existing studies

center primarily on the leader’s behavior within the team and its impact on internal

team processes.

In contrast, studying product development teams, Katz and Allen (1985) note that

leadership both within the team and in terms of broader organizational influence

affects team members’ motivation and behaviors. Consequently, selling team

managers’ behaviors should attempt to not only develop internal bonds but also

succeed in external activities. Smith and Barclay (1993) observe that effective selling

teams are led by experienced sales representatives with strong social and boundary

management skills who inspire team members by example to maintain their

commitment and professionalism. Similarly, Perry et al. (1999) argue that the selling

team manager should focus his or her efforts externally to facilitate good working

relationships between the team and the rest of the organization and garner resources

from outside.

Organizational influence should be a strong characteristic of successful GAM leaders

as well. Teams with influential leaders are more effective because they communicate

better with senior managers and act as advocates for their teams within the

organization, which in turn improves overall communication and collaboration and

reduces potential conflicts. In addition, influential leaders often cause members and

outsiders to infer that the team’s activities are important and prestigious, which

improves both their efforts and team cohesiveness (Scott, 1997). This is of utmost

importance because leaders of GAM teams often have no formal authority, rely

heavily on other members, and are contributing members to team activities (Deeter-

Schmelz and Ramsey, 1995). As a result, they “lead” as opposed to “manage” the

GAM team (Parker, 1994). In these circumstances, strong leaders are those who create

an environment that fosters performance, ensure that team efforts are coordinated with

divisional and organizational efforts, and align team members’ objectives.

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In summary, a GAM team leader can have a strong positive effect on team processes

and performance if he or she (1) possesses broader influence in the organization that

extends to top management and across various functional and divisional boundaries

and (2) creates an effective working environment and motivates team members to

contribute.

Hypothesis 1: GAM team design (i.e., goal and role definition, structure,

empowerment, adequate size, heterogeneity, adequate skills, leadership) has a

positive influence on GAM team processes (i.e., external and internal

communication and collaboration, conflict management, proactiveness).

3.2.2. Organizational context

Teams can be understood best in relation to their external surroundings and internal

processes (Sundstrom et al., 1990). Therefore, many models of team effectiveness

incorporate aspects such as reward systems and training resources that represent the

organizational context of teams (e.g., Campion et al., 1993; Cohen et al., 1996;

Hackman, 1987; Shea and Guzzo, 1987). Cespedes et al. (1989: 55) clearly illustrate

the importance of these factors for GAM: “Sales teamwork is ultimately a by-product

of the organization and has to come from the top down. People in the trenches can be

team players, but they need encouragement and incentives.”

In a similar vein, one of the interviewees highlighted the importance of the

organizational context for changing corporate cultures and mindsets that do not

support global team collaborations:

There are many things that you can do to change behavior, through how

you pay people, how you train people, the systems and processes by which

you want them to interact. These are levers at the disposal of the managers

of the business. If we work on changing those things, before we know

where we are, those behaviors will be how we do things around here.

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Because today’s behaviors are the short term of what culture is all about,

they will eventually turn into a culture.

Recognizing this behavior-shaping role of the organizational environment, the

proposed framework captures context through three constructs: top management

support, rewards and incentives, and training.

Top management support

This quote from one of the interviews effectively represents the opinions of all

interviewed managers:

There is only one golden rule about GAM programs: If you do not have the

undying support of the chief executive, they will go nowhere, it is just a

waste of money.

Top management support was among the top three GAM team success factors

accentuated in the interviews and is often identified as crucial for all aspects of GAM

activities in the literature (Arnold et al., 2001; Homburg et al., 2002). According to

Millman and Wilson (1999a: 330), GAM “is a strategic issue and the process should

therefore be initiated and overseen by senior management.” Because GAM is typically

an organization-wide initiative, it requires shifts of power, responsibilities, and

resources from the local to the global level or from individual countries to GAM units

that span boundaries (Homburg et al., 2000). As a result, managers at various levels

confront trade-off decisions between local business units and global accounts. By

making an unequivocal commitment to the long-term benefits of GAM, top

management emphasizes global firm success as opposed to national or market-based

measures, which helps managers make this trade-off and creates a global

organizational mindset oriented toward the complexities of global strategy, structures,

and processes. As Srivastava et al. (1999) argue, for marketing to be institutionalized

in organizations, it must be infused in the actions of the managers who influence the

work process.

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Support in the form of resource allocation and public recognition of the GAM team’s

efforts conveys the importance attributed by top management to the GAM initiative

and is pivotal for overcoming potential resistance or power struggles and legitimizing

the efforts of the team as a strategic undertaker (Harvey et al., 2003b). Without it, the

team lacks sufficient clout to direct customer activities proactively, and team members

likely ascribe lower importance and significance to their contributions. For example,

when 3M began implementing action teams, senior management sponsors were crucial

to their success. These “enablers” allocated the needed resources and visibly supported

the team, which altered the mindset of middle managers and motivated team members

(Hershock et al., 1994). Empirical studies support this observation in their

identification of the positive relationship between top management commitment and

market orientation (Jaworski and Kohli, 1993), global marketing structure, marketing

performance (Townsend et al., 2004), and, crucially, GAM effectiveness (Workman et

al., 2003).

The other key aspect of top management support is the active involvement of senior

managers in developing relationships with the customer. Senn (2006) observes that a

replicable, systematic process of senior executive interactions with the customer can

represent an essential sales and profitability growth factor. Many of the researched

companies implement executive sponsorship or top-to-top relationship programs that

require top managers to meet with the top management of the customer firm to discuss

business opportunities and set strategies. For example, DHL’s CEO commonly meets

with customer executives such as Michael Dell and discusses the possibilities for joint

business, whereby the two companies can integrate their competencies and develop

tailor-made solutions. This type of support is invaluable to the GAM team working

with Dell, or whichever respective account, and can lead to joint innovations with

superior sales potential. Similarly, Siemens uses a formal, four-step Top Executive

Relationship Process that mandates at least eight executive customer engagements per

year for each team, so the customer contacts are systematically planned, organized,

and reported. This mandate helped shift customers’ perceptions of Siemens to a more

customer-driven and engaged partner. Moreover, four years after the introduction of

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the process, the growth rate of these accounts increased significantly and reached

levels two times higher than other accounts (Senn, 2006).

In summary, top management support has a positive effect on team process and

performance because it enhances internal and external collaboration by embedding a

GAM focus, reduces conflicts, and enforces a proactive approach toward global

customers.

Rewards and incentives

A reward system that recognizes and reinforces contributions to the GAM team can

significantly amplify the motivational incentives provided by top management

commitment or well-designed goals and responsibilities. In most interviews,

respondents pointed to measurement, metrics, and compensation as key success

factors. In the organizational behavior research stream, rewards for team performance

do not produce conclusive empirical results, because some studies find no significant

influence on objective performance (Campion et al., 1993; Magjuka and Baldwin,

1991), whereas others (Cohen et al., 1996; Pritchard et al., 1988) indicate that

introducing group incentive plans increases team ratings of performance and

satisfaction or that perceived inequity in compensation among team members is

positively associated with perceived conflict (Wall and Nolan, 1986).

Nevertheless, compensation is one of the three most important areas in team selling,

according to Cespedes et al. (1989: 45), who note that “in sales management, you

won’t get what you don’t pay for.” Gosselin and Heene (2005) also argue that

remuneration and measurement determine whether account managers act as strategic

global account managers with long-term, relational perspectives or as pure

salespersons with a short-term, transaction orientation. Because GAM teams often

work alongside preexisting national sales organizations, and both units have vital roles

in managing the account, it is important that the incentive structure helps reconcile any

potential tensions between the units and enhances collaboration (Arnold et al., 2001).

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Creating such a compensation structure requires two aspects. First, the supplier must

have a measurement system that tracks the sales of the global account and the implied

results of the GAM team efforts worldwide. It should be flexible and fair enough to

recognize sales contributions in a transparent and consistent manner and share sales

credit. Otherwise, the team might get caught in a situation in which it wastes

significant time and energy arguing about the split of sales credit. Companies whose

interviewed managers stated they had found a workable solution usually double-

credited sales and used shadow P&L accounting. For example, DHL and Citigroup use

a shadow P&L system that helps smooth the tension between the GAM team and local

managers regarding sales to the global account. Furthermore, this system establishes

the unique contributions of different units associated with the revenues.

Second, to stimulate contributions, the compensation system must reward team

members appropriately. Regardless of their content, rewards should be contingent on

performance, and the system should reward only those behaviors that lead to the

required positive outcomes (Hackman, 1987). For example, interviewees indicated that

the compensation system for the GAM team should employ an appropriate timeframe.

Sales to large multinational customers often take months or even years to complete, so

incentives tied to short-term performance will encourage efforts that fail to maximize

overall results. Interviewed executives also mentioned various options, such as

bonuses for multiple-year performance or qualitative objectives like building customer

relationships. Furthermore, to encourage a more holistic and relationship-oriented

view among team members, the researched companies all provide incentives that are

based on both GAM team or program-level goals and personal objectives, and they

include both quantitative and qualitative targets.

In general, the experts indicated that appropriate GAM incentives involve a

compensation package with a significant variable portion (bonus) contingent on certain

prespecified, GAM-related objectives. Although the size of this portion varies

considerably across industries and companies (from 20–30% of total compensation in

companies such as Coca-Cola, HBC, and Wacker to 4–5 times the fixed salary in

banking GAM teams at Citigroup), it remains a strong determinant of willingness to

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communicate and collaborate on issues related to the global account, as well as of the

level of conflict and proactiveness of the team.

Training

As Henke et al. (1993) observe, companies often devote more time and resources to

assembling cross-functional teams than to training them, which could be a key

blockage to their successful functioning. A company’s ability to provide needed

training can be a critical success factor (Hackman, 1987), so organizational behavior

literature has researched the relationship between training and team performance to at

least some extent. For example, Salas et al. (1992) provide an extensive review of

relevant studies, but the results are mixed due to diverse and often weak

methodologies (Campion et al., 1993).

As discussed previously, identifying and recruiting high-caliber professionals with the

required broad skill set for the GAM team is a widespread challenge. Consequently,

developing necessary knowledge and skills demands additional professional

development and training throughout the career of everyone involved with global

customers (Weeks and Stevens, 1997). Even if these skills are available, team

members, who come from different backgrounds and parts of the organization, need

training to help them understand their tasks, processes, objectives, and roles (Parker,

1994). This training also must extend beyond the teams to encompass senior and

middle managers. Because middle managers can represent an important obstacle to

success, they should come to see their role in a new light, which requires “an

organization-wide commitment to learning” (Donnellon, 1993).

The range of topics covered by GAM team training can be substantial, from product

and selling skills to interpersonal and cultural training to technical and service

competence development. For example, when Citigroup started building its GAM

teams in the 1980s, training represented a key element of the initiative. In several

years, thousands of people from hundreds of teams across the organization trained in

the customer relationship field, and top management learned to manage a portfolio of

customers (Galbraith, 2001). In the case of Marriott, the introduction of a GAM

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approach involved sales training classes for more than 500 people every year to

demonstrate how strategic account management would change their jobs and

expectations regarding their contributions (Hennessey and Jeannet, 2003). Siemens

requires all GAM team members to undergo intercultural training, because it believes

that acceptance of different cultures is one of the keys to global success. As David

Macaulay, Siemens’ Managing Director for Corporate Accounts, notes, “If you don’t

understand culture or are not sensitive to another person’s culture, doing business can

be twice as hard.”

Finally, DHL uses training not only to enhance the GAM team’s skills but also to

increase its credibility and improve its relationship with the business units. The

company is designing a structured training program that will resemble a university, in

the sense that it will incorporate detailed assessments of competencies and gaps and a

formal process of education, to the point that account managers might be asked to take

tests. When GAM team members have successfully undergone this rigorous training

process and passed the tests, customers and business units likely will perceive them as

more competent and respectable and therefore be more willing to collaborate with

them.

Hypothesis 2: The organizational context (i.e., top management support, rewards

and incentives, training) has a positive influence on GAM team processes (i.e.,

external and internal communication and collaboration, conflict management,

proactiveness).

3.2.3. Team processes

Although the team’s structure and composition may have a direct impact on team

outcomes, in most cases, this relationship is mediated by the processes that

characterize the interactions of team members (Hackman, 1987). Prior studies based

on the organizational demography approach (Pfeffer, 1983), which focuses on the

direct link between team design variables and outcomes, create a “black box” because

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they fail to measure and test the effect of team processes and other intervening

variables (Lawrence, 1997). These variables might include both internal and external

processes (Ancona and Caldwell, 1992a; Gladstein, 1984; Marks et al., 2001), as well

as group psychosocial traits, such as group norms and shared mental models (Cohen

and Bailey, 1997). The field interviews similarly suggest certain variables moderate

the relationship—specifically, external communication and collaboration, internal

communication and collaboration, conflict management, and proactiveness.

External communication and collaboration

Effective GAM teams must detect changes in the business environment, gain

knowledge about customers, and improve team members’ collective understanding of

the business situation. Because GAM team members have varying functions and are

exposed to multiple sources of information, the team needs to communicate

extensively and effectively both internally and externally and to ensure that it uses all

valuable information to its fullest potential (Jones et al., 2005).

As already discussed in Section 2.3, external activity refers to the process of building

relationships with other members inside and outside the organization to ensure

cooperation and obtain vital information and resources. In previous studies of smaller

and relatively isolated groups, external activities have been either completely omitted

or considered less important than intragroup processes (Gladstein, 1984). However,

studies that extend the focus to new and demanding environments with a high degree

of external dependence recognize that teams whose members communicate actively

with and engage outsiders in the team’s work achieve higher performance (Ancona

and Caldwell, 1992a, b; Keller, 1994, 2001). In the context of key account managers,

for example, Schultz and Evans (2002) examine collaborative communications with

customers along four dimensions—informality, bidirectionality, frequency, and

strategic content—and find that all of them relate positively to role performance, trust

in the key account manager, and synergistic solutions.

Furthermore, the active management of external linkages is crucial from a resource-

dependence perspective (Pfeffer, 1986) and a key determinant of GAM performance

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(Homburg et al., 2002). Because GAM activities are often hampered by a lack of

authority (Arnold et al., 1999), the team must rely on extensive and persuasive

communication to secure sufficient contributions. Thus, its ability to communicate

across functions and hierarchical levels in the organization can be a significant

predictor of GAM effectiveness and hence of market performance and profitability

(Workman et al., 2003).

The GAM team’s activities to overcome obstacles within the supplier organization

often are even more difficult and time consuming than working with external partners.

Consider Marriott’s experience for example: When the company introduced its

Alliance Accounts program in the mid-1990s, it faced the challenge of moving the

sales force from a traditional transactional structure to an organization focused on

global account solutions. This shift meant a loss of P&L control, and hence resistance

to the changes, for some local entities. To address the problem, Marriott conducted an

extensive internal selling campaign; for every presentation given to an external

customer to sell the new GAM strategy, five presentations targeted those involved

internally (Croom et al., 1999). Every success and best practice of the account teams

was celebrated and communicated to all involved. In addition, Marriott identified

audiences in the company that were critical to success and started providing them with

constant information to help them understand the program’s role and gain their support

(Hennessey and Jeannet, 2003).

In addition, in many of the researched companies, communication provides a critical

means for exchanging best practices and experiences among GAM teams. Typically,

these communications encompass both formal channels, such as regular best practice

meetings, forums, or workshops, and informal channels, such as one-on-one meetings,

phone calls, and similar activities. For Coca-Cola HBC, best practice meetings get

GAM teams together to share customer knowledge and best practice examples from

different countries. These exchanges help synchronize account planning and execution

processes across customers. To increase awareness and involvement in GAM activities

among other people in the company, Clariant invites senior managers from other areas

such as product management, as well as the customer, to its best practices event. DHL

even offers virtual team rooms, in which each team can share more detailed

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information about presentations and other materials that may be relevant for managing

DHL’s global customers as a whole.

Internal communication and collaboration

Internal communication and collaboration refers to the interaction within the team. Shi

et al. (2005) argue that the degree of collaboration and coordination of GAM activities

within the team, across both countries and different levels, determines a supplier’s

GAM market performance and profitability. This link receives broad support from

supplier–buyer relationship literature (Buvik and John, 2000; Heide and John, 1990;

Mohr and Spekman, 1994) and organizational behavior studies (Bunderson and

Sutcliffe, 2002; Gladstein, 1984; Pinto et al., 1993). For example, Seers et al. (1995)

link internal communication and collaboration in a 10-item scale that measures

reciprocal exchange and find a positive link to efficiency. Their definition of internal

communication involves members’ contributions and receipt of ideas, feedback,

assistance, and recognition to other members.

Some authors suggest that communication has two distinct aspects—frequency and

content—that might have different effects on performance outcomes. For example,

Ancona and Caldwell’s (1992a) study shows that the frequency of communication is

unrelated to performance, unlike the content of the information. Similarly, Smith et al.

(1994) indicate that the frequency of internal communication relates negatively to the

performance of top management teams, because it tends to indicate high levels of

conflict and requires time that detracts from performance. Pinto and Pinto (1990) find

that highly cooperative project teams make significantly more use of informal

communication methods (particularly phone calls) than do less effective teams. More

important, their reasons for communication tend to revolve around obtaining project-

related information, reviewing progress, brainstorming, and receiving feedback rather

than resolving disputes, which again suggests that the content and effectiveness of

communication are more important predictors of performance than is frequency itself.

The relatively limited evidence from GAM teams, however, reveals slightly different

results. Birkinshaw et al. (2001) argue that the frequency of communication between

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the global account manager and other individuals (both within and outside the team)

on GAM-related matters improves performance in terms of efficiency, sales growth,

and customer partnerships. These authors suggest frequent communication may

convey a sense of where priorities lie and speed up problem-solving processes.

Interviewees generally agreed that both frequency and effectiveness of communication

among team members are important; many complained that dispersed team members

lacked knowledge about what was going on with the account in other parts of the

world and what other team members had discussed with the customer on an individual

basis. As a result, these companies have introduced various channels and tools to share

information within the team. Regular face-to-face or virtual meetings involving all

team members, or at least the core, are the norm, though the frequency varies across

companies and teams, depending on the size and geographical dispersion of the team.

Other tools include regular e-mails or formal reports from the team leader to all

members on anything that seems important about the account, such as the latest

developments and results, account planning updates, and planned meetings and events.

These communications usually employ specialized Web-based tools or customer

relationship management (CRM) systems that enable the sharing of far more detailed

information. For example, IBM, Siemens, and SAP consider their CRM systems key

facilitators of inter- and intrateam collaboration and invest significantly in them. The

systems provide complete transparency about customers and the activities of other

team members and welcome input from team members regarding all relevant

information they might have about the customer—client visits reports, key issues

discussed with the customer, types of products the customer buys from their country or

division, competitive threats, new opportunities, and so forth.

However, as one of the interviewees stated, “CRM systems are just a tool that can

even get in the way of effective team working. There is nothing more effective than

people talking to one another.” This assertion reemphasizes that, regardless of the

infrastructure used to facilitate communication, the key determinant of effective

collaboration remains people’s skills and their willingness to exchange information

and work together as a team.

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Conflict

A commonly studied team process, conflict refers to “the tension between team

members due to real or perceived differences” (De Dreu and Van Vianen, 2001: 309).

It has two main dimensions, amount of conflict and conflict resolution or management,

and comprises two types identified by research, namely, cognitive (task-based) and

affective (relationship-based) (Amason, 1996; Jehn, 1995). Jehn and Mannix (2001)

add a third type: process-based conflict. These various types differ in terms of their

effects on performance (Amason and Sapienza, 1997); high-performing teams likely

encourage functional conflict but avoid or resolve dysfunctional types of conflict.

In GAM teams, conflicts can arise from the diverse aspects of the home organizations

(functions, units, regions) represented on the team, which lead to contrasting views

and divergent task interpretations. Cultural differences associated with different ways

of doing business also emerge. SAP, for example, experienced increased disagreement

and misunderstanding among team members from countries such as Japan and China

when Western team leaders’ expectations about taking initiative and higher degrees of

independence clashed with the Asian members’ need for closer coaching and detailed

directives. Similar issues at Clariant had to be resolved through regular visits from top

management to these countries.

However, an even more problematic area from which conflicts arise, as repeatedly

noted in the exploratory research interviews, is resource disparities and disagreements

about resource allocations (Denison, 1996; Donnellon, 1993). Teams question “Who

owns the customer?” and how the P&L responsibility will split among the involved

parties, which can create performance-inhibiting tensions, as described by one of the

interview respondents:

The typical conflict that we have is that the business units are not always

aligned with what we are trying to achieve. It could be about, we think that

something is important and they don’t. But in most cases, it comes down to

money. They are there to drive short-term P&L, and we are there to

develop long-term cash flows.

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As discussed previously, the level of these conflicts depends on (1) how the

responsibilities are split, (2) how rewards and incentives are defined, and (3) the skills

team members have in terms of demonstrating the value of global collaborations and

enforcing the global goals.

Because conflict is such a common phenomenon in GAM teams, performance depends

significantly on effective mechanisms for managing disputes. Ruekert and Walker

(1987) suggest that when conflicting parties can work out their differences in a

cooperative manner, the perceived effectiveness of the relationship between marketing

and representatives of other parts of the organization increases. Examining selling

teams, Smith and Barclay (1993) also find that unresolved conflicts or conflicts

resolved through avoidance or competition lead to declining trust and team

effectiveness.

The literature identifies several conflict-resolution mechanisms for a dispute within an

organization, including (1) conflict avoidance, (2) focusing on common interests, (3)

openly confronting the issue and resolving the dispute through negotiation and

compromise, and (4) resorting to higher authority to decide the issue unilaterally

(Ruekert and Walker, 1987). In their taxonomy of team processes, Marks et al. (2001)

group these mechanisms into two types: preemptive conflict management that

establishes conditions to prevent, control, or guide conflict management before it

occurs and reactive conflict management that works through disagreements to solve

them. The experts generally confirm these findings; in most cases, they assume the

team will work together and come up with different alternatives to solve the conflict.

In the companies with more mature GAM programs such as Citigroup, teams have

gained experience in handling such situations and manage to solve conflicts internally

in almost 95% of the cases. To a large extent, one of the interviewees noted, this

resolution depends on the skills and training of the account managers:

I believe that a really good account manager can resolve 80% of those

competing goals situations without escalation. It all comes down to the art

of negotiation, which is a function of skills and training.

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When such resolution simply is not possible, a mechanism must specify how the team

should escalate the conflict to senior managers who can make a decision. This

recommendation presumes, of course, that management is receptive to taking a

position in these situations and helping resolve the conflict and that the team does not

perceive escalation as a failure that should be avoided. As one interviewee noted:

The basic value system in our company is that the team has to find a way to

reconcile conflicts. You have to respect one another’s different points of

view and work it out. The escalation process takes place only if this is

impossible. In such situations, teams are advised not to avoid escalating. If

you let a problem stay a problem, you will get beaten up with a vengeance

for not escalating. It is not a failure if the team cannot solve a particular

problem. A failure is if the team has not sorted out the problem and has not

escalated it.

In conclusion, clashes in teams that are as diverse and complex as global customer

teams often are just a fact of life. Therefore, GAM team performance is influenced by

the extent and frequency of conflicts, as well as the existence of working mechanisms

that can help resolve disputes in a timely and effective manner.

Proactiveness

Although many GAM activities result from a customer’s demand, the potential for a

cooperative and synergistic relationship is greatest when the supplier adopts more

proactive behavior (Harvey et al., 2003a). Arnold et al. (1999: 15) note that “the

proactive–reactive dimension matters a great deal,” and Workman et al. (2003)

identify a significant relationship between activity proactiveness and KAM

effectiveness that confirms the importance of supplier-initiated activities. As an

organization-wide strategy that defines future business for both the supplier and the

customer, GAM requires a proactive approach to understanding customers’ explicit

and latent needs and cultivating new value creation opportunities, such as creating new

demand rather than waiting for requests (Shi et al., 2005). Some of the executives in

the field interviews commented on proactivity explicitly:

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Highly successful GAM teams are those that sell not what exists but what

does not exist.

The classical account manager from the past was waiting for interesting

new products that can boost the business and then going to the customer

and trying to sell them. The GAM team of today and the future has to see

business opportunities, come up with solutions that can boost the business.

and then find people to help execute the solutions (e.g., logistics, finance,

etc.). It really has to radar the situation, decide in which direction to go, and

mobilize the needed resources to get there.

Although some research has been conducted at the individual level of analysis

(Bateman and Crant, 1993; Crant and Bateman, 2000), relatively few studies address

this construct at the group level. Bateman and Crant (1993) define proactive behavior

as individual actions that effect environmental change through scanning for

opportunities, showing initiative, taking action, solving problems, and persevering

until changes occur. At the team level of analysis, Hyatt and Ruddy (1997) define

proactiveness as the extent to which team members actively seek areas for continuous

improvement, continuously revise work processes, seek alternative and innovative

solutions to problems, and address issues before they become major problems.

Furthermore, their study implies that proactive team behavior relates to higher

managerial ratings of team performance. Wellins et al. (1991) support this argument

by noting that a team’s ability to take action on problems and improve the quality of its

work by initiating changes can be a critical success factor.

The example of 3M’s IBM global account team illustrates how proactive efforts can

increase customer value, customer satisfaction, and the business potential of the

relationship. Having interviewed IBM managers around the world, the GAM team at

3M managed to identify an opportunity to reduce IBM’s storage losses by using new

materials that make IBM disk drives less vulnerable to contamination. The company

initiated a 3M technology group that spent two years creating a new material that

reduced IBM storage losses by 10% (Sperry, 2000). As this example shows,

companies whose GAM teams proactively and systematically look for new

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opportunities and then are not afraid to take actions to realize those opportunities are

likely to reap the benefits of long-term customer relationships and thus outperform

their competitors. To achieve such benefits though, GAM teams sometimes must

initiate changes in their existing organizations and processes in ways that may be

considered radical but still are justified by the positive outcome (Shi et al., 2005).

Hypothesis 3: GAM team processes (i.e., external and internal communication and

collaboration, conflict management, proactiveness) have a positive influence on

GAM team relational performance.

Hypothesis 4: GAM team processes (i.e., external and internal communication and

collaboration, conflict management, proactiveness) have a positive influence on

GAM team financial performance.

3.2.4. GAM team performance

Although the team’s composition and interactions have implications for individual

members and overall GAM organizational outcomes, it is most appropriate to examine

performance at the team level. Individual performance narrows the focus and provides

only limited insights about the group aspect under investigation; furthermore, overall

GAM performance likely is affected by factors external to the team. Because this

research is designed to gain a better understanding of the team phenomenon, it focuses

on two types of team outcomes: financial, or quantitative, and relational, or qualitative,

performance.

Financial performance

In identifying appropriate team performance measures, Cohen and Bailey (1996)

recommend performance criteria specific to the task and the type of team, as well as

aggregated measures of overall performance. In the selling team context, quantitative

performance measures might include sales volume, profitability, customer retention

rates, and/or the number of new customers acquired (Perry et al., 1999). Because the

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responsibilities of the GAM team are limited to one or a few related customers and

because its goals are to develop long-term collaborative relationships, sales growth and

profitability represent more appropriate criteria (Birkinshaw et al., 2001; Homburg et

al., 2002; Shi et al., 2005). These two measures constitute an integral part of the key

performance indicators of all the researched companies, because they avoid measuring

sales at a certain point and instead assess the team’s ability to identify cross-selling

opportunities through cross-divisional cooperation and work together with the

customer to reduce costs and increase sales to end consumers. Other measures of key

account performance identified in prior research include reduced cost of sales to

customers, more efficient use of salespeople’s time in serving the customer, cross-

selling to divisions of the customer’s operation in which the supplier traditionally has

been weak (Arnold et al., 2001), securing desired market share, and attracting new

customers (Homburg et al., 2002). However, the interviews reveal yet another

important GAM team performance metric: share of the customer’s wallet. This

measure refers to the share of the customer’s total purchasing volume actually devoted

to the specific supplier. Again, growth in the share of wallet may be more important

than the absolute value, because it indicates that the team has been able to gain share

from its competitors who supply the same customer.

