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Interpersonal trust in commercialrelationships

Antecedents and consequences of customertrust in the salesperson

Paolo GuenziDepartment of Management, Bocconi University and

SDA Bocconi School of Management, Milan, Italy, and

Laurent GeorgesIUT TC Tarbes, University of Toulouse III, Toulouse, France

Abstract

Purpose This paper seeks to explore drivers and consequences of customer trust in the salespersonin the financial services industry. Its theoretical foundations are based on literature on customers

interpersonal relationships with salespeople and front-line employees, as well as on literature in the

area of customer trust.

Design/methodology/approach A conceptual model, specifying a set of hypotheses linking a

salespersons behaviours to customer trust, and the latter to behavioural loyalty intentions, was tested

using partial least squares (PLS) on a sample of 150 customers in the Italian banking industry.

Multiple models were compared in order to evaluate the mediating role of customer trust.

Findings The results of the empirical study show that both salespersons customer orientation and

expertise positively influence customer trust in the salesperson. Conversely, selling orientation has anegative impact on it. Moreover, a salespersons likeability does not influence customer trust. Finally,

trust in the salesperson positively influences a customers intentions to re-buy/cross-buy and to

recommend, while it decreases a customers intention to switch to competitors.

Research limitations/implications The study suggests that different relational antecedentsmay have different impacts on different relational mediators and outcomes. Since the mechanisms of

interpersonal relationship formation and development are multifaceted, to understand fully the

complexity of relational phenomena researchers should develop and test models incorporating

multiple relational antecedents and outcomes.

Practical implications The study provides sales managers with some evidence of the behaviours

that salespeople should adopt to influence successfully the creation of long-term relationships,

especially in the context of credence services. The findings suggest that the optimal behaviours of

salespeople may vary, depending on the ultimate goal of the sellers relational strategy. The authors

suggest drivers that managers can leverage to stimulate salespeople to perform the desiredbehaviours.

Originality/value The model tested in the empirical study highlights the mediating role of

customer trust and incorporates a broad set of drivers and consequences of interpersonal trust. As

such, it improves knowledge of trust-building processes in the context of credence services, where

trust and interpersonal relationships are very relevant.

Keywords Trust, Interpersonal relations, Financial services, Sales force, Regression analysis

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0309-0566.htm

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Received November 2006Revised January 2008,May 2008

European Journal of Marketing

Vol. 44 No. 1/2, 2010

pp. 114-138

q Emerald Group Publishing Limited

0309-0566

DOI 10.1108/03090561011008637


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IntroductionFor Gundlach and Murphy (1993, p.. 41) trust is the most universally acceptedvariable as a basis of any human interaction or exchange. Trust is especially relevantin the relationship marketing perspective (e.g. Morgan and Hunt, 1994). Service

industries are particularly suited for the analysis of relationship marketing theoriesand practices (Bitner, 1995). In service environments, the importance of trust has beenidentified by Berry (1996, p. 42), who states that the inherent nature of services [. . .]positions trust as perhaps the single most powerful relationship marketing toolavailable to a company. Nevertheless, as recently underlined by Harris and Goode(2004), within service dynamics trust has been understated, overlooked or ignored.Importantly, encouraging customers to trust the firm is a top priority among the goalsof service firms, and developing trust with customer-contact employees is one of themost cited practices in building long-lasting customer-to-firm relationships (Claycomband Martin, 2002). In fact, in many service environments, the firms relational intentand ability are, to a great extent, personified and expressed in practice by the front-lineemployees (Price and Arnould, 1999), and interpersonal relationships are considered a

key element of the offering (Czepiel, 1990) because they provide social andconfidence/trust benefits (Goodwin and Gremler, 1996). Although the topic of thecontribution of interpersonal relationship in building customer relationships andloyalty with service providers has attracted increasing attention in the last few years(Bove and Johnson, 2001), less is known about how interpersonal relationships withcustomers are created and nurtured (Witkowski and Thibodeau, 1999). In thisperspective, understanding interpersonal trust-building processes is particularlyimportant (Gwinner et al., 1998).

In keeping with this evidence, in the present study trust is used as the focalconstruct for analysing interpersonal loyalty-building processes in the context ofservice selling. The goal of our research is twofold:

(1) to explore what factors explain customer trust in salespersons; and(2) to investigate the consequences of trust on the customers behavioural loyalty

intentions.

These issues are analysed in the context of the financial service industry, which isparticularly well suited to the topic under examination. In fact, we argue that studyingthe contribution of interpersonal relationships to trust-building processes implies thechoice of a setting where:

. interpersonal relationships are relevant to the customers overall evaluation ofthe service; and

. trust is important.

With regard to the first aspect, the importance of interpersonal relationships variesacross different service industries (Iacobucci and Ostrom, 1996). In general terms, thisimportance increases in the types of service industry that are characterised by a highlevel of people focus, customer contact time per interaction, degree of customisationand discretion, and process focus (Silvestro et al., 1992). Financial consulting, similar tomost professional services, strongly fits these characteristics.

With regard to the second aspect, we argue that, in a relational perspective, trust isparticularly important in credence services, because in such services perceived risk is

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higher compared with search and experience services (Mitra et al., 1999). In fact, incredence services, such as the financial industry, the quality of the offering cannot beevaluated accurately and efficiently even after the service has been used extensively,because the consumer lacks technical expertise or feels that the cost of acquiring

accurate information is greater than its expected value (Powpaka, 1996).Our model integrates and broadens previous research by incorporating three

drivers of customer trust namely, the salespersons customer-oriented selling,expertise and likeability and exploring the impact of customer trust on threebehavioural loyalty intentions customer intention to recommend, to purchase and toswitch to competitors.

The article is organised as follows. After a short overview of the literature oninterpersonal relationships, the trust concept is examined, with a specific focus oncustomer trust and interpersonal relationships with salespeople in the context offinancial services. Then the conceptual model is presented, specifying the determinantsand consequences of trust. This is followed by the development of a set of hypotheseslinking a salespersons behaviour to customer trust and the latter to behaviouralloyalty intentions. Next, the method used to test our model is explained, followed by apresentation of the results of the analysis. Finally, theoretical and managerialimplications, limitations of the study, as well as future research opportunities areexamined.

The contribution of interpersonal relationships in building and developingrelationships with customersThe contribution of interpersonal relationships in building customer-to-firmrelationships has been thoroughly investigated in the service literature. However, todate, most of the research on the topic has focused on the impact of front-line employee

characteristics, attitudes and behaviour on customers perceived quality, satisfaction,commitment and loyalty while, unfortunately, little attention has been devoted to theanalysis of the specific role played by contact personnel whose main activity is sellingservices (Gounaris and Venetis, 2002).

