Salespeople affect firm performance in a number of ways, most importantly by generating sales from maintaining and enhancing existing customer relationships (i.e., “farming” activities, or customer retention) and/or prospecting for new customers (i.e., “hunting” activities, or customer acquisition). Each of these activities is critical for firm success in business-to-business (B2B) markets (Moncrief et al. 2006; Sabnis et al. 2013). However, achieving a balance between hunting and farming activities is difficult because not only are the strategic investments of attracting new customers versus managing relationships with existing ones very different from one another (Blattberg and Deighton 1996), but they also exert diverse effects on financial outcomes (Reichheld 1996). As an extension of prior research on the trade-offs between firm-level customer acquisition and retention in the business-to-consumer domain, recent research has started to examine this issue at the individual salesperson level in the business-to-business domain (Carter et al. 2014). However, three research gaps remain.

First, despite the widespread recognition of the need to strategically balance both of these sales activities, research to date has focused on the benefits and costs associated with either farming (e.g., Palmatier et al. 2007) or hunting (e.g., Sabnis et al. 2013). By failing to simultaneously examine farming and hunting behaviors, extant research is limited in its ability to provide insight into why—when left to their own discretion—salespeople develop a preference for farming over hunting, and vice versa. The issue becomes more pronounced when companies adopt a generalist (e.g., territory manager) go-to-market model where B2B salespeople have discretion in determining the strategies they use to achieve sales goals (Zoltners et al. 2004). Given that almost 85% of B2B salesforces adopt this model (Cron and DeCarlo 2010, p. 156), understanding why salespeople tend to gravitate toward one or the other activities when managing a territory holds significant theoretical and managerial importance. In fact, such a study could help explain why, as suggested by practitioners, the specialist organizational model with designated hunters and farmers generally fails and why there is a high attrition rate among hunter reps (Hancock et al. 2011).

Second, little is known about how the trade-offs salespeople experience in engaging in both of these activities (i.e., ambidexterity in customer engagement) may affect sales performance. Recent research on customer service representatives’ ambidextrous behavior indicates that engaging in potentially conflicting tasks such as generating sales while providing services reduces efficiency but enhances customer satisfaction and conversion rates (Jasmand et al. 2012). Carter et al. (2014) show that the effect of salesperson acquisition time allocation on sales revenues is moderated by management control variables, such as salesperson financial incentives, customer portfolio variables, and firm cross-functional coordination capabilities. However, it remains unclear how the inherent trade-off and potential synergy salespeople experience between hunting and farming will influence profit margins, an indicator of both sales revenues and cost efficiency. Third, research has yet to identify factors that can alter a salesperson’s inclination toward hunting or farming. This understanding is important, because it provides guidance to managers who, for strategic reasons, may need to shape salesperson behaviors away from or toward such tendencies.

By addressing these important research gaps, we make a number of contributions to the literature. First, we conceptualize, develop measurement scales for, and provide the initial empirical evidence of the nomological validity of two new customer engagement orientation constructs, namely salesperson hunting and farming orientation. Practitioners commonly use these terms to describe salesperson proclivities toward these two activities, yet academics have lagged behind in their conceptual development. The scales we develop will be useful for future academic examination of salespeople’s customer engagement orientation.

Second, we shed light on why salespeople adopt a certain customer engagement orientation. On the basis of in-depth interviews and regulatory focus theory (Higgins 2002), we develop a theoretical model positing that salespeople tend to self-regulate when striving toward goal attainment using two regulatory systems, namely promotion or prevention focus, which will influence the extent to which they adopt a hunting or farming orientation. Self-regulating through a promotion focus involves strong motivations to attain desired end states, prompting individuals to seek out new opportunities during goal pursuit. In contrast, a prevention focus increases one’s inclination to avoid mistakes in reaching a goal because of an increased sensitivity to negative outcomes (Lanaj et al. 2012). We posit and find that these general regulatory foci are related to hunting and farming orientations, which are situated, context-specific regulatory foci representative of different approaches by which salespeople attain performance goals. In doing so, we demonstrate that managers can identify hunters and farmers in their salesforce by measuring salesperson regulatory foci.

Third, we integrate expectancy theory (Vroom 1964) and regulatory focus theory (e.g., Higgins 2002; Shah et al. 1998) to examine and extend the understanding of the boundary conditions of the effect of regulatory fit on salesperson outcomes. To this end, we establish salesperson expected hunting success and acquisition-based compensation plans as situational factors that not only have a direct impact on salesperson customer engagement orientations, but also interact with salesperson regulatory foci to jointly alter these orientations.

Finally, we extend prior research on firm-level ambidexterity (O’Reilly and Tushman 2008) and the emerging research on individual-level ambidexterity among boundary spanners (e.g., Carter et al. 2014; Jasmand et al. 2012). Specifically, we add to the literature by showing that salesperson ambidexterity in customer relationship management depends on a salesperson’s level of customer orientation. Furthermore, we extend prior firm-level research on the influence of ambidexterity on innovation by showing the influence of ambidexterity on a different outcome, at the individual level, and in the new context of boundary spanning activities. In doing so, our work also contributes to previous research on customer orientation and goal orientations held by salespeople (e.g., Sujan et al. 1994).

Our study is organized as follows. We first review the background literatures on individual orientations in sales research, present a qualitative study on personality traits that drive hunters and farmers in the salesforce, and discuss our dispositional–situational approach. Then, we present the research hypotheses and findings from two empirical studies. We conclude with a discussion about the theoretical and managerial implications.

Conceptual background

Salesperson orientations

In contrast to firm-level orientations such as market orientation and strategic orientations, our research focuses on individual, salesperson-level orientations involving hunting and farming. Two streams of research emerge from a literature review of individual orientations as predictors of salesperson performance, as summarized in Table 1. The first research stream focuses on salesperson customer orientation in salesperson–customer interactions. Customer orientation refers to “the degree to which salespeople practice the marketing concept by trying to help their customers make purchase decisions that will satisfy customer needs” (Saxe and Weitz 1982, p. 344). This research suggests that customer orientation has a positive, although weak, effect on salesperson performance and dismisses selling orientation as undesirable (Franke and Park 2006; Saxe and Weitz 1982). The second research stream considers salespeople’s performance goal achievement using the construct of goal orientation (Harris et al. 2005; Kohli et al. 1998; Sujan et al. 1994). Goal orientation includes learning orientation, which has a positive relationship with customer orientation, and performance orientation, which positively affects selling orientation (Harris et al. 2005). These research streams suggest that salespeople have, or develop, a particular orientation toward their job, which can explain performance outcomes. Moreover, they lend support to our fundamental argument that salespeople may hold different customer engagement orientations toward hunting and farming and that these orientations could influence performance.

Table 1 Prior research on individual orientations in the sales literature
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Customer acquisition and retention orientation at the salesperson level: hunting and farming orientation

At the firm level of analysis, customer acquisition and retention represent two important strategies in customer relationship management (Blattberg and Deighton 1996). As a parallel at the salesperson level of analysis, customer acquisition involves hunting activities used in securing initial orders from new customers, including prospecting, generating leads, pre-call planning, and delivering sales presentations. Retention, on the other hand, entails farming behavior used in selling to existing customers, such as building long-term relationships, creating efficiencies in order-taking, and increasing share of wallet through cross-selling and up-selling efforts (Honeycutt et al. 2009; Moncrief et al. 2006).

