Dean: Prof. Dr. Ernst E. Hildebrand

1st Examiner: Prof. Dr. Siegfried Lewark

2nd Examiner: Prof. Dr. Carl Brønn

Date of oral defense: 24 November 2006

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Abstract Increasing market globalization, the complexity of linked economic relationship structures and modern information and communication technologies have led to radical changes in the forest sector. Knowledge is increasingly becoming recognized as a crucial resource in many fields of endeavor. Increasing pressure of competition through globalization and uncertainty in the forest sector has led to an interest in the concepts of knowledge management and organizational learning. Investigating the relationships between organizational learning processes and social structures this research work wants to identify potential barriers to organizational learning and to develop strategies to overcome these barriers. The study is conducted in the context of the forest sector, using case studies. This dissertation builds on an integrated model of organizational learning. The organizational learning processes are operationalized through the OLI diagnostic tool. The informal organizational structures are revealed through a social network analysis. These methods are combined to allow new insights into the role of informal structures and processes that influence organizational learning and to extend existing organizational learning theory.

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Contents

ABSTRACT ........................................................................................................................................................... I CONTENTS ..........................................................................................................................................................II LIST OF FIGURES............................................................................................................................................ IV LIST OF TABLES................................................................................................................................................V CHAPTER 1 INTRODUCTION....................................................................................................................1

1.1 THE FOREST SECTOR ON THE MOVE .......................................................................................................1 1.2 ORGANIZATIONAL LEARNING AND SOCIAL NETWORK STRUCTURE ........................................................3 1.3 PURPOSE OF THE STUDY ........................................................................................................................5 1.4 ORGANIZATION OF THE DISSERTATION .................................................................................................5

CHAPTER 2 LITERATURE REVIEW........................................................................................................7 2.1 CHAPTER OVERVIEW .............................................................................................................................7 2.2 THEORETICAL FOUNDATIONS OF ORGANIZATIONAL LEARNING .............................................................7

2.2.1 Individual learning as the basis for organizational learning...........................................................8 2.2.2 Knowing and memory ....................................................................................................................12 2.2.3 Organizational learning theories...................................................................................................17 2.2.4 Barriers to organizational learning...............................................................................................24

2.3 THEORETICAL FOUNDATIONS OF SOCIAL NETWORK ANALYSIS ............................................................31 2.3.1 The lineage of social network analysis ..........................................................................................33 2.3.2 Introduction into social network terminology................................................................................35 2.3.3 Social network structure and organizational learning ..................................................................37

CHAPTER 3 SYNTHESIS ...........................................................................................................................44 3.1 CHAPTER OVERVIEW ...........................................................................................................................44 3.2 ORGANIZATIONAL LEARNING AND ITS DETERMINANTS .......................................................................44

3.2.1 Organizational learning characteristics ........................................................................................45 3.2.2 Social network structure and organizational learning processes ..................................................46

3.3 BUILDING A CONCEPTUAL FRAMEWORK..............................................................................................48 3.3.1 Knowledge acquisition...................................................................................................................49 3.3.2 Knowledge sharing ........................................................................................................................55 3.3.3 Knowledge utilization ....................................................................................................................60

3.4 SUMMARY OF PROPOSITIONS ...............................................................................................................66 CHAPTER 4 METHODS .............................................................................................................................67

4.1 CHAPTER OVERVIEW ...........................................................................................................................67 4.2 RESEARCH DESIGN ..............................................................................................................................67

4.2.1 Case study methodology ................................................................................................................67 4.2.3 Selection of cases ...........................................................................................................................70 4.2.4 Selection and operationalization of variables................................................................................71

4.3 DATA ANALYSIS PROCEDURES.............................................................................................................81 4.3.1 Mail questionnaire.........................................................................................................................81 4.3.2 Assessing organizational learning characteristics ........................................................................82 4.3.3 Measurement validity, reliability, error and accuracy of social network data..............................88

CHAPTER 5 EMPIRICAL RESULTS .......................................................................................................91 5.1 CHAPTER OVERVIEW ...........................................................................................................................91 5.2 SURVEY RESPONSE RATE .....................................................................................................................91 5.3 SOCIAL NETWORK ANALYSIS RESULTS ................................................................................................92

5.3.1 “Network Constraint” ...................................................................................................................92 5.3.2 “Effective Size”..............................................................................................................................93 5.3.3 “Network Structure”......................................................................................................................94

5.4 ORGANIZATIONAL LEARNING CAPABILITY RESULTS............................................................................95 5.4.1 Learning orientations ....................................................................................................................95 5.4.2 Facilitating factors ......................................................................................................................103

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CHAPTER 6 DISCUSSION AND CONCLUSIONS................................................................................115 6.1 CHAPTER OVERVIEW .........................................................................................................................115 6.2 BARRIERS TO ORGANIZATIONAL LEARNING AND ITS DETERMINANTS ................................................115

6.2.1 Determinants of barriers to knowledge acquisition.....................................................................116 6.2.2 Determinants of barriers to knowledge sharing ..........................................................................118 6.2.3 Determinants of barriers to knowledge utilization ......................................................................121

6.3 CONCLUSIONS ...................................................................................................................................124 6.3.1 Implications for use: enhancing learning capability ...................................................................126 6.3.2 Limitations ...................................................................................................................................130

6.4 MAJOR CONTRIBUTIONS OF THIS STUDY ............................................................................................132 6.4.1 Suggestions for further research..................................................................................................135

SUMMARY........................................................................................................................................................137 Purpose of the study ...................................................................................................................................137 Research design .........................................................................................................................................138 Main findings and conclusions...................................................................................................................139

KURZFASSUNG ...............................................................................................................................................141 Zielsetzung der Arbeit ................................................................................................................................142 Untersuchungsaufbau ................................................................................................................................142 Ergebnisse und Schlussfolgerungen...........................................................................................................145

REFERENCES ..................................................................................................................................................148 ACKNOWLEDGMENTS.................................................................................................................................159 APPENDIX I GERMAN MAIL QUESTIONNAIRE...............................................................................160 APPENDIX II NORWEGIAN MAIL QUESTIONNAIRE ..................................................................167

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List of Figures FIGURE 1: LEWINIAN EXPERIENTIAL LEARNING MODEL ........................................................................................11 FIGURE 2: OBSERVE-ASSESS-DESIGN-IMPLEMENT-CYCLE ....................................................................................13 FIGURE 3: INDIVIDUAL LEARNING MODEL .............................................................................................................16 FIGURE 4: LEARNING MODES..................................................................................................................................21 FIGURE 5: INTEGRATED ORGANIZATIONAL LEARNING MODEL ..............................................................................22 FIGURE 6: THE INCOMPLETE LEARNING CYCLES....................................................................................................24 FIGURE 7: BARRIERS TO ORGANIZATIONAL LEARNING ...........................................................................................26 FIGURE 8: THE FORMAL VS. THE INFORMAL STRUCTURE OF AN ORGANIZATION .....................................................32 FIGURE 9: THE LINEAGE OF SOCIAL NETWORK ANALYSIS .......................................................................................33 FIGURE 10: STRUCTURAL THEORY OF ACTION.......................................................................................................37 FIGURE 11: STRUCTURAL HOLES IN THE SOCIAL STRUCTURE OF AN ORGANIZATION ..............................................41 FIGURE 12: ENTREPRENEURIAL NETWORK .............................................................................................................42 FIGURE 13: CLIQUE NETWORK................................................................................................................................43 FIGURE 14: ORGANIZATIONAL LEARNING AND ITS DETERMINANTS........................................................................48 FIGURE 15: INCOMPLETE LEARNING CYCLE - KNOWLEDGE ACQUISITION .............................................................50 FIGURE 16: BARRIER TO KNOWLEDGE ACQUISITION - LEARNING UNDER AMBIGUITY.............................................51 FIGURE 17: BARRIER TO KNOWLEDGE ACQUISITION – SITUATIONAL LEARNING.....................................................53 FIGURE 18: BARRIER TO KNOWLEDGE SHARING – ROLE-CONSTRAINED LEARNING ................................................56 FIGURE 19: BARRIER TO KNOWLEDGE SHARING – FRAGMENTED LEARNING ..........................................................58 FIGURE 20: BARRIER TO KNOWLEDGE UTILIZATION – AUDIENCE LEARNING..........................................................60 FIGURE 21: BARRIER TO KNOWLEDGE UTILIZATION – SUPERSTITIOUS LEARNING ..................................................62 FIGURE 22: BARRIER TO KNOWLEDGE UTILIZATION – OPPORTUNISTIC LEARNING .................................................63 FIGURE 23: RESEARCH DESIGN...............................................................................................................................69 FIGURE 24: STRUCTURAL INDICATORS OF REDUNDANCY .......................................................................................83 FIGURE 25: HOLE CONDITIONS OF REDUNDANCY AND CONSTRAINT.......................................................................85

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List of Tables TABLE 1: DEFINITIONS ON “ORGANIZATIONAL LEARNING” ...................................................................................18 TABLE 2: LEARNING ORIENTATIONS.......................................................................................................................28 TABLE 3: FACILITATING FACTORS ..........................................................................................................................29 TABLE 4: BARRIERS TO KNOWLEDGE ACQUISITION ................................................................................................55 TABLE 5: BARRIERS TO KNOWLEDGE SHARING.......................................................................................................59 TABLE 6: BARRIERS TO KNOWLEDGE UTILIZATION.................................................................................................65 TABLE 7: CHARACTERISTICS OF BARRIERS TO ORGANIZATIONAL LEARNING..........................................................66 TABLE 8: VARIABLES OF ORGANIZATIONAL LEARNING CHARACTERISTICS ............................................................71 TABLE 9: NAME-GENERATOR .................................................................................................................................73 TABLE 10: OPERATIONALIZATION OF THE LEARNING ORIENTATIONS .....................................................................76 TABLE 11: OPERATIONALIZATION OF THE FACILITATING FACTORS ........................................................................80 TABLE 12: DEMOGRAPHIC VARIABLES ...................................................................................................................81 TABLE 13: RESPONSE RATE BY CASES ON TOTAL AND BY HIERARCHICAL LEVELS..................................................91 TABLE 14: MEANS AND STANDARD DEVIATIONS OF “NETWORK CONSTRAINT” .....................................................92 TABLE 15: MEANS AND STANDARD DEVIATIONS OF “EFFECTIVE SIZE”..................................................................93 TABLE 16: NUMBER OF INDIVIDUALS IN TYPES OF SOCIAL NETWORK STRUCTURE .................................................94 TABLE 17: MEANS AND STANDARD DEVIATIONS FOR “LOR1: KNOWLEDGE SOURCE”...........................................96 TABLE 18: MEANS AND STANDARD DEVIATIONS FOR “LOR2: CONTENT-PROCESS FOCUS” ...................................97 TABLE 19: MEANS AND STANDARD DEVIATIONS FOR “LOR3: DOCUMENTATION MODE” ......................................98 TABLE 20: MEANS AND STANDARD DEVIATIONS FOR “LOR4: DISSEMINATION MODE” .........................................99 TABLE 21: MEANS AND STANDARD DEVIATIONS FOR “LOR5: LEARNING FOCUS” .................................................99 TABLE 22: MEANS AND STANDARD DEVIATIONS FOR “LOR6: VALUE-CHAIN FOCUS”.........................................100 TABLE 23: MEANS AND STANDARD DEVIATIONS FOR “LOR7: SKILL DEVELOPMENT FOCUS”..............................101 TABLE 24: CROSS-TABULATION - TYPES OF SOCIAL NETWORK STRUCTURE AND LEARNING ORIENTATIONS.........102 TABLE 25: MEANS AND STANDARD DEVIATIONS FOR “FF1: SCANNING IMPERATIVE” .........................................104 TABLE 26: MEANS AND STANDARD DEVIATIONS FOR “FF2: PERFORMANCE GAP”...............................................105 TABLE 27: MEANS AND STANDARD DEVIATIONS FOR “FF3: CONCERN FOR MEASUREMENT” ..............................106 TABLE 28: MEANS AND STANDARD DEVIATIONS FOR “FF 4: EXPERIMENTAL MIND-SET” ....................................107 TABLE 29: MEANS AND STANDARD DEVIATIONS FOR “FF5: CLIMATE OF OPENNESS”..........................................107 TABLE 30: MEANS AND STANDARD DEVIATIONS FOR “FF6: CONTINUOUS EDUCATION”......................................108 TABLE 31: MEANS AND STANDARD DEVIATIONS FOR “FF 7: OPERATIONAL VARIETY”........................................109 TABLE 32: MEANS AND STANDARD DEVIATIONS FOR “FF8: MULTIPLE ADVOCATES” .........................................109 TABLE 33: MEANS AND STANDARD DEVIATIONS FOR “FF 9: INVOLVED LEADERSHIP” ........................................110 TABLE 34: MEANS AND STANDARD DEVIATIONS FOR “FF10: SYSTEMS PERSPECTIVE”........................................111 TABLE 35: CROSS-TABULATION - TYPES OF SOCIAL NETWOK STRUCTURE AND FACILITATING FACTORS ..............112 TABLE 36: DETERMINANTS OF POTENTIAL BARRIERS TO KNOWLEDGE ACQUISITION............................................116 TABLE 37: DETERMINANTS OF POTENTIAL BARRIERS TO KNOWLEDGE SHARING ..................................................119 TABLE 38: DETERMINANTS OF POTENTIAL BARRIERS TO KNOWLEDGE UTILIZATION ............................................122 TABLE 39: OVERCOMING LEARNING UNDER AMBIGUITY......................................................................................127 TABLE 40: OVERCOMING SITUTIONAL LEARNING.................................................................................................128 TABLE 41: OVERCOMING ROLE-CONSTRAINED LEARNING....................................................................................128 TABLE 42: OVERCOMING FRAGMENTED LEARNING ..............................................................................................129 TABLE 43: OVERCOMING AUDIENCE LEARNING....................................................................................................129 TABLE 44: OVERCOMING SUPERSTITIOUS LEARNING............................................................................................130 TABLE 45: OVERCOMING OPPORTUNISTIC LEARNING ...........................................................................................130

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Chapter 1 Introduction Increasing internationalization of markets, the complexity of linked economic relationship

structures, and modern information and communication technologies have led to radical

changes in many sectors of business and social life. Knowledge has become the crucial

resource that has replaced the traditional ranking of the factors of production work, land, and

capital (Al-Laham 2003, Pawlowsky 1998). At the same time, this development is

accompanied by an extremely high rate of innovation and continuously increasing

complexity of new techniques and processes. Consequently, knowledge is increasingly

recognized as a critical resource in many fields of endeavor (De Geus 1988, Nonaka 1998,

Picot et al. 2001). The forest sector is also strongly influenced by these developments.

1.1 The forest sector on the move As generally outlined above, the forest sector faces a number of serious challenges. These

challenges include changes from many different sources, such as increasing mechanization,

structural and social changes, and an increasing pressure of competition through globalization

as well as the use of enabling technologies such as information and communication

technologies.

More specifically, the increased application of advanced mechanization and high

technologies as well as outsourcing of forest work has led to a decrease in the number of

forest workers and the emergence of forest entrepreneurs and forest service organizations

(ILO 2003, Kastenholz 2004, Sachse 2002, Westermayer 2004). In Sweden, for example,

employment in the forest sector decreased by 60% from 1980 to 2000 despite an almost 30%

increase in wood production over the same time period (ILO 2003). For example in

Germany, where after the storms in the years 1990 and 1999 the number of forest contractors

has rapidly increased. During that time, organizations working in the forest sector could

realize a positive economic development and therefore invested in newer, heavier and more

advanced harvesting machines (Duffner and Ketterer 2002, Ripken 2001). The use of

mechanized harvesting systems has led to limitation of costs and also to a decrease in costs in

some fields of wood harvesting (Hecker et al. 1998).

Beside the issues named above, the forest sector is finding itself compelled to react to

mounting pressures resulting from globalization (ILO 2001). This is manifested through

increased competition and the demand for sustainable development from governments and

customers. This requires new approaches to management and transformation of natural

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resources (Borgschulte 2002). At the same time, due to technical innovations and linked

economic relationship structures, the situation is influenced by internationalization and

globalization processes in the wood procurement organizations and pulp and paper industry

(Becker 2004, Hecker et al. 1998, Pfleiderer 1998). These have led and will continue to lead

to mergers and concentration processes in the forest sector and to increasing pressure of

competition.

The forest sector is also facing an increasing importance of new technologies. The enabling

technologies such as transportation, communication and information technologies have

played an important role in increasing productivity and globalization of the forest sector.

Information and communication technologies have enormously improved the availability of

information regarding markets and facilitated communication with distant and wide-spread

suppliers as well as the information transfer within organizations. The development and use

of transportation, communication and information technologies enable new products and

service opportunities. However, the communication and networking enabled by digital

technologies have significantly changed and continue to change the business environment as

well as the practices of business and various services (Frank 2005, Hug 2004, Scheuber 2003,

Werners 2001).

The forces caused by increased mechanization, use of enabling technologies and

internationalization of markets will drive organizations in the forest sector to develop

innovations and promote the spread of new knowledge. In order to succeed in this new

economy based on knowledge, the forest sector needs to be innovative and develop new

competencies. One important step in this direction is to focus on knowledge management

and organizational learning. Given the increasing complexity and uncertainty of the

organizational environment an increasing interest for the concept of learning organization has

been taken place in forestry (Uerpmann 2005).

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1.2 Organizational learning and social network structure It is safe to say that in the future organizations will be confronted with an environment of

increasing complexity and dynamics. In order to successfully meet these challenges, a

“learning organization” not only has to develop its individual and collective problem solving

capacity, but also to strengthen its organizational learning processes as a capability for the

realization of competitive advantages.

The magnitude of change occurring in organizations today has led to a deep interest in the

concepts of knowledge management and organizational learning. Organizational learning

has long been an issue of both practical and theoretical concern (Huber 1991, Mirvis 1996).

The first references to organizational learning appeared as far back as in the early 1960s

(Cyert and March 1963). Cyert and March proposed a general theory of organizational

learning as part of a model of decision making within a firm. They emphasized the role of

rules, procedures, and routines in response to external shocks. Those that are more or less

likely to be adopted according to whether or not they lead to positive consequences for the

organization. Kim argued that,

“[a]ll organizations learn, whether they consciously choose to or not – it is a fundamental requirement for their sustained existence. Some firms deliberately advance organizational learning, developing capabilities that are consistent with their objectives; others make no focused effort and, therefore, acquire habits that are counterproductive. Nonetheless, all organizations learn” (Kim 1993, p. 37).

Argyris and Schön defined organizational learning as the process that leads to possible

change in organizational practices, based on the development of the knowledge of the actors

executing those practices (Argyris and Schön 1978). According to Schreyögg,

“[o]rganisatorisches Lernen ist […] der Prozess, in dem Organisationen Wissen erwerben, in ihrer Wissensbasis verankern und für zukünftige Problemlösungserfordernisse neu organisieren” (Schreyögg 2003, p. 550).

A rich literature in sociology has emphasized information flow through interpersonal

networks (Burt 1992, Cross et al. 2002, Granovetter 1973). Given the importance of social

relationships for acquiring and transferring information (e.g., Cross and Sproull 2004,

Hansen 1999, Rhee 2004) and learning how to do one’s work (e.g., Brown and Duguid 1991,

Lave and Wenger 1991, Wenger et al. 2002) it is important to pay attention to the function of

social network structures of organizational learning. An organization can, however, be only

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partially organized by formal and planned organizational structure. Oesten and Roeder

stated:

“Zu einem wesentlichen Teil finden sich informelle Zielvorstellungen und Normen, Kommunikationswege sowie spezifische informelle Rollen, Status-, Sanktions-, Symbol- und Sprachsysteme, die den Informations- und Wissensfluss maßgeblich mitbestimmen, in informellen Strukturen” (Oesten and Roeder 2002, p. 81).

Parallel to formally regulated information and communication structures, informal networks

emerge in all organizations (Krackhardt and Hanson 1993, Molina 2001). These informal

interpersonal networks are thought to play a critical role in the organizational learning

processes. This can result in the generation of information. Furthermore, it can also serve to

improve one’s own position in an organization and thereby has a dual purpose: the

information itself (necessary knowledge, knowledge of relations, etc.) as a factor in its own

right, and control, i.e. the possibility of using contacts and communication structures and/or

of holding them back. The dynamics inherent in formal and informal organizational structure

provide rules, which both facilitate and limit the exchange of information and thereby the

organizational learning processes in the organization. Thus, learning and knowledge

management within an organization are inhibited and promoted by its organizational

structure and culture.

In his construct of the “structural hole” (Burt 1992), Burt argued that groups of employees,

who are connected by strong ties to one another, have access to similar information and only

little new circumstances are thus exchanged (Burt 2000, Burt 2003). These individuals are

tied together by socializing bonds of interaction through which they come to share beliefs and

behavioral tendencies. They are subject to a strong social control. Group-specific standards

are clearly defined and are strictly sanctioned (c.f., Jansen 2003). In an organization, a

multiplicity of these groups exists, which simultaneously may be separated from each other

by structural holes. Only individual persons bridge the structural holes, by developing weak

ties with persons in other groups. The structural holes in the social structure are opportunities

to promote and take advantage of competition between different contacts. These persons

stand at the intersection of a multitude of different social groups, but do not belong to a

specific one. Due to their position, they may gather non-redundant information from their

contacts in the different groups much faster than others (Granovetter 1973).

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1.3 Purpose of the study The main purpose of this research work is to gain new insights into the role of informal

structures and processes that influence organizational learning. In order to understand the

relationships between social network structures and organizational learning processes, there

is a need for a conceptual framework that determines organizational learning capabilities by a

profil that is composed of learning orientations and facilitating factors, in conjunction with

types of social network structure.

The research work builds on an integrated model of organizational learning (Kim 1993). The

organizational learning processes are operationalized through the “Organizational Learning

Inventory” (OLI) diagnostic tool (Nevis et al. 1995). The organizational aspects are revealed

through social network analysis (Burt 1992). In order to understand the relationships

between social network structures and organizational learning processes a framework is

developed in this dissertation and applied to case studies.

The study is conducted in the context of the German and Norwegian forest sector, which are

experiencing challenges from many sides as described above. The ability of the forest sector

to handle these challenges successfully rests, to a large degree, on its ability to improve the

learning capabilities of its organizations.

1.4 Organization of the Dissertation This dissertation is a work of science that seeks to combine theory and practice and is

organized as follows. After this introduction (Chapter 1), the literature review (Chapter 2) is

divided into two main parts.

The first part discusses the theoretical advances and empirical experiences of the concept of

organizational learning. The objective is to present the concept of organizationa learning and

a model that addresses potential barriers to learning as well as a diagnostic instrument that

focuses on the organization’s structural and cultural learning characteristics. The second part

introduces the concept of social network analysis and presents the link between

organizational learning and social network analysis.

Based upon this review, a conceptual framework that combines aspects of potential barriers

to organizational learning with different types of social network structures and organizational

learning characteristics is developed in “Chapter 3”. In this chapter, propositions that relate

structural components and organizational learning characteristics are derived.

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“Chapter 4” presents a brief overview of the research design by presenting an introduction in

case study methodology, the selction of the subjects and the formalization of the structural

components and organizational learning characteristics. Furthermore the instruments and

procedures used in this study are introduced.

“Chapter 5” displays the empirical result of the case study surveys and the following chapter

(Chapter 6) discusses the empirical results from the case studies on a more general basis.

Based on these results and the discussion of them, conclusions are drawn and implications for

further research are elaborated.

The dissertation ends with an English summary and a German “Kurzfassung”.

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Chapter 2 Literature Review

2.1 Chapter overview This chapter focuses on the theoretical advances and empirical experiences within the

concept of organizational learning and introduces the social network theory. The chapter is

subdivided into three subchapters, a brief chapter overview (2.1), an organizational learning

part (2.2) and a social network theory part (2.3).

After this brief chapter overview, the second part (2.2) starts with a short overview of

individual learning and emphasizes the role of experiences for learning and knowing. The

following sections define organizational learning, and link individual and organizational

learning by presenting an integrated model of organizational learning. Building on this

model, potential barriers to organizational learning are described and the organizational

learning inventory for assessing organizational learning capabilities is highlighted.

The third part (2.3) about the theoretical foundation of social network analysis gives a review

of the historical development leading up to what we now call social network analysis and

then go into the concept of social network analysis, per se. The following sections describe

the link between social network analysis and organizational learning by the concept of social

capital and present the facilitative role of social network structure and their distinct benefits.

2.2 Theoretical foundations of organizational learning During the past 20 years, interest in organizational learning and knowledge management has

grown at a seemingly exponential rate following the publication of Peter Senge’s book, The

Fifth Discipline (Senge 1990). In fact, the roots of current practitioners’ interest go back to

the quality movement and its focus on continuous improvement, hence learning and

knowledge creation, and before that to the notion that organizations comprise systems of

decision making or information processing (Cyert and March 1963, March and Simon 1958).

Whether it was due to the emergence of a global economy or an increase in competitiveness

among firms, learning has become a strategic initiative for many organizations (De Geus

1988) and a focal point for competitive advantage (Stata 1989).

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Organizational theories have proposed a variety of definitions of organizational learning.

Argyris and Schön (1978), for example, state,

“[o]rganizational learning is a process in which members of an organization detect error or anomaly and correct it by restructuring organizational theory of action, embedding the results of their inquiry in organizational maps and images” (Argyris and Schön 1978, p. 58).

In this sense, all organizations learn, whether they consciously choose to support

organizational learning processes or they only attach subordinate importance to

organizational learning (cf., Willke 2004). Some organizations deliberately advance

organizational learning, developing capabilities that are consistent with their objectives;

others make no focused effort and, therefore, acquire habits that may be counterproductive.

The concept of organizational learning will be presented in more detail in section “2.2.3

Organizational Learning Theories” (p. 17). In order to describe the processes through which

individual learning advance organizational learning, we will firstly address the role of

individual learning, and knowing and memory.

2.2.1 Individual learning as the basis for organizational learning First, there appears to be the issue of the importance of individual learning for organizational

learning. Various theories of organizational learning have been based on theories of

individual learning. Kim (1993) writes that

“[t]he importance of individual learning for organizational learning is at once obvious and subtle – obvious because all organizations are composed of individuals; subtle because organizations can learn independent of any specific individual but not independent of all individuals” (Kim 1993, p. 37).

Hence, theories of individual learning are crucial to understanding organizational learning.

Although individual learning has been the subject of research of psychologists, linguists,

educators, and others for a long time, they are all still far from fully understanding the

workings of the human mind. Therefore, to better understand organizational learning, the

following sections present the basic individual learning theories and highlight the role of

experiences for learning.

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Behaviorism There are two basic schools of thought within behaviorism. Behaviorism is a theory of

animal and human learning that only focuses on objectively observable behaviors and

discounts mental activities. Behavior theorists define learning as nothing more than the

acquisition of new behavior (cf., Amelingmeyer 2002, Güldenberg 2001). Experiments by

behaviorists identify conditioning as a universal learning process. There are two different

types of conditioning, each yielding a different behavioral pattern: classic conditioning, and

operant conditioning. Classic conditioning occurs when a natural reflex responds to a

stimulus. The most popular example is Pavlov's observation that dogs salivate when they eat

or even see food (e.g., Pavlov 1927). Essentially, animals and people are biologically

“wired” so that a certain stimulus will produce a specific response. Behavioral or operant

conditioning occurs when a response to a stimulus is reinforced. Operant conditioning is a

simple feedback system: If a reward or reinforcement follows the response to a stimulus,

then the response becomes more probable in the future. For example, leading behaviorist

Burrhus Frederic Skinner (1904-1990) used reinforcement techniques to teach pigeons to

dance and bowl a ball in a mini-alley (e.g., Skinner 1953). To achieve success, according to

this school, the operant school of behaviorism requires active interventions.

Behaviorism is relatively simple to understand because it relies only on observable behavior

and describes several universal laws of behavior. Its positive and negative reinforcement

techniques can be very effective--both in animals, and in treatments for human disorders such

as autism and antisocial behavior. However, behaviorism does not account for all kinds of

learning, since it disregards the activities of the mind (c.f., Staehle 1999).

Cognitivism and cognitive constructivism Cognitivism and cognitive learning theories put emphasis on how humans learn and

understand using internal processes of acquiring, understanding, and retaining knowledge

(e.g., Tolman 1932). Cognitive learning theories focus on the learner, namely to explain

learning in terms of cognitive processes, structures, and representations that are believed to

operate within the learner (Smith and Ragan 1999). The difference of cognitive learning

theory compared with behavioral theory is that cognitivists place much more emphasis on

factors within the learner and less emphasis on factors within the environment. Cognitivists

believe that humans are capable of insights, perceptions, and attributing meaning. Learning

occurs when humans recognize experience, thereby making sense of input from the

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environment. Thus, they take explicit perception, organization of perception and

interpretation of information as elements of learning into consideration.

Despite the shift from seeing learning as change in observable behavior to seeing learning as

developing internal information-processing mechanisms and models, purely cognitivist

models of learning were still predicated on the notion that learning was a matter of finding

ways to assimilate “objective” knowledge (Kolb 1984). The next step in developing

understanding was when theorists became aware that learners themselves played an active

role in constructing the things that they were learning (Berger and Luckman 1966, Mead

1934, Wittgenstein 1953). While cognitivist models of learning focus on learners developing

representative models of knowledge provided to them by their environment, cognitive

construction shifts the focus to the learner’s own process of actively constructing these

models though interaction with their environment . Many of these models are inspired by

Piaget’s (1950, 1970) developmental model of learning.

Experiential learning theory Experiential learning has developed and evolved over the past decades and has been

significantly influenced by J. Dewey, J. Piaget, and K. Lewin. In his work, Dewey (1938)

offers an understanding and synthesis of the conflict between “traditional” education and his

“progressive” approach. The essence of his work was that truth and knowledge is not

absolute but rather it is continuously evolving. According to Dewey, our experiences

influence our knowledge and what is known to us as truth. Another major contribution to

experimental learning is that of Piaget. In his studies of children, he demonstrated that

abstract reasoning and the ability to manipulate symbols arise from the infant’s actions in

exploring and coping with the immediate concrete environment (Piaget 1971). The essence

of his work is the description of how intelligence, and thus learning, is shaped by experience.

The tradition of experimental learning, which led to the development of present-day

educational and organizational developmental work, is attributed to Lewin.

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Dewey’s (1926) philosophical perspective of pragmatism in education and Lewin’s (1951)

work in translating phenomena to concepts and field theory are the two cornerstones in this

school of thought. According to Kolb (1984), this perspective on learning is called

experiential for two reasons:

“The first is to tie it clearly to its intellectual origins in the work of Dewey, Lewin, and Piaget. The second reason is to emphasize the central role that experience plays in the learning process. This differentiates experiential learning theory from rationalist and other cognitive theories of learning that tend to give primary emphasis to acquisition, manipulation, and recall of abstract symbols, and from behavioural learning theories that deny any role for consciousness and subjective experience in the learning process” (Kolb 1984, p. 20).

Observation andReflection

ConcreteExperience

Formation ofAbstract Concepts and Generalizations

Testing Implicationsof Concepts inNew Situations

Figure 1: Lewinian Experiential Learning Model

(Modified from Kolb 1984, p.21)

Figure 1 views a learning cycle that is representative of the experiential theory of learning. A

person continually cycles through a process of moving from having concrete experiences, to

making observations and reflections on those experiences, to forming abstract concepts and

generalizations based on those reflections, to testing those ideas in a new situation that leads

to another concrete experience. Learning is thus conceived as a four-stage cycle. One’s

observations are assimilated into a “theory” from which new implications for action can be

deduced. These implications then serve as guidelines in acting to create new experience.

This four-stage cycle of individual learning is known as the Lewinian Experiential Learning

Model.

12

According to Kolb (1984), two aspects of the “Lewinian Experiential Learning Model” are

noteworthy. First is its emphasis on the central role that immediate personal experience play

in the learning process; giving life, texture, and subjective personal meaning to abstract

concepts and at the same time providing a concrete, publicly shared reference point for

testing the implications and validity of ideas created during the learning process. Second,

Lewin based his model on feedback processes. Describing a social learning and problem-

solving process that generates valid information with which to assess deviations from desired

objectives he borrowed the concept of feedback from electrical engineering (c.f., Ashby

1960). Lewin and his fellows believed that much individual and organizational

ineffectiveness could ultimately be traced to a lack of adequate feedback processes. This

ineffectiveness results from an imbalance between observation and action – either from a

tendency of individuals and organizations to emphasize decision and action at the expense of

information gathering, or from a tendency to become overloaded by data collection and

analysis.

2.2.2 Knowing and memory Although the individual learning theories help to understand learning, they do not

explicitly address the role of memory. Memory plays a critical role in linking individual and

organizational learning. Building on the previous sections, an individual learning cycle with

a clearer connection to activities conducted in an organizational context is presented.

Furthermore, introducing individual mental models the importance of memory for

organizational learning is emphasized.

The individual learning cycle Kim (1993) defines individual learning “as increasing one’s capacity to take effective action”

(Kim 1993, p. 38). For Piaget (1970), the key to learning lies in the mutual interaction of the

process of accommodation (adapting our mental concepts based on experience in the world)

and the process of assimilation (integrating our experience into existing mental concepts). As

Kolb (1984) puts it,

“Learning is the process whereby knowledge is created through the transformation of experience. This definition emphasizes several critical aspects of the learning process as viewed from the experiential perspective. First is the emphasis on the process of adaptation and learning as opposed to content or outcomes. Second is that knowledge is a transformation process, being continuously created and recreated, not an independent entity to be acquired or

13

transmitted. Third, learning transforms experience in both its objective and subjective forms. Finally, to understand learning, we must understand the nature of knowledge, and vice versa” Kolb (1984, p.38).

Thus, both parts of the definition are important: what people learn and how they understand

and apply that learning. Another thing to think about regarding the two facets is as

operational and conceptional learning. Kim’s “Observe-Assess-Design-Implement-Cycle” of

individual learning includes the salient features of the versions mentioned above, but the

terms have clearer connections to activities conducted in an organizational context (Figure 2).