Hence, in order to examine this topic, the personal selling literature is a secondrelevant field of research. Here it is widely recognised that the interaction betweenthe salesperson and the customer can have a substantial impact on importantrelational outcomes for the firm (Doney and Cannon, 1997; Foster and Cadogan,2000). However, a major limitation of this stream of research is that most studiesare focused on industrial or channel relationships (e.g. Beverland, 2001) andbusiness-to-business services (e.g. Haytko, 2004). In contrast, consumer serviceshave been overlooked.

A third, relevant research stream is the literature on retailing, where thecontribution of sales associates in fostering customer relationships with the store hasbeen consistently demonstrated (Beatty et al., 1996; Wong and Sohal, 2003). However,this literature typically analyses the relationship-building role played by salespeoplewho are selling goods, not services.

In short, we argue that all the above-cited streams of research have stronglimitations. This suggests the opportunity to investigate the role played by salespeoplein fostering customer relationships in consumer services markets.

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Customer trust and interpersonal relationships in the context of financialservicesIn the context of customer relationships with salespeople, trust is a key construct(Jolson, 1997). More specifically, customer trust has a future risk contingency

orientation as customers place themselves at some risk of undesirable outcomes if thesalesperson lacks the competence necessary to provide valid information or themotivation to protect the customers interests (Swan et al., 1999, pp. 94-5).

Credence services necessarily imply risk, uncertainty and vulnerability; hence,they are an appropriate context in which to study trust-building processes.

Financial services are highly abstract services characterised by credenceattributes, technical complexity, information asymmetry and long-term return oninvestment: all these factors emphasize the importance of trust in the financial servicesindustry (e.g. Roman and Ruiz, 2005). Moreover, compared with pseudo-relationships,where different employees perform the service from one occasion to another, in the caseof financial services, customers usually interact with a single, dedicated servicerepresentative, which fosters the importance of trust (Gutek et al., 1999). In the context

of financial services relationships, vulnerability and uncertainty arise for severalreasons. First, the exchange process is complex and requires a large amount ofinformation sharing. However, customers generally lack precise information andsufficient expertise. In these circumstances the salesperson becomes a key source ofinformation. Consequently, customer trust in the salesperson is mandatory for theexchange to take place and continue in the long term. Second, once a financial service isacquired, it is almost impossible to change, should the customer dislike it. Under thesecircumstances, a salesperson might be opportunistic and knowingly sell services thecustomer does not really need. A trusted salesperson is not expected to beopportunistic. Third, many users experience difficulty in evaluating the quality of thefinancial services. Hence, in this context, they must rely on the salesperson to ensure alevel of information quality. Finally, in financial services the desirability of long-termrelationships is heightened by the long time period over which costs and benefits canbe spread and shared among counterparts. In fact, it is often only over the long run thateconomic evaluations can be made by both parties. Therefore, building a climate oftrust becomes very salient in order for the relational exchange to develop.

However, there is evidence that personal advisers in consumer banking often adopthard-selling approaches (Verhallen et al., 1997). Moreover, the existence of arelationship manager does not provide, per se, benefits to the customer: these benefitsdepend on how the relationship is implemented (Colgate and Lang, 2005).

Consequently, an in-depth examination of trust and loyalty-building processes ininterpersonal relationships with salespeople in the consumer-banking financialservices industry is needed.

Conceptual model and hypothesesSeveral models have been proposed to explain customer trust in salespeople. Ourframework mainly builds on the extensive meta-analysis reported by Swan et al. (1999),which is the most comprehensive study on the antecedents and consequences of trustin the salesperson. The authors showed that, among salesperson-related drivers, onlybenevolence, competence and likeability/similarity significantly affect customer trust.Hence, a salespersons expertise (i.e. competence), likeability, as well as sellingorientation and customer orientation (i.e. a proxy of benevolence[1]) were included aspotential drivers of trust.

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As for the consequences of trust in the salesperson, Swan et al. (1999) identified fourcategories i.e. customer satisfaction, positive attitudes, intentions and behaviours all of which are significantly affected by trust. As understanding that the formation ofloyalty remains a crucial management issue, in the present study we focus our

attention on customer loyalty intentions (Pritchard et al., 1999).Our model is consistent with Olivers (1997) comprehensive conceptualisation of a

four-phase, sequential loyalty chain. In fact, we posit that customers develop, insequence, cognitive responses (e.g. perceptions about salespeoples behaviours andattitudes), affective responses (which we synthesise as trust), conative responses (i.e.behavioural intentions) and action responses (that is, actual behaviours), which we donot analyse here.

Our model is also consistent with the recent meta-analysis on relationshipmarketing (Palmatier et al., 2006), since it incorporates relational antecedents,relational outcomes and one relational mediator (i.e. trust).

Figure 1 depicts the model that is proposed and tested in the study.

Antecedents of customer trust in the salespersonCustomer orientation. The SOCO scale developed by Saxe and Weitz (1982) includesquestions aimed at evaluating the following characteristics of the interaction processwith customers:

. a desire to help customers make satisfactory purchase decisions;

. helping customers assess their needs;

. offering products that will satisfy customers needs;

. describing products (and services) adequately;

. avoiding descriptive or manipulative tactics; and

.

avoiding the use of high pressure selling.

Figure 1.Antecedents andconsequences of customertrust in the salesperson inthe context of financialservices

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It is generally recognised that customer orientation increases performance (e.g. Saxeand Weitz, 1982; Boles et al., 2001). However, with few notable exceptions (e.g.Langerak, 2001), existing empirical research has not investigated the impact ofcustomer orientation on trust, although a large body of evidence links customer

orientation to relational outcomes like customer satisfaction (e.g. Goff et al., 1997) andthe customers perceived quality of the buyer-seller relationship (e.g. Beverland, 2001).Building on the above-cited theoretical and empirical foundations, we hypothesise

that:

H1. A salespersons customer orientation is positively related to customer trust inthe salesperson.