Relational performance

Measuring GAM team performance using purely quantitative criteria runs the risk of

missing vital aspects of partnering with the customer. The goals and objectives of a

supplier when forming GAM teams and developing relationships with key customers

are much broader than simply sales volume. Some of these goals include the creation

of a long-term relationship, learning, and innovation, the latter of which can result

from various joint initiatives and increased access to new product ideas or leading-

edge practices undertaken by the customer (Birkinshaw et al., 2001). Other qualitative

measures of GAM team performance might include customer assessments of the

relationship and customer satisfaction (Gladstein, 1984; Shea and Guzzo, 1987). In

addition, the team’s ability to meet its goals and objectives (Moon and Gupta, 1997),

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gain broader market access, and achieve better competitive position in the market can

provide an indication of its performance (Smith and Barclay, 1993). Field research by

Shi et al. (2005) indicates that for GAM executives, some of the most important

relational outcomes are customer satisfaction and the ability to provide customer

value. Many of these measures appear integrated into the GAM outcomes proposed by

Homburg et al. (2002)—maintaining long-term relationships, achieving mutual trust,

achieving customer satisfaction, providing customer value, and successfully

introducing new products.

Finally, the relationship between the two performance indicator groups is worth

investigating. In many studies they are considered as ultimate unrelated outcomes

(Cohen and Bailey, 1997). However, if the relational performance construct is

decomposed in its building elements such as customer satisfaction, customer value or

enhanced market position, it becomes clear that it may have an influence on financial

outcomes. The debate about the effects of customer satisfaction has been ongoing in

the marketing literature and some authors argue that customer satisfaction does not

always correlate with organizational performance (Jones and Sasser, 1995). However,

a number of studies support the notion of a positive relationship between satisfied

customers and financial performance (Anderson et al., 1997; Rust and Zahonik, 1993)

as well as the customer’s willingness to pay (Homburg et al., 2005). Similarly,

customer value delivery has been considered a source of competitive advantage and

performance improvement (Naumann, 1995; Woodruff, 1997). Therefore, it is

expected that relational performance will have a positive influence on financial

performance.

Hypothesis 5: Relational performance has a positive influence on financial

performance.

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4. Empirical study

As discussed earlier, the objective of this second empirical research stage was to build

on the findings from the qualitative interview research and to test the framework with

more robust statistical methods. Despite the advantages of qualitative methods in early

research phases, qualitative research usually yields findings that cannot be generalized

to whole populations (Eisenhardt, 1989). Consequently, once the problem becomes

clearer and specific propositions emerge, a quantitative approach that tests hypotheses

and examines cause-and-effect relationships between variables is more appropriate.

Because most surveys have a descriptive or analytical objective, they represent a good

research strategy for theory testing (Black, 1999). Therefore, a survey was selected as

the best strategy to test the framework developed through the first stage of interview

research.

Furthermore, surveys are widely used in the areas of marketing, business, political

science, psychology, and sociology (Babbie, 1990) and have been instrumental in team

research (e.g., Campion et al., 1993; Denison et al., 1996; Gladstein, 1984). Their key

advantage is their generalizability (i.e., high external validity), which results from their

ability to generate large representative samples that can be tested statistically (Snow

and Thomas, 1994). The use of large samples enables the development of

methodologically robust conclusions. Moreover, the abstraction achieved by

quantifying the studied phenomena facilitates an identification of common patterns

that indicate more or less persistent structures (Bentz and Shapiro, 1998).

Finally, recent empirical research that identifies a trend toward increased use of

combined methods in academic publications supports the choice of this research

design (Scandura and Williams, 2000). Combining methodological approaches offers

several advantages and likely leads to more profound insights through the resultant

enhanced validity, reliability, and holistic perspective (Bentz and Shapiro, 1998;

Denzin, 1970). In a review of 57 mixed-method studies, Greene et al. (1989) propose

that linking qualitative and quantitative methods can help sequentially develop ideas

(i.e., the results of the first method appear in the second’s sampling, instrumentation,

and so forth) and expand the scope and breadth of a study. In addition, Rossman and

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Wilson (1985) suggest that the use of combinatory methods is justified when the goal

is to confirm or corroborate findings through triangulation.

4.1. Method

The first step in designing the survey was to decide whether to conduct a cross-

sectional study with many companies represented by one or few respondents per

company or a study of a limited number of companies that each provide many teams

and respondents. Research into top management teams traditionally uses cross-

sectional surveys, because they engage top managers best by requiring less of their

time and efforts. In addition, single-company surveys often are not possible because of

the few top managers. In organizational group research, however, single-company

surveys are more common. In a review of 28 team studies, Cohen and Bailey (1997)

find that most investigate teams within a single company; only one-quarter extend

their setting to at least two organizations, and only two examine more than one

industry. Notable one-company studies not reviewed by Cohen and Bailey (1997)

include Gladstein (1984), Keller (1986), Denison et al. (1996), Bunderson and

Sutcliffe (2002), and, in a marketing context, Ruekert and Walker (1987). Surveys of

just a few companies are also common; for example, Pelled et al. (1999) survey teams

from three companies, Keller (2001) four, and Ancona and Caldwell (1992a) five.

For this study, a research design involving a small number of companies appears more

appropriate, largely because the unit of analysis is the team, not the company. By

examining teams in fewer organizational settings, it becomes possible to reduce the

variance in external factors that may affect team performance. In turn, a more precise

investigation of the hypothesized relationships becomes possible, which also increases

internal validity. Furthermore, the problems associated with cross-sectional surveys of

GAM teams become evident in Workman et al.’s (2003) study of the determinants of

GAM effectiveness. With a sample of 385 companies in Germany and the United

States, their study captures a great variety of teams and team contexts and yet fails to

find significant support for the hypothesis that the use of teams improves

effectiveness. The authors suggest that future research efforts should examine the team

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characteristics that influence effectiveness more precisely, which requires a certain

level of comparability among the surveyed teams.

The selected research design is also preferable to a cross-sectional study because of the

potential difficulties associated with the latter, such as sampling problems and low

response rates. A survey from a small number of companies should induce a higher

response rate, because it would be endorsed by senior managers who could inform the

respondents in advance and ask them to complete the survey. More important, such a

survey likely involves a more appropriate sample of qualified respondents.

Determining who is considered a member of the GAM team and defining the limits of

the team can be a substantial challenge for an outsider and represents a major obstacle

and limitation of a large cross-sectional research design. With a survey of only a few

companies, I avoided this problem by discussing the objectives of the project with the

project sponsor in each of the companies and clearly indicated that all respondents

should be in positions to complete the questionnaire; namely, they should be assigned

to or actively involved in the work of a GAM team as full- or part-time members or

have regular working contacts with the team that qualify them to assess its work. This

specification ensured that the potential respondents were the most qualified in each

company, which is a critical prerequisite for collecting high-quality data.

4.2. Sample

The selection of the companies for the survey followed similar criteria as the interview

research. The companies had to be large MNCs with an established GAM program and

extensive usage of GAM teams. Initially, unlike in the exploratory research, some

industry similarity was sought to limit the variance in external influences. However,

finding a sufficient number of companies from related industries proved difficult, so

this condition had to be abandoned in favor of a larger sample.

The initial sampling frame emerged from the Strategic Account Management

Association (SAMA) membership list, as well as additional contacts from the

interview research and other appropriate projects. The head of SAMA’s Education and

Resources division evaluated the Association’s membership base to identify managers

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from companies that met the criteria for the study and sent them a personal invitation

to participate in it, accompanied by the project description. Either I or my doctoral co-

supervisor approached the other companies directly. The companies that expressed

interest in the study were carefully evaluated through interviews to assess their

suitability. Thus, the final sample includes six companies from various industries with

relatively advanced GAM programs: Halcrow, Hilti, Honeywell, Marriott, Philips, and

Xerox. Table 1 presents a brief overview of the companies and their GAM programs.

Table 1: Overview of study companies

Company Industry Headquarters GAM Program Since

Total Sales (2005)

Global Accounts’ Share of Total Sales

Number of Global Accounts

Number of Teamsin Study

Halcrow Engineering UK 2001 £ 282m 35% 14 14

Hilti Construction Liechtenstein 1999 CHF 3.6b 5% 31 31

Honeywell Diversified USA 1980s $ 27.7b 5% 8 6

Marriott Hotels USA 1997 $ 11.6b 10% 32 18

Philips Electronics Netherlands 1990s € 30b n.a. 7 4

Xerox Office equipment

USA 1986 $ 15.7b n.a. 51 40

The UK-based Halcrow is a multidisciplinary services company specializing in the

planning, design, and management of infrastructure development projects worldwide.

With approximately 6,000 employees in more than 70 countries, Halcrow generated

total sales of £282m in 2005. The company established its GAM program in 2001 to

address the needs of 14 strategic international customers that specialize in various

property, water, and transportation lines of business. These customers account for

approximately 35% of Halcrow’s total sales and are serviced by dedicated customer

teams, supported by extensive back office staff and a central GAM coordination team

of three senior managers.

Hilti is headquartered in Liechtenstein and manufactures a wide range of products for

the construction industry, including drilling, cutting, screw fastening, and measuring

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systems. Its 2005 total sales reached CHF3.6b, and the number of employees was

16,000 in 120 countries. The first global account managers were appointed almost 15

years ago, but a more structured GAM program has been in place only since 1999. The

program encompasses 31 global customers from the construction industry that account

for approximately 5% of Hilti’s total sales. They are managed by 23 global account

executives and their teams.

Honeywell is a diversified technology and manufacturing company, serving customers

worldwide with aerospace products and services; control technologies for buildings,

homes, and industry; automotive products; turbochargers; and specialty materials. It is

based in the United States and generated US$27.7b in 2005 from more than 100,000

employees in 95 countries. Its GAM program dates back to the early 1980s, and in the

last two years, the company has devoted increased efforts to reenergize it. The GAM

program involves 8 global accounts and 10 regional EMEA accounts, primarily from

the oil and gas industry. Each has a dedicated team at Honeywell.

Marriott International is a worldwide hospitality company based in Washington, DC.

It employs 143,000 people and runs more than 2,800 hotels and other lodging

properties in 67 countries. Its total sales in 2005 amounted to US$11.6b, of which 10%

was generated by its 32 global accounts from various industries. The GAM program

was established in 1997 and involves 14 Global Account Directors and more than 400

people in account management or global sales positions.

Royal Philips Electronics, headquartered in the Netherlands, is a leading provider of

products and services in four business areas: consumer electronics, domestic

appliances and personal care, medical systems, and lighting. It employs 125,500

people in more than 60 countries and generated sales in excess of EUR30b in 2005. Its

GAM program targets large retailers such as Wal-Mart and covers 7 large global

customers (Tier 1 accounts) and 13 smaller Tier 2 customers.

Headquartered in Connecticut, in the United States, the Xerox Corporation is a leader

in the document industry, providing a wide range of products such as printing and

publishing systems, digital presses, copiers, and fax machines. With 55,200 employees

worldwide, the company generated revenues of US$15.7b. Since 1986, when Xerox

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started with a GAM test program, the program has expanded to 51 premier global

accounts and hundreds of smaller global or regional ones. Xerox also employs

hundreds of global account managers supported by teams that are responsible for one

or a few accounts.

The overall sample consists of 273 valid responses from 113 GAM teams in these six

companies. Table 1 indicates the number of teams per company. Whereas Halcrow and

Hilti involved all their GAM teams, the other four companies contained some teams

deemed unsuitable for the study for various reasons, such as ongoing restructuring or

low performance in the previous year for reasons external to the team. Even after

excluding these cases, the survey encompassed the majority of teams, which ensures a

sufficient number of representative teams and variance. All respondents met the

selection requirements; they directly supported or were involved in the GAM team, in

positions ranging from key or global account managers to regional or country general

managers to members of the top management team. More detailed sample

characteristics appear in the results section.

4.3. Questionnaire

The data collection instrument consists of a multiple-item survey questionnaire. To

ensure the reliability and validity of the measurements, questionnaire development

occurred in several stages, following the guidelines provided in the literature

(Churchill, 1979; DeVellis, 1991; Venkatraman and Grant, 1986). The first step

involved reviewing all relevant literature again, with a specific focus on existing scales

used in previous empirical research. A content analysis of the 13 expert interviews

provided the means to adapt the validated scales to the context of GAM teams and

develop new measures if no available scales existed. The development of these new

scales also relied on a thorough review of the conceptual literature. Several academic

experts and other doctoral students reviewed the initial list of items in the

questionnaire and provided suggestions for improvement. In accordance with their

comments, I added, deleted, or reworded several questions. Finally, 11 senior GAM

executives, including managers from each of the six research companies and some of

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the interviewees from the exploratory research phase, agreed to pretest the modified

survey. They completed the survey, commented on the importance of the items, and

identified any ambiguous or biased questions or potential quality issues. Their major

concern centered on the length of the survey; therefore, I identified several items to

eliminate that did not cause a significant loss of validity in the constructs.

The final version of the questionnaire appears in Appendix F. To increase the

credibility of the questionnaire and improve the response rate, each company received

a separate questionnaire that contained the same questions but was personalized with

the company name and signed by both the project sponsor and me. The questionnaire

design followed Dillman’s (2000) guidelines for constructing questionnaires; the first

section contained a short description and instructions that stressed that all provided

data would be treated as confidential and aggregated prior to analysis.

The core part of the questionnaire consists of five sections—structure and tasks; skills,

competencies, and leadership; organizational context; processes and behaviors; and

outcomes—that reflect the key dimensions of interest. Following Salancik and

Pfeffer’s (1977) approach to avoiding consistency bias effects, the outcome section,

which contains the dependent variables, appears at the end of the questionnaire, after

the independent variables. Within each section, the questions group around the topics

they assess and appear in a logical sequence, which increases their salience and ease of

understanding (Heberlein and Baumgartner, 1978). Finally, respondents are asked to

provide some background information and could express any relevant comments in a

provided blank space.

4.4. Variable operationalization

As described previously, the questionnaire development followed several steps

designed to ensure the maximum validity of the measures. Wherever possible, the

scales used to measure the proposed constructs were based on existing and tested

scales, though most existing measures had to be adapted to fit the researched

constructs. For example, measures from organizational behavior literature had to be

adapted to the context of GAM, and measures used in account management literature

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to assess performance at a program or company level had to be adapted to the team

level. In addition, several new scales appear in the questionnaire. The meta-analysis

results of Churchill and Peter (1984) provide support for the use of adapted or newly

developed scales in marketing research. Having failed to find significant differences

among the reliabilities of originally developed, borrowed-modified and borrowed-

unmodified scales, they concluded that the properties of the measures themselves are

more influential than the measure development procedures.

All core questions, except those referring to team financial and overall performance,

are based on statements that participants evaluated on five-point Likert scales ranging

from 1 = “strongly disagree” to 5 = “strongly agree.” The questions follow an

informant rather than respondent approach, in that the participants evaluate their

team’s rather than their own personal behaviors or attitudes (Van der Vegt and

Bunderson, 2005).

4.4.1. Dependent variables

Although the six focal companies were requested to provide objective measures of

performance, such as actual sales and profitability figures per team, they all rejected

this request for several reasons. First, not all of the companies had such measures

available at a team level because their financial systems and sales allocation

mechanisms do not track sales in this way. Second, some companies refused for

confidentiality reasons. As a result, this study uses subjective performance ratings.

Subjective assessments of task performance, buyer–seller relationship maintenance,

and other performance outcomes at the group level are appropriate in team selling

research because objective outcomes can be difficult to measure in contexts that

involve long, complex selling cycles and whose results may be influenced by product

and competitive factors extraneous to the team (Gladstein, 1984; Smith and Barclay,

1993). Furthermore, objective measures are appropriate to determine how well the

team achieves its quantitative goals, but in many cases, perceptions of effectiveness

among key stakeholders (e.g., customer satisfaction) represent strong indicators of

team performance as well (Cohen and Bailey, 1996).

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A three-item scale adapted from the performance scale of Homburg et al. (2002) and

the efficiency and sales growth scale of Birkinshaw at al. (2001) measures the

financial performance dependent variable. Respondents evaluated how their team

performed, in the past three years, with respect to (1) growth in sales, (2) profitability,

and (3) growth in the share of global customers’ wallets. These questions use a five-

point Likert scale ranging from 1 = “poor” to 5 = “outstanding.”

The relational performance dependent variable uses a six-item scale adapted from the

KAM effectiveness scale of Homburg et al. (2002) and the partnership with customer

scale of Birkinshaw at al. (2001). The items are as follows: “Our team is characterized

by strong and harmonious long-term relationships with global customers,” “Our

customers are satisfied with the overall performance of our team,” “Our team provides

real value to our customers,” “Our team has successfully learned some critical skills or

capabilities from our customer relationships,” “Our company’s competitiveness has

been enhanced due to our team’s GAM achievements,” and “Our team achieves its

goals and objectives.”

4.4.2. Independent variables

Following McGrath’s (1984) recommendations about studying work groups, multiple

constructs and multiple items per construct represent all team characteristics. At least

three items reflect nearly every construct to ensure adequate internal consistency.

The goal and role definition measure involves a newly developed five-item scale that

consists of the following items: “Our GAM team has well-defined goals and objectives

related to our global customers,” “Our team’s goals and objectives are well aligned

with our overall corporate strategies,” “Team members’ individual objectives and

targets are linked to GAM team objectives,” “The roles and responsibilities of team

members are clearly defined,” and “The roles and responsibilities of team members are

understood across the organization.”

A newly developed five-item scale also measures team structure; the items are “The

team has a workable structure and clear reporting lines,” “The team is well positioned

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and integrated in the overall organizational structure of our company,” “The team

structure provides appropriate cross-geographical coverage for our customers,” “The

team structure provides appropriate cross-functional coverage for our customers,” and

“The team structure provides appropriate cross-divisional coverage for our customers.”

Empowerment consists of a three-item scale, specifically: “Our team has sufficient

authority to make important decisions about our customer business,” “Our team has

sufficient authority to change organizational routines to achieve better results for our

customers,” and “Our team has the resources required to innovate and develop our

global customer relationships continuously.” These items represent adaptations from

the authority scale that Mathieu et al. (2006) use to measure empowerment, and from

the resources measure of Denison et al. (1996).

The size adequacy measure is one item adapted from Cohen et al. (1996): “The

number of people in our team is sufficient for our customer business to be developed

efficiently.”

To measure heterogeneity, this study uses the three-item scale proposed by Campion et

al. (1993): “Team members vary widely in their areas of expertise,” “Team members

have a variety of backgrounds and experiences,” and “Team members have

complementary skills and abilities.”

The adequate skills construct employs a newly developed nine-point scale that asks

respondents to what extent they agree with the statements that the account and sales

managers on their team: “are capable of building strong and trusting relationships,”

“have a good understanding of our customer’s business and organization,” “have a

good understanding of our business and the internal capabilities of our company,” “are

able to think and work in an interdisciplinary way,” “are able to coordinate complex

networks and activities,” “are able to think creatively to deliver value to the customer,”

“are able to work in a diverse and multicultural environment,” “employ strategic, long-

term thinking,” and “possess powers of persuasion.”

A three-item scale adapted from Gladstein’s (1984) leadership measure and Scott’s

(1997) measure of a team leader’s organizational influence serves to measure the

leadership variable. The questionnaire items asked respondents to what extent they

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agreed with the statement that their team leader: “has substantial influence in the

organization (even when he/she has no formal authority),” “has a strong relationship

with top management,” and “is able to motivate team members and create synergies

within the team.”

The items related to top management support come from the top management global

orientation measure of Townsend et al. (2004) and Scott’s (1997) scale of top

management support and recognition. The three items are as follows: “Our top

management is actively involved in the team’s efforts to develop profitable, long-term

relationships with global customers,” “Our top management is committed to deploying

the necessary resources to make our global customer operations succeed,” and “Our

top management publicly promotes our team’s GAM activities to others in the

organization.”

The newly developed three-item scale used to measure rewards and incentives

includes the following items: “Team members’ contributions to developing global

customers are measured in a systematic and transparent manner,” “Our compensation

system promotes global collaboration by appropriately rewarding contributions to the

GAM team,” and “Many professional rewards (e.g., pay, promotions) are determined

in large part by team members’ performance on the GAM team.”

The training scale, adapted from Campion et al. (1993), includes three items: “The

company provides adequate global selling and negotiation training for our team,” “The

company provides adequate team skills training for our team (e.g., communication,

organization, interpersonal),” and “The company provides adequate technical training

for our team.”

The measure of external communication and collaboration employs a newly developed

three-item scale: “Our team regularly exchanges best practices and market knowledge

with other GAM teams,” “Team members communicate proactively on issues related

to our GAM activities across boundaries and hierarchical levels in the entire

organization,” and “Our team has difficulty obtaining support from other parts of the

organization” (reverse coded).

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Internal communication and collaboration entail a four-item scale adapted from

Bunderson and Sutcliffe’s (2002) information-sharing scale and the measure of within-

group collaboration from Campion et al. (1993). The items are as follows:

“Communication in the team is effective, despite the geographical distance of team

members,” “Team members keep one another updated about their activities and key

issues affecting the business,” “Team members are good at coordinating their efforts to

serve the customer efficiently,” and ”Team members collaborate to achieve our global

goals.”

The conflict management scale includes two items adapted from Denison et al. (1996):

“Disputes between the different units represented on our team make it difficult to do

our work” (reverse coded) and “Our team is able to identify and resolve conflicts in a

timely and effective manner,” as well as two newly developed items: “Our team can

easily send problems up the chain of command (escalate to senior management) when

they cannot be resolved within the team” and “The negative politics within the team

are minimal.”

Finally, the proactiveness scale contains a three-item adaptation of Bateman and

Crant’s (1993) measure of individual proactivity, similar to the team adaptation of the

same construct used by Kirkman and Rosen (1997): “Team members proactively

cultivate new business opportunities,” “Our team is not afraid to challenge the status

quo to improve our customer relationships,” and “The team is a powerful force for

constructive change in the organization.”

4.4.3. Control variables

In addition to the dependent and independent variables described above, the

questionnaire included a number of variables that aimed to control for the influence of

key respondents’ characteristics: (1) organizational tenure, (2) team tenure, (3) gender,

and (4) age. All of these variables were measured with single-item scales.

Furthermore, three control variables at a company level were considered: (1) company

industry, (2) company size as measured by sales, and (3) GAM program size as

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measured by the number of global accounts and the percentage of total sales generated

from global customers.

4.5. Data collection procedures

The data collection took place during August 15–September 30, 2006. The entirely

online survey relied on the Internet platform www.questionpro.com because an online

format offers several advantages over mail surveys. First, Internet surveys are

particularly useful for international studies because they overcome geographical

boundaries, one of the primary barriers to conducting surveys (Dillman, 2000). The

very nature of the sample required a fast and efficient way to reach a relatively large

number of people in various countries. Furthermore, in their positions as global

account managers, many respondents spend a large part of their time traveling, which

demands an approach they could access even if they were away from their usual

workplace. Second, common concerns about Internet surveys, such as limited access to

computers or varying degrees of computer literacy (Dillman, 2000), are not a problem

in this study because the sample includes managers with daily access to e-mail and the

Internet and who must possess the required computer skills and feel comfortable with

Internet applications for their jobs (Zhang, 2000). Third, Internet surveys reduce time

and costs substantially. Fourth, an online survey significantly reduces the likelihood of

transcription and coding errors because the data entry takes place as the respondents

fill in the questionnaire and automatically gets stored in an electronic form (Sills and

Song, 2002; Simsek and Veiga, 2001). In addition, a pretest of the survey on the Web

platform confirmed that the interface was operational, the server was reliable, and the

data were collected correctly.

Multiple data collection contacts increase response rates in both mail (Linsky, 1975;

Dillman, 1991) and e-mail (Schaefer and Dillman, 1998) surveys better than any other

technique. Therefore, the data collection followed a four-step approach, similar to that

Dillman (2000) recommends for mail surveys. First, the survey sponsors in each of the

six research companies informed the participants about the forthcoming survey by e-

mail. In all companies, the sponsors were senior managers responsible for global

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accounts, which contributed significantly to the acceptance of the study. In four

companies, these project sponsors held the position of global head of GAM; in the

other two cases, their positions were equivalent for the region of EMEA. This

prenotice e-mail contained an overview of the research project, similar to the cover

page of the questionnaire. In particular, it emphasized the benefits for the participating

company and that the participants’ responses would be greatly appreciated. To avoid

the problem associated with a social desirability bias (Zerbe and Paulhus, 1987) and to

make respondents more comfortable with disclosing information, they were not asked

to provide their names and were assured that all data will be aggregated prior to

analysis and no analyses will be conducted on the individual or team level.

Second, I sent a personalized e-mail, including an individual link and password to the

survey, to all participants approximately two days after the internal notification. In

total, 425 managers received an invitation. Third, approximately two weeks after the

initial mailing, those who had not responded received a reminder e-mail indicating that

their responses had not been received. Before this reminder, 126 managers had

completed and successfully submitted the questionnaire. Afterward, an additional 117

complete questionnaires arrived. Fourth, a second reminder sent to all remaining

nonrespondents two weeks after the first reminder resulted in an additional 33

responses. The survey was closed approximately six weeks after the first mailing, and

the six companies were informed about its successful completion. On the closing date,

the database consisted of 276 responses, all of which were screened for incomplete or

double entries, which led to the exclusion of 3 entries because of their many missing

values. This study therefore is based on 273 complete data sets, an effective response

rate of 64.2%, which compares favorably to similar studies (e.g., Denison et al., 1996;

Gladstein, 1984).

4.6. Data analysis procedures

The sequence of procedures used in the data analysis is depicted in Figure 6. First, the

database was examined and tested for potential biases such as nonresponse bias and

common method bias. Second, the descriptive statistics, distributions and correlations

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of all individual variables were scanned to better understand their characteristics and

implications for the next steps of the analysis. Third, the reliability of all measures was

assessed by studying the item-total correlations and the coefficient alpha.

Figure 6: Summary of the sequence of data analysis

Fourth, exploratory factor analysis (EFA) was performed to assess the validity of the

dimensions identified by the qualitative research. This also led to initial purification of

the measures by reducing the number of items in some of the scales. Next, the

confirmatory factor analysis (CFA) provided further support for the identified

dimensions and for their fit in the respective second-order domains. Finally, structural

equation analysis was used to identify causal relationships among constructs and to

test the full latent variable model.

Tests for potential biases

Examination of all variables

Reliability and item analysis

Confirmatory factor analysis

Exploratory factor analysis

Test of the structural path model

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5. Results 5.1. Sample description

This section provides an overview of the sample in terms of location, positions and

other demographic characteristics of the respondents. Figure 7 shows how the sample

is distributed among the six companies that participated in the survey. The largest

number of responses was obtained from Hilti – 68 complete questionnaires, followed

by Philips – 54, Halcrow – 49, Xerox – 47, Honeywell – 32, and Marriott – 23. The

difference in the sizes of these subsamples reflects the different number of respondents

invited to participate in the survey rather than any significant differences in the

response rate per company.

Figure 7: Respondents by company

Halcrow18%

Hilti25%

Honeywell12%

Marriott8%

Philips20%

Xerox17%

Figure 8 illustrates the geographic distribution of the respondents as described by the

country in which they are based. It demonstrates a very international, yet

predominantly European, sample with managers located in a total of 25 countries in

almost all major regions of the world. Approximately 69% of the respondents are

based in Western Europe, followed by 23% from North America, 3% from Eastern

Europe, 3% from Asia and 2% from Latin America. In terms of countries, the largest

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share is represented by the UK (28%) followed by the USA (23%), Germany (8%) and

France (7%).

Figure 8: Respondents by country

28%

8%

7%5%4%4%3%

10%

3%

23%

2%

3%UKGermanyFranceLiechtensteinBelgiumNetherlandsItalyRest of W. EuropeEastern EuropeUSALatin AmericaAsia

The respondents hold a wide range of positions ranging from technical specialists and

country sales representatives to CEO and board members.