For most B2B firms, the salesforce is typically well positioned to approach potential new customers as well as to sell to current customers. However, the relatively high rejection rates make closing new accounts for B2B salespeople very difficult (e.g., Ingram et al. 2013). This theme is captured by Hancock et al. (2011, p. 2) observation that “[e]ven though the salesforce is typically best placed to find and approach potential clients, individual reps may shun the uncomfortable task of cold-calling in favor of selling to customers they know well.” Thus, the process associated with hunting for new accounts is generally considered a higher-risk endeavor than farming activities with existing customers (e.g., Blattberg and Deighton 1996). Furthermore, hunting activities are generally evaluated in terms of “wins,” such as how many new accounts salespeople acquire, whereas farming activities in terms of “losses,” such as how many existing customers defect. However, the personal characteristics that underlie hunters and farmers are unknown. Therefore, we take a grounded-theory approach by conducting an exploratory study to help develop our research model on salesperson hunting and farming orientations.

Study 1: the exploratory study

We conducted in-depth interviews with seven sales and human resource managers to gain a better theoretical understanding of the personal characteristics that represent hunters and farmers. The participants were identified through the recruitment office from a large southeastern university with interviews lasting 30 min to an hour each. Participants were selected from different B2B companies located in disparate parts of the United States. We continued the purposive sampling procedure and modified the interview script until we reached a saturation point in terms of learning about the phenomenon (i.e., the characteristics of hunting and farming) (Glaser and Strauss 1967). All participants had greater than 10 years of experience in recruiting and hiring salespeople. One of the authors conducted all interviews, which were recorded and transcribed verbatim. Two coders identified and categorized the personality traits that are related to salesperson preference for hunting for new customers and farming existing customers. The intercoder reliability was 95%. We present representative comments from managers in Table 2.

Table 2 Study 1: Practitioners’ representative comments on hunter and farmer characteristics
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These comments were then compared with established constructs in the literature. Two key findings emerge. First, the comments suggest that in personal selling, hunters and farmers possess distinct trait-like individual motivations toward hunting and/or farming activities. Comments such as risk-taker, desire to win, and positive outcome focus were consistently mentioned for hunters, whereas farmer-related comments centered on traits such as a preference for routine, an amiable personality, and less aggressiveness. Second, the data revealed a relatively high correspondence between the motivational traits that are predictive of salesperson hunting and farming orientation and promotion focus and prevention focus in regulatory focus theory, respectively.

Predictors of salesperson hunting and farming orientations

Trait-based predictors

In line with the results from Study 1, we propose that salespeople develop preferred customer engagement orientations that reflect their proclivity for engaging in either hunting or farming activities to achieve sales goals. Given that personal traits are organized hierarchically (broad/basic personality → narrower/surface trait levels → behavioral response; Allport 1961), we also posit that salespeople’s workplace regulatory focus, a more general personal trait, is predictive of their customer engagement orientation, a more situated surface trait. Accordingly, we define salesperson hunting orientation as a situated regulatory focus in which a salesperson is inclined to prospect for new customers in order to achieve sales goals and salesperson farming orientation as a situated regulatory focus in which a salesperson prefers to leverage relationships with existing customers to attain sales goals. We also posit that although salespeople possess hunting and farming orientations that are driven by their regulatory focus, these orientations may change due to situational factors (c.f., Lanaj et al. 2012).Footnote 1

Situational predictors

Because it is managerially relevant to understand why salespeople avoid hunting activities (Hancock et al. 2011), we also examine how situational factors can influence salespeople to develop a hunting orientation. Consistent with this view, sales researchers have examined the interactive effects associated with a variety of individual and organizational variables in predicting firm and salesperson performance (e.g., Sujan et al. 1994; Sabnis et al. 2013). However, research has yet to examine how regulatory focus may interact with situational cues to predict salesperson behaviors. This possibility is, however, suggested by the concept of regulatory fit, which indicates that the influence of one’s regulatory orientation on behavior may be impacted by situational factors (Förster and Higgins 2005). Moreover, expectancy theory (Vroom 1964) also indicates motivation as jointly driven by expectancy, instrumentality, and valence of both internal and external stimuli. Therefore, our framework investigates the joint effects involving, on the one hand, salesperson regulatory foci that capture salesperson valence for particular types of behaviors (e.g., hunting or farming activities) and outcomes and, on the other, situational factors that capture salesperson expectancy and instrumentality of the success and reward from those behaviors and outcomes (Vroom 1964). Together, these variables jointly impact salespeople’s customer engagement orientation.

We focus on two moderators, salesperson expected hunting success and acquisition-based compensation plans. Salesperson expected hunting success corresponds to the notion of expectancy, the probability that extending an effort in hunting will improve performance. We define expected hunting success as a salesperson’s near-term expectation of adding new customers through prospecting. Acquisition-based compensation plans captures instrumentality, the probability that being successful in hunting will improve the compensation salespeople receive. We define acquisition-based compensation plans as the relative percentage of a salesperson’s total annual compensation that is derived by his or her behavior and outcome of acquiring new customers. Because customer acquisition-based compensation plans are promotion-framed task incentives, the valence for such incentives is captured by salesperson promotion focus (e.g., Shah et al. 1998).

We conducted two studies to test our conceptual model. Study 2 employs single-firm data to test dispositional antecedents of hunting and farming orientations. We also examine the joint effect of these two customer engagement orientations on profit margins.Footnote 2 We develop two new scales to measure salesperson hunting and farming orientations that facilitate empirical examination of the above issues. In Study 3, between-firm data are used to test two situational antecedents and moderators of salesperson hunting and farming orientations at the individual (expected hunting success) and firm (acquisition-based compensation plans) levels. The conceptual framework guiding our investigation is summarized in Fig. 1.

Fig. 1
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Conceptual framework. Notes: The dotted paths are also estimated in the empirical model. Profit margins are company archival data

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Study 2

Regulatory focus as an antecedent of salesperson hunting and farming orientations

Regulatory focus represents a basic dispositional trait reflecting the tendency of individuals to pursue goals through one of two self-regulatory motivational systems (Higgins 2002). A promotion focus is an innate motivational state reflecting an individual’s sensitivity toward attaining positive outcomes, leading to an “eagerness” to use relatively creative and risky strategies (Crowe and Higgins 1997), such as exploring for new customers as a means of increasing sales. In contrast, a prevention focus is a motivational state that reflects a desire to avoid negative outcomes, resulting in the adoption of loss-avoidant, “vigilant” strategies for goal attainment such as exploiting existing customer relationships to garner incremental sales.

Importantly, regulatory focus should be predictive of surface traits reflecting the behavioral mindsets adopted by an individual (Higgins 2002) and, consequently, the work behaviors in which that person prefers to engage (Crowe and Higgins 1997; Förster et al. 2003). These notions are supported by research conducted in non-selling workplace contexts indicating that these two self-regulatory goal systems are predictive of distinct strategic behavior inclinations (see Lanaj et al. 2012 for a review). This ability of regulatory focus to influence behavior arises because individuals generally prefer to engage in goal-related activities that provide high levels of “fit” with their regulatory focus (Higgins 2002).

Because a promotion focus motivates employees to have an exploratory orientation and heightened sensitivity for accomplishment toward their work environment (Lanaj et al. 2012), salespeople who are promotion-focused should adopt a stronger customer engagement orientation geared toward hunting for new customers. As noted earlier, prospecting for, and successfully attaining, new accounts embodies a relatively greater approach-focused strategy in making a new sale and more perceived uncertainty than does selling to existing accounts. In addition, a promotion person’s focus is naturally oriented toward achieving “hits” and accomplishments that have been shown to have downstream effects on decision making and preferred job behaviors (Förster et al. 2003; Lanaj et al. 2012). Thus, the higher level of fit between a promotion-based regulatory focus and hunting orientation should facilitate greater levels of hunting behaviors.

Because hunting behaviors are relatively incompatible with the loss- and risk-aversion tendencies possessed by salespeople who are prevention oriented, all else equal, these individuals are less likely to adopt strong hunting orientations. Further, because individuals who are prevention focused tend to prefer more predictable work tasks (Higgins 2002), they should be motivated to engage in farming-related activities aimed at generating sales to known, relationship-based customers versus hunting for new prospects. A prevention focus, therefore, should encourage the adoption of a farming orientation.