Selling orientation. Interestingly, it has been pointed out that the SOCO scale consistsof two distinct, although related, subscales (Thomas et al., 2001). Nevertheless, veryfew studies have simultaneously examined the separate impact of customerorientation, on the one hand, and selling orientation, on the other, on selected

outcome variables. Among these studies, Goff et al. (1997) posited and partiallysupported the existence of opposite consequences of customer orientation and selling

orientation on customer satisfaction. Tam and Wong (2001) demonstrated that

customer orientation positively affects both customer satisfaction and customer trust,while selling orientation has a negative impact on both outputs. Hence, we hypothesise

that selling orientation, compared with customer orientation, will have an opposite (i.e.negative) effect on customer trust in the salesperson:

H2. A salespersons selling orientation is negatively related to customer trust inthe salesperson.

Expertise. Expertise is defined as the salespersons knowledge, technical competence

and ability to provide answers to specific questions. Thanks to his/her expertise thesalesperson can reduce the customers uncertainties and feelings of vulnerabilityduring the encounter. Consequently, perceived expertise should be a predictor of

customer trust in the salesperson (Crosby et al., 1990).Swan et al.s (1999) meta-analysis supports this argument. In the specific context of

services, Hennig-Thurau (2004) has recently shown that the technical skills of service

employees (i.e. their competence, knowledge and expertise) drive other relationaloutcomes, such as customer satisfaction, commitment and retention. Hence:

H3. A salespersons expertise is positively related to customer trust in thesalesperson.

Likeability. Likeability is the extent to which a salesperson is perceived as friendly,courteous and pleasant. Research carried out in the context of channel relationships

shows that interpersonal liking is a key driver of customer trust (Nicholson et al., 2001).In service contexts social skills can foster customer satisfaction, commitment and

retention (Hennig-Thurau, 2004). The general findings provided in the meta-analysisby Swan et al. (1999) suggest the following hypothesis:

H4. A salespersons likeability is positively related to customer trust in thesalesperson.

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The impact of customer trust in the salesperson on behavioural loyalty intentionsWe analyse loyalty in terms of behavioural intentions, i.e. as the customers intention toperform a diverse set of behaviours such as recommending the salesperson to a friend,remaining loyal to the salesperson or doing more business with the salesperson, which

signal a motivation to maintain and develop a relationship with the salesperson(Zeithaml et al., 1996). Such intentions may include the likelihood of a customerrepurchasing and/or making more purchases, providing positive word of mouth andstaying in the relationship with the service provider, as opposed to switching tocompetitors (Wong and Sohal, 2003).

Foster and Cadogan (2000) found that customer trust in the salesperson positivelyaffects the customers anticipated future interaction with the salesperson, repurchaseintentions and willingness to recommend the supplier to other potential buyers.Similarly, Kennedy et al. (2001) showed that customer trust in the salesperson motivatesrepurchase intention and the anticipation of future interaction. Crosby et al. (1990)demonstrated that customer trust in the salesperson stimulates relationship continuityand customer willingness to refer. Finally, Patterson and Smith (2003) showed that the

risk of losing a social relationship is the switching barrier which has the strongestimpact on the customers propensity to stay with their present service provider.To sum up, in the literature many different conceptualisations and measures of

behavioural intentions have been suggested and tested (Curasi and Kennedy, 2002). Inkeeping with the classification of behavioural intentions suggested by Zeithaml et al.(1996), we argue that this construct incorporates different dimensions. Themultifaceted nature of behavioural intentions has been recognised in many studies(e.g. Bendall-Lyon and Powers, 2004). Smith et al. (1999) argue that behaviouralintentions can be grouped into two categories:

(1) economic behaviour; and

(2) social behaviour.

The former impact the financial performance of the firm and can include repeatpurchase behaviours and switching behaviour. The latter impact the response ofotherexisting and potential customers of the firm, and may include complaint behavioursand word-of-mouth communications. We argue that, unfortunately, despite thismultifaceted nature, most studies have only explored one dimension, such as intentionto recommend the salesperson to other customers (Mittal et al., 1999), repatronageintentions (Wakefield and Blodgett, 1996), repurchase intentions (Jones et al., 2000),intentions of future interaction (Tam and Wong, 2001) and propensity to stay(Patterson and Smith, 2003). We see this as a major limitation in the extant literature.As a consequence, consistently with the arguments provided by Smith et al. (1999), inour model we incorporate as consequences of trust two measures of economicbehavioural intentions (intention to repurchase and to switch) and one measure of

social behavioural intention (i.e. intention to recommend). Hence we hypothesise that:H5. Customer trust in the salesperson is positively related to customers intention

to recommend.

H6. Customer trust in the salesperson is positively related to customers intentionto re-buy/cross-buy.

H7. Customer trust in the salesperson is negatively related to customers intentionto switch to competitors.

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Study methodQuestionnaire developmentAs all the constructs used in our research were based on studies carried out inEnglish-speaking countries, the questionnaire was originally developed in English.

Following Brislins (1976) recommendations, this was translated into Italian by oneexpert professional translator. Then, a second bilingual blindly translated the materialback into English. Finally, under the supervision of the research team, both translatorsreconciled any differences. At this point, to assess face validity, three experts, alluniversity professors in marketing, reviewed the measures in cooperation with the firstauthor to make a preliminary judgement about the quality of the translation. To refinethe wording of items, the questionnaire was pre-tested on ten graduate students whohad recently purchased financial services. Based on their comments some minormodifications were made.

Data collection procedure and sampleTo test our hypotheses, a survey was conducted on a sample of individual customers ofdifferent banks located in a major city in Northern Italy. Since the goal of our studywas to obtain data reasonably generalisable to the whole population while capitalisingon the advantages of personal interviews, we used the approach suggested byErffmeyer et al. (1999): we adopted quota sampling and used a variety of locations overa wide range of days and times. This resulted in us obtaining a sufficiently large anddemographically representative sample using a technique that allows for theclarification and explanation of ambiguous or potentially confusing questions.

Importantly, personal interviews are by far the best contact method to minimisenon-response problems (e.g. Yu and Cooper, 1983). In fact, for example, compared withmail and telephone interviewing, satisficing and social desirability response bias are

lower when face-to-face respondents are used (Holbrook et al., 2003). In short,face-to-face interviewing increases response quality and response rate (De Leeuw,1992). Furthermore, research shows that response rate increases when the study isconducted by a university (as in our case) (e.g. Greer et al., 2000).

For each of eight different branches of the banks, 20 respondents were selected andinterviewed outside banks at different times and days during a two-month period.

Sudman (1980) suggests using quota sampling based on age and gender as a meansof reducing potential biases in personal intercept-based data collection. The quotasample constructed for this study was gathered so that it was representative of Italyspopulation based on age and gender distribution. Hence, when selecting respondents,interviewers focused mainly on gender, which is self-evident, and on the approximateage of people.

