Exploratory navigation and salesperson performance - Florida ...

Industrial Marketing Management 39 (2010) 538–550

Contents lists available at ScienceDirect

Industrial Marketing Management

Exploratory navigation and salesperson performance: Investigating selected antecedents and boundary conditions in high-technology and financial services contexts Christopher R. Plouffe a,⁎, Srinivas Sridharan b, Donald W. Barclay b a b

College of Business, Florida State University, United States Ivey Business School, The University of Western Ontario, Canada

a r t i c l e

i n f o

Article history: Received 13 November 2007 Received in revised form 12 November 2008 Accepted 3 February 2009 Available online 13 March 2009 Keywords: Sales behavior Sales performance Internal Intraorganizational Selling

a b s t r a c t Salesperson behavior aimed at improving internal company response to customer requests has received little attention in the industrial marketing literature in comparison to external, customer-directed behaviors. In this study, the phenomenon of “salesperson navigation” (SpN) is developed within the context of a research model of selected antecedents and boundary-conditions that influence a primary form of navigational behavior, or “exploratory navigation”. The research model's utility in predicting sales performance is tested empirically with data from two Fortune 500 sales forces. The findings show that the traits of competitiveness and expert power significantly enhance the salesperson's propensity to engage in exploratory navigation behavior. Exploratory navigation, in turn, is found to have a significant and positive association with salesperson job performance, contingent upon specific boundary conditions within the salesperson's own organization (i.e., sales management support and internal competitive climate). The article concludes by offering sales researchers and industrial marketing managers implications derived from the study as well as directions for further work. © 2009 Elsevier Inc. All rights reserved.

Interest in the sales role and the management of the sales function has seen an increase in both academic (e.g., Franke & Park, 2006; McFarland, Challagalla, & Shervani, 2006) and managerial audiences (e.g., Stevens & Kinni, 2007; Stewart, 2006). Despite this renewed emphasis on sales, research has lagged in its ability to shed sufficient light on the drivers of sales performance at the individual level of analysis. Typical studies explain a relatively modest 10-to-20% of the variance in salesperson performance (Churchill, Ford, Hartley, & Walker, 1985; Rich, Bommer, MacKenzie, Podsakoff, & Johnson, 1999; Vinchur, Schippmann, Switzer, & Roth, 1998). Further, if one considers research examining behavioral determinants of salesperson job performance, the field has had an almost exclusive focus on the salesperson's “externally-directed” behavior — how the salesperson acts and what strategies and tactics he/she employs in dealing with customers and prospects. Even within this context, there is little-to-no consensus amongst scholars or supporting empirical work in the industrial marketing literature to definitively show that any one such externally-directed selling perspective is superior to another, or that different types of sales skills may actually be required for different

⁎ Corresponding author. College of Business, Florida State University, Rovetta Business Annex, 403, Tallahassee, FL USA 32306-1110, United States. Tel.: +1 850 597 9235; fax: +1 850 644 4098. E-mail address: [email protected] (C.R. Plouffe). 0019-8501/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.indmarman.2009.02.003

types of selling contexts (Cron, Marshall, Singh, Spiro, & Sujan, 2005; Franke & Park, 2006; Vinchur et al., 1998). This work begins from the premise that the “internally-directed” dimension of the sales role – salesperson behaviors and job functioning inside one's own organization – has an important influence on salesperson performance. This is consistent with some early perspectives on the sales role (Walker, Churchill, & Ford, 1977; Weitz, 1981) as well as with practitioner and anecdotal accounts (Rasmusson, 1999; Stevens & Kinni, 2007). However, other than a few isolated conceptual pieces (e.g., Sujan, 1999; Weitz & Bradford, 1999), the sales literature has not explicitly paid much attention to the internally-directed dimension of the sales job or the drivers of such behavior (Williams & Plouffe, 2007). The broad phenomenon of interest in this research is labeled salesperson navigation (or SpN, Plouffe & Barclay, 2007). It describes the act of a salesperson purposefully exploring their own organization to interact with key others. These key others may have resources, decision-making authority, and/or the ability to shape policy in the salesperson's favor, all of which could be important influences on the salesperson's ultimate success in dealing with customers and prospects. The specific goal of this paper is to empirically demonstrate the significance of one form of navigational behavior – “exploratory navigation” (as articulated by Plouffe & Barclay, 2007, pp. 531–532) – on salesperson performance, as well as to explore selected individuallevel antecedents to this behavior. In terms of antecedents, we focus

C.R. Plouffe et al. / Industrial Marketing Management 39 (2010) 538–550

539

Fig. 1. Research model.

on some commonly-studied salesperson traits (e.g., competitiveness. Brown, Cron, & Slocum, 1998; Harris, Mowen, & Brown, 2005) and characteristics (e.g., interpersonal power. Busch & Wilson, 1976; Comer, 1984). A secondary objective is to simultaneously explore whether specific characteristics of the sales context might act as boundary conditions on navigation. From a managerial standpoint, the expected utility of this research is two-fold: (i) helping sales managers better understand an overlooked salesperson competency (i.e., internal behaviors) which might, for example, enable better hiring decisions, while also (ii) helping industrial salespeople themselves better understand the types of behaviors which might underlie exemplary sales performance. The balance of the paper is organized as follows. First, since the notion of salesperson navigation is relatively new, a review of relevant literature is undertaken to place the concept within the broader context of work on salesperson traits and performance. Next, the research model and hypotheses driving the study are explicated. The model is then tested using both primary and archival data collected from two Fortune 500 sales organizations. The paper concludes with a discussion of the findings, their implications for sales management research and marketing practice, and directions for future work in this area. 1. Literature review 1.1. Internally-directed selling behaviors and salesperson performance The buyer-seller interface has become more complex today, with customers increasingly demanding integrated cross-functional solutions to business problems (Tuli, Kohli, & Bharadwaj, 2007). As a consequence, the salesperson in many industries has evolved into a customer relationship manager (Weitz & Bradford, 1999), with research showing that firms who best cater to how their customers wish to purchase tend to perform better (Ahearne, Jelinek, & Jones, 2007). This notion of the salesperson as a relationship manager has become all the more acute given the prevalence of national, global,

and major (or key) account selling (e.g., Jones, Dixon, Chonko, & Cannon, 2005; Workman, Homburg, & Jensen, 2003). A pivotal task underlying the success or failure of salespeople focused on solutions and key accounts selling is their ability to identify and subsequently marshal needed resources from key others across their own organization (Rackham & DeVincentis, 1999; Sujan 1999; Weitz & Bradford, 1999). The implication of this is that salespeople need to be able to work well within their own organizations in order to get what they need to satisfy the ever-increasing demands of their customers and prospects (McGregor, 2006). Work in the practitioner realm further informs these trends. For instance, a recent large-scale study of salespeople by consultancy HR Chally found that top-performing salespeople “work” the systems, people, and processes of their own organization to their customer's advantage (Stevens & Kinni, 2007, Ch. 5, p. 89+). Collectively, the research above highlights the emerging importance of internally-directed selling behavior and related activity. It seems reasonable to expect that salespeople who proactively work through the challenges and constraints that their own work environments pose may be more successful than those who do not. The operative questions, then, are what form(s) of internallydirected selling behavior are relied upon by salespeople today, and might this behavior impact sales performance? To effectively answer these questions, stock must be taken of the broader heritage of sales research, and in particular, work which has delineated various traits of the salesperson as antecedents to performance. 2. Research model and hypotheses The constructs and hypotheses in the research model are supported with theory where possible (primarily Social Cognitive Theory, or SCT. Bandura, 1977; Wood & Bandura, 1989). But as the literature reminds us, theory may not be all that useful or compelling a guide when the broader research domain (i.e., that of “internallydirected” selling and salesperson job functioning) is largely uncharted

540


(e.g., Hunt, 1983). Therefore, the primary research question of this study is: Does exploratory navigation affect sales performance, and what role might selected salesperson traits play as antecedents in predicting exploratory navigation behavior? In addition, the research model (see Fig. 1) will assess whether the performance impact of exploratory navigation is moderated by specific contextual variables in the sales organization. The rationale for this is that in certain organizational contexts, there may (or may not) be a significant need to navigate since systems, procedures, and processes may make it easier (or, correspondingly, harder) for salespeople to get what they need internally in order to be successful. 2.1. Exploratory navigation Exploratory navigation is an individual-level internal behavior focused on the salesperson's learning and mastery of their own organization — its capabilities; its personnel and their skills and aptitudes; the nature, location, amount, and/or quality of various resources it possesses; order fulfillment and customer service processes; organizational constraints, etc. Salespeople may engage in such generalized behavior because doing so is intrinsically satisfying on some level, and/or because doing so may garner them yet-to-be defined gains or knowledge-based capital for subsequent leveraging (i.e., instrumental value). Building directly from Plouffe and Barclay's (2007, p. 531) conceptualization of exploratory navigation, exploratory navigation is defined here as: the extent to which salespeople generally seek out new and unfamiliar personnel, departments, or other resources within their own organization. The benefit of engaging in such behavior would be to create (or augment) the salesperson's existing stock of knowledge about their own organization such that this can be brought to bear on the tasks they perform which pertain to their customers (e.g. negotiating delivery terms; offering product guarantees, etc.). Engaging in this behavior should therefore lead to superior in-role job performance for the salesperson. Exploratory navigation is theoretically embedded within the tenets of Social Cognitive Theory (Bandura, 1977; Wood & Bandura, 1989). At its core, SCT emphasizes the notion of a “social intelligence.” This social intelligence has a behavioral component to it, and includes the concepts, memories, and informal rules and heuristics that individuals use when navigating the tasks and challenges inherent in everyday situations. SCT therefore supports exploratory navigation in that it reminds us that individuals are in a constant pattern of seeking out that which is broadly available in their environment, comparing this to that which they possess and/or need, and making appropriate behavioral adjustments to rectify real or perceived imbalances in this regard. 2.2. Exploratory navigation and performance The preceding discussion suggests that if salespeople engaged in exploratory navigation regularly and competently, it may impact their resultant selling performance. This is because the resources, personnel, policy knowledge, etc. identified through exploratory navigation can subsequently be brought to bear on both current customer problems and new sales opportunities. By being better informed and better connected within their own organization (Plouffe & Barclay, 2007; Sujan, 1999), internal navigators are thus better positioned to leverage key intraorganizational resources, as well as bypass key constraints and bottlenecks in serving customers and prospects. Therefore, salespeople who excel at this type of internally-directed behavior should benefit from enhanced sales performance. With this, it is proposed that: H1. The greater the extent of a salesperson's exploratory navigation behavior, the greater his/her sales performance.