In this cycle of individual learning, a person experiences a concrete situation and observes

what is happening. The learner assesses (consciously or subconsciously) his experience by

reflecting on his observations and then designs an abstract concept that seems to be an

appropriate response to the assessment. Afterwards, the person tests the design by

implementing it in the concrete world, which leads to a new concrete experience,

commencing another cycle.

Assess(Reflect on observations)

Observe(Concrete experience)

Design(From abstract concepts)

Implement(Test concepts)

Figure 2: Observe-Assess-Design-Implement-Cycle

(Adapted from Kim 1993, p.39)

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Mental models Although the individual learning cycle (Figure 2) helps to understand learning, it does not

explicitly address the role of memory; which plays a critical role in linking individual and

organizational learning. Psychological research makes a distinction between learning and

memory. Learning has more to do with acquisition, whereas memory has more to do with

retention of whatever is acquired. In reality, however, separating the two processes is

difficult because they are tightly interconnected, in the sense that what we already have in our

memory affects what we learn and what we learn affects our memory. The concept of

memory is commonly understood to be analogous to a storage device where everything we

perceive and experience is filed away. Integrating the role of memory requires a more

explicit distinction between operational and conceptual learning.

In Figure 3, Kim (1993) relates two different levels of learning – operational and conceptual

– to two parts of mental models (see Kim 1993, p. 40). The philosophical distinction

between these forms of learning and knowing is perhaps best described by William James.

The following quotation describes his view of these two kinds of knowledge:

“There are two kinds of knowledge broadly and practically distinguishable: We may call them respectively knowledge of acquaintance and knowledge-about. Most languages express the distinction; thus, γνŵναι, ειδέναι; noscere, scire; kennen, wissen; connâitre, savoir. I am acquainted with many people and things, which I know very little about, except their presence in the places where I have met them. I know the color blue when I see it, and the flavor of a pear when I taste it; I know an inch when I move my finger through it; a second of time, when I feel it pass; an effort of attention when I make it; a difference between two things when I notice it; but about the inner nature of these facts or what makes them what they are, I can say nothing at all. I cannot impart acquaintance with them to anyone who has not already made it himself. I cannot describe them, make a blind man guess what blue is like, define to a child a syllogism, or tell a philosopher in just what respect distance is just what it is, and differs from other forms of relation. At most, I can say to my friends, Go to certain places and act in certain ways, and these objects will probably come. All the elementary natures of the world, its highest genera, the simple qualities of matter and mind, together with the kinds of relation that subsist between them, must either not be known at all, or known in this dumb way of acquaintance without knowledge-about. In minds able to speak at all there is, it is true, some knowledge about everything. Things can at least be classed, and the times of their appearance told. But in general, the less we analyze a thing, and the fewer of its relations we perceive, the less we know about it and the more our familiarity with it is of the acquaintance-type. […] We can relapse at will into a mere condition of acquaintance with an object by scattering our attention and staring at it in a vacuous trance-like way. We can ascend to knowledge about it by rallying our wits and proceeding to notice and analyze and think. What we are only acquainted with is only present to our minds; we have it, or the idea of it. But when we know about it, we do more than merely have it; we seem, as we think

15

over its relations, to subject it to a sort of treatment and to operate upon it with our thought. The words feeling and thought give voice to the antithesis. Through feelings we become acquainted with things, but only by our thoughts do we know about them. Feelings are the germ and starting point of cognition, thoughts the developed tree. […] The mental states usually distinguished as feelings are the emotions, and the sensations we get from skin, muscle, viscus, eye, ear, nose, and palate. The “thoughts,” as recognized in popular parlance, are the conceptions and judgments” (James 1890, pp.221-222; from Kolb1984, p.44).

Argyris and Schön (1978) argue that learning takes place only when new knowledge is

translated into different behavior that is replicable. For Piaget (1970), the key to learning lies

in the mutual interaction of accommodation (adapting our mental models based on our

experience in the world) and assimilation (integrating our experience into existing mental

models). Building on the these two distinct forms of knowing, Kolb (1984) summarized that

learning, the creation of knowledge and meaning, occurs through the active extension and

grounding of ideas and experiences in the external world and through internal reflection

about the attributes of these experiences and ideas.

Thus, both parts of knowledge are important: what people learn and how people understand

and apply that learning. Or as Nevis, DiBella and Gould put it, true knowledge is more than

information; it includes the meaning or interpretation of the information, and a lot of

intangibles such as the tacit knowledge of experienced people that is not well articulated but

often determines collective organizational competence (Nevis et al. 1995). According to the

description above, Nevis and colleagues make a similar distinction as Kim (1993) by

claiming that organizations prefer to accumulate knowledge about how products and services

should be developed/delivered (operational learning according to Kim) or about what future

product and service should be (conceptual learning according to Kim). Similar distinctions

are made by Polanyi (1958). As Willke (2004) puts it:

“Eine […] klassische Unterscheidung von Wissen ist […] die von Michael Polanyi (1958) stammende Differenz von implizitem und explizitem Wissen. Implizites Wissen ist ein Wissen, das eine Person aufgrund ihrer Erfahrungen, ihrer Geschichte, ihrer Praxis und ihres Lernens im Sinne von Know-how hat. […] Explizites Wissen dagegen ist ein ausgesprochenes, formuliertes, dokumentiertes und in diesem Sinne expliziertes Wissen, ein Wissen also, von dem der Wissende weiß und über das er sprechen kann” (Willke 2004, p. 35).

16

IndividualAction

IndividualAction

EnvironmentalResponse


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Conceptual

Assess

Design

Observe

Implement

Individual Learning

Operational

Figure 3: Individual Learning Model


In Figure 3, individual mental models are added to the individual learning cycle. Operational

learning represents learning at the procedural level, where one learns the steps in order to

complete a particular task. This knowledge is captured as routines, such as filling out entry

forms, operating a piece of machinery, handling a switchboard, or retooling a machine. Not

only does operational learning accumulate and change routines, but routines affect the

operational learning process as well. The arrows going in both directions in Kim’s individual

learning cycle with mental models represents this mutual influence (Kim 1993). Conceptual

learning has do with thinking about why things are done in the first place, sometimes

challenging the very nature or existence of prevailing conditions, procedures, or conceptions

and leading to new frameworks in the mental model. The new frameworks, in turn, can open

up opportunities for discontinuous steps of improvement by reframing a problem in radically

different ways.

In this dissertation, knowledge is defined as the capacity for effective action. Whether or not

knowledge leads to effective action depends upon people’s capacity to interpret the

information, generate meaningful options for action, and implement an action that leads to

desired results. Much of the confusion and disappointment today regarding knowledge

management, for example, comes from this lack of clarity. Organizations and people are

investing in systems to capture, organize, and disseminate information, and calling it

17

knowledge. But knowledge cannot be converted into an object and “given” from one person

to another. Knowledge only diffuses when there are learning processes whereby people

develop new capacities for effective action (McDermott 2002). Information technology,

while critical for enabling the spread of information, cannot capture and store knowledge.

Only people can do that.

A good way to understand the role of memory in the learning process itself is the concept of

mental models. As Senge and his colleagues (1994) state,

“[m]ental models are the images, assumptions, and stories which we carry in our minds of ourselves, other people, institutions, and every aspect of the world. Like a pane of glass framing and subtly distorting our vision, mental models determine what we see. Human beings cannot navigate through the complex environments of our world without cognitive “mental maps”; and all of these mental maps, by definition, are flawed in some way” (Senge et al. 1994, p.235).

The concept of mental models differs from the traditional notion of memory as static storage

because mental models play an active role in what an individual sees and does. Mental

models represent a person’s view of the world, including explicit and implicit

understandings. Mental models provide the context in which to view and interpret new

material, and they determine how stored information is relevant to a given situation (cf.,

Schreyögg 2003). However, mental models not only help us to see the world we see, they

can also restrict our understanding to one that makes sense within the mental model we have.

Or as Mark Engel in Bateson (1972) puts it:

“[…] we create the world that we perceive, not because there is no reality outside our heads […], but because we select and edit the reality we see to conform to our beliefs about what sort of world we live in. The man who believes that the resources of the world are infinite, for example, or that if something is good for you then the more of it the better, will not be able to see his errors, because he will not look for evidence of them” (Bateson 1972, p. VII).

2.2.3 Organizational learning theories An organization learns through its individual members, and, therefore, is affected either

directly or indirectly by individual learning. Since organizations always learn through their

members, analogies between individual and organizational learning can be developed.

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Table 1: Definitions on “Organizational Learning”

Author(s) Definition

Daft and Weick (1984) Organizational learning is knowledge about the interrelationships between the

organization’s action and the environment.

Levinthal and March (1993)

Organizational learning copes with the problem of balancing the competing

goals of developing new knowledge and exploiting current competencies in

the face of the dynamic tendencies to emphasize one or the other.

Levitt and March (1988) Organizations are seen as learning by encoding inferences from history into

routines that guide behavior.

Marquardt (1996) An organization that learns powerfully and collectively and is continually

transforming itself to better collect, manage, and use knowledge for success.

Miller (1996)

Learning is to be distinguished from decision making. The former increases

organizational knowledge, the latter need not. Learning may in fact occur

long before, or long after, action is taken.

Mills and Friesen (1992)

A learning organization sustains internal innovation with the immediate goals

of improving quality, enhancing customer or supplier relationships, or more

effectively executing business strategy, and the ultimate objective of

sustaining profitability.

Nadler et al. (1992) Learning requires an environment in which the results of experiments are

sought after, examined and disseminated throughout the organization.

Slater and Narver (1994) At its most basic definition, organizational learning is the development of

new knowledge and insights that have the potential to influence behavior.

Schwandt (1995)

A system of actions, actors, and processes that enables an organization to

transform information into valued knowledge that increases its long-run

adaptive capacity.

However, organizational learning is far more complex and dynamic than a mere

magnification of individual learning. The level of complexity increases tremendously going

from a single individual to a large collection of diverse individuals. Issues of motivation and

reward, for instance, which are an integral part of human learning, become even more

complicated within organizations. Although the meaning of the term “learning” remains

essentially the same as in the individual case, the learning process is fundamentally different

at the organizational level. Table 1 shows a selection of existing definitions of the term

organizational learning. Most of the literature, developed in the past twenty years, that

considers organizational learning or the learning organization follows a normative or

prescriptive tradition (Fiol and Lyles 1985, Garvin 1993, Lee et al. 1992, Senge 1990).

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A widely accepted theory of organizational learning was suggested by Argyris and Schön

(1978). Argyris and Schön (1974) assert that people hold maps in their heads about how to

plan, implement, and review their actions. They further assert that few people are aware that

the maps they use to take action are not the theories they explicitly espouse. In addition,

even fewer people are aware of the maps or theories they do use. To clarify, this is not

merely the difference between what people say and do. Argyris and Schön suggest that there

is a theory consistent with what people say and a theory consistent with what they do.

Therefore the distinction is not between “theory and action” but between two different

“theories of action”: the espouse theory, and the theory-in-use. Building on this concept

Agyris and Schön (1978) distinguish between three different levels of learning: Single-Loop-

Learning, Double-Loop-Learning and Deutero-Learning.

Single-loop-learning By single-loop-learning Argyris and Schön (1978, 1996, 1999) mean instrumental learning

that changes strategies of action or assumptions underlying strategies in ways that leave the

values of a theory of action unchanged. For example, quality control inspectors who identify

a defective product may convey that information to production engineers, who, in turn, may

change product specification and production methods to correct the defect. Line managers

may respond to an increase in turnover of personnel by investigating sources of worker

dissatisfaction, looking for factors they can influence, such as salary levels, fringe benefits, or

job design, to improve the stability of their work force. Organization members react to notice

internal and external changes as they try to identify and eliminate sources of error of their

"normal" behavior. These are learning episodes which function to preserve a certain kind of

constancy – to keep organizational performance within the range set by existing

organizational values and norms. Bateson (1972) argued that the organization’s ability to

remain stable in a changing environment denotes a kind of learning. According to Bateson

(1979), “Learning I will be an appropriate label for the revision of choice within an

unchanged set of alternatives; Learning II will be the label for the revision of the set from

which the choice is to be made […]” (Bateson 1979, p. 287).

According to Argyris and Schön (1996), organizations continually engaged in transactions

with their environments regularly carry out inquiry that takes the form of search for and

correction of error. Following Bateson’s argument (1979), Argyris and Schön call this

learning single-loop-learning. Single-loop-learning is sufficient where error connection can

proceed by changing organizational strategies and assumptions within a constant framework

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of values and norms for performance. It is instrumental and, therefore, concerned primarily

with efficiency: how best to achieve existing goals and objectives, keeping organizational

performance within the range specified by existing values and norms. Single-loop-learning

has also been referred to as lower-level learning by Fiol and Lyles (1985), adaptive learning

or coping by Senge (1990), and non-strategic learning by Mason (1993).

Double-loop-learning By double-loop-learning, Argyris and Schön (1978, 1996) mean learning that results in a

change in the values of theory-in-use, as well as in its strategies and assumptions. The

double-loop refers to the two feedback loops that connect the observed action with strategies

and values served by strategies. Strategies and assumptions may change concurrently with,

or because of, change in values.

Argyris and Schön borrow the distinction between (a) single- and (b) double-loop-learning

from Ashby (1960). Ashby formulates his distinction in terms of (a) the adaptive behavior of

a stable system, “the region of stability being the region of the phase space in which all the

essential variables lie within their normal limits,” and (b) a change in the value of an

effective parameter, which changes the field within the system seeks to maintain its stability.

One of Ashby’s examples is the behavior of a heating or cooling system governed by a

thermostat. In an analogy to single-loop-learning, the system changes the values of certain

variables (for example, the opening or closing of an air valve) in order to keep the

temperature within the limits of a setting. Double-loop-learning is analogous to the process

by which a change in the setting includes the system to maintain temperature within the range

specified by a new setting (c.f., Ashby 1960).

According to Argyris and Schön (1996), double-loop-learning may be carried out by

individuals when their inquiry leads to change in the values of their theories-in-use or by

organizations when individuals inquire on behalf of an organization in such a way as to lead

to change in the values of organizational theory-in-use. Double-loop-learning is also called

higher-level learning by Fiol and Lyles (1985), generative learning (or learning to expand an

organization’s capability) by Senge (1990), and strategic learning by Mason (1993). It is

only through double-loop-learning that individuals or organizations can address the

desirability of the values and norms that govern their theories-in-use.

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Deutero-learning By deutero-learning, Argyris and Schön (1978, 1996) mean learning by reflecting on

previous contexts of learning. The organizational members reflect on and inquire into

previous episodes of organizational learning, or failure to learn. They discover what they did

that facilitated or inhibited learning; they invent new strategies for learning, produce these

strategies and evaluate and generalize what they have produced. This means identifying the

organizational learning orientation or styles, and the factors that promote learning (Nevis et

al. 1995) (c.f., “Chapter 4,” p. 79). The results become encoded in the individual mental

models and are reflected in organizational learning practice. Deutero-learning in particular

takes place by the members of an organization discovering and modifying the learning

systems and by thus stimulating learning readiness.

ActingActing

ChoosingChoosing

Observing consequences(match/mismatch with expectations)

Observing consequences(match/mismatch with expectations)

Mental model(governing variables

and relationships)

Mental model(governing variables

and relationships)

Dou

ble

-Loo

p Le

arn

ing

Single-Loop Learning

Figure 4: Learning modes

(Modified from Brønn 2005)

Double-loop-learning and deutero-learning are concerned with the why and how of changing

the organization while single-loop-learning is concerned with accepting change without

questioning underlying assumptions and core beliefs (Figure 4). However, the type of

organizational learning depends on where in an organization learning occurs. Thus, learning

can occur in different functions of the organization such as research, development, design,

engineering, marketing, and administration.

22

Linking individual and organizational learning Although theories of organizational learning build on analogies to individual learning

theories the critical link between individual and organizational learning was rather

unaddressed. In order to make this connection, Kim (1993) suggested an integrated model of

organizational learning, which is represented in Figure 5.

IndividualAction

IndividualAction

Individual Single-Loop Learning

(ISLL)

Individual Double-Loop Learning

(IDLL) EnvironmentalResponse


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Individual Learning

Operational

OrganizationalRoutines

Weltanschauung

SharedMentalModels

OrganizationalAction


Organizational Single-Loop

Learning (OSLL)

Single-Loop Learning

Double-Loop Learning

Organizational Double-Loop Learning

(ODLL)

Figure 5: Integrated Organizational Learning Model


The “Integrated Organizational Learning Model” incorporates Argyris and Schön’s concept

of single-loop-learning and double-loop-learning on both the individual and organizational

levels. Doublel-loop-learning involves surfacing and challenging deep-rooted assumptions

and norms of an organization that have previously been inaccessible, either because they

were unknown or known but beyond discussion. Individual double-loop-learning is traced

out in Figure 5 as the process through which individual learning affects individual mental

models, which in turn affect future learning. Organizational double-loop-learning occurs

when individual mental models become incorporated into the organization through shared

mental models, which can then affect organizational action. In both cases, double-loop-

learning provides opportunities for discontinuous steps of improvement where reframing a

problem can bring about radically different potential solutions. The distinction between

weltanschauung and organizational routines are integrated throughout the different stages.

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Organizational memory – shared mental models As we have seen, organizational learning is reflected in changing an organization’s behavior

that in turn results from changes in the behavior and beliefs of the organization’s members.

The results of individual learning are captured in the individual’s memory. Individuals, in

turn, contribute to organizational memory. Organizational memory is broadly defined as

everything that is contained within an organization that is somehow retrievable (Kim 1993).

Analogous to individual memory, organizational memory is indispensable for organizational

learning (Argyris and Schön 1996).

The parts of an organization’s memory that are relevant for organizational learning are those

that constitute active memory – they define what an organization pays attention to, how it

chooses to act, and what it chooses to remember from its experiences – that is, individual

mental models and shared mental models. They may be explicit or implicit, tacit or widely

recognized, but they have the capacity to affect the way an individual or organization views

the world and takes action. As Argyris and Schön (1996) state, “organizational knowledge is

embedded in routines and practice which may be inspected and decoded even when

individuals who carry them out are unable to put them into words” (Argyris and Schön 1996,

p. 13). Organizational learning is dependent on individuals improving their mental models.

Additionally, making those mental models explicit is crucial to developing new shared

mental models. This process allows organizational learning to be independent of any specific

individual.

However, mental models are not merely a repository of sensory data. They are active in that

they build theories about sensory experiences (c.f., “2.2.2 Knowing and memory,” p. 12).

Each mental model is a clustering or an aggregation of data that prescribes a viewpoint or a

course of action. Conceptual learning produces new or revised routines that are executed in

lieu of old ones. The revised mental models contain not only the framework routines but also

knowledge about how the routines fit within the new frameworks.

According to Kim (1993), individual frameworks become embedded in the organization’s

weltanschauung (Figure 5). The organization’s weltanschauung is a reflection of the

organization’s culture, deep-rooted assumptions, artifacts, and overts rules of behavior. All

of these things moderate its decision making as it encounters unpredictable, non-routine

events. The organization’s view of the world slowly evolves to encompass the current

thinking of the individual within. In similar fashion, individual routines that are proved

sound over time become standard operating procedures. Standard operating procedures may

include things like a marketing plan to launch a new product, procedures for paying

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suppliers, employee performance reviews, and hiring criteria. The standard operating

procedures allow an organization to respond to routine needs in predictable ways. The

strength of the link between individual mental models and shared mental models is a function

of the amount of influence exerted by a particular individual or group of individuals.

2.2.4 Barriers to organizational learning The most systematic discussion of barriers to organizational learning was offered as early as

1975 by March and Olsen. Their list was later expanded by other authors, such as Kim

(1993).

IndividualAction

IndividualBeliefs



Role-ConstrainedLearning

SuperstitiousLearning

AudienceLearning

Learningunder

Ambiguity4

3

2

1

Figure 6: The Incomplete Learning Cycles

(Modified from March and Olsen 1975)

Using a model of learning that highlighted the linkages between individual beliefs, individual

action, organizational action, and environmental response, March and Olsen (1975) identified

four types of interruption to the learning cycle (Figure 6). Building on March and Olsen

(1975), Kim has identified three additional types (for further details, c.f., “Chapter 3,” p. 44).

With regard to the first incomplete learning cycle, March and Olsen theorized that an

interruption in the connection between individual beliefs and individual action would result if

individuals were limited by their role in the organization and unable to act on their learning.

They called this first barrier “Role-Constrained Learning” (March and Olsen 1975, p.158).

25

A second type of incomplete learning cycle, according to March and Olsen is to be found

when individuals change their own behavior but the effect of these actions on the

organizational behavior and action is ambiguity. That is, individual learning and skill

development takes place, but adaptation by the organizational environment does not

necessarily follow. They termed this barrier “Audience Learning” (March and Olsen 1975,

p.159) to highlight the idea that the link between individual action and organizational action

is interrupted.

The third incomplete learning cycle that March and Olsen describe occurs when

organizational members draw incorrect conclusions with regard to the impact of

organizational actions on the environment. They characterized such conclusions as

“Superstitious Learning” (March and Olsen 1975, p.158; c.f., Levitt and March 1988).

The fourth incomplete learning process, which March and Olsen called “Learning under

Ambiguity” (March and Olsen 1975, p.156) occurs when reasons for changes in the

environment cannot be clearly identified; the linkage between the environmental response

and individual learning is interrupted.

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IndividualAction

IndividualAction

SituationalLearning



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Operational


Weltanschauung

SharedMentalModels



5

Role-ConstrainedLearning1

4

Learning underAmbiguity

SuperstitiousLearning

3

FragmentedLearning

6

OpportunisticLearning7

AudienceLearning

2

Figure 7: Barriers to organizational learning

(Modified from Kim 1993, p.47)

Kim (1993) has extended the model from March and Olsen and has identified three additional

types of incomplete learning cycles that affect organizational learning (Figure 7). When

learning occurs, but is forgotten or not codified for later use, as frequently happens in crisis

management, Kim (1993, p. 46) called it “Situational Learning”. Individual learning occurs,

but is does not change the person’s mental models and therefore has no long-term impact –

the learning is situation specific. Crisis management is an example. A problem is solved, but

no learning is carried over to the next case.

“Fragmented Learning” occurs, when one actor or unit learns but the organization as a whole

does not (Kim 1993, p. 46). This kind of learning barrier is typical in decentralized

organizations that do not have the networking capability to keep the parts connected. Each

unit may have the leading expert on the issue under consideration, but the organization as an

institution cannot apply that expertise.

27

The third type of interrupted learning cycle identified by Kim (1993) is not a barrier to

organizational learning but rather a strategy to bypass standard ways of doing things in an

organization in order to achieve learning in one part of it. Kim called it “Opportunistic

Learning” (Kim 1993, pp. 46-47). This kind of incomplete learning cycles occurs when

actors

“[…] want to sever the link between shared mental models and organizational action in order to seize an opportunity that cannot wait for the whole organization to change (or it may not be desirable for the whole organization to change)” (Kim 1993, p.46).

Organizational learning capability There are many potential reasons for the development of learning barriers. Organizational

learning can be described as dealing with behavioral change and improvement of action

through access to better knowledge and understanding (Fiol and Lyles 1985). Nevis and his

colleagues define organizational learning as “the capacity or processes within an organization

to maintain or improve performance based on experience” (Nevis et al. 1995, p. 73).

Following Huber (1991), they arrived at a three-stage model of knowledge managment:

(1) Knowledge acquisition – The development or creation of insights, skills, or

relationships.

(2) Knowledge sharing – The dissemination to others of what has been acquired by

some one.

(3) Knowledge utilization – The assimilation or integration of learning so that it is

broadly available and can be applied to new situations.

All organizations are engaged in some form of collective learning as part of their

development. All have formal and informal processes and structures for the acquisition,

sharing, and utilization of knowledge and skills. There exists a variety of ways in which

organizations create and maximize learning. In order to assess an organization’s learning

capability, Nevis, DiBella, and Gould have developed an instrument called the

“Organizational Learning Inventory” (OLI) that enables a diagnosis of an organization’s

learning orientation and factors that facilitate learning (Nevis et al. 1995).

Learning orientation (LOr) addresses the organization’s culture as it is related to learning by

describing how learning occurs and what is learned within an organization (Table 2). These

dimensions are based on culture, experiences and core competencies.

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Table 2: Learning orientations

(LOr1): Knowledge Source: Internal – External

Preference for developing knowledge internally versus preference for acquiring knowledge developed externally.

(LOr2): Content-Process Focus: What? – How?

Emphasis on accumulation of knowledge about what products/services are versus how organization develops, makes, and delivers its products.

(LOr3): Documentation Mode: Personal – Public

Knowledge is something individuals possess versus publicly available know-how.

(LOr4): Dissemination Mode: Formal – Informal

Formal, prescribed, organization-wide methods of sharing learning versus informal methods, such as role modeling and casual daily interaction.

(LOr5): Learning Focus: Incremental – Transformative

Incremental or corrective learning versus transformative or radical learning.

(LOr6): Value-Chain Focus: Design – Deliver

Emphasis on learning investments in engineering/production activities (“design and make” functions) versus sales/service activities (“market and deliver” functions).

(LOr7): Skill Development Focus: Individual – Group

Development of individuals’ skills versus team or group skills.

In effect, the learning orientations provide a focal point for depicting the learning capability

in all organizations. In an ideal world, organizations would make learning investments that

have aspects of both approaches. However, in practice, an organization will have a strategy

with tendency to one or the other. Once attitudes or values about what to learn and how to

learn are established, they become absorbed in the everyday life of the organization. They

become powerful processes that guide managerial behavior. The learning orientations can

function as choices made consciously, or they can become covert drivers of decisions and

serve as unquestioned assumptions. Once the assumptions and their consequences are

understood fully, a choice can be made to accept these values and build on them or to look at

and build on other assumptions.

29

The facilitating factors (FF) focus on practices and processes that represent the specific

elements that promote learning (Table 3). The ease and amount of learning depends on the

strength of these elements. The more each is prevalent in an organization, the more

opportunities for learning exist. These elements are based on good practice and common

processes.

Table 3: Facilitating factors

(FF1): Scanning Imperative

Information gathering regarding conditions and practices outside the unit; awareness of the environment; curiosity about the external environment in contrast to the internal environment.

(FF2): Performance Gap

Shared perception of a gap between actual and desired state of performance; performance shortfalls seen as opportunities for learning.

(FF3): Concern for Measurement

Considerable effort spent on defining and measuring key factors when venturing into new areas; striving for specific, quantifiable measures; discussion of metrics as a learning activity.

(FF4): Experimental Mind-set

Support for trying new things; curiosity about how things work; ability to “play” with things; “failures” are accepted, not punished; changes in work processes, policies, and structures are a continuous series of learning opportunities.

(FF5): Climate of Openness

Accessibility of information; open communications within the organization; problems/errors/lessons are shared, not hidden; debate and conflict are acceptable ways to solve problems.

(FF6): Continuous Education

Ongoing commitment to education at all levels of the organization; clear support for all members’ growth and development.

(FF7): Operational Variety

Variety of methods, procedures, and systems; appreciation of diversity; pluralistic rather than singular definitions of valued competencies.

(FF8): Multiple Advocates

New ideas and procedures advanced by employees at all levels; more than one champion.

(FF9): Involved Leadership

Leaders articulate vision, are engaged in its implementation; frequent interaction with members; become actively involved in educational programs.

(FF10): Systems Perspective

Interdependence of organizational units; problems and solutions seen in terms of systemic relationships among processes; connection between the unit’s needs and goals and the company’s.

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The presence of the facilitating factors determines the efficiency and effectiveness of the

organizational learning processes. The facilitating factors do not guarantee that useful

learning will occur within an organization, but if they are lacking, it is almost certain that the

ability of the organization to adapt to its environment or to engage in generative learning will

be severely hampered.

The integrated model of organizational learning addresses the issue of the transfer of learning

through the exchange of individual and shared mental models; the organizational learning

inventory assesses the way in which organizations create and maximize their learning.

According to Nevis and his colleagues (Nevis et al. 1995), an organization can be assessed as

a learning system using the OLI. The learning orientations form patterns that define a given

organization’s learning style. Obtaining a complete picture of an organization’s learning

capability requires more than a description of its learning style. While a style is in place and

some forms of learning occur, most learning is not serendipitous (c.f., “2.2.4 Barriers to

organizational learning,” p. 24). The facilitating factors represent specific initiatives that

may help to overcome the structural and systematic characteristics of organizations that may

block or inhibit learning efforts. The more each facilitating factor is prevalent in an

organization, the more opportunity exists for learning. This set of ten facilitating factors is

based on research from Nevis and his colleagues (Nevis et al. 1995) and is consistent with the

writings of others (Garvin 1993, McGill et al. 1992, Senge 1990, Watkins and Marsick

1993). Collectively, the facilitating factors determine an organization’s learning potential.

The presence of these factors determines the efficiency and effectiveness of the

organizational learning cycle. It can be assumed that specific learning orientations and

lacking facilitating factors will cause potential barriers to organizational learning. As

normative elements, the ten facilitating factors represent the conditions or practice that make

organizations learn. In effect, they provide the reasons or incentives for organizational

learning. Exactly what gets learned and how much depends on how the presence of the

facilitating factors in an organization combines with its learning orientations. Together,

learning orientations and facilitating factors provide a powerful tool for understanding the

role that both the formal and the informal structures play in the organizational learning

process.

However, even though both concepts (Kim’s “Integrated Organizational Learning Model”

and the Nevis and colleagues’ “Organizational Learning Inventory”) are helpful in order to

address potential barriers to learning, they are limited in that they do not consider the role of

the social relationships among the organizational members. Researchers have known for

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some time that relationships are important for acquisition and transfer of information (Allen

1977, Burt 1992, Granovetter 1973, Hansen 1999) and that the creation of knowledge is a

social process (Berger and Luckman 1966, Dewey 1938, Mead 1934, Wittgenstein 1953).

Dewey meant not only that people usually think and act together in a social setting, but that

the very process of inquiry, individual or collective, is conditioned by membership in a social

system that establishes inquiry’s taken-for-granted assumptions (Dewey 1938).

Recently, inquiry into organizational learning as a function of social structure has started.

Learning, whether through formal or informal mechanisms, is a fundamental part of social

life (Lave and Wenger 1991). Learning, however, is social not just because it involves

coordinated action, but because it is a required process in any system wherein individuals

interact. A distinctive feature of our species is the need to create cultures and learn how to

function within them. Culture is a shared set of assumptions, values, and artifacts (Schein

1992). However, the importance of social relationships for acquiring information (e.g., Burt

1992, Granovetter 1973), and learning how to do one’s work (e.g., Lave and Wenger 1991,

Orr 1996) is still undervalued (Borgatti and Cross 2003).

2.3 Theoretical foundations of social network analysis Broadly defined, organizations are simply tools that people use to accomplish whatever they

value or desire (Jones 2001). Organizations consist of people organized to achieve

organizational goals. One of the most important strategic elements of an organization is its

structure. Organization structure refers to “the established pattern of relationships among the

components or parts of the organization” (Kast and Rosenzweig 1988, p. 234). We can

distinguish between formal structure and informal structure.

Formal structure refers to those aspects of the organization that are planed. Here, managers

attempt to establish relationships that will enable the organization to efficiently and

effectively meet its objects. Formalization is an important aspect of structure. It is the

formal decision-making framework by which decisions are made about such issues as formal

reporting and responsibility relationships, task differentiation and activity coordination,

policies and procedures to guide individual and collective behavior, and power and hierarchy

relationships (Staehle 1999). The formal organization can be seen and represented in chart

form. An organization chart displays the formal organizational structure and shows job titles,

lines of authority, and relationships between departments.

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The informal structure, by definition, captures all of the relationship patterns that are not

explicitly designed. In many cases, it emerges spontaneously because of the complex

interactions among the organization’s members. The informal organizational structure can be

described as a social network of social interactions among its employees unrelated to the

firm's formal authority structure.

It is the personal and social relationships that arise spontaneously as people associate with

one another in the work environment. As Rogers (2003) points out,

“[e]very formal organization is characterized by various kinds of informal practices, norms, and social relationships among its members. These informal practices emerge over time and fulfill an important function in an organization” (Rogers 2003, p. 404).

Informal interpersonal networks are thought to play a critical role in the organizational

learning processes. This can result in the generation of false information or resistance to

change desired by management. On the other hand, it is the informal organization that can

make the formal organization more effective by providing support to management, stability

to the environment, and useful communication channels (Staehle 1999).

Figure 8: The formal vs. the informal structure of an organization

Social network analyse makes the invisible network of social relationships between

individuals and organizations more visible (Figure 8). The central tenet of social network

analyse is that the causal motor behind what people feel, believe and do lies in the patterns of

relations between actors in a situation, as opposed to the attributes of the individual actors.

Your race, age, sex, and other indicators matter less than the pattern of relations that position

33

you in social structure. Under the central tenet, any explanation of beliefs and behaviors

requires an analysis of how actors are connected to one another in the situation where beliefs

or behaviors are observed.

2.3.1 The lineage of social network analysis A diversity of strands has shaped the development of what today we refer to as social

network analysis. These strands have intersected with one another in a complex and

fascinating history, sometimes fusing and other times diverging on to their separate paths

(Figure 9).

Gestalt theory Structural – functionalanthropology

Field theory,sociometry

Warner,Mayo Gluckman

Group dynamics Homans

Barnes, Bott, Nadel

Graph theory MitchellHarvard

structuralists

Social network analysis

Figure 9: The lineage of social network analysis

(Adapted from Scott 2004, p. 8)

A clear lineage for the mainstream of social network analysis can, nevertheless, be

constructed from this complex history. In this lineage, there are three main traditions: the

sociometric analysts, who worked on small groups and produced many technical advances

with the methods of graph theory; the Harvard researchers of the 1930s, who explored

patterns of interpersonal relations and the formation of “cliques” (cf., Staehle 1999); and the

Manchester anthropologists, who built on both of these strands to investigate the structure of

“community” relations in tribal and village societies. According to Scott (2004), these

traditions were brought together in the 1960s and 1970s at Harvard, when contemporary

social network analysis was forged (Figure 9).