Table I shows the Italian population and the sample breakdown by gender and agecategories, and demonstrates that the quota sample obtained is reasonablyrepresentative of the Italian population. In addition to this, the breakdown byeducation level in our sample is compared with the findings of a nationwide survey ona representative sample of consumers of banking financial services. Importantly, inthis survey, the breakdown by education level of respondents stating that the financialadvisor is the key influence when they choose investment opportunities is reasonablysimilar to our samples breakdown.

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In fact, in answering the questions, our respondents were asked to refer to a specificfinancial advisor they had bought financial services from. Respondents participatedvoluntarily without any compensation. Overall, 160 questionnaires were collected, butsince ten had to be eliminated because they were not complete, 150 usablequestionnaires were obtained.

MeasuresWe used multi-item scales starting from measures adopted in the literature. For the

salespersons customer orientation and selling orientation, the ten items of the SOCOscale, as proposed by Thomas et al. (2001), were used. Measures for the salespersonsexpertise and likeability were taken from Doney and Cannon (1997). Customer trust inthe salesperson was measured by using a subset of the scale used by Doney andCannon (1997). In keeping with Swan et al. (1999), this subset incorporates items atdifferent levels of abstraction, i.e. items which refer to behaviours of the financialadviser (e.g. this salesperson does not make false claims), to attributes broader than aspecific behaviour (e.g. this salesperson does not seem to be concerned with yourneeds) and to one general trust measure (this salesperson is not trustworthy). Forcustomer behavioural loyalty intentions, customers intention to recommend, tore-buy/cross-buy and to switch to competitors were measured through three-itemscales adapted from Zeithaml et al. (1996).

Our measures are reflective as opposed to formative. In fact, in keeping with thesuggestions provided by Jarvis et al. (2003), the constructs used in this research relate toindividual attitudes or behavioural intentions, not to managerial aspects, and the itemsare better interpreted as manifestations, not characteristics of the underlying constructs.

Model estimationThe structural equation model, represented in Figure 1 as well as the alternativemodels (Models 2, 3 and 4), were estimated using the partial least squares (PLS) latent

SampleItaly

(ISTAT official census data) Eurisko Multifinanziaria 2006a

Gender

Male 56 49.6Female 44 51.4

Age18-20 1.3 0.721-40 44.7 34.841-60 34 32.7Over 60 20 31.8

EducationLess than high school 28.7 39.8High school 42.7 39.1College degree 28.6 21.1

Notes: Figures shown are percentages. aBreakdown of respondents in the survey stating that thefinancial adviser is the key influencer when they choose investment opportunities

Table I.Description of the sample

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path model available in SmartPLS software. PLS is a non-parametric estimationprocedure (Wold, 1982) that can accommodate small samples (Wold, 1982) andprovides measurement assessment, which is crucial to our study as we have a ratherlimited sample size. In addition, it avoids some of the restrictive assumptions imposed

by LISREL-like models (Dawes and Lee, 1996). For instance, most authors (Andersonand Gerbing, 1988; Chin, 1998b; Fornell et al., 1990) recommend that there should be atleast ten to 20 times as many observations as variables, otherwise the estimates ofLISREL are probably very unstable. Since our model contains 30 manifest variables,we needed a minimum sample size of 300. With a sample size of 150, PLS was bettersuited to our study. In addition, compared with LISREL, the component-based PLSavoids two serious problems:

(1) inadmissible solutions; and

(2) factor indeterminacy (Fornell and Bookstein, 1982).

Using the resampling procedures (i.e. bootstrap and jackknife), the standard deviation

can be calculated and an approximate t-statistic generated. This overcomes thedisadvantage of non-parametric methods having no formal significance tests for theestimated parameters. According to Chin (1998b), compared with jackknifing, thebootstrap technique offers two advantages:

(1) the possibility of calculating confidence intervals others than those calculatedfrom a normal distribution; and

(2) the possibility of using a larger number of samples.

Consequently, we adopted the bootstrap technique. Bootstrapping was used to draw arandom bootstrap set. The process was repeated 200 times to obtain stable standarderrors and low differences between entire sample estimates and means of subsamples(Leger et al., 1992).

Analysis and resultsThe PLS results are interpreted in two stages:

(1) By assessment of its measurement model.

(2) By assessment of its structural model (Fornell and Larcker, 1981).

Moreover, a path model can be evaluated at three levels:

(1) the quality of the measurement model;

(2) the quality of the structural model; and

(3) each structural regression equation.

Measurement modelThe properties of the measurement model are detailed in Table II. All factor loadingsare at least 0.79. For all constructs, Cronbachs a and composite reliability are wellabove the 0.70 threshold. For each indicator, standard deviation and mean are alsoavailable in Table II.

Convergent validity is confirmed as the average variance in manifest variablesextracted by constructs (AVE) is at least 0.73, indicative that more variance is

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Constructs and indicators Factor loadings Mean SD a CR AVE

Customer orientation 0.90 0.91 0.86How often this salesperson (1

never; 9

always):

Tries to determine your needs 0.92 4.88 2.07Has your best interest in mind 0.88 3.68 2.23Takes a problem-solving approach in sellingproducts or services to you 0.93 5.04 2.10

Recommends products or services that are bestsuited to solving your problems 0.95 4.95 2.27

Tries to find out which kinds of products or serviceswould be most helpful to you 0.96 4.99 2.30

Selling orientation 0.92 0.92 0.88How often this salesperson (1 never; 9 always):Tries to sell as much as he/she can, rather than

satisfying you 0.95 4.77 2.42

Finds it necessary to stretch the truth in his/her salespresentations 0.91 3.51 2.43

Tries to sell as much as he/she can to convince you tobuy, even if it is more than wise customers would

buy 0.93 4.48 2.45Paints a too rosy picture of the products or servicesto make them sound as good as possible 0.94 5.61 2.51

Makes recommendations based on what he/she cansell and not on the basis of your long-termsatisfaction 0.94 4.97 2.50

Salespersons expertise 0.89 0.93 0.82(1 strongly disagree; 9 strongly agree):This salesperson is very knowledgeable 0.94 6.38 2.05This salesperson knows his/her product line/services

very well 0.86 6.34 1.65This salesperson is not an expert 0.92 5.78 2.28

Salespersons likeability 0.90 0.93 0.83(1 strongly disagree; 9 strongly agree):This salesperson is friendly 0.87 3.53 2.34This salesperson is always nice to you 0.91 5.94 2.25This salesperson is someone you like to have around 0.92 5.08 2.65

Customer trust in the salesperson 0.86 0.87 0.85(1 strongly disagree; 9 strongly agree):This salesperson has been frank in dealing with you 0.94 4.15 2.14

This salesperson does not make false claims 0.90 4.36 1.99This salesperson does not seem to be concerned with

your needs 0.91 5.68 2.21People who know him/her do not trust this

salesperson 0.86 6.32 1.95This salesperson is not trustworthy 0.91 5.60 2.30

(continued)

Table II.Model 1: scale propertiesand PLS measurementmodel

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explained than unexplained in the variables associated with a given construct. Onecriterion for adequate discriminant validity is that the correlation of a construct withits indicators (i.e. the square root of the AVE) should exceed the correlation between theconstruct and any other construct. The findings shown in Table III suggestdiscriminant validity, since all diagonal elements are greater than the non-diagonalelements in the corresponding rows and columns.

