Journal of the Academy of Marketing Science

, Volume 41, Issue 1, pp 73–90

The interactive effects of sales control systems on salesperson performance: a job demands–resources perspective

Authors

    • School of BusinessClarkson University
  • Kenneth R. Evans
    • Michael F. Price College of BusinessUniversity of Oklahoma
Original Empirical Research

DOI: 10.1007/s11747-012-0315-4

Cite this article as:
Miao, C.F. & Evans, K.R. J. of the Acad. Mark. Sci. (2013) 41: 73. doi:10.1007/s11747-012-0315-4

Abstract

Sales control systems represent an important managerial tool in directing the sales force for desired organizational objectives. However, the majority of prior sales control research has focused only on the main effects of sales control systems without explicitly considering their interactive effects and associated intervening mechanisms. Drawing on job demands–resources theory, the authors theorize differential interactive effects of outcome control, activity control, and capability control on job engagement (i.e., adaptive selling behavior and selling effort) and job stress (i.e., role ambiguity and role conflict), which subsequently affect salesperson performance. Empirical results using a sample of industrial salespeople find that (1) outcome control and capability control have positive interactive effects on adaptive selling behavior and selling effort while suppressing role conflict, (2) activity control and capability control have a negative interactive effect on role ambiguity, and (3) outcome control and activity control have a positive interactive effect on selling effort but negative interactive effects on adaptive selling behavior and role clarity. These results indicate that sales control researchers can benefit from considering the complex interactive effects of various control styles as well as the intervening processes, which provide a more refined understanding of this important managerial tool.

Keywords

Sales control interactive effectsAdaptive selling behaviorSelling effortRole ambiguityRole conflictSalesperson performance

For many firms the sales force is the only organizational unit that generates sales revenues, which is especially true for business-to-business relationships where salespeople are key boundary-spanners between the company and its customers (Krafft et al. 2004; Spiro and Weitz 1990). As salespeople play a critical role in relationship marketing (Morgan and Hunt 1994), successful launch of new products (Ahearne et al. 2010b), and the cross-functional product development process (Joshi 2010), many organizations have seen a significant shift in resource allocation to the sales function (Kotler 2003; Piercy et al. 2006). Given the strategic importance of and significant investments in the sales function, “the benefits of effectively managing a sales force have never been greater” (Krafft et al. 2004, p. 265).

Companies typically employ sales control systems—the formalized policies, rules, and procedures—to direct the sales force to reach desired organizational objectives (Anderson and Oliver 1987). One notable limitation in the extant empirical sales control literature is that the great majority of studies have focused on the main effects of behavior- and outcome-based control systems to the neglect of potential interactive effects of sales control systems. Given that most sales organizations employ a combination of behavior- and outcome-based control systems (Cravens et al. 2004; Oliver and Anderson 1995), an understanding of the interactive effects of sales control systems is of both theoretical and managerial interest. While some prior research has suggested potential positive synergies of behavioral and outcome control styles in affecting salesperson performance (Cravens et al. 2004; Jaworski et al. 1993; Onyemah and Anderson 2009), noticeable research gaps remain.

First, studies on hybrid sales control systems treat behavioral control as a unidimensional construct without distinguishing between capability control and activity control. Capability control emphasizes the development of a salesperson’s skills and abilities, which entails active management involvement in setting capability goals, coaching, and providing diagnostic feedback for selling skill improvement (Kohli et al. 1998); activity control, on the other hand, enables sales managers to closely monitor and control required routine activities (e.g., call rate) undertaken by salespeople in the selling process (Challagalla and Shervani 1996). While both are embedded in the global domain of behavioral control, capability control and activity control may have differential and even opposite psychological and behavioral consequences (Evans et al. 2007; Miao et al. 2007). Indeed, prior research on hybrid control systems that did not distinguish between capability control and activity control has produced inconsistent results. For example, in contrast to Jaworski et al. (1993) who found no differences in salesperson performance across different control combinations, Cravens et al. (2004) reported the highest mean performance in the high control group, which begs the question: Do hybrid control strategies characterized by high degrees of behavioral and outcome control elements always produce positive synergies? By treating capability control and activity control as separate behavioral control constructs, we may be able to shed some light on the seemingly inconsistent findings reported in prior research.