Figure 9: Respondents’ position

GAM, IKAM, Head of GAM

24%

Other15%

KAM, NAM, account manager

20%Sales director, sales manager, head of sales

17%

TMT member, managing

director, general manager, head of region , head

of BU24%

118

Figure 9 shows that some 24% of the respondents are in a senior-level position with

general management responsibilities. The same percentage of respondents are

responsible for managing international customer relationships with job titles such as

global account manager or international key account manager. Another 20% are

responsible for customer relationships in a more limited geographical region and had

titles such as key account manager or national account manager. Finally, 17% were

responsible for sales and 15% held other more specialized positions, e.g. technical

support, logistics, marketing etc. Overall, Figure 9 provides support that the

respondents were well qualified for the study because they represent positions and

functions that are expected to be well acquainted with the work of the GAM teams in

an organization.

Figure 10: Respondents’ team tenure

3 to 4 years19% 1 to 2 years

42%

Less than 1 years16%

More than 4 years23%

Figure 10 presents the sample composition in terms of respondents’ team tenure. The

average team tenure is 3 years (SD = 2.8). Respondents who have been involved in

their respective teams for less than one year account for only 16% of the total sample.

Approximately 42% have been working with their teams for a period of one to two

119

years. 19% have been involved for three to four years and 23% for more than four

years. These figures reflect the relatively early age of GAM teamwork in some of the

participating companies. For example, at Halcrow and Hilti, GAM teams have been

more formalized only in the recent years. Also, in many of the other companies, the

GAM programs have started with a smaller number of people and with time larger

teams have gradually formed around the individual account managers.

A profile of the survey participants in terms of organizational tenure is presented in

Figure 11. The average organizational tenure is 12.5 years (SD = 8.6), with

approximately half of the respondents (51%) having worked in their company for 12

years or longer. This indicates a good familiarity with the company and potentially a

strong understanding of the global customer organization and processes.

Figure 11: Respondents’ organizational tenure

More than 20 years17%

16 to 20 years18%

1 to 3 years19%

4 to 7 years16%

8 to 11 years14%12 to 15 years

16%

120

5.2. Examination for potential biases

The first step in analyzing the data was to check the database for potential biases. In

particular, two types of bias – nonresponse and common method – were of concern for

this study.

5.2.1. Nonresponse bias

Nonresponse bias in a database refers to the possibility that the persons who responded

differ significantly from those who did not, which does not allow to draw conclusions

for the entire sample. Literature suggests that the best remedy against this type of bias

is the reduction of nonresponse itself (Armstrong and Overton, 1977). Therefore, the

high response rate (64%) of this study suggests that there is no strong reason to suspect

the existence of nonresponse bias. Nevertheless the first step is to assess it because

surveys have often been criticized for it. Armstrong and Overton (1977) suggest three

methods of estimating nonresponse bias: comparison with known values for the

population, subjective estimates and extrapolation (i.e., comparison between early and

late respondents assuming that late participants resemble nonrespondents). The first

two methods are not applicable in this research because no prior values are available

for any of the variables examined in this study and there is no strong basis for

subjectively estimating differences between respondents and nonrespondents.

Consequently, similar to other studies in GAM and marketing (Townsend et al., 2004;

Workman et al., 2003), the extrapolation procedure was used. Following the procedure

described by Workman et al. (2003), the dataset was divided into thirds within each

company according to the date when the questionnaire was completed. Next,

independent sample t-tests were conducted to check for differences between the first

third (n = 91) and the last third (n = 91) in the mean responses for the items used.

The results for the independent variables do not indicate the existence of substantial

response bias. In most cases (71% of the items) late respondents have given higher

ratings than early respondents but these differences are not significant. The only three

items with significantly different mean responses are: 2.5 “Team members have a

121

good understanding of our customer’s business and organization” (t = -2.28, p = .02);

4.2 “Team members communicate proactively on issues related to our GAM activities”

(t = 1.84. p = .07) and 4.12 “Team members proactively cultivate new business

opportunities” (t = -1.73, p = .09). However, the p-values of two of them are above the

conventional level of .05.

Table 2 presents the results for the dependent variables as well as for two key control

variables. It indicates the mean difference between the ratings of the early and late

respondents and the respective t- and p-values.

Table 2: Assessment of nonresponse bias

Variable Mean difference t-value Sig.

(p-value)

Team tenure of respondents .010 .022 .982

Organizational tenure of respondents 2.059 1.544 .124

5.1 Strong and harmonious long-term customer relation. -.330 -2.730 .007

5.2 Customer satisfaction -.165 -1.455 .148

5.3 Customer value .060 .553 .581

5.4 Learning of skills and capabilities from customer rel. -.064 -.656 .512

5.5 Enhanced competitive position -.073 -.600 .549

5.6 Achievement of goals and objectives -.085 -.664 .508

5.7 Sales growth -.085 -.648 .518

5.8 Profitability -.127 -.965 .336

5.9 Share of wallet growth -.205 -1.605 .110

With the exception of item 5.1 “Our team is characterized by strong and harmonious

long-term relationships with global customers” (t = -2.73, p = .007), these variables do

not exhibit any significant differences. P-values range from .11 to .98. Hence, there is

no strong reason to believe that nonrespondents differ from respondents in terms of

their demographics or the performance of their teams. Overall, nonresponse bias does

not seem to be a concern.

122

5.2.2. Common method bias

Common method bias is of some concern because the data were collected from the

same respondents with the use of a single instrument. When two measures are

collected from same-source self-reports, any defect in that source may contaminate

both measures, presumably in the same fashion and in the same direction. As a result,

the two measures may exhibit a correlation that does not reflect an actual relationship

and may lead to erroneous conclusions (Podsakoff and Organ, 1986).

As discussed, the first step to prevent this bias was made during the study design. The

questionnaire items were arranged so that the dependent variables followed the

independent ones to prevent the possibility of consistency artifacts that leads to

common method variance. Guaranteeing anonymity to respondents was used as a tool

to reduce social desirability effects which are another source of common method

variance. Second, the possibility of a common method bias was checked with

Harman’s (1967) single-factor test using the procedure suggested by Podsakoff and

Organ (1986). The assumption of this test is that if a common method bias is present,

an unrotated factor analysis of all variables of interest will generate either a single

factor or a general factor that accounts for most of the covariance in the independent

and dependent variables. Following this technique, I entered all dependent and

independent variables in an unrotated factor analysis based on the principal

components method and found 14 distinct factors with eigenvalues greater than one.

The first factor explained 26% of the variance in the data, which is not high enough to

conclude that it captures the majority of the variance. Therefore, a substantial common

method bias is not evident in the data and should not be considered a threat.

5.3. Examination of the variables

After no bias was detected in the overall data set, the next step was to examine all

individual items for potential sources of concern. In the first place, I evaluated the data

set by looking for outliers. A careful examination of the frequencies and scatterplots of

all variables revealed that no outliers are present. Second, the distribution properties of

all variables were checked. Because many analytical methods assume that the used

123

variables are normally distributed, it is important to detect any nonnormal distributions

that might threaten the validity of such methods. For this purpose, the histograms for

all variables were reviewed as well as distribution indicators such as skewness and

kurtosis. The negative skewness indicators of the majority of items suggest that their

distribution is skewed to the left. However, almost all skewness values are within the

range of -1.00 to 1.00, and hence provide no strong indication of nonnormality. Only

item 2.1 “Team members vary widely in their areas of expertise” (Skewness = -1.15)

is just outside these boundaries but still well within the -5.00 to 5.00 boundaries of

concern for skewness. Similarly, most of the sample kurtosis are within or close to the

-1.00 to 1.00 range. Only item 2.2 “Team members have a variety of backgrounds and

experiences” (Kurtosis = 2.18) has kurtosis above the conventional cutoff point of 2.00

beyond which nonnormality becomes a concern. Thus, no strong indication of

nonnormal distributions is detected.

The third step was to examine the data for missing values. The average percentage of

missing values per item is 4.6% and no item has more than 12% of missing values,

which is a sign of no serious concern. However, in order to ensure more stable results

in the subsequent analyses, all missing values are replaced with the median, which is

an appropriate method given the distribution properties of the dataset and the relatively

low number of missing values. The key advantage of these imputations is that they

allow calculating modification indexes in AMOS (Arbuckle, 1997), which is very

important for the confirmatory factor analysis and the test of the structural path model.

Finally, the correlations among all items were examined. The correlation matrix in

Appendix G indicates that most of the variables are significantly correlated with each

other. However, the Cohen’s (1977) criteria, whereby correlations of .1 are considered

small, .3 medium and .5 large, indicate that large correlations (marked in bold) are

observed mainly among items from the same scale. This can be expected because these

items are used to measure the same construct and the high correlations provide some

initial support for the reliability of the measures.

124

5.4. Reliability and item analysis

The next step in the analysis is to evaluate the reliability of the scales used. Reliability

can be defined broadly as the degree to which measures are free from error and

therefore yield consistent results. As Peter (1979) notes, behavioral measures are

seldom totally reliable and valid but the degree of their validity and reliability should

be assessed if research is to be truly scientific. Reliability could be examined in terms

of stability and consistency of the measurement whereby stability is related to the

ability to reproduce measurement results at different points in time and consistency

refers to the reliability of alternative scales designed to measure the same characteristic

(McCullough and Best, 1979).

There are three basic methods of assessing the reliability of a measurement scale: test-

retest, internal consistency and alternative forms (Peter, 1979). The test-retest and the

alternative forms methods require that either the same or two different sets of items are

administered to the same objects at two different times. Since the nature of this study

makes these methods impossible to conduct, the internal consistency approach is the

most appropriate. It requires that the measurement scale is applied to subjects at one

point in time and subsets of items within the scale are then correlated. The two major

methods for assessing the internal consistency of an instrument are Cronbach’s (1951)

coefficient alpha and split-half reliability methods such as the Spearman-Brown

coefficient or the Guttman split-half coefficient. However, the results of the split-half

model are strongly influenced by the way the scale is split and it is less reliable when

the number of items in the two halves are not equal (Green and Salkind, 2005).

Because some of the constructs in the questionnaire are measured by only 3 items, this

method is not appropriate and the coefficient alpha is preferred.

As Churchill (1979: 68) suggests coefficient alpha „absolutely should be the first

measure one calculates to assess the quality of an instrument.” Specifically within

marketing research, it has been considered the most useful method for assessing the

reliability of measures (Peter, 1979). According to Nunnally and Bernstein’s (1994)

guidelines, in early stages of research modest coefficient alpha levels of .5 to .6 will

suffice and for basic research in general increasing reliability beyond .8 is unnecessary

12

5

Tab

le 3

: Var

iabl

es m

easu

res a

nd c

onst

ruct

rel

iabi

lity

estim

ates

Con

stru

cts a

nd it

ems

Sour

ce

Item

-tot

al

corr

elat

ion

Coe

ff.

alph

a G

oal a

nd r

ole

defin

ition

1.

1 O

ur G

AM

team

has

wel

l-def

ined

goa

ls a

nd o

bjec

tives

rela

ted

to o

ur g

loba

l cus

tom

ers.

1.2

Our

team

’s g

oals

and

obj

ectiv

es a

re w

ell a

ligne

d w

ith o

ur o

vera

ll co

rpor

ate

stra

tegi

es.

1.3

Team

mem

bers

’ ind

ivid

ual o

bjec

tives

and

targ

ets a

re li

nked

to G

AM

team

obj

ectiv

es.

1.4

The

role

s and

resp

onsi

bilit

ies o

f tea

m m

embe

rs a

re c

lear

ly d

efin

ed.

1.5

The

role

s and

resp

onsi

bilit

ies o

f tea

m m

embe

rs a

re u

nder

stoo

d ac

ross

the

orga

niza

tion.

T

eam

stru

ctur

e 1.

6 Th

e te

am h

as a

wor

kabl

e st

ruct

ure

and

clea

r rep

ortin

g lin

es.

1.7

The

team

is w

ell p

ositi

oned

and

inte

grat

ed in

the

over

all o

rgan

izat

iona

l stru

ctur

e of

our

com

pany

. 1.

8 Th

e te

am st

ruct

ure

prov

ides

app

ropr

iate

cro

ss-g

eogr

aphi

cal c

over

age

for o

ur c

usto

mer

s. 1.

9 Th

e te

am st

ruct

ure

prov

ides

app

ropr

iate

cro

ss-f

unct

iona

l cov

erag

e fo

r our

cus

tom

ers.

1.10

The

team

stru

ctur

e pr

ovid

es a

ppro

pria

te c

ross

-div

isio

nal c

over

age

for o

ur c

usto

mer

s. E

mpo

wer

men

t 1.

11 O

ur te

am h

as su

ffic

ient

aut

horit

y to

mak

e im

porta

nt d

ecis

ions

abo

ut o

ur c

usto

mer

bus

ines

s. 1.

12 O

ur te

am h

as su

ffic

ient

aut

horit

y to

cha

nge

orga

niza

tiona

l rou

tines

to a

chie

ve b

ette

r res

ults

for o

ur c

usto

mer

s.

1.13

Our

team

has

the

reso

urce

s req

uire

d to

inno

vate

and

dev

elop

our

glo

bal c

usto

mer

rela

tions

hips

con

tinuo

usly

. Si

ze a

dequ

acy

1.14

The

num

ber o

f peo

ple

in o

ur te

am is

suff

icie

nt fo

r our

cus

tom

er b

usin

ess t

o be

dev

elop

ed e

ffici

ently

. H

eter

ogen

eity

2.

1 T

eam

mem

bers

var

y w

idel

y in

thei

r are

as o

f exp

ertis

e.

2.2

Tea

m m

embe

rs h

ave

a va

riety

of b

ackg

roun

ds a

nd e

xper

ienc

es.

2.3

Tea

m m

embe

rs h

ave

com

plem

enta

ry sk

ills a

nd a

bilit

ies.

Ade

quat

e sk

ills

The

acco

unt/s

ales

man

ager

s on

our t

eam

2.

4 …

are

cap

able

of b

uild

ing

stro

ng a

nd tr

ustin

g re

latio

nshi

ps.

2.5

… h

ave

a go

od u

nder

stan

ding

of o

ur c

usto

mer

’s b

usin

ess a

nd o

rgan

izat

ion.

2.

6 …

hav

e a

good

und

erst

andi

ng o

f our

bus

ines

s and

the

inte

rnal

cap

abili

ties o

f our

com

pany

. 2.

7 …

are

abl

e to

thin

k an

d w

ork

in a

n in

terd

isci

plin

ary

way

. 2.

8… a

re a

ble

to c

oord

inat

e co

mpl

ex n

etw

orks

and

act

iviti

es.

2.9

… a

re a

ble

to th

ink

crea

tivel

y to

del

iver

val

ue to

the

cust

omer

. 2.

10 …

are

abl

e to

wor

k in

a d

iver

se a

nd m

ultic

ultu

ral e

nviro

nmen

t. 2.

11 …

em

ploy

stra

tegi

c, lo

ng-te

rm th

inki

ng.

2.12

… p

osse

ss p

ower

s of p

ersu

asio

n.

Lea

ders

hip

2.13

Our

team

lead

er h

as su

bsta

ntia

l inf

luen

ce in

the

orga

niza

tion

(eve

n w

hen

he/s

he h

as n

o fo

rmal

aut

horit

y).

2.14

Our

team

lead

er h

as a

stro

ng re

latio

nshi

p w

ith to

p m

anag

emen

t.

2.15

Our

team

lead

er is

abl

e to

mot

ivat

e te

am m

embe

rs a

nd c

reat

e sy

nerg

ies w

ithin

the

team

.

New

N

ew

Den

ison

et a

l. (1

996)

, Mat

hieu

et

al.

(200

6)

Coh

en e

t al.

(199

6)

Cam

pion

et a

l. (1

993)

N

ew

Gla

dste

in (1

984)

, Sc

ott (

1997

)

.559

.4

80

.547

.5

89

.424

.4

80

.532

.6

39

.666

.6

31

.616

.7

39

.496

.6

37

.661

.4

55

.692

.6

87

.515

.6

77

.706

.7

12

.575

.6

83

.637

.7

32

.709

.5

94

.755

.8

04

.776

.7

48

.893

.8

20

12

6

Top

man

agem

ent s

uppo

rt

3.1

Our

top

man

agem

ent i

s act

ivel

y in

volv

ed in

the

team

’s e

ffor

ts to

dev

elop

pro

fitab

le, l

ong-

term

rela

tions

hips

with

glo

bal c

usto

mer

s. 3.

2 O

ur to

p m

anag

emen

t is c

omm

itted

to d

eplo

ying

the

nece

ssar

y re

sour

ces t

o m

ake

our g

loba

l cus

tom

er o

pera

tions

succ

eed.

3.

3 O

ur to

p m

anag

emen

t pub

licly

pro

mot

es o

ur te

am’s

GA

M a

ctiv

ities

to o

ther

s in

the

orga

niza

tion.

R

ewar

ds a

nd in

cent

ives

3.

4 Te

am m

embe

rs’ c

ontri

butio

ns to

dev

elop

ing

glob

al c

usto

mer

s are

mea

sure

d in

a sy

stem

atic

and

tran

spar

ent m

anne

r. 3.

5 O

ur c

ompe

nsat

ion

syst

em p

rom

otes

glo

bal c

olla

bora

tion

by a

ppro

pria

tely

rew

ardi

ng c

ontri

butio

ns to

the

GA

M te

am.

3.6

Man

y pr

ofes

sion

al re

war

ds (e

.g.,

pay,

pro

mot

ions

) are

det

erm

ined

in la

rge

part

by te

am m

embe

rs’ p

erfo

rman

ce o

n th

e G

AM

team

. T

rain

ing

3.7

The

com

pany

pro

vide

s ade

quat

e gl

obal

selli

ng a

nd n

egot

iatio

n tra

inin

g fo

r our

team

. 3.

8 Th

e co

mpa

ny p

rovi

des a

dequ

ate

team

skill

s tra

inin

g fo

r our

team

(e.g

., co

mm

unic

atio

n, o

rgan

izat

ion,

inte

rper

sona

l).

3.9

The

com

pany

pro

vide

s ade

quat

e te

chni

cal t

rain

ing

for o

ur te

am.

Ext

erna

l com

mun

icat

ion

and

colla

bora

tion

4.1

Our

team

regu

larly

exc

hang

es b

est p

ract

ices

and

mar

ket k

now

ledg

e w

ith o

ther

GA

M te

ams.

4.2

Team

mem

bers

com

mun

icat

e pr

oact

ivel

y on

issu

es re

late

d to

our

GA

M a

ctiv

ities

acr

oss b

ound

arie

s and

hie

rarc

hica

l lev

els i

n th

e en

tire

orga

niza

tion.

4.

3 O

ur te

am h

as d

iffic

ulty

obt

aini

ng su

ppor

t fro

m o

ther

par

ts o

f the

org

aniz

atio

n. (r

ever

se c

oded

) * re

mov

ed a

fter r

elia

bilit

y an

alys

is

Inte

rnal

com

mun

icat

ion

and

colla

bora

tion

4.4

Com

mun

icat

ion

in th

e te

am is

eff

ectiv

e, d

espi

te th

e ge

ogra

phic

al d

ista

nce

of te

am m

embe

rs.

4.5

Team

mem

bers

kee

p on

e an

othe

r upd

ated

abo

ut th

eir a

ctiv

ities

and

key

issu

es a

ffec

ting

the

busi

ness

. 4.

6 Te

am m

embe

rs a

re g

ood

at c

oord

inat

ing

thei

r eff

orts

to se

rve

the

cust

omer

eff

icie

ntly

. 4.

7 Te

am m

embe

rs c

olla

bora

te to

ach

ieve

our

glo

bal g

oals

. C

onfli

ct m

anag

emen

t 4.

8 D

ispu

tes b

etw

een

the

diff

eren

t uni

ts re

pres

ente

d on

our

team

mak

e it

diff

icul

t to

do o

ur w

ork.

(rev

erse

cod

ed)

4.9

Our

team

is a

ble

to id

entif

y an

d re

solv

e co

nflic

ts in

a ti

mel

y an

d ef

fect

ive

man

ner.

4.10

Our

team

can

eas

ily se

nd p

robl

ems u

p th

e ch

ain

of c

omm

and

(esc

alat

e to

seni

or m

anag

emen

t) w

hen

they

can

not b

e re

solv

ed w

ithin

the

team

. 4.

11 T

he n

egat

ive

polit

ics w

ithin

the

team

are

min

imal

. Pr

oact

iven

ess

4.12

Tea

m m

embe

rs p

roac

tivel

y cu

ltiva

te n

ew b

usin

ess o

ppor

tuni

ties.

4.13

Our

team

is n

ot a

frai

d to

cha

lleng

e th

e st

atus

quo

to im

prov

e ou

r cus

tom

er re

latio

nshi

ps.

4.14

The

team

is a

pow

erfu

l for

ce fo

r con

stru

ctiv

e ch

ange

in th

e or

gani

zatio

n.

Rel

atio

nal a

nd m

arke

t per

form

ance

5.

1 O

ur te

am is

cha

ract

eriz

ed b

y st

rong

and

har

mon

ious

long

-term

rela

tions

hips

with

glo

bal c

usto

mer

s. 5.

2 O

ur c

usto

mer

s are

satis

fied

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127

because at that level measurement error has a very little impact on correlations. As a

result, coefficients of .7 or above are usually considered satisfactory.

Table 3 reports the results of the reliability analysis of all constructs including the

coefficient alpha and the item-total correlation that describes the extent to which each

item is correlated to its own scale. In general, the Cronbach’s alphas for all

independent constructs meet the minimum recommended level of .7 with the exception

of conflict management which is slightly lower but still satisfactory (α = .65). The

coefficients of the other independent constructs range from .71 to .89. The two

performance constructs are reliable as well, as indicated by their coefficient alphas of

.87 and .83, respectively. Only one item (4.3 “Our team has difficulty obtaining

support from other parts of the organization”) had a low item-total correlation (.135)

resulting in a coefficient alpha below the recommended level (α = .52) and was

considered for elimination from the scale (Churchill, 1979). Its elimination improved

the internal reliability of the measure for external communication and collaboration to

.71.

5.5. Results of the exploratory factor analysis

As suggested by Churchill (1979) and Anderson (1987), the next step after evaluating

the internal consistency of the scales is to assess the validity of all constructs, usually

through factor analysis. Validity refers to the degree to which instruments truly

measure the constructs which they are intended to measure (Peter, 1979) and factor

analysis is an appropriate method for assessing construct validity when an instrument

consists of multiple questions that measure different constructs (Nunnally and

Bernstein, 1994).

Following the procedure employed by Denison et al. (1996) to confirm their three-

domain model of cross-functional team effectiveness, the factor analysis was

conducted in two steps. First, exploratory factor analysis was used to examine the

acceptability of the 16 individual constructs suggested by the literature and by the

qualitative analysis and to determine how the item pool relates to the developed

128

constructs. In a second step, I conducted confirmatory factor analysis to further refine

these results and to determine if the derived factors fit in the four second-order factors

of team design, organizational context, processes and performance. Starting with an

exploratory rather than a confirmatory approach was justified by the exploratory

nature of this study, the lack of developed theory and the relative novelty of some

measures (Thompson, 2004) as well as the large number of items (Bentler and Chou,

1987).

A separate EFA for each of the four second-order domains was chosen as opposed to

EFA on all items for two main reasons. First, a single EFA on all items would require

a significantly larger sample size to compensate for the large number of items.

Gorsuch (1983: 332) suggests that “an absolute minimum ratio is five individuals to

every variable” and Nunnally and Bernstein (1994) recommends that for any kind of

multivariate analysis, there should be at least 10 times as many subjects as items. A

sample size of 273 data points and an item pool of 52 independent and 9 dependent

variables results in a subject-to-item ratio that is much lower than the recommended

value and the ratio in other studies that use this method (e.g. Campion et al., 1993).

This entails the risk of producing unreliable estimates in a single EFA. Second, EFA

per domain is legitimate when the domains are clearly delineated. Conceptually

distinct domains provide a considerable degree of assurance that the items from one

domain do not relate to, and therefore will not load on, another domain. Thus, they can

be separated for the analysis to improve the subject-to-item ratio and increase the

robustness of the results. As the framework is based on extensive review of previous

studies that consistently distinguish between the four domains, the analysis was based

on this method and followed the procedure of Denison et al. (1996).

All EFA used the principle components extraction method with Varimax rotation.

Items were selected for inclusion in a factor based on the Kaiser (1958) criterion of

loading above .50. Items with loading below this cutoff point were excluded from

further analysis. Table 4 presents the results from the EFA of the team design domain.

129

Table 4: Results of exploratory factor analysis of the team design domain

Constructs and items 1 2 3 4 5 6 Goals and roles (α = .777)

1.1 Our GAM team has well-defined goals and objectives related to our global customers.

1.2 Our team’s goals and objectives are well aligned with our overall corporate strategies.

1.3 Team members’ individual objectives and targets are linked to GAM team objectives.

1.4 The roles and responsibilities of team members are clearly defined.1.6 The team has a workable structure and clear reporting lines.

Customer coverage (α = .714) 1.8 The team structure provides appropriate cross-geographical

coverage for our customers. 1.9 The team structure provides appropriate cross-functional

coverage for our customers. 1.10 The team structure provides appropriate cross-divisional coverage

for our customers. Empowerment (α = .776)

1.11 Our team has sufficient authority to make important decisions about our customer business.

1.12 Our team has sufficient authority to change organizational routines to achieve better results for our customers.

1.13 Our team has the resources required to innovate and develop our global customer relationships continuously.

Heterogeneity (α = .748) 2.1 Team members vary widely in their areas of expertise. 2.2 Team members have a variety of backgrounds and experiences. 2.3 Team members have complementary skills and abilities.

Adequate skills (α = .893) The account/sales managers on our team 2.4 … are capable of building strong and trusting relationships. 2.5 … have a good understanding of our customer’s business and

organization. 2.6 … have a good understanding of our business and the internal

capabilities of our company. 2.7 … are able to think and work in an interdisciplinary way. 2.8 … are able to coordinate complex networks and activities. 2.9 … are able to think creatively to deliver value to the customer. 2.10 … are able to work in a diverse and multicultural environment. 2.11 … employ strategic, long-term thinking. 2.12 … possess powers of persuasion.

Leadership (α = .820) 2.13 Our team leader has substantial influence in the organization

(even when he/she has no formal authority). 2.14 Our team leader has a strong relationship with top management. 2.15 Our team leader is able to motivate team members and create

synergies within the team. Eigenvalues Variance explained by factor after Varimax rotation Total variance explained

.763

.698

.716

.701.628

.401

.172

.073

.209

.083

.222

.067 -.010.191

.144

.130

.066 .011 .072 .067 .053 .126 .168

.167

.096 .206

2.80911.5%65.2%

.031

.118

.061

.092 .269

.707

.822

.818

.175

.135

.214

.001

.039

.106

-.047 .055

.185

.195 .069 .053 .176 .058 .019

.069

.093 .025

1.565 8.4%

.130

.202

.168

-.032 .044

.043

.214

.259

.733

.856

.646

-.025 .001 .234

.077 -.061

-.137

.155 .166 .006 .063 .258 .251

.216

.112 .172

1.673 8.6%

.047

.058

.140

.059 -.106

-.054

.118

.061

-.045

.022

.144

.867

.859

.579

.012 -.062

-.108

.028 .124 .143 .192 .078 .167

.123

.074 .077

1.156 7.9%

.193

.000

.034

.176 .126

.124

.151

.203

.121

.182

.131

-.004.154 .322

.733

.765

.612

.749

.769

.802

.684

.726

.651

.208

.107

.233

7.89719.9%

.070

.030

.121

.122 .159

.176

.027

.031

.261

.139

.119

.150

.049

.023

.329

.152

.297

.011

.064

.032

.022

.049

.123

.799

.852

.693

1.8608.9%

The results reveal six factors with eigenvalues greater than 1.0, which correspond

directly to the developed GAM team design constructs. Because the size adequacy

130

variable is measured by a single item, it is not included in this analysis. All items

measuring GAM team empowerment, heterogeneity, skills and leadership load on their

own factors and have loadings above the cutoff point of .50. However, some of the

items from the role and goal definition and the team structure scales did not load as

expected. The item 1.6 “The team has a workable structure and clear reporting lines”

loads on the first factor as opposed to the team structure factor. A possible explanation

is that reporting lines and structural arrangements within the team are more closely

related to the division of roles and responsibilities that is captured by the roles and

goals construct. The items 1.5 “The roles and responsibilities of team members are

understood across the organization” and 1.7 “The team is well positioned and

integrated in the overall organizational structure” cross-load on the first two factors

and have loadings below .50. Consequently, they had to be removed from the analysis.