Research indicates that prevention and promotion represent independent dispositions (Lanaj et al. 2012), which makes it possible for the same person to possess high or low levels of each orientation (Förster et al. 2003). Thus, while a promotion focus should generally motivate salespeople to adopt a hunting orientation and a prevention focus should typically encourage a farming orientation, some individuals may be both hunting and farming oriented (i.e., ambidextrous in that they equally prefer both hunting and farming activities). In hunting endeavors, salespeople might engage in some farming activities when the customers have just been acquired. For example, ambidextrous salespeople may attempt to identify new opportunities for cross-selling or up-selling existing customers. Thus, promotion (prevention) focus will also be related to salesperson farming (hunting) orientation. Nevertheless, because individuals generally prefer to engage in goal-related activities that provide high levels of “fit” with their regulatory focus (Higgins 2002), we hypothesize the relative strength of association between a given regulatory focus and a customer engagement orientation as follows:

  • H1a: Salesperson promotion focus is, ceteris paribus, more positively related to a hunting orientation than is salesperson prevention focus.

  • H1b: Salesperson prevention focus is, ceteris paribus, more positively related to a farming orientation than is salesperson promotion focus.

Interactions involving hunting and farming orientations

Two-way interaction between hunting orientation and farming orientation

Prior research on regulatory focus suggests that a high degree of fit between the goal-oriented activities and a person’s dominant regulatory focus significantly increases a person’s anticipation of task enjoyment, perceived task success, and persistence in repeating the task (Freitas and Higgins 2002). Extending these findings to the sales context, we argue that because farming-oriented (hunting-oriented) salespeople enjoy engaging with existing (new) customers, they are more invested and persistent in meeting existing (new) customer needs. We further argue that when salespeople are expected to carry out both tasks, the dominant customer engagement orientation will alter how salespeople implement the other customer engagement task in their sales funnel management, defined as salesperson behavior to move customers from prospects to established customers and maintain a pipeline of prospects and existing customers.

Because highly farming-oriented salespeople receive more enjoyment from, and thus more persistent at, meeting existing customer needs, their approach to sales funnel management will likely center on extracting revenues and profits from existing customers. As salesperson hunting orientation increases from low to high, we expect highly farming-oriented salespeople to incrementally prioritize prospecting new customers from whom they can subsequently extract revenues and profits. Because salespeople who are high on both hunting and farming orientation will be more selective and efficient in their funnel management, they will generate higher profit margins than those who focus on farming but fail to replenish their pipelines of new prospects.

In contrast, low farming-oriented salespeople receive less enjoyment from exploiting relationships with existing customers. As a result, these salespeople are less likely to fully leverage, from a profit perspective, their prior investments in developing relationships with existing customers. When coupled with a strong hunting orientation, a weak farming orientation should further exacerbate a salesperson’s motivation to seek new customer relationships. Success in gaining first-time orders from new customers often requires significant incentives (e.g., trial orders and/or price cuts; Dwyer et al. 1987), resulting in sales that produce relatively lower profit margins. As salesperson hunting orientation increases from low to high, we expect hunting orientation to dominate salesperson behavior such that these low farming-oriented salespeople incur incrementally higher acquisition costs and lower sales revenues from existing customers. Therefore, we hypothesize:

  • H2: There is a two-way interaction between salesperson hunting orientation and farming orientation on profit margins such that the relationship between hunting orientation and profit margins is positive when farming orientation is high, but negative when farming orientation is low.

Three-way interaction among hunting orientation, farming orientation, and customer orientation

It remains unclear when salesperson ambidexterity in customer engagement orientation, that is pursuing both hunting and farming orientations, poses as an advantage or a physical and mental constraint. In firm-level research on innovation, Kyriakopoulos and Moorman (2004) show that the trade-off between exploration and exploitation does not occur when the firm is market oriented. At the individual level, however, not only is there a lack of understanding of individual motivations that influence boundary spanners’ ambidexterity (Jasmand et al. 2012), but there has been debate about the degree to which employees can succeed when assigned seemingly contradictory tasks (e.g., Gupta et al. 2006). To help shed light on this debate, we posit that while salespeople’s customer engagement orientations determine their investment and perseverance in dealing with existing and/or new customers, customer orientation provides salespeople with the ability and knowledge to understand and solve customer problems to become successful in such activities.

Specifically, when an ambidextrous salesperson is also highly customer oriented, we expect greater efficiency in managing the sales funnel of both new and existing customers, resulting in a positive synergistic effect on profits. Furthermore, customer orientation provides salespeople with profound customer knowledge, a form of resource slack, that relieves ambidextrous salespeople from the mental and physical constraints of engaging with both new and existing customers (c.f., Kyriakopoulos and Moorman 2004; Voss et al. 2008). Conversely, when salespeople experience an imbalance among the three orientations, we expect their performance will suffer for at least two reasons. First, low customer-oriented salespeople will experience greater physical and mental constraints because they lack the skills to effectively perform either hunting or farming activities. Second, high customer-oriented salespeople who are not ambidextrous (e.g., high hunting orientation but low farming orientation, or vice versa) will be suboptimal in extracting profits from their sales funnel, such as missing out on cross-selling and up-selling opportunities or overinvesting in new prospects that they fail to subsequently nurture and retain. Thus, we hypothesize:

  • H3: There is a three-way interaction between salesperson hunting orientation, farming orientation, and customer orientation on profit margins such that the relationship between hunting orientation and profit margins is more positive when both farming orientation and customer orientation are high.

Procedures and sample

B2B salespeople from a publicly-traded industrial distribution firm with over 1,200 profit center locations (i.e., stores) nationwide were selected for the study. According to executives, the large network of stores is strategically important in providing a high level of localized inventory support to each store’s territory. The distributor offers over 325,000 different SKUs of industrial products to the marketplace and employs both inside and outside, territory-based salespeople. The market is highly competitive, with the sponsoring distributor having a 2% market share. The outside salesforce, which is the focus of this study, is the company’s primary revenue-generating function and is responsible for managing all aspects of customer relationships by actively working an assigned territory for prospects, making sales presentations, closing sales, and managing current customers. The salesforce is compensated using a salary plus commission plan, with the variable portion of the performance scorecard based on a combination of average profit margins earned, growth of current customer sales revenues, and number of new customers closed during the month. Importantly, all salespeople have the freedom to invest their time in activities associated with prospecting for new customers (i.e., hunting) or building existing relationships with current customers (i.e., farming).Footnote 3 The firm also provides its salespeople the freedom to determine the final sales price to customers.

We approached executives at the firm for their participation, offering them a report of the overall results and customized analyses for their internal purposes. The company provided us with the email addresses of 1,174 salespeople from the Southeast, Midwest, and Southwest regions involving 22 different states and salesperson profit margins from company records. Salespeople were e-mailed an introductory letter from top management indicating their support for the project. These individuals were subsequently emailed an invitation to participate in the online study that included a brief description of the study, a promise of anonymity, and a Web link to access the survey. A second request was e-mailed 2 weeks later. No incentives for participation in the study were provided.

We received complete responses from 514 salespeople. To ensure the stability of the performance data, we focused on salespeople who worked at the same store for at least 6 consecutive months, 3 months before and 3 months after the study. Because of promotions, changing stores, and salesperson departures during the 6 month study period, 157 salespeople were eliminated from the study, resulting in a final sample of 357 salespeople (or 30.4% response rate). About 43% of salespeople were 30 years old or younger, and 11.8% were female. The salespeople had a mean sales experience of 69.75 months, company tenure of 44.34 months, and industry experience of 56.30 months. The vast majority (85.7%) of respondents held an undergraduate degree or higher. There were no statistically significant differences between early and late respondents on model and demographic variables. To check for nonresponse bias, we used demographic variables from company records to compare characteristics between those responded and those who did not and also found no differences (Armstrong and Overton 1977).