2.3. Antecedents of exploratory navigation In keeping with the preceding review of the literature, it is suggested that exploratory navigation, as a behavior, may be influenced by traits and characteristics of the salesperson themselves. The literature has explicated many traits which may impact selling behaviors (for representative examples see Franke & Park, 2006; Reid & Plank, 2000; Schwepker, 2003; Vinchur et al., 1998). Given this, there are numerous constructs that could potentially impact exploratory navigation. For example, Reid and Plank's (2000) exhaustive review of industrial marketing lists dozens and dozens of constructs operating at different levels of analysis (i.e., individual; workgroup; firm; industry etc.) many of which might be potentially germane to the SpN phenomenon, and exploratory navigation in particular. Clearly it is difficult to include an exhaustive set of antecedents in a single empirical study (Hunt, 1983). We therefore adhered to three principles in identifying a reasonable set of antecedent constructs to test in this study: (i) constructs that could be genuinely argued to possess a logical relevance to exploratory navigation; (ii) constructs that have been shown to be solid predictors of salesperson performance; and pragmatically speaking, (iii) constructs that could all be meaningfully be tested together within the context of the same study and data collection effort (Dillman, 2000). Further, we favored constructs rooted in strong theoretical frameworks with established relevance to the sales role. The next sub-sections will therefore detail which specific antecedents and contextual moderators were included in this study. Trait Competitiveness–Human beings tend to compete for things which are prized, scarce, or otherwise valued or needed (e.g., Gartlan, 1968). In this spirit, both practitioner and anecdotal accounts support the notion that one the key tenets of the successful salesperson is a competitive drive and instinct (Crom, Crom, & Dale Carnegie & Associates Inc., 2003; Page, 2002). This so-called “trait competitiveness” was explored in a sales performance context by Brown et al. (1998), with more recent findings continuing to support the notion that competitiveness is critical when examining performance (e.g., Harris et al., 2005). The basic argument is that the relative presence, or absence, of this trait will drive some of the salesperson's most basic behaviors — for example, the extent to which they proactively learn about their own organization, its capabilities, constraints, personnel, etc. The logic here is that exploratory navigation and the information/ knowledge that this behavior garners should provide an edge in terms of performance, and the inherent competitiveness the salesperson possesses is a basic fuel which may stoke this form of internal behavior. Given this, it is proposed that: H2. The greater the salesperson's trait competitiveness, the greater the extent of exploratory navigation behavior. Power — power has long been acknowledged as residing at the very core of interpersonal interaction and exchange (Blau, 1964). It has also been recognized as a critical resource and predictor of workplace performance (Kramer & Neale, 1998; Pfeffer, 1981). As a result, the possession of one or more forms of power within the organization by a salesperson may be an important consideration in understanding their performance. French and Raven's (1959) five-fold power conceptualization is widely accepted as appropriate to characterize settings where one person may exert power over another. However, of the five different types of power they espouse, just three – “expert”, “legitimate”, and “referent” power – are likely germane to the sales role.1 With the preceding in mind, and given positive past findings 1 The other two types of power in the French and Raven (1959) taxonomy – coercive and reward – are not hypothesized as being salient traits in this research. As for coercive power, salespeople (as opposed to management) usually do not have the latitude to punish other organizational members who do not do as they wish. Similarly, with reward power, salespeople generally do not have the ability to reward key others within their own organization for doing as they need or request.


with respect to these forms of power and sales performance (e.g., Busch & Wilson, 1976; Comer, 1984), the individual salesperson's relative possession of these three power traits is argued to be an important potential determinant of exploratory navigation. Thinking first about “expert power”, salespeople today are generally considered experts in the products and services they represent (Rackham & DeVincentis, 1999), often acting as consultants and solution-providers to their customers (e.g., Tuli et al., 2007). This expertise can be useful when requesting that their own organization does something to support their customer-directed selling efforts. To be in a position to leverage their expert power, salespeople may be inclined to explore their organization to establish this power base before a specific need occurs. In addition, their expertise should allow them to effectively pinpoint people and resources that could be useful to them, therefore encouraging exploratory navigation behavior. Given this: H3. The greater the salesperson's expert power, the greater the extent of exploratory navigation behavior. As revenue generators for their firms and guardians of customers as assets, salespeople are likely perceived by key others in their organizations as having some legitimate power. This is widely recognized in both academic (Rosenbloom, 1994; Zemanek & Pride, 1996) and practitioner circles (Rackham & DeVincentis, 1999; Stewart, 2006). The reason for this is that now more than ever, the sales role is a truly cross-functional, cross-enterprise one (Siebel, 2001). Coworkers and departments across the salesperson's own internal work environment see the salesperson today as a “quarterback” (Weitz & Bradford, 1999) who is responsible for bringing the resources and capabilities of the vending firm to bear on behalf of the customer (Stevens & Kinni, 2007, pp. 4–22 and 134–155). Internal stakeholders are also aware now more than ever before that the salesperson's success in catering to their customers has a direct effect on the firm's revenues, market share etc. (Palmatier, Gopalakrishna, & Houston, 2006). Because of this, salespeople are argued to possess legitimate power through their unique role within the organization. Moreover, they may want to establish this power source by exploring and positioning themselves across the organization prior to subsequent needs or requests (McGregor, 2006). Therefore: H4. The greater the salesperson's legitimate power, the greater the extent of exploratory navigation behavior. Referent power is suggested as being a more general power trait that should also be germane. Referent power emerges when one individual has a high esteem for another, and perhaps more subtly wishes to be associated with, or be like, them. Because salespeople often possess charismatic and gregarious personalities (e.g., Ahearne, Gruen, & Jarvis, 1999), it stands to reason that as a function of this, they may also possess a good degree of referent power. The argument is thus that they may attempt to preemptively establish and set-up this power base for later exploitation by first exploring their own organization, thus: H5. The greater the salesperson's referent power, the greater the extent of exploratory navigation behavior. 2.4. Contextual influences Will exploratory navigation always make a difference to performance? In attempting to answer this question, the argument to now be advanced is that that there may be context-specific boundary conditions that may dampen (or enhance) the exploratory navigation → performance relationship. Examining such contextual variables has a long history in sales research and this work thus picks up upon this theme. There is, however, a long list of potential moderating

541

influences from which to choose (e.g., Baldauf & Cravens, 2002; Churchill et al., 1985; Franke & Park, 2006). Given the nascent and preliminary nature of this research, the approach which was taken was to seek out one construct that would represent each of the salesperson's immediate “sales context” as well as the broader “organizational context” within which they work, plus the impact of the industry itself. The ultimate choices were guided by theory, past research findings, and anecdotal and conceptual accounts suggesting that a specific moderating construct was appropriate. Sales Management Support (SMS) — is defined as the degree to which the salesperson's immediate sales manager assists them with selling-related tasks and challenges and provides them with guidance, mentoring, and reassurance. As such, because it is designed to help salespeople effectively meet the demands of their customers and prospects, SMS has been theorized to enhance their sales performance (e.g., Jolson, Dubinsky, Yammarino, & Comer, 1993; Rich, 1997; Sujan, 1999). While this is a valid premise regarding management supportiveness, we contend it is not without potential pitfalls if overdone. For instance, if “supportive” managers overly protect salespeople from having to tackle internal process-related hurdles (instead of entirely taking on themselves the responsibility of “cutting through redtape”), they could in effect be undermining important salesperson learning that could accrue from engaging in that process. Second, managerial “navigation” efforts are likely to elicit a higher proportion of favorable responses from internal others in comparison to salesperson efforts, because of the differences in hierarchy and authority. In addition, both the salesperson and their manager could be chasing down the same issue(s) and personnel internally, thereby creating confusion and perhaps wasteful redundancies. Thus, although the literature provides enough evidence that management support can help salespeople perform better, the above nuances considered from an internal navigation perspective point to caveats. In effect, we propose that more than adequate sales manager support can reduce the relevance and efficacy of the salesperson's own navigation behavior with respect to sales performance (using both ‘learning’ and ‘process’ rationales). This is not akin to proposing a direct negative impact of over-support on sales performance (i.e., a direct effect), although that is plausible as well. Rather, we propose a lowering of the performance-relevance of salesperson navigation by managers being over-supportive (i.e., a moderation effect): H6. The perceived level of sales management support will negatively moderate the relationship between a salesperson's exploratory navigation and his/her sales performance. Competitive Psychological Climate (CPC) — While trait competitiveness was characterized earlier as the salesperson's predisposition to adopt competitive behavior and tactics (e.g., Brown et al., 1998), competitive psychological climate (CPC) assesses salesperson perceptions of the competitive forces at the level of the organization (e.g., Strutton, Pelton & Lumpkin, 1993; Swift & Campbell, 1998). It is the felt sense of inherent competition in the organization. It captures the extent to which salespeople feel they work in a “dog-eat-dog” environment (Workman et al., 2003). If a salesperson perceives their organization as being highly competitive, she/he may feel compelled to look for ways to gain a needed edge over their sales peers (Martin & Bush, 2006; Strutton et al., 1993; Swift & Campbell, 1998). In doing so, as we know that competitive organizations breed imitation and one-upmanship behaviors (Brown et al., 1998), a sizeable proportion of the sales force will likely end up engaging in proactive intraorganizational behaviors — such as exploratory navigation. However, if most salespeople navigate to ‘get ahead,’ ironically, it is likely very difficult for any one salesperson to actually get ahead in reality. With everyone employing the same behavioral strategy to angle for resources and favors, the competitive advantage to be gleaned from engaging in

