Firms are increasingly deploying a value-based selling (VBS) approach in their sales organizations to drive growth for new offerings. However, VBS adoption remains challenging, signaling that leaders need guidance to motivate VBS. Drawing from the systems theory of motivation, we examine motivational mechanisms at two levels—salesperson and sales team—to understand how to motivate, and benefit from, VBS. Using multisource data (i.e., salespeople, managers, archival performance) from 70 sales teams in a U.S.-based manufacturing and services provider, our findings illustrate drivers and outcomes of VBS. Specifically, we uncover a framework of salesperson, leader, customer, and team factors that help explain salesperson motivation for VBS. Importantly, we link VBS to customers’ adoption of new products to support VBS’s role for selling new products. Critical for sales team strategy, our model also integrates a team-level motivational mechanism to provide a comprehensive framework for salesperson and sales team motivations and outcomes.
In modern business markets, the focus of sales activity has shifted from presenting feature-benefit propositions to engaging in value-based selling (VBS) (Blocker et al. 2012; Töytäri and Rajala 2015). The momentum behind VBS can largely be attributed to two factors: (1) B2B buyers increasingly expect vendors to quantify value (Hinterhuber and Snelgrove 2016), and (2) VBS has demonstrated improvements on firm customer retention and growth (Aberdeen Group 2011) as well as salesperson performance (Terho et al. 2015). Despite this attention, motivating salespeople for VBS remains an issue (Moorman and Vogel 2012), with sales leaders citing their salesforce’s “inability to articulate unique business value” as their top challenge (SiriusDecisions 2015). Motivating VBS is difficult because it requires significant effort from salespeople to deeply understand the customer’s business and quantify value. Yet, until sales leaders understand how to implement VBS successfully, customers are less likely to differentiate between vendors (CSO Insights 2018) or try new offerings (Steenburgh and Ahearne 2018).
Practitioner interest in this topic has sparked research on the antecedents and outcomes of VBS. Existing empirical studies have begun to uncover motivational drivers, but results are mixed and have largely neglected the impact of leadership and boundary conditions (see Table 1). Because VBS requires significant effort, especially for new products where customer specifications are more uncertain, research needs to identify all potential sources for motivating salesperson VBS. Sales managers, teams, and customers have all shown to be influential in motivating behavior (e.g., Ahearne et al. 2005; Auh et al. 2014; Lam et al. 2019), emphasizing a need for broadening the drivers of VBS to include these factors. The mounting evidence that firms struggle to sell the value of new products (Steenburgh and Ahearne 2018) and the limited attention to customer-focused behaviors for new product selling behavior (Table 1) makes this research void critical to address.
Recent case studies suggest that firms who communicate value can win over tentative customers to new products (van Wyk et al. 2018). Yet research to date on VBS outcomes has relied on self-rated measures of overall sales performance, leaving it unclear whether VBS improves new product performance. Additionally, existing new product selling research emphasizes product-focused efforts, such as salespeople emphasizing new over established products (e.g., Ahearne et al. 2010a, 2010b; Fu et al. 2010). Identifying customer-focused efforts (e.g., understanding the customer’s business) for selling new products would broaden our understanding of how salespeople drive new product sales. Therefore, examining the impact of VBS on new product performance would not only expand knowledge on VBS outcomes but also provide practical guidance for selling new products.
Lastly, sales teams, rather than firm leadership, are proposed to offer more leverage for successfully implementing VBS in the salesforce (Moorman and Vogel 2012). Despite the evident importance of teams across sales research (Mullins and Panagopoulos 2018), our understanding of top-down influences on VBS and its outcomes is isolated to a select few firm-level factors. A lack of team influences is also present across the new product selling literature (Table 1). This absence of knowledge on team-level factors influencing the execution as well as the outcomes of VBS is an important gap both from an academic and practitioner perspective. From the academic side, research has shown that individual motivational processes and outcomes are strongly influenced by team motivational processes (Auh et al. 2014; Chen et al. 2009; Wang and Howell 2012). Sales research currently lacks studies that incorporate sales team-level processes alongside salesperson processes to align outcomes as a system, rather than as disparate parts. The need to integrate sales team and salesperson processes within a system for motivating VBS and outcomes suggests a focus that draws from Chen and Kanfer’s (2006) multilevel theory of team motivation (i.e., systems theory), which “aims to understand and predict: (1) collective motivation and its outcomes, and (2) individual motivation and its outcomes in the context of teams” (p. 226). This integrated VBS framework also helps sales practitioners. Misalignment between sales team and salesperson may create suboptimal performance outcomes at either or both levels. Sales leaders need frameworks to ensure processes at the team level complement salesperson processes to optimize team management.
Against this background, we conceptualize and test drivers and outcomes of salesperson VBS behavior—defined as the degree to which the salesperson works with the customer to craft a market offering in such a way that benefits are translated into monetary terms, based on an in-depth understanding of the customer’s business model, thereby convincingly demonstrating their contribution to customers’ profitability (Terho et al. 2012). Specifically, we draw from systems theory to articulate why and when multilevel motivation processes occurring at the salesperson and team levels increase goal-directed behavior (i.e., salesperson’s self-reported VBS, sales team goal pursuit), and, in turn, improve performance outcomes (i.e., customers’ adoption of new products, % of team quota achievement) across levels (see Fig. 1).
Our systems theory approach to studying motivation and outcomes of VBS makes multiple novel contributions. First, we provide an integrative framework of VBS drivers (salesperson regulatory focus and perceived empowering leader behaviors, hereafter PELB) contextualized within task (perceived empowering customer behaviors, hereafter PECB) and team contexts (team monitoring climate), shedding light on motivational differences across salespeople. Examining drivers alongside task and team boundary conditions not only addresses gaps in the VBS domain, but also helps broaden the sales team literature to include a systematic perspective of sales motivation and goal attainment, which are core aspects to selling. Notably, our findings demonstrate salesperson VBS is determined by motivational fit between salesperson, leader, task, and team factors. For example, results show that PECB has differential effects on each type of regulatory focus—strengthening the relationship between prevention focus and VBS while weakening the relationship between promotion focus and VBS. We also find evidence that team monitoring climate weakens the relationship between prevention focus and VBS while strengthening the relationship between PELB and VBS. Similar to other domains such as ambidexterity, uncovering these contextual influences provides valuable insights to motivate salesperson VBS amidst the complex demands and challenges of the field.Footnote 1 Our findings highlight that situational factors (i.e., team, customers) and leadership play a key part in motivating VBS, extending previous studies focused on firm and individual factors (Terho et al. 2015, 2017). Thus, our systems approach to VBS within sales teams helps answer calls for research on team dynamics and interpersonal interactions as drivers of salesperson motivation (Khusainova et al. 2018). Furthermore, testing systems theory within a sales context extends the empirical applications of the framework beyond previous studies that rely on undergraduates, service employees, and R&D teams (Chen et al. 2007; Chen et al. 2009; Chen et al. 2013).
Second, by linking VBS and team goal pursuit with customers’ adoption of new products via systems theory, we help bridge the literature between VBS and new product selling within the sales team domain. Given value-differentiation strategies emphasize how new products provide greater value (Anderson et al. 2007) and the anecdotal evidence for the importance of VBS in new product selling (Steenburgh and Ahearne 2018), the lack of empirical research attention to VBS in new product selling is surprising. We offer an empirical linkage demonstrating VBS as a customer-focused selling behavior, relative to product-focused selling behaviors, that drives customers’ adoption of new products. By relying on managerial performance evaluation, rather than self-reports, this finding not only expands the potential outcomes of VBS to include new product performance but also bolsters the validity of VBS as a strong predictor of performance. Furthermore, we build on previous research on individual new product goal effort (Fu et al. 2009) by providing evidence of the unique contribution sales team goal pursuit has on new product performance. Our findings offer two potential avenues to improve new product performance without reducing emphasis on existing products (c.f. Van der Borgh et al. 2017).
Lastly, our systems theory framework helps demonstrate how salesperson and sales team motivational processes have interwoven effects on performance. This perspective also illustrates why integrating motivation processes across levels helps sales leaders to strategically align sales team and salesperson VBS outcomes. For example, previous research at the sales team level demonstrates that the sales team’s potency has a positive impact on sales team outcomes such as effort and performance (Ahearne et al. 2010b). Yet, multilevel studies show that sales team potency has a negative impact on individual behavior (Schmitz 2013). Our integrative VBS framework avoids this ambiguity and helps sales leaders align sales team and salesperson behavior and outcomes. Examining team-level processes helps us uncover the novel link between team monitoring climate, team goal pursuit, and team performance. Importantly, by integrating team-level and salesperson processes, we see that a high monitoring climate is not a simple solution. A high team monitoring climate is costly for motivating prevention focused salespeople for VBS but promotes VBS under an empowering leader. Conversely, a low team monitoring climate is costly not only for team performance, but also for the unique contribution from team goal pursuit in improving customers’ adoption of new products. Without this holistic understanding, sales team variables that improve team-level behaviors and performance could inhibit salesperson VBS and consequent outcomes.