Second, prior studies on hybrid control strategies take the configuration perspective by linking hybrid sales control systems directly to sales performance. Therefore, how exactly such hybrid sales control systems may bring about positive or negative interactive effects remains in a black box because these studies did not explicitly investigate the intervening processes through which such effects may be created. While sales control researchers have explored various mediation mechanisms for sales control main effects (e.g., Challagalla and Shervani 1996; Evans et al. 2007; Kohli et al. 1998; Miao et al. 2007), they have not considered how interactive effects of sales control systems may operate through intervening variables, which may be responsible for observed patterns of results across hybrid sales control studies (e.g., Cravens et al. 2004; Jaworski et al. 1993; Onyemah and Anderson 2009).

In this study we rely on job demands–resources (JD-R) theory (Bakker and Demerouti 2007; Bakker et al. 2010) as an integrative framework to investigate the interactive effects on salesperson performance of outcome control, activity control, and capability control (Fig. 1). According to JD-R theory, while each job has its own associated characteristics, most job-related factors can be grouped into two general categories: job demands and job resources. Because job demands and resources in a given organization depend on the particular characteristics of the job, the specific constructs considered in JD-R–based studies tend to vary considerably (Bakker et al. 2010; Nahrgang et al. 2011; Zablah et al. 2012). In the terminology of JD-R, outcome control can be understood as a type of job demand imposed on salespeople due to outcome control’s high pressure and risky nature that requires salespeople to expend sustained physical and/or psychological effort in the selling task (Anderson and Oliver 1987); likewise, activity control is a job demand because it specifies a combination of activities (e.g., call rate, number of new accounts to visit) salespeople must fulfill in the selling process (Challagalla and Shervani 1996; Fang et al. 2005). In contrast, capability control can be perceived as a job resource because capability control is specifically designed to enable salespeople to achieve their work goals and to cope with job demands with appropriate selling skills and knowledge (Kohli et al. 1998). JD-R theory further posits that job demands and job resources can often interact to affect job outcomes (e.g., salesperson performance) via the dual mediating processes. One process focuses on employee job engagement, and the other process involves job stress/strain effects (Bakker and Demerouti 2007).
https://static-content.springer.com/image/art%3A10.1007%2Fs11747-012-0315-4/MediaObjects/11747_2012_315_Fig1_HTML.gif
Fig. 1

The JD-R-Based sales control interactive effects

We use adaptive selling behavior and selling effort as indicators of a salesperson’s job engagement because (1) they represent the salesperson’s level of investment in the selling task and (2) these two types of personal selling behaviors have received considerable amount of research attention in sales literature as immediate precursors to sales performance (Brown and Peterson 1994; Spiro and Weitz 1990; Sujan et al. 1994). Finally, consistent with recent marketing research in a similar boundary-spanning context (Zablah et al. 2012), role ambiguity and role conflict depict the job stress effect in our model. Therefore, the causal ordering of the constructs in our conceptual model is consistent with the overarching framework of the JD-R perspective, which is tested with a cross-sectional sample of salespeople in the manufacturing sector.

A partial least squares (PLS) structural equation model finds substantive support for most hypothesized relationships as predicted by JD-R theory. The results provide compelling evidence that outcome control, activity control, and capability control have differential interactive effects on salesperson’s job engagement and job stress, which subsequently affect salesperson performance. Therefore, sales control researchers can benefit from considering the complex interactive effects of various control styles as well as the intervening processes, which provide a more refined understanding of this important managerial tool.

In the remainder of this paper, we first provide a review of relevant theories, which serve to inform our research hypotheses development. We then describe our research method and report the results of the analyses, followed by a discussion of the findings and their implications. Finally, the paper concludes with limitations and future research directions.

Theoretical background

Job demands–resources (JD-R) theory

At the heart of JD-R theory lies the assumption that whereas every occupation has its own set of factors associated with job engagement and job stress, these factors can be grouped in two general categories—job demands and job resources—which can be applied to various occupational settings irrespective of their idiosyncratic demands and resources involved (Bakker and Demerouti 2007). Job demands are aspects of the job that require employees to expend sustained physical and/or psychological effort (e.g., achieving a challenging sales quota), whereas job resources are aspects of the job (e.g., supervisor coaching and feedback) and/or of the individual employee (e.g., experience and knowledge) that can facilitate work goal attainment, stimulate learning and personal growth, and reduce or cope with job demands (Bakker and Demerouti 2007; Nahrgang et al. 2011; Xanthopoulou et al. 2007).