Measures

Scales

We developed multi-item scales assessing hunting and farming orientations by following Churchill’s (1979) recommended procedures. First, we developed an initial pool of items from our exploratory research efforts and then refined the wording of several items based on suggestions from distributor salespeople and academic experts. We then pretested the scale using a small sample of the focal firm’s salespeople (N = 49) who did not participate in the main study. Following this pretest, we made minor wording changes to the scales. The final measures consist of four items for each customer engagement orientation. We measured salesperson promotion focus and prevention focus by adapting a scale from Neubert et al. (2008) to fit with a work-related context. The firm calculated profit margins by subtracting costs of goods sold from total sales revenues a salesperson generates. To test for evidence of causality and to smooth out abnormality, we used the average profit margins of 3 consecutive months following the survey. A 3-month lag was deemed sufficient to observe the outcome from salesperson hunting and farming activities in the industrial distribution context.

Controls

For antecedents to salesperson hunting and farming orientation, we controlled for two business unit variables (store size, prior store sales growth) and several salesperson variables (prior salesperson sales growth, sales experience, age, education, and job satisfaction). Gender was not included in Study 2 due to the predominantly male sample. We included two growth-related variables as covariates because salespeople are likely to become either more hunting oriented (to achieve long-term growth) or more farming oriented (to maintain short-term growth) when the business is growing. We calculated prior salesperson and store sales growth as the average of sales growth of the salesperson and each store, respectively, in the 3 months prior to our survey. Store size, a proxy of the market power of the company in the territory, is based on the company’s categorization of the average sales revenue of each store. For outcomes, we controlled for salesperson job satisfaction, measured using three items from Hackman and Oldham’s (1975) scale. Appendix 1 contains a list of all the key measurement scales.

Analytical strategy

While we collected the data from multiple stores, the majority (90%) of the stores had only one outside salesperson. Therefore, we used salespeople as the level of analysis. Because it includes latent variable interactions, we specified the structural equation model to be estimated by a numerical integration algorithm, using Mplus 7 (Muthén and Muthén 1998–2012). This method has been shown to be superior to many other alternative techniques (Klein and Moosbrugger 2000). It does not produce standard fit indexes as in a traditional main-effects only structural model; rather, this method produces fit indices in the form of log-likelihood (LL), Akaike’s information criterion (AIC) and Bayesian information criterion (BIC). We compared main effects and models with interactions using –2LL, with the differences in the number of free parameters between the two models as the degrees of freedom. In the model specification, we allow the residuals of hunting orientation and farming orientation to be correlated.

To reduce common method biases, we followed Podsakoff et al. (2012)’s recommendations. First, as a procedural remedy, we used a variety of scale types and reversed the wording on several items. Second, we conducted the Harman’s single-factor test, which indicated that the single-factor model had a significantly worse model fit than the multifactor measurement model. In the principal components factor analysis including all corresponding items without rotation, the highest variance explained by one factor is 28.6%. Third, as a more stringent test, we specified a structural model that included a common method factor with all of the measures as indicators and all the salesperson-reported variables of our theoretical model. The common method factor is constrained to be unrelated to all the constructs (Podsakoff et al. 2012). In this model, the path coefficients remain stable, providing further evidence that the empirical results are not affected by common method bias.

Measurement model

We assessed reliability and validity for each measure using exploratory and confirmatory factor analyses. The exploratory factor analysis showed that the new scales of hunting and farming orientations were well-behaved and did not cross load heavily onto unintended factors. The confirmatory factor analysis (CFA) model indicated a good fit (comparative fit index [CFI] = 0.95, Root Mean Square Error of Approximation [RMSEA] = 0.06; χ 2 = 678.44, d.f. = 215, χ 2/d.f. = 3.15; Bagozzi and Yi 2012). For all the constructs, no Cronbach alpha values were lower than 0.70. The average variance extracted (AVE) was at least 0.50 for all the constructs, with the only exception of farming orientation (AVE = 0.45). All of the constructs also possessed discriminant validity, given that the AVE exceeds the squared correlations between all pairs of constructs (Fornell and Larcker 1981). The correlation matrix, reliability indices, and variable descriptive statistics appear in the lower triangle in Table 3.

Table 3 Means, standard deviations, and intercorrelation matrix
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We also assessed whether there is discriminant validity among hunting orientation, farming orientation and customer orientation. The five-item customer orientation scale adapted from Thomas et al. (2001) exhibited good scale properties with a Cronbach alpha of 0.97 and AVE of 0.87. An exploratory factor analysis without rotation of all the items from the three constructs showed that the items loaded on three distinct factors. We then tested discriminant validity among the three constructs by conducting a series of nested chi-square analyses. The results show that constraining the correlation of any pair of these variables to one resulted in models with a poorer fit with a significant increase in the chi-square statistics (Δχ2 [d.f. =1] = 359.71; 353.05; and 1,005.39, p < 0.00 for the farming orientation–customer orientation, farming orientation–customer orientation, and hunting orientation–customer orientation pairs, respectively). All the three constructs also satisfy the stringent test of discriminant validity because the AVE exceeds the squared correlations between all pairs of constructs (Fornell and Larcker 1981). The zero-order correlations between farming orientation–customer orientation and hunting orientation–customer orientation are 0.20 and 0.14, respectively. Thus, there is concrete evidence of discriminant validity among the three constructs, hunting orientation, farming orientation and customer orientation.Footnote 4

Results

We report the empirical results of Study 2 in Table 4. We present the unstandardized coefficients from the main-effects model first (the baseline model, Model 1) followed by those from the models with a two-way interaction (Model 2) and a three-way interaction (Model 3).

Table 4 Study 2: Empirical results
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Main-effects only model

To test H1a and H1b, we first estimated a main-effects model. We found that salesperson promotion focus is positively related to hunting orientation (γ = 0.176, p < 0.01), and salesperson prevention focus is positively related to farming orientation (γ = 0.201, p < 0.01). Neither the relationship between salesperson prevention focus and hunting orientation nor that between promotion focus and farming orientation is significant. We examined whether the relationship between promotion focus and hunting orientation is stronger than that between prevention focus and hunting orientation (H1a) and the relationship between prevention focus and farming orientation is stronger than that between promotion focus and farming orientation (H1b) by imposing two constraints to the model. The Wald test with two constraints is significant (χ2[d.f. = 2] = 6.50, p < 0.05), indicating the relative strength of each regulatory focus in predicting salesperson hunting and farming orientation. Thus, H1a and H1b are supported.

Full model with the interaction effects

We added the two-way interaction effect between hunting orientation and farming orientation to test H2. Unlike Model 1, the effects of hunting and farming orientation on profit margins in Model 2 are conditional effects. We found that the interaction was positive and significant (β = 1.154, p < 0.05), but the main effects of both hunting and farming on profit margins are not significant. This result and the interaction plot in Fig. 2 show support of H2. To test H3, we added all the lower-order interactions and a three-way interaction among hunting, farming, and customer orientations to Model 2 and found it to be significant in predicting profit margins (β = 1.298, p < 0.05, Model 3). As Table 4 shows, the full model with interactions had a better model fit than the main-effects only model.

Fig. 2
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Study 2: Joint impact of hunting and farming orientations on profit margins

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Additional analysis

As a robustness test to see whether the interaction effect between hunting orientation and farming orientation is confounded by their quadratic effects on profit margins (Ganzach 1997), we added the quadratic terms of these independent variables to the model. None of these terms have any significant effects on profit margins. In addition, an assumption we made is that salespeople will devote more time toward hunting (farming) with greater hunting (farming) orientation. We measured salesperson hunting and farming time allocation by asking salespeople how they normally allocate their time to a battery of multiple selling activities during a week. We found a significant correlation between a composite measure of time allocated toward hunting activities and hunting orientation (0.17, p < 0.05) and farming orientation (−0.09, p < 0.10). These intuitive relationships further support our theorization.