542


‘more’ navigation is likely to be eroded. For instance, if sitting in on manufacturing planning meetings were considered useful and several salespeople began to do this, it can quickly either become a farcical routine disliked by their manufacturing counterparts or become mandated by the organization as a useful and necessary process for all salespeople. In both cases, the variance in benefits that can accrue from this process for salespeople would be greatly reduced. Conversely, in a sales organization perceived to have benign levels of internal competition, a lack of pressure to resort to imitation and one-upmanship strategies may mean the potential advantages accruing from exploratory navigation may not become apparent or salient to salespeople (Workman et al., 2003). In other words, when salespeople don't feel threatened by high levels of peer-oriented pressure, they may not necessarily consider proactive behaviors such as exploratory navigation to be advantageous or essential for success. As a result, it is conceivable that a much smaller proportion of the sales force will engage in exploratory navigation. However, as above, this can ironically be very promising for the few salespeople who do decide to exhibit proactive behaviors because it can cause variance in the benefits received relative to those who are not as proactive. In summary, the expectation is that there will be diminishing (increasing) returns to navigation behavior as it is conducted in increasingly competitive (benign) sales force climates. Thus: H7. The perceived competitive psychological climate of the salesperson's own organization will negatively moderate the relationship between a salesperson's exploratory navigation and his/her sales performance. Industry — The supposition here is that the impact of exploratory navigation on sales performance will vary depending on the specific selling context – or industry – which is being considered (Jones et al., 2005). For example, in a technology-intensive industry, the customer offering is often a complex solution requiring some combination of: pre- and post-sales service, price negotiation, design support or customization, delivery and installation, and end-user training etc. Here the impact of exploratory navigation may be substantial as the salesperson has to rally a number of internal resources, inputs, and people to ultimately satisfy the customer (Tuli et al., 2007). In contrast, in a relatively mature business-to-business context such as financial services (e.g., commercial lending), exploratory navigation, while still perhaps useful for building social capital across the organization, may not translate into notable performance effects. As the preceding logic for the industry/sales context acting as a moderator is both intuitive and somewhat necessarily underdeveloped, no formal hypothesis is offered in this regard. Instead, the approach taken is simply test the research model in two unique industry contexts in an arm's length effort to see what differences might arise. 2.5. Control variables In order to control for well-known effects of employee demographic variables on job performance (in this case, salespeople and sales performance. Ingram & Bellenger, 1983; Pfeffer, 1985), a formative construct was specified which was comprised of: age, gender, educational attainment level, and experience. In effect, this construct acts as a form of respondent bias that can affect performance levels independent of the focal constructs of interest. 3. Method The study's methodological approach centered around the collection of both primary and archival data from salespeople to test the research model. This approach helps assuage potential common methods concerns and the problems associated with single measures of dependent variables (i.e., sales performance. Chonko, Loe, Roberts, & Tanner, 2000; Jaramillo, Carrillat, & Locander, 2003; Rich et al., 1999).

3.1. Samples, data collection and response rates The sales forces of two Fortune 500 companies were recruited for this research:2 TechCo (high-tech industry), a technology and office automation products vendor whose account managers (salespeople) sold across diverse industries; and BankCo (financial services), a global bank whose commercial account managers arranged loans to, and increased deposits from, corporate clients. TechCo provided a database of 364 account managers while BankCo provided a database of 144 account managers. The TechCo database comprised the entire sales force of the firm's operations in one prominent G-8 nation (Canada), while the BankCo database comprised its entire commercial sales force worldwide. The intent in capturing data from two distinct sales forces within the context of a single study was to allow for a greater deal of generalizability of the empirical findings (i.e., not having to rely solely upon findings associated with a single industry). It also allowed for basic comparisons across sales contexts — something which would be expected to vary according to industry, sales task, and customer setting, etc. (Rackham & DeVincentis, 1999; Stevens & Kinni, 2007; Weitz & Bradford, 1999). An internet and email-based survey was employed which followed Dillman's (2000) Tailored Design Method (TDM) in its design and execution. At TechCo, 56.6% of the salespeople completed the survey (n = 206), while at BankCo the response rate was 75.7% (n = 109). These response rates are well above the norms typically achieved in industrial marketing or sales research (e.g., Williams & Plouffe, 2007). Each of the achieved samples for the two firms exhibited a wide range of respondent ages; a reasonable gender split (approximately 60% male, 40% female); showed that the salespeople were highly educated (73% had at least one university degree); and had significant sales experience (14.6 years in sales for the TechCo respondents, and 15.9 years at BankCo). Missing data was not a significant issue (less than 4% of all respondent cases), and for what cases there were, standard data imputation procedures were followed (Allison, 2002; Hair, Anderson, Tatham, & Black, 1995, pp. 43–57). To assess nonresponse bias, Armstrong and Overton's (1977) procedure was used to compare early (survey wave 1) with late (post wave 3) responses. There were no significant differences (p b .05) on key variables between early and late respondents. 3.2. Measures Well-established scales were employed to operationalize most of the constructs in the research model (see the Appendix A for all constructs and items). A key exception was the focal construct of exploratory navigation (see below). The prescriptions offered for the operationalization of new constructs were heeded (as per Churchill, 1979; DeVellis, 1991), including pilot testing and preliminary assessments of construct reliability. Other than as noted below, constructs were measured by seven-point disagree/agree Likert scales. Exploratory Navigation — was operationalized using seven items that capture the extent to which a salesperson actively seeks out colleagues, learns about the internal dynamics and resources of his/ her organization, etc. The measures for exploratory navigation were developed directly from the ideas and qualitative fieldwork reported by Plouffe and Barclay (2007). Because it is a behavior, exploratory navigation was measured with five-interval behavioral frequency scaling (Spector, 1992. i.e., ‘never’ to ‘always’). Performance — Though infrequently practiced or reported, an emerging body of research converges to suggest that multiple measures of job performance of personnel such as salespeople is most prudent (e.g., Chonko et al., 2000; Jaramillo et al., 2003; Rich et al., 1999; Viswesvaran, Schmidt, & Ones, 1996). The approach taken

2

Both participating firms asked to remain anonymous.


543

Table 1 Descriptive statistics and correlations: pooled dataset. Variable 1

1 2 3 4 5 6 7 8 9

Exploratory navigation Trait competitiveness Expert power Legitimate power Referent power Comp climate SM support Subjective performance1 Objective performance2

10 11 12

Control variables Age Education Experience

Mean

SD

Comp. Rel

AVE3

1

4.14 5.82 5.67 3.50 5.14 5.47 5.13 8.58 117.7

.49 1.06 .64 1.17 .75 1.18 1.24 1.40 69.7

.83 .90 .90 .88 .90 .80 .86 .94 n/a

.40 .64 .61 .64 .60 .46 .57 .73 n/a

.63 .25 .31 .15 .15 .11 .05 .24 − .05

.80 .22 .19 .23 .28 .15 .39 .06

.78 .27 .51 .12 .05 .50 .09

3.59 5.55 9.58

.87 1.31 6.23

− .01 .03 − .02

− .24⁎ .12⁎ − .17⁎

.10 .06 .19⁎

2

3

4

5

.80 .28 .21 .16 .17 .03

− .02 − .05 .02

.77 .03 .03 .23 .01

− .15⁎ .09 − .11⁎

6

7

.68 .11 − .36 .01

.76 − .10 .03

− .21⁎ .11⁎ − .18⁎

− .08 .04 − .08

8

9

10

11

.09 − .06 .14⁎

− .27⁎⁎ .68⁎⁎

− .26⁎⁎

.85 .14⁎

− .05 .11 .08

1

Note that exploratory navigation was measured with 5-point Likert scaling; subjective performance was measured with an 11 Likert format; and all other latent constructs (e.g., trait competitiveness, the power measures etc.) were measured with 7-point Likert agree/disagree scaling. Please refer to pp. 19–22 of the manuscript for detailed explanations/sources on why these specific measurement approaches were employed. 2 Percentage of sales quota achieved, most recent fiscal year. 3 Square root of AVE on diagonal. ⁎ = p b .05, and ⁎⁎ = p b .01.