While the initial JD-R theory used a global definition of job demands, later work differentiated between challenge demands (e.g., high workload) and hindrance demands (e.g., organizational politics), which have opposite effects on job engagement (Crawford et al. 2010; Podsakoff et al. 2007; Van den Broeck et al. 2010). Unlike hindrance demands that can constrain employees’ professional development and work accomplishment, challenge demands may actually enhance employees’ job engagement because employees may perceive challenging job demands as a potential opportunity to promote their professional competence and/or personal growth, thereby triggering positive emotions and active engagement in the job (Crawford et al. 2010; Van den Broeck et al. 2010). Moreover, in contrast to hindrance demands, which consistently have been found to cause job stress due to exhaustion of employees’ mental and physical resources (Bakker et al. 2005), challenge demands need not create job stress (Zablah et al. 2012) because they do not necessarily “yield an energy-depleting effect” (Van den Broeck et al. 2010, p. 753). In fact, the presence of demand-relevant resources can further alter the way employees perceive these challenging demands, thereby aiding in job stress resistance (Zablah et al. 2012). In this study outcome control and activity control are both considered as challenge demands as opposed to hindrance demands because both outcome control and activity control entail high levels of work pressure to complete tasks within required timeframes, which tends to elicit an active problem-focused coping style to meet these quantitative and subjective workloads (Crawford et al. 2010; Podsakoff et al. 2007; Van den Broeck et al. 2010).1

A central thesis of JD-R is that combinations of job demands and job resources can affect employee performance via their interactive effects on job engagement and job stress. In particular, challenge demands and job resources are expected to have a positive interactive effect on job engagement, resulting in enhanced job performance. While challenge demands can directly motivate employees to stay engaged in their work, such effect will be more pronounced when job resources are available. This is because when employees are confronted with challenging demands, they are likely to assess the linkage between effort required to deal with these demands and the probability of obtaining favorable outcomes (Van den Broeck et al. 2010). When job resources are made available for employees, the utility of job resources can be fully tapped by employees to reduce or cope with high job demands, thereby boosting employee confidence and motivating them to keep actively engaged in the otherwise demanding task (Bakker et al. 2010). In other words, salespeople who have access to job resources tend to consider such job demands as more controllable, which are more likely to be met with an active coping strategy, thereby leading to higher levels of job engagement (LePine et al. 2005). Empirically, the relationship between challenge demands and job stress has been inconclusive (LePine et al. 2005; Van den Broeck et al. 2010), suggesting such a relationship may be contingent upon the specific measures of job stress or some moderating factors such as job resources. When demand-relevant job resources are available, employees will have an improved ability to cope with an otherwise demanding task, thereby reducing perceived workload and associated job stress (Bakker and Demerouti 2007). Stated differently, challenge demands and job resources should have a negative interactive effect on job stress.

Sales control systems

To help employees focus on sales-related organizational objectives, sales organizations usually use some type of formal sales control system. Anderson and Oliver (1987, p. 76) define a control system as “an organization’s set of procedures for monitoring, directing, evaluating, and compensating its employees.” They further suggest that a formal sales control system can be designed as either behavior- or outcome-based control. Behavior-based control often requires active management involvement in directing, coaching, evaluating, and rewarding salespeople according to what they do during the selling process (e.g., selling activities and/or strategies). Under behavioral control, required selling behaviors (e.g., call rate) are assumed to eventually lead to sales outcomes (e.g., sales quota); hence, salespeople are evaluated and compensated for the fulfillment of required activity goals and not the immediate sales outcomes. In contrast, outcome control is like a market contract arrangement that uses incentives such as commissions to reward salespeople on the basis of their immediate sales outcomes (e.g., sales volume). This type of control philosophy holds salespeople accountable for sales outcomes, but managerial involvement in directing the selling process is minimal, which shifts performance risk to the salespeople.