Constructs and indicators Factor loadings Mean SD a CR AVE

Customers intention to recommend 0.82 0.85 0.86How likely it is that you will (1

not at all likely;

7 very likely):Say positive things about this salesperson to other

people 0.94 3.59 2.15Recommend this salesperson to someone who seeksyour advice 0.88 3.81 1.90

Encourage others to do business with thissalesperson? 0.95 3.00 1.99

Customers intention to re-buy/cross-buy 0.81 0.84 0.84How likely it is that you will (1 not at all likely;7 very likely):Do more business with this salesperson in the future 0.93 3.59 1.59Buy new products/services offered by this

salesperson 0.91 3.43 1.93Buy more products/services from this salesperson? 0.89 3.00 1.87

Customers intention to switch to competitors 0.81 0.89 0.73(1 not at all likely; 7 very likely): How likely itis that you will

Switch to a competitors salesperson, paying thecosts that this action implies 0.79 2.10 1.46

Switch to a competitors salesperson, in case thisaction implies no costs 0.90 3.98 2.09Take some of your money managed by thissalesperson to a competitors salesperson? 0.85 3.41 1.88 Table II.

Construct 1 2 3 4 5 6 7 8

1. Customer orientation 0.932. Selling orientation 20.79 0.94

3. Salespersons expertise 0.682

0.72 0.904. Salespersons likeability 0.83 20.81 0.70 0.915. Customer trust 0.85 20.82 0.72 0.76 0.926. Intention to recommend 0.85 20.73 0.71 0.76 0.79 0.937. Intention to rebuy/crossbuy 0.82 20.82 0.65 0.79 0.75 0.80 0.928. Intention to switch 20.76 0.80 20.66 20.76 20.79 20.77 20.81 0.85

Note: Figures in italics on the diagonal show the square root of the AVE; numbers below the diagonalrepresent construct correlations

Table III.Model 1: discriminant

validity

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Structural modelsAsoneoftheobjectivesofthisstudyistotestacausalorderbetweenrelationalconstructs,it is necessary to examine whether customer trust in the salesperson acts as a mediatingvariable between perceived behaviours/traits (i.e. customer orientation, selling

orientation, expertise and likeability) and customer intentions (i.e. intention torecommend, intention to re-buy/cross-buy and intention to switch to competitors).After presenting the results for our initial model (i.e. Model 1), we address this issue bycomparingourbaselinemodeltoalternativemodelformulations(Models2and3).Finally,based on these results, an alternative model formulation, called Model 4, is discussed.

Results for the initial model (Model 1)Table IV reports the standardized parameters obtained from bootstrap simulation.Bootstrapped standard deviations and t-values (Chin, 1998a) confirm that hypothesesH1, H2, H3, H5, H6and H7are supported. One hypothesis (H4) is not supported: thereis no significant relationship between salespersons likeability and customer trust.

Selling orientation has the strongest, negative impact on customer trust (B2

0:

41).Customer trust positively affects all types of customer intentions.

Construct Model 1a Model 2 Model 3 Model 4

Dependent variable: customer trustCustomer orientation 0.28b (1.97c) * ( ) 0.28 (2.16)* 0.23 (1.70) *

Selling orientation 20.41 (2.69) * ( ) 20.41 (2.63) * 20.36 (2.46) *

Expertise 0.16 (1.75) * ( ) 0.16 (1.67)* 0.15 (1.67) *

Likeability 20.12 (0.82) ( ) 20.12 (0.84) ( )

Dependent variable: intention to cross-buy/rebuyCustomer orientation ( ) 0.39 (4.21) * 0.27 (3.23) * 0.31 (3.77) *

Selling orientation ( ) 20.38 (4.00) * 20.21 (1.98) * 20.25 (2.95) *

Expertise ( ) 0.11 (2.01) * 0.04 (0.92) ( )Likeability ( ) 0.01 (0.03) 0.06 (0.66) ( )Trust 0.78 (15.60) * ( ) 0.42 (3.99)* 0.43 (4.20) *

Dependent variable: intention to switchCustomer orientation ( ) 20.13 (1.09) 0.04 (0.35) ( )Selling orientation ( ) 0.62 (5.03) * 0.37 (3.29) * 0.37 (4.10) *

Expertise ( ) 20.17 (1.71) * 20.09 (1.00) ( )Likeability ( ) 0.11 (0.88) 0.03 (0.29) ( )Trust 20.78 (18.29) * ( ) 20.53 (5.08) * 20.54 (5.24) *

Dependent variable: intention to recommendCustomer orientation ( ) 0.31 (3.38) * 0.16 (2.38) * 0.17 (2.52) *

Selling orientation ( ) 20.27 (3.00) * 20.05 (0.53) ( )Expertise ( ) 0.22 (3.26) * 0.13 (2.80) * 0.13 (3.05) *

Likeability ( ) 0.10 (0.88) 0.17 (2.07) * 0.19 (2.93) *

Trust 0.83 ( ) 0.54 (8.12) * 0.55 (9.60) *

Notes: aModel 1, original model; Model 2, customer trust excluded; Model 3, saturated model; Model 4,final model. bStandardized path coefficients. ct-values. *Significant if above 1.64 for one-tailed test

Table IV.Parameter estimation ofthe PLS causal Models 1,2, 3 and 4 by thebootstrap method

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The indices for explained variance (R2 ), redundancy and communality are shown inTable V. Redundancy and R2 are not computed, of course, for exogenous constructs.The structural model demonstrates strong predictive power as the variance explained(R2 ) in key endogenous constructs was 0.45 for trust in the salesperson, 0.70 for

intention to recommend, 0.61 for intention to re-buy/cross-buy and 0.60 for intention toswitch to competitors. The findings show that our initial model (i.e. Model 1) explains alarge part of the variance in the endogenous variables with an average global R2 of0.59. Communality and redundancy coefficients can be used essentially in the sameway as the R2, since they reflect the relative amount of explained variance for latentand manifest variables.