Since all the remaining items in the structure factor relate to the extent that the team

structure provides adequate customer coverage, this construct is named customer

coverage to better reflect its content. Despite the changes in the first two scales, they

remain reliable with Cronbach’s alphas of .78 and .71 respectively. The six factors

explain 65.2% of the total variance with skills having the largest share (19.9%)

followed by roles and goals (11.5%) and leadership (8.9%). In summary, these results

provide satisfactory support for the validity of the six team design dimensions.

131

Table 5: Results of exploratory factor analysis of the organizational context domain

Constructs and items 1 2 3 Top management support (α = .811)

3.1 Our top management is actively involved in the team’s efforts to develop profitable, long-term relationships with global customers.

3.2 Our top management is committed to deploying the necessary resources to make our global customer operations succeed.

3.3 Our top management publicly promotes our team’s GAM activities to others in the organization.

Rewards and incentives (α = .810) 3.4 Team members’ contributions to developing global customers are

measured in a systematic and transparent manner. 3.5 Our compensation system promotes global collaboration by

appropriately rewarding contributions to the GAM team. 3.6 Many professional rewards (e.g., pay, promotions) are determined in

large part by team members’ performance on the GAM team. Training (α = .790)

3.7 The company provides adequate global selling and negotiation training for our team.

3.8 The company provides adequate team skills training for our team (e.g., communication, organization, interpersonal).

3.9 The company provides adequate technical training for our team. Eigenvalues Variance explained by factor after Varimax rotation Total variance explained

.817

.830

.800

.377

.260

.240

.090

.138

.164

4.255 25.8% 72.9%

.235

.236

.307

.676

.825

.804

.434

.230

-.056

1.404 24.8%

.145

.163

.113

.133

.153

.145

.690

.844

.837

.904 22.4%

Table 5 presents the results from the EFA of the organizational context domain. The

initial solution based on the eigenvalues-greater-than-one rule yielded only two factors

whereby all top management support and rewards items loaded on the same factor.

However, since all theoretical and conceptual arguments as well as the qualitative

research findings point to a clear distinction between these two constructs, imposing a

three-factor EFA solution is acceptable (Thompson, 2004: 32). The results of this

analysis show that all items load on their own factor and have loadings above the .50

level, which supports the separation of top management support and rewards and the

validity of the three constructs. These three factors explain 72.9% of the total variance

in this domain.

132

Table 6 presents the results from the EFA of the team processes domain.

Table 6: Results of exploratory factor analysis of the team processes domain

Constructs and items 1 2 3 Communication and collaboration (α = .823)

4.1 Our team regularly exchanges best practices and market knowledge with other GAM teams.

4.2 Team members communicate proactively on issues related to our GAM activities across boundaries and hierarchical levels in the entire organiz.

4.4 Communication in the team is effective, despite the geographical distance of team members.

4.5 Team members keep one another updated about their activities and key issues affecting the business.

4.6 Team members are good at coordinating their efforts to serve the customer efficiently.

4.7 Team members collaborate to achieve our global goals. Conflict management (α = .602)

4.8 Disputes between the different units represented on our team make it difficult to do our work. (reverse coded)

4.9 Our team is able to identify and resolve conflicts in a timely and effective manner.

4.11 The negative politics within the team are minimal. Proactiveness (α = .721)

4.12 Team members proactively cultivate new business opportunities. 4.13 Our team is not afraid to challenge the status quo to improve our

customer relationships. 4.14 The team is a powerful force for constructive change in the organization.

Eigenvalues Variance explained by factor after Varimax rotation Total variance explained

.592

.707

.742

.810

.631

.642

.050

.250

.125

.245 .090

.344

4.600 26.1% 58.1%

-.055

.098

.154

.115

.236

.208

.802

.631

.701

.098 .245

.127

1.008 14.7%

.259

.291

.043

.041

.287

.287

-.025

.268

.274

.737 .795

.656

1.36817.3%

The results indicate three factors with eigenvalues greater than 1.0. All communication

and collaboration variables load on the same factor, contrary to the expected

distinction between internal and external collaboration. To further explore this result,

the six communication and collaboration items were entered in a separate factor

analysis independent of the other process variables, which yielded only one factor as

well. Consequently, these items are combined into one more general construct named

communication and collaboration. The item 4.10 “Our team can easily send problems

up the chain of command (escalate to senior management) when they cannot be

resolved within the team” has a loading below the cutoff point and had to be removed

from the conflict management scale. The three extracted factors explain 58.1% of the

133

total variance with communication and collaboration accounting for almost half of this

percentage (26.1%).

Table 7 presents the results from the EFA of the performance domain.

Table 7: Results of exploratory factor analysis of the team performance domain

Constructs and items 1 2 Relational performance (α = .859)

5.1 Our team is characterized by strong and harmonious long-term relationships with global customers.

5.2 Our customers are satisfied with the overall performance of our team. 5.3 Our team provides real value to our customers. 5.4 Our team has successfully learned some critical skills or capabilities

from our customer relationships. 5.5 Our company’s competitive position has been enhanced due to our

team’s GAM achievements. Financial performance (α = .826)

How has your team, over the past three years, performed with respect to 5.7 … growth in sales? 5.8 … profitability? 5.9 … growth in the share of your global customers’ wallets?

Eigenvalues Variance explained by factor after Varimax rotation Total variance explained

.745

.767 .800 .728

.736

.251

.296

.282

4.416 38.6% 67.9%

.305

.291 .277 .211

.214

.881

.779

.789

1.020 29.4%

The results indicate two distinct factors with eigenvalues above 1.0 which correspond

to the expected relational and financial performance constructs and explain 67.9% of

the total variance. Only item 5.6 “Our team achieves its goals and objectives” did not

have a satisfactory loading and had to be removed. Nevertheless the two measures

show evidence of a high degree of reliability and validity.

134

Table 8: Means, standard deviations and correlations of the constructs

Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14

GAM team design1) Goals and roles 3.60 .682) Customer coverage 3.50 .78 .463) Empowerment 3.09 .87 .39 .474) Adequate size 3.20 1.05 .21 .41 .355) Heterogeneity 3.86 .61 .22 .19 .22 .106) Adequate skills 3.86 .53 .32 .37 .37 .16 .307) Leadership 3.81 .70 .36 .23 .35 .12 .24 .40

Organizational context8) Top management support 3.57 .76 .43 .38 .49 .30 .26 .31 .419) Rewards and incentives 2.64 .81 .40 .27 .47 .17 .24 .38 .39 .5810) Training 3.37 .77 .28 .21 .21 .19 .18 .27 .27 .31 .37

GAM team processes11) Communication/ collaboration 3.50 .59 .46 .38 .32 .19 .21 .44 .39 .48 .42 .3012) Conflict management 3.66 .60 .22 .32 .35 .19 .12 .36 .32 .27 .16 .16 .3413) Proactiveness 3.66 .66 .40 .41 .42 .18 .29 .56 .43 .40 .41 .20 .50 .37

GAM team performance14) Relational performance 3.86 .61 .39 .41 .44 .24 .21 .49 .49 .48 .44 .27 .49 .41 .6415) Financial performance 3.69 .72 .33 .21 .23 .03 .08 .33 .41 .35 .34 .16 .36 .29 .46 .58

Note: Correlations of .16 are significant at the .05 level (two-tailed). Large correlations (> .50) are

indicated in bold.

Table 8 reports the means, standard deviations and correlations of the factors derived

through EFA. Many of the correlations are significant. Nevertheless the lack of large

correlations of .50 or above (Cohen, 1977) among the constructs per domain provides

some additional support for the independence of the identified dimensions.

5.6. Results of the confirmatory factor analysis

Following the initial support for the validity of the proposed constructs, the next step

was to provide a confirmatory measurement through CFA. Unlike EFA, CFA allows

constructs to intercorrelate freely and helps to estimate relations with higher-order

constructs (Thompson, 2004), which is needed to determine how the dimensions fit in

the four domains. Moreover, CFA models are the critical starting point in structural

equation modeling. As Anderson and Gerbing (1988) argue, model-testing can be

thought of as the analysis of two distinct models: 1) a confirmatory measurement, or

135

factor analysis, model that specifies the relations of the observed measures to their

posited underlying constructs and 2) a confirmatory structural model that specifies the

causal relations of the constructs to one another. Following the recommendations for a

two-step approach in which the measurement model is estimated separately prior to the

simultaneous estimation of the measurement and structural submodels (Anderson and

Gerbing, 1988; Fornell and Yi, 1992), I conducted CFA first and then tested the full

structural model. This approach is consistent also with other studies in the marketing

area (e.g. Townsend et al., 2004; Zou and Cavusgil, 2002)

Unlike EFA which does not require the researcher to have or to declare any specific

expectations about the number and the nature of underlying factors, CFA explicitly

and directly requires expectations regarding the number of factors, the variables which

reflect given factors and the correlations among factors (Thompson, 2004). Therefore,

to increase precision, the CFA was based on the results of the EFA. Similarly to EFA,

CFA was conducted per domain because sample size is of even greater concern in this

type of analysis (Jackson, 2001; MacCallum et al., 1996). All measured items were

modeled as observed variables and all first- and second-order constructs were modeled

as latent variables. The CFA was performed using the maximum likelihood method in

AMOS 5.0.

The analysis of the CFA results of all domains follows the procedure recommended by

Bagozzi and Yi (1988). First, the output is screened to detect any anomalies or

problems in the minimization process. This screening showed that in all cases the

estimation process converged properly. Second, to obtain a comprehensive assessment

of the model fit, a number of fit statistics are examined as recommended in the

literature (Hu and Bentler, 1999). Third, the internal structure of the models is

examined and the convergent and discriminant validity of the dimensions are tested

(Campbell and Fiske, 1959).

The team design domain required some modifications due to the large number of

variables, which are briefly discussed before presenting the overall CFA results.

Before pooling all constructs together to form the second-order factor team design, I

tested if instead the constructs split into two second-order factors of team organization

136

and team composition as suggested in some studies (Gladstein, 1984). These two

factors have a strong relation between each other (covariance coefficient = .81) and the

model does not fit very well (RMSEA = .06), which is an indication for a single

second-order factor that captures the entire team design domain.

The examination of the parameter estimates and variances as well as the residuals and

modification indexes of the initial CFA model of this domain does not reveal any

irregular or insignificant results. However, several items are not very strongly

correlated with their constructs and are removed from further analysis for two reasons.

First, this modification improves the validity of the scales and has been justified in

studies of similar nature (Gladstein, 1984). Second, actions to decrease the number of

measures are often necessary in complex models with a large number of indicators and

first-order factors such as the model of this domain As Cohen et al. (1996) observe, in

previous studies using structural equation models, researchers attempting to model

relationships among a large number of variables have found it difficult to fit such

models even to predictions with strong theoretical support. As a result, the elimination

of the least correlated items is recommended to obtain an acceptable model (Arbuckle,

1997).

The indicators that do not have a strong correlation with their respective constructs and

had to be removed are 1.3 “Team members’ individual objectives and targets are

linked to GAM team objectives”, 2.3 “Team members have complementary skills and

abilities”, 2.6 “The account/sales managers on our team have a good understanding of

our business and the internal capabilities of our company”, 2.8 “The account/sales

managers on our team are able to coordinate complex networks and activities”, 2.15

“Our team leader is able to motivate team members and create synergies within the

team”. Surprisingly, the size indicator does not load well on the second-order factor

and is not included in subsequent analysis either.

Table 9 presents the results of the second-order CFA of all domains. Because the

results of the first-order factors to a large extent confirm the EFA results, with the

exceptions described above, they are reported in Appendix H to avoid a lengthy and

repetitive discussion here. It suffices to say that all indicators have positive and

137

significant loadings on their posited constructs. The two performance constructs are

not pooled in a second-order factor because the goal is to understand the impact of the

team dimensions on each of these two distinct types of performance (Hypotheses 3 and

4) as well as their interrelation (Hypothesis 5). The results also show that the two

factors have covariance of only .28, indicating that a second-order factor should not be

extracted (Thompson, 2004: 145).

Table 9: Results of the second-order confirmatory factor analysis

Variables Standardizedloading t-value Fit statistics

Team design Goals and roles Customer coverage Empowerment Heterogeneity Adequate skills Leadership

Organizational Context

Top management support Rewards and incentives Training

Team processes

Communication and collaboration Conflict management Proactiveness

Performance Relational performance

5.1 Long-term relationships 5.2 Customer satisfaction 5.3 Customer value 5.4 Learning 5.5 Competitive position

Financial performance 5.7 Sales growth 5.8 Profitability 5.9 Share of wallet growth

.660 .690 .710 .273 .621 .515

.746

.938

.486

.836

.750

.813

.737

.763

.778

.673

.672

.849 .726 .739

5.731 6.393

- 2.534 6.007 5.855

5.207 4.641

-

5.818 5.433

- -

11.73511.95310.39010.371

-

11.74411.930

χ²/df RMSEA RMR CFI NFI

χ²/df RMSEA RMR CFI NFI

χ²/df RMSEA RMR CFI NFI

χ²/df RMSEA RMR CFI NFI

1.587 .046 .037 .952 .883

1.745 .052 .038 .980

.955

1.356 .036 .029 .980 .929

1.158 .024 .016 .997 .976

Notes: All factor loadings are significant at the .01 confidence level. - indicates a fixed parameter.

138

The fit statistics of the models of all four domains indicate a very good fit with the

data. The first assessed fit indicator is the chi-square/ degrees of freedom ratio (χ²/df)

and all models have values below the maximum acceptable cutoff point of 2.0.

Although the χ² and the χ²/df statistics are frequently reported in academic

publications, they have often been criticized for using the unrealistic hypothesis-

testing assumption that a model fits exactly the data (Cudeck and Browne, 1983) and

for their dependence on the sample size (Bentler and Bonnett, 1980). Therefore, four

other indexes are considered of higher importance and examined thoroughly – the

root-mean-square error of approximation RMSEA (Steiger, 1990), the root-mean-

square residual RMR (Jöreskog and Sörbom, 1981), the comparative fit index CFI

(Bentler, 1990) and the normed fit index NFI (Bentler and Bonnett, 1980). The

RMSEA indexes for the four models equal .046, .052, .036 and .024 respectively, and

therefore, meet the criteria of Browne and Cudeck (1993) who suggest that a value

below .05 indicates close fit and that values up to .08 are reasonable. The RMR values

are respectively .037, .038, .029 and .016 and below the maximum tolerable level of

.08. CFI equals .952, .980, .980 and .997, which is well above the recommended

minimum value of .90. Finally, the NFI indexes are equal to .883, .955, .929 and .976

respectively and compare well to the recommended minimum value of .90 for a good

model fit.

Next, the convergent validity, or the extent to which the factors within a domain are

correlated, was assessed by checking the internal structure of the models, i.e. factor

loadings, variances and modification coefficients. Appendix H and Table 9 show that

the coefficients linking the indicators with their latent constructs are all significant at

the .01 level (t-values ranging from 3.376 to 12.815). In addition, the variance

estimates are all significantly greater than zero. Similarly, the loadings of all first-order

constructs on the second-order factors are also positive and significant at the .01

significance level (t-values from 2.534 to 6.393). Also, with the exception of

heterogeneity, all loadings are high in magnitude. The modification indexes did not

indicate that any respecifications are needed. As a result, the CFA models of the four

domains present satisfactory convergent validity (Anderson, 1987).

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Finally, the discriminant validity was tested to assess to what extent the factors are

unique. The analysis follows the procedure described by Bagozzi and Phillips (1982)

and Bagozzi et al. (1991). The correlations between each pair of first-order factors

within each domain were constrained to equal 1 in a series of consecutive CFAs. In

each case, the χ2 statistic of the constrained model was compared to the χ2 of the initial

model with no constraints. The premise is that a better fit (i.e., significantly lower χ2

value) for the unconstrained model would indicate that the constructs whose

correlations are constrained to unity are not perfectly correlated. Therefore,

discriminant validity is achieved and these constructs can be considered distinct, or

unique.

Table 10 presents the results of this analysis, indicating the χ2 value for the

unconstrained model and the models of each constrained pair. In all cases, the χ2 value

increases significantly when factors are constrained suggesting that all measures

possess discriminant validity. Moreover, whereas in most of the unconstrained models

the χ2 statistic is not significant at the .01 level, in all constrained models the p-value is

below .01 indicating a less than adequate model fit. In summary, the CFA models fit

the data adequately and there is evidence that all factors possess sufficient convergent

and discriminant validity.

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Table 10: Results of discriminant validity tests

Models χ2 p-value Team design

Unconstrained model Goals and roles ↔ Customer coverage Goals and roles ↔ Empowerment Goals and roles ↔ Heterogeneity Goals and roles ↔ Skills Goals and roles ↔ Leadership Customer coverage ↔ Empowerment Customer coverage ↔ Heterogeneity Customer coverage ↔ Skills Customer coverage ↔ Leadership Empowerment ↔ Heterogeneity Empowerment ↔ Skills Empowerment ↔ Leadership Heterogeneity ↔ Skills Heterogeneity ↔ Leadership Skills ↔ Leadership

Organizational context

Unconstrained model Top management support ↔ Rewards/incentives Top management support ↔ Training Rewards/incentives ↔ Training

Team processes

Unconstrained model Communication/collaboration ↔ Conflict management Communication/collaboration ↔ Proactiveness Conflict management ↔ Proactiveness

Team performance

Unconstrained model Relational performance ↔ Financial performance

285.67 444.33 441.20 419.75 473.95 367.87 338.67 416.78 403.32 377.99 412.49 402.30 350.14 448.33 396.48 397.30

41.87 103.20 170.12 153.64

66.47 224.83 162.26 183.50

22.01 114.65

.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

.013

.000

.000

.000

.049

.000

.000

.000

.284

.000

Note: ↔ indicates the pair of factors whose correlation was constrained to equal 1

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5.7. Test of the structural path model

Structural equation modeling (SEM) has been widely used in the marketing and

consumer behavior research because it offers a number of advantages over other

methods. First, by explicitly taking into account the inherent imperfections of

behavioral science data, SEM allows to assess and correct unreliable measures when

multiple indicators of each construct are available. Second, the key advantage of SEM

is that it makes it possible to investigate comprehensive theoretical frameworks in

which the effects of constructs are manifested across multiple levels of variables

through direct, indirect and bi-directional paths of influence (Baumgartner and

Homburg, 1996). Because the model involves a number of such relations, which

would be hard to test with other methods such as regressions or correlations, this

method was selected for testing the hypothesized links. This approach is consistent

also with a wide range of studies that have tested various teamwork models (e.g.,

Ancona and Caldwell, 1992b; Gladstein, 1984; Mathieu et al., 2006).

5.7.1. Results of the model test

Following the purification of the measurement model through CFA, the complete

structural path model was tested using the maximum likelihood procedure in AMOS.

The full model included the structural model as well as the multiple indicators of all

first- and second-order constructs obtained in the previous stages. In addition, an

attempt was made to model the control variables in order to account for the differences

among the six companies and among the respondents. However, the employed method

of structural equation modeling and the AMOS software do not allow for the

introduction of control variables, and therefore, these variables were not included in

the analysis. This represents a trade-off that had to be accepted in order to benefit from

the significant advantages of the SEM method.

In the first step, the model with the initially posited relationships among the domains

(Figure 5) was tested. Although the coefficients were positive and significant, with the

exception of the processes - financial performance link, the fit statistics (χ²/df = 1.575,

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RMSEA = .046, RMR = .087, CFI = .889, NFI = .747) indicated that this model did

not fit the data in a satisfactory manner. A thorough examination of the solution helped

to identify the reason for the inadequate fit. The modification index for the link from

organizational context to team design equaled 82.571, which is significantly higher

than the maximum tolerable value of 10. This result indicates that organizational

context actually predicts team design; that is, the relationship to team process is not

direct but mediated by the team design domain. Therefore, this indirect link was

modeled as well.

Figure 12 shows the results for the modified model, including the structural

relationships, the standardized estimates of the path coefficients and the fit indexes.

The t-values for all estimates are reported in brackets.

Figure 12: Structural path model 1

Notes: ** significant at p < .01 Model fit statistics: χ²/df = 1.469, RMSEA = .042, RMR = .041, CFI = .909, NFI = .765

This solution was thoroughly examined by inspecting the parameter estimates,

standard errors, and modification indexes as well as any possible irregularities and no

problems were detected. The specification of the context-design relation improved the

fit indexes and indicated that the direct influence of organizational context on team

processes is not significant (t = -.035, p > .1). The χ² for this model is 1621.29, which

with 1104 degrees of freedom results in χ²/df ratio equal to 1.469 and indicates a good

.826**(6.446)

Organizational context

GAM team design

Team processes

Relational performance

Financial performance

-.035(-.208)

.963**(4.608)

.850**(9.163)

-.016(-.099)

.674**(4.101)

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fit. The other fit indexes are respectively RMSEA = .042, RMR = .041, CFI = .909,

NFI = .765. Given the relatively complex nature of the model, which includes a large

number of indicators and three second-order factors, these indexes suggest a

satisfactory fit (Bollen, 1989).

One surprising result is the insignificant effect of team processes on financial

performance. In order to further analyze this result and to ensure that all potentially

important paths among the domains are captured, a number of alternative models were

tested. Thus, models incorporating the direct links from team design and

organizational context to the two performance variables were also tested but they did

not yield significant results, and therefore, provided support for the mediating role of

team processes. An interesting result emerges when a model without the path of

influence between relational performance and financial performance is analyzed.

Figure 13 reports the results for this model.

Figure 13: Structural path model 2

Notes: ** significant at p < .01 Model fit statistics: χ²/df = 1.482, RMSEA = .042, RMR = .041, CFI = .907, NFI = .762

The fit indexes demonstrate an adequate fit (χ²/df = 1.482, RMSEA = .042, RMR =

.041, CFI = .907, NFI = .762). The direct influence of organizational context on team

processes is not significant again (t = -.063, p > .1). All other coefficients are positive,

significant and high in magnitude, indicating that when the link between the relational

.827**(6.448)

Organizational context

GAM team design

Team processes

Relational performance

Financial performance

-.010(-.063)

.921**(4.524)

.882**(9.527)

.618**(7.956)

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and financial performance is not taken into account, the direct influence of team

processes on financial performance becomes significant.

These results confirm the positive effect of the process domain on both types of

performance. The comparison of model 1 and model 2 indicates that their fit

characteristics are equally good. However, model 2 is less preferable than model 1

because it is less informative and inclusive. It fails to capture one important

relationship that elucidates better the underlying links. If we take model 2 and do not

recognize the link between relational and financial performance, we may conclude that

team processes lead directly to financial results whereas in fact this relationship is less

straightforward with a number of intermediate outcomes playing a role. As a result,

model 1 was considered a more appropriate solution and is discussed in more detail

below. The full model is presented in Appendix I.

In summary, the estimates of the path coefficients of model 1 show the following

results in relation to the hypothesized relationships. Hypothesis 1 states that GAM

team design will have a positive effect on team processes and it is supported. Figure 12

shows that the standardized regression coefficient from GAM team design to team

processes equals .963, which indicates a positive and significant link (t = 4.608, p <

.01).

Hypothesis 2 posits that the organizational context will have a positive effect on team

processes. As discussed, this hypothesis is not supported. The path coefficient is

insignificant (t = -.208, p > .1). However, although no direct link is detected, another

path of influence through which organizational context impacts the performance of

GAM teams is identified. As the coefficients in Figure 12 show, organizational context

has a positive and significant effect on team design (t = 6.446, p < .01), which supports

the importance of the context construct.

Hypothesis 3 predicts that team processes will have a positive influence on team

relational performance. The path coefficient of .850 indicates that in fact relational

performance is positively and significantly influenced by team processes (t = 9.163, p

< .01), which provides support for this hypothesis.

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Hypothesis 4, which states that team processes will have a positive effect on team

financial performance, is not supported (t = -.016, p > .1). As discussed, however, an

indirect link from team processes to financial performance is found, i.e. team

processes influence relational performance, which in turn influences financial

performance.

This result supports Hypothesis 5, which states that relational performance has an

effect on financial performance. The path coefficient (.674) is positive and significant

(t = 4.101, p < .01).

In summary, although two of the hypotheses are not supported, the test of the

structural model reveals alternative or indirect relationships through which all

identified factors play a role and influence performance to a different extent. Overall,

the good model fit indicates that this model reflects the underlying factors and their

relationships well.

5.7.2. Effect sizes

To shed more light on how the individual dimensions of team design, organizational

context and team processes are related to the influenced constructs, the total effect

sizes for model 1, including the direct and indirect effects, are estimated (Fox, 1980).

This analysis followed the procedure of Zou and Cavusgil (2002) where the loadings

of the first-order dimensions on their second-order scales are multiplied by the path

coefficients from the respective second-order construct to the other constructs of

interest. Table 11 summarize the results.

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Table 11: Effects of individual dimensions on their dependent domains

Dimensions Team design

Team processes

Relational performance

Financial performance

Top management support

Rewards and incentives

Training

Goals and roles

Customer coverage

Empowerment

Heterogeneity

Adequate skills

Leadership

Communication and collaboration

Conflict management

Proactiveness

.689

.683

.419

.634

.629

.386

.631

.576

.634

.276

.679

.576

.539

.535

.328

.536

.489

.539

.235

.577

.489

.666

.653

.768

.300

.298

.183

.299

.273

.300

.131

.321

.273

.436

.428

.503

The variables of highest interest are relational performance and financial performance.

As expected, these two variables are influenced the most by the three process

dimensions due to their direct relationship. In the order of the effect size, the

dimension with strongest impact on both types of performance is proactiveness

followed by communication and collaboration and conflict management. Other

dimensions with a strong influence on performance, also in the order of their effect

sizes, are adequate skills, empowerment, top management support, goals and roles, and

rewards and incentives. It is interesting that although organizational context has an

indirect impact on performance mediated by both team design and processes, two of

the context variables have effects similar in magnitude to the design dimensions which

are only mediated by the team process. Finally, the effects of customer coverage and

leadership on the performance indicators are medium in size and the effects of training

and heterogeneity are relatively small.

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5.8. Comparison among performance groups

As a final assessment of the importance of the identified team characteristics and their

relationship to performance, I compared the high-performance and the low-

performance teams along the 12 design, context and process dimensions that emerged

from the exploratory and confirmatory factor analyses. For that purpose, in the first

step I compared groups based on individual scores and then in a second step based on

team scores. In the first step, the sample was divided into three groups according to the

average scores of the five team relational performance measures and the three financial

performance measures. Average scores of between 4 and 5 on the five-point Likert

scale are classified as high-performing teams. Out of the 273 data points, 82 responses,

or 30% of the sample, are in this category. The next category, mid-performance teams,

includes the responses with an average score of more than 3 and less than 4. The total

number of responses in this group is 111, which represents 41% of the sample. The

remaining 80 responses (29% of the sample) that have average scores below or equal

to 3 are classified as low-performers.

One-way analysis of variance (ANOVA) tests were performed to compare the mean

scores of the high-performance, mid-performance and low-performance groups on the

12 variables from the design, context and process domains. The mean scores and the

F-values are presented in Table 12.