here was to focus on outcome-based performance measures (as opposed to behavioral. Anderson & Oliver, 1987). Both self-reported performance (i.e., survey responses from salespeople) as well as an archival, objective performance measure were garnered for each salesperson (the objective performance measures were provided by each firm's management).3 For the self-reported measures of sales performance, Johlke, Duhan, Howell, and Wilke's scale was employed (2000), this scale itself being modeled on the popular scale of Behrman and Perreault (1982). This resulted in an 11-point Likert scale (where the salesperson rates themselves from −5 {much worse than the other salespeople in this company} to ‘average’ to + 5 {much better than the other salespeople in this company}).4 The six self-reported measures of performance were summed into a single composite measure of subjective performance.5 There would thus be two formative indicators of sales performance: one an objective measure provided by each firm's management; the other a subjective, salesperson-reported measure. Though seldom employed in the literature, the logic is that this approach is both theoretically robust and methodologically defensible (e.g., Bagozzi, Verbeke, & Gavino, 2003; Viswesvaran et al., 1996). Trait competitiveness — was operationalized from the work of Brown et al. (1998). Their original four measures (#'s 1–4, in the Appendix A) were included, with one additional measure (#5) added based upon reflection of this construct's underlying domain and the specific focus of this research. Power — was operationalized based on the foundational work of French and Raven (1959). The chief sources leveraged were Comer's work (1984), Holzbach's “attributed power index” (or API, 1974), and Swasy's operationalization (1979). As per Holzbach (1974) and Comer 3 Here we used percentage of quota achieved for TechCo salespeople for the most recently completed fiscal year. For BankCo, we used a composite measure of performance based equally on the salesperson's achieved loan and deposit growth for the most current fiscal year compared to their assigned targets. The percentage of quota measure at TechCo ranged from 2 to 464%, with a mean of 109.4% sales quota achievement. At BankCo, the range for the objective performance measure was 63– 600%, with an average of 128.8%. 4 Because this scale has a “zero” mid-point, after the survey was closed, data for each item in the scale was reorganized as a continuous variable ranging from 1 to 11. 5 We went with this approach because if all seven items (one objective; six subjective) were used to estimate sales performance, there would be a marked overweighting of the subjective measures. Supporting analyses indicated that the subjective salesperson performance scale was highly reliable (α = .91 for the TechCo dataset and .94 for BankCo) which further justifies a composite scale score and this approach.

(1984), the three power antecedents were measured with seven-point Likert scales: (1) “extremely inaccurate” … to (4) “neither accurate nor inaccurate” … to (7) “extremely accurate”. Competitive Psychological Climate — was also operationalized from Brown et al.'s (1998) work. One additional item (# 5; see the Appendix A) was added to this scale based on a belief that it tapped the underlying conceptual domain. This construct is specified with reflective indicators. Sales Management Support — While existing constructs tap various facets of sales management behavior and supervisory style (e.g., Challagalla & Shervani, 1996; Jaworski & Kohli, 1991; Oliver & Anderson, 1994), none were found which tapped precisely what was being sought out for this research; thus, measures for this construct were aggregated from pre-existing, salient sources. Items #1–4 (see Appendix A) were adapted from the “supervision” sub-scale of Churchill, Ford and Walker's (1974) INDSALES instrument; item #5 was adapted from the “supportive leadership/leadership consideration” scale of House and Dressler (1974); and items #6–7 are from Oliver and Anderson's “extent of supervision” scale (1994, p. 64). This construct is also specified with reflective indicators. Controls — The control variables were measured as follows. Experience was assessed by five items from Churchill, Ford, and Walker (1976) and Behrman and Perreault (1984). Gender was captured by a radio-button check box on the survey. Age was captured with an openended numerical input question. Educational Attainment was measured with seven response choices ranging from “junior high school or less” to “graduate university degree”. 4. Analysis and results To test the hypotheses pertaining to the moderating effects of the contextual variables, we constructed the relevant interaction terms (each involving a contextual variable and exploratory navigation) using the product-indicator approach (Chin, Marcolin, & Newsted, 2003). This approach entails mean-centering the items of both constructs to avoid problems of collinearity, and computing the interaction term by multiplying each item of one construct with all the items of the other. For example, if construct X has two items (X1, X2) and construct Y has three (Y1, Y2, Y3), then their interaction term X ⁎ Y will have six items (X1 ⁎ Y1, X1 ⁎ Y2, X1 ⁎ Y3, X2 ⁎ Y1, X2 ⁎ Y2, X2 ⁎ Y3). The two datasets (TechCo and BankCo) were analyzed separately using the Partial Least Squares (PLS) approach to structural equation modeling (Barclay, Higgins, & Thompson, 1995; Chin, 1998; Fornell & Bookstein, 1982). To assess whether or not common

544


Table 2 Measurement model results (item loadings): pooled dataset. Exploratory navigation Comp. psych. climate Sales management support Trait competitiveness Expert power Legitimate power Referent power Performance Exn1 Exn2 Exn3 Exn4 Exn5 Exn6 Exn7 Cmpclim1 Cmpclim2 Cmpclim3 Cmpclim4 Cmpclim5 Smsupp1 Smsupp2 Smsupp4 Smsupp5 Smsupp6 Tcomp1 Tcomp2 Tcomp3 Tcomp4 Tcomp5 Powexp1 Powexp2 Powexp3 Powexp4 Powexp5 Powexp6 Powleg1 Powleg2 Powleg3 Powleg4 Powref1 Powref2 Powref3 Powref4 Powref5 Powref6 ObjPerf⁎ SbjPerf⁎

.73 .69 .59 .58 .63 .60 .62 .48 .72 .84 .71 .57 .82 .67 .81 .85 .57 .83 .85 .81 .73 .77 .83 .77 .86 .87 .70 .65 .86 .87 .77 .70 .68 .68 .88 .91 .87 .58 .03 .97

⁎The numbers represent the weights of the indicator variables of objective performance and subjective performance in making up the formative construct of performance.

methods bias may be an issue in our research (i.e., because we had predictors and outcomes {i.e., self-reported job performance} emerging from the same source — salespeople), a supplemental analysis was run (Harman's single-factor test. Aulakh & Gencturk, 2000; Gopal, 2006). No untoward findings were unearthed, thus ruling out the threat of a common methods bias influencing the results.6 The results of the measurement model are first detailed using a pooled dataset consisting of data from both studied organizations.7 Next, the results of the structural models are presented beginning with the parameter estimates for TechCo, then BankCo, respectively.

6 According to Harman's single-factor test (Gopal, 2006), if the data contains a substantial amount of common method variance, then an exploratory factor analysis should result in a single factor accounting for all (or the statistical majority) of the covariance among the otherwise posited measures (for an application of this, see Aulakh and Gencturk, 2000). Upon loading all of the study measures into one large EFA (except for gender, which as the literature suggests, is highly unstable because of its dichotomous nature), we found that as many as 12 factors emerged to account for the total variance which was explained. We thus concluded that common methods variance is not an issue in this research, and does not introduce present a bias to our results. 7 Because our objective in analyzing the measurement model results is to demonstrate the psychometric properties of the various measures, our use of the pooled dataset does not pose problems while allowing the benefit of more statistical power. Since PLS makes measurement model estimations simultaneously with structural model estimations, we also have an opportunity to cross-check our measurement model results in the context of subsequent structural model assessments using the two separate datasets.

5. Measurement results The results of the measurement model indicate that the measures employed in this work demonstrated adequate psychometric properties. Two different measures of scale reliability indicate that all measures are adequately reliable. The composite reliabilities are reported in Table 1; all were above .80 and therefore acceptable (Nunnally, 1978).8 An examination of the average variance extracted (AVE) of individual constructs reveals that except for exploratory navigation and competitive psychological climate, all other constructs in the model have AVEs of greater than 0.5 (see Table 1), which indicates convergent validity of the constructs. In the case of exploratory navigation and competitive psychological climate, AVEs of 0.40 and 0.46 (respectively) are admittedly below the 0.5 threshold. However, since the items of both of these load well on their respective constructs (see Table 2), do not exhibit significant crossloadings on any other measures, and demonstrate good composite reliability, there is a reasonable degree of confidence in the measurement properties of these two constructs. Moreover, given that this is early-stage/ preliminary research (with this point being particularly salient to the exploratory navigation construct), one could take the view that some instability is to be expected and can be improved upon as the constructs are deployed and perhaps refined in subsequent work. At the item level, all items of all measurement scales exhibited item loadings of greater than 8 Cronbach's alphas are not reported in Table 1, but were run on the data. As would be expected, the alpha values closely mirrored the composite reliability scores reported in Table 1.

C.R. Plouffe et al. / Industrial Marketing Management 39 (2010) 538–550 Table 3 PLS structural model results: TechCo. Predicted associations Hypothesized paths Exploratory navigation → performance Competitiveness → exploratory navigation Expert power → exploratory navigation Legitimate power → exploratory navigation Referent power → exploratory navigation Competitive psychological climate × Exploratory Navigation → Performance Sales management support × exploratory navigation → performance Control variable paths Age → performance Education → performance Experience → performance Gender → performance Direct paths Competitive climate → performance Sales management support → performance

Hypothesis

Standard path coefficient

t-value for path

H1 H2

.13 .27

1.7 4.35

H3 H4

.29 .05

4.09 .71

H5 H6

− .07 − .21

.91 1.96

H7

− .10

.60

− .06 .10 .21 − .14

.76 1.46 2.76 2.01

.29 .09

2.28 1.20

Notes: ○ Bolded values are significant structural model estimates (the manuscript details specific levels). ○ Variance explained (R2) for: • Exploratory navigation = 17.7%. • Performance = 26.9%.