In the 1930s, a group of German émigrés influenced by Wolfgang Köhler’s “Gestalt” theory

were working in the United States on cognitive and social psychology. This work led to a

34

considerable amount of research on the problem of sociometry and ‘group dynamics’. Using

laboratory methods or laboratory-like case studies, they looked at group structure and the

flow of information and ideas through groups. At the same time, anthropologists and

sociologists at Harvard University were developing some of the ideas of the British social

anthropologist Radcliffe-Brown. He is the one who introduced the term ‘social structure’ as a

metaphor: “I use the term ‘social structure’ to denote this network of actually existing

relations” (Radcliffe-Brown 1940; from Schenk 1984, p.3). Their work produced important

factory and community studies that emphasized the importance of informal, interpersonal

relations in social systems.

In Britain, at Manchester University, a parallel line of development from the work of

Radcliffe-Brown emphasized the analysis of conflict and contradiction and applied these

ideas to the study of African tribal societies and to rural and small town Britain. Building on

earlier traditions, they made considerable advances in allying mathematics with substantive

social theory. Not until well into the 1960s, however, did the final breakthrough to a well-

developed methodology of social network analysis occur. Mitchell (1969) defined a social

network as

“a specific set of linkages among a defined set of persons, with the additional property that the characteristics of these linkages as a whole may be used to interpret the social behavior of the persons involved” (Mitchell 1969, p. 2; from Schenk 1984).

At Harvard University, Harrison White began to extend his investigations of the

mathematical basis of social structure, forging together some of the key insights of his North

American predecessors and creating a unique synthesis which was developed and enlarged by

the students that he trained. They were also engaged in studying the important of gaps, as

opposed to the ties, in social structure. As these students moved through their careers to

departments across the world, the arguments of White and the work of the British researchers

were united into the complex but increasingly coherent framework of social network

analysis. White (1970) shifted perspective to focus on the hole, or opportunities, created

when a person leaves a position. Looking at social structure at social structure more

generally, White, Boorman, and Breiger (1976) stressed the structural hole metaphor as a

substantive motivation for their network blockmodels.

35

One of the students, Mark Granovetter, found a troubling result in his dissertation research.

Hoping to link network structure to job searches, he interviewed men about how they found

their current job and included sociometric items asking for the names of close contacts. The

troubling result was that the men almost never found work through close contacts. He

developed the point in a widely cited article, “The strength of Weak Ties” (Granovetter

1973), and in the book “Getting a Job” (Granovetter 1974).

Building on these findings, a recent stream of literature has examined the role of different

network structures in facilitating outcomes for network constituents. Introducing the social

network terminology, it is possible to represent the social relationships of any organization,

and the link between social network and organizational learning is described.

2.3.2 Introduction into social network terminology Social network analysis focuses on analyzing the relationship among actors within a network.

The actor can be persons, teams, organizations, concepts, etc. A common framework for

social network analysis is the mathematical approach of graph theory (Scott 2004).

Graph theoretical dimensions of informal organizations The basic idea of a social network is very simple. A social network is a set of actors (or

nodes) that may have relationships (or ties) with another. Social network analysis represents

networks as graphs. Krackhardt (1994) argued that graph theory provides a rich descriptive

language for assessing organizational structure. A graph (G) is defined as a set of N points P

= {Pi} and a set of unordered pairs of those points L = {Pi, Pj}; these latter elements are often

referred to as lines connecting those points (L). For example, these points can represent

people in an organization, and the pairs of points, or line between them, represent

relationships (such as interaction, communication) between those organizational members. If

person “i” interacts with person “j”, then the ordered pair (Pi, Pj) is included in set L that

defines the relationship interaction.

Ties connecting pairs of actors can be directed (i.e., potentially one-directional, as in giving

advice to someone) or undirected (as in being physically proximate) and can be dichotomous

(present or absent, as in whether two people are friends or not) or valued (measured on a

scale, as in strength of friendship). A directed graph, or digraph (D), is defined as a set of

points P = {Pi} and a set of ordered pairs of those points L = {Pi, Pj}. A digraph is used to

represent relations that are potentially asymmetric, such as authority or giving advice. For

36

example, if “i” is the immediate supervisor to “j”, and L is defined as the set of formal

authority relationships, then L would contain the ordered pair (Pi, Pj) but would not contain

the ordered pair (Pi, Pj). With these tools and definitions developed so far, it is possible to

represent the informal structure of any organization.

The ego-network The network for which j is ego consists of all persons with whom j has a direct relation and

the relations among these persons. Since relations in a system are only considered when they

are present for a specific actor as ego, models of these relations describe an ego-network

anchored on a single actor (Mitchell 1969). Such network models are also discussed as

primary stars, primary zones, first-order zones, and personal networks. They can be

anchored on any aggregation of persons as an actor, for example, families (e.g., Bott 1957) or

corporations. To some extent, Moreno (1934) provides subsequent work with his network

analysis of the social network centered round a specific person. Models of ego-networks

have been most extensively developed, however, by anthropologists extending sociometry in

order to conduct empirical research on large populations (cf., Barnes 1969, Mitchell 1969).

Since the actor on whom an ego-network is anchored can be treated as a randomly selected

survey respondent, models of ego-networks have become popular in sociology, especially for

describing the social psychology of urban life (Wellman 1979). Among the many aspects of

ego-networks, the extent to which ego can rely on her/his network for social support can be

measured. Furthermore, an ego-network has range to the extent that it includes a diversity of

actors as ego’s contacts.

37

2.3.3 Social network structure and organizational learning According to Burt’s structural theory of action, actors find themselves in a social structure

(Burt 1982). Thus, the social structure causes action and by itself is modified by action

(Figure 10).

ACTORINTERESTS

ACTION

SOCIAL STRUCTUREAS THE

CONTEXT OF ACTION

4

3

3

2

1

Figure 10: Structural Theory of Action

(Adapted from Burt 1982, p. 9)

Burt argues that the social structure, for example the organizational structure, defines its

actors’ social similarities, which in turn pattern their perceptions of the advantages to be had

by taking each of several alternative actions (2). At the same time, social structure

differentially constrains actors in their ability to take actions (3). Actions are therefore a joint

function of actors pursuing their interests to the limit of their ability where both interests (3a)

and ability are patterned by social structure (3b). Finally, actions taken under social

structural constraints can modify social structure itself and these modifications have the

potential to create new constraints to be faced by actors within the structure (4) (cf., Burt

1982, Jansen 2003).

Within the literature on organizational learning, one can often find the idea that learning is an

individual affair. As explained above, individual learning (action) is always embedded

within a social structure. Within all organizations, there exists a formal structure that is

described by boxes, arrows, documented policies and procedures. Behind every organization

chart lies informal clusters and networks of employees who work together - sharing

38

knowledge, solving common problems and exchanging insights, stories and frustrations.

Thus, the social network structure influences organizational learning. The network serves an

important function in the development of social constraint directing information flow in the

building and maintaining of social capital (Borgatti and Foster 2003, Cross et al. 2002,

Hansen 1999, Reagans and McEvily 2003).

The network structure of social capital Social capital has become a ubiquitous metaphor in the study of organizations. In general

social capital is defined as the resources that result from social structure. Bourdieu is often

quoted in defining social capital as

“[…] the sum of the resources, actual or virtual, that accrue to an individual or group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition” (Bourdieu and Wacquant 1992, p. 119, c.f., Bourdieu 1980).

Two schools of thought dominate social network theory and argue for their distinct

advantages: cohesion or closure theorists and structural holes or brokerage theorists.

Cohesions theorists propose that densely embedded networks with many connections are

more beneficial. Dense networks of direct ties appear to foster the development of shared

norms, routines, and the trust necessary for the sharing of proprietary information. Structural

hole theorists, by contrast, posit that networks are open social structures where advantages

derive from brokerage. Given greater homogeneity within than between groups, people

whose networks bridge the structural holes between groups have earlier access to a broader

diversity of information and have experience in translating information across groups (Burt

2003).

39

Network closure as social capital

The traditional view of social capital (Coleman 1988, Coleman 1990, Putman 1993) stresses

the positive effect of cohesive social ties or “network closure” on the production of social

norms and sanctions that facilitate trust and cooperative exchanges. Coleman states that

“[s]ocial capital is defined by its function. It is not a single entity but a variety of different entities, with two elements in common: they all consist of some aspect of social structures, and they facilitate certain actions of actors – whether persons or corporate actors – within the structure. Like other forms of capital, social capital is productive, making possible the achievement of certain ends that in its absence would not be possible. […] Unlike other forms of capital, social capital inheres in the structure of relations between actors and among actors” (Coleman 1988, p. 98).

Burt (2001) refers to Coleman’s view as a closure argument. Grounding his influential work

in Colemans’s metaphor, Putman (1993) preserves the focus on action facilitated by social

structure. According to Putman (1993), social capital refers “[t]o features of social

organization, such as trust, norms, and networks that can improve the efficiency of society by

facilitating coordinated action” (Putman 1993, p. 167; from Burt 2001, p. 32). According to

the traditional view, members of a closely-knit network can trust each other to honor

obligations, which diminish the uncertainty of their exchanges and enhance their ability to

cooperate in the pursuit of their interests. Network closure affects access to information.

Coleman emphasizes that

“[a]n important form of social capital is the potential for information that inheres in social relations. […] a person who is not greatly interested in current events but who is interested in being informed about important developments can save the time of reading a newspaper by depending on spouse or friends who pay attention to such matters” (Coleman 1988, p. 104).

The benefit emphasized by Coleman is that network closure facilitates sanctions that make it

less risky for people in the network to trust one anther. Coleman summarizes that

“[t]he consequence of this closure is, […] a set of effective sanctions that can monitor and guide behavior. […] Closure of the social structure is important not only for the existence of effective norms but also for another form of social capital: the trustworthiness of social structures that allows the proliferation of obligations and expectations. Defection from an obligation is a form of imposing a negative externality or another. Yet, in a structure without closure, it can be effectively sanctioned, if at all, only by the person to whom the obligation is owed. Reputation cannot arise in an open structure, and collective sanctions that would ensure trustworthiness cannot be applied” (Coleman 1988, pp. 107-108).

40

In a different form, the same argument is also advanced by Granovetter (1985), who stresses

the positive effect of common third parties in facilitating trust between people and in

diminishing the risk of opportunism that can affect cooperative relationships. According to

Granovetter, being mutual friends is a condition of structural embeddedness. As Granovetter

(1992) puts it,

“[m]y mortification at cheating a friend of long standing may be substantial even when undiscovered. It may increase when the friend becomes aware of it. But I may become even more unbearable when our mutual friends uncover the deceit and tell one another” (Granovetter 1992, p. 44).

Thus, actors in a network with closure – that is to say networks in which everyone is

connected such that no one can escape the notice of others, which in operational terms

usually means a dense network – are argued to be more likely to conform to the norm of

reciprocity. Failure to reciprocate may result in strong sanctions and in serious damage to the

defector’s reputation as a trustful contact, damage that can have negative consequences for

the defector’s ability to enjoy future benefits from social capital.

Structural holes as social capital

Structural hole theory proposes an alternative view of the relationship between network

structure and the benefits of social capital. Rather than stressing the utility of consistent

norms fostered by cohesive networks, structural hole theory claims that the benefits of social

capital result from the diversity of information and the brokerage opportunities created by the

lack of connection between different actors in a social network. In his work, Burt (1992)

argues that participation in, and control of, information diffusion underlies the social capital

of structural holes. According to this, creativity and learning are central to the competitive

advantage of structural holes (Burt 2000). Furthermore, Burt’s argument on structural holes

describes social capital as

“a function of brokerage opportunities, and draws on network concepts that emerged in sociology during the 1970s; most notably Granovetter (1973) on the strength of weak ties, Freeman (1977) on between-ness centrality, Cook and Emerson (1978) on the benefits of having exclusive exchange partners, and Burt (1980) on the structural autonomy created by complex networks. More generally, sociological ideas elaborated by Simmel (1955 [1922]) and Merton (1968 [1957]) on the autonomy generated by conflicting affiliations are mixed in the hole argument with traditional economic ideas of monopoly power and oligopoly network models of competitive advantage” (Burt 2001, p. 34).

41

A

YOU

B

D

C

Figure 11: Structural holes in the social structure of an organization


The weaker connections (dashed lines) between groups in Figure 11 are holes in the social

structure of an organization. These holes in social structure – or more simply, structural

holes – create a competitive advantage for an individual whose relationships span the holes.

The structural hole between two groups does not mean that people in the groups are unaware

of one another. It means that the people are focused on their own activities, which means

that they do not attend to the activities of people in the other group. Holes are buffers, like an

insulator in an electric circuit. People on either side of a structural hole circulate in different

flows of information. Structural holes are thus an opportunity to bridge the flow of

information between people, and control the projects that bring together people from opposite

of the hole. According to Burt, individuals with a contact network rich in structural holes

“monitor information more effectively than it can be monitored bureaucratically. They move

information faster, and to more people, than memos” (Burt 1997, p. 343). By focusing on the

opportunity side of networks, structural hole theory argues that those individuals have high

levels of social capital because they are not part of cohesive, embedded networks. The

implication is that network closure does not help, but rather hinders organizational

coordination.

42

Entrepreneurial networks versus clique networks According to Burt (1992), structural holes separate non-redundant sources of information,

sources that are more additional than overlapping. There are two indicators of redundancy:

cohesion and equivalence. Cohesive contacts (contacts strongly connected to each other) are

likely to have similar information and therefore provide redundant information benefits.

Structurally equivalent contacts (contacts that link a manager to the same third parties) have

the same source of information and therefore also provide redundant information benefits.

Thus, individuals with contact networks rich in structural holes are the individuals who know

about and have a hand in the information flow. The behaviors by which they develop the

opportunities are many and varied, but the opportunity itself is at all times defined by a hole

in the social structure.

EntrepreneurialNetwork

Individual

Figure 12: Entrepreneurial network

(Modified from Burt 2000)

In terms of the argument of structural hole theory, networks rich in the entrepreneurial

opportunities of structural holes are transformative networks (Figure 12). And, actors in

entrepreneurial networks are people skilled in building the interpersonal bridges that span

structural holes (Burt 2000). The advantages of bridging structural holes emerge from an

individual generating a constituency for new ideas synthesized from the diverse information

clusters to which a network entrepreneur has access. This type of social network structure is

associated with more creativity and innovation.

43

Individual

CliqueNetwork

Figure 13: Clique network

(Modified from Burt 2000)

Unlike the entrepreneurial networks are the clique networks (Figure 13). According to the

traditional view, this type of social network structure is associated with social support and

trust. The more emotionally involved two individuals are with each other, the more time and

effort they are willing to put forth on behalf of each other, including effort in the form of

learning. In addition, this is the network structure associated with more incremental learning.

44

Chapter 3 Synthesis

3.1 Chapter overview Building on the second chapter, this chapter integrates the concept of organizational learning

and social network analysis to develop a conceptual framework.

Following this chapter overviev (3.1), the second part presents the determinants relevant to

organizational learning (3.2). Thereafter, the focus is on the informal organizational structure

as reflected by the social network structure within the organization. By describing

organizational learning characteristics as a joint function of formal and informal

organizational structures as well as cultural learning orientations the link between

organizational learning and social network structure.

In the third part (3.3), the conceptual framework is developed that combines aspects of

barriers to organizational learning with different types of social network structure and

organizational learning charactieristics combines aslinkages between the informal structure

and the learning orientation are developed and operationalized. Developing this framework,

propositions that relate barriers to organizational learning with structural components and

learning characteristics are derived.

The chapter ends with a brief summary (3.4) table of the study’s propositions, which are

presented in a summary table.

3.2 Organizational learning and its determinants The forest sector faces a number of serious challenges. The ability to manage knowledge

effectively within an organization is critical to a number of organizational learning processes.

All organizations have formal and informal processes and structures in place for the

acquisition, sharing, and utilization of knowledge and skills. In this sense, organizations can

be thought of as learning systems. Values, norms, procedures, and business performance

data are communicated and assimilated by employees, starting with early socialization and

continuing through all types of group communication, both formal and informal. The nature

of learning and the way it takes place are determined in large measure by the culture und

structure of the organization.

45

3.2.1 Organizational learning characteristics Kim’s model addresses the issue of the transfer of collective learning through the exchange

of individual and shared mental models (Kim 1993). The organization is therefore dependent

on the individuals improving their mental models and on the individuals making those mental

models explicit so that they can contribute to developing the organization’s shared mental

models. According to Kim (1993), shared mental models are what make the organizational

memory usable to the other employees in an organization. Building on March and Olsen

(1975), Kim identified seven potential reasons for the development of barriers to learning

(Kim 1993). Using the OLI (Nevis et al. 1995) the organization can be assessed as a learning

system. However, both concepts do not consider the role of social relationships among

organizational members. Even though, researchers have known for some time that social

relationships are important for acquisition and transfer of information and that the creation of

knowledge is a social process, inquiry into organizational learning as a function of social

structure has only recently begun (Borgatti and Cross 2003). From this perspective,

information seekers do more than obtain “answers” from experts or fellows. By gathering

information and knowledge, they “construct” their own understanding of the world; thus,

individuals’ experiences are transferred into mental models, which provide the context in

which to view and interpret new information, and they determine how stored information is

relevant to a given situation.

All organizations consist of formal and informal social structures. The formal structure can

be seen and represented in an organizational chart form. The informal social structure

displays the network of social relationships among the organization’s employees. As Burt

(1982) states, social structure causes action and itself is modified by action. Thus, social

network structure, the network of social relationships among individuals in an organization,

influence individual and group behavior and serve to constrain and facilitate change.

According to the social network theory, the social network serves an important function in

the development of social constraint directing information flow in the building and

maintaining of social capital (Borgatti and Foster 2003, Reagans and McEvily 2003). In this

way social network structure influences organizational learning processes. Thus,

organizational learning emerges from, is constrained by, and is enabled by social network

structure.

46

3.2.2 Social network structure and organizational learning processes Social network analysis has made significant contributions to a variety of fields including

sociology, social psychology, anthropology, epidemiology, and management studies (Cross

2004). Application of social network analysis techniques to organizational learning and

knowledge management is relatively new. Social network analysis helps identify the

strengths and inefficiencies in knowledge flow. It makes the invisible network of social

relationships between individuals and organizations more visible and thus gives valuable

input to managers in making decisions for improving the performance of their organizations

(Cross et al. 2003, Krackhardt and Hanson 1993).

Two schools of thought dominate social network theory and argue for their distinct

advantages: cohesion or closure theorists and structural holes or brokerage theorists.

Cohesions theorists propose that densely embedded networks with many connections are

more beneficial (Coleman 1988, Walker et al. 1997). Dense networks of direct ties appear to

foster the development of shared norms, routines, and the trust necessary for the sharing of

proprietary information. Structural hole theorists, by contrast, posit that networks are open

social structures where advantages derive from brokerage. A structural hole indicates that the

people on either side of the hole have access to different flows of information (Hargadon and

Sutton 1997). Networks rich in structural holes imply access to mutually unconnected

partners, and consequently, to many distinct information flows. Given greater homogeneity

within than between groups, people whose networks bridge the structural holes between

groups have earlier access to a broader diversity of information and have experience in

translating information across groups (Burt 2003).

Social network analysis can be applied to focus on analyzing the social relationships (ties)

among the employees of an organization (actors) in terms of knowledge acquisition.

Viewing organization as social systems, in which employees are continually generating

experiences, organizations can potentially be learning all the time. Knowledge is generated

when individuals give meaning to information or experiences. Social network analysis helps

to increase the visibility of knowledge sources and thus can facilitate and accelerate the

process of locating relevant expertise or experiences in an organization. One of the most

important ways that people learn new ideas is by associating those ideas with what they

already know. In a more abstract sense, two individuals who are similarly positioned in an

informal communication network will come to share common knowledge and information

(Burt 1987, Rogers 2003, Strang and Turna 1993). Furthermore, dense networks of direct

ties appear to foster the development of shared norms of behavior and explicit

47

interorganizational knowledge-sharing routines (Burt 2001, Dyer and Noboeka 2000, Uzzi

1997) and a set of effective sanctions that can monitor and guide behavior (Coleman 1988).

This redundant contact might lead to an effective acquisition of knowledge, which requires a

certain level of co-presence, social affinity, and socialization.

Unlike close networks with homogeneity, creativity and learning are central to the

competitive advantage of structural holes (Burt 2000). According to Hansen (1999), actors

that rely on weak ties as sources of ideas are more likely to be innovative than actors that rely

on strong ties. The benefits of individuals embedded in Entrepreneurial networks result from

the diversity of information and the brokerage opportunities created by the lack of connection

between different actors in a social network. Burt (1992) argued that individuals whose

relationships connect multiple bodies of knowledge broker the flow of information between

people, and control the projects that bring together people from opposite sides of the hole.

This leads to greater opportunity to acquire non-redundant information and fewer constraints.

Following Hansen’s argument, a disadvantage might be that weak ties (ties bridging

structural holes) promote the acquisition of simple knowledge (Hansen 1999).

Since organizations are continually creating experiences and thus creating or acquiring

knowledge, the potential for learning is always there. To realize that potential, organizations

must have the capacity to disseminate and use that knowledge. Knowledge can be

disseminated within an organization and between employees in a variety of ways. Some

modes of dissemination are more formal than others; some are based on written

communication (e.g., formal reports and documents), others on oral presentation (e.g., staff

presentations and telephone conversations). Prior research in organizations has indicated that

the effective sharing of knowledge that is difficult to codify requires a certain level of face-

to-face communication, social affinity, and socialization (Nonaka and Takeuchi 1995).

Hansen (1999) argues that strong ties promote the transfer of complex knowledge, while

weak ties promote the transfer of simple knowledge. Furthermore, trust plays an important

role in knowledge sharing. Network closure facilitates sanctioning that makes it less risky

for individuals in the network to trust one another (Coleman 1988).

Knowledge may be generated and disseminated throughout an organization, but unless it is

used to alter our decisions, our behaviors, or our culture organizational learning remains

incomplete. Several studies exemplified the approach of inferring knowledge transfer from

the association between network structure and organizational performance. Ingram and

Roberts (2000) described how dense friendship networks affect organizational performance.

Further, Reagans and Zuckerman (2001) described how interaction among scientists with

48

non-overlapping networks outside their team improved productivity. They argued that

collaboration among scientists with different external contacts bridges gaps, or structural

holes in the network outside the team. The advantage of bridging structural holes emerges

from an individual generating a constituency for new ideas synthesized from the diverse

information clusters to which a network entrepreneur has access. This network structure is

associated with more creativity and innovation. Network closure, in contrast, can improve

the efficiency of social systems by facilitating coordinated action. The more emotionally

involved individuals are with each other (like in network structure associated with trust and

social support) the more time and effort they are willing to put forth on behalf of each other,

including effort in the form of learning. This network structure is associated with more

incremental learning.

3.3 Building a conceptual framework As stated earlier, the organization’s ability to learn is a joint function of the formal and

informal organization structure and the cultural learning orientation. This research work

considers how different features of informal networks affect organizational learning.

Cultural Dimension –Learning Orientations

(Nevis et al. 1995)

Facilitating Factors(Nevis et al. 1995)

Relational Dimension –Social Network Form

(Burt 1992)

OrganizationalLearning Capabilities

Barriers to Organizational Learning

(Kim 1993)

Figure 14: Organizational learning and its determinants

As a complement to previous research that has emphasized the linkage between individual

and organizational learning, we focus on how the relationships between types of social

49

network structure and organizational learning is asscociated with potentioal barriers to

organizational learning (Figure 14).

Nevis, DiBella, and Gould defined organizations as learning systems and identified 14

different learning approaches (Nevis et al. 1995). They identified relationships between

learning orientations and facilitating factors and provided guidance on developing capability

in specific approaches by focusing on specific facilitating factors. Combining social network

theory with Nevis and colleague’s framework enables us to operationalize relationships

between organizational learning characteristics and social network structures (Nevis et al.

1995). According to them, learning orientations form patterns that define a given

organization’s learning style. The facilitating factors represent specific initiatives that help to

overcome the structural and systematic characteristics of organizations that may block or

inhibit learning efforts. Exactly what and how much gets learned depends on how the

presence of the facilitating factors in an organization combined with its learning orientations.

Learning orientations and facilitating factors contribute to understanding organization’s

learning capability.

Building on social network theory (Borgatti and Cross 2003, Burt 1992, Granovetter 1973,

Hansen 1999), it can be argued that specific social network structures facilitate different

organizational learning processes. According to the social network literature, there are two

dominating types of network structure: clique networks and entrepreneurial networks. Both

types of social network structure have their distinct advantages. Clique networks appear to

foster the development of shared norms and routines, and the trust necessary for sharing of

personal information and complex knowledge. Entrepreneurial networks, by contrast, are the

source for new ideas synthesized from diverse information clusters to which a network

entrepreneur has access. This type of social network structure is associated with more

creativity and innovation.

3.3.1 Knowledge acquisition Organizations gain knowledge directly through the experiences of their own employees or

indirectly through the adaptation of experiences of other organizations. Hence the first phase

of the learning cycle involves either the creation or acquisition of knowledge.

Building on experiential learning theory, a person continually cycles through a process of

having a concrete experience, making observations and reflecting on that experience, forming

abstract concepts and generalizations based on those reflections, and testing those ideas in a

50

new situation, which leads to another concrete experience (Figure 15). In his model, Kim

(1993) distinguished between operational and conceptual learning. Operational learning

represents learning at the procedural level, where one learns the steps in order to complete a

particular task. At this level individuals learn through the acquisition of skills or know-how,

which implies the physical ability to produce some action. This know-how is captured as

routines, such as operating a piece of machinery or filling out an entry form. Conceptual

learning has more to do with the integration of experiences into existing mental models, such

as the thinking about why things are done a certain way in the first place. Conceptual

learning may challenge the existence of prevailing conceptions and lead to new frameworks

in the mental model.

Observationand reflection

Concreteexperience

Formation ofabstract concepts

and generalizations

Testing implicationsof concepts innew situations

SituationalLearning

5

4

Learningunder

Ambiguity

Incomplete Learning Cycle:Knowledge Acquisition

Figure 15: Incomplete Learning Cycle - Knowledge Acquisition

(Modified from Kolb 1984, p.21)

Kim’s model identifies two barriers to organizational learning in the knowledge acquisition

process: learning under ambiguity and situational learning (Kim 1993).

Learning under ambiguity Building on Kim’s model, learning under ambiguity occurs when the linkage between the

environmental response and their individual beliefs is interrupted; reasons for changes in the

environment cannot be clearly identified. The individual mental models are not updated in

any effective manner by the observed response of the environment to organizational action

51

(Figure 16). Using the “Lewian Experimental Learning Model”, individual learning is

constrained as it moves through the incomplete learning cycle (Figure 2), forms concepts,

generalizise, and act on them (test the concepts), but cannot observe what is happening.

According to this, learning under ambiguity occurs when the individual cannot understand

the observed experiences.

Learning under Ambiguity:

Embedded in Clique Networks

“LOr1: Knowledge Source” mostly internal

Low support on “FF1: Scanning Imperative”

Low support on “FF4: Experimental Mind-set”

Social Network StructureClique Network

ConceptualAssess

DesignObserve

Implement

Individual Learning

Operational



IndividualAction

IndividualAction



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Figure 16: Barrier to knowledge acquisition - Learning under ambiguity

Individuals who are embedded in a “closed” network structure (clique network) are often tied

to contacts that view the issue in similar ways. That is, they are more strongly constraint by

their social network and have fewer options to gather non-redundant information that could

clarify the ambiguities. As Granovetter, social network researcher, pointed out, “[…]

individuals with few weak ties will be deprived of information from distant parts of the social

system and will be confined to the provincial news and views of their close friends”

(Granovetter 1983, p. 202).

52

Furthermore, organizational learning researchers argue that sound learning can only occur

with a foundation of enhanced consciousness or a thorough understanding of one’s

environment (Nevis et al. 1995). Building on their research, they claimed that the facilitating

factor “FF1: Scanning Imperative” is a basic process for increasing awareness that can lead to

learning. Furthermore, they argued that organizations that want to improve their learning

capabilities need an environment in which people are encouraged to try out new things on an

ongoing basis.

This point of view is supported by Dorothy Leonardo-Barton (1992), who considers the next

production frontier to be the organization of the factory as a learning laboratory. Thus, a high

organization’s effectiveness in the facilitating factor “FF4: Experimental Mind-set” refers to

organizational support for trying new things and curiosity about how things work. Learning

under ambiguity is held to occur if individuals are highly constrained by their social network

structure and have less people to gather non-redundant information from, the organization

prefers to develop knowledge internally, and there is only little evidence to support

organizational curiosity and gathering of information about conditions and practice outside

the unit/organization.

Proposition: learning under ambiguity

Learning under ambiguity is more likely to occur in organizations characterized by employees embedded in clique networks (high network constraint and small effective size of network), and which tend to have the following learning orientation:

“LOr1: Knowledge Source”: a preference for developing knowledge internally;

and which tend to be associated with low support on the following facilitating factors:

“FF1: Scanning Imperative”: gathering information outside the unit/organization, and

“FF4: Experimental Mind-set”: organizational curiosity about conditions and practices.

Situational learning According to Kim, situational learning occurs “when the individual forgets or does not codify

the learning for later use; the link between individual learning and individual mental model is

severed” (Kim 1993, p. 46). Here, the link between making observations, reflecting on their

experience and the generalization based on those reflections is interrupted (Figure 17). Thus,

the individuals’ mental models are not changed and the organization does not have a way of

absorbing the learning either. Crisis management is a good example of situational learning,

where there is a strong focus on products and outcome. Each problem is solved, but no

learning is carried over to the next case. An organization with a formal and rehearsed crisis

management plan may be said to have overcome at least one situational learning barrier.

53

Situational Learning:

Embedded in Entrepreneurial Networks

“LOr2: Content-Process Focus” mostly “how”

Low support on “FF2: Performance Gap”

Low support on “FF3: Concern for Measurement”

IndividualAction

IndividualAction





Social Network StructureEntrepreneurial Network

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ConceptualAssess

DesignObserve

Implement

Individual Learning

Operational


Weltanschauung

SharedMentalModels

Figure 17: Barrier to knowledge acquisition – Situational learning

Burt (1992) argued that individuals whose relationships connect multiple bodies of

knowledge broker the flow of information between people. This leads to greater opportunity

to acquire non-redundant information and fewer constraints. In contrast, Hansen (1999)

argued that only strong ties promote the transfer of complex knowledge. Consistent with

these findings, we suggest that while weak ties are instrumental for the transfer of solutions,

or simple information, people relied heavily on strong ties to help frame and solve problems.

Situational learning is held to occur when individuals have access to a great deal of

information but have no ability to reflect on them, and generalize and codify them for later

use. This may happen as the individuals are embedded in large networks with many non-

redundant contacts. Individuals in this position have access to a great deal of information.

However, by gathering information the emphasis is more on the accumulation of knowledge

about how products/services are developed, or improved, then on thinking about what or why

things are done. In the case of situational learning, there is only little evidence to support the

discourse over metrics as a learning activity, and people do not use performance shortfalls as

opportunities for learning. Organizational learning researchers point out that the potential for

learning is proportional to how widely performance gap concerns are shared (Nevis et al.

1995). According to them, facilitating factor “FF2: Performance Gap” describes the shared

perception among the organizational members of a gap between the actual and the desired

state of performance. The awareness of a performance gap – asking anyone to face up to and

54

accept feedback or to accept a vision that means giving up a deeply held world view – opens

the door to learning by providing the initial awareness that new knowledge needs to be

generated. As part of the organizational feedback system, extended consideration of

measurement issues become a critical part of learning. Examining and developing metrics

for strategic initiatives needs a great deal of time and requires the surfacing of the underlying

assumptions about what needs to be measured. The facilitating factor “FF3: Concern for

Measurement” indicates how much the discourse about measurements, and the search for the

most appropriate ones, is a critical aspect of learning in the organization.

Proposition: situational learning

Situational learning is more likely to occur in organizations characterized by employees embedded in entrepreneurial networks (low network constraint and large effective size of network), and which tend to have the following learning orientation:

“LOr2: Content-Process Focus”: a preference for accumulation of knowledge about how products/services are developed or improved;


“FF2: Performance Gap”: seeing performance shortfalls as learning opportunities, and

“FF3: Concern for Measurement”: defining or measuring key factors.

55

Table 4: Barriers to knowledge acquisition

Barriers to knowledge

acquisition

Type of social network

structure

Learning orientations Facilitating factors

“FF1: Scanning

Imperative”

low support Learning under

ambiguity

Clique networks

high network constraint;

small effective size of

network

“LOr1:Knowledge

Source”

mostly internal

“FF4: Experimental

Mind-set”

low support

“FF2: Performance

Gap”

low support

Situational learning

Entrepreneurial

networks

low network constraint;

large effective size of

network

“LOr2: Content-Process

Focus”

mostly how?

“FF3: Concern for

Measurement”

low support

3.3.2 Knowledge sharing One way to think about the dissemination of knowledge and its role in the learning cycle

comes from the distinction between conceptual learning and operational learning (c.f.,

“Mental model,” p. 16). Building on Kim’s model, conceptual learning has to do with

thinking about why things are done (Kim 1993). This sometimes challenges the very nature

of existing conceptions and leads to new frameworks in the individual mental model.

Operational learning represents learning where one learns the steps to complete a particular

task. Operational learning accumulates and changes routines.

Knowledge is disseminated in organizations through a variety of ways or channels. Hansen

(1999) showed that different kinds of network ties or interpersonal relationships are

necessary for different kinds of knowledge transfer. Having a large number of “weak ties,”

that is, infrequent, distant relationships and acquaintanceships, facilitates the search for new

knowledge. A person with a broad network can find new information easily, for example, by

using e-mail and web searches. If the information is relatively simple and easy to transfer,

weak ties are very efficient and useful. However, weak ties can actually slow the

transmission of complex information, which requires a strong connection among individuals

or groups. Kim’s model identifies two barriers toorganizational learning in the knowledge

dissemination process: role-constrained learning, and fragmented learning.