Within the global construct of behavioral control there are two qualitatively different dimensions—activity control and capability control (Challagalla and Shervani 1996). Activity control requires salespeople to perform a pre-determined combination of selling-related activities deemed important for achieving desirable levels of performance. Under activity control, sales managers will monitor salespeople’s actual behavior and reward them on the basis of the performance of these required activities. In contrast, capability control does not specify a set of required activities/procedures for salespeople to follow. Instead, managers set personalized goals for salespeople regarding the levels of skills and abilities they must master, provide diagnostic feedback for those skills and abilities, coach salespeople when necessary, and reward them on the basis of their demonstrated skills and abilities. The sales manager can, for instance, illustrate why a particular course of action in a sales encounter is more effective in one situation but less so in another. A key difference between activity control and capability control is that activity control usually does not provide detailed diagnostic feedback that enables salespeople to recalibrate their strategies in different sales encounters (Mallin and Pullins 2009). For example, a salesperson may be required to visit a certain number of new accounts every month. Activity control may not inform the salesperson as to why some new accounts declined a request for a salesperson’s visit. Prior research has shown that activity control and capability control have differential and even opposite effects (Evans et al. 2007; Miao et al. 2007). Therefore, they should be treated as separate constructs as opposed to a global behavioral control construct.

The great majority of empirical research on sales control systems has focused on the main effects of behavior versus outcome control, and most findings seem to favor behavior-based control due to its stronger positive associations with various salesperson psychological, behavioral, and performance outcomes (Baldauf et al. 2005). Only a few researchers, however, have explored the more complex nature of behavior and outcome control styles by investigating their synergistic effects. For example, Oliver and Anderson (1995) demonstrated that a hybrid control strategy, compared to straight outcome or behavior control, is more effective in enhancing sales reps’ work planning behavior and achievement of company sales/profit goals. A more recent empirical study by Onyemah and Anderson (2009) found support for the positive synergistic effects of sales control systems as long as the behavior and outcome control elements are internally congruent. However, inconsistent findings have also been reported, showing that hybrid sales control systems may not always lead to higher performance (Cravens et al. 2004; Jaworski et al. 1993). Therefore, more research is needed to uncover the intervening mechanisms (e.g., job engagement) responsible for the differential effects of hybrid sales control on salesperson performance. It is also not clear how outcome control, activity control, and capability control may have differing and even conflicting interactive effects with one another; hence, there is a need to bring clarity to the previous research on hybrid control strategies, which has yet to distinguish between these two different types of behavior control.

Job engagement—adaptive selling behavior and selling effort

The sales literature suggests two important ways in which salespeople adjust their goal-related behaviors—adaptive selling behavior and selling effort2 (Brown and Peterson 1994; Fang et al. 2004; Spiro and Weitz 1990). Adaptive selling behavior is defined as “the altering of sales behaviors during a customer interaction or across customer interactions based on perceived information about the nature of the selling situation” (Spiro and Weitz 1990, p. 62). In contrast, selling effort refers to “the force, energy, or activity by which work is accomplished” (Brown and Peterson 1994, p. 71). Because both adaptive selling behavior and selling effort reflect the extent to which salespeople are actively involved in their job, we consider them as indicators of salespeople’s job engagement in our JD-R–based model.

Adaptive selling behavior has been of particular interest to sales researchers as no single selling behavior can be equally effective across different situations (Weitz et al. 1986). While stable personality traits such as self-monitoring, androgyny, and intrinsic reward orientation have been found to have a significant relationship with adaptive selling, a more managerially interesting question is to what extent and how sales organizations can encourage a salesperson’s practice of adaptive selling. Unfortunately, prior research has not successfully linked managerial practice to adaptive selling (Spiro and Weitz 1990; Vink and Verbeke 1993). For example, Spiro and Weitz (1990) tested the relationships between three management styles—initiation of structure, production emphasis, and tolerance of freedom—and adaptive selling but failed to find significant correlations. In this study we rely on JD-R theory to propose and test the interactive effects on adaptive selling behavior of sales control systems, which represent one of the most important sales management tools.