An important part of model evaluation is the examination of fit indices reflecting thepredictive power of estimated inner and outer model relationships. As pointed out byTenenhaus et al. (2005, p. 173), differently from SEM-ML, PLS path modeling does notoptimize any scalar function so that it naturally lacks of an index that can provide theuser with a global validation of the model (as it is instead with x2 and related measuresin SEM-ML). The GoFrepresents an operational solution to this problem as it may be

meant as an index for validating the PLS model globally. A general criterion forevaluating goodness-of-fit (GoF) is to calculate the geometric mean of the averagecommunality and the average R2 (Tenenhaus et al., 2005). According to the results inTable V, the GoF index is satisfactory: GoF p0:84 0:59 0:70 (Tenenhauset al., 2005; Ringle et al., 2008).

The blindfolding approach, proposed by Wold (1982), was followed to calculatethe cross-validated (CV) communality and redundancy (see Table VI). TheCV-communality index (H2) measures the quality of the measurement model for eachblock of variables. The mean of the CV-communality indices can be used to measure theglobal quality of the measurement model if they are positive for all blocks of variables.The quality of each structural equation is measured, instead, by the CV-redundancyindex (i.e. Stone-Geissers Q2, which Tenenhaus et al., 2005, p. 174, callF2). The mean of

the CV-redundancy indices (F2

) related to the endogenous blocks can be used to measurethe global quality of the structural model, if they are positive for all endogenous blocks.

For Model 1, the quality of both the measurement model and the structural model issatisfactory (see Table VI). In fact, average CV-communality (H2 0:76) and averageCV-redundancy (F2 0:49) indexes are well above the recommend standard of 0.30(Tenenhaus et al., 2005).

The mediating role of customer trust in the salesperson: comparing alternative modelsTo test the mediating role of customer trust, we compared Model 1 with Model 2 andModel 3. In Model 2, customer trust was excluded and customer orientation, sellingorientation, expertise and likeability were directly linked to intention to recommend,intention to re-buy/cross-buy and intention to switch to competitors. As indicated inTable IV, all but four path coefficients are significant: salespersons likeability has nosignificant impact on the three exogenous variables, and salespersons customerorientation has no significant impact on customers intention to switch to competitors.

In comparison with our initial model (i.e. Model 1), excluding customer trustresulted in a drop of R2 for customers intention to recommend and customersintention to switch to competitors (see Table V). Conversely, there was an increase ofR2 for intention to re-buy/cross-buy. The GoF increased slightly from 0.70 (Model 1) to0.72 (Model 2). Moving to the blindfolding results (Table VI), Model 2 has lower

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Construct

Mo

del

1a

Mo

del

2

Mo

del

3

Model

4

Trust

0.4

5b

(0.8

5)c

0.2

2d

()

0.45

(0.8

5)

0.2

2

0.4

5

(0.8

5)

0.1

9

Intentiontorecommen

d

0.7

0

(0.8

6)

0.6

0

0.6

5

(0.8

6)

0.3

2

0.82

(0.8

6)

0.1

8

0.8

2

(0.8

6)

0.1

9

Intentiontore

buy

/cross-

buy

0.6

1

(0.8

4)

0.5

1

0.6

8

(0.8

4)

0.3

8

0.78

(0.8

4)

0.2

9

0.7

8

(0.8

4)

0.3

3

Intentiontosw

itch

0.6

0

(0.7

3)

0.4

3

0.5

6

(0.7

2)

0.1

0

0.69

(0.7

3)

2

0.0

3

0.6

9

(0.7

3)

0.2

7

Customerorientation

(0.8

7)

(0.8

6)

(0.8

6)

(0.8

6)

Sel

lingorientation

(0.8

8)

(0.8

8)

(0.8

8)

(0.8

8)

Expertise

(0.8

2)

(0.8

2)

(0.8

2)

(0.8

2)

Likea

bility

(0.8

3)

(0.8

3)

(0.8

3)

(0.8

3)

Average

0.5

9

(0.8

4e)

0.4

4

0.6

3

(0.8

3)

0.2

6

0.68

(0.8

4)

f

0.6

8

(0.8

4)

0.2

2

Go

Findex

g

0.7

0

0.7

2

0.76

0.7

6

Notes:aMo

del

1,

base

line;

Mo

del

2,

customertrustexclu

ded;M

odel

3,

saturated;

Mo

del

4,

fina

lmo

del.

bExp

lainedvariance.

cCommuna

lity:communa

lity

coef

ficientsareequa

ltothesquare

dcorrelations

betweenmani

festvaria

blesan

dtheirassociatedlatentvaria

bles.

dRedun

dancy:re

dun

dancycoef

ficients

reflectthe

jointpre

dictivepowero

fthe

inneran

doutermo

del

relationsh

ips.

eComputedasaweightedaverageo

fthe

differentcommunal

itiesw

iththe

weig

htsbeingthenumbero

fman

ifestvaria

blespereachcons

truct

(see

Tenen

hausetal.

,2005

,p.1

80).f

Cannotbeca

lcu

latedasa

lltheb

locksarenot

positive

(Tenen

hausetal.

,2005).gGo

F

paveragecommun

ality

averageR2)]

.Averagecomm

una

lityiscomputedasaweig

htedaverageo

fthe

differentcommuna

litiesw

iththeweig

htsbeingthenum

berof

indicatorsper

latentvaria

ble(Tenen

hausetal.

,2005)

Table V.Explained variance (R2),communality,redundancy and GoFindex for Models 1, 2, 3and 4

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average CV-communality (H2

0:

76 in Model 1 and H2

0:

67 in Model 2) andaverage CV-redundancy (F2 0:67 in Model 1 and F2 0:42 in Model 2). Theseresults suggest that, compared with Model 1, Model 2 has a lower quality of bothmeasurement and prediction.