The findings show that the mean scores on 11 out of the 12 characteristics are

significantly different among the three groups. Furthermore, the high-performance

group has higher ratings on all dimensions than the mid-performance group and the

mid-performance group has higher ratings than the low-performance group. The only

dimension for which the difference is not significant is heterogeneity (F = 2.184, p =

.067). To identify the source of this result, I conducted independent samples t-tests for

each pair of performance groups. The results indicated that the difference in

heterogeneity between the high-performance and the mid-performance groups is not

significant (t = 1.181, p = .239) as is not the difference between the mid-performance

and low-performance teams (t = 1.344, p = .181). However, the high-performance and

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the low-performance groups are significantly different at the .05 level (t = 2.280, p =

.024).

Table 12: Comparison among performance groups based on individual scores

Mean scores Variable High

(n = 82) Medium (n = 111)

Low (n = 80)

F-value Sig. level

Goals and roles 4.05 3.73 3.33 27.464 .000

Customer coverage 3.86 3.47 3.19 16.389 .000

Empowerment 3.44 3.05 2.77 13.090 .000

Heterogeneity 3.99 3.86 3.74 2.724 .067

Adequate skills 4.21 3.82 3.57 33.934 .000

Leadership 4.24 3.73 3.41 27.092 .000

Top management support 3.95 3.59 3.16 25.263 .000

Rewards and incentives 3.08 2.55 2.34 20.241 .000

Training 3.65 3.29 3.21 8.271 .000

Communication and collaboration 3.79 3.56 3.12 33.722 .000

Conflict management 3.95 3.62 3.39 20.013 .000

Proactiveness 4.19 3.63 3.18 70.282 .000

In order to assess the differences between the three groups in more detail, the same

type of one-way ANOVA tests were conducted for the individual items. With the

exception of the two heterogeneity items where insignificant differences were to be

expected given the results above and item 3.7 which is significant at the .05 level (F =

4.291, p = .015), in all cases the mean ratings of the three groups are significantly

different at the .01 level (F-values ranging from 5.102 to 58.292). Moreover, the

directions of the differences are as predicted, i.e. the scores of the high-performance

group are higher that those of the mid-performance group and the mid-performance

group scores are higher than the group with low performance ratings.

In the second step, the same procedure was conducted but the delineation was made

based on the average team scores. The sample of 113 teams was divided into three

groups according to the each team’s average score of the five team relational

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performance measures and the three financial performance measures. The same

grouping criteria were applied and resulted in the following split. The number of high-

performing teams (average scores between 4 and 5 on the five-point Likert scale) was

36, or 32% of all teams. The number of mid-performance teams (average scores

between 3 and 4) was 47, which represents 42% of the sample. The remaining 30

teams (26% of the sample) are low-performing. Table 13 presents the results of the

ANOVA tests that compare the mean results among the three groups.

Table 13: Comparison among performance groups based on team scores

Mean scores Variable High

(n = 36) Medium (n = 47)

Low (n = 30)

F-value Sig. level

Goals and roles 4.07 3.76 3.45 7.724 .001

Customer coverage 3.11 3.32 3.30 13.435 .000

Empowerment 3.67 3.15 2.73 13.100 .000

Heterogeneity 4.14 3.95 3.93 1.193 .307

Adequate skills 4.23 3.90 3.48 13.286 .000

Leadership 4.43 3.85 3.40 27.862 .000

Top management support 4.17 3.52 3.34 16.367 .000

Rewards and incentives 3.13 2.58 2.36 10.829 .000

Training 3.68 3.29 3.28 3.578 .031

Communication and collaboration 3.96 3.52 3.25 15.688 .000

Conflict management 4.10 3.61 3.42 13.279 .000

Proactiveness 4.25 3.68 3.10 41.100 .000

The results largely confirm the findings based on individual results, indicating that the

scores along all variables except heterogeneity differ significantly among high-, mid-

and low-performing groups. The differences between mid- and low-performance teams

in the area of training seem to be small but still significant at the .05 level. Overall, the

results provide support for the proposition that high-performing teams are

characterized by significantly higher ratings in all critical team areas.

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6. Discussion and conclusions

Global account management teams represent an important part of many GAM

programs, but existing research has provided few directions for clarifying their

formation, structure, composition, interaction processes, and performance.

Furthermore, GAM teams differ from other forms of group work because of their

complexity in terms of their cross-regional, cross-functional, and cross-divisional

span; blurred responsibilities and lines of authority; heightened external activity

requirements; and long-term tasks. This further complicates the managerial task of

building and managing high-performance customer teams. Consequently, this study

aimed to examine what determines the performance of GAM teams by developing and

empirically testing an integrative model of team composition, interaction and

performance.

Based on the comprehensive literature review, the exploratory interview research and

the analysis of survey data from six multinational companies, 12 determinants of team

performance in three broad domains were identified. They were then evaluated against

both qualitative and quantitative performance criteria. The findings can be summarized

as follows:

First, the empirical results indicate that GAM team design is a complex,

multifaceted construct that encompasses six key dimensions: role and goal

definition, customer coverage, empowerment, heterogeneity, adequate skills, and

leadership.

Second, these structural and composition dimensions influence team performance

through the mediating role of three key processes: communication and

collaboration, conflict management, and proactiveness.

Third, three key elements of the organizational context of GAM teams – top

management support, rewards and incentives, and training – are positively

associated with team design and therefore have an indirect influence on team

performance.

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Fourth, GAM team performance has two associated components, relational and

financial performance. The empirical results show that relational performance is

an intermediate outcome that leads to improved financial performance in terms of

sales growth, profitability and growth in the customer’s share of wallet.

Fifth, and most important, all design, process and context dimensions have either

direct or indirect positive influence on performance. Therefore, companies may

improve their relational and financial outcomes with global customers by

improving their performance in these areas of team functioning.

This chapter discusses the major findings and their implications for theory and practice

in more detail. The discussion is organized around the three research questions that

guides the study. It is then followed by a summary of the contributions to theory,

managerial implication, limitations and directions for further research.

6.1 Key findings: What are the key elements of GAM team design?

This research question aimed to conceptualize and delineate the specific aspects of

team structure and composition that may have an impact on team performance. The

literature review and the qualitative findings pointed to seven key dimensions: role and

goal definition, structure, empowerment, size adequacy, heterogeneity, adequate skills,

and leadership. Although some studies distinguish between team structure and team

composition (Gladstein, 1984; Smith and Barclay, 1993) as two distinct sub-

components of team design, the results did not provide support for the split of the

identified dimensions in these two groups. This result is not surprising in light of the

lack of agreement in literature on what constitutes structure and composition. For

example, abilities are a part of the composition domains of Gladstein (1984) and

Cohen et al. (1996) whereas Gist et al. (1987) refer to them as an aspect of group

structure. Therefore, the identified seven dimensions were kept within one single

second-order construct.

The exploratory and confirmatory factor analyses confirmed the validity of six of the

design aspects but indicated that size adequacy does not correlate well with the design

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domain. Thus, this variable was not analyzed further in order to avoid incorrect

conclusions. One possible explanation is that the measure of size adequacy lacked a

certain degree of reliability. The measure was adopted from prior studies (Campion et

al., 1993; Cohen et al., 1996) and contained only one item that asked whether the

number of people on the team is sufficient for the team to develop its customer

business. However, these studies examined small organizational groups with clear

membership and predominantly full-time members so the measure may have failed to

capture all aspects pertinent to team size in GAM teams where membership is often

fluid and virtual. For example, many GAM team members participate on a part-time

basis and the percentage of time dedicated to the team may be an additional indicator

of human resource availability along with the number of people. This result indicates

that the issue of GAM team size is more complex than predicted. The implication is a

need for the development of more detailed scales and a more thorough investigation

into related concepts such as time dedication or the number of full-time and part-time

members.

Five of the remaining six dimensions – roles and goals, customer coverage,

empowerment, skills and leadership – loaded very well on the design domain and, as

demonstrated by their effect sizes, have a strong influence on team processes and

hence indirectly on team performance. The implication is that building high-

performance GAM teams starts with a clear specification of their objectives in relation

to the customer and to the overall corporate goals and delineation of the roles and

responsibilities that team members are expected to fulfill. This is important because

GAM team members often have other responsibilities in addition to their involvement

with global customers. Furthermore, the composition of the team should be given

sufficient attention in order to identify individuals with adequate skills who are

capable of developing relationships and navigating the interface between two global

organizations. Team performance may be further enhanced if these arrangements are

accompanied by a leader with a strong presence and influence in the organization and

the provision of sufficient resources and authority to the team.

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These findings are largely consistent with organizational behavior literature. However,

a contribution of this study is the identification of specific skills required for GAM

team members. Support was found for seven out of the nine initial skills indicators and

the results showed that adequate skills is the design dimension with the strongest

impact on team processes. These results extend previous research in GAM that has

identified critical account manager skills but has not tested the skills-performance

relationship empirically (Harvey et al., 2003a; Millman, 1996).

Another novel dimension identified in this study is customer coverage. Following the

purification of the team structure construct, customer coverage emerged as the most

relevant aspect of team structure. This result is not surprising because the key role of a

GAM team is to provide the required services and support to its global customer,

which demands an appropriate cross-geographical, cross-functional and cross-

divisional representation on the team. Although some GAM studies have referred to

the need for a structural fit with the customer (Shi et al., 2004) and team selling

literature has discussed ways to achieve better alignment between buying and selling

teams (Puri and Korgaonkar, 1991), no aspect similar to customer coverage within a

GAM team context has been studied from an empirical perspective before. Therefore,

the identification of a link between this factor and team process and performance is an

important extension of existing research in GAM and team selling.

Finally, one key design dimension – heterogeneity in terms of experience and

expertise – did not yield unequivocal results, and therefore, needs further clarification.

The results of the literature review and the qualitative research indicated that expertise

diversity is desirable in teams with complex and uncertain tasks because it enhances

creativity and facilitates problem-solving. Consequently, it was predicted that the

variety in backgrounds and skills will facilitate the work of the GAM team. The

empirical results showed that this factor indeed had a significant and positive loading

on team design, and hence a positive effect on team processes, but these effects were

relatively small in size. Furthermore, the comparison of the three performance groups

did not detect substantial differences in this aspect. Thus, these findings provided

support for the positive consequences of expertise heterogeneity in dynamic and

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demanding environments and yet raised questions about its predictive power and

importance relative to the other design elements.

A number of arguments can explain these moderate results. First, as already discussed

in Section 3.2.1 in a GAM context various types of diversity are often a given because

of the multicultural and multifunctional nature of GAM teams. The expertise

heterogeneity variable (M = 3.86, SD = .61) provided some evidence of this; it had one

of the highest mean scores and one of the lowest standard deviations of all variables,

implying that a high degree of expertise diversity was present in all studied teams. The

low variance among teams might explain the low predictive power of this variable and

the heightened relative importance of other factors such as communication skills,

leadership and empowerment. Second, a potential explanation can be the existence of

negative consequences that outweigh the benefits of expertise diversity but are not

captured in the model, or any alternative relations also not reflected in the model. Most

types of heterogeneity are complex phenomena that may have contradictory effects

depending on the contextual moderators and the types of processes studied (Milliken

and Martins, 1996; Pelled et al., 1999). Although an attempt was made to maximize

precision by focusing on the most relevant type of diversity, delving into its

multidimensional consequences was outside the scope of this exploratory study. The

objective was to create a holistic model of GAM team performance determinants

rather than focus on specific elements so this result is satisfactory. In general, the

results provided evidence for the conceptualization of these six characteristics as the

key elements of GAM team design.

6.2 Key findings: How does GAM team design influence team

performance?

This second research question sought to analyze if there is a relationship between team

design and performance and to investigate the mechanisms through which they are

linked. To that end, three hypotheses were formulated. Hypothesis 1 stated that team

design will have a positive influence on team processes. Hypothesis 3 and 4 posited

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that team processes will have a positive influence on team relational performance and

on team financial performance.

The first key finding is that team design has an indirect link to performance mediated

by team processes. The test of the structural path model provided support for

Hypothesis 1 and did not indicate a significant direct relationship between design and

performance. This result is in line with studies that call for opening the “black box” of

team interactions (Lawrence, 1997) as a way to explain how demographic

characteristics influence outcomes. By including team processes in the model, this

study managed to avoid the pitfall of earlier research that has omitted this vital link,

and therefore, failed to reach conclusive results (e.g. Campion et al., 1993; Cohen et

al., 1996). The implication is that decisions in relation to a team’s structure and

composition should be based on a careful assessment of their potential consequences

on the way team members interact within the team and with their environment.

An important related finding is the identification of three distinct GAM team

processes. The empirical results reported that communication and collaboration,

conflict management, and proactiveness are all useful predictors of team performance.

Whereas communication and collaboration (Ancona and Caldwell, 1992a; Bunderson

and Sutcliffe, 2002) and conflict (Amason, 1996; Jehn, 1995) have been more

extensively researched in the context of organizational groups, proactiveness is a less

understood concept. In GAM teams, however, proactiveness had the strongest

influence on performance, as indicated by its effect size and the largest mean

difference across the three performance groups. Thus, this study makes a contribution

by providing further evidence for its relevance and identifying its relation to structural

elements and performance outcomes on the team level.

A novel result is that the findings failed to draw an explicit delineation line between

internal and external communication and collaboration. Although the distinction

between internal and external team activity has traditionally been accepted as a norm

in group research, this research brought some new insights. All communication and

collaboration indicators formed a single factor including both internal and external

aspects. This can be explained by the blurred GAM team boundaries and the need for

156

team members to build multilateral relationships in a complex network within the

supplier and the customer organization. Therefore, it is often difficult, and not

necessary, to determine what part of the ongoing information exchange and

collaboration efforts take place among team members and what part may be defined as

external. The implications are primarily theoretical. They suggest that the examination

of teams with less structured boundaries and more complex interactions with the

environment may require a modified multidimensional definition of communication

and collaboration that accounts for the differences from more conventional and

isolated teams.

The second key finding concerns the relationship between team processes and

performance. This study identified two distinct types of team performance – relational

and financial, whereby relational performance refers to the development of long-term

customer relationships and outcomes such as customer satisfaction, customer value

and learning from the customer, and financial performance involves sales growth,

profitability and growth in the share of the customer’s wallet. Team processes related

significantly to the relational performance criteria, providing support for Hypothesis 3.

However, the direct link to financial performance (Hypothesis 4) was confirmed only

when no link between the two types of performance was assumed. When the influence

of relational on financial performance is introduced (Hypothesis 5), Hypothesis 4 is no

longer substantiated. This result suggests that relational performance is only an

intermediate outcome that ultimately enhances financial results.

Hypothesis 4 was formulated on the basis of prior studies that have found a direct

influence of GAM processes such as communication and coordination on financial

measures of profitability and sales growth (Birkinshaw et al., 2001; Shi et al., 2005).

Therefore, the findings of this dissertation are a valuable contribution that provides

one missing link in prior research in this area. It was outside the scope of this

exploratory inquiry to investigate each performance element separately. However, in

light of individual influences of the relational performance components such as

customer satisfaction and customer value, it is not surprising that relational

performance plays a mediating role between processes and financial results.

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This finding has also implications for organizational behavior research. The majority

of studies in this research stream distinguish between performance outcomes such as

goal achievement, productivity or other relevant criteria and attitudinal outcomes in

terms of team satisfaction, commitment and trust. However, these two performance

groups are usually treated as ultimate outcomes of effective teamwork with no explicit

interrelation. The findings in this study imply that the relationship between the two

may require a more close consideration.

6.3 Key findings: What other factors have an influence on GAM team

performance?

The third research question helped to investigate if there are additional factors from

the team’s environment that influence team performance. The literature review and the

interview research led to the discovery of three such factors – top management

support, rewards and incentives, and training. The empirical results indicated that these

factors explain a large portion of the variance in environmental factors (73%) and

therefore capture well the influence of the organizational context. The implication is

that the development of high-performance GAM teams is unlikely to succeed if it is

not accompanied by genuine support from senior managers as well as proper

incentives that encourage contributions to the team and stimulate global thinking

throughout the organization. Furthermore, building well-functioning teams requires

that team members are offered well-rounded training opportunities to develop the

relevant selling, interpersonal and technical skills.

In contrast to prior studies that have posited a direct path of influence from

organizational context to team processes (e.g., Hackman, 1987), I did not find support

for such a link. However, organizational context proved to be a significant predictor

of team design, which represents a worthwhile contribution to the team literature

where this relationship has not been widely recognized. An examination of each of the

three context dimensions may help uncover an explanation for this finding. Top

management support was predicted to influence team processes by helping the team in

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its work with the customer, and more importantly, by creating a more cooperative and

customer-oriented organizational environment. However, some other aspects of top

management activities, which have possibly been overlooked, may explain the link to

team design. For example, in some cases senior management support may take the

form of involvement in the goal and role setting process or in the appointment of a

team leader and members, thus influencing all team composition elements. In addition,

top management support can be a direct predictor of team empowerment (Kirkman and

Rosen, 1999). Similarly, the indirect relationship between training and processes can

be explained by the mediating role of skills. The only aspect in which the context-

design link remains hard to explain is rewards and incentives. Clearly, the reward and

compensation practices are related to behavioral and attitudinal outcomes such as the

efforts applied to the team tasks and the degree of collaboration (Hackman, 1987),

which belong in the process domain rather than the design area. This suggests that the

results may have been primarily driven by the effects of top management support and

training or that there are other aspects that remain implicit in this study. In any case,

these findings prompt further investigation of the specific elements of the context

domain.

6.4 Contributions to theory

The major contribution of the study is the development and empirical validation of an

integrative model that sheds light on the performance determinants of a relatively new

and underresearched phenomenon – GAM teams. The novel approach lies in the

combination of concepts from the account management and organizational behavior

research streams. To my knowledge, this is the first study to conduct such an

interdisciplinary research. Thus, it makes contributions to both of these academic

areas.

First, the focus was on the team level which has not been sufficiently investigated

within the GAM or customer management literature. Second, the detailed delineation

of domains of team functioning with their specific dimensions extends prior research

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on the GAM team that has addressed only the fundamental organizational decisions

(Kempeners and van der Hart, 1999). The introduction of concepts from research on

organizational groups and cross-functional teams broadened the understanding of

GAM teamwork by taking a step further from purely structural issues. Thus, a more

complete set of performance determinants including a number of processual and

behavioral aspects were identified. Moreover, I suggested and inspected variables that

have never been empirically tested in a GAM context, such as empowerment,

leadership, conflict management and training.

Third, this study makes a contribution to the organizational behavior field by

identifying factors that gain increased importance in teams with more complex and

dynamic structures, tasks and environments. For example, constructs such as

proactiveness, top management support, skills and rewards have not been broadly

emphasized or have produced inconclusive results in studies of teams with more

structured and stable settings. However, they received a strong confirmation in this

study implying a distinctive set of individual, team and organizational capabilities that

can be generalizable to teams of similarly dynamic nature.

Fourth, I examined a number of alternative relationships among all domains and

detected important intermediating influences. Thus, the analysis provided a more

realistic and informative picture of the existing interrelations than the approach of

directly linking input variables to outcomes. This led to some novel findings such as

the intermediate role of relational performance.

Fifth, a key strength is the empirical validation of the model with a relatively large

data sample from truly global companies. Following the solid grounding in literature

and exploratory interview research, the model was tested in six companies where

GAM teams represent a core element of global customer operations. The results

indicated that it reflects well the realities of such teams.

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6.5 Managerial implications

From a managerial viewpoint, this study highlights, in the first place, the importance

of adopting a holistic approach to the composition and management of GAM teams.

As the results point out, all team characteristics as well as the context in which the

teams work have important performance implications. Consequently, neglecting some

or all of these issues will represent a key barrier to team effectiveness and to the

overall GAM operations of the supplier.

More importantly, I developed a comprehensive model that can guide companies in

their implementation of customer teams and GAM programs and help them improve

their global customer relationships. First, the model has a practical value because of its

focus on dimensions that management can influence. Many of the design elements are

directly within the control of the senior managers responsible for global sales and

teams. The context and process attributes may be less directly controllable but

management can influence them through encouragement, reinforcement and internal

negotiation. Although the degree of control may vary, all of these characteristics offer

a valuable direction for management in learning how to build and manage high-

performance GAM teams. Thus, even if some of these parameters cannot be changed

in the short term, they represent a useful foundation for creating a clear vision and

strategies for the longer run.

Second, the complete set of 12 team characteristics and their corresponding 42

questionnaire items (after scale optimization) along with the 9 performance indicators

can be used as a practical tool for designing new GAM teams or enhancing the

effectiveness of existing ones. With due consideration of the limitations of the

research, this set can be converted into a checklist for optimizing GAM team

performance and assist companies in assessing current performance, mapping out

areas for improvement, developing courses of action and tracking the progress along

each dimension.

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Third, the tested linkages among the variables illustrate the mechanisms through which

different aspects of GAM teamwork are interrelated. This can help managers better

understand and foresee the potential consequences of their actions in each domain.

Fourth, the model has practical implication for human resource (HR) practices. Many

of the team dimensions (e.g., skills, training, leadership, rewards) relate to HR

activities performed by line managers or HR managers. Thus, by calling attention to

the need for appropriate GAM team-related recruitment, training and compensation

practices at various levels in the organization, the model might enhance awareness of

this aspect and help to embed a global market-oriented approach in the organization.

More specifically, managers can take several key points from this study and

implement them immediately to create new teams or optimize the performance of

existing ones:

Situation analysis

In the first place, the findings indicate that it is important to understand very well all

factors underlying team performance. All observable behaviors and results are

determined by factors that are more subtle and difficult to control. Therefore, GAM

team improvements should start with a profound analysis of the current situation along

the groups of factors in the design and context domains. Managers need to assess

where potential blockages or outright problems lie and address them in the most

efficient manner. This study assists them in this process by providing a framework and

diagnostic questions to conduct this assessment.

Holistic approach

A common mistake in the implementation of teams is to focus resources and efforts

primarily on the team design and neglect the larger settting in which it works. As the

results show, the team cannot be isolated from the rest of the organization and the

creation of an environment that fosters GAM cooperation and supports teamwork is a

critical success factor. Managers should, therefore, take a more comprehensive view

and try to understand what factors from the internal and external team environment are

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important in their companies. The three factors that they can more easily influence,

and hence, should concentrate on are top management support, rewards and incentives,

and training. By continuously seeking support from and involving top management,

managers responsible for GAM can benefit from increasing support in the organization

and leverage with the customer. On the other hand, developing appropriate incentive

plans and training schemes looks rather inside the team and ensures that team

members are, first, more capable of fulfilling their goals and, second, more motivated

to overcome hurdles and excel in their jobs.

Quick wins

The results indicated that five factors – adequate skills, goal and role definition,

empowerment, top management support, rewards and incentives – have a stronger

impact on the way teams work and perform than others. These are the areas which

could lead to larger performance improvements, and therefore, should be the main

focal point for managers who seek to achieve such “quick wins” or to start

implementation/ optimization programs with only a few key elements before rolling

them out to all elements. The implication is that one of the starting points for building

and improving GAM teams is the question “Do we have the right people for this job?”

Team members’ skills are a crucial factor implying that the recruitment and training of

account managers should be done with great care. Simply promoting sales people into

global account managers is unlikely to succeed if they do not possess a broader skill

set including managerial skills, strategic thinking and ability to work in a

multidisciplinary way and multicultural surroundings. As discussed above, stringent

selection and hiring processes should be also accompanied by well-designed

compensation systems and appropriate training to develop the required selling, team

working and technical skills.

Similarly, the results suggest that managers could achieve larger performance

improvements by optimizing the teams’ goal and role structure. Many teams operate in

settings where individual, team and company goals contradict with each other and

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expectations about members’ roles and responsibilities are unclear. Changing such

established organizational arrangements could be one of the most difficult managerial

tasks but it is an absolute prerequisite for creating a functioning and collaborative

team. Setting the right objectives and role expectations could relatively quickly

improve results by increasing the level of motivation, efforts and time dedicated to the

team.

Finally, among the top critical areas that should be tackled first are team empowerment

and top management support. Providing GAM teams with sufficient resources,

decision-making power and support from senior management means that they would

be able to make fast and efficient decisions and take courses of action that are in the

best interest of the customer but may be unpopular or face opposition within the

supplier organization. As a result, teams will experience fewer conflicts and higher

levels of collaboration and proactive behavior. As the experience of the interviewed

companies shows, these two factors may be also difficult to implement but in all cases

the payoff could be significant. The key instrument for managers to achieve this is

what interviewees defined as internal selling, i.e. continuous communication with top

management and other key people in the organization to clearly demonstrate the

benefits of empowered GAM teams and highlight specific success stories of the teams

that contributed to the success of the entire company.

6.6 Limitations and suggestions for further research

Potential limitations of the dissertation can be outlined from both a methodological

and conceptual point of view. First, the quantitative research was conducted in six

companies. The small number of study companies has been consistently justified in

previous team studies and larger samples have hardly ever been used in this type of

research. Moreover, the companies were carefully selected to be representative of the

population of multinational companies that use global or international customer teams.

Nevertheless, it must be recognized that this approach may raise some generalizability

issues. To confirm the generalizability of the model to other settings, future studies

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may build on this initial research through replication and extension, which are

effective methods for research enhancement (Easley and Madden, 2000).

Another limitation is the use of subjective performance ratings. This approach was

justified by the lack of objective measures in some of the study companies as well as

the unwillingness of others to share them. Further research, however, should seek to

test the effect of the identified independent variables on objective measures like actual

sales growth, profitability, share of wallet or customer satisfaction ratings obtained in

customer surveys. For the measures which are rather subjective by nature (e.g.,

relationship building, customer value, learning and competitive position), a more

unbiased rating can be obtained through managerial or customer assessments of these

indicators. To ensure independence, these ratings should be collected separately from

the assessments of team members who complete the questionnaire.

Finally, a worthwhile extension from a methodological perspective could be obtain

also data from the customer. Although some dimensions may be less visible and

difficult to evaluate from the customer side, testing the model with a sample of

members from the customers' buying teams who work with the GAM teams could add

some insights.

From a conceptual point of view, the exploratory nature of the study did not provide

for a really profound exploration of each variable. The goal was to develop a broad

framework of performance determinants and gain understanding of the key paths of

influence. Therefore, future studies may identify the individual antecedents and

consequences of each team characteristic. This could help to better explain the

unpredicted results like the influence of organizational context on team design or the

low predictive power of team size and heterogeneity. Furthermore, additional research

could build on the model to identify other dimensions and measure their performance

outcomes, or study the effects of other facilitating or moderating conditions.

Finally, another interesting avenue for further research is to conduct a multi-level

analysis to better understand the individual-, group- and organization-level variables

that impact team performance. This will require a slightly different approach to

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sampling and variable conceptualization to ensure that variables are correctly defined

at the corresponding hierarchical levels (Hox, 2002).

6.7 Concluding remarks

Designing and implementing global account management teams represents a critical

task for suppliers that are expanding the scope of their relationships with global

customers. However, to date, research had not provided an explanation of how these

teams should be built and what determines their performance. The objective of this

study was to fill this research gap by identifying key critical success factors and

linking them in a holistic model. I hope that the findings shed some light on these

interesting issues and have a practical value for companies that strive for improving

their strategic customer relationships. Finally, I hope that this study spurs future

research that will further enhance our understanding in this emerging field of inquiry.

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19

0

App

endi

x A

: Acc

ount

Man

agem

ent L

itera

ture

G

loba

l Acc

ount

Man

agem

ent

Aut

hors

Fo

cus

Met

hod

and

Sam

ple

Find

ings

C

ontr

ibut

ions

L

imita

tions

Mill

man

(199

6)

Org

aniz

atio

n,

syst

ems

inte

grat

ion,

role

of

the

glob

al

acco

unt

man

ager

, im

plem

enta

tion

Des

crip

tive,

cas

e st

udie

s •

GA

M ro

le re

quire

men

ts fo

r suc

cess

ful s

yste

ms

selli

ng in

clud

e co

ordi

natio

n, k

ey a

ccou

nt p

lann

ing,

ex

tern

al a

nd in

tern

al re

latio

nshi

p m

anag

emen

t, sa

les a

nd p

rofit

resp

onsi

bilit

y, n

egot

iatio

n,

mul

ticul

tura

l tea

mw

ork

One

of t

he fi

rst a

rticl

es to

st

udy

vario

us G

AM

as

pect

s

Yip

and

Mad

sen

(199

6)

Glo

baliz

atio

n,

glob

al st

rate

gy

of G

AM

Cas

e st

udie

s •

The

use

and

perf

orm

ance

eff

ects

of G

AM

are

co

ntin

gent

on

indu

stry

- and

firm

-spe

cific

fact

ors.