Theoretical framework and hypotheses
Salespeople who practice VBS adopt proactive, value co-creator roles and strive to influence the customer’s value creation process and, therefore, the emergence of value-in-use (Blocker et al. 2012). VBS is conceptualized as a multidimensional concept comprising behaviors for (1) understanding the customer’s business model, (2) crafting the value proposition, and (3) communicating value (Terho et al. 2015). Thus, VBS is a set of behaviors in line with, but distinct from, other well-established selling behaviors such as adaptive selling or customer-oriented selling. However, VBS is a challenging activity that requires significant effort, and, as such, it is critical to understand the drivers and contexts best suited to motivate this behavior. Existing approaches to salesperson motivation, such as attribution theory (Schmitz 2013) and motivation-opportunity-ability (MOA) framework (Terho et al. 2017), have offered useful insights for understanding salesperson motivation. However, these frameworks are inadequate for conceptualizing salesperson motivation processes occurring within the context of team motivation processes. Given the need to understand team influences on salesperson motivation, as well as the potential outcomes associated with team motivation to guide sales team strategy, we ground our framework in the systems theory of motivation (Chen and Kanfer 2006).
Systems theory of motivation
Earlier work from organizational psychology supports the notion of team influences on individual motivation. Hackman (1992) theorized that person and situation stimuli affect motivation by providing team members with informational, attitudinal, and behavioral cues which he later categorized as ambient (i.e., team-oriented) and discretionary (i.e., individual-oriented) stimuli. Building on Hackman’s (1992) classification, Chen and Kanfer (2006) proposed their systems theory of motivation where ambient and discretionary inputs differentially influence team and individual motivation, and also interact to influence individual motivation. Ambient inputs pervade the team as a whole, and therefore strongly influence team motivation. In contrast, discretionary inputs are directed at each member, and hence strongly influence individual, relative to team, motivation. Ambient inputs can also synergistically interact with discretionary inputs to influence individual motivation, since the alignment of inputs provides a more conducive environment for individual motivation (e.g., Chen et al. 2007). Based on these distinctions, system theory proposes that ambient and discretionary inputs stimulate functionally parallel goal-striving processesFootnote 2 toward achieving performance across levels (e.g., Chen et al. 2009). This provides a way to explore the unique and complementary means by which individuals and collectives are motivated, as related to individual and collective performance (Chen et al. 2013; D’Innocenzo et al. 2016).
We posit that such motivational processes function independently and in combination to drive salesperson- and team-level goal-striving and performance outcomes. Goal-striving is defined as the ongoing self-processes by which an entity regulates affect, cognitions, and actions for the purpose of accomplishing a goal (Chen and Kanfer 2006). Thus, in sales teams, the multilevel goal-striving mechanism captures effort at the salesperson level—salesperson’s allocation of personal effort directed at performing their individual role—as well as the sales team level—teams’ allocation of collective effort toward team goals. In line with this, we conceptualize sales team goal pursuit—the collective sales team effort directed toward goal accomplishment—as team-level goal-striving, which helps improve team performance. At the same time, the definition of VBS emphasizes that “the monetary aspect of customer value opportunity represents the goal of value-based selling” (Terho et al. 2012, p. 178). Uncovering, defining, and communicating opportunities to create financial value for customers represents an effortful goal innate to VBS. Thus, we conceptualize VBS as a salesperson’s goal-striving to create financial value for customers, which helps improve individual performance. In the next sections, we build our rationale for the goal-striving processes occurring at each level, followed by our proposed top-down relationships.
Salesperson-level antecedents of VBS and salesperson performance
Given the boundary spanning nature of the salesperson role, the salesperson and the environment should be considered simultaneously in order to predict salesperson behavior. However, in what seems to be a limitation of systems theory, there is no guidance for conceptualizing the relationship between various discretionary inputs. Previous sales research guides us here, consistently emphasizing the interplay between three critical factors—salesperson, task, and supervisor—to model salesperson effort (e.g., Jaramillo and Mulki 2008), behavior (e.g., Rapp et al. 2006), and performance (e.g., Kohli 1989). We follow in the footsteps of these frameworks in our selection of discretionary input variables that focus on salesperson (i.e., regulatory focus), task (i.e., PECBs), and supervisory inputs (i.e., PELBs) that influence salesperson VBS.
A regulatory focus represents individuals’ basic tendencies to pursue goals through one of two self-regulatory motivational systems: promotion and prevention focus. A promotion focus reflects an innate sensitivity toward attaining positive outcomes, emphasizing an attention to desired gains, ideal goals, and risky strategies, while a prevention focus is an innate desire to avoid negative outcomes, resulting in the adoption of loss-avoidant strategies for goal attainment (Higgins 1997). Importantly, a person’s regulatory focus is predictive of the behaviors in which that person prefers to engage (Crowe and Higgins 1997).
Building from previous work showing promotion and prevention focus are predictive of different selling orientations (DeCarlo and Lam 2016), we believe that the behavioral tendencies associated with each type of regulatory focus are aligned with separate aspects of VBS. Specifically, under a goal-striving perspective, VBS salespeople strive to create financial value for customers in one of two forms: (1) incremental revenue growth or (2) quantifiable cost reduction (Anderson et al. 2006). The salesperson’s pursuit of these two facets of value reflect different avenues for goal-striving within VBS that should align with each type of regulatory focus. Specifically, we expect that goal-striving for incremental revenue growth is aligned with a promotion focus (gain-seeking), whereas goal-striving for quantifiable cost reduction is aligned with a prevention focus (avoiding losses). Thus, we expect that each dimension of regulatory focus positively affects VBS, albeit through different mechanisms.
Because a promotion focus motivates the use of relatively creative strategies, and a behavioral mindset suited to risk and growth (Crowe and Higgins 1997), promotion salespeople should be more motivated for VBS geared toward incremental revenue growth. Uncovering solutions for, and successfully improving, customers’ revenue growth embodies a great deal of uncertainty in that salespeople cannot accurately predict future demand and revenue improvement for their customer’s business. In addition, a promotion focus is naturally oriented toward generating ideas with the potential to grow business (Brockner et al. 2004), which is foundational for VBS salespeople to demonstrate potential value via revenue growth.
Since the behavioral strategies for seeking revenue growth via VBS are misaligned with the predictable, risk-averse tendencies of a prevention focus, we expect prevention focused salespeople to be less motivated for VBS via revenue generation. However, the second key component of VBS focuses on finding value through cost saving. Salespeople’s efforts to demonstrate cost savings involve more predictable processes to reduce customers’ expenses. In other words, VBS via cost reduction is akin to a “due diligence” activity that requires salespeople to uncover a customer’s inefficiencies through a routinized process. This attention to loss avoidance aligns with a preventive focus, thus helping salespeople be motivated for VBS. Critical here, research indicates that prevention and promotion focus represent independent dispositions, making it possible for one individual to possess high levels of each simultaneously (Lanaj et al. 2012). Thus, individuals may be both promotion and prevention focused, which, for our framework, means that both should encourage VBS.
Salesperson promotion focus is positively related to VBS.
Salesperson prevention focus is positively related to VBS.
Prior research shows that salespeople are influenced by perceptions of their manager’s leadership behavior (Mullins and Syam 2014). Accordingly, we believe perceived empowering leader behaviors (PELB) offer another way to motivate VBS given the influence of ELB’s on employee engagement (see Sharma and Kirkman 2015 for a review). We examine perceptions of empowering leadership given previous research shows that leader self-reports can be a poor predictor of employee behavior and outcomes, relative to the employee’s perceptions (Amundsen and Martinsen 2014). Consistent with previous work (Ahearne et al. 2005), we conceptualize ELBs as implementing conditions that increase employees’ feelings of meaningfulness, competence, self-determination, and impact. Employees who perceive managers exhibiting empowering leadership demonstrate greater intrinsic motivation, engagement, as well as extra-role behaviors (Auh et al. 2014; Zhang and Bartol 2010).
Building on these previous findings, we expect that salespeople who perceive ELBs are more motivated to create financial value for customers (i.e., striving toward the goal of VBS). VBS salespeople adopt proactive, value co-creator roles in three main ways: (1) innovate offerings that offer superior customer value, (2) help customers implement the offering, and (3) match business processes between parties for better financial results (Terho et al. 2017). The nature of these activities requires VBS salespeople to nimbly adjust to each customers’ unique needs to create and capture value. Thus, salespeople should be more motivated to perform VBS when they feel unconstrained and supported by leadership. In line with this, PELB’s help salespeople perceive greater decision-making autonomy, more confidence in their capabilities, and fewer hindrances to performance (Ahearne et al. 2005). Indeed, PELB’s unencumber salespeople to deploy more improvisational selling approaches (Rapp et al. 2006) and enhance employees’ motivation (Zhang and Bartol 2010), key factors for implementing new ideas and uncovering value-in-use for each customer’s business. In sum, salespeople who perceive fewer bureaucratic prescriptions about how to work, alongside increasing freedom and confidence to operate as needed, should be more likely to seek out decision makers and pursue critical knowledge about customers’ unique usage situations, both critical determinants for VBS (Terho et al. 2017).
Perceived empowering leader behaviors are positively related to VBS.
Moderating effect of task input on salesperson input and leadership input
Customer factors often have substantial influence over salesperson task motivation. For example, customer base characteristics change the motivation for hunting activities (Lam et al. 2019) while demanding customers influence salespeople’s intrinsic motivation for selling effort (Jaramillo and Mulki 2008). Given that understanding the customer’s business model is a critical task of VBS, we believe a customer’s willingness to share value-relevant knowledge should influence the salesperson’s perceived effort needed to uncover and realize value—key tasks for VBS. For example, salespeople should feel more empowered when they perceive their customers seeking advice during decision-making, delegating control over tasks, and expressing confidence in their ability to provide value. Accordingly, we examine the role of perceived empowering customer behaviors (PECB)—customer-created conditions that help salespeople feel motivated and capable of making important decisions during customer interactions (Dong et al. 2015)—as a task input that moderates salesperson and leader motivations for VBS. It is important to examine salesperson perceptions of their customers as they are a strong driver of salespeople’s behaviors such as relationship building (Mullins et al. 2014).