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Role-constrained learning With regard to the first incomplete learning cycle, March and Olsen (1975) theorized that an

interruption in the connection between individual beliefs and individual action would result if

individuals were limited in their ability to act by their role in organization (Figure 18).

Consequently, their learning would not be reflected in their actions.

Clique networks are argued to do two things. First, they affect the way people are socialized

into a social circle. Specifically, a strong tie could ease the transfer of complex knowledge

because it is more likely than a weak tie to be embedded in a dense web of third-party

relationships (Granovetter 1973, Hansen 1999). Thus, an individual embedded in a clique

network is surrounded by contacts who view the issues in similar ways. Second, clique

networks facilitate sanctioning that makes it less risky for people in the network to trust one

another. At the same time, it promotes the formation of norms and shared rules. Thus,

individual behavior is guided by norms and rules defining what is considered appropriate and

inappropriate behavior.




IndividualAction

IndividualAction



Role-constraint Learning:


“LOr4: Dissemination Mode” mostly informal

Low support on “FF77: Operational Variety”

Low support on “FF8: Multiple Advocates”

ConceptualAssess

DesignObserve

Implement

Individual Learning

Operational

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Figure 18: Barrier to knowledge sharing – Role-constrained learning

57

According to organizational researchers, operational variety is an important factor in

organizational learning capability because it provides an opportunity to understand the

implications and consequences of different ways of working (Nevis et al. 1995). The

organization’s effectiveness in the facilitating factor “FF7: Operational Variety” provides

more options and, perhaps even more important, allows the development of multiple role

models. Although operational variety is a prerequisite for enhancing the dissemination of

knowledge, in order for knowledge to be effectively disseminated and utilized support for

new ideas is essential.

Organizational laerning reserchers found that unless a significant number of individuals act

as champions, a developing base of knowledge does not become broadly used (Nevis et al.

1995). The greater the number of advocates who promote new ideas and the greater the

number of “knowledge brokers” who bring knowledge into the organizational system, the

more rapidly and extensively will organizational learning take place. The facilitating factor

“FF8: Multiple Advocates” indicates the organization’s effectiveness in this factor.

Proposition: role-constrained learning

Role-constrained Learning is more likely to occur in organizations characterized by employees embedded in clique networks (high network constraint and small effective size of network), and which tend to have the following learning orientation:

“LOr4: Dissemination Mode”: knowledge is mostly shared through informal methods;


“FF7: Operational Variety”: appreciate different methods, procedures, and competences, and

“FF8: Multiple Advocates”: support for new ideas and methods advanced by employees.

Fragmented learning As employees gain experiences and presumably expertise, firms need processes to transmit

those experiences throughout the organization. One result is that knowledge, which at one

time was very personal or tacit, is shared and becomes available to others in the organization.

If an organizational unit is interested in enhancing its learning capability, it must examine the

extent to which it has open boundaries. When there are tight controls over information or

rigid rules about who belongs at planning and problem-solving meetings, only the fortunate

few who are allowed to participate in the largest number and variety of events are provided

with rich learning opportunities. Fragmented learning is more likely to occur when there are

instances in which individuals learn but the organization as a whole does not. Kim (1993)

58

points out that this happens when the link between individual mental model and shared

mental model is interrupted (Figure 19).

Social learning researchers have espoused the notion of “legitimate peripheral participation”

as a guideline for examining this (Lave and Wenger 1991, Wenger et al. 2002). According to

Nevis and colleagues, two facilitating factors are especially relevant to knowledge sharing:

“FF5: Climate of Openness” and “FF6: Continuous Education” (Nevis et al. 1995). Climate

of openness is the extent to which the existence of information boundaries influences the

exchange of knowledge and experiences. The factor “FF6: Continuous Education” assesses

the organization’s commitment to lifelong learning at all levels of the organization. This

includes formal education programs but goes well beyond that to more pervasive support of

any kind of development experience. In many ways, this factor is another way of expressing

what Senge and his colleagues call “personal mastery” (Senge et al. 1994).

Fragmented Learning:


“LOr3: Documentation Mode” mostly personal

Low support on “FF5: Climate of Openness”

Low support on “FF6: Continuous Education”

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IndividualAction

IndividualAction




Weltanschauung

SharedMentalModels


ConceptualIndividual Learning

Operational



Figure 19: Barrier to knowledge sharing – Fragmented learning

According to Burt (1992), participation in and control of information diffusion underlies the

social capital of structural holes. Consequently, structural holes are an opportunity to broker

the flow of information between people, but at the same time they enable the people to

control the issue that bring together people from opposite sides of the hole. Whether or not

knowledge is disseminated in an organization depends also on the outcome of doing so. If

analysis of the organization indicates that useful knowledge is acquired reasonably well but is

not readily available to significant others, the knowledge sharing phase of the learning cycle

59

may be interrupted. Many organizations devote considerable effort to dissemination of what

they consider important and useful knowledge, but are generally frustrated in achieving the

results they desire. One of the reasons may be an overload of information being distributed,

such that few things truly stand out as being critical. Another reason is described by the

expression “knowledge is power”. The expression suggests that tacit knowledge will not

readily be shared. Individuals may fear that by sharing knowledge their colleagues and

employers will be less dependent on them.

Proposition: fragmented learning

Fragmented learning is more likely to occur in organizations characterized by employees embedded in entrepreneurial networks (low network constraint and large effective size of network), and which tend to have the following learning orientation:

“LOr3: Documentation Mode”: knowledge is mostly possessed by individuals


“FF5: Climate of Openness”: open communication among the organizational members;

“FF6: Continuous Education”: commitment of quality resources for learning.

Table 5: Barriers to knowledge sharing


sharing

Type of social network

structure


“FF7: Operational

Variety”

low support Role-constrained

learning

Clique networks



network

“LOr4: Dissemination

Mode”

mostly informal

“FF8: Multiple

Advocates”

low support

“FF5: Climate of

Openness”

low support Fragmented

learning

Entrepreneurial

networks

low network constraint


network

“LOr3: Documentation

Mode”

mostly personal

“FF6: Continuous

Education”

low support

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3.3.3 Knowledge utilization Knowledge may be generated and disseminated throughout an organization, but unless it is

used to alter the decisions, behavior, or culture within an organization then the learning cycle

remains incomplete. The area of knowledge utilization represents the ultimate payoff of the

learning process. How or if knowledge is used reflects our values and indicates preferences

for certain outcomes. For true assimilation of knowledge to take place, individuals must be

able to embrace new mental models and make their own meaning out of their experiences in

approaching something new. When knowledge is created and disseminated around an

organization, it can challenge our view of the world and point out the need for change.

Whether this knowledge is used to alter the organizational actions depend on a variety of

factors. Kim’s model (1993) identifies three barriers to organizational learning in the

knowledge utilization process: audience learning, superstitious learning, and opportunistic

learning.

Audience Learning:


“LOr5: Learning Focus“ mostly incremental

Low support on “FF7: Operational Variety”

Low support on “FF8: Multiple Advocates”

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Operational



IndividualAction

IndividualAction



Figure 20: Barrier to knowledge utilization – Audience learning

Audience learning March and Olsen (1975) termed this barrier audience learning to highlight the idea that the

link between individual action and organizational action is interrupted. Audience learning

may occur when individuals change their own behavior, but the effect of these actions on the

organizational actions is ambiguity. In this way, individuallearning takes place, but adaption

by the organizational environment does not necessarily follow.

61

Individual behavior in Clique networks is guided by norms and rules defined by the people in

the network. In addition, network members react to internal and external changes as they try

to identify and eliminate sources of error for their normal behavior (Putman 1993; from Burt

2001). Thus, even if the individual has learned something new it can be only used within the

range specified by existing norms and values. In other words, new knowledge can only be

utilized as it is accepted by her/his network members. As already discussed above, the

facilitating factors “FF7: Operational Variety” and “FF8: Multiple Advocates” are important

factors in organizational learning processes. Both of these facilitating factors should make it

possible for a significant number of employees to be involved in learning and in

demonstrating that there are many ways to accomplish something. By working in different

ways, individuals see variety and diversity as a way of internalizing the value of multiple

approaches. A lack of support in this first factor in an organization indicates that there is

only little support for different methods, procedures, and competencies; individuals tend to

follow similar routines, although the change to new structure should lead to greater variation.

The factor “FF8: Multiple Advocates” has an impact because it allows many individuals to

propose or support learning initiatives. A lack of support in this second factor indicates that

there may be only few opportunities to promote new ideas. Clique networks do insist that

everybody follows the same work rules or use the same processes. There is only little

organizational support for experimentation, and interest in creative ideas or new

technologies, and only few “champions” who set the stage for learning. This may create an

energized coalition that educates itself, and assimilates the skills and attitudes that are

necessary, but then do not act as a teacher to others.

Proposition: audience learning

Audience learning is more likely to occur in organizations characterized by employees embedded in clique networks (high network constraint and small effective size of network), and which tend to have the following learning orientation:

“LOr5: Learning Focus”: preference for knowledge related to the improvement of existing products and services;


“FF7: Operational Variety”: appreciation for different methods, procedures, and competences, and

“FF8: Multiple Advocates”: support for new ideas and methods advanced by employees.

62

Superstitious learning Superstitious learning occurs when organizational members draw incorrect conclusions

regarding the impact of organizational actions on the environment (Figure 21). It represents

an interruption in the value-chain (design-make-market-deliver). Organizations put a strong

emphasis on learning investment in engineering or production activities, but there is no real

basis for the connections made between organizational action and environmental response

(“market-and-deliver” functions).

Superstitious Learning:


“LOr6: Value-Chain Focus” is mostlydesign/make functions

Low support on “FF1: Scanning Imperative”

Low support on “FF10: Systems Perspective”


IndividualAction

IndividualAction






Weltanschauung

SharedMentalModels

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Operational

Figure 21: Barrier to knowledge utilization – Superstitious learning

Individuals embedded in clique networks are surrounded by contacts that view the issues in

similar ways. In order to draw correct conclusions about the impact of organizational actions

on the environment it is important to gather data from the external environment is needed

(“FF1: Scanning Imperative”). There is substantial agreement that many organizations get

themselves into trouble because of limited or poorly directed efforts in this regard.

According to the social network literature, individuals embedded in close networks have

access to similar information and develop shared norms and routines. Thus, there is only little

need to reflect on the organizational impacts on the external environment to acquire new

information. Furthermore, Senge and colleagues claimed that the “discipline” of systems

perspective enables people to think in terms of whole systems and the interdependence of

parts (“FF10: Systems Perspective”) (Senge et al. 1994). When individuals lack a systems

perspective, their actions often lead to unanticipated consequences. Furthermore,

63

organizational learning researchers added that organizational learning is limited when

individuals cannot recognize the relationships among processes, structures, and disperse

action (Nevis et al. 1995). We assume that individuals who are socialized into a tight group

tend to have very rigid boundaries. In other words, their network is closed and there is only a

weak awareness of time delay between action and its outcome.

Proposition: superstitious learning

Superstitious learning is likely to occur in organizations characterized by employees embedded in clique networks (high network constraint and small effective size of network), and tend to have the following learning orientation:

“LOr6: Value-Chain Focus”: emphasis on learning investments in engineering or production activities;


“FF1: Scanning Imperative”: gathering information outside the unit/organization, and

“FF10: Systems Perspective”: support for recognition of interdependences among organizational units.

Opportunistic Learning:


“LOr7: Skill-Development Focus” mostly individual

Low support on “FF9: Involved Leadership”

IndividualAction

IndividualAction







Weltanschauung

SharedMentalModels


Operational

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Figure 22: Barrier to knowledge utilization – Opportunistic learning

Opportunistic learning

According to Kim (1993), opportunistic learning occurs when organizations purposely try to

bypass the standard procedures because their established ways of doing business are seen as

an impediment to a particular task. It takes place when organizational action is based on an

individual’s actions and not on the organization’s widely shared mental models (Figure 22).

64

Individuals accustomed to interacting with contacts from diverse communities of practice are

presented with a greater opportunity of learning how to convey complex ideas than are

individuals limited to interactions within a single body of knowledge. These individuals are

more independent to behave differently from group norms. On the contrary, Nevis and

colleagues argued that for truly assimilated, actionable learning to occur, leaders have to be

involved in knowledge sharing and utilization (Nevis et al. 1995). To avoid opportunistic

learning leaders have to be engaged in hands-on implementation of the vision by being an

early participant in any learning effort. In addition, organizational learning researchers argue

that “[o]nly through direct involvement that reflects coordination, vision, and integration can

leaders obtain important data and provide powerful role models” (Nevis et al. 1995, p. 82).

Proposition: opportunistic learning

Opportunistic learning is held to occur in organizations, in which employees are embedded in entrepreneurial networks (low network constraint and large effective size of network), and which tend to have the following learning orientation:

“LOr7: Skill Development Focus”: development of knowledge and skills pertaining to individuals;


“FF9: Involved Leadership”: leaders’ involvement in ensuring a learning environment.

65

Table 6: Barriers to knowledge utilization


utilization

Types of social network

structure


“FF7: Operational

Variety”

low support Audience learning

Clique networks



network

“LOr5: Learning Focus”

mostly incremental

“FF8: Multiple

Advocates”

low support

“FF1: Scanning

Imperative”

low support Superstitious

learning

Clique networks



network

“LOr6: Value-Chain

Focus”

mostly design/make

“FF10: Systems

Perspective”

low support

Opportunistic

Learning

Entrepreneurial

networks

low network constraint;


network

“LOr7: Skill

Developmental Focus”

mostly individual

“FF9: Involved

Leadership”

low support

66

3.4 Summary of propositions As we have already stated, the organization’s ability to learn is a joint function of the formal

and informal organization’s structure and the cultural learning orientation. The combination

of Kim’s (1993) model with the notion of learning orientations and facilitating factors (Nevis

et al. 1995), and the two specific social network structures (clique networks and

entrepreneurial networks) allows deriving propositions that characterize potential barriers to

organizational learning. These propositions are listed in the summary table (Table 7).

Table 7: Characteristics of barriers to organizational learning

Types of social network structure

Clique network Entrepreneurial network

Learning under ambiguity Situational learning “FF1”: Low support

“FF2”: Low support

Knowledge acquisition “LOr1”:

Mostly internal “FF4”: Low support

“LOr2”: Mostly how “FF3”:

Low support

Role-constrained learning Fragmented learning “FF7”: Low support


Knowledge sharing “LOr4”:

Mostly informal “FF8”: Low support

“LOr3”: Mostly personal “FF6”:

Low support

Audience learning Opportunistic learning “FF7”: Low support “LOr5”:

Mostly incremental “FF8”: Low support

“LOr7”: Mostly individual


Superstitious learning “FF1”: Low support

Knowledge utilization

“LOr6”: Mostly design/make “FF10”:

Low support

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Chapter 4 Methods

4.1 Chapter overview This chapter presents the research design and the operationalization of variables.

Furthermore, the instruments and procedures used in this study are explained. The chapter is

subdivided into a brief chapter overview (4.1), the research design part (4.2) and the data

analysis procedure part (4.3).

The research design part (4.2) begins with an introduction into the case study methodology to

familiarize the reader with the case organization, describes the selection of subjects and

variables and the instruments.

The data analysis procedure part (4.3) describes the execution of data collection and data

analysis. Furthermore, it presents important concerns in social network measurement.

4.2 Research design Every type of empirical research has an implicit, if not explicit, research design. In the most

elementary sense, the design is the logical sequence that connects the empirical data to a

study’s initial research question and, ultimately, to its conclusions. In this sense, the purpose

of the design is to help to avoid the situation in which the evidence does not address the

initial research question.

4.2.1 Case study methodology Case study research excels at bringing us to an understanding of a complex issue or object

and can extend experience or add strength to what is already known through previous

research. Case studies emphasize detailed contextual analysis of a limited number of events

or conditions and their relationships. Researchers have used the case study approach for

many years across a variety of disciplines. Social scientists, in particular, have made wide

use of this research methodology to examine contemporary real-life situations and provide

the basis for the application of ideas and extension of methods. Robert K. Yin defines the

case study methodology as an empirical inquiry that investigates a contemporary

phenomenon within its real-life-context; when the boundaries between phenomenon and

context are not evident; and in which multiple sources of evidence are used (Yin 2003a). In

the 1960s, researchers were becoming concerned about the limitations of quantitative

68

methods. Hence, there was a renewed interest in case study. Strauss and Glaser (1967)

developed the concept of “grounded theory”. This, along with some well-regarded studies,

accelerated the renewed use of the methodology.

A frequent criticism of case study methodology is that its dependence on a few cases renders

it incapable of providing a generalizing conclusion. Hamel and colleagues (Hamel et al.

1993) and Yin (2003a, 2003b) forcefully argued that the relative size of the sample, whether

two, 10, or 100 cases are used, does not transform a multiple case into a macroscopic study.

The body of literature on case study research is limited in comparison to that of experimental

or quasi-experimental research. The requirements and inflexibility of the latter forms of

research make case studies the only viable alternative in some instances. It is an advantage

that case study research does not need to have a minimum number of cases or randomly

selected cases. The researcher is called upon to work with the situation that presents itself in

each case. Case study research can be single or multiple-case design, where a multiple

design must follow a replication rather than a sampling logic. When no other cases are

available for replication, the research is limited to single-case design. Yin (2003a) pointed

out that generalization of results, from either single or multiple designs, is made to theory and

not to populations. Multiple case study research strengthens the results by replicating the

pattern matching, thus increasing confidence in the robustness of the theory.

Building theory from case study research The research design, used in this work, is described in Figure 23. From a review of the

organizational learning and social network literature in “Chapter II”, organizational learning

capability has been described as a joint function of the formal and informal organizational

structure and the cultural learning dimension. After a review of the literature a framework

was developed. In the framework the seven barriers to organizational learning were

operationalized by developing linkages the between the informal structure (social network

structure) and the organizational learning orientation and the factors that facilitate learning.

The dimensions have been discussed in the literature review and will be operationalized in

this chapter (Table 8).

69

developframework

selectcases conduct 1st

case study

conduct 2ndcase study

conduct 3rdcase study

conduct 4thcase study

write individualcase report




draw cross-case conclusions

modify theory

develop implications

design data collection

DEFINE & DESIGN PREPARE, COLLECT & ANALYZE ANALYZE & CONCLUDE

Figure 23: Research design

After the specification of the research question and propositions, four cases in Germany and

Norway were selected and a questionnaire for the data collection has been developed. The

case studies were carried out by collecting data with a mail survey. The survey was

organized in three parts: (Part I) collection of network data, (Part II) assessing learning

orientations, and (Part III) assessing evidence on facilitating factors and collection of

demographic data. The first part asked the respondents to identify the five people that they

have the most important professional contact within their organization. The second part

asked the respondents to assess their organization as a learning system. The third part of the

survey asked the respondent to reflect on the amount of evidence of a set of processes that

influence the ease with which learning occurs. Additive, demographic data was collected.

Using the case study approach, we sought to explore the relationship between organizational

learning processes and social network structure. A multiple case study approach was used.

Since the research work was carried out in close collaboration with the firm management,

after executing each case study an individual case report was written. Coupled with the

within-case analyses, a cross-case search for patterns was undertaken to explore the effects of

social network structures on organizational learning processes in a range of settings. The

objective was to discuss the results from the case studies on a more general basis and to list

similarities and differences between the cases. After conducting the four case studies cross-

case conclusions were drawn and implications for further research developed.

70

4.2.3 Selection of cases The source material for this research work was from four forest sector organizations located

in Germany and Norway. All cases were located in the forest sector, three cases in Germany

and one case in Norway. The organizations are either responsible for organizing timber

harvesting activities and supply chain management, or are dependent on wood supply and

produce wood-based products. Even so all firms are forest sector organizations there have

been differences in locations and structure, operational tasks, and size.

The first case is a forestry-contractor firm located in Germany. This firm provides the link

between all kinds of forest owners and the wood processing industries. The organization

offers a broad spectrum of services, from mechanized wood harvesting to wood procurement

management. The heads of operations are contact persons for private forest owner and public

forest owners. People working in this organization organize timber harvesting processes,

lead sub-contractors doing harvesting and steer the single steps in the timber harvesting

process chain. Given the need to provide integrated solutions that draw on expertise from

different areas of expertise, working success depends critically on individuals’ ability to

acquire, transfer and utilize knowledge. The firm distinguishes itself from other the other

cases by offering an advanced service for the entire timber harvesting process chain.

Additionally, the firm has a strongly developed organizational structure. The main office is

located in southern Germany. Together with four other agencies with regional management

and offices customer proximity and the consideration of the regional features are ensured.

Several heads of operations are assigned to every office and form the base of the

organization. At the time of the case study, the firm employed 49 employees. The majority

of employees are working at the operational level.

The second case is a forestry-contractor firm also located in Germany. The firm

distinguishes itself from the other cases by having a very flat organizational structure. There

is only one main office located in southern Germany and several heads of operations

throughout Germany. At the time of the case study, the firm employed 13 employees. The

majority of employees are working at the operational level.

The third case is a family-owned sawmill located in southern Germany. The firm

distinguishes itself from the other cases by being family-owned and mainly producing sawn

timber. Additionally, the firm has a strong organizational structure with a strong formal

hierarchy. At the time of the case study, the firm employed 94 employees. The majority of

employees are working at the operational level.

71

The fourth case is a forestry-contractor firm located in Norway. The organization is

responsible for a broad spectrum of services, from mechanized wood harvesting to wood

procuremnet management. The main office is located in Middle Eastern Norway. Despite a

huge area that is being covered, the firm has a flat organizational structure with a moderate

formal hierarchy. At the time of the case study, the firm employed 61 employees. The

majority of employees are working at the operational level.

4.2.4 Selection and operationalization of variables The literature review in Chapter 2 (p. 31) emphasized the information flow through

interpersonal networks and its importance for organizational learning. The conceptual

framework in the synthese-chapter (“Chapter 3,” p. 48) linked organizational learning

orientation and factors that facilitate learning with types of social network structure.

Furthermore, in the framework we postulate that potential barriers to organizational learning

can be associated with specific types of social network structure and profiles of

organizational learning capabilities.

Table 8: Variables of organizational learning characteristics

Type of social network structure Learning orientations Facilitating factors

Clique networks “LOr1: Knowledge Source” “FF1: Scanning Imperative”

Entrepreneurial networks “LOr2: Content-Process Focus” “FF2: Performance Gap”

“LOr3: Documentation Mode” “FF3: Concern for Measurement”

“LOr4: Dissemination Mode” “FF4: Organizational Curiosity”

“LOr5: Learning Scope” “FF5: Climate of Openness”

“LOr6: Value-Chain Focus” “FF6: Continuous Education”

“LOr7: Skill Development Focus” “FF7: Operational Variety”

“FF8: Multiple Advocates”

“FF9: Involved Leadership”

“FF10: Systems Perspective”

72

In this section, these barriers are operationalized by different variables listed in Table 8. The

social network structure is described in terms of two different network types, “clique

networks” and “entrepreneurial networks”. How the two different social network structures

are derived and calculated is presented in section “Organizational learning capability (p. 27)”.

Beside the informal structure, the organizational learning characteristics are described by

seven learning orientations and assessed through ten facilitating factors describing the

organizations’ preferences in these factors. The selection and operationalization are

described in the following sections.

Social network data Network data can be collected using sociometric or egocentric techniques (Wassermann and

Faust 1994). In sociometric techniques, each respondent is provided with a fixed contact

roster and asked to describe his or her relationship with every individual on the roster. A

virtue of the sociometric approach is that it provides information on all interactions inside a

network. However, the technique can also introduce inaccuracies into the network data.

Defining an appropriate boundary around the network, the set of individuals who are

interconnected, is critical (Laumann et al. 1983). To the extent that the network boundary

varies from one person to the next, asking each respondent to report on connections that lie

outside her or his frame of reference can be problematic, too. Individuals provide more

accurate network data on that part of the network with which they are most familiar

(Kumbasar et al. 1994). Their assessment of network connections involving distant

individuals is less accurate (Krackhardt and Kilduff 1999).

An alternative approach is to collect network data using egocentric techniques. Each

individual responds to a series of questions that generate names, resulting in a roster of

contacts (Burt 2000, Fischer 1982). Next, the respondent describes the relationship with each

cited contact. In some applications of egocentric techniques, respondents are asked to

describe the relationships among their contacts. In this study, since we collected network

data from all organizational members, we constructed information about ties among a

respondent’s contacts using the responses from the contacts themselves. A virtue of the

egocentric technique is that it asks an individual to report on that part of the network with

which they are most familiar. Additionally, individual responses can be aggregated to

describe the total network. A network can be constructed between different members of the

organization based on their reported relationships with each other. A potential drawback of

the egocentric technique is that it can miss important interactions that lie outside a

73

respondent’s frame of reference. More generators mean larger networks at a cost of

interview time and respondent patience (Burt 1984, Marsden 1990). An implication of Burt’s

review is that personal discussion relations should be a first priority. The discussion relations

are the informal relations through which the information and control benefits of structural

holes most clearly operate.

In the current study, we used the egocentric technique to gather network data (Table 9). Each

respondent was asked to respond to the following name-generator question: “Please, identify

the five people that you have the most important professional contact within your

organization (in decreasing order of frequency).” For the name generator, a respondent could

nominate up to five contacts. The respondents were asked to indicate the intensity of their

connection in terms of communication frequency (Granovetter 1973, Hansen 1999).

Table 9: Name-generator

From time to time, most people discuss important issues with other. Please, when you think about your organization – who are the people, you discuss your business issues with that are important to you?

“Please, identify the five people that you have the most important professional contact

within your organization (in decreasing order of frequency).” Contacts First Name Last Name

Most frequent Second most Third most Fourth most Fifth most

By using egocentric technique to gather network data, each person’s ego network can be

described and structural indicators of the networks can be computed. From the structural

indicators different types of social network structure can be identified as explained in more

detail in the part “Data Analysis Procedures (p. 81)”.

Learning orientation and facilitating factors The organizational learning orientation and the factor that facilitate learning were

operationalized through an “Organizational Learning Inventory” (OLI) diagnostic tool (Nevis

et al. 1995). The first section of the OLI, consisting of learning orientations, represents the

critical dimensions in describing how organizational learning takes place and the content of

learning. Each respondent was asked to describe the seven learning orientations in her or his

74

organization. The dimensions define the practices by which knowledge is acquired,

disseminated, or used. Each learning dimension represents an extreme position on a

continuum. The dimensions were measured with items adapted from an instrument

developed by (Nevis et al. 1995) and modified according to the target groups. The items are

displayed in Table 10 and are operationalized by closed question.

The second section of the OLI consists of ten facilitating factors. The facilitating factors are

the practice or conditions that promote learning within an organization. Each respondent was

asked to evaluate the degree to which her or his organization is effective in each factor.

Collectively, the factors determine an organization’s learning potential. Each facilitating

factor was measured with items adapted from an instrument developed by (Nevis et al. 1995)

and modified according to the target groups. The items are displayed in Table 11. Each item

was measured with a 5-point Likert scale, ranging from (1) “strongly agree” to (5) “strongly

disagree”. Several items were used to define each single facilitating factor.

Learning orientations

The learning orientation “LOr1: Knowledge Source” is the mean of the items (LOr1a, LOr1b,

LOr1c) shown in Table 10. Each item defines a single measure of the extent to which the

organization prefers to develop new knowledge versus the extent to which it is more likely to

seek knowledge developed by external sources. The implications of a value-choice in this

learning orientation are important. Choosing the assumption that internally developed

knowledge is the source of competitive advantage is to decide to invest heavily in one’s own

research and development. Conversely, to choose the path of emphasizing external

knowledge is to decide to make heavy in environmental scanning or ways of evaluating what

others are doing.

The learning orientation “LOr2: Content-Process Focus” is the mean of the items (LOr2a,

LOr2b, LOr2c) (Table 10). Each item defines a single measure of the preference for

knowledge about what products or services are as opposed to knowledge about how those

products or services are developed, delivered, or improved. On the one hand, there are strong

focus product-related issues themselves; on the other hand, the focus is on development of

core capabilities that can be applied to make the product service better.

Borrowing from Polyani’s distinction between tacit and explicit knowledge (Nonaka 1998),

the Learning orientation “LOr3: Documentation Mode” is the mean of the items (LOr3a,

LOr3b) shown in Table 10. Each item defines a single measure of the extent to which

knowledge is seen as something that an individual possesses versus something that can

75

always be made explicit so that it is available to others. The difference in these approaches is

between “manualized” know-how and an individual craftsman’s knowledge.

The learning orientation “LOr4: Dissemination Mode” pertains to differences between

establishing an atmosphere in which learning evolves informally and an atmosphere in which

a more structured, controlled approach is taken to introduce learning. This learning

orientation is the mean of the items (LOr4a, LOr4b, LOr4c) (Table 10). Each item defines a

single measure of the dissemination mode applied within the organization. In the formal

approach, a decision is made that a valuable insight or method should be shared and used by

others on a broad, institutionalized basis. In the informal approach, learning is spread

through encounters with role models and gatekeepers who actualize the insight or method by

behaving in a compelling way.

The learning orientation “LOr5: Learning Focus” is the mean of the items (LOr5a, LOr5b,

LOr5c) shown in Table 10. Each item defines a single measure of the preference for

incremental learning as opposed to transformative learning. The difference is whether

learning is directed at the enhancement of existing paradigms, products, or services, or

toward creating new ones. Argyris and Schön (1978) argue that organizational performance

problems are more likely to require a change in prevalent assumptions than improvement in

exsisting modes. The difference is between an experimental approach and an efficiency

exercise, between transformative learning and incremental learning.

The learning orientation “LOr6: Value-Chain Focus” is the mean of the items (LOr6a,

LOr6b, LOr6c) (Table 10). Each item defines a single measure of which core competences

and learning investments are valued and supported. Prahalad and Hamel (1990) argued in

their paper on core competences, that a decision to exit a function or stage in the value chain

and to have an alliance with another firm that performs that service amounts to a decision to

de-invest in ongoing learning in that are. It reflects an assumption that the organization can

gain a competive advantage through the ability to add value at a particular point in the value-

chain than other firms are able to do.

76

Team or group learning has received much attention in recent literature on organizational

learning. Nevis and colleagues support the need for team skills but believe that both,

individual and group skill development, are necessary (Nevis et al. 1995). The learning

orientation “LOr7: Skill Development Focus” is the mean of the items (LOr7a, LOr7b,

LOr7c) shown in Table 10. Each item defines a single measure of the preference for learning

geared to individual skill development and learning focused on team or group skill

development.

Table 10: Operationalization of the learning orientations

LOr1 Knowledge Source Internal External

LOr1a Your organization… views itself as a pioneer and encourages learning from your own experiences.

emulates the work of others and encourages learning from the action of others.

LOr1b Your organization attaches importance...

in being the first to develop a new product/idea.

in improving on the work of others.

LOr1c Your organizations performance… is based on your own knowledge. is measured by using data from the external environment.

LOr2 Content-Process Focus What? How? LOr2a Your organization invests mainly in

research on… what the future products/services should be.

how to realize future products / services.

LOr2b Employees are appreciated, who are able to… create new ideas realize products/services.

LOr2c In your organization you focus on… what your goals should be. how you should accomplish your goals.

LOr3 Documentation Mode Personal Public LOr3a When you need knowledge, you turn

to… the person most expert in that domain.

an organized source such as a library or databank.

LOr3b A successful knowledge exchange… only takes place if the people know each other well.

is possible, by making knowledge explicit so that it is available to others.

LOr4 Dissemination Mode Formal Informal LOr4a You learn desired operational

methods… by using written procedure guidelines and manuals.

by verbally sharing knowledge between members.

LOr4b New ideas and processes are disseminated…

in formal and regularly education program that large numbers of people attend.

through a selected group of people, who embrace them and share the knowledge informally.

LOr4c New staff members will be incorporated…

mainly through formal methods or written documents.

mainly through informal or verbal methods.

LOr5 Learning Focus Incremental Transformative LOr5a Learning / Education in your

organization focuses on… improving what you already know or do

further development in new areas / capabilities.

LOr5b To improve your work / processes… you use existing tools and methods. you focus on developing new instruments and methods.

LOr5c When things are going well… you tend to leave them as they are. you still think about how to improve them.

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Table 10: Operationalization of the learning orientations (continued)

LOr6 Value-Chain Focus Design/Make Market/Deliver LOr6a The organizational activities are… mainly focused on technical issues. mainly focused on service or market

issues.

LOr6b You focus on developing skills… needed to design and make products.

to market and deliver products or services.

LOr6c Other organizations benchmark themselves with you areas of…

production and technological development. the service and market functions.

LOr7 Skill Development Focus Individual Group LOr7a Your organization believes in… the skills and knowledge of

individual staff members. what can be accomplished by teams and task forces.

LOr7b Learning and educational programs are geared toward… the development of individuals. the improvement of cooperation in

teams.

LOr7c When hiring individuals, your organization is most interested in their ability to…

perform a specific function. to work well with others.

Facilitating factors

The second section of the OLI consists of ten facilitating factors. The facilitating factors are

the practice or conditions that promote learning within an organization. Each respondent was

asked to consider the degree to which her or his organization is effective in each factor.

Collectively, they determine an organization’s learning potential. Each factor was measured

with items adapted from an instrument developed by (Nevis et al. 1995) and modified

according to the target groups. The items are displayed in Table 11. Each item was

measured with a 5-point Likert scale, ranging from (1) strongly agrees to (5) strongly

disagree. The items define a single facilitating factor.

The facilitating factor “FF1: Scanning Imperative” is the mean of the items (FF1a, FF1b,

FF1c) (Table 11). Scanning imperative is a basic process for increasing awareness that can

lead to organizational learning. Each item defines a single measure of the extent to which the

organization is sensing development problems or opportunities and acting on them rather

than waiting until a problem is full-blown or a window of opportunities has closed. Scanning

imperative is a basic process for increasing awareness that can lead to learning.