•

GA

M su

cces

s fac

tors

: con

sist

ent w

orld

wid

e se

rvic

e, o

ne p

oint

of c

onta

ct, p

artn

erin

g w

ith th

e cu

stom

er, o

utso

urci

ng, c

oord

inat

ion

acro

ss re

gion

s.

Dev

elop

s a fr

amew

ork

linki

ng g

loba

lizat

ion

driv

ers a

nd n

eed

for G

AM

w

ith o

rgan

izat

ion

resp

onse

, stra

tegy

, and

im

plem

enta

tion

Mai

nly

qual

itativ

e an

d de

scrip

tive.

Arn

old,

B

irkin

shaw

, and

To

ulan

(199

9)

Org

aniz

atio

n an

d co

ordi

natio

n of

ac

tiviti

es

Expl

orat

ory

field

re

sear

ch, 5

0+ in

terv

iew

s w

ith e

xecu

tives

from

15

com

pani

es in

Eur

ope

and

Nor

th A

mer

ica

• G

AM

stru

ctur

es a

re c

ostly

to im

plem

ent.

• M

ain

obst

acle

s: d

iffic

ultie

s to

alig

n lo

cal a

nd g

loba

l or

gani

zatio

ns, G

AM

as a

pro

cess

dev

elop

s fas

ter

than

GA

M a

s a fo

rm. G

AM

stru

ctur

es a

re m

ainl

y ex

perim

enta

l and

subj

ect t

o re

-des

ign.

Sugg

ests

an

agen

da fo

r im

plem

entin

g G

AM

st

ruct

ures

, add

ress

ing

thre

e le

vels

of i

ssue

s:

stra

tegi

c, o

rgan

izat

iona

l ,a

nd m

arke

ting.

Qua

litat

ive

rese

arch

w

ith a

rela

tivel

y sm

all

sam

ple

size

.

Mon

tgom

ery,

Y

ip, a

nd

Vill

alon

ga

(199

8a, 1

998b

);

Mon

tgom

ery

and

Yip

(199

9,

2000

)

Driv

ers o

f G

AM

, m

anag

ers,

info

rmat

ion,

pe

rfor

man

ce

Surv

ey re

spon

ses o

f 191

ex

ecut

ives

from

165

co

mpa

nies

from

four

so

urce

s: m

ail s

urve

y an

d th

ree

conv

enie

nce

sam

ples

from

exe

cutiv

e ed

ucat

ion

prog

ram

s

• Si

gnifi

cant

rela

tions

hip

betw

een

cust

omer

gl

obal

izat

ion

and

dem

and

for G

AM

. •

Dem

and

for G

AM

is in

crea

sing

, and

supp

liers

will

m

ake

grea

ter u

se o

f it i

n th

e fu

ture

. •

Glo

bal p

rices

not

the

mos

t dem

ande

d as

pect

; rat

her,

cons

iste

ncy

in se

rvic

e is

. •

The

grea

ter t

he c

usto

mer

dem

and,

the

grea

ter i

ts

use

by th

e su

pplie

r.

• Su

pplie

rs p

rovi

de a

lagg

ed re

spon

se to

GA

M

dem

and.

•

The

mos

t use

d to

ols o

f GA

M a

re a

ccou

nt m

anag

ers

and

supp

ort t

eam

s.

• Th

e be

tter t

he re

spon

se to

dem

and,

the

grea

ter i

ts

effe

ct o

n su

pplie

r per

form

ance

.

Firs

t em

piric

al m

easu

res

and

test

s of G

AM

.

Prov

ides

evi

denc

e of

pe

rfor

man

ce

impr

ovem

ents

.

Test

s the

fram

ewor

k of

Y

ip a

nd M

adse

n (1

996)

an

d de

velo

ps a

mod

el o

f G

AM

link

ing

cust

omer

s’

dem

and

for G

AM

and

su

pplie

rs’ r

espo

nses

.

Poss

ible

bia

ses d

ue to

th

e ch

oice

of s

ampl

es.

19

1

A

utho

rs

Focu

s M

etho

d an

d Sa

mpl

e Fi

ndin

gs

Con

trib

utio

ns

Lim

itatio

ns

Senn

(199

9)

GA

M

impl

emen

tatio

n pr

oces

s

Fram

ewor

k de

velo

pmen

t : s

urve

y of

87

Swis

s bu

sine

ss-to

-bus

ines

s co

mpa

nies

, 10

impl

emen

tatio

n pr

ojec

ts.

Firs

t tes

t: su

rvey

re

spon

ses f

rom

18

man

ager

s of a

Sw

iss

man

ufac

turin

g co

mpa

ny.

• G

AM

impl

emen

tatio

n pa

sses

thro

ugh

thre

e st

eps

each

incl

udin

g th

ree

key

deci

sion

s: d

efin

ing

goal

s (b

usin

ess p

artn

ers,

prod

ucts

and

serv

ices

, peo

ple)

, al

igni

ng b

usin

ess p

roce

sses

(rel

atio

nshi

p m

anag

emen

t, ta

sk m

anag

emen

t, m

anag

emen

t st

ruct

ures

), an

d in

stal

ling

lear

ning

pro

cess

es

(lear

ning

pro

cess

es, s

ucce

ss m

easu

rem

ent,

info

rmat

ion

man

agem

ent).

Com

bine

s tw

o pe

rspe

ctiv

es o

f GA

M, t

he

wor

king

leve

ls a

nd th

e w

orki

ng p

roce

ss, t

o cr

eate

a

dyna

mic

and

hol

istic

fr

amew

ork

for G

AM

im

plem

enta

tion.

Dev

elop

men

t of t

he

fram

ewor

k is

at a

n ea

rly

stag

e. F

urth

er

poss

ibili

ties t

o te

st a

nd

valid

ate

it.

Senn

and

A

rnol

d (1

999)

G

AM

im

plem

enta

tion

proc

ess

Surv

ey re

spon

ses f

rom

20

0 m

anag

ers i

n 6

com

pani

es; i

nter

view

s an

d fo

cus g

roup

s

• C

usto

mer

satis

fact

ion

is p

ositi

vely

rela

ted

to n

ine

field

s on

thre

e le

vels

(stra

tegi

c, o

pera

tiona

l, an

d ta

ctic

al).

•

Seve

n of

the

field

s hav

e a

sign

ifica

nt im

pact

.

Empi

rical

ly te

sts a

nd

valid

ates

a m

odel

that

al

low

s tak

ing

an

inte

grat

ed a

ppro

ach

to

GA

M im

plem

enta

tion

cove

ring

thre

e pr

oces

s le

vels

of i

mpo

rtanc

e.

Wils

on (1

999)

G

AM

pro

gram

in

trodu

ctio

n,

capa

bilit

y de

velo

pmen

t, su

stai

ning

the

effo

rt

Pane

l pre

sent

atio

n in

clud

ing

shor

t cas

e st

udie

s of f

ive

com

pani

es.

• Id

entif

ies p

robl

ems o

f ini

tiatin

g G

AM

pro

gram

. •

The

mai

n su

cces

s fac

tors

are

seni

or m

anag

emen

t co

mm

itmen

t, go

ing

beyo

nd p

rodu

ct-r

elat

ed is

sues

, sk

illed

peo

ple.

Des

crib

es b

est p

ract

ices

. R

elat

es c

usto

mer

se

lect

ion

crite

ria to

the

stag

es o

f GA

M

deve

lopm

ent.

Stud

ies a

sm

all n

umbe

r of

com

pani

es.

Rel

ativ

ely

inco

nclu

sive

fin

ding

s tha

t do

not

allo

w fo

r ge

nera

lizat

ions

.

Nar

ayan

das,

Que

lch,

and

Sw

artz

(200

0)

Glo

bal p

ricin

g co

ntra

cts

Fiel

d re

sear

ch, 5

0+ in

-de

pth

inte

rvie

ws w

ith

seni

or G

AM

exe

cutiv

es

• Id

entif

ies s

ix so

urce

s of v

aria

nce

acro

ss m

arke

ts.

• Su

cces

s of g

loba

l pric

ing

cont

ract

dep

ends

on

cust

omer

’s to

p m

anag

emen

t sup

port,

syst

emat

ic

impl

emen

tatio

n at

all

leve

ls, s

imila

r loc

al m

arke

ts,

cust

omer

focu

s on

valu

e en

hanc

emen

t, sh

are

of

purc

hase

s, re

latio

nshi

ps w

ith lo

cal m

anag

ers.

Ove

rvie

w o

f the

mai

n ch

alle

nges

and

risk

s of

glob

al p

ricin

g co

ntra

cts.

A c

heck

list f

or su

pplie

rs.

Wils

on, C

room

, M

illm

an, a

nd

Wei

lbak

er

(200

0)

Glo

bal a

ccou

nt

sele

ctio

n, G

AM

co

mpe

tenc

ies,

stra

tegi

c ap

proa

ches

to

GA

M, b

arrie

rs

to

impl

emen

tatio

n

159

resp

onse

s fro

m a

qu

antit

ativ

e po

stal

su

rvey

, 21

in-d

epth

st

ruct

ured

inte

rvie

ws,

stru

ctur

ed w

orks

hops

, se

cond

ary

data

ana

lysi

s, 5

long

itudi

nal c

ase

stud

ies

• Fi

ndin

gs c

over

driv

ers o

f glo

baliz

atio

n, a

ccou

nt

sele

ctio

n, o

rgan

izat

iona

l sup

port,

GA

M

com

pete

ncie

s, G

AM

stra

tegi

es, r

ole

of th

e gl

obal

ac

coun

t man

ager

, blo

cks t

o G

AM

.

Ana

lyze

s a b

road

rang

e of

is

sues

. Pro

vide

s a

fram

ewor

k fo

r ana

lyzi

ng

the

role

of t

he g

loba

l ac

coun

t man

ager

.

Des

crip

tive

rese

arch

, la

ck o

f mor

e ad

vanc

ed

quan

titat

ive

supp

ort.

19

2

A

utho

rs

Focu

s M

etho

d an

d Sa

mpl

e Fi

ndin

gs

Con

trib

utio

ns

Lim

itatio

ns

Arn

old,

Bel

z,

and

Senn

(200

1)

GA

M

know

ledg

e m

anag

emen

t

Expl

orat

ory,

4 c

ase

stud

ies b

ased

on

wor

ksho

ps a

nd 7

0 in

terv

iew

s

• G

AM

driv

es k

now

ledg

e tra

nsfe

rs.

• K

now

ledg

e m

anag

emen

t is p

artly

dev

elop

ed. M

ain

obst

acle

s: in

suff

icie

nt u

se o

f com

mon

IT p

latfo

rms

wor

ldw

ide,

mar

ketin

g in

form

atio

n sy

stem

s, an

d m

arke

ting

know

-how

.

Gui

delin

es fo

r im

plem

entin

g th

e th

ree

know

ledg

e pr

oces

ses:

ge

nera

tion,

dis

sem

inat

ion,

re

spon

sive

ness

.

Smal

l sam

ple

of

com

pani

es.

Arn

old,

B

irkin

shaw

, and

To

ulan

(200

1)

Stra

tegy

and

im

plem

enta

tion

of G

AM

; cu

stom

er

asse

ssm

ent.

Stag

e I:

face

-to-f

ace

inte

rvie

ws w

ith 3

5 m

anag

ers i

n 10

co

mpa

nies

. St

age

II: S

urve

y re

spon

ses f

rom

107

gl

obal

acc

ount

m

anag

ers,

55 n

atio

nal

sale

s man

ager

s, an

d 10

ex

ecut

ives

in 1

6 co

mpa

nies

.

• G

AM

lead

s to

cent

raliz

atio

n of

act

iviti

es a

nd

dow

nwar

d pr

essu

re o

n pr

ices

and

has

no

sign

ifica

nt

impa

ct o

n sa

les g

row

th.

• It

is im

porta

nt to

cla

rify

the

role

of t

he G

AM

team

, in

trodu

ce a

real

istic

ince

ntiv

e st

ruct

ure,

pic

k th

e rig

ht G

AM

, cre

ate

a st

rong

supp

ort n

etw

ork,

and

bu

ild c

usto

mer

rela

tions

hips

on

mor

e th

an o

ne

leve

l.

• A

mod

el to

ass

ess

bala

nce

of p

ower

and

ap

prop

riate

type

of

rela

tions

hip.

•

Five

less

ons f

or

GA

M im

plem

enta

tion

succ

ess.

Doe

s not

pro

vide

co

mpl

ete

rese

arch

m

etho

dolo

gy a

nd

resu

lts.

Birk

insh

aw,

Toul

an, a

nd

Arn

old

(200

1)

Fact

ors

dete

rmin

ing

acco

unt

perf

orm

ance

Surv

ey re

spon

ses f

rom

10

6 gl

obal

acc

ount

m

anag

ers f

rom

16

com

pani

es.

• Ef

ficie

ncy

and

sale

s gro

wth

pre

dict

ed b

y bo

th

info

rmat

ion-

proc

essi

ng a

nd re

sour

ce-d

epen

denc

y va

riabl

es (s

cope

of a

ccou

nt, s

uppo

rt sy

stem

s, co

mm

unic

atio

n, a

ccou

nt c

entra

lizat

ion,

cus

tom

er

depe

nden

ce).

• Pa

rtner

ship

with

cus

tom

er is

pre

dict

ed b

y cu

stom

er

depe

nden

ce a

nd c

ontro

l var

iabl

es (e

xper

ienc

e of

ac

coun

t man

ager

and

firm

dum

my

varia

bles

).

Take

s a n

ew a

ppro

ach

to

GA

M b

y lin

king

it to

two

orga

niza

tion

theo

ries—

info

rmat

ion

proc

essi

ng

and

reso

urce

dep

ende

ncy.

All

data

col

lect

ed fr

om

the

sam

e so

urce

(c

omm

on-m

etho

d bi

as).

Onl

y 16

com

pani

es;

risk

that

resp

onse

s are

no

t ind

epen

dent

.

Mon

tgom

ery,

Y

ip, a

nd

Vill

alon

ga

(200

1, 2

002)

Dem

and

and

supp

ly o

f GA

M

Surv

ey re

spon

ses o

f 191

ex

ecut

ives

from

165

co

mpa

nies

from

four

so

urce

s: m

ail s

urve

y an

d th

ree

conv

enie

nce

sam

ples

from

exe

cutiv

e ed

ucat

ion

prog

ram

s

• Su

pplie

r’s i

ndus

try g

loba

lizat

ion

driv

ers d

eter

min

e cu

stom

er’s

deg

ree

of g

loba

l pur

chas

ing.

•

Supp

lier’

s ind

ustry

glo

baliz

atio

n dr

iver

s det

erm

ine

cust

omer

dem

and

for G

AM

. •

Sign

ifica

nt re

latio

nshi

p be

twee

n cu

stom

er’s

deg

ree

of g

loba

l pur

chas

ing

and

dem

and

for G

AM

. •

The

grea

ter t

he c

usto

mer

dem

and,

the

grea

ter t

he

use

by th

e su

pplie

r.

• Th

e be

tter t

he re

spon

se to

dem

and,

the

grea

ter t

he

effe

ct o

n su

pplie

r per

form

ance

.

Add

s to

earli

er

fram

ewor

ks. P

rovi

des

quan

titat

ive

evid

ence

for

perf

orm

ance

im

prov

emen

ts.

Poss

ible

bia

ses d

ue to

th

e ch

oice

of s

ampl

es.

Use

of t

he sa

me

data

as

prev

ious

stud

ies.

19

3

A

utho

rs

Focu

s M

etho

d an

d Sa

mpl

e Fi

ndin

gs

Con

trib

utio

ns

Lim

itatio

ns

Birk

insh

aw a

nd

Terje

sen

(200

2)

Org

aniz

atio

n de

sign

/stru

ctur

e C

ase

stud

ies

• C

usto

mer

-foc

used

org

aniz

atio

nal s

truct

ures

are

co

mpe

ting

with

and

repl

acin

g tra

ditio

nal s

truct

ures

su

ch a

s pro

duct

div

isio

n, a

rea

divi

sion

, or p

rodu

ct-

area

mat

rix.

An

over

view

of t

he

vario

us ty

pes o

f cu

stom

er-f

ocus

ed

stru

ctur

es w

ith th

eir p

ros

and

cons

.

Insu

ffic

ient

em

piric

al

supp

ort.

Hom

burg

, W

orkm

an, a

nd

Jens

en (2

002)

Act

iviti

es,

man

ager

s, re

sour

ces,

stru

ctur

e

Num

eric

al ta

xono

my

base

d on

surv

ey

resp

onse

s fro

m 2

64

Ger

man

firm

s and

121

U

.S. f

irms

• Th

e m

ain

KA

M d

imen

sion

s are

act

iviti

es, a

ctor

s, re

sour

ces,

and

appr

oach

form

aliz

atio

n.

• Th

ere

are

eigh

t KA

M a

ppro

ache

s: to

p-m

anag

emen

t K

AM

, mid

dle-

man

agem

ent K

AM

, ope

ratin

g-le

vel

KA

M, c

ross

-fun

ctio

nal d

omin

ant K

AM

, un

stru

ctur

ed K

AM

, iso

late

d K

AM

, cou

ntry

-clu

b K

AM

, and

no

KA

M.

• Th

ere

are

sign

ifica

nt p

erfo

rman

ce d

iffer

ence

s am

ong

the

appr

oach

es w

ith c

ross

-fun

ctio

nal

dom

inan

t KA

M sh

owin

g th

e hi

ghes

t val

ues f

or

alm

ost a

ll va

riabl

es.

• C

onso

lidat

es p

revi

ous

rese

arch

usi

ng a

co

nfig

urat

iona

l pe

rspe

ctiv

e.

• Ex

tend

s res

earc

h to

co

mpa

nies

with

out

form

aliz

ed K

AM

pr

ogra

ms.

• Th

e fir

st st

udy

to

empi

rical

ly c

lass

ify

orga

niza

tiona

l ap

proa

ches

to se

lling

.

Stat

ic si

ngle

-info

rman

t re

sear

ch d

esig

n, w

hich

fo

cuse

s on

only

one

si

de o

f the

selle

r–bu

yer

dyad

.

Toul

an,

Birk

insh

aw, a

nd

Arn

old

(200

2)

Cus

tom

er–

supp

lier m

atch

Su

rvey

resp

onse

s fro

m

106

glob

al a

ccou

nt

man

ager

s fro

m 1

6 co

mpa

nies

.

• Ef

ficie

ncy

and

sale

s gro

wth

are

pre

dict

ed b

y st

rate

gic

impo

rtanc

e, a

ctiv

ity c

onfig

urat

ion,

and

ex

ecut

ive

supp

ort f

it.

• Pa

rtner

ship

s with

cus

tom

ers a

re p

redi

cted

by

mar

ketin

g st

rate

gy a

nd e

xecu

tive

supp

ort f

it.

App

lies c

once

pts t

hat

have

not

bee

n st

udie

d in

re

latio

n to

GA

M b

efor

e.

Doe

s not

con

side

r the

cu

stom

er p

ersp

ectiv

e.

Ana

lyze

s a li

mite

d nu

mbe

r of f

it as

pect

s.

Har

vey,

Mye

rs,

and

Nov

icev

ic

(200

3a)

GA

M

rela

tions

hips

C

once

ptua

l •

Use

s rel

atio

nal c

ontra

ctin

g th

eory

to st

udy

the

ratio

nale

and

cha

lleng

es o

f dev

elop

ing

GA

M

rela

tions

hips

.

Dev

elop

s a st

ep-b

y-st

ep

GA

M im

plem

enta

tion

plan

.

Entir

ely

liter

atur

e ba

sed.

Har

vey,

N

ovic

evic

, H

ench

, and

M

yers

(200

3b)

Supp

ly

man

ager

s’ ro

le

adap

tatio

ns/v

ari

atio

ns

Con

cept

ual,

theo

ry-

base

d •

The

supp

ly m

anag

er’s

role

is c

hang

ing

due

to th

e de

man

ds o

f glo

bal c

usto

mer

s.

• G

AM

role

var

iatio

ns d

epen

d on

the

envi

ronm

enta

l co

nditi

ons a

nd th

e sc

ope

of th

e su

pplie

r–cu

stom

er

rela

tions

hip.

•

The

chal

leng

es fo

r GA

M te

ams a

re p

lura

lity

of

man

ager

ial a

utho

ritie

s and

mul

tiplic

ity o

f GA

M

units

, mai

ntai

ning

flex

ibili

ty to

ach

ieve

con

certe

d ef

ficie

ncy,

secu

ring

cont

inui

ng in

tern

al

orga

niza

tiona

l sup

port,

mul

tidim

ensi

onal

ity o

f tas

k m

anag

emen

t, m

ultis

kill

dem

ands

for e

ffec

tive

supp

ly m

anag

emen

t.

Firs

t stu

dy to

use

re

latio

nal c

ontra

ctin

g th

eory

and

the

know

ledg

e-ba

sed

view

in

rela

tion

to G

AM

.

Entir

ely

liter

atur

e ba

sed.

19

4

Aut

hors

Fo

cus

Met

hod

and

Sam

ple

Find

ings

C

ontr

ibut

ions

L

imita

tions

Wils

on a

nd

Mill

man

(200

3)

Glo

bal a

ccou

nt

man

ager

role

Li

tera

ture

-bas

ed

• D

epen

ding

on

the

degr

ee o

f the

glo

bal a

ccou

nt

man

ager

’s id

entif

icat

ion

with

the

acco

unt a

nd

empl

oyer

, the

re a

re fo

ur G

AM

role

s: se

lf-se

rver

, re

nega

de, p

artis

an, a

nd a

rbite

r.

• Th

e de

gree

to w

hich

the

GA

M e

xerc

ises

pol

itica

l an

d en

trepr

eneu

rial s

kills

dep

ends

on

the

leve

ls o

f or

gani

zatio

nal c

ompl

exity

and

cul

tura

l div

ersi

ty

and

the

degr

ee o

f org

aniz

atio

nal i

nter

depe

nden

ce

and

inte

grat

ion.

Bui

lds o

n th

e po

litic

al

entre

pren

eur m

odel

of

Wils

on e

t al.

(200

0) a

nd

rela

tes d

iffer

ent G

AM

ro

les t

o th

e R

elat

ions

hip

Dev

elop

men

t Mod

el o

f M

illm

an a

nd W

ilson

(1

994)

.

Con

cept

ual w

ith li

mite

d em

piric

al su

ppor

t.

Wor

kman

, H

ombu

rg, a

nd

Jens

en (2

003)

Act

iviti

es,

man

ager

s, re

sour

ces,

stru

ctur

e

Surv

ey re

spon

ses f

rom

26

4 G

erm

an fi

rms a

nd

121

U.S

. firm

s

• In

tens

ity a

nd p

roac

tiven

ess o

f KA

M a

ctiv

ities

, top

m

anag

emen

t inv

olve

men

t, an

d re

sour

ces a

re

posi

tivel

y re

late

d to

KA

M e

ffec

tiven

ess.

• K

AM

eff

ectiv

enes

s is p

ositi

vely

rela

ted

to

perf

orm

ance

in m

arke

t, w

hich

affe

cts p

rofit

abili

ty.

• K

AM

app

roac

h fo

rmal

izat

ion

is n

egat

ivel

y re

late

d to

KA

M e

ffect

iven

ess.

• Pe

rfor

man

ce e

ffec

ts a

re m

oder

ated

by

mar

ket

dyna

mis

m a

nd c

ompe

titiv

e in

tens

ity.

• D

evel

ops a

mod

el th

at

links

spec

ific

capa

bilit

ies t

o pe

rfor

man

ce.

• Te

sted

em

piric

ally

.

• Po

ssib

le b

iase

s due

to

cro

ss-s

ectio

nal

desi

gn w

ith a

sing

le

info

rman

t. •

Inte

ract

ions

bet

wee

n co

nstru

cts a

nd

mod

erat

ors a

re n

ot

cons

ider

ed.

Shi,

Zou,

and

C

avus

gil (

2004

) G

AM

ca

pabi

litie

s C

once

ptua

l •

Col

labo

ratio

n or

ient

atio

n, G

AM

stra

tegi

c fit

, and

G

AM

con

figur

atio

n ha

ve p

ositi

ve e

ffect

s on

the

dyad

ic c

ompe

titiv

e ad

vant

age

and

the

join

t pro

fit

perf

orm

ance

. •

Goa

l con

grue

nce

and

com

plem

enta

ry re

sour

ces

have

a p

ositi

ve e

ffec

t on

colla

bora

tion

orie

ntat

ion,

G

AM

stra

tegi

c fit

, and

GA

M c

onfig

urat

ion.

• B

uild

s on

earli

er

rese

arch

to d

evel

op a

br

oade

r mod

el o

f GA

M

capa

bilit

ies a

nd

perf

orm

ance

. •

Incl

udes

bot

h si

des o

f th

e bu

yer–

selle

r dya

d an

d th

eir j

oint

pe

rfor

man

ce.

Con

cept

ual,

not t

este

d em

piric

ally

.

19

5

Nat

iona

l and

Key

Acc

ount

Man

agem

ent

A

utho

rs

Focu

s M

etho

d an

d Sa

mpl

e Fi

ndin

gs

Con

trib

utio

ns

Lim

itatio

ns

Stev

enso

n (1

981)

B

enef

its o

f N

AM

In

terv

iew

s w

ith 3

4 ex

ecut

ives

in 3

3 co

mpa

nies

• Th

e m

ain

bene

fits o

f int

rodu

cing

nat

iona

l acc

ount

m

anag

emen

t are

incr

ease

d sh

are

of sa

les,

impr

oved

pr

ofita

bilit

y, a

nd im

prov

ed tw

o-w

ay b

uyer

–sel

ler

com

mun

icat

ion.

•

Add

ition

al a

dvan

tage

s inc

lude

mai

ntai

ning

pos

ition

at

the

acco

unt,

parti

cipa

ting

in p

resu

med

gro

wth

at

the

acco

unt,

and

impr

oved

new

pro

duct

acc

epta

nce.

Find

s evi

denc

e of

pos

itive

pe

rfor

man

ce e

ffect

s.

Shap

iro a

nd

Mor

iarty

(198

4)

NA

M st

ruct

ures

Fi

eld

rese

arch

of 1

9 co

mpa

nies

•

The

maj

or o

rgan

izat

iona

l opt

ions

are

no

NA

M

prog

ram

, par

t-tim

e pr

ogra

m, f

ull-t

ime

prog

ram

at

oper

atin

g un

it le

vel,

corp

orat

e-le

vel p

rogr

am, a

nd

natio

nal a

ccou

nt d

ivis

ion.

How

ever

, the

re is

no

perf

ect s

olut

ion,

and

the

mos

t app

ropr

iate

stru

ctur

e de

pend

s on

the

envi

ronm

ent.

• R

elat

ed is

sues

are

the

amou

nt o

f sup

port

by th

e sa

les f

orce

, the

inte

grat

ion

of sa

les-

rela

ted

func

tions

, loc

atio

n an

d ro

le o

f sta

ff sp

ecia

lists

, ca

reer

pat

hs, p

ower

, and

aut

horit

y.

Ver

y de

taile

d an

alys

is o

f st

ruct

ural

alte

rnat

ives

w

ith th

eir a

dvan

tage

s and

di

sadv

anta

ges.

Mos

tly c

once

ptua

l.