The original adaptive selling scale developed by Spiro and Weitz (1990) was later demonstrated to be composed of two dimensions: adaptive selling belief and adaptive selling behavior (Marks et al. 1996). Consistent with other sales studies involving adaptive selling, we focus on salespeople’s actual adaptive selling behavior in our model because only adaptive selling behavior, but not adaptive belief, is expected to be an immediate precursor of sales performance (Fang et al. 2004; Marks et al. 1996; Roman and Iacobucci 2010).

Job stress—role ambiguity and role conflict

Role ambiguity and role conflict represent the job stress mechanism in our JD-R–based model. Both role ambiguity and role conflict have been demonstrated to have significant effects on boundary-spanning employees’ well-being and performance (e.g., Brown and Peterson 1993; Singh et al. 1994). In the sales literature a salesperson’s role ambiguity refers to “the perceived lack of information a salesperson needs to perform his or her role adequately (e.g., effort instrumentalities) and his or her uncertainty about the expectations of different role set members” (Singh 1998, p. 70), whereas role conflict is defined as “the degree of incongruity or incompatibility of expectations communicated to a role incumbent by role senders” (Michaels et al. 1987, p. 30). In the sales control context, for example, role conflict may arise from incompatible expectations of the sales manager and the customer.

Hypotheses development

Sales control interactive effects on job engagement

As we discussed earlier, although outcome control and activity control are both considered challenge demands in our model, the characteristics of these two types of demands differ significantly. Outcome control allows salespeople a great deal of autonomy in taking their own courses of action when accomplishing sales goals (Anderson and Oliver 1987); in contrast, under activity control salespeople must follow and fulfill a pre-specified set of activities in the selling process (Challagalla and Shervani 1996). Empirical evidence in the sales control literature suggests that outcome control can enhance both salespeople’s learning orientation and performance orientation (Kohli et al. 1998), which can motivate salespeople to be more adaptive and to expend higher levels of selling effort. Therefore, we expect outcome control to have a positive effect on both adaptive selling behavior and selling effort, and such effects will be accentuated by the presence of capability control. This is because capability control can facilitate outcome goal attainment by enhancing salespeople’s ability to effectively deal with different sales situations.

Capability coaching and feedback are instrumental for sales goal achievement by enabling salespeople to work smarter (Sujan et al. 1994). Moreover, capability coaching and associated diagnostic feedback provide a rich pool of resources that serve to reduce salespeople’s cognitive burden in identifying and fixing problems responsible for poor sales outcomes in varying selling situations. For example, capability feedback may inform the salesperson that some promising new leads were not successfully converted to sales due to a mismatch of the salesperson’s selling approach (e.g., relationship marketing) and the new account’s preferences (e.g., transaction-oriented as opposed to a long-term relationship). Through such diagnostic feedback, the salesperson may be rendered a shortcut to the core of the problem without incurring an undue amount of cognitive resources trying to identify and fix it. Consequently, when capability coaching and feedback are available, salespeople will be able to draw on capability control as a job resource to meet demanding outcome goals by learning and practicing different sales approaches under varying selling circumstances. In the absence of capability coaching and feedback, the effect of outcome control on adaptive selling behavior will likely be dampened due to lower levels of perceived adaptive competence and uncertain expectancy of sales outcomes.

Outcome control is also expected to have a positive interactive effect with capability control on selling effort. As salespeople are able to draw on capability resources to develop and master effective selling approaches and skills, they will have a higher level of perceived efficacy of their effort expended during the selling process. When salespeople are confident that their effort is channeled in the right direction (e.g., selling approach matched with customer characteristics), they will be more likely to expend a greater amount of selling effort in order to maximize overall sales and associated rewards (e.g., commission) due to higher perceived expectancy (Vroom 1964). When capability resources are not available, the positive effect of outcome control on selling effort may be weakened because salespeople may not be sure whether their effort is channeled in the correct direction. We advance the following hypothesis:
  1. H1:

    Capability control amplifies the positive effect of outcome control on (a) adaptive selling behavior and (b) selling effort.