Within the systems theory perspective, Chen and Kanfer (2006) emphasize the importance of “motivational fit” between the person and environment. That is, individuals are unlikely to be motivated in situations that prohibit their motivational tendencies. We integrate this perspective with regulatory fit theory (Higgins 2000) which suggests that people are more motivated to pursue goals and perform better when the situational task characteristics align, or “fit,” with the individual’s regulatory focus. We believe that PECBs influence regulatory fit, and thus differentially impact the relationship between regulatory focus and VBS.
Salespeople who perceive more ECBs experience fewer barriers to gaining the information and autonomy needed to uncover value, which should bolster salespeople’s motivation for VBS. Fewer barriers also signal the customers’ desire for lower exchange control (Mullins et al. 2015) creating a more predictable context for VBS. As a result, we expect that higher PECBs will strengthen the motivational fit for prevention-focused salespeople motivated to avoid mistakes. On the other hand, we expect PECBs and promotion focus to act as substitutes. Specifically, salespeople with low promotion focus are less likely to put effort into uncertain VBS tasks such as quantifying and crafting a unique value proposition. Higher PECBs provide salespeople with more certainty and reduce the efforts required for each customer, such that they reduce perceived VBS task requirements. This provides a more motivating context for VBS with low promotion focused salespeople, who are less willing to devote efforts toward uncertain selling tasks (DeCarlo and Lam 2016). High promotion focused salespeople are less likely to be motivated when they perceive customers providing greater autonomy and predictability. Thus, PECBs act as a substitute for the riskier mindset of high promotion focused salespeople, weakening the impact of promotion focus on VBS. Together, we posit:
A higher level of perceived empowering customer behaviors will weaken the positive relationship between salesperson promotion focus and VBS.
A higher level of perceived empowering customer behaviors will strengthen the positive relationship between salesperson prevention focus and VBS.
We also expect that customer empowerment should play a contextual role for leader motivations for salesperson VBS. As mentioned previously, we expect salespeople who perceive an empowering managers’ expressed confidence, collective vision, and heightened autonomy will be more motivated for VBS. When PECBs are also high, salespeople should perceive customers’ openness to co-creation as a signal of alignment between their heightened ability to pursue VBS goals. As a result, PELBs offer greater motivation for VBS when accompanied by PECBs because salespeople believe they have fewer obstacles to overcome during VBS.
A higher level of perceived empowering customer behaviors will strengthen the positive relationship between perceived empowering leader behaviors and VBS.
VBS and customers’ adoption of new products
Customers may often prefer a well-established product over a new product that bears more risk and outcome uncertainty (van der Borgh and Schepers 2018). This uncertainty heightens the importance of the salesforce in increasing customers’ adoption of new products, defined as the propensity for customers to purchase a new product. Previous research shows that salesperson intentions to promote new products increases new product sales (Fu et al. 2010). Relatedly, Ahearne et al. (2010b) show that greater salesperson effort to promote new products helps improve customers’ new product perceptions. These findings indicate that the salesperson’s motivation to provide customers with relevant and useful new product information should be influential in selling new products. Extending this premise, we posit that salespeople with higher VBS are more likely to increase customers’ adoption of new products. VBS helps customers make purchase decisions that offer cost reductions or revenue generation results (Terho et al. 2012). Thus, VBS helps salespeople communicate not only the relevance of new products, but more importantly, the financial justification for buying a new, uncertain, and potentially higher-priced product. In sum, VBS should increase customers’ confidence and thus, increase customers’ adoption of new products.
VBS is positively related to customers’ adoption of new products.
Team-level antecedents of sales team goal pursuit and performance
Parallel to individual motivational processes, team motivational processes contribute to members’ collective motivation directed at accomplishing team goals (e.g., Chen et al. 2013). In line with this, we posit a team-level goal-striving mechanism for team performance acting in parallel to the individual goal-striving mechanism for salesperson performance.
Specifically, we propose that sales team goal pursuit captures a key team goal-striving mechanism relevant to sales team performance. Team goal pursuit reflects the collective team effort directed toward team goal accomplishment (Burmeister et al. 2019). Team-level goals spur the emergence of a unique collective entity for each sales team to adhere to as a group, which is distinct from, yet aligned with each member’s individual goals. In other words, each sales team member is accountable to their own individual goal but also to the team goal. For example, sales teams typically have performance quotas, such as the number of new customers acquired as team-level goals, which drive their collective actions over time. Because each team member contributes via their individual goals to the achievement of team goals, sales team members are ultimately interdependent on the behaviors and outcomes of other members (Menguc et al. 2013). It is critical to note here that while individual goal-striving is manifested through cognitive-behavioral responses, team goal-striving is manifested through social-behavioral processes at the team level (Chen et al. 2009; Morgeson and Hofmann 1999). As part of a team, members each take part in a system of interpersonal interaction and provide social stimulus for one another’s subsequent actions that influence each members’ goal pursuit efforts.
Sales team monitoring climate and sales team goal pursuit
Stemming from this logic and drawing from previous research (De Jong and Elfring 2010; Langfred 2004), we propose sales team monitoring climate, defined as the degree of team members’ surveillance and awareness of other team members’ activities, as an ambient input that collectively motivates sales team goal pursuit. In line with previous conceptualizations of climate (e.g., Jong et al. 2004), a sales team monitoring climate reflects the collective beliefs of members with regard to their activities being evaluated by each other. Because team members recognize that their activities are evaluated, a monitoring climate helps reduce motivational losses by increasing the likelihood that “social loafing” will be detected, thus directing efforts of team members towards the realization of team goals over individual interests (Rapp et al. 2014; Stanton 2000). Furthermore, a monitoring climate helps reduce process losses by increasing awareness of each other’s activities. Collective awareness helps group members envision the interrelatedness of their actions toward team goals, supported by members’ contribution and subordination to the group’s success (Bijlsma-Frankema et al. 2008). Thus, a sales team monitoring climate should act as a motivational driver for the teams’ goal-focused actions.
Sales team monitoring climate is positively related to sales team goal pursuit.
Sales team goal pursuit and sales team performance
Prior sales research has demonstrated the link between goal effort and performance at the salesperson level. For example, Fu et al. (2009) find that effort directed toward meeting new product selling was positively related to new product performance. At the sales team level, the role of collective goal effort towards team performance is less clear. However, sales teams who devote more overall effort (e.g., sales calls) and exhibit helping behaviors demonstrate higher team performance (Ahearne et al. 2010b). Similarly, teams who are more committed to the team’s goals show greater team performance (Rapp et al. 2006). Building on these studies, along with evidence showing that team-focused efforts improve team’s performance (DeShon et al. 2004), we believe that sales teams devoting effort toward team goals should improve sales team performance.
Sales team goal pursuit is positively related to sales team percentage of quota achievement.
Top-down influences on salesperson’s VBS and salesperson performance outcomes
Chen and Kanfer (2006) highlight that the top-down influence from team-motivational processes helps uniquely shape individual motivation and outcomes, emphasizing the importance of “motivational fit” between the person and environment. Drawing from this, we first consider the moderating impact of sales team monitoring climate on the relationships between each salesperson-level motivational driver and VBS. Then we propose the cross-level impact of team goal pursuit on customers’ adoption of new products.
Moderating effects of sales team monitoring climate
Sales team monitoring climate offers a means of VBS motivation by cultivating an awareness of team members’ progress and collective responsibility for contributing to the team’s performance (Marks and Panzer 2004). Because salesperson behaviors are more identifiable when monitoring climate is high, team members feel more responsible to the team, and to one another. In contrast, when monitoring climate is low, there is less perceived transparency regarding the effort members exert toward creating value in customer accounts. Thus, monitoring climate can increase the incidence and quality of critical, sales-related activities because it increases the accountability to achieve goals and aligns efforts more closely with established goal priorities. This collective accountability makes mistakes or areas for improvement more salient for members of the sales team.
Our framework builds on the systems theory approach (Chen and Kanfer 2006) by integrating a regulatory fit perspective (Higgins 2000) to argue that sales team monitoring climate makes areas for improvement (mistakes) more salient for promotion (prevention-focused) salespeople. Individuals with a promotion focus are driven by their motivation to avoid errors of omission. As a result, promotion-focused individuals are eager to learn from mistakes and to avoid missing opportunities for improvement. Previous research finds that challenge stressors provide situational demands that strengthen the motivational fit for promotion-focused individuals’, heightening their persistence to achieve and uncovering more creative solutions (Sacramento et al. 2013). Similarly, individuals in a promotion focus find more solutions and solve tasks faster when facing difficulty or experiencing failure (Crowe and Higgins 1997). Therefore, a stronger monitoring climate heightens the salience of areas for improvement for promotion-focused salespeople. We believe this regulatory fit strengthens the relationship between promotion focus and salesperson VBS.