Consequently, scanning is an organization’s scouting function and provides the stimulation

and direction of knowledge generation.

The facilitating factor “FF2: Performance gap” describes the shared awareness of

organizational members that there is a difference between the organization’s desired

performance and the actual performance. This factor is the mean of the items (FF2a, FF2b,

FF2c) shown in Table 11. Each item defines a single measure of the extent of the factor

“FF2: Performance Gap”. The potential for learning is proportional to how widely

performance gap concerns are shared. The second aspect of this factor is related to a growing

78

awareness of achieving a higher level of performance or a way of being more effective than

what is envisioned before.

The facilitating factor “FF3: Concern for Measurement” is the mean of the items (FF3a,

FF3b, FF3c) (Table 11). Each item defines a single measure of the extent considerable effort

is spend defining and measuring key factors and having a discourse over measurement

practices. All organization members measure performance in one way or another. In doing

so, they accept the general measurement practices that are customary in their function, their

firm, or industry. However, discussion on the development of measures by the people

involved and extended consideration of measurement issues are a critical part of learning.

The facilitating factor “FF4: Experimental Mind-set” refers to support for trying out new

ideas/concepts and the ability to experiment with methods and procedures. This factor is the

mean of the items (FF4a, FF4b, FF4c) shown in Table 11. Each item defines a single

measure of the extent of organizational curiosity about conditions and practices and the

support for experimentation within the organization. This factor is related to the support of

an environment in which people are encouraged new ideas. If we believe that experience

creates learning, it follows that the more experiences of different kinds that are engaged in,

the more learning will occur.

The facilitating factor “FF 5: Climate of Openness” is related to the degree to which

opportunities to observe and participate are available to organizational members, and the

ability to establish informal contacts to pursue interesting relationships on their own. This

factor is the mean of the items (FF5a, FF5b, FF5c) (Table 11). Each item defines a single

measure of the extent of open communication among organizational members. If an

organization is interested in enhancing its learning capabilities, it must examine the extent to

which it has open boundaries.

To constantly develop organizational learning capability is to engage in an ongoing, life-long

learning process. The facilitating factor “FF6: Continuous Education” is the mean of the

items (FF6a, FF6b, FF6c) shown in Table 11. Each item defines a single measure of the

extent of commitment of quality resources for learning. To constantly develop organizational

learning capability is to engage in an ongoing, never-ending process. It is all but impossible

to accept the notion of knowledge as a competitive aspect without realizing that one does not

reach a finishing point and stop learning.

The facilitating factor “FF7: Operational Variety” describes if the organization has manifest

flexible work rules, and allow the development of multi role models. This factor is the mean

of the items (FF7a, FF7b, FF7c) (Table 11). Each item defines a single measure of the extent

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of the support for different methods, procedures, and competences and the acceptance for

diversity. It assumes that an organization that supports variation in strategy, process and

personnel is more adaptable to uncertainty and allows the development of multiple role

models.

To make any skill or piece of knowledge useful to many members of an organization, key

members of the organization must be seen as using it and trying to influence others regarding

its value. The facilitating factor “FF8: “Multiple Advocates” is the mean of the items (FF8a,

FF8b, FF8c) listed in Table 11. Each item defines a single measure of the extent to support

generalization of learning to new situations. The greater the number of advocates who

promote a new idea and the greater the number of gatekeepers who bring knowledge into the

different organizational units, the more rapidly and extensively organizational learning takes

place.

The facilitating factor “FF9: Involved Leadership” is the mean of the items (FF9a, FF9b,

FF9c) (Table 11). Each item defines a single measure of the extent to which leaders in the

organization are personally and actively involved in learning initiatives and in ensuring that a

learning environment is maintained. Strong leadership is often a key factor in driving

knowledge acquisition, however, the importance for them to be involved in knowledge

dissemination and utilization is often underestimated.

The facilitating factor “FF10: Systems Perspective” has to do with the ability to think in

terms of whole systems and the interdependence of parts. This factor is the mean of the items

(FF10a, FF10b, FF10c) listed in Table 11. Each item defines a single measure of the extent

to which organizational members are aware of time delays between action and their outcome

and the interdependence of their organizational units. Organizational learning is limited

when employees cannot recognize the relationships among processes, structures, and

dispersed action.

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Table 11: Operationalization of the facilitating factors

(FF1a) We periodically ask clients and customers about their perception of our performance.

(FF1b) We maintain close contact with customers, suppliers, etc.

“FF1: Scanning

Imperative”

(FF1c) Information from the external environment provides opportunities for learning.

(FF2a) Most staff members agree that the performance could be improved.

(FF2b) There is a general awareness that the current standard of performance should be set higher.

“FF2: Performance

Gap”

(FF2c) We use unanticipated outcomes to make improvements.

(FF3a) We value the process of benchmarking with other organizations as a learning process.

(FF3b) We talk regularly about the outcomes and impacts of our work.

“FF3: Concern for

Measurement”

(FF3c) We use feedback, process analysis, and constructive critic for improvements.

(FF4a) We reward staff members who develop and try out new ideas, even when their actions lead to negative results.

(FF4b) We always try to understand how things work.

“FF4: Experimental

Mind-set”

(FF4c) Joint brainstorming exercises and experimentations is an important part of our work.

(FF5a) New ideas are shared openly and can be discussed by all interested employees.

(FF5b) New employees are given frequently opportunities to learn from others at all levels in the organization.

“FF5: Climate of

Openness”

(FF5c) Teams working on similar task can readily share their experiences and problems.

(FF6a) All employees are encouraged to acquire new knowledge and expertise.

(FF6b) We support education at all levels, ranging from basic skills to advanced professional development.

“FF6: Continuous

Education”

(FF6c) We are good at providing developmental, on-the-job learning in the daily business.

(FF7a) We do not insist that everybody follow the same work rules or use the same processes.

(FF7b) We hire people with different educations, backgrounds, and variety of experiences.

“FF7: Operational

Variety” (FF7c) In staffing working groups, we strive for a mix of people with different experiences and cultures.

(FF8a) Individuals with new knowledge to share are rarely blocked by the pressure to conform.

(FF8b) Initiatives from all staff to promote new areas of learning are encouraged.

“FF8: Multiple

Advocates”

(FF8c) All employees within the organization voluntarily share their experiences.

(FF9a) Leadership is encouraged at all levels.

(FF9b) If leaders espouse a new idea, they „walk their talk“ by being a good role model.

“FF9: Involved Leadership”

(FF9c) Everyone is a teacher, and everyone is a learner.

(FF10a) We have a long-range performance; we do not make changes based simply upon our current abilities or activities, (FF10b) When making a significant change we give considerable attention to the potential impact of the change on other parts.

“FF10: Systems

Perspective”

(FF10c) When we experience performance problems, we are more likely to examine our internal activities than we are to attribute our problems to external factors.

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Demographic data Background information about the respondents was collected in order to determine if age,

sex, hierarchical level and duration of firm membership influence their perceptions of the

organizations learning capacity. Data on individual backgrounds were taken from company

personnel records. This provided the organizational position and the name (sex).

Information about age and duration of firm membership were collected at the end of the mail

questionnaire. The demographic variables are listed in Table 12.

Table 12: Demographic variables

Organizational Position Age Sex Duration of firm membership • (Executive level); • (Administrative level); • (Operational level)

Year of Birth: 19 __ __ • (M); • (F)

You are working at “organization

XYZ” since: 19 __ __

4.3 Data analysis procedures

4.3.1 Mail questionnaire The data collection was done using standardized mail questionnaires. The mail questionnaire

included three parts: a name generator collecting network data, the Learning orientations

section, and the Facilitating factors section together with the demographic variables. The

greatest strength of mail surveys is that they allow one to minimize sampling errors at

relatively low cost. Because of lower staff requirements, the extra cost of sending out and

processing more mail questionnaires is less than conducting additional telephone or face-to-

face interviews (Salant and Dillman 1994).

Pre-tests The questionnaire format was tested by interviewing a small number of people working on

different levels in the forest sector. The pre-test respondents gave comments on the

questionnaire and enabled the researcher to modify and optimize the survey. Both versions

(the German and the Norwegian) were translated from the original version, in English, into

the original language of subjects and back to assure the accurate translation and context of

both questionnaire forms (e.g. Rosenthal and Rosnow 1991).

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4.3.2 Assessing organizational learning characteristics The following sections present how the assessment of organizational learning characteristics

by organizational learning profiles, types of sozial network structure and barriers to

organizational learning have been proceed.

Organizational learning capability The organizational learning capability was operationalized through two sections in the mail

survey. The first section consists of learning orientations representing the critical dimensions

in describing how organizational learning takes place and the content of learning. Each

respondent was asked to describe the seven learning orientations in her or his organization

operationalized by different items. The items are displayed in Table 10. Each item defines a

single measure of the preference how organizational learning takes place and the content of

learning. The respondents had to choose between 1 (lowest score) and 2 (highest score).

Each learning dimension represents an extreme position on a continuum. Using the

“Statistical Package for the Social Sciences” (SPSS) computer software program was used to

assess each Learning orientation calculating the mean value of the items related to the

relevant Learning orientations.

The second section consists of the facilitating factors representing the practice or conditions

that promote learning within the organization. Each respondent was asked to consider the

degree to which her or his organization is effective in each factor operationalized by different

items. The items are shown in Table 11. Each item was measured with a 5-point Likert

scale, ranging from (1) “strongly agree” to (5) “strongly disagree”. Using the “Statistical

Package for the Social Sciences” (SPSS) computer software program was used to assess the

organization’s effectiveness on each factor calculating the mean value of the items related to

the relevant Facilitating factor.

Types of social network structure Using egocentric technique to gather network data each person’s ego network can be

described and structural indicators of the networks can be computed. From the structural

indicators different types of social network structure can be identified. Data collected in this

way cannot directly inform us about the overall embeddedness of the networks in a

population, but it can give us information on the prevalence of various kinds of ego networks

in even very large populations. Collecting social network data this way, we essentially have

83

a data structure that is composed of a collection of networks. In several important works,

Burt (1987, 1992, 2000, 2001) developed and popularized the term “structural holes” (c.f.,

“Chapter 2,” p. 40) to refer to some important aspects of positional advantage/disadvantage

of individuals that result from how they are embedded in social networks. Burt’s

formalization of his idea of structural holes has facilitated further thinking about how and

why the ways that a person is connected to another person affect her/his constraints and

opportunities, and hence their behavior.

Burt (1992) uses the term “structural hole” for the separation between non-redundant

contacts. In this way, nonredundant contacts are disconnected in some way, either directly in

the sense that they have no direct contact with one another, or indirectly in the sense that one

has contacts that exclude others. Burt argued that cohesion and structural equivalence are the

respective empirical conditions that indicate a structural hole. Both conditions define holes

by indicating where they are absent.

YOURedundancyby Cohesion

YOURedundancyby StructuralEquivalence

Figure 24: Structural indicators of redundancy

(Adapted from Burt 1992, p.18)

Redundancy by cohesion is illustrated at the top of Figure 24. The three contacts are

connected to one another, and so provide the same network benefits. Under the cohesion

criterion, two contacts are redundant to the extent that they are connected by a strong

relationship. Thus, a strong relationship indicates the absence of a structural hole (e.g. father

and son, brother and sister, husband and wife, close friends, people who have been partners

84

for a long time, people who frequently get together for social occasions). Individuals have

easy access to both people if either is a contact.

Structural equivalence is a useful second indicator for detecting structural holes. Two people

are structurally equivalent to the extent that they have the same contacts. Regardless of the

relation between structurally equivalent people, they lead to the same sources of information

and so are redundant. Cohesion concerns direct connection; structural equivalence concerns

indirect connection by mutual contact. Redundancy by structural equivalence is illustrated at

the bottom of Figure 24. The three contacts have no direct ties with one another. They are

nonredundant by cohesion. But each leads you to the same cluster of more distant players.

The information that comes to them, and the people to whom they send information, are

redundant. Both networks in Figure 24 provide one nonredundant contact at a cost of

maintaining three.

Considering the setting at the top of Figure 25, the information access, timing, and referrals

actor i gets through contact j are redundant to the extent that: (a) actor i has a substantial

investment of time and energy in a relationship with another contact, q, and (b) to whom j has

a strong tie:

,jqiq mp

where piq is the proportion of i’s network time and energy invested in the relationship with q

(the interaction with contact q divided by the sum of i’s relations),

,,)(

)(ji

zzz

p

jjiij

qizijiq ≠

+

+=∑

and mjq is the marginal strength of contact j’s relation with contact q (the interaction with

contact q divided by the strongest of j’s relationship with anyone),

,,)(max

)(kj

zzzz

mkjjkk

qjjqjq ≠

+

+=

where max(zjk) is the largest of j’s relations with anyone ( )10 ≥≤ jqm , and zjq is the tie

intensity of a network connection from contact j to contact q, measured as communication

frequency in the network matrix (e.g. Burt 1992, p. 51).

85

q

ji

q

ji

piq

piq

mjq

qqj

qij

Redundant contactis connectedwith others

Constraint contactalso has the

dependence of others

Figure 25: Hole conditions of redundancy and constraint


Burt argues that the redundancy of contact j is measured by the tie strength of j’s connection

to other contacts. He looks at the tie strength of j’s connection to another contact q (mjq at

the top of Figure 25) under the presumption that information access and referrals from j are to

some extent second-hand from the contacts with whom j has strong relations (Burt 1992).

Constraint is different. The constraint from contact j is measured with proportional relations

from other contacts q (pqj at the bottom of Figure 25). Thus, network constraint refers to how

much room an individual has to negotiate or exploit potential structural holes in her or his

network. Burt states, that “opportunities are constraints to the extent that: (a) another of your

contacts q, in whom you invested a large proportion of your network time and energy, has (b)

invested heavily in a relationship with contact j (Burt 1992, p. 54)” (Figure 25). Collecting

network data using a name generator ties between individuals can be identified. A tie exists

from the respondent to the contact if the respondent reports a relationship. It was not

required that the contact corroborate the tie. The tie intensity of a network connection, zij,

was measured as communication frequency. A high value of zij indicates a strong

relationship from person i to person j.

86

A person’s network constraint

“Network Constraint” measures the extent to which a person i’s network is directly or

indirectly invested in a relationship with contact j. If the person i’s potential communicating

partners all have one another as potential communication partners, the person i is highly

constrained. If the person i’s communication partners do not have alternatives in their social

networks, they cannot constrain the person’s behavior so much. Burt (1997) defines the

network constraint index as:

∑j

ijc .

The network constraint index starts with a measure of the extent to which all contacts of

person i’s network are directly or indirectly investing in her or his relationship with contact j,

.,,2

jiqpppcq

qjiqijij ≠

+= ∑

pij is the proportion of i’s network time and energy invested in contact j (direct investment)

and the sum across q in the expression of the indirect investment. The total in parenthesis is

the proportion of i’s relations that are directly or indirectly invested in the relationship with

contact j. The sum of squared proportions, ∑j

ijc , is the network constraint index.

The effective size of a person’s network

An individual embedded in a large network spreads his or her network connections across

multiple pools, bridges holes between people in the broader community of knowledge and, as

a result, is exposed to more diverse knowledge. Network range is therefore high. Our

indicator of network range is the “Effective Size” of a person’s network. Effective size of a

person’s network is the number of people an individual is connected to, minus the

redundancy in the network. It reduces the size of the network to the non-redundant elements

of the network.

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Burt (1992) defines the effective size of i's network as:

[ ] .,,1 jiqmpj

q jqiq ≠−∑ ∑

where j indexes all of the people that person i has contact with, and q is every third person

other than i or j. The quantity (piqmjq) inside the brackets is the level of redundancy between

person i and a particular contact, j. If contact j is completely disconnected from all other

primary contacts then the bracketed term equals one, indicating that j provides one non-

redundant contact in the network. As relations between j and the other contacts strengthen,

the bracketed terms approaches pij, indicating that j is completely redundant with other

contacts in i’s network. The effective size value across contacts varies from one, indicating

that the network only provides a single contact, up to the observed number of contacts in the

network, N, indicating that every contact in the network is non-redundant. Using UCINET 6

for Windows (Borgatti et al. 2002) each person’s network constraint and the effective size of

each person’s network was calculated.

Entrepreneurial networks and clique networks

Collecting network data two variables, representing different dimension of interaction

intensity, have been computed: “Network Constraint” and “Effective Size”. In order to

investigate the effect of social network structures on organizational learning processes we

created the variable “Network Structure”.

Following the social network literature, two schools of thought dominate social network

theory and argue for their distinct advantages: cohesion or closure theorists and structural

holes or brokerage theorists (c.f., “Chapter 2,” p. 38). Closure theorist proposes that densely

embedded networks with many connections are more beneficial. Individuals socialized into

tight groups – that is to say networks in which everybody is connected such that no one can

escape the notice of others - appears to foster the development of shared norms, routines, and

the trust necessary for the sharing of proprietary information. According to Burt (1992,

2000, 2001), these networks are named “clique networks”, which in operational terms means

networks with more “Network Constraint” and lower “Effective Size”. Individuals who are

embedded in “clique networks” are indicated by an effective size value that is lower than or

equal to the average value and a network constraint value that is equal to or higher than 0.5.

Thus, clique networks are networks with few structural holes and high redundancy, which

create social support but minimal information and control benefits.

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Structural holes or brokerage theorists claim that networks rich in structural holes present

opportunities for entrepreneurial behavior (Burt 1997, 2000). Rather than stressing the utility

of consistent norms fostered by cohesive networks, structural hole theory claims that the

benefits of social capital result from the diversity of information and the brokerage

opportunities created by the lack of connection between different actors in a social network.

Networks rich in the entrepreneurial opportunities in terms of the structural holes – that is to

say networks with a larger Effective Size and a low Network Constraint represent networks

that span structural holes and are named “entrepreneurial networks”. Individuals who are

embedded in “entrepreneurial networks” are indicated by an Effective Size value that is

higher than the average value and a Network Constraint value that is lower than 0.5.

Assessing organizational learning breakdowns The previous sections outlined how the variables have been selected and operationalized, and

how the calculation of the data describing the structural components and learning

characteristics of the organizations processed. As argued in the Chapter 3 (p. 45),

organizational learning is a function of how and what organizations learn. In the integrated

framework, how organizations learn is represented by the informal organizational structure

(structural components) and the organizational learning characteristics (the seven learning

orientations and the ten facilitating factors). It is postulated that social network structure

effects organizational learning, and thus barriers to organizational learning can be identified.

The comparison of the mean values of the structural component “Network Structure” will be

used to determine if the distribution of values on either side of the organizational learning

characteristics differs for individuals embedded in entrepreneurial networks from individuals

embedded in clique networks.

4.3.3 Measurement validity, reliability, error and accuracy of social network data In their discussion of measurement error in sociometry, Holland and Leinhardt (1973) refer to

social structure, in the sense of interpersonal relations, as the true, in contrast to the observed

contained in the measured network data, which might contain errors. Important concerns in

social network measurement are the validity, reliability, and measurement error in these data.

In addition, since social network data are often collected by having people reported on their

own interactions, the accuracy of these self-report data is also a concern.

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Validity A measure of a concept is valid to the extent that it actually measures what it is intended to

measure. Often, researchers assume that the measurements of a concept are indeed valid.

For example, one might assume that asking people “Which people in this group are friends?”

has face validity as a measure of friendship, in the sense that the answer to the question gives

a set of actors who are related to the respondent through friendship ties. However, the

validity of measure of a concept is seldom tested in a rigorous way. A more formal notion of

validity, construct validity, arises when measures of concepts behave as expected in

theoretical predictions. Thus, the construct of validity of social network measures can be

studied by examining how these measures behave in a range of theoretical propositions

(Mouton et al. 1955b). In their study, Mouton et al. (1955b) reviewed dozens of sociometric

studies and found that sociometric measures, such as number on choices received by an actor,

were related to a number of actor characteristics, such as leadership and effectiveness, thus

demonstrating the construct validity of those sociometric measures.

Reliability

A measure of a variable or concept is reliable if repeated measurements give the same

estimates of the variable. Three approaches have been used to assess reliability of social

network data: test-retest comparison, comparison of alternative questions formats, and the

reciprocity of sociometric choices (Conrath et al. 1983, Hammer 1985, Laumann 1969, Tracy

et al. 1990). Although it is difficult to draw general conclusions from the research on the

reliability of social network data collected from interviews or surveys, several findings are

noteworthy. Sociometric questions using ratings or full rank orders are more reliable than

fixed choice design in which just a few responses are allowed (Mouton et al. 1955a).

Responses to sociometric questions about more intense relations have higher rates of

reciprocation than sociometric questions about less intense relations (Hammer 1985). Lastly,

the reliability of aggregate measures is higher that the reliability of “choices” made by

individual actors.

Measurement error Measurement error occurs when there is a discrepancy between the “true” score or value of a

concept and the observed (measured) value of that concept. Holland and Leinhardt (1973)

present a thorough discussion of measurement error and its implications in social network

research. As the note, in social network research the measurements are the collection of ties

90

among actors in the network, represented in the sociomatrix or sociogram. Of particular

importance in the discussion presented by Holland and Leinhardt (1973) is the error that

arises in fixed choice data collection designs. Recall that in a fixed choice design, the

respondent is instructed to nominate or name some fixed number of others for each relation.

This design introduces error since it is quite unlikely that all people have the same number of

best friends, etc.

Accuracy Often sociometric data are collected by having people report on their interactions with other

people. For example, a research might ask each actor to report: “With whom did you talk last

week?” The respondent is asked to recall her or his interactions. Considerable research has

been done on the question of information accuracy in social network data. Much of this

research was conducted by Bernard, Killworth, and Sailer using very clear data collection

designs in which they observed interaction among people in several different communities

and asked the same people to report on their interactions (Bernhard et al. 1980, 1982). They

concluded that about half of what people report about their own interactions is incorrect in

one way or another. Thus, people are not very good at reporting on their interactions in

particular situations.

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Chapter 5 Empirical Results

5.1 Chapter overview This chapter presents the survey results of the four case studies and is subdivided into four

parts.

The first part (5.1) is a brief overview. The second part (5.2) presents an overview of the

respondents’ rate to the mail questionnaires. Following, the social network data of the four

cases are presented in part three (5.3). The distribution of the different types of social

network structure in the organizations is outlined. The final part (5.4) presents data that

focuse on organizational learning capability. Results are displayed in cross-tabulation format

with supplementary information presenting the mean values and standard deviations of the

variables.

Building on the outlined survey results, “Chapter 6” (p. 115) expands on them linking the

relationship between social network structures and organizational learning to potential

barriers to learning.

5.2 Survey response rate The response rates from the case studies are shown in Table 13. Out of the 159 mail

questionnaires sent to the three German companies 34 responses were sent back from Case I,

10 responses were received from Case II and 31 responses from the third case. Out of the 61

mail questionnaires sent out in Norway, 37 were returned. The response rate ranges from 33

% to 77 %. The average response rate for the four cases is about 59 %.

Table 13: Response rate by cases on total and by hierarchical levels

Case I Case II Case III Case IV Response Response Response Response

Population

Total % Population

Total % Population

Total % Population

Total %

Total 49 34 63 13 10 77 94 31 33 61 37 61

Executive level 6 6 100 2 2 100 7 6 86 5 4 80

Administrative level 14 11 79 5 5 100 11 7 64 16 15 94

Operational level 29 17 59 6 3 50 76 18 24 40 18 45

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The four companies were investigated at organizational levels, the executive level, the

administrative level, and the operational level. We see that employees at the executive level,

in general, had a higher response rate than individuals at the other levels. In Case IV,

however, individuals at the administrative level had the highest response rate. In all cases,

individuals at the operational level had the lowest response rate (ranging from 24 % in Case

III to 59 % in Case I). An explanation for this lower response rate is that the questionnaire

covered complex issues and individuals at this level possibly felt difficult to reflect on the

organizational status quo regarding the issue under consideration.

5.3 Social network analysis results Collecting network data the tie strength of the respondents was computed (c.f., “Chapter 4,”

p.84). Consistent with prior research, the network data was transformed into “Network

Constraint” and “Effective Size” of the ego-networks.

5.3.1 “Network Constraint” The “Network Constraint” is a measure of the extent to which the organizational employees’

connections are to others who are again connected to one another (c.f., “Chapter 4,” p. 86).

Descriptive statistics for this variable is shown in Table 14. It describes the mean values and

standard deviations for each case, and lists these measures for each case, on total and by

hierarchical levels.

Table 14: Means and standard deviations of “Network Constraint”

Case I Case II Case III Case IV Mean St.d. Mean St.d. Mean St.d. Mean St.d.

Total 0.37 0.14 0.42 0.05 0.29 0.08 0.37 0.15 Executive level 0.26 0.07 0.37 0.01 0.27 0.10 0.21 0.04

Administrative level 0.40 0.12 0.43 0.05 0.41 0.10 0.31 0.11 Operational level 0.39 0.15 0.43 0.07 0.57 0.20 0.51 0.17

Network Constraint ranges from 0.00 to 1.00, with higher values indicating higher

concentration in redundant contacts. The means on cases level of “Network Constraint”

range from 0.29 in Case III to 0.42 in Case II. According to this, 29% of the connections of

the individuals in Case III are to contacts which are connected to one another. In other

words, in an individual knowledge network about each third communication partner is

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connected to another connected communication partner. The Network Constraint decreases

as a network expands. This is an issue, as “Network Constraint” measures the extent to

which a person (“A”) is constrained by her/his personal social network. If the person (“A”)’s

potential communicating partners all have one another as potential communication partners,

the person (“A”) is highly constrained (Clique network). Thus, in a small organization it is

more likely that an individual’s potential communicating partners all have one another as

potential communication partners than in a large organization. This is an explanation why

individuals in Case III are less constrained by their social networks than individual in Case II.

Contrary, connections among the contacts will slow the decrease. As a result, the

individuals’ network constraints from people working at the operational level are lower than

for people working at the executive level.

5.3.2 “Effective Size” The “Effective Size” is calculated as the number of individuals that organizational employees

have in their own communication network, minus the average number of ties that each

connected employee has to other connected employees (c.f., “Chapter 4,” p.86). Table 15

presents the descriptive statistics for the variable “Effective Size” and describes the means

and standard deviations (St.d.) for each case as well as on total and according to the

hierarchical levels.

Table 15: Means and standard deviations of “Effective Size”


Total 5.02 2.34 4.47 1.45 5.64 3.33 5.39 3.64 Executive level 7.79 3.00 5.23 0.22 8.64 6.74 10.83 5.26

Administrative level 4.44 1.51 3.34 1.32 4.19 1.12 6.25 3.28 Operational level 4.30 1.74 2.37 0.51 2.83 1.81 3.08 1.17

The means of the effective size of a person’s network, in general, are about 5 people in all

four cases. Thus, the means of the effective sizes of the organizational employees’ networks

seem to be similar in all four cases. Investigating the hierarchical levels of the different cases

in more detail, the effective size of an individual’s network ranges from about two people in

Case II at the operational level to about 11 people in Case IV at the executive level. An

explanation is that all cases were different by size.

94

Consequently, people in Case II had fewer contacts to refer to than individuals in Case III.

Furthermore, the effective size of a person’s network increases from people working at the

operational level to people working at the executive level. That is, individuals at the

operational level, in general, have fewer non-redundant contacts in their personal

communication networks than employees at the executive level do.

5.3.3 “Network Structure” By using the “Network Constraint” and “Effective Size” the variable “Network Structure”

has been defined (c.f., “Chapter 4,” p. 87). Building on this variable, two different types of

social network structure were distinct: entrepreneurial networks and clique networks. Table

16 shows the number of respondents embedded in these different types of social network

structure. The table displays the number of employees according to the social network

structures they are embedded in for each case, and lists the number of individuals for each

case according by the hierarchical levels.

Table 16: Number of individuals in types of social network structure

Case I Case II Case III Case IV Entrepreneurial

networks Clique

networks Entrepreneurial

networks Clique


networks Clique


networks Clique

networks

Total 11 23 4 6 11 20 13 24

Executive level 5 1 2 0 5 1 4 0

Administrative level 4 7 2 3 4 3 8 7

Operational level 2 15 0 3 2 16 1 17

The results presented in Table 16 indicate that individuals are more frequently embedded in

clique networks than in entrepreneurial networks. However, investigating the embeddedness

of the individuals at the different hierarchical levels, individuals working at the executive

level are more frequently embedded in entrepreneurial networks. In Case II and Case IV,

individuals working at the executive level are only embedded in entrepreneurial networks. In

contrast, individuals working at the administrative level and operational level are mostly

embedded in clique networks. However, the results indicate that the formal organizational

position dominates the informal social organization. Furthermore, in Case II, individuals at

the operational are only embedded in clique networks. An explanation for these results is

that individuals at the executive level, due to their work tasks, have to focus stronger on

95

contacts to different groups of people. On the other hand, individuals at the operational level

do not have the opportunities to broaden the personal communication networks as they are

constrained by the formal organizational structure.

5.4 Organizational learning capability results The following section presents the survey results on the organizational learning aspects. The

data are presented in the form of cross-tabulation tables that display the cases, the

hierarchical levels and social network types. At the end of the “learning orientations” and the

“facilitating factors” sections, the results are summarized and highlighted.

5.4.1 Learning orientations The learning orientations represent the critical dimensions that describe how organizational

learning takes place and the content of learning. The respondent were asked to evaluate the

seven Learning orientations in their organization. The learning orientation dimensions define

the approaches by which knowledge is acquired, disseminated, and used. The orientations

were measured with items adapted from an instrument developed by Nevis and colleagues

(Nevis et al. 1995). These items are listed in more detail in Chapter 4 (p. 74). The values

range from “1” (lowest value) to “2” (highest value).

The grouping variable “Network Structure” has been aggregated using the variables

“Network Constraint” and “Effective Size” and displays the means and standard deviations

(St.d.) of the learning orientations for two different types of social network structure,

entrepreneurial networks and clique networks.

“LOr1: Knowledge Source” The variable “LOr 1: Knowledge Source” is the extent to which an organization prefers to

develop new knowledge internally versus the extent to which it is more likely to seek

inspiration in ideas developed by external sources. Lower scores indicate an emphasis on

internal sources (1 = mostly internal); higher scores represent the external sources (2 =

mostly external). Descriptive statistics for the variable “LOr1: Knowledge Source” are

presented in Table 17. It displays the means and standard deviations (St.d.) for each case and

lists these measures, on total and hierarchical levels as well as types of social network

structure.

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Table 17: Means and standard deviations for “LOr1: Knowledge Source”


Total 1.16 0.19 1.27 0.21 1.32 0.38 1.32 0.33 Entrepreneurial networks 1.20 0.23 1.42 0.17 1.24 0.30 1.41 0.39

Clique networks 1.14 0.17 1.17 0.18 1.36 0.41 1.26 0.29 Executive level 1.22 0.27 1.33 0.00 1.33 0.30 1.50 0.33

Entrepreneurial networks 1.27 0.28 1.33 0.00 1.27 0.28 1.50 0.33 Clique networks 1.00 . . . 1.67 . . .

Administrative level 1.17 0.18 1.27 0.28 1.19 0.33 1.31 0.41 Entrepreneurial networks 1.11 0.19 1.50 0.24 1.17 0.34 1.33 0.44

Clique networks 1.19 0.18 1.11 0.19 1.22 0.39 1.29 0.41 Operational level 1.14 0.17 1.22 0.19 1.36 0.42 1.28 0.26

Entrepreneurial networks 1.17 0.24 . . 1.34 0.47 1.67 . Clique networks 1.14 0.17 1.22 0.19 1.36 0.43 1.25 0.25

From the results, we see that in general all four organizations preferred internally generated

knowledge. In addition, the executive level was more open to externally generated

knowledge than the lower levels. However, there were hierarchical differences. This is

consistent with the nature of the work and responsibility of the hierarchical levels.

Furthermore, there appears to be a tendency for clique networks to focus more on internal

sources.

“LOr2: Content-Process Focus” The organization’s preference for knowledge related to product and service outcomes as

opposed to knowledge about the basic processes behind various products is measured with

the variable “LOr2: Content-Process Focus”. Lower scores indicate a focus on product

related knowledge (1 = mostly what?); higher scores indicate a focus on process related

knowledge (2 = mostly how?). Table 18 presents the means and standard deviations (St.d.)

for each case and lists these measures, on total, for hierarchical levels and types of social

network structure.

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Table 18: Means and standard deviations for “LOr2: Content-Process Focus”








From the results, we see that in general all four organizations emphasized the acquisition of

process related knowledge. Furthermore, the executive level was more focussed on process

related knowledge than the lower levels. This is consistent with the nature of the work and

responsibility of the organizational levels. However, the results are mixed in that there is no

clear pattern across hierarchical levels although there appears to be a tendency for

entrepreneurial networks to be process-focused.

“LOr3: Documentation Mode” The variable “LOr3: Documentation Mode” refers to the variations in behavior and attitudes

regarding the repositories of knowledge. At one pole, knowledge is seen in very personal

terms. At the other pole, the emphasis is on defining knowledge in more objective terms as

being a consensually supported result of information processing. Lower scores indicate a

perception of knowledge in more personal terms (1 = mostly personal); higher scores a

perception of knowledge in more objective terms (2 = mostly public).

Descriptive statistics for the variable “LOr3: Documentation Mode” are shown in Table 19.

The table describes the means and standard deviations (St.d.) for each case and lists these

measures, on total, for hierarchical levels and types of social network structure. The results

indicate for the first three cases a strong preference to see knowledge as something

individuals possess. However, individuals in Case IV also emphasize organizational memory

or a publicly documented body of known things. The results are mixed in that there is no

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clear pattern across hierarchical levels although there appears to be a weak tendency for

entrepreneurial networks to see knowledge more in personal terms.