We also examined whether job satisfaction predicts salesperson hunting and farming orientation. We found that salespeople who are satisfied with the job are more likely to be hunting oriented (γ = 0.238, s.e. = 0.064, p < 0.01), but there is no significant relationship between job satisfaction and farming orientation (γ = −0.042, s.e. = 0.029, p > 0.10). The quadratic term of job satisfaction on profit margins is not significant (β = 0.058, s.e. = 0.152, n.s.). The inclusion of these additional paths does not influence the results in the more parsimonious model that we reported.

As a test of the robustness of the time lag, we reestimated the entire model with profit margins for each of the 3 months after the survey instead of an average of all the 3 months after the survey. We also included the average of profit margins of the 3 months prior to the survey to control for unobserved explanatory variables (Wooldridge 2003). The results of all these analyses show that the hypotheses are again supported in the predicted direction.

Moderated mediation analysis

The results suggest that the profit impact of promotion focus is mediated by hunting orientation and moderated by farming and customer orientations. We conduct a moderated mediation analysis using the PROCESS model (Hayes 2013). In the presence of hunting orientation as the mediator, the direct effect from promotion focus on profit margins is not significant. As the confidence intervals in Appendix 2 shows, the conditional indirect effect is negative when the salesperson is not farming oriented but highly customer oriented, but positive when the salesperson is farming oriented and highly customer oriented.

Discussion

Discussion of findings

Study 2 provides strong support for our theorizing. In addition to the hypothesized antecedents to salesperson hunting and farming orientation, we also found significant effects of salesperson prior sales growth and education level on hunting orientation but not farming orientation. Both of the conditional effects of hunting and farming orientation on profit margins are not significant while their interaction effect on profit is positive. This cross-over, two-way interaction, illustrated in Fig. 2, Panel A, is important because it suggests that the relationship between hunting orientation and profit margins can be negative when farming orientation is low, but positive when farming orientation is high.

The three-way interaction is plotted in Fig. 2, Panel B. It indicates that the interaction in Fig. 2, Panel A occurs when salespeople are highly customer oriented. When salespeople are not customer oriented, however, there is no interaction effect between salesperson hunting orientation and farming orientation in predicting profit margins. Customer-oriented salespeople who are farming oriented produce higher profit margins when they are also hunting oriented. Furthermore, relative to salespeople who are not customer oriented, the relationship between hunting orientation and profit margins is more negative among salespeople who are highly customer oriented but not farming oriented.

Study limitations

A main limitation of Study 2, a single-firm study, is that there is no significant variation among salespeople with regard to factors such as the compensation plan and expected hunting success in the remaining time of the current quota cycle. More broadly, it did not control for between-firm situational factors that might influence salespeople’s customer engagement orientation. We address these issues in Study 3 with a between-firm dataset.

Study 3

Since salespeople tend to shy away from hunting activities, we focus on salesperson hunting orientation as the dependent variable in Study 3. The primary goal of Study 3 is to extend Study 2 by examining two between-firm situational factors that influence the relationship between salespeople’s regulatory focus and their hunting orientation: salesperson expected hunting success and acquisition-based compensation plans. As we explained earlier, these two variables capture expectancy and instrumentality components of salesperson motivation to engage in hunting, respectively. For completeness, we also include farming orientation as another dependent variable.

Expected hunting success

Main effect

Classic motivation theories, such as expectancy theory (Vroom 1964), hold that strong a priori expectations of task success often results in a self-fulfilling prophecy as people anticipate putting forth the appropriate task-related effort necessary to achieve the goal (also known as the “typical shifts” phenomenon). The implication is that, all else equal, stronger hunting success expectations should enhance salesperson hunting-oriented activities. This prediction is also supported, in part, by Higgins et al.’s (2001) findings suggesting that when people hold promotion-related success expectations about a goal, they tend to adopt an eagerness orientation that is normally attributed to a promotion focus. As a result, greater hunting success expectations should motivate salespeople to adopt a stronger hunting orientation.

  • H4: Salespeople’s expected hunting success is positively related to their hunting orientation.

Interaction effect between promotion focus and expected hunting success in predicting hunting orientation

While expectancy theory predicts an increase in motivation to hunt under strong expectations of task success, empirical work in regulatory focus theory suggests that one’s regulatory focus can play an important moderating role in shaping an individual’s goal-related behaviors when the situation matches with the regulatory focus (Crowe and Higgins 1997; Higgins et al. 2001). We reconcile these two seemingly contradictory predictions by drawing from research on situation strength (Mischel 1977), which indicates that when the situation is weak, or considered not salient to task accomplishment, personal factors will be more predictive of people’s behavior, as suggested in regulatory focus research. In contrast, when the situation is perceived as salient or strong, the predictive value of personality trait is attenuated and the situation becomes more predictive of individual behavior, as suggested in expectancy theory.

All else equal, promotion-focused people tend to engage in more risky behaviors than prevent-focused people to achieve performance goals (Lanaj et al. 2012) and are more persistent in continuing to complete difficult tasks following failure (Crowe and Higgins 1997). Extending this notion to our context, salespeople with a strong promotion focus would be inherently motivated to implement riskier strategies to attain achievement-related goals, such as prospecting for new customers. Thus, in situations wherein expectations of hunting success are unfavorable, we expect promotion-oriented salespeople to be more likely to assume a hunting orientation. Conversely, when expected hunting success is high, the effect of the situation is strong. Under these conditions, salespeople are likely to adopt a stronger hunting orientation, regardless of the extent to which they are promotion-focused—similar to our main effect prediction above (H4). Therefore, we hypothesize:

  • H5a: The relationship between promotion focus and hunting orientation becomes stronger and positive when expected hunting success is low than when it is high.

Interaction effect between prevention focus and expected hunting success in predicting hunting orientation

Regulatory focus theory suggests that prevention focus and expected hunting success will jointly influence salesperson hunting orientation under certain situations. Given the underlying tendency of prevention-focused individuals to adopt more “vigilant” strategies during goal pursuit and avoid mistakes in goal attainment (Higgins et al. 2001), prevention-focused salespeople holding lower expectations of future hunting success should exhibit significantly weaker tendencies to engage in hunting activities. Higgins et al.’s (2001) findings also suggest that strong situational inducements can temporarily alter a person’s regulatory focus from promotion to prevention and vice versa. Thus, the presence of positive expectations regarding future hunting success should encourage salespeople to temporarily assume hunting orientations as they develop strategies to make quota. Consistent with our earlier argument, the influence of such expectations on the adoption of a hunting orientation should occur even for prevention-oriented salespeople who might otherwise be farming oriented. Again, our prediction is also consistent with research on situation strength, attributing a weaker role to dispositional variables (here, regulatory focus) in the presence of salient situational factors (c.f. Mischel 1977).

  • H5b: The relationship between salesperson prevention focus and hunting orientation becomes stronger (weaker) and positive (negative) when expected hunting success is high (low).

Acquisition-based compensation plans

Main effect

Both expectancy theory and regulatory focus theory inform the same prediction. Under high customer acquisition–based compensation plans, salespeople are provided a strong extrinsic incentive that underscores the instrumentality component of salesperson motivation to engage in hunting. Acquisition-based compensation plans also provide an incentive that emphasizes the “win,” which directly prompts the adoption of an eagerness strategy of hunting orientation. As a result, we expect high customer acquisition–based pay plans to be positively related to salesperson hunting orientation.

  • H6: Customer acquisition–based compensation plans are positively related to salesperson hunting.