In contrast, salespeople with a prevention focus are more motivated to avoid errors of commission (i.e., making mistakes). Due to this sensitivity, individuals with a prevention focus adopt a more conservative response bias when facing difficulty. Difficult situations weaken the motivational fit for prevention-focused individuals, sparking them to quit to avoid explicitly committing an error (Crowe and Higgins 1997). This demotivation occurs because prevention focused individuals experience discomfort and agitation when faced with their own mistakes. Therefore, a stronger monitoring climate heightens the salience of mistakes for prevention-focused salespeople. We believe this lack of regulatory fit weakens the relationship between prevention focus and VBS. Taken together, we posit:
A higher level of sales team monitoring climate will strengthen the positive relationship between salesperson promotion focus and VBS.
A higher level of sales team monitoring climate will weaken the positive relationship between salesperson prevention focus and VBS.
While empowering salespeople is generally assumed to yield more effective salesperson behavior and results, previous research shows that this relationship is not straightforward. In particular, the salesperson’s previous experience and knowledge may act as a situational variable that influences whether a more autonomous, empowered environment improves salesperson behavior. For example, Rapp et al. (2006) find that salesperson experience acts as a substitute for empowering leadership, weakening the link between leaders’ empowering behavior and working smart. Relatedly, employee readiness—the extent to which an employee possesses an array of task-relevant knowledge and experience that will enable them to benefit from an empowered environment—weakens the relationship between empowering leadership and adaptability (Ahearne et al. 2005). Both of these findings suggest that PELBs may provide the best motivational fit for salespeople with lower reliance on their past experience and knowledge. Against this background, we posit that PELBs provide a stronger fit for motivating VBS when salespeople also perceive a higher sales team monitoring climate. In other words, sales team monitoring climate provides ongoing awareness of other team members’ misaligned behavior and performance gaps, thus providing a source of formalized leadership to motivate VBS in more autonomous environments.
A higher level of sales team monitoring climate will strengthen the positive relationship between perceived empowering leader behaviors and VBS.
Effect of sales team goal pursuit on customers’ adoption of new products
Invoking Chen and Kanfer’s (2006) framework, we expect sales team goal pursuit to influence salesperson performance, given the behaviors executed in sales team goal pursuit are directly related to the achievement of sales performance goals. In particular, sales team goal pursuit reflects a state of collective goal-striving within a sales team, which is fundamental to team motivation (Chen and Kanfer 2006). As teams collectively strive toward a goal, team members communicate and exchange information to help each member contribute to goal achievement. This guidance gives each member the opportunity to allocate resources, adjust their strategic plans, or change behaviors to contribute to the team goal (De Jong and Elfring 2010), as well as to their own individual goals (DeShon et al. 2004). Therefore, we expect:
Sales team goal pursuit is positively related to customers’ adoption of new products.
Sample and data collection
We test our model using lagged, multisource data (i.e., salespeople in each team, managers responsible for salespeople in each team, and objective, archival data) gathered from a large, U.S.-based manufacturing and services provider in B2B markets (see Fig. 1 for data sources). Salespeople in this firm are tasked with meeting individual sales quotas but are also organized in teams to sell and service geographic territories to meet formally defined sales team quotas. Depending on the business potential of the geographic territories, team size ranged from six to fifteen salespeople, with an average of 10.2 salespeople per team. Importantly, sales team members routinely rely on each other for assistance in the execution of their roles. For example, teams sell and support a wide range of products and services linked to different mechanical systems. To be successful, team members rely on each other’s knowledge of these offerings not only to make initial sales, but also to provide on-site customer support and make cross-sell offers. In this way, members operate interdependently with opportunities to monitor activities. Before data collection, we conducted in-depth interviews with executives, managers, and front-line salespeople to ensure our materials were appropriate for the firm’s context. In particular, our interviews uncovered that the firm’s salesforce recently underwent VBS training to enable salespeople to uncover and communicate value when visiting customers, particularly for improving new product sales. Importantly, the focal firm introduces new products every year, so new product success is a key performance indicator monitored by sales and firm leadership.
Our data collection spans an 8-month period (see Fig. 1). At Month 1, we distributed surveys to the entire sales organization (705 salespeople and 95 team managers). We received usable responses from 433 salespeople (61.42%) and 70 managers (73.68%). Starting at Month 2, we collected archival performance data at the individual and team level over a seven-month period (Months 2–8). Additional analyses showed that the full sample did not differ significantly from our final sample on any of the variables included in our model. Consistent with Deeter-Schmelz and Ramsey’s (2003) suggestions, we received more than 50% of responses from each team. We did not exclude any teams from the study since we received at least three salesperson surveys from each team. Specifically, in the final sample, sales team size ranged from three to eleven salespeople, with an average of 6.2 salespeople per team. Finally, we found no significant differences between early and late respondents. The final sample of salespeople had an average age of 37.3 years (SD = 10.1) with 15.53 years of sales experience (SD = 7.92).
We use well-established scales to measure our key constructs and covariates (see Web Appendix Table 5). Unless otherwise mentioned, all scales are measured with a seven-point Likert scale (1-strongly disagree; 7-strongly agree).
Promotion and prevention focus are each measured with a six-item scale (Neubert et al. 2008). Perceived empowering leader behaviors are measured with an eight-item scale (Rapp et al. 2006). Perceived empowering customer behaviors are measured with a six-item scale adapted from Dong et al. (2015) and Ahearne et al. (2005) for our study. We measure VBS with a seven-item scale borrowed from Terho et al. (2015). Salespeople in our sample were encouraged to uncover and communicate to business owners the cost reductions (e.g., reduced inventory, lower waste) and additional revenue (e.g., improved customer experience) resulting from new product adoption. Thus, the VBS scale provides an appropriate measure of these behaviors in this context.
Sales team monitoring climate is measured with a three-item, seven-point Likert scale (1-never; 7-frequently) scale borrowed from De Jong and Elfring (2010). Because we conceptualize team monitoring climate as a team-level variable, we aggregate salespeople’s responses to create a single score for each team. The within-team agreement (median rwg = .90), intraclass correlation (ICC1 = .38), and reliability of team-level means (ICC2 = .80) are well above threshold values (LeBreton and Senter 2008), providing evidence to justify data aggregation.
Team manager-reported measures
We asked team managers to rate each salesperson’s performance compared to an average salesperson over the past year (1-below average; 7-above average) by using customers’ adoption of new products as a performance criterion (e.g., Ahearne et al. 2010a). Managers also responded to a six-item, seven-point Likert scale (1-much weaker; 7-much stronger) to measure sales team goal pursuit, adapted from Wilden and Gudergan (2015).
Objective, archival measures
We measure team performance using objective, archival data provided by the firm. Sales team performance is measured by the percent of quota achieved by each team as it takes into consideration between-team heterogeneity caused by internal and external environmental factors (Ahearne et al. 2013). Sales team quota achievement indicated negative skewness and kurtosis scores. We thus log transform sales team quota.
Drawing on systems theory (Chen and Kanfer 2006) and previous research (e.g., Ahearne et al. 2010b), we consider covariates that may explain significant variance in VBS, customers’ adoption of new products, sales team goal pursuit, and sales team % of quota achievement. Doing so may also help minimize the observed heterogeneity bias due to omitted variables. Specifically, we control for salesperson- and team-level covariates.
At the salesperson level, we control for sales experience (in years), salesperson knowledge, customer orientation, and time allocated to customer service. Salesperson knowledge and experienceFootnote 3 can help capture the variation in VBS due to other causes (e.g., efficacy and perceived ability). Indeed, previous research finds that sales experience is correlated with self-efficacy (e.g., Menguc et al. 2011) making it a strong proxy in our empirical model. Additionally, the tenets of the adaptive selling behaviors domain suggest that more effective salespeople have a larger knowledge base for different selling situations (e.g., Weitz et al. 1986), making knowledge a suitable proxy for ability in selling contexts. We log transform sales experience as the data for this variable were not normally distributed. Salesperson knowledge is measured with a four-item scale adapted from Ahearne et al. (2013).
We expect that salespeople who try to help customers achieve their goals and take a problem-solving approach with customers (i.e., customer orientation) are more likely to use a VBS approach. However, the more time a salesperson spends in non-selling based activities, the less likely that the salesperson focuses on identifying opportunities to improve customers’ business profits. Accordingly, customer orientation is measured with a three-item scale borrowed from Thomas et al. (2001). We also asked salespeople to share how many hours a week they allocated to a variety of activities (e.g., administrative duties, customer service, district meetings). Next, we divided the time allocated to customer service by the time allocated to all activities to obtain the relative time salespeople spent on customer service.
At the team level, we control for task and outcome interdependence, and we measure each with a three-item scale (Menguc et al. 2013). Specifically, in a team setting, where salespeople are accountable for their contributions to team performance and their rewards and gains are determined largely by their contributions to team performance, salespeople are more motivated for VBS. In addition, working along with other salespeople in a team setting, where team members are more able to communicate and cooperate to achieve team goals and higher team performance, a salesperson is likely to focus more on VBS to improve customers’ performance. To operationalize team-level covariates, we aggregate salespeople’s responses to create a single score for each team. The within-team agreement (median rwg) (task interdependence = .91, outcome interdependence = .92), intraclass correlation (ICC1) (task interdependence = .37; outcome interdependence = .31), and the reliability of team-level means (ICC2) (task interdependence = .80; outcome interdependence = .82) are well above threshold values (LeBreton and Senter 2008), thus justifying data aggregation. In addition, we control for team manager’s weekly interaction frequency with team members, which is reported by team managers. It is likely that team managers’ regular interaction with team members can motivate them to pursue team goals and achieve higher team performance.