Table 19: Means and standard deviations for “LOr3: Documentation Mode”








“LOr4: Dissemination Mode”

The organization’s difference between establishing an atmosphere in which learning evolves

informally and an atmosphere in which a more structured, controlled approach is taken to

induce learning is measured with the variable “LOr4: Dissemination Mode”. Lower scores

represent a focus on formal methods of sharing knowledge (1 = mostly formal); higher scores

indicate an emphasis on informal communication methods (2 = mostly informal).

Table 20 presents the means and standard deviations (St.d.) for each case and lists these

measures, on total, for hierarchical levels and types of social network structure. In general,

knowledge sharing is perceived as mostly an informal affaire. The results are mixed

although in the first three cases appear to be a tendency for clique networks to focus more on

informal communications. However, the Norwegian case does not support this finding.

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Table 20: Means and standard deviations for “LOr4: Dissemination Mode”








“LOr5: Learning Focus” The variable “LOr5: Learning Focus” pertains to whether knowledge is focused on methods

and tools to improve what is already known or being done, versus knowledge that challenge

the assumptions about what is known or done. Lower scores indicate a focus on

incrementally improving knowledge (1 = mostly incremental) and higher scores represent the

willingness to question the current state of knowledge (2 = mostly transformative).

Table 21: Means and standard deviations for “LOr5: Learning Focus”








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The results of “LOr5: Learning Focus” are shown in Table 21. In general, clique networks

prefer incremental improvement and entrepreneurial networks focus more on challenging

assumptions. The differences are not dramatic and there are exceptions to this conclusion

within the organizations.

“LOr6: Value-Chain Focus” Which learning investments and core competencies are valued and supported by the

organization is indicated by the variable “LOr 6: Value-Chain Focus”. Lower scores indicate

a focus on the technical aspects of the work tasks (1 = mostly design/make), while higher

scores are associated with a service/market orientation (2 = mostly deliver/market).

Table 22: Means and standard deviations for “LOr6: Value-Chain Focus”








Table 22 displays the means and standard deviations (St.d.) for each case and lists these

measures, on total, for hierarchical levels and types of social network structure. In general,

individuals in Case I and Case II focus mostly on learning investments in deliver/market

functions. An explanation is that the core competence of these two cases is the wood

procurement management. There appears to be a tendency for individuals in clique networks

to have a stronger focus on the technical aspects of the work tasks. The pattern across

hierarchical levels indicates that individuals at the higher level emphasize more learning

investments in deliver/market activities, which is to be expected given the nature of the

activities.

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“LOr7: Skill Development Focus” The variable “LOr7: Skill Development Focus” makes a distinction between learning geared

to individual skill development versus learning focused on team skill development. A focus

on individual skill development is associated with lower scores (1 = mostly individual);

group skill development has higher scores (2 = mostly group).

Table 23: Means and standard deviations for “LOr7: Skill Development Focus”







Entrepreneurial networks 1.83 0.24 . . 1.17 0.23 1.67 . Clique networks 1.76 0.40 1.44 0.51 1.42 0.46 1,25 0.31

Descriptive statistics for the variable “LOr7: Skill Development Focus” are shown in Table

23. The table presents the means and standard deviations (St.d.) for each case and lists these


are mixed in that there is no clear pattern across organizational functions although there

appears to be a tendency for entrepreneurial networks to focus more on individual skill

development. However, the fourth case does not support this finding.

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Linking cases to social network structure and learning orientations In Table 24, cases are assigned to social network types when there is a strong relationship

between the effects of social networks and the learning orientations. For example, for the

learning orientation “LOr1: Knowledge Source”, Case I, Case II and Case IV support the

claim that individuals embedded in clique networks are associated with an emphasis on

internal knowledge generation. This is reflected in Table 17 and elaborated in more detail

below the table.

Table 24: Cross-tabulation - types of social network structure and learning orientations

Entrepreneurial networks Learning orientations Clique networks

More external “LOr1: Knowledge Source” (Case I, Case II, Case IV) More internal

More how “LOr2: Content-Process Focus” (Case I, Case II, Case IV) More what

More personal “LOr3: Documentation Mode” (Case I, Case IV) More public

More formal “LOr4: Dissemination Mode” (Case I, Case II, Case III) More informal

More transformative “LOr5: Learning Focus” (Case I, Case III, Case IV) More incremental

More deliver/market “LOr6: Value-Chain Focus” (All Cases) More design/make

More individual “LOr7: Skill Development Focus” (Case I, Case II, Case III) More group

The learning orientations address the organization’s culture as it is related to learning. As

argued in “Chapter 3“ (p. 51) organization’s employees in Clique Networks tend to acquire

new knowledge internally (“LOr1: Knowledge Source””). In other words, they value

knowledge gained from their own experiences more than knowledge created by others.

Consequently, they encourage learning from their own actions and are more likely to be

innovators in the way they do things. Furthermore, we argued that employees in

entrepreneurial networks accumulate mostly know-how (“LOr2: Content-Process Focus”).

That is, people focus more on how they should accomplish their goals. The results from the

CaseI, Case II and Case III support both claims.

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We postulated that individuals in entrepreneurial networks put more emphasis on knowledge

possessed by individuals (“LOr3: Documentation Mode”). While doing so, they can monitor

information more effectively and gain brokerage advantages (Burt 1992). However, the

survey results show a different picture. Only in Case IV, individuals in entrepreneurial

networks have a tendency to see knowledge in more personal terms. In addition, the survey

results indicate that knowledge sharing is perceived as mostly an informal affaire (“LOr4:

Dissemination Mode”). We claimed that individuals in clique neworks share knowledge by

more informal approaches than other do. The results from Case I, Case III and Case IV

support this claim.

The learning orientation “LOr5: Learning Focus” pertains to wether knowledge is focus on

incremental improvement, versus challenging assumptions. The results from Case I, Case III

and Case IV support weakly the claim that employees in clique networks focus more on

incremental improvement. Furthermore, results from all cases support the claim that

individuals in clique networks have a stronger focus on the technical aspects of the work

(“LOr6: Value-Chain Focus”). They prefer to focus on developing skills needed to design

and make products. Consequently, their quest for technical superiority outweighs everything.

In our framework (“Chapter 3,” p. 63), we stated that individuals in entrepreneurial networks

emphasize learning that is geared towards individual skill development (“LOr7: Skill

Development Focus”). The results from Case I and Case III support this. However, Case II

weakly supports this finding and Case III does not support this finding at all.

5.4.2 Facilitating factors The facilitating factors are the practice or conditions that promote learning within an

organization. Unlike the learning orientations, facilitating factors are normative. The

presence of these factors determines the efficiency and effectiveness of the organizational

learning cycle. The respondents were asked to consider the degree to which their

organization is characterized on each factor. The variable was measured with the items listed

in Table 11 (p. 80). The items were measured with a 5-point Likert scale ranging from

“strongly agree” to “strongly disagree”. Lower scores represent stronger agreement that this

factor is a part of the organization’s learning cycle. As with the learning orientations, the

data are cross-tabulated by hierarchical levels and types of social network structure.

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“FF1: Scanning Imperative” The facilitating factor “FF1: Scanning Imperative” indicates the degree to which the external

environment is scanned for new information. Each respondent was asked to describe the

extent to which there is support for this activity in his or her unit/organization (1 = strong

agreement; 5 = strong disagreement). The results of this factor are shown in Table 25.

Table 25: Means and standard deviations for “FF1: Scanning Imperative”


Total 1,96 0.66 2.30 0.70 2.52 0.94 2.16 0.65 Entrepreneurial networks 1.91 0.56 2.33 1.09 2.21 0.52 2.23 0.64

Clique networks 1,99 0.71 2.28 0.39 2.69 1.08 2.11 0.66 Executive level 1.61 0.33 1.67 0.94 1.95 0.25 1.83 0.79





In general, the results are mixed in that there is no clear pattern across organizational

functions although there appears to be a tendency for individuals at the executive level to

focus more on scanning the environment. In addition, there appears to be a tendency for

individuals embedded in clique networks to focus less on scanning the environment.

However, the results indicate that the formal organizational position dominate the informal

social organization.

“FF2: Performance Gap” The facilitating factor “FF 2: Performance Gap” is tied to the shared awareness among

organization members that in the organization is a gap between the organization’s desired

performance and the actual performance (1 = strong agreement; 5 = strong disagreement).

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Table 26: Means and standard deviations for “FF2: Performance Gap”










show that individuals in entrepreneurial networks and the individuals at the executive level

are more clearly concerned with this facilitating factor then are the other clarifications.

Again, this may reflect the formal organizational position of the respondents. Individuals at

the executive level are held to be more accountable for organizational performance then are

lower level workers.

“FF3: Concern for Measurement”

How much the discourse about measurements and the search for the most appropriate ones is

a critical aspect of learning in the organization is represented in the facilitating factor “FF3:

Concern for Measurement” (1 = strong agreement; 5 = strong disagreement).

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Table 27: Means and standard deviations for “FF3: Concern for Measurement”




Entrepreneurial networks 2.13 0.45 1.33 0.00 2,13 0.87 2.08 0.57 Clique networks 2.33 . . . 1.33 . . .




The results in Table 27 are mixed in that there is no clear pattern across organizational

functions although the results from Case II indicate that individuals in entrepreneurial

networks are less concerned with this factor. The results clearly indicate that individuals at

the executive level are more concerned with the issue of measurement. However, the results

are consistent with the expectations of the organizational position and indicate that formal

organizational position dominates the informal social organization.

“FF4: Experimental Mind-set” The facilitating factor “FF4: Experimental Mind-set” refers to the organizational support for

trying new things, curiosity about how things work, and the ability to “play around” with

policies, methods, and procedures (1 = strong agreement; 5 = strong disagreement). Table 28

presents the means and standard deviations (St.d.) for each case and lists these measures, on

total, for hierarchical levels and types of social network structure. The results are mixed. An

experimental mind-set seems to require a trusting environment, where mistakes are tolerated.

This is a characteristic of clique network structure, and there is some support for these in

Case II and Case IV. However, experiments can also be associated with new ideas, which

are supported by entrepreneurial network structure (Case I and Case III).

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Table 28: Means and standard deviations for “FF 4: Experimental Mind-set”




Entrepreneurial networks 2.67 0.47 2.00 0.95 2,47 0.76 2.00 0.27 Clique networks 4.00 0.00 . . 1.67 . . .




“FF5: Climate of Openness” The facilitating factor “FF5: Climate of Openness” is related to the permeability of

information boundaries and the degree to which opportunities to observe and to participate

are available to organization members (1 = strong agreement; 5 = strong disagreement).

Table 29: Means and standard deviations for “FF5: Climate of Openness”




Entrepreneurial networks 2.07 0.43 1.34 0.47 3,27 0,86 2.21 0.37 Clique networks 4.00 . . . 1.67 . . .

Administrative level 2,84 1.15 2.60 0.72 3.05 0.76 2.96 0.85 Entrepreneurial networks 2.08 1.20 2.67 0.00 2.84 0.69 2.88 0.77



The results in Table 29 indicate that entrepreneurial network structure support a more open

climate then clique networks do. In addition, individual at the executive level were generally

more open, consistent their role in the formal organizational structure of that level. However,

we expected that individuals in entrepreneurial networks are more active in contacting

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people, but that the climate is less open as they control the information flow. This is

supported by Case IV.

“FF6: Continuous Education” The factor “FF6: Continuous Education” estimates the organization’s commitment to lifelong

education at all levels of the organization (1 = strong agreement; 5 = strong disagreement).

Table 30: Means and standard deviations for “FF6: Continuous Education”








From the social network perspective, the results are mixed with respect to the factor; there is

no clear pattern (Table 30). From the organization’s hierarchy viewpoint, it appears that

individuals at the operational level are more supportive of this factor (except Case III).

However, the results from Case II and Case IV indicate that individuals in entrepreneurial

networks are less supportive of this factor.

“FF7: Operational Variety” How much different methods, procedures, and competencies are valued is measured with the

facilitating factor “FF7: Operational Variety” (1 = strong agreement; 5 = strong

disagreement). Table 31 presents the means and standard deviations (St.d.) for each case and

lists these measures, on total, for hierarchical levels and types of social network structure.

From a social network perspective, in general, individuals in clique networks report less

supportive of this factor (Case IV is an exception). Additionally, individuals at the executive

level are more concerned with this factor then at the other two levels.

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Table 31: Means and standard deviations for “FF 7: Operational Variety”








“FF8: Multiple Advocates” The facilitating factor “FF8: Multiple Advocates” pertains how easy it is to promote new

ideas, or to bring new knowledge into the organization’s system (1 = strong agreement; 5 =

strong disagreement).

Table 32: Means and standard deviations for “FF8: Multiple Advocates”







Entrepreneurial networks 1.67 0.00 . . 3.17 1.65 3.00 . Clique networks 2.31 0.87 1.56 0.20 3,34 0.84 2.38 0.70

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are mixed. From the organization’s hierarchy perspective there is no clear pattern. In three

of the cases, individuals at the administrative level reported the least support for the use of

multiple advocates. However, in Case I and Case III individuals in clique networks reported

less support for the use of multiple advocates then individuals in entrepreneurial networks.

“FF9: Involved Leadership” The Facilitating factor “FF9: Involved Leadership” indicates how strongly leaders are

involved in the learning processes within the organization (1 = strong agreement; 5 = strong

disagreement).

Table 33: Means and standard deviations for “FF 9: Involved Leadership”








Table 33 presents the means and standard deviations (St.d.) for each case and lists these

measures, on total, for hierarchical levels and types of social network structure. However,

only in Case I the results show that members in clique networks perceive support for the

factor “FF 9: Involved Leadership” more than entrepreneurial network members do. From

the organization’s hierarchy perspective, individuals at the executive and operational level

generally reported more support on this factor then did individuals at the administrative level.

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“FF10: Systems Perspective” The facilitating factor “FF10: Systems Perspective” estimates the employees’ ability to think

in terms of whole systems and the interdependency of organizational parts (1 = strong

agreement; 5 = strong disagreement). In Table 34, the response to this factor is presented.

Table 34: Means and standard deviations for “FF10: Systems Perspective”








The response to factor “FF10: Systems Perspective” were mixed. From the social network

perspective, individuals in clique networks seem to receive less support for this factor then

individuals in entrepreneurial networks do. However, in Case IV, only individuals at the

operational level show this patter too.

Linking cases to social network structure and facilitating factors In Table 35, cases are assigned to social network types when there is a relationship between

the effects of social networks and the facilitating factors. For example, for the facilitating

factor “FF7: Operational Variety”, Case I, Case II and Case III support the claim that

individuals embedded in clique networks are associated with less support for different

methods, procedures and competences. This is reflected in Table 31.

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Table 35: Cross-tabulation - types of social netwok structure and facilitating factors

Entrepreneurial networks Facilitating factors Clique networks

“FF1: Scanning Imperative” (Case I, Case III, Case IV) Little organizational support

Little organizational support “FF2: Performance Gap” (Case III, Case IV)

Little organizational support “FF3: Concern for Measurement” (Case II, Case III)

“FF4: Experimental Mind-set” (Case I, Case III) Little organizational support

Little organizational support “FF5: Climate of Openness” (Case IV)

Little organizational support “FF6: Continuous Education” (Case II, Case III; Case IV)

“FF7: Operational Variety” (Case I, Case II, Case III) Little organizational support

“FF8: Multiple Advocates” (Case I, Case III) Little organizational support

Little organizational support “FF9: Involved Leadership” (Case I, Case II, Case IV)

“FF10: Systems Perspective” (All Cases) Little organizational support

Table 35 summarizes the effect of specific types of social network structure on the

employees’ perception of the organizational support for this factor to facilitate learning. The

facilitating factors focus the practices and processes that promote learning. The more each is

prevalent in an organization, the more opportunity exists for learning. Analyzing the effect

of types of social network structure on “FF 1: Scanning Imperative”, the survey results from

Case I and Case III support the claim that individuals in clique network focus less on

scanning the environment. That is, these individuals perceive less organizational support for

maintaining contact with customers, suppliers, and competitors in areas they do business in.

However, the results from Case II do not support these findings.

On the other hand, we claimed in the framework (“Chapter 3,” p. 52) that entrepreneurial

network members lack a shared awareness that there is a performance gap, because

performance gap concerns are not widely shared. Furthermore, they perceive less awareness

that the current standard of performance should be set higher. The survey results do not

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support this findings although, in Case III and Case IV, at some hierarchical level is possible

to see a network structure effect. Furthermore, we claimed that individuals in entrepreneurial

networks perceive less organizational support for valuing the process of measurement as a

learning opportunity or for using feedback and evaluation to enhance performance.

However, only in Case II, individuals in entrepreneurial networks are less concerned about

this factor (weak support also in Case III).

In Case I and Case III, individuals in clique networks perceive less organizational support for

trying out new ideas or concepts than other do. Further, they recognize less support for

brainstorming exercises to inquire into innovative and creative ideas. However, the results

from Case II and Case IV didn’t support these findings.

We also postulated that entrepreneurial network members report less organizational support

for the factors “FF 5: Climate of Openness” and “FF 6: Continuous Education”. The survey

results weakly supported that individuals in these network structure experienced less support

for disseminating critical information and for sharing ideas openly and discuss them.

Furthermore, these individuals reported and less support for all organizational members’

development and developing systems for capturing, codifying, and aggregating experiences

than do clique network members.

In addition, we proposed that individuals in clique networks tend to perceive that their

organization’s support for the factors “FF 7: Operational Variety” and “FF 8: Multiple

Advocates” is low. The survey results from Case I and Case III support both claims. On the

other hand, the results from Case II only support the first claim.

Analyzing the effect of the social network structure on this factor, the results from Case I

provide weak support for argument that individuals embedded in entrepreneurial networks

perceive less organization’s effectiveness to support the factor “FF 9: Involved Leadership”.

These individuals perceived less organizational support that leadership is encouraged at all

levels in the organization than did clique network members. Furthermore, they perceived

less support that top people participate in learning initiatives as learners.

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In contrast, the Case II and Case III (weak support from Case I and Case IV) support the

claim that individuals in clique networks experience less organizational support for “FF 10:

Systems Perspective” than do entrepreneurial network members. Consequently, clique

network mebers reported less organizational support for developing long-range perspective or

the recognitions of interdependence among organizational units and between actions and

their outcomes.

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Chapter 6 Discussion and Conclusions

6.1 Chapter overview Until now, little research has been done that considers how informal organizational structure

is related to organizational learning, and thus affects organizational learning capability. In

this research work, it is hypothesized that specific capabilities determined by a profile that is

composed of the learning orientation and facilitating factors, in conjunction with a type of

social network structure may result in different barriers to organizational learning.

After this brief chapter overview (6.1) two following parts are presented. The second part

(6.2) of this chapter expands on the results presented in the previous chapter and combines

them with the concept of barriers to organizational learning. The potential occurrence of

each barrier to learning is discussed. Based on the empirical results and the discussion of

them, (6.3) conclusions are drawn and the steps to enhance organizational learning

capabilities, and thus implications for enhancing organizational learning capability, are

highlighted. The chapter ends with a summary of the study’s major contribution (6.4),

including limitations and identification of areas for further research.

6.2 Barriers to organizational learning and its determinants The ability to learn is an important organizational resource. That is, the critical resource

embedded within organizations is the knowledge that skilled individuals bring to work on a

day-to-day basis. Researchers in learning and social network theory have known for some

time that the creation of knowledge is a social process, and consequently social relationships

are important for the acquisition and transfer of knowledge and experiences. According to

this, learning – the process whereby knowledge is created through the transformation of

experiences – is facilitated and constrained by membership in a social system.

Linking individual and organizational learning, organizational learning is reflected in

changing organizations’ action that in turn results from changes in the behavior and beliefs of

the organization’s members. The results of individual learning are captured in the

individual’s memory. Therefore, the parts of an organization’s memory that are most

relevant for organizational learning are those that constitute active memory; those are

individual mental models and shared mental models (c.f., “Chapter 2,” p. 23). Kim (1993)

identified seven potential sources for barriers to organizational learning. In the following

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section, the linkages between potential barriers to organizational learning and their

determinants are discussed. The presention is organized under the basic three-stage model of

learning, which consists of knowledge acquisition, knowledge sharing and knowledge

utilization (c.f., “Chapter 2,” p. 27).

6.2.1 Determinants of barriers to knowledge acquisition Organizations gain knowledge directly through the experiences of their own employees or

indirectly through the experiences of other organizations. Since organizations are continually

creating experiences and thus creating or acquiring knowledge, the potential for learning is

always present. Two barriers to organizational learning can occur in the knowledge

acquisition process (March and Olsen 1975, Kim 1993): learning under ambiguity and

situational learning (c.f., “Chapter 3,” p. 51). Table 36 summarizes the determinants of the

potential barriers to learning in knowledge acquisition.

Table 36: Determinants of potential barriers to knowledge acquisition

Determinants Potential barriers to knowledge acquisition

Type of social network structure: Clique networks

Learning orientation: “LOr1: Knowledge Source”: More internal

Facilitating factors: “FF1: Scanning Imperative”: Low support “FF4: Experimental Mind-set”: Low support

Learning under ambiguity Case I

Type of social network structure Entrepreneurial networks

Learning orientation: “LOr2: Content-Process Focus”: More how?

Facilitating factors: “FF2: Performance Gap”: Low support “FF3: Concern for Measurement”: Low support

Situational learning

Learning under ambiguity is primilary influenced by “LOr1: Knowledge Source” and by

“FF1: Scanning Imperative” and “FF4: Experimental Mind-set”, as well as social network

structure. An organization that is composed of clique networks, an internally focused

knowledge source orientation and low environmental scanning imperative as well as low

experimental mind-set will be more susceptable to this barrier to organizational learning then

organizations with other profiles. Learning under ambiguity occurs when reasons for

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changes in the environment cannot be clearly identified; the linkage between the

environmental response and individual learning is broken. It was claimed that learning under

ambiguity is potentially more likely to occur for individuals in clique networks than for

individuals who are embedded in entrepreneurial networks. This proposition was based on

the statement that individuals in clique networks are more strongly constrained by their social

network and have fewer options to gather non-redundant information that could clarify the

ambiguities in the cause-effect relationships. The results from the case studies support the

claim that individuals embedded in clique networks tend to perceive the organization’s

learning orientation “LOr1: Knowledge Source” as more focused on internal knowledge

acquisition (Case I, Case II, and Case IV). These results are consistent with other findings

that individuals in clique networks will be deprived of information from distant parts of the

social system and will be confined to the news and views of their close contacts (e.g.,

Granovetter 1983).

In addition, it was claimed that the potential for learning under ambiguity occurs because

individuals in clique networks perceive their organization’s support for the factors “FF1:

Scanning Imperative” and “FF4: Experimental Mind-set” as lower than those in

entrepreneurial networks. In the survey, individuals in clique networks perceive their

organization’s effectivness in supporting information gathering about conditions and practice

outside the unit/organization as lower than members of entrepreneurial networks do.

Furthermore, clique network members also tend to perceive their organization’s effectiveness

with respect to support trying out new ideas as lower than individuals in entrepreneurial

networks do. The survey result from Case I and Case III support weakly both claims.

In general, the survey results provide support for all individual claims. However, learning

under ambiguity is a complex phenomenon and, as Figure 16 shows, is influenced by a

combination of factors. From the surveys, only Case I provides weak evidence for the

potential occurrence of learning under ambiguity.

Situational learning occurs when the connection between individual learning and individual

mental model is interrupted. That is, individuals learn but forget or do not codify the learning

for later use. We hypothesized that individuals in entrepreneurial networks are more

frequently confronted with situational learning than individuals embedded in clique networks

do. This claim was based on the assumption that individuals in entrepreneurial networks

have access to more information but the information is not complex or deep (e.g., Hansen

1999). The results from the case studies support this claim. Compared to clique networs are

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individuals in entrepreneurial networks tending to perceive the organization’s learning

orientation “LOr2: Content-Process Focus” as more focused on the process, on the

accumulation of know-how rather than know-what (Case I, Case II and Case III).

According to the conceptual framework (c.f, “Chapter 3,” p. 53), it was claimed that

situational learning is more likely to occur when individuals in entrepreneurial networks tend

to perceive less organizational support for the factors “FF2: Performance Gap” and “FF3:

Concern for Measurement”. The survey results provide some support that entrepreneurial

network members tend to observe less organizational support for sharing perceptions of gaps

between current performance and desired performance. Consequently, there was little

organizational support for seeing performance shortfalls as opportunities for learning.

Furthermore, the results provide some support that these individuals rate organization’s

effectivness to support defining and measuring key factors when the organization ventures

into new areas as lower as do clique network members.

As learning under ambiguity also situational learning is complex as shown in Figure 17.

Although the survey results tend to support all individual propositions regarding the type of

social network structure, learning orientation and facilitating factors, none of the case studies

provides direct support for the occurrence of this potential barrier to organizational learning.

6.2.2 Determinants of barriers to knowledge sharing Knowledge can be disseminated within an organization and between employees in a variety

of ways. Some modes of dissemination are more formal than others; some are based on

written communications (formal reports and documents, for example), others on oral

presentation (staff presentations and telephone conversations, for example). The expression

“knowledge is power” suggests that tacit knowledge will not readily be shared because

employees may fear that by sharing knowledge their employer will become less dependent on

them. Organizations need to engender values that will produce exactly the opposite feeling.

By sharing knowledge, individuals can demonstrate their contribution to organizational

learning and firms need to recognize such individuals. Building on Kim (1993), two

potential barriers to learning in the knowledge dissemination process were identifed in the

framework: role-constrained learning and fragmented learning.

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Table 37 summarizes the determinants of these two barriers to knowledge sharing.

Table 37: Determinants of potential barriers to knowledge sharing

Determinants Potential barriers to knowledge sharing

Type of social network structure Clique networks

Learning orientation: “LOr4: Dissemination Mode”: More informal

Facilitating factors: “FF7: Operational Variety”: Low support “FF8: Multiple Advocates” : Low support

Role-constrained learning Case I, Case III

Type of social network structure Entrepreneurial networks

Learning orientation: “LOr3: Documentation Mode”: More personal

Facilitating factors: “FF5: Climate of Openness”: Low support “FF6: Continuous Education”: Low support

Fragmented learning Case IV

March and Olsen (1975) theorized that an interruption in the connection between individual

learning and individual action would result if individuals were limited by their role in

organization and were unable to act on their learning. We claimed that individuals in clique

networks face role-constrained learning more frequently than do individuals embedded in

entrepreneurial networks (c.f., “Chapter 3,” p. 56). This proposition was based on the claim

that individuals embedded in clique networks promote the formation of norms and rules and

facilitate sanctioning that makes it less risky for people in the network to trust one another

(e.g., Coleman 1988). According to this, clique network members perceive the

organization’s learning orientation “LOr4: Dissemination Mode” as more informal. The

survey results from Case I, Case II and Case III support the claim that members of clique

networks tend to share learning in a more informal manner, such as role modeling and

through daily interaction.

In addition, we claimed that the potential for role-constrained learning is increased as clique

network members experience little organizational support for the factors “FF7: Operational

Variety” and “FF8: Multiple Advocates”. The survey results support both claims.

Individuals in clique networks tend to rate their organization’s support for different varieties

of methods and procedures and for new ideas and methods advanced by employees at all

levels as lower than individuals in entrepreneurial networks do.

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In general, the results from Case I and Case III provide support for this proposition. In

addition, there was some weak support from Case II even though there were some scoring

differences on the factor “FF8: Multiple Advocates”.

Another potential barrier to knowledge sharing, identified in the framework, was fragmented

learning (c.f., “Chapter 3,” p. 57). Fragmented learning can occur when one actor or unit

learns but the collective does not (Kim 1993). As stated earlier, the parts of an organization’s

memory that are relevant for organizational learning are the individual mental models and the

shared mental models. The organization’s view of the world slowly evolves to encompass

the current thinking of the individuals within. In similar fashion, individual routines that are

proved sound over time become standard operating procedures. When the link between the

individual mental models and the organization’s shared mental model is severed the

organization’s knowledge become fragmented among its members. Individual learning takes

part but does not contribute to collective learning. This kind of learning barrier is typical in

highly decentralized organizations that do not have the networking capability to keep their

parts connected. In addition, very compartmentalized organizations can suffer from this

barrier to learning.

We claimed that individuals in entrepreneurial networks face the potential of fragmented

organizational learning more frequently than individuals in clique networks. This claim was

based on the proposition that individuals in entrepreneurial networks have networks that are

rich in structural holes, which help them to “monitor information more effectively than it can

be monitored bureaucratically” (Burt 1997, p. 343). At the same time, they gain brokerage

advantages from social network structures that make it more risky for people in the network

to trust one another. As a result, individuals in entrepreneurial networks tend to perceive the

organization’s learning orientation “LOr3: Documentation Mode” as more personal. The

survey results from Case IV provide some support that individuals in entrepreneurial

networks tend to see knowledge as something that individuals possess.

In addition, it was claimed that the potential for fragmented organizational learning is

increased as entrepreneurial network members tend to perceive less organizational support

for the factors “FF5: Climate of Openness” and “FF6: Continuous Education”. The survey

results provide weak support that individuals in entrepreneurial networks experience less

support for open communication within the organization (Case IV) and also less

organizational support for all organizational members’ growth and development than do

clique network members (Case II and Case IV).

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In conclusion, the survey results provide some support for the individual claims. The

empirical case results from Case I and Case III provide moderate evidence for the potential

occurrences of role-constraint learning and Case IV provides somewhat less support for the

potential occurrences of fragmental learning. As with role-constrained learning (Figure 18),

fragmented learning is also complex, as shown in Figure 19.

6.2.3 Determinants of barriers to knowledge utilization Knowledge may be generated and disseminated throughout an organization, but unless it is

used to alter our decisions, our behavior, or culture then the learning cycle remains

incomplete. Whether and how knowledge is used reflects our values and points out our

preferences for certain outcomes. Building on Kim’s model (1993), the conceptual

framework identifies three barriers to organizational learning in the knowledge utilization

process: audience learning, superstitious learning, and opportunistic learning (c.f., “Chapter

3,” p. 60). Table 38 summarizes the factors that influence the potential occurrence of those

barriers to learning.

Audience learning can occur when individuals change their own behaviors, but the effect of

these actions on the organizational behavior and action is ambiguity. In this way, individual

learning and skill development takes place, but adaptation by the organizational environment

does not necessarily follow. March and Olsen (1975) termed this barrier “Audience

Learning” to highlight the idea that the link between individual action and organizational

action is interrupted (c.f., “Chapter 2,” p. 24). According to this, we propose that clique

network members are more likely to experience audience learning than are entrepreneurial

network members as a consequence of the characteristics of the clique network structure.

When the linkage between individual action and organizational action is unclear, there is an

implied feedback (knowledge acquisition) process. Individuals receive information regarding

the effect of their unique contributions, but the effects on the organization are unclear; so

regardless of the quality of the individual action, the effect on the organization’s collective

action is not obvious.

Clique network members are more susceptible to this potential barrier to learning because of

the “tight” nature of their network structure type. Strong norms, rules, and sanctioning

processes can limi the ability of any single network member to reveive the necessary

feedback that is needed to understand the relationship of individual to collective action.

Entrepreneurial networks’ boundaries are more open and these network members are not as

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constrained by the psychosocial forces, and therefore are less likely to experiences this

barrier. Clique network members tend to perceive the organization’s learning orientation

“LOr5: Learning Focus” as more incremental and the survey results from Case I, Case III and

Case IV provide support for this claim. In addition, we proposed that the potential for

audience learning is increased as individuals in clique networks tend to perceive that their

organization’s support for the factors “FF7: Operational Variety” and “FF8: Multiple

Advocates” is low. The survey results from Case I and Case III provide support for both

clams. On the other hand, the results from Case II only support the claim regarding “FF7:

Operational Variety”.

However, audience learning is a complex issue, as shown in Figure 20. In general, the results

from Case I and Case III provide moderate support for this potential barrier to learning to

occur. An explanation is that both, Case I and Case III, are characterized by a high degree of

hierarchy.

Table 38: Determinants of potential barriers to knowledge utilization

Determinants Potential barriers to knowledge utilization

Types of social network structure Clique networks

Learning orientation: “LOr5: Learning Focus”: More Incremental

Facilitating factors: “FF7: Operational Variety”: Low support “FF8: Multiple Advocates”: Low support

Audience learning Case I, Case III

Types of social network structure Clique networks

Learning orientation: “LOr6: Value-Chain Focus”: More Design/Make

Facilitating factors: “FF1: Scanning Imperative”: Low support “FF10: Systems Perspective”: Low support

Superstitious learning Case I, Case III, Case IV

Types of social network structure Entrepreneurial networks

Learning orientation: “LOr7: Skill Development Focus”: More Individual

Facilitating factors: “FF9: Involved Leadership”: Low support

Opportunistic learning Case I, Case II

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Building on research from March and Olsen (1975) and Kim (1993), the framework

identified superstitious learning as a potential barrier to knowledge utilization (c.f., “Chapter

3,” p. 62). Superstitious learning can occur when organizational members draw incorrect

conclusions with regard to the impact of either individual or organizational actions on the

environment. In this case, the decision and action cycle proceeds and learning happens, but

the inferences that are drawn from the observations of the environmental response do not

have any real basis. For example, the conclusion may rest on untested assumptions that are

no longer valid. Essentially, the mental models that guide the feedback information

processing is erroneous, but is believed regardless. Thus, we hypothesized that individuals

embedded in clique networks face the possibility to superstitious learning more often than do

members of entrepreneurial networks. This proposition was based on the claim that

individuals embedded in clique networks have stronger ties that ease the transfer of complex

knowledge but at the same time are surrounded by contacts that view the issues in similar

ways. There is a common worldview in a clique network.

According to this, individuals in clique networks will identify the organization’s learning

orientation “LOr6: Value-Chain Focus” as focusing more on learning investments in

engineering/production activities. This is a more inwardly focused perspective that

emphasizes internal improvements. The survey results from the all four cases support this

claim.