Model 3 includes both indirect and direct paths. Thus, it is a saturated model. Just asin Model 1, hypotheses H1, H2, H3, H5, H6and H7are supported and one hypothesis(H4) is not supported (see Table IV). On the contrary, several differences exist betweenModel 3 and Model 2. For instance, whereas in Model 2 salespersons expertise had asignificant impact on intention to re-buy/cross-buy (B 0:11) and intention to switchto competitors (B 20:17), in Model 3 these paths become not significant (B 0:04and B 20:09, respectively). In addition to this, whereas in Model 2 salespersonslikeability had no significant impact on intention to recommend (B

0:10), in Model 3

this path became significant (B 0:

17).As shown in Tables IV and V, average explained variance, average CV-communality

and average CV-redundancy are higher in Model 3 than in Model 1 and Model 2. Owingto the higher R2, the goodness of fit index (GoF 0:75) is also higher. Thus, Model 3shows a better quality in terms of both measurement and prediction.

Starting from these results, we evaluated the mediating role of customer trust in thesalesperson. According to Baron and Kenny (1986), to establish mediation thefollowing conditions must be satisfied:

. in Model 2, the exogenous constructs (i.e. customer orientation, sellingorientation, expertise and/or likeability) must have a significant effect on thedependent variables (i.e. intention to recommend, intention to re-buy/cross-buyand/or intention to switch);

. in Model 3, the exogenous constructs must have a significant effect on customertrust;

. in Model 3, customer trust must have a significant effect on intention torecommend, intention to re-buy/cross-buy and/or intention to switch; and

. the effect of the exogenous construct on intention to recommend, intention tore-buy/cross-buy and/or intention to switch must be lower in Model 3 than inModel 2. There is full mediation if the direct effect is not significant in Model 3.

Construct Model 1 Model 2 Model 3 Model 4

Trust 0.85b

(0.37)c

( ) 0.85 (0.37) 0.76 (0.37)Intention to recommend 0.82 (0.59) 0.67 (0.55) 0.82 (0.69) 0.68 (0.69)

Intention to rebuy/cross-buy 0.64 (0.59) 0.84 (0.38) 0.64 (0.63) 0.65 (0.63)Intention to switch 0.73 (0.42) 0.44 (0.34) 0.73 (0.48) 0.45 (0.47)Customer orientation 0.78 ( ) 0.78 ( ) 0.78 ( ) 0.78 ( )Selling orientation 0.88 ( ) 0.81 () 0.88 ( ) 0.88 ( )Expertise 0.60 ( ) 0.60 ( ) 0.60 ( ) 0.60 ( )Likeability 0.83 ( ) 0.61 ( ) 0.82 ( ) 0.82 ( )Average 0.76 (0.49) 0.67 (0.42) 0.76 (0.54) 0.70 (0.54)

Notes: aModel 1, baseline; Model 2, customer trust excluded; Model 3, saturated; Model 4, final. bCVcommunality H2. cCV redundancy F2

Table VI.Blindfolding results: CV

communality and CVredundancy for Models 1,

2, 3 and 4

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Looking at the results presented in Table IV, trust partially mediates the relationshipbetween:

. customer orientation and intention to recommend;

.

customer orientation and intention to re-buy/cross-buy;. selling orientation and intention to re-buy/cross-buy;

. selling orientation and intention to switch to competitors; and

. expertise and intention to recommend.

Trust fully mediates the relationship between:

. selling orientation and intention to recommend;

. expertise and intention to re-buy/cross-buy; and

. expertise and intention to switch to competitors.

Finally, customer trust does not play a mediating role between:

. customer orientation and intention to switch;

. likeability and intention to recommend;

. likeability and intention to re-buy/cross-buy; and

. likeability and intention to switch to competitors.

The final modelFigure 2 depicts our final model (Model 4), as well as the path coefficients, t-tests andR2. It incorporates the conclusions of the above-cited analysis and includes onlysignificant direct and indirect paths. Scores regarding communality, redundancy,goodness-of-fit, CV-communality and CV-redundancy are satisfactory and presented inTables IV and V.

DiscussionOur study focused on drivers and consequences of interpersonal trust in commercialrelationships. Four different models have been tested and compared. Our findings shedfurther light on the loyalty-building process in interpersonal relationships in the contextof a consumer service industry. More specifically, results of this study indicate thatcustomer trust in the salesperson significantly influences loyalty intentions bothpositively (i.e. intention to recommend and intention to re-buy/cross-buy) and negatively(i.e. intention to switch to competitors). Our research also shows that the adoption ofcustomer-oriented selling, as well as salespersons expertise, can contribute to thecreation of strong and long-lasting positive relationships with customers, by increasingcustomer trust and hence by fostering loyalty intentions. On the other hand, our studyalso demonstrates that a selling orientation can negatively affect the development ofcustomer trust in the salesperson. Salespersons likeability has no significant directeffect on customer trust, but it has a direct, positive impact on the customers intention torecommend. Therefore, it seems that salespersons likeability may be helpful in gainingnew customers but not in developing long-lasting relationships with existing ones. Therelatively high average evaluation provided by respondents on this construct (seeTable II) may also indicate that, in the financial industry, likeability is a necessaryprerequisite for playing the game, but it is not a driver of trust. In other words, customers

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apparently recognise (or possibly take for granted) their financial advisers likeability.However, they do not feel secure that he/she has the necessary motivation or expertise tobe trusted. This may be because of recent financial scandals, as in the cases of Parmalatand Argentina Bonds, which have undermined Italian investors confidence in financialorganizations fairness and ethical credibility. As recently demonstrated by Roman andRuiz (2005), in the context of financial services, perceived ethical behaviours andexpertise are major drivers of customer trust in the salesperson. Obviously, this topicdeserves special attention in future research.

Theoretical contributionThe theoretical contribution of our research to current knowledge can be summarisedas follows.

Figure 2.Final model

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Generally speaking, our findings confirm an important foundation of relationshipmarketing theory: trust is a key relational mediator between relational drivers andconsequences (Morgan and Hunt, 1994; Palmatier et al., 2006). More specifically, ourstudy suggests that customer trust plays a mediating role between customer

orientation, selling orientation, salespersons expertise and the customers loyaltyintentions.

Importantly, compared with previous studies on similar topics, which mainlyfocused on one or few determinants or consequences of customer trust in thesalesperson, our research took into account a relatively broad set of both drivers andconsequences of trust. As pointed out by Palmatier et al. (2006), customer relationshipsdo not equally influence all exchange outcomes, and different relational antecedentsmay have different impacts on different relational mediators and outcomes.Interestingly, for example, our findings suggest that salespersons social competence(expressed by his/her likeability) only affects the customers intention to recommend,which is a social behavioural intention, while it does not influence economicbehavioural intentions. Conversely, selling orientation (i.e. opportunistic behaviour)has a direct impact on the latter intentions, but no effect on the former. From atheoretical standpoint, these findings suggest that the mechanisms of interpersonalrelationship formation and development are multifaceted: hence, researchers tendencyto examine only one type of relational antecedent and outcome, and to use onlyaggregate measures of salesperson performance or behavioural intentions, makesit difficult to understand fully the complexity of relational phenomena.