We conduct confirmatory factor analysis (CFA) to assess the validity and reliability of the measures. The CFA for the multi-item scales (reported by salespeople) indicate a good fit to the data (χ2 = 2180.93, df = 1082, GFI = .891, TLI = .921; CFI = .927, RMSEA = .049). All constructs indicate a high level of reliability as Cronbach’s alphas and composite reliabilities are above .70, whereas the average variance extracted (AVE) values are greater than .50. All factor loadings are statistically significant, supporting convergent validity (see Appendix Table 5). The discriminant validity of the constructs is supported as the AVE estimates are greater than the squared intercorrelations between all pairs of constructs (Fornell and Larcker 1981). Team goal pursuit (reported by sales team managers), also indicate a good fit to the data (χ2 = 18.45, df = 9, GFI = .891, TLI = .921; CFI = .927, RMSEA = .049) with high reliability (see Table 2).
Due to the nested nature of our data, we estimate the model using a two-level path analysis with Mplus 7. This method enabled us to estimate the model relationships and standard errors more accurately by modeling distinct variances between- and within-levels.
It is worth noting that we control for three types of heterogeneity in the model estimation. First, causal heterogeneity is taken into consideration as the proposed model examines the moderating role of salesperson/within-level (i.e., customers’ empowering behaviors) and team/between-level (i.e., team monitoring climate) variables while testing the effect of the antecedent variables on VBS. Second, we controlled for salesperson- and team-level covariates to minimize observed heterogeneity in model estimation (see Fig. 1). Third, the treatment of unobserved heterogeneity is embedded in the multilevel modeling technique. As Rabe-Hesketh, Skrondal, and Pickles (2004, pp. 167–168) state “[T]he latent variables, or random effects, can be interpreted as unobserved heterogeneity at the different levels inducing dependence among all lower-level units in the same higher-level unit. Whereas random intercepts represent heterogeneity between clusters in the overall response, random coefficients represent heterogeneity in the relationship between the response and explanatory variables.”
Besides the main effect hypotheses (i.e., H1a-b, H2, H4-H6, and H8), our model proposes two additional types of hypotheses: (1) within-level moderation (i.e., H3a-c) and (2) between-level moderation (i.e., H7a-c). We employ the Bayesian estimation option with Markov Chain Monte Carlo (MCMC) algorithms to compute bootstrapped estimates (i.e., 1000 samples) while testing the moderation hypotheses. To this end, the Bayesian estimation is more effective than the maximum-likelihood option in computing the standard errors of the interaction effects that are not normally distributed. In addition, we monitor posterior distributions through trace/autocorrelation plots and convergence through Gelman-Rubin’s potential scaling reduction. We test within- and between-level interactions by using the latent moderated structural equation technique (Preacher et al. 2016). We create the interaction terms using group mean centering for salesperson-level constructs and grand mean centering for team-level constructs to obtain unbiased estimates of cross-level interactions (Hofmann and Gavin 1998).
Because salespeople might engage in VBS with an expectation of high levels of performance, the construct of VBS may well be endogenous to the model. We use the control function approach to mitigate the endogeneity bias (Petrin and Train 2010; Wooldridge 2010). That is, we compute the residual term of VBS by regressing it against the covariates, the moderating variables, and the instrument that met the requirements of relevance (i.e., significant correlation with VBS) and exclusion restriction (i.e., uncorrelated with the error term in the outcome variables) (Wooldridge 2010). We chose competitive intelligenceFootnote 4 as the instrumental variable, given that it fulfilled the requirements of relevance and exclusion restriction. First, the instrument is correlated significantly with VBS (r = .51, p < .01) but not with product adoption (r = .02, p > .10). Second, the Sargan test indicates that the instrument is exogenous (χ2 = 1.19, p > .10). The Anderson–Rubin test supports that there was significant correlation between the error term in product adoption (F = 4.03, p < .05). We compute the product of the residual term and the scores of VBS. Accordingly, we correct and control for the endogeneity bias by including both the residual and the interaction term of the residual term and the scores of VBS as covariates while estimating the model.
We next present the results of the main effect hypotheses (i.e., H1a-b, H2, H4-H6, and H8) followed by within-level (i.e., H3a-c) and between-level moderation hypotheses (i.e., H7a-c).
We first fit the main effects–only model. We find that all main effects are statistically significant, yet the assessment of modification indices reveals that adding a direct path from PELB to customers’ adoption of new products reduces the model deviance with a lower value of Akaike Information Criterion [AIC] and Bayesian Information Criterion [BIC]) (ΔAIC = 8.946; ΔBIC = 7.671). Table 3 reports the results of the modified, main-effects only model (see Model 1).
Overall, promotion focus (γ = .094, p < .01), prevention focus (γ = .172, p < .01), and leader behaviors (γ = .062, p < .01) are related significantly to VBS, and VBS is related significantly to customers’ adoption of new products (γ = .138, p < .05), supporting H1a, H1b, H2, and H4, respectively. We also found that sales team monitoring climate is related significantly to team goal pursuit (γ = .182, p < .05), which is significantly related to sales team performance (γ = .008, p < .01) and customers’ adoption of new products (γ = .384, p < .01). These findings supported H5, H6, and H8, respectively. In addition, PELB (γ = .097, p < .05) are related significantly to customers’ adoption of new products.
Within- and between-level interaction effects
Table 3 (Model 2) reports the results of the full model that includes the within- and between-level interaction effects. Figure 2 shows significant within- and between-level interaction effects. Regarding the within-level interactions, the interaction effect of PECB with promotion focus is negative and significant (γ = − .055, p < .05). Promotion focus is related positively to VBS at lower levels of PECB (γ = .143, p < .01, CI [.054, .258]) but not related to VBS at higher levels of PECB (γ = .029, ns, CI [−.070, .123]). Hence H3a is supported (see Fig. 2, Panel A). The interaction effect of PECB with prevention focus is positive and significant (γ = .076, p < .05). Prevention focus is related positively to VBS at higher levels of PECB (γ = .260, p < .01, CI [.061, .405]) but not related to VBS at low levels of PECB (γ = .103, ns, CI [−.055, .275]). Therefore, H3b is supported (see Fig. 2, Panel B). Table 3 also indicates that the interaction effect of PELB with PECB is not significant (γ = .001, p > .10). Thus, H3c is not supported.
Regarding the between-level interaction effects, the interaction effect of promotion focus with sales team monitoring climate on VBS is not significant (γ = .012, p > .10). Thus, H7a is not supported. The interaction effect of prevention focus with sales team monitoring climate is negative and significant (γ = − .164, p < .05). Prevention focus is related positively to VBS under a lower monitoring climate (γ = .234, p < .01, CI [.084, .413]) but not related to VBS in a higher monitoring climate (γ = .129, ns, CI [−.041, .317]). Hence, H7b is supported (see Fig. 2, Panel C). The interaction effect of PELB with sales team monitoring climate on VBS is positive and significant (γ = .118, p < .01). PELB is related positively to VBS at higher levels of monitoring climate (γ = .115, p < .01, CI [.027, .225]) but not related to VBS at lower levels of monitoring climate (γ = .001, ns, CI [−.064, .064]). Thus, H7c is supported (see Fig. 2, Panel D).
Finally, we test the conditional effects of PELB, promotion focus, and prevention focus on VBS at varying levels of sales team monitoring climate and PECB (see Table 4). Specifically, PELB has the strongest effect on VBS at high levels of sales team monitoring climate and PECB. Also, promotion focus has the strongest effect on VBS at high levels of sales team monitoring climate but low levels of PECB. Finally, prevention focus has the strongest effect on VBS at low levels of sales team monitoring climate but at high levels of PECB.
While our model proposes customers’ adoption of new products as the performance outcome of VBS at the salesperson level, we also test whether VBS has an effect on salesperson performance mediated by customers’ adoption of new products. Using objective, archival data, we measure each salesperson’s performance in terms of sales quota achievement (dollar salest /sales quotat). We obtained data on each salesperson’s dollar sales and sales quota for seven months immediately after the survey was completed. We calculate sales quota achievement for each month and then averaged the quota scores (Ahearne et al. 2013). We find that customers’ adoption of new products has a positive, significant effect on salesperson performance (γ = .019, p < .01). In addition, VBS (γ = .003, p < .05, 95% confidence interval [CI] [.001, .007]) has a significant indirect effect on salesperson percent of quota achievement through customers’ adoption of new products.Footnote 5 Yet, the direct effect of VBS on salesperson percent of quota achievement is not significant. These findings suggest that customers’ adoption of new products serves as an indirect mediator in the VBS salesperson percent of quota achievement relationship.
Although VBS is gaining momentum as an important sales approach in modern business markets, empirical research on the topic is surprisingly scarce. Against this backdrop, our study makes several contributions to sales theory and practice.
Providing an integrative framework of VBS drivers
As shown in Table 1, our current understanding of the motivational drivers of VBS is limited to either salesperson- or firm-level influences (e.g., Terho et al. 2015, 2017). Informed by systems theory (Chen and Kanfer 2006), we expand prior research by proposing and testing an integrative framework of salesperson-level VBS drivers, which are occurring within a wider system comprising task and team inputs (Fig. 1). Adopting this systemic perspective of sales motivation and goal striving in a VBS context allows us to unveil several novel insights.