Col

letti

and

Tu

brid

y (1

987)

Pr

ogra

m

stru

ctur

e, th

e ac

coun

t m

anag

er,

mea

sure

men

t

Surv

ey o

f 105

NA

MA

m

embe

r com

pani

es

• Id

entif

ies t

he m

ost c

omm

on p

atte

rns i

n or

gani

zatio

n st

ruct

ure

and

job

defin

ition

, tim

e al

loca

tion,

ac

coun

t man

ager

skill

s req

uire

d, c

ompe

nsat

ion,

and

m

easu

rem

ent a

rran

gem

ents

.

Prov

ides

an

over

view

of

the

acco

unt m

anag

er ro

le

supp

orte

d by

em

piric

al

resu

lts.

Bas

ed o

n re

spon

ses

only

from

NA

MA

m

embe

rs.

Wot

ruba

and

C

astle

berr

y (1

993)

NA

M m

anag

er

107

surv

ey re

spon

ses

from

NA

MA

mem

bers

•

Mos

t firm

s con

duct

form

al jo

b an

alys

is a

nd re

crui

t N

AM

s fro

m w

ithin

thei

r ow

n fir

m.

• Pe

rfor

man

ce o

f NA

Ms i

s affe

cted

by

leng

th o

f te

nure

, age

of p

rogr

am, a

nd ti

me

devo

ted

to k

ey

acco

unts

.

Det

aile

d an

alys

is o

f all

aspe

cts o

f NA

M H

R

prac

tices

.

Serv

ice

and

cons

umer

go

ods c

ompa

nies

un

derr

epre

sent

ed in

the

sam

ple.

Pard

o, S

alle

, an

d Sp

ence

r (1

995)

Key

acc

ount

ad

apta

tion

proc

ess

Cas

e st

udy

of a

tele

com

co

mpa

ny, i

nclu

ding

10

inte

rvie

ws

• Th

e ke

y ac

coun

tizat

ion

proc

ess i

s a re

sult

of

adap

tatio

n to

var

ious

act

ors.

• Th

e ke

y ac

coun

t rel

atio

nshi

p de

velo

ps o

ver t

ime

goin

g th

roug

h fo

ur p

hase

s: n

aive

ty, a

war

enes

s, co

nsol

idat

ion,

and

fine

-tuni

ng.

Com

bine

s ele

men

ts o

f the

In

tera

ctio

n M

odel

(H

akan

sson

, 198

2) a

nd

the

netw

ork

appr

oach

to

expl

ain

the

evol

utio

n of

K

AM

rela

tions

hips

ove

r tim

e.

Sing

le c

ase

stud

y.

19

6

Aut

hors

Fo

cus

Met

hod

and

Sam

ple

Find

ings

C

ontr

ibut

ions

L

imita

tions

Lam

be a

nd

Spek

man

(199

7)

NA

M a

llian

ces

Surv

ey re

spon

ses f

rom

11

8 m

anag

ers,

mos

tly

U.S

. bas

ed

• C

lass

ifies

NA

M re

latio

nshi

ps o

n ba

sis o

f deg

ree

of

colla

bora

tion.

•

NA

M a

llian

ces a

re n

o le

ss c

olla

bora

tive

than

oth

er

form

s of s

trate

gic

allia

nces

. •

The

leve

l of c

olla

bora

tion

in a

NA

M re

latio

nshi

p de

term

ines

the

appr

opria

te st

rate

gies

.

Firs

t stu

dy to

com

pare

N

AM

col

labo

ratio

n w

ith

othe

r for

ms o

f re

latio

nshi

ps a

nd d

raw

im

plic

atio

ns.

Expl

orat

ory

stud

y,

furth

er e

mpi

rical

wor

k is

nee

ded.

McD

onal

d,

Mill

man

, and

R

oger

s (19

97)

KA

M

rela

tions

hips

an

d pr

oces

s, ch

alle

nges

In-d

epth

inte

rvie

ws w

ith

11 c

usto

mer

–sup

plie

r dy

ads

• Th

e m

ost c

omm

on c

usto

mer

sele

ctio

n cr

iteria

are

vo

lum

e-re

late

d fa

ctor

s, po

tent

ial f

or p

rofit

, and

st

atus

-rel

ated

fact

ors.

• Th

e m

ost c

omm

on su

pplie

r sel

ectio

n cr

iteria

are

ea

se o

f doi

ng b

usin

ess,

qual

ity o

f pro

duct

/ser

vice

, an

d qu

ality

of p

eopl

e.

• Sk

ills r

equi

red

for K

AM

s are

inte

grity

, pr

oduc

t/ser

vice

kno

wle

dge,

com

mun

icat

ions

, un

ders

tand

ing

of c

usto

mer

bus

ines

s, an

d se

lling

/neg

otia

tion

skill

s. •

The

bigg

est K

AM

cha

lleng

es a

re g

loba

l/loc

al

orga

niza

tiona

l iss

ues,

proc

ess e

xcel

lenc

e,

deve

lopm

ent o

f KA

Ms,

and

deve

lopm

ent o

f KA

M

team

s.

• Pr

ovid

es so

me

supp

ort

for t

he R

elat

ions

hip

Dev

elop

men

t Mod

el o

f M

illm

an a

nd W

ilson

(1

994)

. •

One

of t

he fe

w st

udie

s to

add

ress

bot

h th

e cu

stom

er a

nd su

pplie

r si

des o

f the

rela

tions

hip.

Nap

olita

no

(199

7)

Evol

utio

n of

K

AM

rela

tions

, K

AM

stra

tegy

NA

MA

sur

vey

amon

g Fo

rtun

e 10

00

com

pani

es, s

ampl

e no

t sp

ecifi

ed

• Th

e nu

mbe

r of N

AM

pro

gram

s and

man

ager

s tri

pled

in th

e pe

riod

1992

–199

6.

• 53

% o

f the

com

pani

es c

onsi

der t

he e

ffec

tiven

ess o

f th

eir p

artn

erin

g w

ith c

usto

mer

s to

be p

oor.

• Pr

ovid

es e

vide

nce

for

the

incr

easi

ng

impo

rtanc

e of

NA

M.

• A

naly

zes t

he k

ey

elem

ents

of a

succ

essf

ul

natio

nal a

ccou

nt

stra

tegy

.

Sam

ple

and

met

hod

not

disc

lose

d.

Pard

o (1

997)

C

usto

mer

pe

rcep

tion

20 in

terv

iew

s with

key

ac

coun

ts o

f ele

ctric

ity

and

tele

phon

e co

mpa

nies

• K

ey a

ccou

nts p

erce

ive

KA

M in

thre

e w

ays

acco

rdin

g to

the

adde

d va

lue:

dis

ench

antm

ent,

inte

rest

, and

ent

husi

asm

. •

Mod

erat

ors o

f KA

M p

rogr

am p

erce

ptio

n by

cu

stom

ers a

re p

erce

ived

pro

duct

impo

rtanc

e an

d ce

ntra

lizat

ion

of p

urch

ase

deci

sion

s.

• O

ne o

f the

few

stud

ies

to fo

cus e

ntire

ly o

n th

e cu

stom

er p

ersp

ectiv

e.

Expl

orat

ory

stud

y,

furth

er e

mpi

rical

wor

k is

nee

ded.

19

7

A

utho

rs

Focu

s M

etho

d an

d Sa

mpl

e Fi

ndin

gs

Con

trib

utio

ns

Lim

itatio

ns

Seng

upta

, K

rapf

el, a

nd

Pusa

teri

(199

7)

Switc

hing

co

sts

Surv

ey o

f 176

NA

MA

m

embe

r com

pani

es

• Th

e gr

eate

r the

ada

ptat

ion

unde

rtake

n by

the

selli

ng

firm

, the

ince

ntiv

es o

ffer

ed b

y th

e se

lling

firm

, and

th

e cu

stom

er’s

rela

tions

hip-

spec

ific

inve

stm

ent,

the

grea

ter t

he c

usto

mer

switc

hing

cos

ts.

• Th

e gr

eate

r the

cus

tom

er sw

itchi

ng c

osts

, the

hig

her

the

obje

ctiv

e an

d su

bjec

tive

perf

orm

ance

of t

he k

ey

acco

unt m

anag

er.

• D

emon

stra

tes t

he

impo

rtanc

e of

cus

tom

er

switc

hing

cos

ts in

KA

M

rela

tions

hips

.

Shar

ma

(199

7)

Buy

ing

beha

vior

10

9 te

leph

one

ques

tionn

aire

s of b

uyer

s of

tele

phon

e eq

uipm

ent

• C

usto

mer

s’ p

refe

renc

e fo

r KA

M p

rogr

ams d

epen

ds

on le

vels

invo

lved

in p

urch

asin

g, fu

nctio

ns

invo

lved

in p

urch

asin

g, a

nd ti

me

take

n fo

r pu

rcha

sing

.

• Pr

ovid

es im

plic

atio

ns fo

r ke

y ac

coun

t sel

ectio

n.

Sam

ple

cove

rs o

nly

one

supp

lier i

ndus

try

and

one

resp

onde

nt

per c

ompa

ny.

Wee

ks a

nd

Stev

ens (

1997

) N

AM

trai

ning

, N

AM

sk

ills/

abili

ties

Surv

ey o

f 133

NA

MA

m

embe

r com

pani

es

• N

AM

s are

not

satis

fied

with

NA

M sa

les t

rain

ing

prog

ram

s. •

Com

pani

es a

re n

ot a

dequ

ate

in a

ddre

ssin

g 13

out

of

21 sk

ills/

abili

ties r

equi

red

for t

he N

AM

role

.

• A

focu

sed

inve

stig

atio

n of

N

AM

trai

ning

nee

ds.

Wei

lbak

er a

nd

Wee

ks (1

997)

Ev

olut

ion

of

KA

M p

roce

ss

Con

tent

ana

lysi

s of

exis

ting

liter

atur

e •

The

evol

utio

n of

the

natio

nal a

ccou

nt m

anag

emen

t pr

oces

s clo

sely

rese

mbl

es th

e pr

oduc

t life

cyc

le

and/

or th

e ad

optio

n cu

rve.

• Ex

tens

ive

liter

atur

e re

view

.

Dis

hman

and

N

itse

(199

8)

Cha

ract

eris

tics

of n

atio

nal

acco

unts

27 in

terv

iew

s with

N

AM

A m

embe

rs

• Pr

ovid

es d

escr

iptiv

es o

f the

num

ber a

nd si

ze o

f key

ac

coun

ts a

nd sa

les f

orce

in th

e st

udie

d co

mpa

nies

. •

The

mos

t com

mon

org

aniz

atio

n m

odel

is

mul

tinat

iona

l acc

ount

s ser

vice

d by

sing

le-ti

ered

sa

les f

orce

.

• Id

entif

ies s

ome

sign

ifica

nt

diffe

renc

es to

pre

viou

s re

sear

ch.

Expl

orat

ory

stud

y.

Kem

pene

rs a

nd

van

der H

art

(199

9)

Acc

ount

m

anag

emen

t st

ruct

ures

Seve

n ca

se st

udie

s of

diffe

rent

com

pani

es

• D

esig

ning

KA

M p

rogr

ams r

equi

res 1

5 de

cisi

ons i

n th

e ar

eas o

f pos

ition

ing

of K

AM

pro

gram

, po

sitio

ning

of a

ccou

nt m

anag

ers,

leve

ls o

f acc

ount

m

anag

ers,

and

orga

niza

tion

of a

ccou

nt te

ams.

• B

uild

s on

Shap

iro a

nd

Mor

iarty

(198

4) to

cre

ate

a de

cisi

on-m

akin

g m

odel

fo

r dev

elop

ing

KA

M

stru

ctur

es.

Expl

orat

ory

stud

y.

Mill

man

and

W

ilson

(199

9a)

KA

M

proc

esse

s C

once

ptua

l, ba

sed

on

sour

ces s

uch

as fo

rmal

re

sear

ch p

roje

cts,

stud

ying

buy

er/s

elle

r dy

ads,

indu

stry

surv

eys,

and

obse

rvat

ions

from

w

orks

hops

and

con

sulti

ng

proj

ects

• Th

e K

AM

pro

cess

is u

nlik

ely

to su

ccee

d w

ithou

t se

nior

man

agem

ent i

nvol

vem

ent.

• Tr

aditi

onal

vie

ws o

n bu

yer/s

elle

r rel

atio

nshi

ps a

nd

cultu

ral b

arrie

rs m

ust b

e re

plac

ed.

• Th

e pr

oces

s inv

olve

s “to

tal”

supp

ly c

hain

m

anag

emen

t. •

The

proc

ess m

ust f

acili

tate

“re

al”

invo

lvem

ent w

ith

the

cust

omer

.

• Id

entif

ies a

nd a

naly

zes

eigh

t key

KA

M p

roce

ss

elem

ents

.

Unc

lear

dat

a so

urce

s.

198

Appendix B: Organizational Team Models Source: Gladstein (1984). Source: Hackman (1987).

PROCESS CRITERIAOF EFFECTIVENESS

• Level of effort brought to bear on the group task

• Amount of knowledge and skill applied to task work

• Appropriateness of the task performance strategies used by the group

GROUP SYNERGY

Assistance to the group by interacting in the ways that:•Reduce process•Create synergistic process gains

ORGANIZATIONAL CONTEXT

A context that supports and reinforces competent task work, via: •Reward system•Educational system•Information system

GROUP DESIGN

A design that prompts and facilitates competent work on the task, via:•Structure of the task•Composition of the group•Group norms about performance processes

GROUP EFFECTIVENESS

• Task output acceptable to those who receive or review it

• Capability of members to work together in future is maintained or strengthened

• Member’s need are more satisfied than frustrated by group experience

MATERIAL RESOURCES

Sufficiency of material resources required to accomplish the task well and on time

GROUP LEVEL

GROUP COMPOSITION

• Adequate Skills • Heterogeneity • Organization Tenure • Job Tenure

• Role & Goal Clarity • Specific Work Norms • Task Control • Size • Formal Leadership

GROUP STRUCTURE

INPUTS

RESOURCES AVAILABLE

• Training & Technical Consultation • Markets Served

• Reward for Group Performance • Supervisory Control

ORGANIZATIONAL STRUCTURE

ORGANIZATIONAL LEVEL

PROCESS OUTPUTS

GROUP PROCESS

GROUP TASK

GROUP EFFECTIVENESS

• Open Communication • Supportiveness

• Conflict

• Discussion of Strategy • Weighting Individual Inputs • Boundary Management

• Task Complexity • Environmental Uncertainty • Interdependence

Performance Satisfaction

x

GROUP LEVEL

GROUP COMPOSITION

• Adequate Skills • Heterogeneity • Organization Tenure • Job Tenure

• Role & Goal Clarity • Specific Work Norms • Task Control • Size • Formal Leadership

GROUP STRUCTURE

INPUTS

RESOURCES AVAILABLE

• Training & Technical Consultation • Markets Served

• Reward for Group Performance • Supervisory Control

ORGANIZATIONAL STRUCTURE

ORGANIZATIONAL LEVEL

PROCESS OUTPUTS

GROUP PROCESS

GROUP TASK

GROUP EFFECTIVENESS

• Open Communication • Supportiveness

• Conflict

• Discussion of Strategy • Weighting Individual Inputs • Boundary Management

• Task Complexity • Environmental Uncertainty • Interdependence

Performance Satisfaction

x

199

Source: Gist, Locke, and Taylor (1987). Source: Cohen and Bailey (1997).

PROCESSES

InfluenceFacilitationSocial ImpactLoafing

DevelopmentIdentificationTeam Development

Decision MakingParticipationAlternative/InformationGenerationAlternative EvaluationConsensus BuildingTotal Process

OUTCOMES

Group Performance

Quantity

Quality

TimelinesQuality of Work Life of

Group Members

Capability of Working Independently in the

Future

Task Characteristics

INPUTS

Group Structure

SizeAbilityPersonalityGenderRace

Group StrategiesLeadershipReward Allocation

Internal Processes e.g. conflict,

communication

External Processes e.g. conflict

communication

Effectiveness

-Performance Outcomes e.g. quality productivity

-Attitudinal Outcomes e.g. job satisfaction, trust

-BehavioralOutcomes e.g. turnover absenteeism

Group Psychosocial Traits

e.g. norms, shared mental models

Task Designe.g. autonomy, interdependency

Group Composition e.g. size, tenure

Organizational Context e.g. Rewards, supervision

Environmental Factors e.g. turbulence, industry characteristics

200

Source: Campion, Medsker, and Higgs (1993).

Job DesignSelf-ManagementParticipationTask VarietyTask SignificanceTask Identity

ProcessPotencySocial SupportWorkload SharingCommunication/Cooperation Within Group

InterdependenceTask InterdependenceGoal InterdependenceInterdependence Feedback and Reward

CompositionHeterogeneityFlexibilityRelative SizePreference for Group Work

ContextTrainingManagerial SupportCommunication/Cooperation Between Group

Productivity

Satisfaction

Manager Judgment

201

Appendix C: Selling Team Models Source: Ruekert and Walker (1987). Source: Smith and Barclay (1993).

Transaction between Marketing and Another Functional Area

•Work

•Resources

•Assistance

Internal Environmental Condition

•Resource Dependence

•Domain Similarity

•Strategic Imperative

External Environmental Condition

•Complexity

•Turbulence

Psycho-Social Outcomes• Perceived

Effectiveness of the relationships

• Conflicts

Functional Outcomes•Accomplishment of Marketing’s Goals

•Accomplishment of Other Area’s Goals

•Accomplishment of Joint GoalsCommunication Flows between

Marketing and Another Functional Area

•Amount

•Difficulty

•Formal versus informal

Situational Dimensions Structural and Process Dimensions Outcome Dimensions

Communication Patterns between Marketing and Another Functional

Area•Formal Rules and Procedures

•Informal Influence

•Conflict Resolution Mechanism

INPUTS PROCESSES OUTPUTS

PARTICIPATIVE LEADER

BEHAVIOR

SUPPORTIVE LEADER

BEHAVIOR

MEMBER INTER-DEPENDENCY

POTENCY

MARKET ORIENTATION

BOUNDARY MANAGEMENT

SELLING CENTER

COHESIVENESS

PERCEIVED TASK

PERFORMANCE

INDIVIDUAL

LEVEL

GROUP

LEVEL

ORGANIZATION

LEVEL

202

Source: Moon and Gupta (1997). Source: Perry, Pearce and Sims (1999).

Communication Flows Between Selling Center

Participants

ConnectednessDifficulty

Internal Environment

Market Orientation

Information Infrastructure

Size and Shape of the Selling Center

Extensivity

Vertical Involvement

Lateral Involvement

Nature of the Selling Task

Environmental Complexity

Goal Achievement

Customer Responsiveness

Perceived Effectiveness

Perceived Conflict Level

Psycho-Social Outcomes

Situational Dimensions Structural and Process Dimensions Outcome Dimensions

Shared Leadership•Transactional

• Transformational

• Directive

• Empowering

• Social Supportive

Selling Task Characteristic•Interdependence

•Complexity

Vertical Leadership Role•Team Design

•Facilitative

•Boundary Management

•Contingent

Team Characteristics•Ability

•Proximity

•Maturity

•Diversity

•Size

Behavioral Responses

• Effort

• Communication

• Citizenship Behavior

Affective/Cognitive Responses•Commitment

•Satisfaction

•Potency

•Cohesiveness

Team EffectivenessQualitative

• Team Self Ratings

• Manager Rating

• Customer Rating

Quantitative• Sales Volume

• Profitability

20

3

App

endi

x D

: Lis

t of I

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Inte

rvie

w d

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Com

pany

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Com

pany

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ame

Posi

tion

Dat

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Indu

stry

T

otal

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s (2

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205

Appendix E: Interview Questionnaire

Interview questionnaire

Meeting with Mr./Ms. XXXX, COMPANY, XX.XX.2006 1. Global account management activities at COMPANY Please describe briefly the current set-up of global account management at COMPANY:

• How many customers have special status as global key accounts? How many people at COMPANY are dedicated to these customers?

• What share of COMPANY’s total sales is generated by global accounts? • How is the GAM program integrated into the organization? Is it a separate organizational unit? How dispersed

is it geographically? Who has the profit-and-loss responsibility for the global account? 2. GAM team characteristics Please describe the structure and responsibilities of a typical GAM team at COMPANY:

• How many GAM teams does COMPANY have, and what is the average number of team members? • What are the key responsibilities of each team? • Where are the teams positioned in the organization? To whom do they report? • Where are the teams usually based geographically? Do team members work close to one another? • How are the teams structured? How many hierarchical levels and functions are represented? Why? • How diverse are the teams in terms of nationality, background and international experience? What is the

significance of these characteristics?

Please describe the factors that determine the structure and composition of a team: • What are the most important decisions to be made when a team is formed? What is COMPANY looking for in

a GAM team? • To what extent do the customer organization and customer requirements influence the design of the team?

Does the team structure reflect the customer organization? • To what extent do the existing organizational arrangements at COMPANY influence the design of the team?

3. GAM team processes Please describe the most important aspects of teamwork and the factors that facilitate and hinder it:

• How do team members communicate with one another, and how often do they meet? • What mechanisms do they use to coordinate work within the team and with people outside the team? Any

support systems? • How does COMPANY ensure commitment and collaboration within the teams; between the teams and other

parts of the organization; between the teams and the customers? • What are the most common types of conflicts/problems? Why do they arise? How are they usually resolved? • How does a team’s structure and composition influence its ability to perform its tasks effectively? • What are the key factors facilitating team functioning? • What are the key factors hindering team functioning?

4. Performance criteria Please describe the most important team performance criteria:

• Does COMPANY measure team performance? How (key performance indicators)? • How are team members rewarded (incentive/remuneration systems)? • What does a high-performing team look like? • What does a low-performing team look like?

Note: We would greatly appreciate any additional soft- or hardcopy materials that would extend and inform the research process. All information provided during interviews or in any other form will be treated as strictly confidential.

Thank you for your valuable support!

206

Appendix F: Survey questionnaire

COMPANY logo

Questionnaire

High-Performance Global Account Management Teams

Dear colleagues, Thank you for taking the time to complete this survey. Your contribution is very important. The purpose of this survey As part of our commitment to continuously improving our global customer relationships, we are conducting this study, together with the University of St. Gallen (Switzerland), to evaluate the factors that can enhance the performance of global account management (GAM) teams. Benefits Your responses to this questionnaire will provide us with valuable feedback about how to optimize our GAM team processes and achieve higher levels of customer excellence. Furthermore, as you reflect on the questions, you likely will obtain new ideas to improve your own daily work with global customers.

The information you provide will be treated as strictly confidential. All analyses will be conducted on an aggregate level with no reference to individual responses. Definition For the purposes of this questionnaire, we define a global account management team as all persons involved in developing and maintaining relationships with one or a few related key customers on a worldwide basis. The team may include a core team fully dedicated to the customer as well as an extended team whose members participate on a part-time basis for specific support tasks.

Thank you for your cooperation!

NAME Yana Atanasova POSITION University of St. Gallen

Instructions Please answer all questions with reference to your own GAM team. If you are not dedicated

to a single team, please consider the team with which you work most often or are most familiar.

The whole process will take you about 15 minutes. Please note that you cannot save your answers and come back, so please be prepared to finish the survey in one sitting.

If you have any questions regarding the questionnaire, please contact Yana Atanasova (E-mail: yana.atanasova@unisg.ch, Phone: +41 76 389 2262, Fax: +41 71 224 2447; University of St. Gallen, FIM-HSG, Dufourstrasse 40a, 9000 St. Gallen, Switzerland).

207

Team information Which account team are you evaluating? (Please indicate the name of the customer or the team leader) 1. Structure and tasks 1.1 Our GAM team has well-defined goals and objectives related to our

global customers.

1.2 Our team’s goals and objectives are well aligned with our overall corporate strategies.

1.3 Team members’ individual objectives and targets are linked to GAM team objectives.

1.4 The roles and responsibilities of team members are clearly defined.

1.5 The roles and responsibilities of team members are understood across the organization.

1.6 The team has a workable structure and clear reporting lines.

1.7 The team is well positioned and integrated in the overall organizational structure of COMPANY.

1.8 The team structure provides appropriate cross-geographical coverage for our customers.

1.9 The team structure provides appropriate cross-functional coverage for our customers.

1.10 The team structure provides appropriate cross-divisional coverage for our customers.

1.11 Our team has sufficient authority to make important decisions about our customer business.

1.12 Our team has sufficient authority to change organizational routines to achieve better results for our customers.

1.13 Our team has the resources required to innovate and develop our global customer relationships continuously.

1.14 The number of people in our team is sufficient for our customer business to be developed efficiently.

2. Skills, competencies, and leadership 2.1 Team members vary widely in their areas of expertise.

2.2 Team members have a variety of backgrounds and experiences.

2.3 Team members have complementary skills and abilities.

The account/sales managers on our team 2.4 … are capable of building strong and trusting relationships.

2.5 … have a good understanding of our customer’s business and organization.

1 2 3 4 5

1 2 3 4 5

Strongly disagree

Strongly agree Disagree Agree

Neutral

Please answer all questions with reference to your own GAM team. If you work with more than one team, please consider the team with which you work most often or are most familiar.

Strongly disagree

Strongly agree Disagree Agree

Neutral

208

2.6 … have a good understanding of our business and the internal capabilities of COMPANY.

2.7 … are able to think and work in an interdisciplinary way.

2.8 … are able to coordinate complex networks and activities.

2.9 … are able to think creatively to deliver value to the customer.

2.10 … are able to work in a diverse and multicultural environment.

2.11 … employ strategic, long-term thinking.

2.12 … possess powers of persuasion.

Our team leader 2.13 … has substantial influence in the organization (even when

he/she has no formal authority).

2.14 … has a strong relationship with top management.

2.15 … is able to motivate team members and create synergies within the team.

3. Organizational context 3.1 Our top management is actively involved in the team’s efforts to

develop profitable, long-term relationships with global customers.

3.2 Our top management is committed to deploy the necessary resources to make our global customer operations succeed.

3.3 Our top management publicly promotes our team’s GAM activities to others in the organization.

3.4 Team members’ contributions to developing global customers are measured in a systematic and transparent manner.

3.5 Our compensation system promotes a global collaboration mindset by appropriately rewarding contributions to the GAM team.

3.6 Many professional rewards (e.g., pay, promotions) are determined in large part by team members’ performance on the GAM team.

3.7 The company provides adequate global selling and negotiation training for our team.

3.8 The company provides adequate team skills training for our team (e.g., communication, organization, interpersonal).

3.9 The company provides adequate technical training for our team.

4. Processes and behaviors 4.1 Our team regularly exchanges best practices and market

knowledge with other GAM teams.

4.2 Team members communicate proactively on issues related to our GAM activities across boundaries and hierarchical levels in the entire organization.

4.3 Our team has difficulty obtaining support from other parts of the organization.

4.4 Communication in the team is effective, despite the geographical distance of team members.

4.5 Team members keep one another updated about their activities and key issues affecting the business.

4.6 Team members are good at coordinating their efforts to serve the customer efficiently.

1 2 3 4 5

1 2 3 4 5

Strongly disagree

Strongly agree Disagree

Neutral

Strongly disagree

Strongly agree Disagree Agree

Neutral

Agree

209

4.7 Team members collaborate to achieve our global goals.

4.8 Disputes between the different units represented on our team make it difficult to do our work.

4.9 Our team is able to identify and resolve conflicts in a timely and effective manner.

4.10 Our team can easily send problems up the chain of command (escalate to senior management) when they cannot be resolved within the team.

4.11 The negative politics within the team are minimal.

4.12 Team members proactively cultivate new business opportunities.

4.13 Our team is not afraid to challenge the status quo to improve our customer relationships.

4.14 The team is a powerful force for constructive change in the organization.

5. Outcomes 5.1 Our team is characterized by strong and harmonious long-term

relationships with global customers.

5.2 Our customers are satisfied with the overall performance of our team.

5.3 Our team provides real value to our customers.

5.4 Our team has successfully learned some critical skills or capabilities from our customer relationships.

5.5 COMPANY’s competitive position has been enhanced due to our team’s GAM achievements.

5.6 Our team achieves its goals and objectives.

How has your team, over the past three years, performed with respect to

5.7 … growth in sales?