Interaction effect between promotion focus and acquisition-based compensation plans

A key attribute of promotion-focused people is that they are chronically predisposed to self-regulate to attain “hits” by engaging in greater task diversity to ensure goal attainment (Förster and Higgins 2005). Promotion-oriented people are also more prone to focus on “maximal goals” that are in the upper range of performance outcomes (Pennington and Roese 2003). As a result, they tend to have a natural inclination to engage in risker activities in attaining goals (Crowe and Higgins 1997). In combination, these studies suggest that as promotion focus becomes stronger, salespeople will be more likely to engage in a greater variety of behaviors that include hunting activities to ensure they reach performance goals, even when the reward system does not place an emphasis on such behavior. In other words, we expect a positive relationship between promotion focus and hunting orientation under low acquisition-based compensation plans.

However, under a high acquisition-based compensation plan, the inducement of the external factor (i.e., acquisition-based compensation) should temporarily alter the effect of regulatory focus on customer engagement orientation behaviors. Specifically, for both high and low promotion focus salespeople, we expect a greater emphasis on hunting activities. This prediction is in line with our earlier discussion regarding the potential for the strength of a situation to overpower the influence of a personal factor. Accordingly, we do not anticipate a significant moderating relationship between promotion focus and salesperson hunting orientation under a high acquisition-based compensation plan. Thus, we hypothesize:

  • H7a: The relationship between salesperson promotion focus and hunting orientation becomes stronger and positive for low acquisition-based compensation plans.

Interaction effect between prevention focus and acquisition-based compensation plans

Following our previous discussion, customer acquisition–based compensation plans provide a signal to salespeople that hunting activities are critical to successfully achieving performance goals. Thus, high acquisition-based compensation plans are likely to make hunting activities more extrinsically attractive to prevention-focused salespeople who otherwise do not enjoy engaging in such activities. Importantly, prevention-focused salespeople tend to have a strong sense of vigilance of obligation for meeting performance standards. That is, their concern for duty and obligation enhances their sensitivity to negative outcomes, such as not meeting minimum goals (Lanaj et al. 2012). The external inducement of a high customer acquisition–based compensation plan should engender a prevention-focused salesperson’s sense of vigilance to engage, at least temporarily, in a different type of regulatory focus (i.e., promotion focus) to avoid not meeting performance standards. Thus, we expect high acquisition-based compensation plans to have a positive moderating effect on the relationship between prevention focus and hunting orientation. This again is in line with the strength of the situation argument. In contrast, under low acquisition-based compensation, such external motivation is less likely to be experienced by prevention-focused salespeople, and therefore their prevention focus will dictate their disinclination for hunting. Therefore, we expect a negative relationship between prevention focus and a hunting orientation under a low acquisition-based pay plans.

  • H7b: The relationship between salesperson prevention focus and hunting orientation becomes stronger (weaker) and positive (negative) for high (low) acquisition-based compensation plans.

Study procedures and sample

Study 3 participants were randomly selected from a nationwide panel of salespeople provided by a market research firm that adheres to the ESOMAR International Code on Market and Social Research. Participants were e-mailed an invitation to the online study that included a brief description of the study, a promise of anonymity, and a Web link to access the survey. A filter question was used to exclude participants who were not (1) responsible for both hunting and farming, (2) quota-based, and (3) B2B sales executives responsible for generating revenue. After 2 weeks of data collection, responses were garnered from 225 salespeople. Of these responses, we excluded 25 respondents who did not correctly answer the quality check question, which was placed halfway through the questionnaire (“For quality purposes, please click ‘Poor-Extremely Low’ for this question”). The resulting sample involved 200 participants (73% female; 65.5% below 50 years of age), the majority of whom (74%) had some college education or higher. The sample composition is provided in Appendix 3.

Measures

The measures for regulatory foci, hunting orientation, and farming orientation were identical to the ones we used in Study 2. We measured salesperson expected hunting success by asking salespeople to rate their likelihood of being successful in hunting for new customers in the current quarter (1 = not at all, 7 = very high). Acquisition-based compensation was measured by asking salespeople to report the percentage of their compensation that is based on hunting versus farming activities and outcomes.

To address an alternative explanation that the relationship between regulatory focus and salesperson hunting orientation results from supervisors’ selection, we added a three-item scale to measure the supervisor selection. We also controlled for (1) the time left in the current quota cycle (measured in months), (2) quota achievement in the current quota cycle (%), (3) firm market share (%), (4) competitive intensity (measured with three items adapted from Jaworski and Kohli 1993), (5) salesperson experience in sales, and (6) demographic variables. Information about these additional measures is available in Appendix 1. We report the correlation matrix and reliability indices in the upper triangle in Table 3.

Measurement model

We validated the measurement model before estimating the structural model. The results showed that the CFA model exhibited good fit (CFI = 0.94, RMSEA = 0.07; χ 2 = 416.62, d.f. = 174, χ 2/d.f. = 2.39; Bagozzi and Yi 2012). All the scales have an internal consistency reliability index (Cronbach alpha) that is greater than 0.70. In addition, all measures possessed AVE that was above the recommended threshold of 0.50 and exhibited discriminant validity based on Fornell and Larcker’s (1981) recommendation to compare the squared correlation and the AVE of each pair of constructs. We also adopted the procedural and statistical remedies for common method biases as we did in Study 2. We found that in the principal components factor analysis that includes all corresponding items without rotation, the highest variance explained by one factor is 27.5%. The paths coefficients remain stable even when we include a common method factor in the structural model. Therefore, we concluded that these biases were not a major concern. Furthermore, the focus of Study 3 is on interaction effects, making respondents’ hypothesis-guessing highly unlikely. Podsakoff et al. (2012, p. 565) also note that method bias can only deflate and not inflate quadratic and interaction effects.

Results

Table 5 provides the empirical results of Study 3. For each dependent variable, we present the unstandardized coefficients from the main effects model first (the baseline model, Model 4), and those from the full model second (Model 5). Similar to Study 2, we allow for the residuals of hunting and farming orientations to be correlated. In the main effects model, the effect of expected hunting success on salesperson hunting orientation is positive and significant (γ = 0.323, p < 0.01), supporting H4. We also found support for H6, which predicts a positive effect of acquisition-based compensation plans on hunting orientation (γ = 0.011, p < 0.01). Although not hypothesized, we also found that expected hunting success does not influence salesperson farming orientation at all, but acquisition-based compensation plan does have a negative effect on farming orientation (γ = −0.01, p < 0.05).

Table 5 Study 3: Empirical results
Full size table

In the full model with interaction effects (Model 5), we found support for the predicted interactions between expected hunting success and regulatory focus. Specifically, the interaction between promotion focus and expected hunting success is negative (β = −0.333, p < 0.05, H5a) while that between prevention focus and expected hunting success is positive and significant (β = 0.233, p < 0.01, H5b). The results also show a significant interaction between regulatory focus and acquisition-based compensation plans. Specifically, the interaction between promotion focus acquisition-based compensation plans is negative (β = −0.014, p < 0.05, H7a) while that between prevention focus and acquisition-based compensation plans is positive (β = 0.007, p < 0.10, H7b). Thus, both H7a and H7b are supported. As Table 5 shows, the full model significantly fits the data better than the main-effects only model.

Additional analysis

Industries as clusters

In Study 3, individual responses are from multiple industries and might be interdependent. However, we found no significant between-industry variance for salesperson hunting orientation (χ 2 [d.f. = 20] = 24.79, n.s.), but a significant, albeit very small (ICC1 = 0.02), between-industry variance for salesperson farming orientation (total variance = 0.97, between-industry variance = 0.02, χ 2 [d.f. = 20] = 35.94, p < 0.02). In comparison with the general threshold in multilevel models (e.g., Schneider et al. 1998), this between-industry variance is not considered a threat to our findings. Nevertheless, as a robustness test, we retested the hypotheses with a two-level model wherein level 1 included all the individual responses nested within industries at level 2. We found that the results remain robust as hypothesized.