First, we offer the notion that a sales team can be viewed as a VBS system. In particular, our conceptual model and empirical testing illustrate parallel processes occurring at the team and individual level that motivate goal-striving and performance outcomes across both levels. This novel conceptualization helps us examine motivations occurring at the team level that influences VBS and performance outcomes at the individual level. As such, our multilevel perspective offers a unique lens to study VBS, given that extant research has focused on either the individual or firm level (e.g., Terho et al. 2015) without considering the importance of individual-level processes occurring within team contexts. These multilevel motivational processes also provide greater explanatory power of customers’ adoption of new products and team quota achievement, thus providing unique contributions that go above and beyond prior investigations in the area (e.g., Terho et al. 2017). In particular, results reveal that—through social interactions that increase collective awareness of team members’ activities—sales team monitoring climate motivates sales team goal pursuit. Because team-level goal pursuit entails that members work together to accomplish commons goals, team members exhibit increased communication and feedback behaviors to help individuals reach individual objectives that contribute to common goals. Beyond these findings, we believe systems theory has many applications in future sales team research such as goal-setting and can be useful in examining the role of team dynamics as drivers of salesperson motivation (Khusainova et al. 2018).
Second, we address gaps in prior VBS work, which has not examined the dynamic interplay between salesperson and sales team motivational processes (see Mullins and Panagopoulos 2018). Specifically, our study extends prior work by articulating a set of contextual nuances at the salesperson and team levels that influence salesperson motivation for VBS. At the salesperson level, we find two discretionary inputs act as drivers of VBS: (1) a goal-focused motivational trait (i.e., promotion and prevention focus) and (2) a leader-focused motivational source (i.e., PELBs). Results reveal that both discretionary inputs contribute significantly in motivating VBS. Specifically, in line with previous research on regulatory focus and salesperson behavior (e.g., DeCarlo and Lam 2016), we find that regulatory focus also shapes salespeople’s goal-striving to provide value in each customer’s unique business situation (i.e., VBS). Each facet of regulatory focus motivates VBS via a mindset of seeking gains (promotion focus), or a mindset of avoiding losses (prevention focus), illustrating a dual-pathway to motivating VBS.
In addition, our study shows that salespeople who perceive their leaders inspiring more work meaningfulness and offering more autonomy are encouraged to find ways to help improve their customers’ businesses. However, the interplay between each salesperson regulatory foci and a discretionary task input (i.e., PECB) is intriguing in that promotion (prevention) focus positively influences VBS at lower (higher) levels of PECB. This highlights key motivational differences between types of regulatory focus depending on the perceived customer context. Specifically, when salespeople perceive customers’ signals for discovering value that contributes to their business, VBS motivated from a risk-avoiding (versus a risk-taking) mindset is strengthened (weakened), because it less uncertain for salespeople to uncover value-creating opportunities.
At the team level, results also reveal an interesting pattern of insights. Specifically, prevention focus leads to higher levels of VBS only at lower levels of sales team monitoring climate, which has not been examined in prior work. It is plausible that salespeople with a prevention focus, who are motivated to avoid losses and errors (Crowe and Higgins 1997), fit better with a team that does not put members’ behaviors, and perhaps mistakes, under the spotlight. In contrast, we find that monitoring climate does not play a significant role in motivating salespeople that have a mindset of exploiting potential positive outcomes and opportunities via VBS. It seems that salespeople high on promotion focus are already holding themselves accountable for contributions to team tasks, thus buffering the influence of a monitoring climate. Results also reveal that the positive effects of PELB on VBS are strengthened at higher levels of monitoring climate, perhaps because monitoring provides the ongoing guidance needed when salespeople perceive more empowered environments. Collectively, these results highlight the “motivational fit” between sales team monitoring climate and VBS drivers, a novel finding that has not been uncovered in prior research.
One limitation of the systems theory of motivation is that it does not distinguish among different types of discretionary inputs; rather, these inputs are viewed as one generic category (Chen and Kanfer 2006). Although not the core focus of our work, our conceptual model allows for the distinction of different types of discretionary inputs: salesperson-, perceived task-, and perceived leader-focused inputs. As such, our work also helps expand the systems theory of motivation by introducing a categorization of discretionary inputs relevant to sales research.
Although not formally hypothesized, we also find that two covariates (i.e., salesperson knowledge and customer orientation) positively influence salesperson VBS (Table 3). Consistent with the tenets of adaptive selling research (Weitz et al. 1986), salespeople that have a larger knowledge base for different selling situations can better understand a customer’s unique problems and, as such, better demonstrate the benefits of their product offerings to customer profitability. Also, because customer orientated salespeople try to help customers take a problem-solving approach while interacting with them (Terho et al. 2015), they are motivated to identify opportunities to improve customers’ business profits, thus engaging in VBS. In summary, these findings provide novel insights into motivating salesperson VBS.
Integrating VBS outcomes with the new product selling literature
We also contribute to research that examines the role of the salesforce in new product performance (e.g., Fu et al. 2010). While product-focused effort can have both a beneficial effect on the performance of new products (Ahearne et al. 2010b) and a detrimental effect on existing product performance (Van der Borgh et al. 2017), it is not well understood whether VBS provides benefits to salespeople in this context. We broaden this line of research by linking VBS (i.e., customer-focused effort) with customers’ adoption of new products. Furthermore, our multilevel model extends prior studies by illustrating team-level mechanisms that influence customers’ adoption of new products at the salesperson level. We find that sales team goal pursuit provides a unique contribution to increasing customers’ adoption of new products above and beyond individual-level influences. These findings not only document the anecdotal evidence for the importance of VBS in new product selling (Steenburgh and Ahearne 2018), but also bridge the literature between VBS and new product selling within the sales team domain, thus extending research on individual goal-striving for new product performance (Fu et al. 2009).
Interestingly, while not hypothesized, we find that PELB are significantly and positively related to customers’ adoption of new products (Table 3). This finding highlights the role leaders play in new product sales by allowing salespeople to work under perceived decision-making autonomy and confidence in the employee’s capabilities (Ahearne et al. 2005), conditions needed in mitigating the uncertainty surrounding new product selling.
Our research also provides useful guidance to sales leaders interested in implementing VBS. Specifically, our study provides answers to two key questions that sales leaders face.
What can sales leaders do to motivate VBS?
With 57% of buyers today finding little differentiation between sellers’ offerings (CSO Insights 2018), sales leaders face significant challenges to motivate a VBS approach in their salesforce. Our findings offer actionable guidance to sales leaders in this important domain. First, our findings suggest that driving VBS involves a complex interplay between motivational factors residing at the salesperson and sales team level. Accordingly, we recommend that sales leaders do not view motivational factors in isolation, but, rather, design initiatives that account for salesperson- and team-level factors simultaneously. For example, prevention focused salespeople are less motivated for VBS within sales teams who surveil team members’ activities. Leaders should consider each motivational factor in combination for their teams to have higher chances of success. Our conditional effects analysis (Table 4) offers concrete direction to sales leaders in this regard. Specifically, prevention focused salespeople will be more motivated when they perceive lower levels of monitoring from their team and, at the same time, high levels of empowerment from their customers. Critical here, managers must recognize that this approach has a downside in that monitoring climate is an important antecedent to sales team goal pursuit, which consequently drives team performance and customers’ adoption of new products. In contrast, promotion focused salespeople will be more motivated when they perceive higher levels of monitoring from their team and, simultaneously, fewer empowering signals from their customers. These nuances in motivating salespeople with different regulatory foci highlight the need for leaders to accommodate varying motivations in a team. One way of doing this is by employing the items in our study (see Web Appendix Table 5) in a survey to diagnose the perceptions salespeople regarding their regulatory foci, sales team, and customer characteristics. Based on the results, sales leaders can design carefully crafted interventions such as enacting individualized coaching or training sessions that address differing perceptions about customers and sales teams.
Second, sales leaders should acknowledge that a broad set of dynamic stimuli contribute toward motivating VBS. For example, sales leaders can identify salespeople’s regulatory focus or perceived leader behaviors during selection and later during employment through surveying and tracking their salespeople over time. PELB can positively influence VBS, aligning with practitioner advice that sales leaders allocate time for VBS coaching interventions (Moorman and Vogel 2012). Leaders can increase salespeople’s perceptions of exhibiting more empowering behaviors in day-to-day activities by offering salespeople opportunities to express opinions, demonstrating confidence in salespeople’s ability, and increasing salespeople’s autonomy in completing tasks. Leaders can also employ survey results to develop benchmarking plans, or team assignments, based on these varying levels of motivations.
Third, sales leaders should be aware that the customer context, as perceived by salespeople, influences the impact of salespeople’s regulatory foci in motivating VBS. In particular, when salespeople perceive customers granting influence and autonomy over important decisions during customer interactions, salespeople with a preventive rather than a promotion focus are motivated for VBS. This finding is important in light of recent work, which shows that business customers often limit suppliers’ communication with buyers during the purchasing process (Chase and Murtha 2019). In such contexts, salespeople may perceive customers granting little, if any, influence during customer interactions, which hampers the development of VBS. Leaders should, therefore, segment customers based on the level of customer empowerment perceived by salespeople, using the survey items employed in our study. Based on the results, sales leaders can coach or support salespeople with specific selling approaches for customers that hinder salesperson efforts to act on their goal motivations during customer interactions.
Does engaging in VBS pay-off?