0.5, demonstrating good item-level validity (see Table 2). The lone exception pertains to one item of competitive psychological climate, which at a loading of 0.48, is very close to recommended levels (Nunnally, 1978). As for discriminant validity, Table 1 shows the square roots of AVE on the diagonal of the correlation matrix (italicized). Discriminant validity is supported in that the rows and columns of correlations associated with each square root are less than the square root of the corresponding AVE (Fornell & Bookstein, 1982), which is the case in this research. An interesting measurement result pertains to the performance construct which was deliberately specified to be a formative construct composed of two indicator variables — subjective performance and objective performance. The results (i.e., weights) suggest that subjective performance makes up the majority of this formatively-specified construct (see Table 2). This type of empirical finding is not uncommon amongst those relatively few studies in industrial marketing which have simultaneously embedded both subjective/self-reported as well as objective/archival measures of job performance into the same model, or study (Chonko et al., 2000; Jaramillo et al., 2003; Rich et al., 1999). 5.1. Structural results Consistent with the study's hypotheses, a structural PLS model was tested that included the antecedents and consequences of exploratory navigation (EXN) as described earlier (see Fig. 1). The statistical significance of paths in the model was estimated by a bootstrapping procedure using 300 resamples, thus generating robust standard error estimates (Chin, 1998). The results of the structural model are now detailed, beginning with TechCo. TechCo Findings — The structural results for TechCo are shown in Table 3. The path between exploratory navigation and performance was positive (β=.13) but not significant (t-value=1.93), thereby providing only indicative but not conclusive support for H1. At first glance, this is a surprising result — one which is taken up further in the ensuing discussion section. In terms of antecedents, the path between trait competitiveness

545

and exploratory navigation was significant and positive (β =.27 pb.01), lending support to H2. Among the paths between the three forms of salesperson power and exploratory navigation, the only significant path was that pertaining to expert power (β =.29 p b.01), lending support to H3, but not H4 (β=.05, n/s) or H5 (β=−.07, n/s). It therefore appears that referent and legitimate power do not increase a salesperson's propensity to navigate internally — at least at TechCo. The path between the interaction term of competitive psychological climate, exploratory navigation, and performance was significant and negative (β = −.21 p b .05), lending support to the moderating effect espoused by H7. This effect was just significant (t-value= 1.97), which suggests a closer examination in the discussion section. The path between the interaction term of sales management support, exploratory navigation, and performance was negative (as was hypothesized), but not statistically significant (β = −.10, n/s). Thus, the second moderating effect hypothesis H6 is not supported given the TechCo data. In addition to the hypothesized paths, the study design also attempted to control for the effects of demographic variables known to impact sales (or job) performance. The path between the formatively specified control construct and performance was positive and significant (β=.24 pb.01). Upon examining the weightings estimated and assigned in PLS to the individual variables making up this construct, it became apparent that the construct was mainly driven by sales experience (w=+.96), education (w=+.6), and age (w=−.6), with the other variables contributing much less. This is broadly consistent with prior sales research which has indicated a positive performance impact of such demographic variables (e.g., Bartkus, Peterson, & Bellenger, 1989; Ingram & Bellenger, 1983). Although logic and intuition indicate that age and experience should correlate positively with performance, it could be that one experiences diminishing returns in terms of performance benefits as one's career stages unfold or other life priorities evolve (Cron, 1984; Feldman & Weitz, 1988).

Table 4 PLS structural model results: BankCo. Predicted associations Hypothesized paths Exploratory navigation → performance Competitiveness → exploratory navigation Expert power → exploratory navigation Legitimate power → exploratory navigation Referent power→exploratory navigation Competitive psychological climate × Exploratory Navigation → Performance Sales management support × exploratory → navigation performance Control variable paths Age → performance Education → performance Experience → performance Gender → performance Direct paths Competitive climate → performance Sales management support → performance

Hypothesis

Standard path coefficient

t-value for path

H1

.37

2.06

H2

.16

1.73

H3

.37

4.03

H4

− .01

.17

H5

.08

.51

H6

.35

1.50

H7

− .11

.37

− .23 .05 .26 − .02

1.75 .53 1.42 .25

.14 − .04

1.54 .32

Notes: ○ Bolded values are significant structural model estimates (the manuscript details specific levels). ○ Variance explained (R2) for: • Exploratory navigation = 21.9%. • Performance = 42.0%.

546


Based on the above results and accounting for the influence of the control construct, the hypothesized model explained variance of 17.7% in exploratory navigation and 26.9% in salesperson performance at TechCo. These results are comparable – arguably even stronger – than those typically achieved in “customer-directed” behavioral studies which seek to explain variance in salesperson job performance (e.g., Churchill et al., 1985; Franke & Park, 2006; Schwepker, 2003). It is thus suggested that the sales field's tendency to overlook internally-directed selling behaviors such as exploratory navigation may, in part, be a contributor to the field's ongoing inability to surpass figures of N20% variance explained in the job performance of salespeople (Rich et al., 1999; Vinchur et al., 1998; Williams & Plouffe, 2007). In other words and in a different light, it is proposed that if both internally- and externally-directed selling behaviors were simultaneously modeled within the context of a single research design and setting, there may well be a high-upside opportunity to potentially explain much more variance in salesperson performance than has historically been achieved. Further comments in this regard are offered in the article's concluding sections. BankCo Findings — The structural results for BankCo are shown in Table 4. Here the path between exploratory navigation and performance was found to be positive and significant (β = .37, p b 0.01). Therefore, H1 is supported. In contrast to the corresponding result in TechCo, the EXN → Performance effect in BankCo is substantive and remains highly significant when compared to several other paths in the model. This is an interesting outcome in terms of how this effect has played out across the two companies/industries which were studied. Therefore, our a priori expectation was roughly opposite what the data ultimately showed (i.e., for the effect to be stronger in TechCo). This result is considered further in the ensuing discussion. In terms of antecedents, the path between trait competitiveness and exploratory navigation was significant and positive (β=.16 pb0.01), supporting H2. Among the paths between the three forms of salesperson power and exploratory navigation, expert power (β=.37 pb0.01) was again significant, thus lending support to H3, but not H4 (β=−.01, n/s) or H5 (β=.08, n/s). The hypothesized moderating effect of EXN–Competitive Psychological Climate→Performance (i.e., H6, β=.35, n/s) and EXN–Sales Management Support→Performance (i.e., H7, β=−.11, n/s) were not supported given the BankCo context. As for the controls, the path between the formatively specified control construct and performance was positive and significant (β=.25 pb .01). As with TechCo, sales experience (w= +1.36) and age (w=−.77) form large contributions to the construct in the expected directions. Different from the TechCo result however, education does not figure prominently in the formative control construct. Based on the above results and accounting for the influence of demographic differences, the hypothesized model explained a variance of 21.9% in exploratory navigation and 42.0% in salesperson performance. Again, these results are quite strong when juxtaposed against findings typically reported for sales antecedents/behaviors as these are modeled against job performance. Comparing results across the two companies, it therefore becomes clear that: (1) the pattern of results pertaining to the antecedents of navigation are identical; (2) the pattern of results pertaining to the performance implications of navigation are different; and (3), all things being equal, the variables in the research model have more explanatory power in the BankCo sales context. It thus appears to be relatively straightforward to predict the common triggers of exploratory navigation behavior regardless of industry context, whereas its usefulness as a driver of job performance appears to be somewhat tempered by the specific industry context. Attention now turns to elaborating upon these findings from both academic and managerial viewpoints. 6. Discussion 6.1. Research implications The key finding in this work is that exploratory navigation, an internally-directed salesperson behavior, does impact sales perfor-

mance, although the results suggest that it does so more in a stable, mature industry (i.e., BankCo) and less in an industry characterized by market, customer, and technological turbulence (i.e. TechCo). Recall that most sales performance studies generally explain between 10 and 20% of the variance in performance, relying mainly on customerfocused behaviors and antecedents to such behaviors (Churchill et al., 1985; Franke & Park, 2006; Vinchur et al., 1998). The fact that the research model in this study explains a comparable percent of the variance in performance, but does so using an internal behavior, suggests that future research which incorporates both internallydirected and externally-directed selling behaviors and antecedents can potentially explain sales performance significantly better than has been typically achieved to-date. A key issue, however, is that the results indicate that exploratory navigation makes more of a difference in the BankCo case. This is contrary to what was originally expected (i.e., a more prominent role in the TechCo case). The initial reasoning was that the complexity, instability, and turbulence of a high tech industry might mean that getting one's “ducks in a row” internally might be more critical for salespeople there than in the relatively simpler, more stable, and mature financial services context. This merits closer examination. It may be that in the more volatile high tech context, salespeople have to more sharply focus on sizable short-term challenges in order to survive and thrive. Focusing on these challenges, however, might entail narrower, more focused activities that are tightly aligned with the immediate sales task (i.e., customer-facing activities). This would reduce the relative impact of exploratory navigation on performance in such sales contexts. An alternative explanation is that the original argument, as offered and tested, was not entirely complete. The notion of salespeople engaging in exploratory navigation might be important. However, if this is understood by all salespeople in the high-tech context, then they will all engage in this behavior. This might reduce the variance in exploratory navigation to the point that it does not empirically impact performance — no variance; no covariance. As to BankCo, in a more mature industry where turbulence is generally less, there may not be as much need to keep overcoming short-term challenges, and engaging in the proactive stance embodied by exploratory navigation more readily correlates with performance in addition to positioning the salesperson for sustained success. Also, perhaps salespeople in this more stable context may not see the need to navigate since resources and policies allow them to perform adequately. However, some salespeople may see the opportunity to excel if they did navigate; separating themselves away from the pack. This might generate more variance in exploratory navigation which could thus become a more important driver of performance. The conclusion to this point is that viable explanations for the unexpected results across both studied industry contexts can be crafted, but these explanations should be tested and corroborated with further research. From an antecedents' perspective, it was not surprising to find that the relationships between the studied antecedents and exploratory navigation held constant across the two industry contexts. Since the antecedents are individual-level characteristics, their impact should be industry invariant. Trait competitiveness and expert power exert a strong influence, just as proposed. Competitive salespeople are known to be proactive and will thus navigate to get well positioned within their own organization. Although the trait competitiveness → performance relationship has received a moderate amount of attention in the literature (e.g., Brown et al., 1998; Harris et al., 2005), the relationship has typically been proposed as a direct effect. The same may be said for the expert power → performance relationship (Busch & Wilson, 1976; Comer, 1984). A key contribution of this research is thus to provide some evidence that these traits can result in mediating behaviors – in this case exploratory navigation — that then go on to impact performance in a process-based, sequential manner (Mackenzie, 2000).