Mediation or moderation

Research in expectancy theory suggests that salesperson traits can influence their evaluation of expectancies (Teas 1981). We examine this possibility by regressing expected hunting success on salesperson regulatory focus. We found that both promotion focus and prevention focus are unrelated to expected hunting success; therefore, we can rule out this alternative model specification.

Additional covariates

Prior research suggests that extraversion might predict how comfortable salespeople are in interacting with new versus existing customers (e.g., Barrick and Mount 1991). Therefore, we included extraversion, measured with a four-item scale adapted from Donnellan et al. (2006), as a covariate in predicting salesperson hunting and farming orientation. This scale has a Cronbach alpha of 0.74. There was no significant zero-order correlation between extraversion and hunting orientation (ρ = 0.083, n.s.) or farming orientation (ρ = 0.13, n.s.). We also included extraversion in our analysis and found no significant effect on either hunting orientation (γ = −0.143, s.e. = 0.140) or farming orientation (γ = 0.099, s.e. = 0.104).

Discussion

We plot the interactions associated with H5 and H7 in Fig. 3. When salespeople have low expected hunting success, the relationship between promotion focus and hunting orientation is significantly positive (Fig. 3, Panel A), while prevention- focused salespeople become even less likely to become hunting oriented (Panel B). In contrast, when salespeople have high expectations about their hunting success, the relationship between promotion focus and hunting orientation is not significant whereas that between prevention focus and hunting orientation becomes positive. Figure 3, Panel C, shows that when the firm does not reward salespeople based on hunting, salesperson promotion focus is highly predictive of salesperson hunting orientation. In contrast, when the firm rewards salespeople largely based on hunting, then a promotion focus is not related to hunting orientation. Figure 3, Panel D suggests that acquisition-based compensation plans are effective in making prevention-focused salespeople become more hunting oriented. The relationship between a prevention focus and hunting orientation is downward when the firm’s compensation plan does not place an emphasis on hunting.

Fig. 3
figure 3

Study 3: Interaction plots with hunting orientation as the dependent variable

Full size image

Interestingly, the interaction pattern in H5a is similar to that in H7a, and the interaction effect in H5b is similar to that in H7b. This similarity has two implications. First, compensation pay plans, an extrinsic firm-level factor, appears to be a substitute for expected hunting success, an individual factor, in altering the effect of general regulatory focus (i.e., work-related promotion and prevention focus) on situated regulatory focus (i.e., hunting and farming orientations). Second, it shows that expectancy theory and the strength of a powerful situation (e.g., high expected hunting success, high acquisition-focused compensation plans) is able to explain the non-significant relationship between promotion focus and hunting orientation in Panel A and C. In weaker situations (e.g., low expected hunting success, low acquisition-focused compensation plans), regulatory fit theory is at work to explain the positive effect of promotion focus on hunting orientation. Finally, in the full model with interaction effects, the main effect of supervisor selection, a control variable, is significant and positive in predicting farming orientation. However, its relationship with hunting orientation is not significant. We also found that market share is positively related to farming orientation, whereas salesperson experience in sales is positively related to hunting orientation.

General discussion

Discussions of salesperson hunting and farming activities are commonplace among practitioners (e.g., Bowen 2012). A reoccurring theme in such discussions is that salespeople often over-focus on farming, which runs counter to firms’ strategic imperatives on recruiting new customers. Yet, research has not investigated why some salespeople prefer farming over hunting and vice versa. Along these lines, research to this point has failed to (1) theoretically develop and provide nomological evidence of salespeople’s customer engagement orientation, (2) elucidate why salespeople prefer to engage in a particular type of customer engagement when doing so may be contrary to firm objectives, (3) understand how managers can align salespeople’s customer engagement orientation and strategic imperatives, and (4) how to leverage salesperson ambidexterity when they enjoy engaging with both existing and new customers to achieve sales goals. Our research addresses these important gaps by examining antecedents and moderating factors that influence salesperson hunting and farming orientations. We next discuss the theoretical and managerial implications of our findings.

Summary of findings and theoretical implications

Salespeople’s customer engagement orientation

On the basis of in-depth interviews and two empirical studies, we are the first to propose two new constructs, salesperson hunting orientation and farming orientation. We then integrate expectancy theory and regulatory focus theory to show that the nomological validity of these constructs includes regulatory focus, compensation plans, and expected hunting success as personal and situational antecedents with profit margins as an efficiency-based consequence. While prior research has primarily focused on salesperson time allocation to hunting activities and performance (Carter et al. 2014; Sabnis et al. 2013), our study sheds light on not only the antecedents but also the situational factors that might alter salespeople’s customer engagement orientation. Our findings therefore contribute to the literature on salesperson orientation and more specifically to the emerging research on customer acquisition and retention at the individual salesperson level of analysis.

Joint effects of customer engagement orientation on profit margins

Our research is the first to demonstrate the influence of regulatory focus on both subjective (customer engagement orientations) and objective (profit margins) salesperson performance outcomes. We empirically demonstrate that in the generalist sales organization model where salespeople have discretion in how to allocate their effort on either existing or new customers to achieve sales goals, a high farming orientation coupled with a low hunting orientation can be as suboptimal in profit generation as a high hunting orientation combined with a low farming orientation. On the surface, this finding may seem somewhat counterintuitive vis-a-vis the combination of low hunting orientation and low farming orientation. However, regulatory fit theory provides the most direct explanation. According to our model, a salesperson low on both hunting and farming indicates s/he does not enjoy doing either activity; therefore the person is ambivalent about performing one activity over the other. As a result, it appears that these salespeople will conduct a more profitable mix of customer engagement activities than those that prefer farming over hunting activities. While this finding may be unique to the industrial distribution industry, our data lend support to the notion that current customers may leverage the salesperson relationship to extract greater discounts and less profitable sales (e.g., Dwyer et al. 1987). In addition, our finding is also consistent with Johnson and Selnes (2004), who found that the key to increasing overall customer portfolio profitability lies in the ability to successfully acquire new customers.

In contrast, high hunting and farming orientations significantly increased profit margins. These results suggest that ambidextrous salespeople who are high on both hunting and farming orientations are better able to achieve efficiencies in managing their customer portfolio, as evidenced by higher profit margins. Study 2 also demonstrated that ambidextrous salespeople who are highly customer-oriented have the skills to overcome the physical and mental constraints to produce significantly greater profit margins.

Taken together, our results enrich the current understanding of salesperson ambidexterity (e.g., Carter et al. 2014; Jasmand et al. 2012) by examining the effect of both synergy (high hunting, high farming) and tradeoffs (e.g., low hunting, high farming and vice versa). Our focus on the profit impact of salesperson ambidexterity extends Carter et al.’s (2014) focus on factors that improve the top line effect (i.e., sales revenue) of salesperson hunting time allocation. We also extend their findings by demonstrating that customer-acquisition based compensation plans and expected hunting success can temporarily change a salesperson’s hunting and farming orientation. Our results also build on Jasmand et al.’s (2012) findings that show boundary spanners’ ambidexterity in the form of selling both products and services can reduce salesperson efficiency for a specific customer. Our results indicate that efficiency across a customer base can be achieved when salespeople have the ability, that is, high on customer orientation, to deal with varied demands from both new and existing customers.

Furthermore, our findings are the first to answer Lanaj et al.’s (2012) calls for research on the joint effects of these two situated regulatory foci on work outcomes. Our findings reveal that the interaction effect of regulatory foci is not necessarily a cancelling effect wherein the existence of one regulatory focus weakens the effect of the other. Quite the contrary, we show that a synergistic effect can be achieved if people have the ability, or a slack of resource, to pursue both goals. This finding is in line with prior research at the firm level on how firms can pursue both exploration of building new capabilities and knowledge, such as hunting for new customers in our context, and exploitation of existing capabilities and knowledge, such as farming existing customers in our context (e.g., Kyriakopoulos and Moorman 2004; Voss et al. 2008).