First, using manager ratings of salesperson performance, we clearly show that VBS matters significantly since it increases customers’ adoption of new products, which, in an additional analysis, is related to objective, time-lagged data on percent of quota achievement. Although other strategies may be linked to customers’ adoption of new products, our findings suggest that initiatives for increasing VBS also pays off. Accordingly, we recommend that sales leaders monitor the adoption of new products in the context of VBS, since this key metric operates as an immediate outcome, which influences quota achievement over time. Second, we uncover mechanisms that occur at the team level but also influence customers’ adoption of new products. Specifically, sales team goal pursuit provides a unique contribution to increasing not only customers’ adoption of new products at the salesperson level but also the percent of quota achievement at the sales team level. Accordingly, we suggest that managers regularly monitor sales team dynamics and, specifically, whether team members collectively exert effort toward team goal achievement, such as collecting and sharing customer insights.
Limitations and future research
Our study is subject to some limitations that present fruitful avenues for future research. First, although we employ time-lagged data, a longitudinal research design is needed to address how salesperson VBS changes over time. For example, during times of increased competition, salespeople may prefer a VBS approach to fend off competitive attacks. In contrast, introducing a radically innovative product might offer more opportunities for differentiation and lessen the need for VBS. We also point out that our measure of VBS is self-reported rather than observed by customers. In light of this, future research should explore how VBS changes from the customer’s perspective using observational data (e.g., coding of recorded salesperson–customer interactions) to gain a deeper understanding of how salespeople change their VBS approaches and whether VBS behaviors remain salient to customers. Second, in today’s environment, salespeople may belong to more than one team. Accordingly, future researchers could examine how multiple team membership influences the multilevel processes present in our study. Third, our data come from a single firm. Future research should compare the implementation of VBS across firms to examine whether firm characteristics (e.g., training, hiring, compensation) exert an influence on the relationships examined in our study. Finally, our model is limited to a focus on discretionary and ambient inputs’ impact on goal-striving. Future researchers should empirically test mediating mechanisms such as motivational states (e.g., self-efficacy, gain seeking, loss avoidance) and goal selection.Footnote 6
We acknowledge that we do not include motivational states and goal generation as mediators in our model between discretionary inputs and goal-striving. However, multiple studies propose direct links between empowering leadership behaviors and sought-after behaviors such as service-oriented citizenship behaviors (Auh et al. 2014) and adaptive selling behaviors (Ahearne et al. 2005). Similarly, meta-analytic results provide robust evidence that regulatory foci predict unique variance in work behaviors after controlling for personality, motivation, and attitudinal predictors (Lanaj et al. 2012). As such, we follow similar work on systems theory that favors a parsimonious theoretical model (e.g., Chen et al. 2007).
We thank an anonymous reviewer for contributing this insight.
Competitive intelligence (Cronbach’s alpha = .86) was measured with a three-item, seven-point scale (i.e., “I try to gather and transmit reliable information about competitors”; “I always assign myself objectives to obtain information about competitors”; “I ask customers about our competition’s strategies”) drawn from Rapp et al. (2011).
It is possible that VBS has an inverted U-shaped effect on customers’ adoption of new products. The rationale is that at very high levels of VBS, salespeople completely focus on the customer’s bottom line, creating value for the customer, but this might involve existing rather than new (and usually more expensive) products. As such, adoption of new products might be hampered at very high levels of VBS. We test this alternative model by entering the squared term of VBS in addition to its main-effect in the model. Although we find that the main effect of VBS is related significantly to adoption of new products (γ = .089, p < .05), the squared term of VBS is not related significantly to adoption of new products (γ = −.001, p > .10).
We thank an anonymous reviewer for suggesting these potential process variables.
Aberdeen Group (2011). Value-based selling: Building a best-in-class capability for sales effectiveness. Accessed on July 31, 2018.
Ahearne, M., Mathieu, J., & Rapp, A. (2005). To empower or not to empower your sales force? An empirical examination of the influence of leadership empowerment behavior on customer satisfaction and performance. Journal of Applied Psychology, 90(5), 945.
Ahearne, M., Rapp, A., Hughes, D. E., & Jindal, R. (2010a). Managing sales force product perceptions and control systems in the success of new product introductions. Journal of Marketing Research, 47(4), 764–776.
Ahearne, M., MacKenzie, S. B., Podsakoff, P. M., Mathieu, J. E., & Lam, S. K. (2010b). The role of consensus in sales team performance. Journal of Marketing Research, 47(3), 458–469.
Ahearne, M., Lam, S. K., Hayati, B., & Kraus, F. (2013). Intrafunctional competitive intelligence and sales performance: A social network perspective. Journal of Marketing, 77(5), 37–56.
Amundsen, S., & Martinsen, Ø. L. (2014). Self–other agreement in empowering leadership: Relationships with leader effectiveness and subordinates' job satisfaction and turnover intention. The Leadership Quarterly, 25(4), 784–800.
Anderson, J. C., Narus, J. A., & Van Rossum, W. (2006). Customer value propositions in business markets. Harvard Business Review, 84, 1–4.
Anderson, J., Kumar, N., & Narus, J. A. (2007). Value merchants: Demonstrating and documenting superior value in business markets. Harvard Business School Press, Boston, MA.
Auh, S., Menguc, B., & Jung, Y. S. (2014). Unpacking the relationship between empowering leadership and service-oriented citizenship behaviors: A multilevel approach. Journal of the Academy of Marketing Science, 42(5), 558–579.
Bijlsma-Frankema, K., de Jong, B., & van de Bunt, G. (2008). Heed, a missing link between trust, monitoring and performance in knowledge intensive teams. The International Journal of Human Resource Management, 19(1), 19–40.
Blocker, C. P., Cannon, J. P., Panagopoulos, N. G., & Sager, J. K. (2012). The role of the sales force in value creation and appropriation: New directions for research. Journal of Personal Selling & Sales Management, 32(1), 15–27.
Brockner, J., Higgins, E. T., & Low, M. B. (2004). Regulatory focus theory and the entrepreneurial process. Journal of Business Venturing, 19(2), 203–220.
Burmeister, A., Li, Y., Wang, M., Shi, J., & Jin, Y. (2019). Team knowledge exchange: How and when does transformational leadership have an effect? Journal of Organizational Behavior.
Chase, K. S, & Murtha, B. (2019). Selling to barricaded buyers. Journal of Marketing, 83(6), 2–20.
Chen, G., & Kanfer, R. (2006). Toward a systems theory of motivated behavior in work teams. Research in Organizational Behavior, 27, 223–267.
Chen, G., Kirkman, B. L., Kanfer, R., Allen, D., & Rosen, B. (2007). A multilevel study of leadership, empowerment, and performance in teams. Journal of Applied Psychology, 92(2).
Chen, G., Kanfer, R., DeShon, R. P., Mathieu, J. E., & Kozlowski, S. W. (2009). The motivating potential of teams: Test and extension of cross-level model of motivation in teams. Organizational Behavior and Human Decision Processes, 110(1), 45–55.
Chen, G., Farh, J. L., Campbell-Bush, E. M., Wu, Z., & Wu, X. (2013). Teams as innovative systems: Multilevel motivational antecedents of innovation in R&D teams. Journal of Applied Psychology, 98(6), 1018.
Crowe, E., & Higgins, E. T. (1997). Regulatory focus and strategic inclinations: Promotion and prevention in decision-making. Organizational Behavior and Human Decision Processes, 69(2), 117–132.
CSO Insights (2018). The growing buyer-seller gap: Results of the 2018 buyer preferences study.
D’Innocenzo, L., Luciano, M. M., Mathieu, J. E., Maynard, M. T., & Chen, G. (2016). Empowered to perform: A multilevel investigation of the influence of empowerment on performance in hospital units. Academy of Management Journal, 59(4), 1290–1307.
De Jong, B. A., & Elfring, T. (2010). How does trust affect the performance of ongoing teams? The mediating role of reflexivity, monitoring, and effort. Academy of Management Journal, 53(3), 535–549.
DeCarlo, T. E., & Lam, S. K. (2016). Identifying effective hunters and farmers in the salesforce: A dispositional–situational framework. Journal of the Academy of Marketing Science, 44(4).
Deeter-Schmelz, D. R., & Ramsey, R. P. (2003). An investigation of team information processing in service teams: Exploring the link between teams and customers. Journal of the Academy of Marketing Science, 31(4), 409–424.
DeShon, R. P., Kozlowski, S. W., Schmidt, A. M., Milner, K. R., & Wiechmann, D. (2004). A multiple-goal, multilevel model of feedback effects on the regulation of individual and team performance. Journal of Applied Psychology, 89(6), 1035.
Dong, Y., Liao, H., Chuang, A., Zhou, J., & Campbell, E. M. (2015). Fostering employee service creativity: Joint effects of customer empowering behaviors and supervisory empowering leadership. Journal of Applied Psychology, 100(5), 1364.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 39–50.
Fu, F. Q., Richards, K. A., & Jones, E. (2009). The motivation hub: Effects of goal setting and self-efficacy on effort and new product sales. Journal of Personal Selling & Sales Management, 29(3), 277–292.
Fu, F. Q., Richards, K. A., Hughes, D. E., & Jones, E. (2010). Motivating salespeople to sell new products: The relative influence of attitudes, subjective norms, and self-efficacy. Journal of Marketing, 74(6), 61–76.
Hackman, J. R. (1992). Group influences on individuals in organizations. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (2nd ed.), 3, 199–267. Palo Alto, CA: Consulting Psy chologists Press.
Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52(12), 1280.
Higgins, E. T. (2000). Making a good decision: Value from fit. American Psychologist, 55(11).