As to the moderation effects which were proposed, the results suggest that sales management support does not impact the EXN → Performance relationship, whereas the competitive psychological climate of the sales unit has a more meaningful role, at least in the case of TechCo. The original idea behind proposing an intervening role for sales management support was that if a sales manager engaged in navigation, the salesperson would not have to. A simple explanation for the non-significant findings here could be that the effect exists, but because the two datasets were analyzed separately, perhaps there was not enough statistical power to detect significant results. Alternatively, it may be that even if sales managers and salespeople both navigate extensively, their navigation efforts might actually focus on different things (e.g., the salesperson being focused on transaction-level customer issues or opportunities, whereas sales managers might take care of other types of issues, such as operational bottlenecks). The results also indicate that the sales unit's competitive psychological climate as perceived by the salesperson plays a role, at least in the case of TechCo. As expected, as the sales unit's climate becomes increasingly competitive, navigation may gradually become a waste of time and effort (i.e., since a competitive sales force may already be prone to engaging in navigation, the competitive advantage to be garnered from it is potentially mitigated). Alternatively, if within an organization everyone is navigating, this could create friction with those who hold the resources, influence, or power to act on the salesperson's behalf. The result could be a group of co-workers who become immune to navigation, thereby reducing its impact on performance. 6.2. Managerial implications At a high level, the results suggest that sales managers may benefit from a mind-set shift and recognize that internal sales behaviors do matter, and that attention should be focused on this dimension of the sales role in addition to those dimensions which pertain to customers and explicitly defined sales opportunities. More specifically, given the impact of internal navigation on job performance, we suggest that this behavior be present or developed in salespeople. The issue thus becomes one of: “is this something you hire or develop through training?” We recommend that since trait competitiveness has a strong and consistent impact on exploratory navigation, this trait should be sought out in the hiring process. On the other hand, sales force trainers and sales managers should coach salespeople to execute navigational behaviors given that exploratory navigation is not all trait-driven. This conclusion is supported further in that sales management support doesn't seem to diminish the need to navigate. Finally, from a power perspective, the results show the promise inherent in the salesperson's further nurturing, and ultimately channeling, the expert power which they might possess internally. Although there are no specific results from this study for the point which is now offered, we feel it compelling to mention the following idea because it was the predominant theme in our discussions when we presented these research findings to the sponsoring companies. Senior executives at both TechCo and BankCo interpreted the results in the following manner. If salespeople need to spend time and effort navigating and influencing key others in the organization, perhaps it is the case that the organization and its systems, procedures, processes etc. are themselves broken. One way to resolve this would be to work on fixing the broader organization so that the importance of internally-directed selling behavior – e.g., the need to navigate – is reduced. The cost of not doing so may be a heavy one for sales organizations to bear, because salespeople may ultimately end up spending far too much time on internal activities, and not enough working with customers and prospects. Given the pragmatic challenges associated with “fixing” such broken organizations,

547

however, perhaps a more tenable suggestion for managers would be: (i) to instruct their salespeople that EN behavior is likely beneficial to some threshold, and (ii) to introduce them to key personnel internally, provide basic contact information for such personnel, and offer rules and policies germane to interacting with such key departments etc. What this work exposes is that if salespeople are to develop complex knowledge structures of their organization and its products, people, resources, and processes and, more importantly, how these ingredients interact to produce superior outcomes for the firm, it therefore becomes vital to develop this behavioral competency in sales personnel. Our conjecture is that some baseline competency in navigational ability probably exists in virtually all salespeople; the questions for future researchers then become: (i) what are the requisite “make or break” thresholds of navigational behavior (in terms of performance), and (ii) can salespeople who are commensurately “low” on navigational ability have these low levels augmented through training, mentoring, or the like? In the end, we submit that the salesperson's need to navigate will remain for a long time in today's increasingly complex organizations and that this, in turn, will require that sales managers work on better understanding and managing such behavior within their immediate work groups. 6.3. Limitations and future research directions We begin the discussion of the limitations associated with this research by highlighting several caveats related to construct and measurement-related issues. One such issue concerns the new constructs introduced in this work. While every effort was taken to ensure robust theoretical development of the new constructs, there may have been additional work which was not considered in their development which may have assisted.9 As such, future researchers deploying the new constructs offered here (i.e., exploratory navigation; sales management support) should consider them works-inprogress and open to theoretical and conceptual improvements. A second measurement-related limitation pertains to the relative importance of the objective and subjective measures of performance which together formed the sales performance construct. As shown by the weights of these measures, the formative construct was dominated by the subjective measure. This is a somewhat disappointing finding, as intuitively, performance should be performance — regardless of measurement (Rich et al., 1999; Sharma, Rich, & Levy, 2004; Vinchur et al., 1998). Perhaps it is the case that exploratory navigation could have longer-term effects on objective performance that the present study/data could not detect. A longitudinal study might help elucidate this (Chonko et al., 2000), as might studying additional industries and selling contexts. A third limitation of this work pertains to the nature of the hypothesized relationship of exploratory navigation to performance. H1 in the research model assumed a direct effect, linear relationship between the salesperson's exploratory navigation behavior and performance. However, in actuality, this relationship is probably non-linear and is likely to exhibit characteristics of an inverted U-shape distribution at different levels of navigational behavior as this behavior pertains to performance. The notion here, which the research model could not explicitly assess, is that at some point, navigational behavior, though beneficial to the salesperson in general terms, probably decreases the salesperson's

9 For example, the following article could likely contribute to the further development and refinement of our “sales management support” construct with its construct of “perceived organizational support”. See: Babakus, Emin, David W. Cravens, Mark Johnston, and William C. Moncrief (1996), “Examining the Role of Organizational Variables in the Salesperson Job Satisfaction Model,” Journal of Personal Selling & Sales Management, 16 (3), 33–46.

548


achieved performance simply because they are now spending so much time internally, as opposed to doing externally-directed activities such as interacting with customers, prospecting for new business etc. In terms of future research, several directions and extensions of this work are proposed. First, it would be interesting to collect data not just from salespeople but also from the key others with whom they interact internally. Would their views of the salesperson's traits and behaviors be consistent? Though likely cumbersome, such a network approach (Iacobucci, 1996) would be enlightening because it would include key others from functions such as administrative support; order fulfillment and coordination; accounting and finance; marketing; legal, R&D etc. Finally, the range of constructs included in this research should be expanded. The antecedents of EN which were examined in this research were salesperson traits and characteristics, which tend to be invariant, deep-set, and numerous (Cron et al., 2005; Reid & Plank, 2000). Beyond this, it is clear that situational, organizational, and taskrelated factors could also influence exploratory navigation — both from the perspective of antecedents to, and potential moderators of, the EN → performance relationship. Along these lines, future research may be well-served by simultaneously including variables such as a customer-centric orientation (e.g., Sheth, Sisodia, & Sharma, 2000), formal and informal lines of authority (here the Leader-Member Exchange theoretical framework might be helpful. See Dienesch & Liden, 1986), and the degree of bureaucratization within the organization (e.g., Adler & Borys, 1996).

4. When at company functions (e.g., conferences, social events), I network and meet coworkers I did not know before. 5. I think about ways this company could better help me meet my customer's needs. 6. I utilize my existing contacts and network within this organization. 7. I keep up-to-date with personnel changes within my company. Trait competitiveness 1. 2. 3. 4. 5.

Expert power 1. 2. 3. 4. 5. 6.

Appendix A. Measurement details Subjective performance 1. My ability to sell products / services with higher profit margins. 2. My ability to generate a high dollar amount of sales in my territory. 3. My ability to quickly generate sales of new company products / services. 4. My ability to produce a high market share for my company in my territory. 5. My ability to exceed the sales targets and objectives that are assigned to me. 6. My ability to identify and sell to major accounts / customers in my territory. Objective performance Defer to footnote # 4, which describes the archival measures of performance for both studied firms. Exploratory navigation 1. I learn as much as possible about my organization. 2. I examine my own company's organization charts and personnel directories. 3. When in an office or facility of this company that is not my own, I seek to understand what goes on there.

As a salesperson, I am knowledgeable. As a salesperson, I am experienced. As a salesperson, I am proficient. As a salesperson, I am qualified. Others in this organization trust my judgment. Others in this organization agree that I generally know what is best. Legitimate power

1. 2. 3. 4.