Identifying hunters and farmers in the salesforce

We show that all else equal, salespeople possessing a regulatory focus involving promotion (prevention) hold relatively strong hunting (farming) orientations toward new (existing) customers, which in turn, affected profit margins. In providing such evidence, our results document the usefulness of employing regulatory focus to identify salespeople who are prone to adopt hunting or farming orientations, even after controlling for variables such as supervisor selection, time left in the quota cycle, and the firm’s market share. Given the widespread availability of valid measures of regulatory focus, this variable can easily be incorporated into existing personality batteries used by companies in evaluating the potential fit of an individual with firm sales objectives.

Firm-level antecedents of hunting and farming orientation

Results from Study 3 also suggest a high expectation of hunting success does not reduce salesperson farming orientation. In contrast, a customer acquisition–based compensation plan will gravitate salespeople to more hunting and less farming. However, we also found that these factors will also significantly disrupt the impact of regulatory focus on salesperson customer engagement orientations.

In particular, compensation plans that emphasize customer acquisition have a strong positive effect on high prevention-focused salespeople’s hunting orientation. This supports the notion that high prevention-focused people are particularly sensitive to fulfilling the requirements of their position and avoiding the negative consequences for failing (Lanaj et al. 2012). As such, compensation can provide an important boundary effect of regulatory focus in that, despite the a priori preferences of prevention-focused salespeople for safer, less risky activities (e.g., a farming orientation) may be over-ridden by their motivations to engage in behaviors (e.g., hunting) that increase their probability of making quota. Our results also indicate that customer acquisition pay plans that fit with a salesperson’s innate regulatory focus can have an altering effect on salesperson hunting orientation and farming orientation. Theoretically, this result contributes to research that suggests regulatory fit is associated with higher levels of motivation toward goal pursuit (Lanaj et al. 2012) by demonstrating the influence of firm-level factors. Our study is the first, to our knowledge, to examine how such expectations and compensation plans can alter, yet under other situations fail to disrupt, salesperson customer engagement orientations.

Managerial implications

In response to recent economic downturns and the ensuing need for lean management, sales managers are likely to ask their salesforces to be ambidextrous, that is, to engage in both hunting and farming activities. In that regard, our research offers useful insights for managers seeking to identify, allocate, and balance hunting and farming efforts within the salesforce.

Salesperson ambidexterity

Findings from Study 2 extend prior firm-level research suggesting inherent trade-offs in the simultaneous pursuit of oppositional firm goals such as exploration and exploitation (Voss et al. 2008). Our results suggest that salespeople who pursue both hunting and farming are actually more efficient: they able to generate higher profit margins. Specifically, our findings showed at least a 3% increase in profit margins associated with salespeople possessing high farming and hunting orientations. This synergy is even higher when salespeople are customer oriented. The profit impact of ambidexterity is also surprisingly robust to the specification of time. As mentioned earlier, when we reestimated the empirical model with profit margins for each of the 3 months after the survey instead of using an average of 3 months, the results are almost identical and supportive of our hypothesis. This robustness further demonstrates that the benefit of being ambidextrous can be sustainable.

Using situational factors to alter salespeople’s customer engagement orientation

Our results also demonstrate the importance of understanding the influence of two situational factors, expected hunting success and customer acquisition–based compensation plans. While high expected hunting success will enhance hunting orientation regardless of a salesperson’s regulatory focus, lowered expectations appear to significantly reduce the relationship between prevention focus and hunting orientation (Fig. 3, Panels A and B). Thus, a manager who pursues a customer acquisition strategy is cautioned to understand the potential lack of fit, discouragement, and/or poor performance prevention-focused salespeople may experience when they hold pessimistic outlooks regarding the outcome of their hunting activities. In contrast, sales managers can shift the selling orientations of prevention-focused salespeople who are inherent farmers by shaping their perceptions of expected hunting success, thereby promote the adoption of hunting orientations by such individuals. Furthermore, management can affect salesperson customer engagement orientations through the use of customer acquisition-based compensation plans. In particular, salespeople who are high in prevention focus can be “managed” into becoming more hunting-oriented through compensation plans that emphasize customer acquisition given our results indicating that prevention-focused salespeople who are farmers by design become more hunting oriented in response to such plans. In contrast, highly promotion-focused salespeople tend to remain hunting oriented even under compensation schemes that do not primarily reward customer acquisition (Fig. 3, Panels C and D). These findings enable managers to better understand how compensation affects the balance between customer acquisition and retention at the individual salesperson during a quota cycle.

Implications for salesforce selection process

The risks and opportunity costs of hiring an unproductive salesperson can be substantial. Accordingly, having assessment tools to place employees into the proper sales positions is a critical imperative in sales management. Given that general personality factors have been “poor to modest predictors of salesperson performance” (Krishnan et al. 2002, p. 286), our findings suggest an important role for employing measures assessing regulatory focus as a basic personality trait of self-motivation and hunting/farming orientation as a surface trait in salesperson customer strategies. Our findings inform managers to match a salesperson’s regulatory focus and customer engagement orientation with the appropriate sales position in order to maximize salesperson and firm performance. Selecting salespeople without relying on these important variables has the potential to increase “misses” in salesforce deployment strategies (as suggested by the nonsignificant effect of supervisor selection on hunting orientation in Study 3).

Limitations and further research

Our work has some limitations. First, it can be argued that there might be reverse causality between regulatory focus and salesperson hunting and farming orientation. With cross-sectional survey data, showing causality is difficult. Theoretically, however, the causality should flow from more generic measure of a trait (workplace regulatory focus as more basic traits) to a more task-specific trait (hunting and farming orientation as surface traits), and not the other way around. Nevertheless, further research with longitudinal data is needed to empirically prove causality. Second, given that our analyses were focused primarily at the salesperson level, future research efforts emphasizing organizational-level factors using a multilevel approach would seem warranted. For example, it would be of interest to examine whether sales team effectiveness and organizational strategic orientations is positively related to the extent to which various team members share a common customer engagement orientation.

Third, on the antecedents side, we recognize that the moderating role of other situational variables deserves further investigation. For example, it will be worthwhile to explore the role of the qualifying processes, frequency of repeat purchase, customer characteristics, cross-functional collaboration, CRM technology, and firm-level market orientation on the relationships between regulatory focus and the various sales outcomes examined in the present investigation (e.g., Kyriakopoulos and Moorman 2004; Sabnis et al. 2013).

Fourth, on the consequences side, we focus on the joint effect of customer engagement orientations on profit margins. While important, we have not examined the underlying process, other outcomes, and other contextual factors. The fairly moderate relationship between salesperson hunting and farming orientation and salespeople’s actual time allocation suggests it will be useful to examine the moderators thereof. Additional work is needed to explore other outcome variables, such as salesperson total profits, salesperson job attitude (e.g., when assigned to tasks that do not fit with their hunting/farming orientation), and customer outcomes (e.g., breadth of products purchased). Future research is also needed to examine if this joint effect on profit margins is more sensitive to characteristics of the existing customer base, such as customer power, transaction costs (see Carter et al. 2014) and the relative dependence of firms on hunting and farming activities. For example, if a firm competes in an industry characterized by a “market exchange” customer base, then most accounts would be considered new customers as compared with a company focusing on longer-term contractual arrangements. Thus, we may see stronger emphasis on hunting and farming activities under these varying customer markets.

Finally, we encourage future research to extend our findings using longitudinal designs or field experiments. These research designs allow for a more dynamic examination of salespeople’s customer engagement orientation, such as sequential alternations between hunting and farming over time (e.g., temporal sequencing of ambidexterity, Gupta et al. 2006) instead of a concurrent pursuit of hunting and farming. It will also be interesting to examine the consequences of salesperson ambidexterity in industries that require a longer selling cycle.