Hinterhuber, A., & Snelgrove, T. C. (Eds.). (2016). Quantifying and documenting value in business markets. In A. Hinterhuber and T. C. Snelgrove (Eds.), Value first then price: quantifying value in business to business markets from the perspective of both buyers and sellers, 3–13. Routledge, Abingdon, UK.
Hofmann, D. A., & Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 24(5), 623–641.
Jaramillo, F., & Mulki, J. P. (2008). Sales effort: The intertwined roles of the leader, customers, and the salesperson. Journal of Personal Selling & Sales Management, 28(1), 37–51.
Johnson, J. S., Friend, S. B., & Horn, B. J. (2014). Levels of analysis and sources of data in sales research: A multilevel-multisource review. Journal of Personal Selling & Sales Management, 34(1), 70–86.
Jong, A. D., Ruyter, K. D., & Lemmink, J. (2004). Antecedents and consequences of the service climate in boundary-spanning self-managing service teams. Journal of Marketing, 68(2).
Khusainova, R., de Jong, A., Lee, N., Marshall, G. W., & Rudd, J. M. (2018). (re) defining salesperson motivation: Current status, main challenges, and research directions. Journal of Personal Selling & Sales Management, 38(1), 2–29.
Kohli, A. K. (1989). Effects of supervisory behavior: The role of individual differences among salespeople. Journal of Marketing, 53(4), 40–50.
Lam, S. K., DeCarlo, T. E., & Sharma, A. (2019). Salesperson ambidexterity in customer engagement: Do customer base characteristics matter? Journal of the Academy of Marketing Science, 1–22.
Lanaj, K., Chang, C. H., & Johnson, R. E. (2012). Regulatory focus and work-related outcomes: A review and meta-analysis. Psychological Bulletin, 138(5), 998.
Langfred, C. W. (2004). Too much of a good thing? Negative effects of high trust and individual autonomy in self-managing teams. Academy of Management Journal, 47(3), 385–399.
LeBreton, J. M., & Senter, J. L. (2008). Answers to 20 questions about interrater reliability and interrater agreement. Organizational Research Methods, 11(4), 815–852.
Marks, M. A., & Panzer, F. J. (2004). The influence of team monitoring on team processes and performance. Human Performance, 17(1), 25–41.
Menguc, B., Auh, S., & Kim, Y. C. (2011). Salespeople’s knowledge-sharing behaviors with coworkers outside the sales unit. Journal of Personal Selling & Sales Management, 31(2).
Menguc, B., Auh, S., & Uslu, A. (2013). Customer knowledge creation capability and performance in sales teams. Journal of the Academy of Marketing Science, 41(1), 19–39.
Moorman, M. & Vogel, L. (2012). The race to excel at value-based selling: A new and necessary approach to talent transformation. Accessed July 31, 2018, https://www.zs.com/publications/whitepapers/the-race-to-excel-at-value-based-selling-a-new-and-necessary-approach-to-sales-talent-transformation.aspx
Morgeson, F. P., & Hofmann, D. A. (1999). The structure and function of collective constructs: Implications for multilevel research and theory development. Academy of Management Review, 24(2), 249–265.
Mullins, R. R., & Panagopoulos, N. G. (2018). Understanding the theory and practice of team selling: An introduction to the special section and recommendations on advancing sales team research. Industrial Marketing Management, 77, 1–3.
Mullins, R., & Syam, N. (2014). Manager–salesperson congruence in customer orientation and job outcomes: The bright and dark sides of leadership in aligning values. Journal of Personal Selling & Sales Management, 34(3), 188–205.
Mullins, R. R., Ahearne, M., Lam, S. K., Hall, Z. R., & Boichuk, J. P. (2014). Know your customer: How salesperson perceptions of customer relationship quality form and influence account profitability. Journal of Marketing, 78(6), 38–58.
Mullins, R. R., Bachrach, D. G., Rapp, A., Grewal, D., & Beitelspacher, L. S. (2015). You don’t always get what you want, and you don’t always want what you get: An examination of control–desire for control congruence in transactional relationships. Journal of Applied Psychology, 100(4), 1073–1088.
Neubert, M. J., Kacmar, K. M., Carlson, D. S., Chonko, L. B., & Roberts, J. A. (2008). Regulatory focus as a mediator of the influence of initiating structure and servant leadership on employee behavior. Journal of Applied Psychology, 93(6), 1220.
Petrin, A., & Train, K. (2010). A control function approach to endogeneity in consumer choice models. Journal of Marketing Research, 47(1), 3–13.
Preacher, K. J., Zhang, Z., & Zyphur, M. J. (2016). Multilevel structural equation models for assessing moderation within and across levels of analysis. Psychological Methods, 21(2).
Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Generalized multilevel structural equation modeling. Psychometrika, 69(2), 167–190.
Rapp, A., Ahearne, M., Mathieu, J., & Schillewaert, N. (2006). The impact of knowledge and empowerment on working smart and working hard: The moderating role of experience. International Journal of Research in Marketing, 23(3), 279–293.
Rapp, A., Agnihotri, R., & Baker, T. L. (2011). Conceptualizing salesperson competitive intelligence: An individual-level perspective. Journal of Personal Selling and Sales Management, 31(2), 141–155.
Rapp, T. L., Bachrach, D. G., Rapp, A. A., & Mullins, R. (2014). The role of team goal monitoring in the curvilinear relationship between team efficacy and team performance. Journal of Applied Psychology, 99(5), 976.
Sacramento, C. A., Fay, D., & West, M. A. (2013). Workplace duties or opportunities? Challenge stressors, regulatory focus, and creativity. Organizational Behavior and Human Decision Processes, 121(2), 141–157.
Schmitz, C. (2013). Group influences of selling teams on industrial salespeople’s cross-selling behavior. Journal of the Academy of Marketing Science, 41(1), 55–72.
Sharma, P. N., & Kirkman, B. L. (2015). Leveraging leaders: A literature review and future lines of inquiry for empowering leadership research. Group & Organization Management, 40(2).
SiriusDecisions (2015). It's 2015. What have you done for me lately? Accessed 1 Aug 2018.
Stanton, J. M. (2000). Reactions to employee performance monitoring: Framework, review and research directions. Human Performance, 13, 85–113.
Steenburgh, T. & Ahearne, M. (2018) How to sell new products. Harvard Business Review, November.
Terho, H., Haas, A., Eggert, A., & Ulaga, W. (2012). ‘It's almost like taking the sales out of selling’—Towards a conceptualization of value-based selling in business markets. Industrial Marketing Management, 41(1), 174–185.
Terho, H., Eggert, A., Haas, A., & Ulaga, W. (2015). How sales strategy translates into performance: The role of salesperson customer orientation and value-based selling. Industrial Marketing Management, 45, 12–21.
Terho, H., Eggert, A., Ulaga, W., Haas, A., & Böhm, E. (2017). Selling value in business markets: Individual and organizational factors for turning the idea into action. Industrial Marketing Management, 66, 42–55.
Thomas, R. W., Soutar, G. N., & Ryan, M. M. (2001). The selling orientation-customer orientation (SOCO) scale: A proposed short form. Journal of Personal Selling & Sales Management, 21(1), 63–69.
Töytäri, P., & Rajala, R. (2015). Value-based selling: An organizational capability perspective. Industrial Marketing Management, 45, 101–112.
Van der Borgh, M., & Schepers, J. (2018). Are conservative approaches to new product selling a blessing in disguise? Journal of the Academy of Marketing Science, 46(5), 857–878.
Van der Borgh, M., De Jong, A., & Nijssen, E. J. (2017). Alternative mechanisms guiding salespersons’ ambidextrous product selling. British Journal of Management, 28(2), 331–353.
van Wyk, J., Bornstein, J., & Brooke, P. (2018). Selling industry 4.0: The new sales mind-set for connected products. Deloitte insights. Accessed 1 Feb 2019.
Wang, X. H. F., & Howell, J. M. (2012). A multilevel study of transformational leadership, identification, and follower outcomes. The Leadership Quarterly, 23(5), 775–790.
Weitz, B. A., Sujan, H., & Sujan, M. (1986). Knowledge, motivation, and adaptive behavior: A framework for improving selling effectiveness. Journal of Marketing, 50(4), 174–191.
Wieseke, J., Ahearne, M., Lam, S. K., & Dick, R. V. (2009). The role of leaders in internal marketing. Journal of Marketing, 73(2), 123–145.
Wilden, R., & Gudergan, S. P. (2015). The impact of dynamic capabilities on operational marketing and technological capabilities: Investigating the role of environmental turbulence. Journal of the Academy of Marketing Science, 43(2), 181–199.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). Cambridge: MIT Press.
Yu, T., Patterson, P., & de Ruyter, K. (2015). Converting service encounters into cross-selling opportunities: Does faith in supervisor ability help or hinder service-sales ambidexterity? European Journal of Marketing, 49(3/4), 491–511.
Zhang, X., & Bartol, K. M. (2010). Linking empowering leadership and employee creativity: The influence of psychological empowerment, intrinsic motivation, and creative process engagement. Academy of Management Journal, 53(1), 107–128.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Son K. Lam served as Area Editor for this article.
About this article
Cite this article
Mullins, R., Menguc, B. & Panagopoulos, N.G. Antecedents and performance outcomes of value-based selling in sales teams: a multilevel, systems theory of motivation perspective. J. of the Acad. Mark. Sci. 48, 1053–1074 (2020). https://doi.org/10.1007/s11747-019-00705-2
- Value-based selling
- Systems theory
- Salesperson motivation
- Sales teams
- New product selling
- Sales performance