In addition, it was claimed that the potential for superstitious learning to occur is increased as

clique network members assess their organization’s support for the factors “FF1: Scanning

Imperative” and “FF10: Systems Perspective” to be lower. The empirical results provide

support for the factor “FF1: Scanning Imperative”, as described under “Learning under

Ambiguity”. Furthermore, the results from Case II and Case III provide support for the claim

that individuals in clique networks tend to perceive their organization’s support for breaking

down or opening up the boundaries that develop in organizations and for pushing people

towards adopting a larger perspective than they would usually do in their day-to-day work

(“FF10: Systems Perspective”).

In general, the case results support the proposition that individuals in clique networks are

more at risk to experience superstitious learning. Results from two cases support this

proposition (Table 38).

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Opportunistic learning may occur when an organization takes action that is known to not fit

in with the commonly shared understandings in the organization; the linkage between the

shared mental models and organizational action is severed (c.f., “Chapter 3,” p. 63). This is

not necessarily a bad thing because it can enable the organization to bypass many

institutional hindrances and develop new skills and capabilities. The classic example is

Lockheed Aircraft Corporation’s (now Lockheed Martin) use of “skunkworks” to develop

high performance aircraft outside the normal business of manufacturing cargo aircraft. We

proposed that individuals in entrepreneurial networks might experience opportunistic

learning situations more often than do clique network members. This claim was based on the

proposition that individuals in entrepreneurial networks have networks rich in structural

holes. Following Burt’s argument (1992), structural holes are an opportunity to broker the

flow of information between people, but at the same time control the issues that bring

together people from opposite sides of the hole. Whether knowledge is disseminated in an

organization also depends on the outcome of doing so. Consequently, individuals embedded

in entrepreneurial networks will experience the organization’s learning orientation “LOr9:

Skill Developmental Focus” to be more geared to individual skill development than group

development. The survey results from Case I, Case II, and Case IV provide moderate support

to the claim that these individuals see knowledge utilization dedicated to individual

development.

In addition, we hypothesized that the potential opportunistic learning increases, as

entrepreneurial network individuals perceive that their organization’s support for the

facilitating factor “FF9: Involved Leadership” decreases. The results from Case I and Case II

(Table 38) provide support for the proposition.

6.3 Conclusions We undertook this work to understand better the effects of social network structure on

organizational learning processes. To this end, we introduced and explored the concepts of

organizational learning and social network analysis. Building on research from Nevis,

DiBella and Gould (1995) and Burt (1992) a conceptual framework for diagnosing barriers to

organizational learning was developed. Based on the work from March and Olsen (1975)

and Kim (1993), this framework identified barriers to organizational learning as a joint

function of the formal and informal organizational structure and the cultural learning

orientations. The formal organizational structure and the cultural learning orientations were

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operationalized through the OLI diagnostic tool (Nevis et al. 1995). The informal

organizational structure was revealed through social network analysis (Burt 1992).

According to the social network literature, we defined two different types of social network

structures: clique networks and entrepreneurial networks. We developed a model that

identified the determinants of barriers to organizational learning in terms of social network

structures, learning orientations, and facilitating factors. Data from four case studies in

Germany and Norway were gathered where the relationships of these factors (social network

type, learning orientation and facilitating factors) on organizational learning processes were

investigated. The cases provided mixed support for the derived propositions; some were

strongly supported, others were not. The nature of the organizations that provided data

clearly limited their associative power.

The characteristics of informal communication relationships can be expected to influence

organizational learning by creating or removing barriers to learning. First, individuals in

clique networks are more constrained by their network members and have less access to non-

redundant information sources than entrepreneurial network members. Consistent with

findings from closure theorists (e.g., Coleman 1988, Putman 1993), clique networks support

the production of social norms and roles, and facilitate sanctioning. Members of this network

type are surrounded by contacts that view issues in similar ways. On the one hand, this

reduces the risk for trusting network members and thereby enables and facilitates the

exchange of complex knowledge (e.g., Hansen 1999).

On the other hand, the network constraints can make it difficult to bring new knowledge into

the system. Consequently, any corrective learning that occurs can only be used within the

implicitly allowed range of normal behavior, which is defined by the existing norms and

values of the network members. Thus, clique networks tend to support single-loop learning,

and therefore is primarily concerned with efficiency: how best to achieve existing goals and

objectives, while at the same time keeping organizational performance within the range

specified by existing values and norms.

Second, individuals embedded in entrepreneurial networks, have access to more information

sources and tend more often to search for external knowledge sources. They tend to be more

innovative and focused on sales, distribution, and service activities. These findings are

consistent with the structural holes arguments, and contribute to its applicability. Thus, the

results from the four case studies strongly provided support for characteristics of the

relationship between the social network structures and learning orientations. However, there

was only some support for characteristics between the social network structure and the

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facilitating factors. Both, represented specific elements that facilitate learning. The social

network structure focuses on informal communication relationships, and the facilitating

factors focus on the formal organizational structure. Due to the fact, that there is only some

support, there might be other reasons why learning is facilitated or constrained. Considering

the fact, that the four case studies were selected all from the forest sector, it could be an

industry or sector-related bias or be explained by the small number of cases. Another reason

might be that knowing about an opportunity and being in a position to develop it are distinct

from doing about it. Here, motivation and opportunities are an issue. Hierarchy affects the

perception of the organization’s effectiveness to promote organizational learning.

Hierarchical superiors interact in different information environments than those lower in the

hierarchy (Burt 1992, Cross and Sproull 2004). Further, hierarchical position conveys

authority, enabling one to be an effective source of validation and legitimating or, in contrast,

control the flow of information.

However, highlighting the linkage between social network structures and organizational

learning processes, four potential barriers to organizational learning in clique networks, and

two potential barriers to organizational learning in entrepreneurial networks could be

identified. According to the results, the development or creation of insight, skills, or

relationships (knowledge acquisition) in organization is limited by learning under ambiguity

in clique networks; situational learning did not occur. The dissemination to others of what

has been acquired by someone (knowledge sharing) is limited by role-constrained learning in

clique networks, and fragmented learning in entrepreneurial networks. The assimilation or

integration of learning so that it is broadly available and can be applied to new situations

(knowledge utilization) is limited by audience learning and superstitious Learning in clique

networks and opportunistic learning in entrepreneurial networks. Role-constrained learning,

audience learning, and superstitious learning can be seen as the two incomplete learning

cycles that have the greatest impact on organizational learning in the forest sector.

6.3.1 Implications for use: enhancing learning capability In order to improve learning capabilities organizations have to focus on those elements that

have a meaningful impact on potential barriers to organizational learning in particular phases

of the learning cycle. In this section we briefly discuss the elements critical in terms of their

specific significance at a respective phase. The recommendations and guidelines represent

only a fraction of what should be considered in making choices about the best way to

improve organizational effectiveness.

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The following tables provide a summary of initiatives that can be taken to limit the potential

occurrence of the barriers to learning under three-step framework of knowledge management.

Each table focuses on the specific barrier that has been discussed earlier. Under each barrier,

the main determinants are presented, along with prescriptions for managerial and

organizational actions to overcome the barrier (Organization Transitions 1998).

Enhancing knowledge acquisition Among other things, knowledge acquisition has to do with the development of concepts and

methods that support the purpose of the organization. It addresses issues of how to identify

the insights, and relationships that can lead to improved organizational performance as well

as to increased interest in ongoing learning. Two potential barriers to organizational learning

can be identified: learning under ambiguity and situational learning.

Table 39: Overcoming learning under ambiguity

Clique networks “LOr1: Knowledge Source”:

More internal “LOr1: Knowledge Source”:

More external

Facilitating factors: “FF1: Scanning Imperative” “FF4: Experimental Mind-set”

Facilitating factors: “FF10: Systems Perspective” “FF6: Continuous Education” “FF7: Operational Variety” “FF1: Scanning Imperative”

To overcome learning under ambiguity, organizations need to decide whether they are able to

increase emphasis on facilitating factors associated with learning under ambiguity or to move

from a more internal to a more external knowledge source approach (Table 39). To move

from a more internal to a more external knowledge source, organizations may benchmark

other organizations that have been successful, or support those organization members who

reach outside the organization. In general, we believe that management efforts need to be

shifted to include the skills of observation, data gathering, and risk taking to support

employees in opening up to ways of improving acquisition of knowledge.

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Table 40: Overcoming situtional learning

Entrepreneurial networks “LOr2: Content-Process Focus”:

More how? “LOr2: Content-Process Focus”:

More what?

Facilitating factors: “FF2: Performance Gap” “FF4: Concern for Measurement”

Facilitating factors: “FF4: Concern for Measurement”

To overcome situational learning (Table 40), organization needs to increase the emphasis on

the facilitating factors that have low organizational support or to move from a more process

to a more content oriented focus. To move from process to content focus the organization

may establish focus groups to obtain ideas and reactions for product development. To

strengthen the support for concern for measurement, up-front work on development of

metrics in all new initiatives has to be included.

Enhancing knowledge sharing Knowledge can be disseminated within an organization and between employees in a variety

of ways. For an organization to learn as a whole, it is depending on making individual

mental model explicit to develop shared mental models. That is, two barriers to

organizational learning can be identified: role-constrained learning and fragmented learning.

Table 41: Overcoming role-constrained learning

Clique networks “LOr4: Dissemination Mode”:

More informal “LOr4: Dissemination Mode”:

More formal

Facilitating factors: “FF7: Operational Variety” “FF8: Multiple Advocates”

Facilitating factors: “FF5: Climate of Openness”

Increasing the emphasis on the factors “FF7: Operational Variety” and “FF8: Multiple

Advocates” or moving from a more informal to a more formal knowledge sharing will tend to

overcome role-constrained learning (Table 41). That is, the climate of openness has to be

improved. To encourage openness people may be teached in the value of legitimate

peripherical participation, the practice of inviting select people to meetings and other events

for exposing them to a broader perspective.

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Table 42: Overcoming fragmented learning

Entrepreneurial networks “LOr3: Documentation Mode”:

More personal “LOr3: Documentation Mode”:

More public

Facilitating factors: “FF5: Climate of Openness” “FF6: Continuous Education”

Facilitating factors: “FF5: Climate of Openness” “FF6: Continuous Education”

Establishing more public dissemination mode may help the organization to overcome

fragmented learning (Table 42). To do so, the emphasis on the two factors “FF5: Climate of

Openness” and “FF6: Continuous Education” has to be increased.

Enhancing knowledge utilization Three barriers to organizational learning, audience learning, superstitious learning, and

opportunistic learning may decrease the ability to utilize knowledge. The area of knowledge

utilization represents the ultimate payoff for learning.

Table 43: Overcoming audience learning

Clique networks “LOr5: Learning Focus”:

More incremental “LOr5: Learning Focus”:

More transforative

Facilitating factors: “FF7: Operational Variety” “FF8: Multiple Advocates”

Facilitating factors: “FF4: Experimental Mind-set” “FF1: Scanning Imperative”

Shifting the learning orientation towards a more transformative learning approach or

improving the listed facilitating factors will help to overcome audience learning (Table 43).

Enhancing transformative learning requires a powerful vision of the new, supported by

emotional appeals and even som coercion.

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Table 44: Overcoming superstitious learning

Clique networks “LOr6: Value-Chain Focus”:

More design/make “LOr6: Value-Chain Focus”:

More market/deliver

Facilitating factors: “FF1: Scaning Imperative” “FF10: Systems Perspektive”

Facilitating factors: “FF1: Scaning Imperative”

Superstitious learning is more likely to occur when the individuals are embedded in clique

networks. Increasing the emphasis on the facilitating factors “FF1: Scanning Imperative”

and “FF10: Systems Perspective” will help to overcome this barrier to learning (Table 44).

Table 45: Overcoming opportunistic learning

Entrepreneurial networks “LOr7: Skill Development Focus”:

More individual “LOr7: Skill Development Focus”:

More group

Facilitating factors: “FF9: Involved Leadership”

Facilitating factors: “FF8: Multiple Advocates” “FF10: Systems Perspective”

Shifting the learning orientation to develop team skills or improving the listed facilitating

factors will help to overcome opportunistic learning (Table 45).

6.3.2 Limitations All research has limitations that result from many different sources. Research in

organizations and organizational learning is additionally burdened by the nature of the

subject. Organizations and organizational processes are extremely complex and do not easily

lend themselves to controlled experimentation. Additionally, the presence of thinking,

independent actors interacting in a formal structure creates additional challenges. A very

common approach to studying organizations is via the case study research design. This

research design can provide a balance of control and accessibility that is important for

untangling complex socio-technical processes. Therefore, this research project was based on

four forest sector firms that provide a reasonable coverage of size and diversity.

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Research design The data used in this dissertation were limited to four case studies in the forest sector. The

four firms and the industry sector they are in, will limit the generalization of the results and

this may raise questions about the applicability of the insights to other industries and firms.

Controlling for industry and firm-specific factors could reduce this problem, so that the

results regarding the relationship between organizational learning processes and

organizational structures can be generalized across different organizations and industries.

We attempted to mitigate the potential biases by sampling across multiple types of forest

organization and countries. Nevertheless, different national, occupational or organizational

cultures could engender different types of organizational communication networks.

Moreover, the study is characterized by common method’s variance because the variables

were based on self-report.

Data collection and procedure An important concern in a social network study is the question of which actors to include.

For this research project, the boundary of the set of actors was difficult to determine as there

were many contacts between them and external organizations. The quality of generalizations

about social networks is limited in part by the quality of the data on which the generalizations

are based. Although some work has been done on the measurement error of network data

and the accuracy of network data collection considerably less is known about the reliability

and validity of network data (Marsden 1990).

In addition, a high response rate is necessary to make confident statements about the informal

structure of a social system at the organizational level. The response rates for the survey

instrument used in this research work were in some cases not as high as desired. The

response rates range from 33% -77%. Ideally, the response rate should be at least 80%

(Lesser and Prusak 2004). Because of the moderate response rate, the networks in the cases

are similar to egocentric networks (ego only). An egocentric network is one that has

information on ego (i.e., the actor) connections to alters (i.e., the one whom the actor relates

to), but no information on the connections among those alters (c.f., “Chapter 2,” p. 36). Such

data give only an incomplete picture of the network as a whole, but it still gives a reasonably

good picture of the local networks or neighborhoods of individuals.

Moreover, the study is also limited in that it assesses the organizational learning capability

from a one-sided work and retrospective perspective. Ethnographic work has demonstrated

that problem spaces and solutions are established and change dynamically in interaction with

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people and environment (Brown and Duguid 1991). According to this, our view of

organizational learning capability is constrained to ways that interpersonal interactions

facilitate its creation; our view of social network structures is constrained to characteristics

associated with elements of organizational learning capability.

Yet, while these constraints limit our understanding of how organizational learning capability

is enhanced and how social network structure is initiated and sustained, our work does begin

to illuminate the role the processes play in complex systems of decision making, information

processing and organizational learning. So despite the limitations above, the results of this

investigation are suggestive of significant relationship between the variables.

6.4 Major contributions of this study The contributions of this dissertation are three-fold:

1. It extends the theory of organizational learning and social network analysis by

developing a framework that makes an explicit link between organizational learning

processes and informal organizational structures.

2. It refines the development of tools for enhancing organizational learning capabilities

by providing a method that operationalizes the organizational learning processes and

reveals organizational aspects through a social network analysis.

3. It applies the framework and methods to four case studies in the forest sector allowing

valueable insights into the role of social network structures and organizational

learning.

In this dissertation, we extended the theoretical and empirical experiences within the concept

of organizational learning and social network analysis. That is, two types of social network

structure and their effect on organizational learning processes were identified and linked to

potential barriers to organizational learning.

In response to the challenge of improving an organization’s ability to learn, there has been a

significant amount of theoretical and action research conducted under the name of

organizational learning. Kim’s integrated model of organizational learning links individual

and organizational learning through the exchange of individual and shared mental models

(Kim 1993). Furthermore, it identifies seven potential barriers to organizational learning that

can limit an organization’s ability to learn effectively.

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Nevis, DiBella and Gould (1995) have developed an instrument, the ”Organizational

Learning Inventory,” that enables a diagnosis of an organization’s learning capability. Their

diagnosis model defines two dimensions that describe an organization’s learning orientation

and factors that facilitate learning. Together, these two dimensions provide a powerful tool

for understanding the role that both the formal and the informal structure play in

organizational learning processes. However, even though both concepts (Kim’s “Integrated

Organizational Learning Model” and the Nevis and fellows’ “Organizational Learning

Inventory”) are helpful in order to address potential learning barriers, they are limited in that

they do not explicitly consider the role of the social relationships among the organizational

members.

Thus, the major contribution of this research work is the development of a conceptual

framework that explicitly links social relationships with potential barriers to organizational

learning. The framework suggests that two aspects play an important role in organizational

learning processes, organizational learning capability and social network structure. In this

framework, the organizational learning processes are operationalized through the

“Organizational Learning Inventory” diagnostic tool (Nevis et al. 1995). The organizational

aspects are revealed through a social network analysis (Burt 1992).

This framework was motivated by two theoretical puzzles of social network structure and

their implications for potential barriers to organizational learning. First, clique networks,

which are densely embedded networks with many redundant connections, foster the

development of shared norms, routines, and the trust necessary for the sharing of complex

information. Second, entrepreneurial networks, which are networks with many structural

holes and non-redundant contacts, are networks that cross institutional, organizational, or

social boundaries. Thus, this type of network structure connects people to multiple bodies of

knowledge. Both network structures where hypothesized to have different implications for

organizational learning processes. Addressing the effect of a specific social network

structure for organizational learning processes, certain types of social network structure were

associated with certain types of potential barriers to organizational learning. The result is a

set of propositions that relate potential learning breakdowns with unique combinations of

OLI and social network determinants.

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Although the facilitative role of networks has led their identification as social capital (Burt

1997, Gulati 1999), and network attributes have been associated with several distinct

benefits, such as trust, information, and power, scholars have been unable to agree on the

form of social network structure that constitutes social capital. For instance, closure theorists

have presented densely interconnected networks as the normative (Coleman 1988).

Conversely, others have emphasized the benefits of structural-holes-rich networks (Burt

1992).

At one level, the arguments of this work add further complexity to this problem by

highlighting the fact that each of these social network structure choices in fact entails a

significant impact on the organization’s culture as it relates to learning. Clique networks

enable trust but limit the inflow of diverse and new insights. Thus, corrective learning takes

place, but there is no surfacing and challenging of deep-rooted assumptions and norms.

Although the opportunity to exchange complex knowledge, such as experiences, exists, the

social structure inhibits its utilization. By contrast, entrepreneurial networks provide

informational benefits but inhibit trust development. Thus, the opportunity for radical

learning exists but the uncertainties of the environment may prevent dialogue and skillful

discussion.

At another level, however, the conclusion of this study offers strategies to overcome this

dilemma. The arguments and results from this dissertation suggest that social network

structures cause different potential barriers to organizational learning. Organizational

learning emerges from, is constrained by, and is facilitated by social network structure. What

constitutes an enabling social network structure for one set of actions may well be disabling

for others. Thus, the strategies to change social network structure and to overcome barriers

to organizational learning are likely to be highly contingent on what actors seek to enable or

prevent through it.

Entrepreneurial networks, which are social network structures consisting of many non-

overlapping ties, provide know-how and control benefits (Burt 1992). However, although

individuals have access to multiple bodies of knowledge and are less constrained by their

social network structure, their learning will not automatically be more radical and

transformative as they might be overloaded by information or the information does not have

the quality to start inquiry. Nevertheless, due to their position in the social structure, they can

connect otherwise disconnected actors with each other. Consequently, identifying and

understanding the implications of social network structure as an indicator of potential barriers

to organizational learning is likely to be critical in order to develop successfully strategies to

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overcome these barriers. This study has shown is that there are no simple, universal answers

to these questions. The results, however, indicate more rigorous study is called for.

The study was conducted in the context of the forest sector, which is experiencing challenges

on many sides. Increasing market globalization, modern information and communication

technologies, and rationalization caused by increased application of advanced mechanization

and high technologies, have led to radical changes in the forest sector. The ability of the

forest sector to successfully handle these challenges rests to a large degree on its ability to

improve its learning capabilities.

The ability to learn is an important organizational resource. A critical resource embedded

within organizations is the knowledge that highly skilled employees bring to work on a day-

to-day basis. However, aside from human resource policies targeted at the attraction,

development and retention of skilled workers, there has been little effort put into systematic

ways of leveraging knowledge that is embedded in people and relationships. Given the

extent to which people rely on their own knowledge and the knowledge of their contacts to

solve problems, this is a significant shortcoming.

The framework developed in this research project and applied to four sample cases allows us

to understand how a given informal organizational structure affects organizational learning

processes. Based on the new insights, this framework identified potential barriers to

organizational learning and suggests strategies to overcome these barriers to organizational

learning. We are a long way from having solved the problems of organizational learning, but

thinking about the impact of the formal and the informal organizational structure and the

cultural learning orientation on organizational learning processes is a step in the right

direction.

6.4.1 Suggestions for further research Historically, considerable effort has been spent on working to understand knowledge. The

current mantra is that knowledge creation; sharing and utilization are fundamentally human

and above all social processes (Brown and Duguid 2000). Consequently, the importance of

informal networks in organizations cannot be ignored. In order to capture the flow of

knowledge through these informal networks, social network analysis needs to be more clearly

associated with research in organizational learning.

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Through case studies, it has been shown that the “Organizational Learning Inventory” and

social network analysis are very powerful diagnostic methods in assessing organizational

learning characteristics and analyzing the effects of social network structure on

organizational learning processes. The recent explosion of interest and research in the

properties of networks is providing insights into the dynamics of social networks, which we

believe, will be particularly useful in planning and diagnosing organizational processes,

including learning and knowledge management. Another interesting direction of research

would be exploring how the role of different kinds of social networks differs across industries

with differences in complexity and codifiability of knowledge (Hansen 1999, Sorenson and

Fleming 2004).

Further, as this research work was conducted within a limited numbers of case studies future

research has to be done on a larger number of research examples. In addition, to investigate

the effects of social network structures on organizational learning processes by considering

the different occupational and organizational cultures in more detail, the effect of hierarchy

has to be included.

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Summary

The forest sector faces a number of serious challenges. These challenges include many

different aspects, which may be characterized by social and structural changes, increasing

mechanization and use of new technologies, and increasing pressure of competition through

globalization. The ability of the forest sector to cope with these challenges successfully rests

to a large degree on the ability of the sector to improve the learning capabilities of its

organizations. Consequently, the increasing complexity of linked economic relationship

structures and the uncertainty in the forest sector, as described above, have led to an

increased interest in the concepts of knowledge management and organizational learning.

Organizational learning has long been an issue of both practical and theoretical concern.

Researchers in the field of learning and social network theory have known for some time that

the creation of knowledge is a social process and thus social relationships are important for

learning how to do one’s work, and for acquiring and transferring knowledge and experiences

within teams and organizations. However, until now, little inquiry has been made into the

role that social relationships within an organization have for organizational learning.

Purpose of the study The importance of informal organizational structures on organizational learning and

knowledge management has become widely accepted. This shift of focus towards a social

perspective as an important paradigm in organizational learning studies takes into account the

observation that the major part of individual knowledge transfer does not follow formal

hierarchies or processes but is instead driven by personal and informal communications.

The main purpose of this research work was therefore to gain new insights into the role of

informal structures and processes that influence organizational learning. A secondary

objective was to identify strategies to overcome these barriers so that managers would be able

to position their organizations better in the global market.

Seeking to address the relationship between organizational learning processes and the social

network structures in which they are embedded, there was a need for a framework that

combines both potential barriers to organizational learning as well as the relationship

between social network structures and organizational learning processes. The development

of this framework is the main content of this dissertation.

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Research design The research question was addressed using a comparative multiple-case study research

design based on an exploratory approach. A review of literature on organizational learning

and social network theory was used to present an integrated organizational learning model

that identifies barriers to organizational learning and a diagnostic instrument that focuses on

the organization’s structural and cultural learning characteristics. In addition, the link

between organizational learning and social network analysis was elaborated. Based upon this

review, a conceptual framework was developed that combines aspects of organizational

learning barriers with different types of social network structure and organizational learning

characteristics. Propositions that relate structural components and learning characteristics

were derived. Formalizing the organizational learning characteristics through the

“Organizational Learning Inventory” (OLI) diagnostic tool and revealing the structural

components through a social network analysis, the conceptual framework was applied to the

case studies and a comparative analysis was carried out.

The study was conducted based on four organizations, three from Germany and one from

Norway. These four organizations are either responsible for organizing timber harvesting

activities and wood procurement management, or responsible for production of customized

and/or further processed wood products. All of the organizations are experiencing challenges

from the increasing pressure of competition and uncertainty in the forest sector. The case

studies were conducted by collecting data via a mail questionnaire. The mail questionnaire

was organized in three steps: (I) collection of network data, (II) assessing learning

orientations, and (III) assessing evidence on facilitating factors, including demographic data.

Building on these data, the relationships between organizational learning capability and

social network structure were investigated. For each case, within-case analyses were carried

out and these were followed by a cross-case search for patterns. After conducting the cross-

case analyses, the propositions derived from the framework were discussed. Based on the

empirical results and discussion of them, conclusions were drawn and implications for further

research developed.

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Main findings and conclusions Knowledge is now considered an important organizational resource. The ability to learn and

effectively use this resource is an important organizational capability. This crucial resource

is embedded in organizations through skilled individuals and applied by them on a day-to-

day basis. Research on organizational learning has focused on cognitive, social, and

organizational impediments to acquiring, sharing, and using knowledge in organizations and

a growing body of literature has documented the importance of social relationships for

acquiring information, knowledge transfer, and learning how to do one’s work.

In this dissertation, a conceptual framework was developed that links structural components

with learning characteristics in order to identify potential sources of barriers to organizational

learning. Furthermore, strategies are developed to overcome these barriers to learning. The

results from the case studies support the proposition that specific social network structures,

clique networks and entrepreneurial networks, have different effects on organizational

learning processes. In this sense, the arguments and results from this dissertation suggest that

social network structure cause different potential barriers to organizational learning.

Consequently, organizational learning emerges from, is constrained by, and is facilitated by

social network structure. However, the relationships are complex and what constitutes an

enabling social network structure for one set of actions may well be disabling for others.

Clique networks, social network structures with many connected and redundant ties, facilitate

the development of trust and cooperation. The study highlights the fact that such social

network structures are useful from an organization’s perspective, as individuals have to

improve their work within the range specified by existing values and norms. This may be

sufficient where error correction can proceed by changing collective shared strategies and

assumptions within a constant framework of norms for performance. However, the results of

this research work demonstrate that employees in clique networks tend to be challenged by

role-constrained learning, audience learning, superstitious learning, and learning under

ambiguity. Due to constraints imposed by their social network structure, individuals in

clique networks are more frequently limited by their organizational role and shared norms

and behavior than individuals in entrepreneurial networks. In this sense, they are often

unable to act based on their individual learning, gather external data, and avoid risk taking in

opening up to ways of improving knowledge acquisition.

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On the other side, entrepreneurial networks, which are network structures with many non-

overlapping ties that provide know-how and control benefits, are ideal for employees whose

primary business is the information brokering. However, although these individuals have

access to multiple bodies of knowledge and are less constrained by their network structure,

their learning will not automatically be more radical and transformative. According to the

results of this research work, situational learning, fragmented learning, and opportunistic

learning may occur more frequently in this social network structure than in clique networks.

As stated earlier, organizational learning depends on individuals improving their mental

models and communicating those mental models to others. This is crucial for the

development of shared mental models and thus works to improve the organization’s memory.

In order to overcome the barriers to situational learning, fragmented learning, and

opportunistic learning, individuals embedded in entrepreneurial networks have to increase

trust to enable open communication and to codify and to store learning for later use.

Thus, the major contribution of this research work is the development of a conceptual

framework that explicitly links social relationships with potential barriers to organizational

learning. The arguments of this work emphasize the fact that each of the social network

structure choices, clique network or entrepreneurial network, in fact entails a significant

impact on the organization’s culture as it relates to learning. Consequently, identifying social

network structure as an indicator for potential barriers to organizational learning is therefore

likely to be critical to develop successfully strategies to overcome these barriers.

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Organisatorisches Lernen und soziale Netzwerkstrukturen

- Untersuchung an Fallbeispielen deutscher und norwegischer Unternehmen der Forst-

und Holzwirtschaft -

Kurzfassung Die Forst- und Holzwirtschaft ist seit Jahrzehnten Veränderungen unterworfen, die sich

sowohl in einer zunehmenden Mechanisierung und dem Einsatz neuer Technik als auch in

einem zunehmenden Konkurrenzdruck durch Globalisierung ausdrücken. Um diese

Veränderungen erfolgreich zu meistern, müssen Unternehmen der Forst- und Holzwirtschaft

sich beständig weiterentwickeln. In diesem Zusammenhang gewinnt besonders die effiziente

Erfassung und Weiterleitung von Informationen und Wissen innerhalb von Organisationen

zunehmend an Bedeutung. Konsequenterweise stellt Wissen auch wirtschaftlich eine immer

bedeutsamer werdende Ressource für die Unternehmen dieses Sektors dar. So haben die

zunehmende Komplexität innerhalb der Forst- und Holzwirtschaft und die sich rasch

ändernden Anforderungen zu einem verstärkten Interesse an den Konzepten des

Wissensmanagements und des organisatorischen Lernens geführt.

Die zunehmenden Veränderungen im wirtschaftlichen Umfeld und die sich daraus

ergebenden Konsequenzen haben dazu geführt, dass organisatorisches Lernen seit langem

Gegenstand der Forschung ist. Zur Erklärung der Reaktion von Unternehmen auf veränderte

Umweltbedingungen und der dabei maßgeblichen Entscheidungsprozesse entwickelten

Organisationsforscher das Konzept des organisatorischen Lernens. Dieses Konzept beginnt

mit der Idee, dass Organisationen lernen müssen. Organisatorische Entscheidungsprozesse

stellen sich somit als komplex verlaufende, untereinander vielfältig verknüpfte Lernprozesse

dar. Das Verständnis von Lernprozessen beruht darauf, dass sich Umweltbedingungen

ständig verändern und Unternehmen hierdurch gezwungen sind, zu lernen und sich diesen

Veränderungen anzupassen. Diese Prozesse müssen auf allen Ebenen der Organisation

stattfinden.

Wissenschaftler, die sich mit Lerntheorien und sozialen Netzwerktheorien beschäftigen,

haben bereits seit längerem darauf hingewiesen, dass Lernen ein sozialer Prozess ist.

Folglich sind soziale Beziehungen für den Erwerb, die Vermittlung und Anwendung von

Wissen und Erfahrungen, also für Lernprozesse in Organisationen, von zentraler Bedeutung.

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Jedoch wurden bis heute nur wenige Erkenntnisse darüber gewonnen, welchen Einfluss

soziale Beziehungen auf organisatorische Lernprozesse haben.

Zielsetzung der Arbeit Das Hauptziel der vorliegenden Forschungsarbeit war es, einen konzeptionellen

Bezugsrahmen zu entwickeln, der die unterschiedlichen Aspekte der informellen

Kommunikationsstrukturen mit organisatorischen Lernprozessen verknüpft, um mögliche

Ursachen für Widerstände gegen Lernen in Unternehmen zu identifizieren und zu erklären.

Ein weiteres Ziel war die Entwicklung von Strategien zur Überwindung dieser potentiell

auftretenden Widerstände des organisatorischen Lernens.

Untersuchungsaufbau Innerhalb des Literaturüberblickes zur Theorie des organisatorischen Lernens und der

sozialen Netzwerktheorie wurden das Modell des organisatorischen Lernens nach Kim

(1993), das Instrument zur Ermittlung der Lernfähigkeit von Unternehmen (Nevis et al. 1995)

und die soziale Netzwerkanalyse in Anlehnung an Burt (1992, 2001) vorgestellt.

Kim verbindet in seinem integrierten Modell individuelles und organisatorisches Lernen

(Kim 1993). Das in diesem Modell implizierte Lernkonzept kann als „erfahrungsbasiertes“

Lernen bezeichnet werden. In diesem Sinne versuchen die Organisationsmitglieder und die

Organisation, aus den in der Vergangenheit erfahrenen Umweltreaktionen in kontinuierlich

verbesserter Weise situationsgerechte Handlungsentwürfe zu entwickeln. Das Modell

definiert sieben verschiedene mögliche Widerstände gegen organisatorisches Lernen und

bildet den Ausgangspunkt des Bezugsrahmens in dieser Arbeit.

Weiterhin wurde das Diagnosewerkzeug „Organizational Learning Inventory“ (OLI) von

Nevis, DiBella und Gould vorgestellt (Nevis et al. 1995). Dieses Werkzeug ermöglicht es,

für jeden Lernprozess (Wissensakquisition, Wissenskollektivierung und Wissensumsetzung)

die individuelle Wahrnehmung der “Lernkultur” und der “organisatorischen Unterstützung

des Lernens“ innerhalb des Unternehmens zu erfassen. Die Organisationsforscher

identifizierten 17 Indikatoren, die die Anpassungs- und Veränderungsfähigkeit von

Organisationen („Lernfähigkeit“) beschreiben. Sieben davon repräsentieren die Lernkultur,

die beschreibt, wie und was in Unternehmen gelernt wird. Die weiteren zehn Indikatoren

beschreiben unterschiedliche Initiativen, wie zum Beispiel „offene Kommunikation“

zwischen Mitarbeitern in einem Unternehmen, die Lernen unterstützen.

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Ebenso wurde die soziale Netzwerkanalyse in Anlehnung an Burt (1992, 2001) beschrieben.

Sie stellt ein Instrument dar, mit dem die informellen Kommunikationsstrukturen eines

Unternehmens analysiert werden können. Diese informellen Netzwerke bauen auf

persönliche Beziehungen mit relevanten Wissensträgern auf. Hierbei lassen sie oft formale

Strukturen in einer Organisation, und damit verbundene offizielle

Kommunikationsstrukturen, unberücksichtigt. Durch die Dynamik in formellen sowie

informellen Beziehungen entstehen Strukturen, die einen effizienten Austausch von Wissen

beeinflussen, und dadurch Lernprozesse anregen oder aber begrenzen können. Zwei soziale

Netzwerkstrukturen wurden definiert: „Cliquen-Netzwerke“ und „Unternehmerische

Netzwerke“.