     

While the general premise of JD-R holds that job demands interact with job resources to produce a positive effect on job engagement, JD-R also recognizes that how particular pairs of demands and resources interact depends on the specific characteristics of such demands and resources in the work environment (Bakker and Demerouti 2007). In other words, not all types of demands may have an interactive effect with a given job resource. Unlike outcome control that can motivate salespeople to learn new selling skills/approaches in the selling process, activity control is not related to learning orientation (Kohli et al. 1998). Instead, activity control induces a performance orientation by focusing salespeople’s attention and energy on completing a pre-specified set of activities in the selling task. Due to its limited autonomy in the selling process, activity control may actually dampen intrinsic motivation for learning new selling strategies beyond what has been prescribed in activity control requirements (Kohli et al. 1998; Weitz et al. 1986). Therefore, activity control is not likely to be conducive to adaptive selling behavior, even when capability control is rendered. This is because salespeople may not find capability control a particularly useful resource to redirect their courses of action when activity demands are imposed on them. For example, salespeople may be required to push a new product with maximal effort during all sales encounters. Even if capability control suggests that it would be more appropriate to focus on other products when dealing with certain customers, salespeople may not have the motivation or flexibility to leverage this market knowledge due to compliance with the firm’s behavioral directives (Ahearne et al. 2010b). Activity control, however, may motivate salespeople to expend higher levels of effort in performing required selling activities because they are being evaluated and compensated for completing these activities (Ahearne et al. 2010b; Kohli et al. 1998).

When activity control is complemented by capability control, capability coaching and feedback can make salespeople more efficient and effective in carrying out activity requirements during the selling process. For example, while activity control may require that a pre-determined number of new accounts be acquired in a given time period, salespeople can draw on capability control resources to become not only more adept in identifying appropriate new leads (e.g., through leads screening) but also more skilled at converting customers (e.g., identifying appropriate solutions), thereby cutting down overall time and energy needed to fulfill these pre-specified workloads. Therefore, when capability coaching and feedback are made available to salespeople, they may find it possible to expend less effort in performing activity tasks. In other words, we expect capability control to have a negative interactive effect with activity control on selling effort. We hypothesize:
  1. H2:

    Capability control weakens the positive effect of activity control on selling effort.

     

Because both outcome control and activity control are challenge demands, JD-R theory does not provide direct references to their interactive effects. We nonetheless anticipate opposite interactive effects of activity–outcome control on selling effort (positive) vis-à-vis adaptive selling behavior (negative). When salespeople face high levels of activity and outcome requirements at the same time, perceived job demands will multiply because fulfillment of activity requirements may compromise overall sales. For example, under activity control salespeople may be required to contact a predetermined number of clients regarding a recently introduced product, which can hurt overall sales outcomes when it is more appropriate to focus on other products in the product portfolio in dealing with certain types of customers (Ahearne et al. 2010b). According to JD-R theory, when salespeople are dealing with escalated job demands, they tend to use “performance-protection strategies” by increasing overall effort on the task (Bakker and Demerouti 2007). Therefore, the combination of outcome control and activity control may mobilize salespeople to actively cope with the dual challenge demands by increasing intensity and persistence at work, or overall selling effort (Sujan et al. 1994).

Whereas activity control is expected to have a positive interactive effect with outcome control on selling effort, it is expected to inhibit the positive effect of outcome control on adaptive selling behavior. Activity control significantly restricts salespeople’s autonomy and flexibility to adapt in the selling process because the actions of salespeople are under close scrutiny and their evaluations and compensations largely depend on following prescribed selling behaviors (Ahearne et al. 2010b; Kohli et al. 1998). The practice of adaptive selling behavior may require salespeople to adjust or even abandon the prescribed behaviors altogether, which will compromise the fulfillment of activity requirements. For example, given that all customers do not have the same reasons for objections, salespeople should apply different procedures/strategies to cope with different customers/situations. However, under activity control salespeople may be required to apply the standard procedure for overcoming customer objections, making it difficult for salespeople to make necessary adaptations. Therefore, it is anticipated that the positive effect of outcome control on adaptive selling behavior is weaker when activity control is employed concurrently.
  1. H3:

    Activity control (a) weakens the positive effect of outcome control on adaptive selling behavior and (b) amplifies the positive effect of outcome control on selling effort.