Acknowledgments A grant for this research was provided to the first author by Direct Selling Education Foundation (DSEF) of Canada. The authors gratefully acknowledge the financial support of the Ivey Business School at the University of Western Ontario. The third author acknowledges support from the Social Sciences and Humanities Research Council of Canada. This work has benefited from the helpful comments of Michael Ahearne, Kersi Antia, Bill Cron, Robert Fisher, Jerry Goodstein, Adrian Ryans and Mark Vandenbosch.

I enjoy working in situations involving competition with others. It is important to me to perform better than others on a task. I feel that winning is important in both work and games. I try harder when I am in competition with other people. Being # 1 is important to me.

Most here have an obligation to accept my requests. Most here have a duty to obey me. Compared to most people in this organization, I have authority. As a salesperson here, I have the right to influence the behavior of my coworkers. Referent power

1. 2. 3. 4. 5. 6.

Most here admire me. Most here identify with me. Most here respect me as a person. Most here find me likable. Most here find me friendly. Most here want to be similar to me. Competitive psychological climate

1. My manager frequently compares my results with those of other salespeople. 2. The amount of recognition you get in this company depends on how your sales rank compared to other salespeople. 3. Everybody is concerned with finishing at the top of the sales rankings. 4. My coworkers frequently compare their results with mine. 5. My manager often compares my sales effort to that of the other salespeople here. Sales management support (stem – “my immediate sales manager…”) …has taught me a lot about sales. …does a good job of helping salespeople develop to their potential. …lives up to his/her promises.⁎ …sees that we have the things we need to do our jobs. …helps me overcome internal problems which stop me from carrying out my sales tasks. 6. …makes sure everyone knows what to do and how to do it. 7. …stays in close contact with me.⁎ 1. 2. 3. 4. 5.

Experience 1. How long have you been in sales (in years)? 2. How long have you been in sales in this industry (in years)? 3. How long have you been in sales with your current employer (in years)? 4. How long have you been selling the products or services you currently represent (in years)?


5. How long have you been selling in your current sales territory (i.e., the specific customer accounts, industry verticals, and/or geographies you cover – in years)? ⁎These two items were subsequently deleted from the analysis of the structural model. References Adler, Paul S., & Borys, Bryan (1996). Two types of bureaucracy: Enabling and coercive. Administrative Science Quarterly, 41(1), 61−89. Ahearne, Michael, Gruen, Thomas W., & Jarvis, Cheryl Burke (1999). If looks could sell: Moderation and mediation of the attractiveness effect on salesperson performance. International Journal of Research in Marketing, 16(4), 269−284. Ahearne, Michael, Jelinek, Ronald, & Jones, Eli (2007). Examining the effect of salesperson service behavior in a competitive context. Journal of the Academy of Marketing Science, 35(4), 603−616. Allison, Paul D. (2002). Missing data. Thousand Oaks, CA: Sage. Anderson, Erin, & Oliver, Richard L. (1987). Perspectives on behavior-based versus outcome-based salesforce control systems. Journal of Marketing, 51(4), 76−88. Armstrong, J. Scott, & Overton, Terry S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396−402. Aulakh, Preet S., & Gencturk, Esra F. (2000). International principal-agent relationships: Control, governance and performance. Industrial Marketing Management, 29(6), 521−537. Babakus, Emin, Cravens, David, W., Johnston, Mark, Moncrief, & William, C. (1996). Examining the role of organizational variables in the salesperson job satisfaction model. Journal of Personal Selling & Sales Management, 16(3), 33−46. Bagozzi, Richard P., Verbeke, Willem, & Gavino, Jacinto C. (2003). Culture moderates the self-regulation of shame and its effects on performance: The case of salespersons in The Netherlands and the Philippines. Journal of Applied Psychology, 88(2), 219. Baldauf, Artur, & Cravens, David W. (2002). The effect of moderators on the salesperson behavior performance and salesperson outcome performance and sales organization effectiveness relationships. European Journal of Marketing, 36(11/12), 1367−1388. Bandura, Albert (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall. Barclay, Donald W., Higgins, Christopher A., & Thompson, Ronald (1995). The partial least squares (pls) approach to causal modeling: Personal computer adoption and use as an illustration. Technology Studies, 2(2), 285−309. Bartkus, Kenneth R., Peterson, Mark F., & Bellenger, Danny N. (1989). Type a behavior, experience, and salesperson performance. Journal of Personal Selling & Sales Management, 9(2), 11−18. Behrman, Douglas N., & Perreault, William D., Jr. (1982). Measuring the performance of industrial salespersons. Journal of Business Research, 10(3), 355−370. Behrman, Douglas N., & Perreault, William D., Jr. (1984). A role stress model of the performance and satisfaction of industrial salespersons. Journal of Marketing, 48 (4), 9−21. Blau, Peter M. (1964). Exchange and power in social life. New York, NY: John Wiley & Sons. Brown, Steven P., Cron, William L., & Slocum, John W., Jr. (1998). Effects of trait competitiveness and perceived intraorganizational competition on salesperson goal setting and performance. Journal of Marketing, 62(4), 88−98. Busch, Paul, & Wilson, David T. (1976). An experimental analysis of a salesman's expert and referent bases of social power in the buyer-seller dyad. Journal of Marketing Research, 13(1), 3−11. Challagalla, Goutam N., & Shervani, Tasadduq A. (1996). Dimensions and types of supervisory control: Effects on salesperson performance and satisfaction. Journal of Marketing, 60(1), 89−105. Chin, Wynne W. (1998). The partial least squares approach for structural equation modeling. In George A. Marcoulides (Ed.), Modern methods for business research (pp. 295−336). New York, NY: Lawrence Erlbaum Associates. Chin, Wynne W., Marcolin, Barbara L., & Newsted, Peter R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/ adoption study. Information Systems Research, 14(2), 189. Chonko, Lawrence B., Loe, Terry N., Roberts, James A., & Tanner, John F. (2000). Sales performance: Timing of measurement and type of measurement make a difference. The Journal of Personal Selling & Sales Management, 20(1), 23−36. Churchill, Gilbert A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64−73. Churchill, Gilbert A., Jr., Ford, Neil M., Hartley, Steven W., & Walker, Orville C., Jr. (1985). The determinants of salesperson performance: A meta-analysis. Journal of Marketing Research, 22(2), 103−118. Churchill, Gilbert A., Jr., Ford, Neil M., & Walker, Orville C., Jr. (1974). Measuring the jobsatisfaction of industrial salesmen. Journal of Marketing Research, 11(3), 254−260. Churchill, Gilbert A., Jr., Ford, Neil M., & Walker, Orville C., Jr. (1976). Organizational climate and job satisfaction in the salesforce. Journal of Marketing Research, 13(4), 323−332. Comer, James M. (1984). A psychometric assessment of a measure of sales representatives' power perceptions. Journal of Marketing Research, 21(2), 221−225. Crom, J. Oliver, & Crom, MichaelDale Carnegie & Associates Inc. (2003). The sales advantage: How to get it, keep it, & sell more than ever New York, NY: The Free Press. Cron, William L. (1984). Industrial salesperson development: A career stages perspective. Journal of Marketing, 48(4), 41−52. Cron, William L., Marshall, Greg W., Singh, Jagdip, Spiro, Rosann L., & Sujan, Harish (2005). Salesperson selection, training, and development: Trends, implications,