5.8 … profitability?

5.9 … growth in the share of your global customers’ wallets?

1 2 3 4 5

1 2 3 4 5 Poor Outstanding

Strongly disagree

Strongly agree Disagree Agree

Neutral

210

Background information

What is your position at COMPANY?

What is your position/role within the GAM team?

How long have you been involved in this team?

How long have you been working at COMPANY (in years)?

What is your functional background?

In which country are you based?

What is your gender?

How old are you?

Additional comments/suggestions for improvement

30 or less 31-40 41-50 51-60 Above 60

Female Male

Sales

Marketing

Service

General Management

Manufacturing / Operations

Engineering

Research and Development

Finance

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Purchasing Other

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.23

.19

.21

.23

.03

.09

.11

.22

.22

.22

.25

.28

.20

.14

.19

.18

4.1

Exte

rnal

com

mun

icat

ion

13.

47.9

0.2

2.2

4.3

1.2

0.1

6.2

1.2

9.1

3.1

5.2

7.1

7.1

9.3

3.3

1.0

0.0

1.0

7.1

6.1

6.1

7.1

6.1

8.0

7.1

3.2

1.1

0.2

8.3

4.2

5.3

6.3

3.3

1.2

4.2

2.1

54.

2 Ex

tern

al c

omm

unic

atio

n 2

3.52

.79

.34

.18

.30

.32

.33

.25

.45

.28

.39

.37

.28

.28

.36

.29

.12

.16

.31

.30

.31

.26

.37

.47

.39

.36

.37

.34

.41

.25

.29

.37

.36

.40

.36

.27

.26

4.3

Exte

rnal

com

mun

icat

ion

32.

99.9

5.2

1.1

5.3

0.2

5.3

9.2

9.3

8.3

4.2

1.1

6.3

8.3

1.3

1.2

1.0

9.1

0.1

6.4

1.3

0.2

0.2

3.3

0.2

7.2

3.3

0.2

8.3

4.2

5.2

4.3

3.3

2.3

6.2

7.2

2.2

64.

4 In

tern

al c

omm

unic

atio

n 1

3.66

.75

.36

.31

.21

.38

.21

.23

.21

.20

.24

.18

.09

.01

.09

.10

.04

.05

.15

.29

.18

.19

.22

.19

.18

.27

.22

.24

.26

.16

.33

.27

.28

.22

.22

.11

.14

4.5

Inte

rnal

com

mun

icat

ion

23.

66.7

8.2

7.1

9.1

9.3

3.3

2.1

7.3

2.1

6.3

1.3

2.1

4.0

9.2

1.1

1.0

7.1

1.2

3.2

9.2

1.2

2.2

7.3

6.3

2.3

3.3

9.3

2.2

8.1

8.2

0.1

5.2

2.1

3.2

3.1

6.1

54.

6 In

tern

al c

omm

unic

atio

n 3

3.69

.71

.24

.10

.28

.44

.37

.26

.34

.21

.40

.28

.28

.22

.22

.15

.15

.11

.23

.36

.34

.24

.21

.34

.38

.34

.39

.35

.28

.31

.31

.26

.30

.31

.34

.24

.29

4.7

Inte

rnal

com

mun

icat

ion

43.

73.7

1.2

1.2

0.2

9.2

3.3

0.2

1.4

4.3

0.4

0.4

4.2

8.3

0.4

0.3

4.0

9.0

5.3

2.2

8.2

6.3

1.3

4.3

6.3

2.2

4.4

3.3

2.2

8.2

8.3

2.4

3.3

6.3

7.4

0.2

9.2

24.

8 C

onfli

ct re

solu

tion

13.

57.9

3.0

1-.0

4.0

4.0

1.1

7.1

9.1

8.2

2.1

8.1

2.2

5.2

0.1

8.1

0.0

3.0

7.0

8.3

1.1

6.1

0.1

5.1

3.0

4.1

3.1

1.1

3.1

7.1

0.1

8.0

7.1

3.1

5-.0

2-.0

7-.0

54.

9 C

onfli

ct re

solu

tion

23.

73.6

4.2

7.1

6.1

5.3

2.3

4.2

7.4

2.3

0.3

9.3

0.4

3.3

2.4

1.2

7.1

1.1

5.2

2.4

9.3

7.3

1.4

0.3

4.3

1.3

2.4

3.3

9.3

7.2

7.4

6.3

5.3

5.2

7.2

2.2

2.2

54.

10 C

onfli

ct re

solu

tion

33.

89.7

4.3

0.2

8.2

4.3

3.2

8.3

4.4

0.3

1.2

7.2

5.3

7.3

4.2

9.2

0.1

7.1

3.1

9.3

4.2

2.3

4.3

6.3

4.2

6.1

8.2

4.3

3.3

7.4

2.4

5.4

6.3

8.3

5.2

5.1

8.2

74.

11 C

onfli

ct re

solu

tion

43.

92.7

8.1

7.1

0.1

9.1

0.1

4.2

0.1

9.3

6.2

6.2

0.2

0.2

1.2

3.2

6.0

6.0

2.2

2.2

6.3

0.2

5.3

1.2

5.2

4.1

2.2

6.2

1.1

3.1

9.3

1.2

2.1

8.2

0.1

9.1

5.1

34.

12 P

roac

tiven

ess 1

3.87

.74

.31

.28

.25

.23

.28

.11

.22

.16

.39

.34

.29

.19

.29

.19

.20

.24

.28

.30

.35

.26

.23

.26

.36

.27

.36

.43

.22

.23

.27

.20

.23

.17

.16

.24

.24

4.13

Pro

activ

enes

s 23.

94.7

1.2

3.1

4.2

0.2

3.2

3.1

8.2

9.2

7.3

1.2

9.3

0.2

2.3

1.1

3.1

8.2

6.2

9.3

7.2

7.2

1.2

6.2

4.4

5.3

5.3

9.4

1.2

2.2

4.3

3.1

8.1

6.2

1.1

7.2

6.2

34.

14 P

roac

tiven

ess 3

3.53

.98

.28

.20

.31

.29

.33

.12

.43

.20

.24

.22

.42

.32

.36

.20

.13

.14

.31

.46

.30

.23

.32

.35

.30

.34

.40

.50

.42

.43

.40

.40

.41

.53

.35

.36

.47

5.1

Rel

atio

nal p

erfo

rman

ce 1

3.82

.87

.33

.27

.26

.30

.36

.25

.34

.34

.33

.30

.45

.41

.43

.28

.07

.10

.22

.46

.36

.26

.40

.34

.34

.36

.47

.47

.45

.48

.52

.40

.37

.37

.31

.31

.40

5.2

Rel

atio

nal p

erfo

rman

ce 2

3.85

.79

.27

.19

.19

.21

.32

.24

.34

.35

.36

.31

.41

.40

.39

.30

.11

.07

.26

.40

.33

.31

.38

.35

.31

.25

.36

.39

.37

.40

.43

.42

.38

.34

.26

.31

.33

5.3

Rel

atio

nal p

erfo

rman

ce 3

3.99

.76

.32

.24

.19

.21

.25

.22

.28

.32

.40

.33

.31

.30

.36

.26

.13

.09

.22

.31

.14

.23

.35

.25

.28

.17

.28

.47

.37

.37

.48

.42

.37

.33

.26

.22

.35

5.4

Rel

atio

nal p

erfo

rman

ce 4

4.11

.67

.28

.19

.16

.22

.26

.21

.23

.32

.41

.28

.27

.31

.32

.25

.18

.11

.27

.36

.27

.32

.39

.34

.35

.25

.31

.36

.36

.34

.45

.33

.27

.28

.27

.24

.32

5.5

Rel

atio

nal p

erfo

rman

ce 5

3.97

.89

.34

.28

.27

.23

.12

.18

.29

.35

.35

.24

.27

.21

.37

.29

.14

.12

.17

.27

.18

.27

.25

.23

.16

.16

.23

.23

.41

.45

.44

.52

.37

.38

.27

.18

.31

5.6

Rel

atio

nal p

erfo

rman

ce 6

3.81

.84

.37

.27

.30

.26

.38

.28

.40

.26

.31

.24

.31

.15

.27

.12

.12

.12

.21

.39

.34

.32

.35

.34

.37

.40

.31

.31

.36

.43

.41

.37

.29

.32

.28

.26

.28

5.7

Fina

ncia

l per

form

ance

13.

94.8

7.3

0.2

3.2

5.2

2.2

3.1

8.1

9.1

9.0

9.0

8.2

3.1

5.1

8.0

6.0

1-.0

7.0

8.3

4.2

1.1

5.1

8.1

7.2

1.2

1.2

2.2

2.3

1.3

7.2

8.2

4.2

8.3

0.2

2.1

9.3

15.

8 Fi

nanc

ial p

erfo

rman

ce 2

3.65

.84

.39

.28

.25

.20

.19

.24

.30

.25

.19

.14

.38

.19

.18

.10

.13

.07

.07

.31

.24

.15

.18

.18

.30

.24

.26

.30

.37

.43

.36

.28

.29

.25

.19

.26

.34

5.9

Fina

ncia

l per

form

ance

33.

73.8

6.2

9.2

9.2

1.1

5.1

7.2

0.1

2.2

3.2

0.1

5.2

8.1

7.2

2.1

6.1

1-.0

4.0

9.2

2.1

5.0

9.1

9.1

8.1

8.2

0.1

9.2

0.3

0.4

0.3

3.2

9.2

8.3

5.2

4.1

4.2

5

Not

e: C

orre

latio

ns o

f .16

are

sign

ifica

nt a

t the

.05

leve

l (tw

o-ta

iled)

. Lar

ge c

orre

latio

ns (>

.50)

are

indi

cate

d in

bol

d.

21

2

Mea

ns, s

tand

ard

devi

atio

ns a

nd c

orre

latio

ns (c

ontin

ued)

V

aria

ble

Mea

nSD

3.7

3.8

3.9

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

4.10

4.11

4.12

4.13

4.14

5.1

5.2

5.3

5.4

5.5

5.6

5.7

5.8

3.8

Trai

ning

23.

40.8

8.6

13.

9 Tr

aini

ng 3

3.47

.84

.42

.62

4.1

Exte

rnal

com

mun

icat

ion

13.

47.9

0.1

9.3

0.3

14.

2 Ex

tern

al c

omm

unic

atio

n 2

3.52

.79

.20

.31

.29

.48

4.3

Exte

rnal

com

mun

icat

ion

32.

99.9

5.1

5.1

2.0

4.0

6.3

24.

4 In

tern

al c

omm

unic

atio

n 1

3.66

.75

.19

.32

.28

.29

.38

.05

4.5

Inte

rnal

com

mun

icat

ion

23.

66.7

8.0

6.1

6.1

3.2

2.4

8.0

8.5

54.

6 In

tern

al c

omm

unic

atio

n 3

3.69

.71

.27

.32

.24

.23

.50

.20

.46

.57

4.7

Inte

rnal

com

mun

icat

ion

43.

73.7

1.2

8.3

5.2

9.3

0.5

1.1

6.3

2.4

1.4

44.

8 C

onfli

ct re

solu

tion

13.

57.9

3-.0

3-.0

8-.1

3-.0

6.0

0.5

2.0

0.0

2.1

1.1

04.

9 C

onfli

ct re

solu

tion

23.

73.6

4.1

6.2

8.2

1.2

4.3

3.3

1.3

2.3

7.4

2.3

7.3

04.

10 C

onfli

ct re

solu

tion

33.

89.7

4.2

4.3

0.2

7.3

6.3

6.2

1.2

9.2

7.3

9.3

7.2

0.4

54.

11 C

onfli

ct re

solu

tion

43.

92.7

8.3

5.3

7.2

4.0

9.2

2.2

1.1

8.1

2.3

5.4

0.2

8.4

3.3

24.

12 P

roac

tiven

ess 1

3.87

.74

.12

.30

.14

.30

.33

.15

.32

.34

.37

.33

.00

.33

.30

.37

4.13

Pro

activ

enes

s 23.

94.7

1.1

4.2

9.1

7.1

2.3

0.2

4.2

2.3

0.3

6.2

7.1

0.3

8.3

5.3

5.5

54.

14 P

roac

tiven

ess 3

3.53

.98

.21

.23

.08

.27

.40

.35

.24

.24

.35

.33

.16

.41

.38

.18

.42

.51

5.1

Rel

atio

nal p

erfo

rman

ce 1

3.82

.87

.28

.25

.10

.40

.32

.32

.29

.21

.38

.33

.23

.46

.36

.34

.45

.45

.59

5.2

Rel

atio

nal p

erfo

rman

ce 2

3.85

.79

.24

.24

.19

.36

.28

.33

.23

.12

.34

.32

.23

.53

.39

.35

.40

.38

.45

.68

5.3

Rel

atio

nal p

erfo

rman

ce 3

3.99

.76

.22

.21

.14

.19

.31

.23

.21

.15

.27

.35

.09

.44

.43

.26

.42

.42

.52

.62

.62

5.4

Rel

atio

nal p

erfo

rman

ce 4

4.11

.67

.29

.33

.21

.23

.35

.21

.27

.23

.37

.37

.12

.47

.38

.39

.40

.44

.36

.58

.56

.64

5.5

Rel

atio

nal p

erfo

rman

ce 5

3.97

.89

.20

.21

.24

.33

.36

.27

.20

.09

.30

.31

.07

.32

.37

.29

.28

.43

.48

.54

.50

.59

.53

5.6

Rel

atio

nal p

erfo

rman

ce 6

3.81

.84

.21

.18

.04

.28

.39

.34

.32

.24

.41

.42

.13

.46

.37

.30

.38

.43

.47

.53

.52

.50

.50

.57

5.7

Fina

ncia

l per

form

ance

13.

94.8

7.1

8.1

4.0

6.2

1.2

5.4

1.2

5.1

2.2

9.1

7.1

7.2

3.2

3.1

0.3

2.3

5.5

1.5

1.4

2.4

1.3

6.3

8.5

25.

8 Fi

nanc

ial p

erfo

rman

ce 2

3.65

.84

.20

.20

.17

.25

.32

.34

.24

.13

.34

.21

.13

.41

.32

.23

.34

.30

.38

.49

.48

.47

.42

.36

.53

.64

5.9

Fina

ncia

l per

form

ance

33.

73.8

6.1

9.1

6.1

1.2

3.3

2.3

3.2

0.0

8.2

7.2

4.1

3.2

4.2

7.1

5.2

4.2

9.4

7.5

0.4

3.4

7.3

9.4

0.5

7.7

4.5

6

Not

e: C

orre

latio

ns o

f .16

are

sign

ifica

nt a

t the

.05

leve

l (tw

o-ta

iled)

. Lar

ge c

orre

latio

ns (>

.50)

are

indi

cate

d in

bol

d.

21

3

App

endi

x H

: CFA

resu

lts fo

r the

firs

t-ord

er c

onst

ruct

s Fa

ctor

s and

item

s St

anda

rdiz

ed

load

ing

t-va

lue

Goa

ls a

nd r

oles

1.

1 O

ur G

AM

team

has

wel

l-def

ined

goa

ls a

nd o

bjec

tives

rela

ted

to o

ur g

loba

l cus

tom

ers.

1.2

Our

team

’s g

oals

and

obj

ectiv

es a

re w

ell a

ligne

d w

ith o

ur o

vera

ll co

rpor

ate

stra

tegi

es.

1.4

The

role

s and

resp

onsi

bilit

ies o

f tea

m m

embe

rs a

re c

lear

ly d

efin

ed.

1.6

The

team

has

a w

orka

ble

stru

ctur

e an

d cl

ear r

epor

ting

lines

. C

usto

mer

cov

erag

e 1.

8 Th

e te

am st

ruct

ure

prov

ides

app

ropr

iate

cro

ss-g

eogr

aphi

cal c

over

age

for o

ur c

usto

mer

s. 1.

9 Th

e te

am st

ruct

ure

prov

ides

app

ropr

iate

cro

ss-f

unct

iona

l cov

erag

e fo

r our

cus

tom

ers.

1.10

The

team

stru

ctur

e pr

ovid

es a

ppro

pria

te c

ross

-div

isio

nal c

over

age

for o

ur c

usto

mer

s. E

mpo

wer

men

t 1.

11 O

ur te

am h

as su

ffic

ient

aut

horit

y to

mak

e im

porta

nt d

ecis

ions

abo

ut o

ur c

usto

mer

bus

ines

s. 1.

12 O

ur te

am h

as su

ffic

ient

aut

horit

y to

cha

nge

orga

niza

tiona

l rou

tines

to a

chie

ve b

ette

r res

ults

for o

ur c

usto

mer

s.

1.13

Our

team

has

the

reso

urce

s req

uire

d to

inno

vate

and

dev

elop

our

glo

bal c

usto

mer

rela

tions

hips

con

tinuo

usly

. H

eter

ogen

eity

2.

1 Te

am m

embe

rs v

ary

wid

ely

in th

eir a

reas

of e

xper

tise.

2.

2 Te

am m

embe

rs h

ave

a va

riety

of b

ackg

roun

ds a

nd e

xper

ienc

es.

Ade

quat

e sk

ills

The

acco

unt/s

ales

man

ager

s on

our t

eam

2.

4 …

are

cap

able

of b

uild

ing

stro

ng a

nd tr

ustin

g re

latio

nshi

ps.

2.5

… h

ave

a go

od u

nder

stan

ding

of o

ur c

usto

mer

’s b

usin

ess a

nd o

rgan

izat

ion.

2.

7 …

are

abl

e to

thin

k an

d w

ork

in a

n in

terd

isci

plin

ary

way

. 2.

9 …

are

abl

e to

thin

k cr

eativ

ely

to d

eliv

er v

alue

to th

e cu

stom

er.

2.10

… a

re a

ble

to w

ork

in a

div

erse

and

mul

ticul

tura

l env

ironm

ent.

2.11

… e

mpl

oy st

rate

gic,

long

-term

thin

king

. 2.

12 …

pos

sess

pow

ers o

f per

suas

ion.

L

eade

rshi

p 2.

13 O

ur te

am le

ader

has

subs

tant

ial i

nflu

ence

in th

e or

gani

zatio

n (e

ven

whe

n he

/she

has

no

form

al a

utho

rity)

. 2.

14 O

ur te

am le

ader

has

a st

rong

rela

tions

hip

with

top

man

agem

ent.

T

op m

anag

emen

t sup

port

3.

1 O

ur to

p m

anag

emen

t is a

ctiv

ely

invo

lved

in th

e te

am’s

eff

orts

to d

evel

op p

rofit

able

, lon

g-te

rm re

latio

nshi

ps w

ith g

loba

l cus

tom

ers.

3.2

Our

top

man

agem

ent i

s com

mitt

ed to

dep

loyi

ng th

e ne

cess

ary

reso

urce

s to

mak

e ou

r glo

bal c

usto

mer

ope

ratio

ns su

ccee

d.

3.3

Our

top

man

agem

ent p

ublic

ly p

rom

otes

our

team

’s G

AM

act

iviti

es to

oth

ers i

n th

e or

gani

zatio

n.

Rew

ards

and

ince

ntiv

es

3.4

Team

mem

bers

’ con

tribu

tions

to d

evel

opin

g gl

obal

cus

tom

ers a

re m

easu

red

in a

syst

emat

ic a

nd tr

ansp

aren

t man

ner.

3.5

Our

com

pens

atio

n sy

stem

pro

mot

es g

loba

l col

labo

ratio

n by

app

ropr

iate

ly re

war

ding

con

tribu

tions

to th

e G

AM

team

. 3.

6 M

any

prof

essi

onal

rew

ards

(e.g

., pa

y, p

rom

otio

ns) a

re d

eter

min

ed in

larg

e pa

rt by

team

mem

bers

’ per

form

ance

on

the

GA

M te

am.

.5

84

.457

.6

95

.680

.671

.8

67

.787

.884

.7

66

.705

.671

.9

54

.6

95

.645

.6

76

.724

.6

22

.763

.7

12

.9

58

.722

.721

.7

62

.763

.681

.8

09

.757

7

.194

5

.854

7

.821

-

10.7

60

12.8

15

- 9

.233

8

.562

- -

3.3

76

10

.422

9

.688

10

.186

10

.868

9

.408

11

.396

- -

7.2

02

10

.570

10

.971

-

10.2

41

11.5

94

-

21

4

Tra

inin

g 3.

7 Th

e co

mpa

ny p

rovi

des a

dequ

ate

glob

al se

lling

and

neg

otia

tion

train

ing

for o

ur te

am.

3.8

The

com

pany

pro

vide

s ade

quat

e te

am sk

ills t

rain

ing

for o

ur te

am (e

.g.,

com

mun

icat

ion,

org

aniz

atio

n, in

terp

erso

nal).

3.

9 Th

e co

mpa

ny p

rovi

des a

dequ

ate

tech

nica

l tra

inin

g fo

r our

team

. C

omm

unic

atio

n an

d co

llabo

ratio

n 4.

1 O

ur te

am re

gula

rly e

xcha

nges

bes

t pra

ctic

es a

nd m

arke

t kno

wle

dge

with

oth

er G

AM

team

s. 4.

2 Te

am m

embe

rs c

omm

unic

ate

proa

ctiv

ely

on is

sues

rela

ted

to o

ur G

AM

act

iviti

es a

cros

s bou

ndar

ies a

nd h

iera

rchi

cal l

evel

s in

the

entir

e or

gani

zatio

n.

4.4

Com

mun

icat

ion

in th

e te

am is

eff

ectiv

e, d

espi

te th

e ge

ogra

phic

al d

ista

nce

of te

am m

embe

rs.

4.5

Team

mem

bers

kee

p on

e an

othe

r upd

ated

abo

ut th

eir a

ctiv

ities

and

key

issu

es a

ffec

ting

the

busi

ness

. 4.

6 Te

am m

embe

rs a

re g

ood

at c

oord

inat

ing

thei

r eff

orts

to se

rve

the

cust

omer

eff

icie

ntly

. 4.

7 Te

am m

embe

rs c

olla

bora

te to

ach

ieve

our

glo

bal g

oals

. C

onfli

ct m

anag

emen

t 4.

8 D

ispu

tes b

etw

een

the

diff

eren

t uni

ts re

pres

ente

d on

our

team

mak

e it

diff

icul

t to

do o

ur w

ork.

(rev

erse

cod

ed)

4.9

Our

team

is a

ble

to id

entif

y an

d re

solv

e co

nflic

ts in

a ti

mel

y an

d ef

fect

ive

man

ner.

4.11

The

neg

ativ

e po

litic

s with

in th

e te

am a

re m

inim

al.

Proa

ctiv

enes

s 4.

12 T

eam

mem

bers

pro

activ

ely

culti

vate

new

bus

ines

s opp

ortu

nitie

s. 4.

13 O

ur te

am is

not

afr

aid

to c

halle

nge

the

stat

us q

uo to

impr

ove

our c

usto

mer

rela

tions

hips

. 4.

14 T

he te

am is

a p

ower

ful f

orce

for c

onst

ruct

ive

chan

ge in

the

orga

niza

tion.

.7

04

.889

.6

47

.4

40

.660

.5

80

.647

.6

76

.727

.438

.6

60

.610

.664

.7

01

.652

9.51

3

9.68

3 -

6.35

3 9.

421

8.28

5 9.

204

9.62

8 -

5.24

0 6.

484

- 8.

077

8.29

4 -

Not

es: A

ll fa

ctor

load

ings

are

sign

ifica

nt a

t the

.01

leve

l.

-

indi

cate

s a fi

xed

para

met

er.

21

5

App

endi

x I:

Full

stru

ctur

al p

ath

mod

el

Not

e: n

s = n

ot si

gnifi

cant

. All

othe

r coe

ffic

ient

s are

sign

ifica

nt a

t the

.01

leve

l.

1.1

1.2

1.4

1.6

Rol

es a

nd g

oals

1.8

1.9

1.10

Cus

tom

erco

vera

ge

1.111.

12

1.13

Empo

wer

men

t

Tea

m d

esig

n

2.1

2.2

Het

erog

enei

ty

2.9

2.10

Skill

s2.

13

2.14

Lead

ersh

ip

2.4

2.5

2.7

2.11

2.12

3.1

3.2

3.3

TM su

ppor

t

3.4

3.5

3.6

Rew

ards

3.7

3.8

3.9

Trai

ningO

rgan

izat

iona

lC

onte

xt

4.1

4.2

4.4

Com

mun

icat

ion/

colla

bora

tion

4.8

4.9

4.11

Con

flict

man

agem

ent

4.12

4.13

4.14

Proa

ctiv

enes

s

Tea

m P

roce

sses

5.1

5.2

5.3

Rel

atio

nal

perf

orm

ance

5.4

5.5

5.7

5.8

Fina

ncia

lpe

rfor

man

ce5.

9

4.5

4.6

4.7

.826

.963

-.035

ns

-.016

ns

.850

.674

.604

.450 .701

.660.6

68.8

64.7

92

.707

.773

.870

.722

.886

.706

.643

.671

.723

.612

.761

.718

.907 .763

.655.598

.658

.287

.705

.598

.728

.769

.750

.684

.792 .770

.695

.898

.648

.834 .827

.507

.473

.676

.571

.620

.671

.728

.546

.703

.529

.624

.660

.714

.783

.768

.904

.740 .7

49

.781

.677 .677

.849 .7

27 .737

1.1

1.2

1.4

1.6

Rol

es a

nd g

oals

1.8

1.9

1.10

Cus

tom

erco

vera

ge

1.111.

12

1.13

Empo

wer

men

t

Tea

m d

esig

n

2.1

2.2

Het

erog

enei

ty

2.9

2.10

Skill

s2.

13

2.14

Lead

ersh

ip

2.4

2.5

2.7

2.11

2.12

3.1

3.2

3.3

TM su

ppor

t

3.4

3.5

3.6

Rew

ards

3.7

3.8

3.9

Trai

ningO

rgan

izat

iona

lC

onte

xt

4.1

4.2

4.4

Com

mun

icat

ion/

colla

bora

tion

4.8

4.9

4.11

Con

flict

man

agem

ent

4.12

4.13

4.14

Proa

ctiv

enes

s

Tea

m P

roce

sses

5.1

5.2

5.3

Rel

atio

nal

perf

orm

ance

5.4

5.5

5.7

5.8

Fina

ncia

lpe

rfor

man

ce5.

9

4.5

4.6

4.7

.826

.963

-.035

ns

-.016

ns

.850

.674

.604

.450 .701

.660.6

68.8

64.7

92

.707

.773

.870

.722

.886

.706

.643

.671

.723

.612

.761

.718

.907 .763

.655.598

.658

.287

.705

.598

.728

.769

.750

.684

.792 .770

.695

.898

.648

.834 .827

.507

.473

.676

.571

.620

.671

.728

.546

.703

.529

.624

.660

.714

.783

.768

.904

.740 .7

49

.781

.677 .677

.849 .7

27 .737

Curriculum Vitae Name Yana Atanasova

Date of Birth 5 January 1979

Nationality Bulgarian

Education 2003-2007 University of St. Gallen, Switzerland

Doctor of Business Administration

2001-2003 University of St. Gallen, Switzerland Master of International Management

2002 George Washington University, USA MBA Exchange Semester

1997-2001 Sofia University, Bulgaria Bachelor of Business Administration

2000 Copenhagen Business School, Denmark Academic year abroad

Work Experience 2007 – now Credit Suisse, Zurich, Switzerland

Corporate Development, Assistant Vice President

2002-2006 Account Management Center, Zurich, Switzerland Various consulting and research projects in the area of global account management, some of which in cooperation with the Research Institute for International Management, University of St. Gallen

2005-2006 Rieter AG, Winterthur, Switzerland

Corporate Development, Part-time analyst

2004 Lehman Brothers, Zurich, Switzerland Investment Banking Division, Intern

2002 George Washington University, USA Vice President and Treasurer’s Office, Intern