In addition to this, our study has contributed to extant knowledge in many ways.First, our findings fill a gap in the literature on personal selling, where there is a lack

of empirical research specifically investigating the contribution of salespeoplesbehaviours in fostering customer trust and loyalty intentions in the context ofconsumer services. Second, our findings fill a gap in the service literature, where

studies on interpersonal relationships, especially in consumer services, have mainlyfocused on frontline employees who are not involved in selling. In fact, compared withfrontline employees, salespeople play a different role in developing relationships withcustomers: hence, their contribution to customer trust and loyalty intentions needed tobe investigated. Third, we used customers as respondents: this is relevant, becausemany empirical studies on the topic used salespeople as key informants, thus incurringthe risk of bias in relying on self-reported measures of customer-based outcomes.Furthermore, this is one of the very few empirical studies on selling behaviours carriedout in non-English speaking countries. Since cross-cultural validation of constructs hassometimes led to controversial findings (e.g. Herche et al., 1996), this contribution willhopefully be appreciated. Finally, our study tests separately distinct hypotheses for thetwo dimensions of the SOCO scale, and finds evidence of opposite impacts on trust.

This is important because there is a lack of knowledge concerning the drivers ofmistrust and the negative drivers of trust (Swan et al., 1999).

Managerial contributionThis study provides sales managers with some evidence of the behaviours thatsalespeople should adopt to influence successfully the creation of long-termrelationships, especially in the context of credence services. The generalmanagerial implication is that companies willing to build and foster long-term

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relationships with their accounts should encourage their salespeople to adopt thesebehaviours. Our goal here was to develop and test a comprehensive model thatincorporated multiple attributes and behaviours of salespeople, which can act asrelational drivers, i.e. as operational means of improving customer trust and

behavioural intentions. Importantly, our findings suggest that the optimal behavioursof salespeople may vary, depending on the ultimate goal of the sellers relationalstrategy. For example, salespersons likeability may not be relevant when the mainobjective of the organisation is to develop business with the existing customer base,but it may become important if the company strategy is strongly aimed at acquiringnew customers by leveraging on the current customer bases willingness to recommendthe seller to new potential customers (this could be the case of member-gets-memberprogrammes).

There are many drivers that managers can leverage to stimulate salespeople toperform the desired behaviours. First, companies should carefully select candidates forsales positions, investigating their attitudes and skills. Second, companies shoulddesign training programmes specifically aimed at helping salespeople develop theskills, abilities and competences that are necessary for successfully adopting acustomer orientation and developing a strong expertise. Such programmes should alsoemphasise the negative consequences of cultivating a selling orientation when themain goal is to increase customer loyalty. Third, when designing reward systems forsalespeople, sales managers could, at least in part, take into account their behaviouralperformance as well as indicators of relational performance, such as customer retentionrate or customer trust (Sharma, 1997). Fourth, companies may change the salesdepartments organisational structure, as well as their sales force control systems. Forexample, managers may decide to create two separate sales forces (a transactional oneopposed to a relational one) (Schultz and Good, 2000). Similarly, firms may shift froman independent to an employee sales force or from outcome-based to behaviour-based

sales force control systems, in order better to control the actual implementation ofrelational behaviours on the part of their salespeople.

Limitations and directions for future researchThis study has several limitations. First, the sample size is relatively small. Usinglarger samples would allow subsample analysis. However, by using statisticalmethods that are well suited for small samples (e.g. PLS and the bootstrap method),complex models can still be stably estimated.

Second, because this study is cross-sectional, one should be cautious aboutassigning causality. Since it is focused on a dynamic phenomenon (i.e. relationships), alongitudinal study would be more appropriate (Frankwick et al., 2001). Third, ourstudy is limited to a single industry: hence, cross-validation across different service

industries (e.g. search, experience and credence services) is required. Fourth, futureresearch should ideally focus on actual customer behaviours (Garland, 2002). Finally,the measurement scales adopted in this study, although in most cases successfullyused in previous research on the topic, may not fully capture all the facets of theunderlying constructs. Hence, measures that are more comprehensive would bewelcome.

Future studies on the topic should broaden our framework by including otherrelational behaviours and possibly transactional behaviours (e.g. using hard sell

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techniques) which may lower customer trust (Hawes et al., 1996). We also suggestinvestigating the moderating effect of selected variables (e.g. length of theinterpersonal relationship between the salesperson and the customers, sellers brandreputation, etc.) on the paths included in our model. In addition, different measures of

performance may be considered to compare the impact of relational behaviours onlong-term versus short-term performance indicators. In fact, relational behaviours maypay off only over the long run, while being even detrimental to immediate sales.Moreover, there is a need to understand better both the organizational factors (e.g. salesforce control systems and training programmes) and the personal variables (e.g.personality traits and skills) that encourage the salesperson to adopt relationalbehaviours. Furthermore, future research should investigate the interdependenciesbetween customer trust in a salesperson and in the selling organisation. Future studiesmay also incorporate satisfaction in addition to trust. Finally, we underline theimportance of making international comparisons, by means of replications andextensions of research in different cultural contexts.

Note

1. Customer orientation can be interpreted as a substitute for benevolence because it reflectsnon-opportunistic behaviour that stresses customer-focused solutions and mutual benefits(Schwepker, 2003).

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About the authorsPaolo Guenzi is an Associate Professor of Marketing, Department of Management, BocconiUniversity and SDA Bocconi School of Management, Milan, Italy. His main research interests arein the fields of sales management, personal selling and service marketing. His research has beenpublished in Journal of Business Research, Industrial Marketing Management, European Journalof Marketing, Journal of Marketing Management, Journal of Service Management, International

Journal of Sport Marketing & Sponsorship and International Journal of Service IndustryManagement. Paolo Guenzi is the corresponding author and can be contacted at:[email protected]

Laurent Georges is an Associate Professor of Marketing, IUT TC Tarbes, University ofToulouse III, LCG Research Center EA 2043, Toulouse, France. His main research interests are inthe fields of key account management, personal selling and business-to-business marketing. Hisresearch has been published in Journal of Business to Business Marketing, Industrial Marketing

Management and Journal of Selling and Major Account Management.

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