549

and research opportunities. The Journal of Personal Selling & Sales Management, 25 (2), 123−136. DeVellis, Robert F. (1991). Scale development: Theory and applications. Newbury Park, CA: Sage. Dienesch, Richard M., & Liden, Robert C. (1986). Leader-member exchange model of leadership: A critique and further development. Academy of Management Review, 11(3), 618−634. Dillman, Don A. (2000). Mail and internet surveys: The tailored design method. New York, NY: J. Wiley & Sons. Feldman, Daniel C., & Weitz, Barton A. (1988). Career plateaus in the salesforce: Understanding and removing blockages to employee growth. Journal of Personal Selling & Sales Management, 8(3), 23−32. Fornell, Claes, & Bookstein, Fred L. (1982). Two structural equation models: Lisrel and pls applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440−452. Franke, George R., & Park, Jeong Eun (2006). Salesperson adaptive selling behavior and customer orientation: A meta-analysis. Journal of Marketing Research, 43(4), 693−702. French, John R., & Raven, Bertram (1959). The bases of social power. In D. Cartwright (Ed.), Studies in social power, Vol. 1. (pp. 150−167)Ann Arbor, MI: University of Michigan Institute for Social Research. Gartlan, J. S. (1968). Structure and function in primate society. Folia Primatologica, 8(1), 89−120. Gopal, Kanji J. (2006). 100 statistical tests, 3rd edition Thousdand Oaks, CA: Sage. Hair, Joseph F., Anderson, Ralph E., Tatham, Ronald L., & Black, William C. (1995). Multivariate data analysis with readings. Upper Saddle River, NJ: Prentice-Hall. Harris, Eric G., Mowen, John C., & Brown, Tom J. (2005). Re-examining salesperson goal orientations: Personality influencers, customer orientation, and work satisfaction. Journal of the Academy of Marketing Science, 33(1), 19−35. Holzbach, Robert L. (1974), ‘An investigation of a model for managerial effectiveness: The effects of leadership style and leader attributed social power on subordinate job performance’, (Unpublished Doctoral Dissertation, Carnegie-Mellon University). House, Robert J., & Dessler, Gary (1974). The path-goal theory of leadership: Some post hoc and a priori tests. In James G. Hunt, & Lars L. Larson (Eds.), Contingency approaches to leadership. (pp. 46−48) (1; Carbondale, IL: Southern Illinois University Press). Hunt, Shelby D. (1983). General theories and the fundamental explananda of marketing. Journal of Marketing, 47(4), 9−17. Iacobucci, Dawn (1996). Networks in marketing. Thousand Oaks, CA: Sage. Ingram, Thomas N., & Bellenger, Danny N. (1983). Personal and organizational variables: Their relative effect on reward valences of industrial salespeople. Journal of Marketing Research, 20(2), 198−205. Jaramillo, Fernando, Carrillat, Francois A., & Locander, William B. (2003). Starting to solve the method puzzle in salesperson self-report evaluations. Journal of Personal Selling & Sales Management, 23(4), 369. Jaworski, Bernard J., & Kohli, Ajay K. (1991). Supervisory feedback: Alternative types and their impact on salespeople's performance and satisfaction. Journal of Marketing Research, 28(2), 190−201. Johlke, Mark C., Duhan, Dale F., Howell, Roy D., & Wilkes, Robert W. (2000). An integrated model of sales managers' communication practices. Journal of the Academy of Marketing Science, 28(2), 263−277. Jolson, Marvin A., Dubinsky, Alan J., Yammarino, Francis J., & Comer, Lucette B. (1993). Transforming the salesforce with leadership. Sloan Management Review, 34(3), 95−106. Jones, Eli, Dixon, Andrea L., Chonko, Lawrence B., & Cannon, Joseph P. (2005). Key accounts and team selling: A review, framework, and research agenda. Journal of Personal Selling & Sales Management, 25(2), 181−198. Kramer, Roderick M., & Neale, Margaret A. (Eds.). (1998). Power and influence in organizations Thousand Oaks, CA: Sage. Mackenzie, Kenneth D. (2000). Processes and their frameworks. Management Science, 46(1), 110−125. Martin, Craig A., & Bush, Alan J. (2006). Psychological climate, empowerment, leadership style, and customer-oriented selling: An analysis of the sales managersalesperson dyad. Journal of the Academy of Marketing Science, 34(3), 419−438. McFarland, Richard G., Challagalla, Goutam N., & Shervani, Tasadduq A. (2006). Influence tactics for effective adaptive selling. Journal of Marketing, 70(4), 103−117. McGregor, Jena (2006). The office chart that really counts: Mapping informal relationships at a company is revealing — and useful. BusinessWeek (pp. 48−49). 2006. Nunnally, Jim C. (1978). Psychometric theory. New York, NY: McGraw-Hill. Oliver, Richard L., & Anderson, Erin (1994). An empirical test of the consequences of behaviorand outcome-based sales control systems. Journal of Marketing, 58(4), 53−67. Page, Rick (2002). Hope is not a strategy: The six keys to winning the complex sale. New York, NY: Nautilus Press. Palmatier, Robert W., Gopalakrishna, Srinath, and Houston, Mark B. (2006). Returns on business-to-business relationship marketing investments: Strategies for leveraging profits, Marketing Science, 25 (5), 477-493, 548-49. Pfeffer, Jeffrey (1981). Power in organizations. Marshfield, MA: Pitman. Pfeffer, Jeffrey (1985). Organizational demography: Implications for management. California Management Review, 28(1), 67−81. Plouffe, Christopher R., & Barclay, Donald W. (2007). Salesperson navigation: The intraorganizational dimension of the sales role. Industrial Marketing Management, 36(4), 528−539. Rackham, Neil, & DeVincentis, John R. (1999). Rethinking the sales force: Redefining selling to create and capture customer value. New York, NY: McGraw-Hill. Rasmusson, Erika (1999). The 10 traits of top salespeople. Sales and Marketing Management, 151(8), 34−37.

550


Reid, David A., & Plank, Richard E. (2000). Business marketing comes of age: A comprehensive review of the literature. Journal of Business-to-Business Marketing, 7 (2/3), 9−185. Rich, Gregory A. (1997). The sales manager as a role model: Effects on trust, job satisfaction, and performance of salespeople. Journal of the Academy of Marketing Science, 25(4), 319−328. Rich, Gregory A., Bommer, William H., MacKenzie, Scott B., Podsakoff, Philip M., & Johnson, Jonathan L. (1999). Apples and apples or apples and oranges? A metaanalysis of objective and subjective measures of salesperson performance. The Journal of Personal Selling & Sales Management, 19(4), 41−52. Rosenbloom, Bert (1994). Wholesale distribution channels: New insights and perspectives. Greenwhich, CT: Haworth Press Inc. Schwepker, Charles H., Jr. (2003). Customer-oriented selling: A review, extension, and directions for future research. Journal of Personal Selling & Sales Management, 23(2), 151−171. Sharma, Arun, Rich, Gregory A., & Levy, Michael (2004). Comment: Starting to solve the method puzzle in salesperson self-report evaluations. The Journal of Personal Selling & Sales Management, 24(2), 135−139. Sheth, Jagdish N., Sisodia, Rajendra S., & Sharma, Arun (2000). The antecedents and consequences of customer-centric marketing. Journal of the Academy of Marketing Science, 28(1), 55−66. Siebel, ‘Essential new sales competencies for customer-centric organizations’, White Paper, Siebel Systems eBusiness, Vol. August, (2001). Spector, Paul E. (1992). Summated rating scale construction: An introduction. Newbury Park, CA: Sage. Stevens, Howard, & Kinni, Theodore (2007). Achieve sales excellence: The 7 customer rules for becoming the new sales professional. New York, NY: Platinum Press. Stewart, Thomas A. (2006). The top line: From the editor.Harvard Business Review, 74, 10 (6-7 {Special Double-Issue on Sales}). Strutton, David, Pelton, Lou E., & Lumpkin, James R. (1993). The relationship between psychological climate and salesperson-sales manager trust in sales organizations. Journal of Personal Selling & Sales Management, 13(4), 1−14. Sujan, Harish (1999). Optimism and street-smarts: Identifying and improving salesperson intelligence. The Journal of Personal Selling & Sales Management, 19(3), 17−33. Swasy, John L. (1979). Measuring the bases of social power. In William L. Wilkie (Ed.), Advances in Consumer Research, vol. 6. (pp. 340−346)Ann Arbor, MI: Association for Consumer Research (ACR). Swift, Cathy Owens, & Campbell, Constance (1998). Psychological climate: Relevance for sales managers and impact on consequent job satisfaction. Journal of Marketing Theory & Practice, 6(1), 27−37. Tuli, Kapil R., Kohli, Ajay K., & Bharadwaj, Sundar G. (2007). Rethinking customer solutions: From product bundles to relational processes. Journal of Marketing, 71 (3), 1−17. Vinchur, Andrew J., Schippmann, Jeffrey S., Switzer, Fred S., III, & Roth, Philip L. (1998). A meta-analytic review of predictors of job performance for salespeople. Journal of Applied Psychology, 83(4), 586−597.

Viswesvaran, Chockalingam, Schmidt, Frank L., & Ones, Deniz S. (1996). Comparative analysis of the reliability of job performance ratings. Journal of Applied Psychology, 81(5), 557−574. Walker, Orville C., Jr., Churchill, Gilbert A., & Ford, Neil M. (1977). Motivation and performance in industrial selling: Present knowledge and needed research. Journal of Marketing Research, 14(2), 156−168. Weitz, Barton A. (1981). Effectiveness in sales interactions: A contingency framework. Journal of Marketing, 45(1), 85−103. Weitz, Barton A., & Bradford, Kevin D. (1999). Personal selling and sales management: A relationship marketing perspective. Journal of the Academy of Marketing Science, 27 (2), 241−254. Williams, Brian C., & Plouffe, Christopher R. (2007). Assessing the evolution of sales knowledge: A 20-year content analysis. Industrial Marketing Management, 36(4), 408−419. Wood, Robert, & Bandura, Albert (1989). Social cognitive theory of organizational management. Academy of Management Review, 14(3), 361−384. Workman, John P., Jr., Homburg, Christian, & Jensen, Ove (2003). Intraorganizational determinants of key account management effectiveness. Journal of the Academy of Marketing Science, 31(1), 3−21. Zemanek, James E., & Pride, William M. (1996). Distinguishing between manufacturer power & manufacturer salesperson power. Journal of Business and Industrial Marketing, 11(2), 20−63. Christopher R. Plouffe (PhD Ivey Business School, University of Western Ontario) is an Assistant Professor in the Dept. of Marketing, College of Business, Florida State University. Prior to earning his PhD, Chris spent several years selling enterprise-level computing and consulting solutions for Hewlett-Packard (Canada) Ltd. in various territories and industry verticals across Canada. Chris's research has appeared in the Journal of the Academy of Marketing Science, Information Systems Research, Industrial Marketing Management, the Journal of Personal Selling & Sales Management, the Journal of Product Innovation Management, European Journal of Marketing, and elsewhere. Srinivas Sridharan is an Assistant Professor of Marketing at the Richard Ivey School of Business. Prior to entering the academic field, Srinivas worked as an engineer in India. His research has appeared in the Journal of Public Policy & Marketing, Journal of Business Research, Journal of Consumer Marketing, and the Ivey Business Journal. Donald W. Barclay (PhD the University of Michigan) is an Emeritus Associate Professor of Marketing at the Ivey Business School, the University of Western Ontario. Donald held several sales and account management positions for IBM in Canada prior to earning his PhD and his career as an academic at the Ivey Business School. Donald's research has appeared in Journal of Marketing, Journal of Marketing Research, and elsewhere.