Basierend auf Kims (1993) Modell und der Kombination der beiden oben genannten

Instrumente konnten die unterschiedlichen Aspekte der informellen

Kommunikationsstrukturen und des organisatorischen Lernens mit möglichen Widerständen

gegen organisatorisches Lernen in Unternehmen in einem konzeptionellen Bezugsrahmen

verbunden werden. Dieser Ansatz ermöglichte es, Vermutungen abzuleiten, die die

strukturellen Komponenten und die Lerncharakteristika miteinander in Beziehung setzen.

Es wird vermutet, dass das Lernen der Organisationsmitglieder in Cliquen-Netzwerken durch

folgende Widerstände begrenzt ist: (1) „Rollen-beschränktes Lernen“, (2) „Publikums-

Lernen“, (3) „Abergläubisches Lernen“ und (4) „Mehrdeutiges Lernen“. Diese vier

Widerstände gegen organisatorisches Lernen werden im Folgenden kurz beschrieben.

(1) Können individuelle Handlungskonzepte nicht in entsprechende Handlungen umgesetzt werden und finden deshalb keine Lernprozesse statt, so wird dies als „Rollen-beschränktes Lernen“ bezeichnet. Konkret geht es um eine „Diskrepanz zwischen individuellen Lernen und Handeln“. Mitarbeiter eines Unternehmens wollen zwar gemäß ihren Überzeugungen handeln (individuelles Lernen findet statt), können dies aber wegen auferlegter Rollenbeschränkungen nicht. (2) Eine weitere Störung ist anzutreffen, wenn zwar eine individuelle Handlung stattfindet, aus ihr aber kein unternehmerisches Handeln resultiert. In diesem Fall wird von „Publikums-Lernen“ gesprochen. Hier geht es darum, dass individuelle Handlungen (z.B. der Vorschlag wegen veränderter Bedingungen den Planungsansatz zu ändern) nicht in das organisatorische Verhaltenssystem aufgenommen werden. Das Unternehmen aktiviert seine eigenen Handlungsgesetze gegen die (neu gelernte) Handlungsinitiative. Der Impuls verläuft im Sande.

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(3) Von „Abergläubischem Lernen“ ist die Rede, wenn entweder organisatorische oder individuelle Handlungen vorliegen und gleichzeitig Umweltreaktionen beobachtet werden können, aber ein kausaler Zusammenhang objektiv nicht nachvollziehbar ist. Hier geht es um das potentielle Unvermögen der Organisation, auf der Basis von Erfahrungen zu erkennen, dass ihre Sicht des Organisation/Umwelt-Zusammenhangs unzutreffend ist, bzw. dass die Zusammenhänge unzutreffend rekonstruiert werden. Organisationsmitglieder formen sich ihr Bild von der Umweltsituation unter Unsicherheit und vergleichen es mit ihren Erwartungen. Alle Umweltereignisse werden (notwendigerweise) durch einen Filter wahrgenommen. (4) „Mehrdeutiges Lernen“ besagt, dass die Lernstörung hierbei durch Fehlinterpretationen und Vereinfachungen entsteht. Die Unklarheit der Umweltsituation wird verleugnet und die Zusammenhänge finden ihre Erklärung in altbekannten Mustern.

Weiterhin wird die Vermutung aufgestellt, dass organisatorische Lernprozesse für

Organisationsmitglieder in „Unternehmerischen Netzwerken“ verstärkt durch (5) „Situatives

Lernen“, (6) „Fragmentarisches Lernen“, und (7) „Opportunistisches Lernen“ unterbrochen

sind.

(5) „Situatives Lernen“ ist anzutreffen, wenn individuelles Handeln zwar die Notwendigkeit von Veränderungen mentaler Modelle nahe legt, dies aber nicht immer geschieht. Diese Unterbrechung des Lernprozesses kann z.B. unter hohem Arbeitsdruck vorkommen, wobei nach der Lösung eines Problems sofort zur Bewältigung des nächsten übergegangen wird, ohne die vorgenommenen Handlungen zu reflektieren und das mentale Modell zu hinterfragen und gegebenenfalls zu verändern. (6) „Fragmentarisches Lernen“ liegt vor, wenn der Austauschprozess zwischen den individuellen mentalen Modellen und den kollektiven mentalen Modellen gestört ist. Hierbei findet das individuelle Wissen einzelner Mitarbeiter keinen Eingang in die kollektiven mentalen Modelle und trägt somit nicht zur Entwicklung des organisationsweiten Wissenspools bei. (7) „Opportunistisches Lernen“ bezeichnet die unterbrochene Verbindung zwischen den kollektiven mentalen Modellen und der unternehmerischen Handlung. Es tritt auf, wenn nicht gemäß den kollektiven mentalen Modellen gehandelt wird (dies muss nicht zwangsläufig negativ sein). Dies bedeutet z.B. die Abkehr von Standardabläufen, von der Unternehmenskultur oder von den Unternehmenswerten. Diese Unterbrechung des organisatorischen Lernens ist anzutreffen, wenn es notwendig ist, Chancen wahrzunehmen ohne das gesamte Unternehmen verändern zu wollen oder zu können.

Die organisatorischen Lerncharakteristika wurden mit Hilfe des Diagnosewerkzeuges (OLI)

präzisiert und die strukturellen Komponenten wurden durch die netzwerkanalytischen

Kennzahlen der „strukturellen Löcher“ erfasst. Der empirische Teil der Untersuchung wurde

exemplarisch an vier Fallbeispielen aus der Forst- und Holzwirtschaft in Deutschland und

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Norwegen durchgeführt. Das Ziel war es, die theoretischen Erkenntnisse durch empirische

Ergebnisse zu untermauern. Alle Unternehmen haben ihre Aufgabenfelder entweder in der

Holzbereitstellung oder der Weiterverarbeitung von Holz. Die erforderlichen Grunddaten

wurden innerhalb der Fallstudien mit Hilfe einer schriftlichen Befragung der Mitarbeiter

erhoben. Der Fragebogen basierte auf dem konzeptionellen Bezugsrahmen und war in drei

thematische Abschnitte unterteilt. Im ersten Abschnitt wurden die Befragten aufgefordert,

ihre fünf wichtigsten beruflichen Kontakte innerhalb des Unternehmens zu benennen. Im

zweiten und dritten Abschnitt des Fragebogens sollten die Befragten ihre individuelle

Wahrnehmung der Lernkultur innerhalb des Unternehmens und der Unterstützung des

Lernens durch Initiativen des Unternehmens bewerten sowie demographische Daten zu ihrer

Person angeben.

Basierend auf diesen Daten wurden Zusammenhänge zwischen organisatorischer

Lernfähigkeit und sozialer Netzwerkstruktur untersucht. Die Datenanalyse wurde sowohl

innerhalb der Fallbeispiele als auch fallübergreifend durchgeführt. Aufbauend auf der

fallübergreifenden Analyse wurden die im konzeptionellen Bezugsrahmen aufgeworfenen

Vermutungen eingehender diskutiert und mit Ursachen für Widerstände gegen Lernen in

Organisationen verknüpft.

Ergebnisse und Schlussfolgerungen Diese Arbeit verknüpft erstmalig soziale Netzwerkstrukturen mit Widerständen gegen

organisatorisches Lernen. Hierfür wurde ein konzeptioneller Bezugsrahmen entwickelt, der

die strukturellen Komponenten innerhalb einer Organisation mit ihren Lerncharakteristika

verbindet, um mögliche Ursachen für Widerstände gegen organisatorisches Lernen zu

identifizieren und Strategien zu deren Überwindung zu entwickeln.

Die Ergebnisse der Fallstudien untermauern die Vermutung, dass spezifische soziale

Netzwerkstrukturen – „Cliquen-Netzwerke“ und „Unternehmerische Netzwerke“ –

organisatorische Lernprozesse auf unterschiedliche Art und Weise beeinflussen. Soziale

Netzwerkstrukturen ermöglichen somit organisatorisches Lernen, gleichzeitig wird

organisatorisches Lernen durch soziale Netzwerkstrukturen eingeschränkt. Was für manche

Lernprozesse eine fördernde soziale Netzwerkstruktur ist, kann für andere Lernprozesse

einengend wirken.

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„Cliquen-Netzwerke”, also soziale Netzwerkstrukturen mit vielen miteinander verbundenen

und redundanten Kontakten, erleichtern die Entwicklung von Vertrauen und

Zusammenarbeit. Diese sozialen Netzwerkstrukturen sind für ein Unternehmen förderlich,

wenn Mitarbeiter ihre Handlungen an veränderte Umstände zwar anpassen müssen, aber die

zugrunde liegenden gemeinsamen Denk- und Handlungsmuster, die durch

unternehmensweite Normen definiert sind, nicht ändern. Aufgrund der dicht geknüpften

sozialen Netzwerkstruktur sind gruppenspezifische Normen klar definiert und abweichendes

Verhalten wird sanktioniert. Dies hat zur Folge, dass Mitarbeiter in „Cliquen-Netzwerken”

es vermeiden sich Veränderungen auszusetzen, die die Art und Weise ihres Wissenserwerbs

verbessert. Im Rahmen der Untersuchung zeigte sich, dass Mitarbeiter, die in „Cliquen-

Netzwerke” eingebunden sind, verstärkt zu „Mehrdeutigem Lernen“, „Rollen-beschränktem

Lernen“, „Publikums-Lernen“ und „Abergläubischem Lernen“ tendieren. „Mehrdeutiges

Lernen“ konnte für das Fallbeispiel (I) festgestellt werden. Durch den Mechanismus der

sozialen Abgrenzung haben Mitarbeiter in „Cliquen-Netzwerken” nur einen begrenzten

Zugang zu neuen Informationen. Bedingt durch die Beschränkungen ihrer sozialen

Netzwerkstruktur sind Individuen in „Cliquen-Netzwerken” häufig an ihre Rolle und

gemeinsame Normen gebunden und in ihrem Verhalten eingeschränkt. Dies konnte in

Fallbeispielen (I) und (III) als „Rollen-beschränktes Lernen“ identifiziert werden, was oft zur

Folge hat, dass die Mitarbeiter nicht imstande sind, das Gelernte umzusetzen. „Publikums-

Lernen“ war in Fallbeispielen (I) und (III) anzutreffen. Hier findet zwar eine individuelle

Handlung statt, jedoch trifft sie auf Widerstände im Unternehmen. Der Impuls verläuft im

Sande. In diesen Fallbeispielen konnte auch „Abergläubisches Lernen“ beobachtet werden.

Bei dieser Lernstörung ist es den Individuen in „Cliquen-Netzwerken” nur bedingt möglich,

externe Informationen zu erfassen und somit zutreffende Organisations-Umwelt-

Zusammenhänge herzustellen.

Die zweite untersuchte soziale Netzwerkstruktur ist das „Unternehmerische Netzwerk”, in

das Personen eingebunden sind, deren soziale Netzwerke strukturelle Löcher überbrücken.

Diese sozialen Netzwerkstrukturen bieten ihren Akteuren Informations- und Kontrollvorteile.

Dementsprechend bieten diese Netzwerkstrukturen günstige Voraussetzungen für Personen,

deren Tätigkeitsschwerpunkt die Arbeit mit Informationen ist. Durch ihre vermittelnde

Position zwischen getrennten Gruppen haben sie Zugang zu neuen Informationen und können

Denk- und Handlungsmuster miteinander vergleichen und somit neue Erkenntnisse

generieren. Obwohl diese Personen Zugang zu einer Vielzahl von unterschiedlichen

Wissensquellen haben und keiner Gruppe so verbindlich angehören, dass sie sich deren

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Normen und Präferenzen anschließen müssten, lernen sie nicht automatisch besser als

Individuen in „Cliquen-Netzwerken”. Ein Ergebnis dieser Arbeit war, dass Individuen, die in

„Unternehmerische Netzwerke“ eingebunden sind, verstärkt Unterbrechungen der

organisatorischen Lernprozesse durch „Situatives Lernen“, „Fragmentarisches Lernen“, und

„Opportunistisches Lernen“ ausgesetzt sind. Organisatorisches Lernen ist davon abhängig,

dass Einzelpersonen ihre individuellen mentalen Modelle verbessern und dem Unternehmen

mitteilen, um kollektive mentale Modelle zu entwickeln. „Situatives Lernen“ konnte nur

bedingt in Fallbeispiel (IV) angenommen werden. Des Weiteren kann der Austauschprozess

zwischen den individuellen mentalen Modellen und den kollektiven mentalen Modellen,

bedingt durch die strategische Position im sozialen Netzwerk und das Handlungspotential

von Mitarbeitern, in „Unternehmerischen Netzwerken” gestört sein. Dies wird als

„Fragmentarisches Lernen“ bezeichnet und kann ebenfalls für Fallbeispiel (IV) angenommen

werden. Um diesen Widerstand erfolgreich zu umgehen, müssen Mitarbeiter in

„Unternehmerischen Netzwerken” verstärkt Vertrauen entwickeln und eine offene

Kommunikationskultur etablieren. Hierdurch ist es möglich, individuelle Erkenntnisse für

das ganze Unternehmen verfügbar zu machen. Ähnliches gilt für „Opportunistisches

Lernen“. Mitarbeiter, die in ein „Unternehmerisches Netzwerk” eingebunden sind, können

Chancen wahrnehmen und umsetzen, die sich für Mitarbeiter in „Cliquen-Netzwerken”

aufgrund der sozialen Kontrolle nicht ergeben. Dies konnte für die Fallbeispiele (I) und (III)

angenommen werden.

Das Hauptergebnis dieser Arbeit ist die Entwicklung eines konzeptionellen Bezugsrahmens,

der es ermöglicht, explizit die sozialen Netzwerkstrukturen mit potentiellen Widerständen

gegen organisatorisches Lernen zu verbinden. Die Ergebnisse der Arbeit bestätigen, dass

soziale Netzwerkstrukturen organisatorische Lernprozesse auf unterschiedliche Art und

Weise beeinflussen. Zusammenfassend lässt sich feststellen, dass für die erfolgreiche

Entwicklung von Strategien zur Überwindung dieser Widerstände eine Identifizierung der

sozialen Netzwerkstruktur unumgänglich ist.

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Acknowledgments

Pursuing a Ph.D. project is an honor and a privilege for which I am grateful, although the

process of finishing it is another matter. Like most worthwhile things in life, the completion

of this dissertation would not have been possible without the help and involvement of

numerous people. Though I won’t be able to mention every name, I am very thankful to all

who have supported and accompanied me through the past several years, both within and

outside of the Faculty of Forest and Environmental Sciences at the Albert-Ludwigs-

University in Freiburg.

First and foremost, I thank my wife Tinka for her unwavering support throughout the

whole process. She always encouraged me to stick with my dreams.

Writing my dissertation has proven to be challenging and excellent learning experience. I

wish to extend my deepest gratitude to:

Siegfried Lewark, as my supervisor, for his advice and his financial assistance as well as

contributing to my understanding of the forest sector and the forest profession in general.

Carl Brønn, for his enthusiastic response to my ideas, and his guidance, encouragement,

and willingness to complete this dissertation. He was an instructive mentor. I have learned

much from him over the years.

Sincere thanks are extended to all the organizations and colleagues whose openness and

commitment to my work have been a key to its success, further to all the employees who

participated in the case studies for allowing me to study and learn from them.

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Appendix I German mail questionnaire

Analyse des Informations- und Wissensflusses innerhalb

der „UNTERNEHMEN“

Der vorliegende Fragebogen ist Teil einer Studie des Instituts für Forstbenutzung und Forstliche Arbeitswissenschaft der Albert-Ludwigs-Universität Freiburg in Zusammenarbeit mit der „UNTERNEHMEN“, die die Erfassung und Weiterleitung von Informationen und Wissen innerhalb des Unternehmens untersucht.

Alle Angaben werden streng vertraulich behandelt und die Anonymität in der Auswertung ist gewährleistet!

Teil I von III Die meisten Menschen diskutieren wichtige Angelegenheiten von Zeit zu Zeit mit anderen. Wenn Sie an Ihr Unternehmen denken – wer sind die Personen, mit denen Sie berufliche Angelegenheiten diskutieren, die Ihnen wichtig sind? Bitte nennen Sie die 5 Personen, zu denen Sie die wichtigsten beruflichen Kontakte innerhalb Ihres Unternehmens haben (Bitte in abnehmender Häufigkeit!).

Kontakte Vorname Nachname

Am häufigsten

Am zweit häufigsten

Am dritt häufigsten

Am viert häufigsten

Am fünft häufigsten

ALBERT-LUDWIGS-

UNIVERSITÄT FREIBURG

Bitte senden Sie den Fragebogen bis zum

DD.MM.YYYY zurück, damit Ihre Antwort in

die Auswertung einfließen können!

Bitte senden Sie den Fragebogen bis zum

DD.MM.YYYY zurück, damit Ihre Antwort in

die Auswertung einfließen können!

161

Teil II von III

Kreuzen Sie bitte die Aussage an, die Ihrer Meinung nach derzeit jeweils am besten auf Ihr Unternehmen zutrifft.

LOr1a: Ihr Unternehmen…

sieht sich als Vorreiter und lernt aufgrund eigener Erfahrungen!

ahmt oft andere nach und lernt

aufgrund der Erfahrungen anderer!

LOr1b: Für Ihr Unternehmen ist es besonders wichtig,...

unter den Ersten zu sein, die neue

Produkte / Ideen entwickeln!

die Produkte / Ideen anderer zu

verbessern!

LOr1c: Die Wettbewerbs-fähigkeit Ihres Unternehmens wird…

aufgrund eigener Erfahrungen

bewertet!

aufgrund externer Maßstäbe bewertet!

LOr2a: Ihr Unternehmen investiert hauptsächlich in die Frage,…

was die zukünftigen Produkte /

Dienstleistungen sind!

wie zukünftige Produkte /

Dienstleistungen realisiert werden!

LOr2b: Die Mitarbeiter müssen vor allem…

Ideen entwickeln können!

Produkte / Dienstleistungen realisieren

können!

LOr2c: Der Schwerpunkt der Überlegungen in Ihrem Unternehmen liegt darauf,…

was die Ziele sind!

wie die Ziele erreicht werden!

LOr3a: Wenn Sie Informationen benötigen, dann…

fragen Sie die Person, die sich am

Besten damit auskennt!

suchen Sie in einer Datenbank /

Dokumenten danach!

LOr3b: Ein erfolgreicher Informationsaustausch…

findet nur statt, wenn sich die

Personen kennen!

kann stattfinden, ohne dass sich die

Personen kennen, indem alles schriftlich festgehalten wird!

LOr4a: Die Einarbeitung in neue Arbeitsverfahren erfolgt…

mit Hilfe von schriftlichen Notizen und

mit Handbüchern!

durch Gespräche zwischen den

Beteiligten!

LOr4b. Neue Ideen und Verfahren werden…

allen Mitarbeitern mit Hilfe von regelmäßigen Veranstaltungen

weitervermittelt!

werden erst im kleinen Kreis

ausprobiert und informell weitervermittelt!

LOr4c: Neue Mitarbeiter werden…

hauptsächlich schriftlich eingewiesen!

hauptsächlich mündlich eingewiesen!

LOr5a: Lernen / Weiterbildung dient in Ihrem Unternehmen…

der Verbesserung des bereits

vorhandenen Wissens!

der Weiterentwicklung in neuen

Bereichen / Tätigkeiten!

LOr5b: Um sich zu verbessern,…

werden bereits bekannte Instrumente

und Verfahren verwenden!

werden spezifische Instrumente und

Verfahren selbst entwickelt!

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Kreuzen Sie bitte die Aussage an, die Ihrer Meinung nach derzeit jeweils am besten auf Ihr Unternehmen

zutrifft.

LOr5c: Wenn etwas gut funktioniert,…

wird es belassen, wie es ist!

wird trotzdem über Verbesserungen

nachgedacht!

LOr6a: Der Schwerpunkt der Unternehmenstätigkeiten…

ist auf technische Aspekte

ausgerichtet!

liegt im Service- /

Dienstleistungsbereich!

LOr6b: Ziel ist die Verbesserung der Kompetenzen,…

die im Bereich Produktion und Herstellung benötigt werden!

die im Bereich Service und

Dienstleistung zum Einsatz kommen!

LOr6c: Andere Unternehmen vergleichen sich mit Ihrem im Bereich…

der Produktion und technischen

Entwicklung!

des Services und der Dienstleistung!

LOr7a: Ihr Unternehmen schätzt besonders…

das Können und Wissen einzelner

Mitarbeiter!

das Arbeiten im Team!

LOr7b: Lernen und Weiterbildung dienen…

der Förderung einzelnen

Mitarbeitern!

der Verbesserung der Zusammenarbeit

in Gruppen!

LOr7c: Bei Neueinstellungen ist es am Wichtigsten, dass…

spezielle Erfahrungen und

Kenntnisse im gewünschten Bereich vorliegen!

der neue Mitarbeiter teamfähig ist!

163

Teil III von III


Stimme voll und ganz zu

Stimme eher zu

Stimme eher nicht

zu

Stimme gar nicht

zu

FF1a: Kunden werden regelmäßig nach ihrer Zufriedenheit befragt!

FF1b: Es besteht ein enger Kontakt zu Kunden, Zulieferern, etc.!

FF1c: Externe Informationen werden als Möglichkeit zum Lernen wahrgenommen!

FF2a: Die Mehrzahl der Mitarbeiter ist der Meinung, dass die Leistung noch verbessert werden könnte!

FF2b: Es besteht ein allgemeines Bedürfnis den jetzigen Leistungsmaßstab höher anzusetzen!

FF2c: Unvorhersehbare Geschehnisse werden im Unternehmen als Möglichkeit für Verbesserungen genutzt!

FF3a: Der Vergleich mit anderen Unternehmen wird als Möglichkeit für Lernprozesse genutzt!

FF3b: Aufwand und Ergebnis Ihrer Arbeit werden regelmäßig besprochen!

FF3c: Rückmeldungen und Evaluationen werden gewöhnlich für Verbesserungen verwendet!

FF4a: Mitarbeiter mit neuen Ideen –wenn auch nicht immer zielführend– sind hoch angesehen!

FF4b: Die Mitarbeiter sind bestrebt, zu verstehen, wie die Dinge funktionieren!

FF4c: Gemeinsames Ideensammeln und Experimentieren ist ein wichtiger Teil der Arbeit im Unternehmen!

164



Stimme eher zu

Stimme eher nicht

zu

Stimme gar nicht

zu

FF5a: Neue Ideen werden offen mit allen Mitarbeitern diskutiert!

FF5b: Neuen Mitarbeitern wird regelmäßig die Möglichkeit geboten, von anderen Mitarbeitern zu lernen!

FF5c: Bei ähnlichen Problemen können sich verschiedene Arbeitsgruppen gemeinsam über ihre Erfahrungen austauschen.

FF6a: Alle Mitarbeiter werden ermutigt, sich weiter zu bilden!

FF6b: Weiterbildung ist sowohl auf Basiswissen wie auf Spezialwissen ausgerichtet!

FF6c: Lernen am Arbeitsplatz während des Tagesgeschäfts nimmt einen besonderen Stellenwert ein!

FF7a: Es wir nicht auf vorgegebenen Arbeitsabläufen beharrt!

FF7b: Ihr Unternehmen stellt Mitarbeiter mit unterschiedlicher Ausbildung, Hintergrund und Erfahrungsvielfalt ein!

FF7c: Das Unternehmen ist bestrebt, in den einzelnen Gruppen Personen mit unterschiedlichen Erfahrungen und Sichtweisen zu kombinieren!

FF8a: Es passiert nur selten, dass Mitarbeiter neues Wissen nicht weitergeben, um nicht aufzufallen!

FF8b: Eigene Bemühungen der Mitarbeiter, sich in neuen Bereichen weiter zu entwickeln, werden immer unterstützt!

FF8c: Alle Mitarbeiter innerhalb von „UNTERNEHMEN“ teilen ihr Wissen freiwillig anderen mit!

165



Stimme eher zu

Stimme eher nicht

zu

Stimme gar nicht

zu

FF9a: Die Übernahme von Verantwortung auf allen Ebenen innerhalb des Unternehmens wird gefördert!

FF9b: Die Vorgesetzten gehen bei der Umsetzung von neuen Ideen durch ihr Verhalten als gutes Beispiel voraus!

FF9: Jeder ist sowohl Lehrer als auch Lernender!

FF10a: Entscheidungen beruhen auf einer längerfristigen Planung und sind nicht von aktuellen Ereignissen und Bedürfnissen abhängig!

FF10b: Durchgeführte Veränderungen werden auf ihre Auswirkungen auf andere Tätigkeitsbereiche hin untersucht!

FF10c: Gründe für eine Verschlechterung der Wettbewerbsfähigkeit werden eher in Ihrem Unternehmen als in externen Ursachen gesucht!

Ihr Geburtsjahr: 19 __ __ (Jahr, z.B 1974) Im Unternehmen „UNTERNEHMEN“ beschäftigt, seit: __ __ __ __ (Jahr, z.B 2001)

166

Herzlichen Dank für Ihre Mithilfe und Ihre wertvolle Zeit! Haben Sie noch Fragen? Erreichen können Sie uns unter:


Institut für Forstbenutzung und Forstliche Arbeitswissenschaft Universität Freiburg

Adresse: Telefon:

E-Mail:

167

Appendix II Norwegian mail questionnaire

Spredning av informasjon og kunnskap i “BEDRIFT”

Dette spørreskjemaet er en del av et doktorgradsarbeid hos “Institute for Forest Utilization and Work Science” ved Albert-Ludwigs-Universitetet i Freiburg. Undersøkelsen gjennomføres i sammarbeid med “BEDRIFT” som vil undersøke og lære mer om læring og spredning av informasjon og kunnskap i sin bedrift.

Vi garanterer at alle svar vil bli konfidensielt behandlet, og vi garanterer at dataene vil vil bli presentert slik at anonymitet sikres.

Del I av III "De fleste mennesker diskuterer viktige saker med andre fra tid til annen. Når du tenker på din bedrift – hvem er de personene som du diskuterer faglige spørsmål med som er viktigst for deg?" Være så snill å oppgi de 5 personene som er dine viktigste faglige kontakter i “BEDRIFT” (vennligst oppgi disse i prioritert rekkefølge!).

Kontakter Fornavn Etternavn

Den hyppigste

Den nest hyppigste

Den tredje hyppigste

Den fjerde hyppigste

Den femte hyppigste

ALBERT-LUDWIGS-

UNIVERSITÄT FREIBURG

Vennligst send detutfylte spørreskjematilbake, senest den

DD.MM.YYYY.

Vennligst send detutfylte spørreskjematilbake, senest den

DD.MM.YYYY.

168

Del II av III

Sett et “X” i feltet for den påstand som best beskriver din bedrift ut fra ditt perspektiv.

LOr1a: Vår bedrift…

ser seg som trendsetter og lærer av egne erfaringer.

etterligner andre bedrifter og lærer av

andre sine erfaringer.

LOr1b: For vår bedrift er det veldig viktig å...

være den første som utvikler nye

produkter/ideer.

forbedre produkter/ideer fra andre.

LOr1c: Våre resultater blir evaluert…

på grunnlag av vår egne erfaringer.

på grunnlag av data fra eksterne miljø.

LOr2a: Vår bedrift investerer hovedsaklig i spørsmålet,…

hva våre fremtidige produkter/service

skal være.

hvordan våre fremtidige

produkter/service skal bli realisert.

LOr2b: Vår bedrift verdsetter medarbeidere…

som utvikler nye ideer mer enn de

som bare støtter dem.

som bidrar til å realisere våre

produkter/service mer enn de som utvikler nye produkter/service.

LOr2c: Vår bedrift fokuserer på…

hva våre mål skal være.

hvordan vi skal nå våre mål.

LOr3a: Når vi trenger kunnskap, vender vi oss mot…

personer som er eksperter på dette

området.

etablerer vi en ny ressurs som en

databank / håndbok.

LOr3b: En vellykket spredning av informasjon…

er bare mulig hvis folk kjenner

hverandre!

er mulig, uten at folk kjenner

hverandre, idet det blir skriftlig dokumenteret!

LOr4a: Vi lærer ønskede framgangsmåter,…

ved å bruke skriftlige notater og

håndbøker.

ved hjelp av samtaler mellom

deltakerne.

LOr4b: Nye idé eller har utviklet en ny metode…

sprer vi informasjonene til all

medarbeidere gjennom regelmessige arrangementer.

blir det testet ut i en liten gruppe og

sprer unformell!

LOr4c: Nye medarbeider blir hovedsaklig…

skriftlig satt inn i sitt arbeid.

muntlig satt inn i sitt arbeid.

LOr5a: Vår læring og videreutdanning fokuserer på…

forbedring av hva vi allerede vet.

utvikling av nye områder /

virksomheter.

LOr5b: Når vi lærer hvordan ting kan gjøres bedre,…

bruker vi kjente hjelpemidler og

metoder.

fokuserer vi på utvkling av nye

hjelpemidler og metoder.

169

Sett et “X” i feltet for hver påstand som best beskriver din bedrift ut fra ditt perspektiv.

LOr5c: Når noe fungerer bra, …

forandrer vi ikke på det.

tenker vi likevel på endring og

forbedring.

LOr6a: I våre anstrengelser …

overskygger teknologisk evne alt

annet

overskygger service/tjenester for

kundene alt annet.

LOr6b: Vi fokuserer på utvikling av kompetansene som …

trengs for å utvikle og produsere

varer og tjenester.

trengs for salg og levering av service /

tjenester.

LOr6c: Andre bedrifter samligner seg med vår bedrift med…

hensyn til produksjon og tekniske

utvikling.

evne til service- / tilbud av tjenester.

LOr7a: Vår bedrift tror på…

kompetanse og kunnskap hos hver

enkelt medarbeider sine ferdigheter og beslutningsevne..

det som kan oppnås gjennom samarbeid.

LOr7b: Læring og videreutdanning er rettet mot…

å utvikle hver enkelte medarbeider

forbedring av samarbeidet i gruppen.

LOr7c: Ved nyansettelse er det viktigste at…

den nye medarbeideren har spesielle erfaringer og kunnskap i et ønsket

området.

den nye medarbeideren kan jobbe i team.

170

Del III av III


Jeg er helt enig

Jeg er noe enig Jeg er noe

uenig Jeg er

helt uenig

FF1a: Våre kunder blir regelmessig spurt etter deres tilfredshet.

FF1b: Vi har en god kontakt til våre kunder, underleverandører osv.

FF1c: Informasjon fra eksterne miljø oppfates som mulighet til å lære noe.

FF2a: De flest av våre medarbeidere tror at vi forsatt kan forbedre vår konkurransevene.

FF2b: Det fins et generelt behov for å øke den nåværende standard i vår bedrift

FF2c: Vi bruker uforutsette hendelser til å forbedre oss.

FF3a: Samligningen med andre bedrifter blir vår bedrift sett på som en mulighet til å lære av.

FF3b: Innsatts og resultater av vårt arbeid diskuteres regelmessig.

FF3c: Tilbakemeldninger og evaluaering brukes vanligvis til forbedring.

FF4a: Medarbeider med nye ideer, selv om de ikke er innenfor gjeldende målsettinger, er høyt ansett.

FF4b: Vi ønsker å forstå hvordan ting fungerer.

FF4c: Å samle ideer og eksperimentere sammen er en viktig del av vår jobb

171


Jeg er helt enig


uenig Jeg er

helt uenig

FF5a: Nye ideer diskuteres åpent med alle medarbeidere

FF5b: Nye ansatte har regelmessig muligheten til å lære av andre medarbeidere.

FF5c: I vår bedrift kan forskjellige arbeidsgrupper med lignende problemer utveksler sine erfaringer.

F6a: Alle våre medarbeidere oppmuntres til videreutdanning.

FF6b: Videreutdanning er rettet mot både grunnleggende kunnskap og spesialkunskap.

FF6c: Vi tilbyr arbeidsplassbasert opplæring til våre medarbeidere

FF7a: Vi insisterer ikke på like arbeidsrutiner og at alle arbeider etter samme arbeidsmetoder.

FF7b: Vi ansetter medarbeider med forskjellig utdanning, bakgrunn og erfaring.

FF7c: Vår bedrift prøver å kombinere folk med forskjellig kompetanse og holdninger i de enkelte arbeidsgruppene.

FF8a: Det skjer skjelden at medarbeidere med ny kunnskap, ikke sprer denne fordi de ikke ønsker oppmerksomhet om seg.

FF8b: Egne anstrengelser med hensyn til videreutdanning på nye områder støttes alltid.

FF8c: Det er helt vanlig at medarbeidere i vår bedrift på eget initiativ deler sin kunnskap med andre

172


Jeg er helt enig


uenig Jeg er

helt uenig

FF9a: Å ta ansvar støttes innenfor alle områder i vår bedrift.

FF9b: Ledelsen er gode eksempler når det gjelder å realisere nye ideer.

FF9c: Alle er både lærer og student samtidig.

FF10a: Alle våre beslutninger baserer seg på langsikt planlegging og er ikke avhengig av aktuelle hendelser og behov.

FF10b: Gjennomførte forandringer undersøkes med hensyn til deres virkning mot andre arbeidsområder.

FF10c: Årsaker til forverring av vår konkuranseevne søkes som oftest i indre årsaker i vår bedrift enn i årsaker utenfor vår bedrift.

Fødselsår: 19 __ __ (År, f.eks. 1974) Ansatt hos “BEDRIFT” siden: __ __ __ __ (År, f.eks. 2001)

173

Tusen takk for din innsatts og bruk av verdifull tid. Hvis du har spørsmål, vennligst ta kontakt med:


Institute for Forest Utilization and Work Science University of Freiburg

Adresse:

Tel:

E-post:

Organizational Learning and Social Network Structures

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