     

Sales control interactive effects on job stress

According to JD-R theory, job resources and job demands should produce a suppressing interactive effect on job stress/strain (Bakker and Demerouti 2007). While it has been well supported that hindrance demands are positively related to job strain, the relationship between challenge demands and job strain has been found to be positive, non-significant, or even negative (Demerouti and Bakker 2011; LePine et al. 2005; Van den Broeck et al. 2010), which suggests their relationship is more complex and may depend on some moderators and/or the specific type of challenge demand and/or job stress. As mentioned earlier, outcome control and activity control are challenge demands that induce salespeople to take an active problem-focused coping strategy for goal achievement (Van den Broeck et al. 2010). The sales control literature actually suggests that both outcome control and activity control may reduce salespeople’s role ambiguity (Challagalla and Shervani 1996). Outcome control clearly spells out detailed output goals through which the sales manager’s role expectation of the salesperson is understood; meanwhile, outcome control motivates the salesperson to proactively seek out and understand customers’ distinct needs/problems, thereby clarifying role expectations of the customer. In a similar vein, activity control is a type of organizational formalization expected to clarify role expectations of the sales manager (Michaels et al. 1988), which also brings opportunities (e.g., mandatory attendance at trade shows) for the salesperson to gain better understanding of the customer’s expectations (Challagalla and Shervani 1996).

The presence of capability control as a job resource will likely magnify the negative effect of outcome control on role ambiguity. Under outcome control, for example, salespeople must quickly and accurately figure out the customer’s preferences and expectations, especially during initial contact with the customer (Evans et al. 2000). Capability coaching and feedback help refine and improve the salesperson’s skills for extracting and utilizing valuable information during customer encounters, thereby making it easier for the salesperson to clarify and understand the customer’s expectations. Capability control may also amplify the negative effect of activity control on role ambiguity because capability diagnostics and feedback can enable the salesperson to fine-tune role information when fulfilling activity tasks. For example, a salesperson who follows the required selling procedures may lose a prospective account although the product seems a good solution for the customer’s problem. When this happens, capability control may help the salesperson identify the cause of a customer’s objection, which could be that the salesperson lacked assertiveness in communicating with the customer. Therefore, capability control is a particularly useful job resource in this context because it provides necessary role information for successfully fulfilling activity goals.

When activity control is used concurrently with outcome control, however, the suppressing effect of outcome control on role ambiguity may be weakened. The simultaneous requirements of outcome control and activity control will increase role ambiguity because they may send out incompatible role information, thereby confusing salespeople. For example, by accomplishing activity goals (e.g., predetermined call plan), salespeople may have to make suboptimal decisions in targeting, planning and executing sales calls, which will compromise sales outcomes (Ahearne et al. 2010b). As such, outcome control and activity control are not cognitively consistent in terms of role information. In summary, we hypothesize:
  1. H4a:

    Capability control amplifies the negative effect of outcome control on the salesperson’s role ambiguity.

     
  2. H4b:

    Capability control amplifies the negative effect of activity control on the salesperson’s role ambiguity.

     
  3. H4c:

    Activity control weakens the negative effect of outcome control on the salesperson’s role ambiguity.

     
Outcome control gives the salesperson the discretion to make decisions about job-related activities because management’s active involvement in directing the selling process is minimal (Anderson and Oliver 1987). Under outcome control the sales manager expects the salesperson to generate a high level of sales dollars by empowering the salesperson to be flexible and adaptive in satisfying customers’ needs. Given that successfully satisfying customers’ needs usually translates to sales orders, perceived role conflict of customer and manager expectations should be minimal. Therefore, we expect a negative association between outcome control and role conflict. Although in outcome control the salesperson has a high degree of flexibility in satisfying customers’ needs, customers’ unique situations may still give rise to requests that may run counter to the salesperson’s understanding of his/her role obligations (Zablah et al. 2012). Capability control represents the sales manager’s active support and commitment to the salesperson in solving customer-related issues, which can facilitate the salesperson in coping with such situations, thereby mitigating the salesperson’s perceived role conflict in the selling process (Jones et al. 2003). Therefore, it is expected that the negative effect of outcome control on role conflict is amplified when capability control is employed at the same time.
  1. H5a:

    Capability control amplifies the negative effect of outcome control on the salesperson’s role conflict.

     
In contrast to outcome control, activity control is a type of organizational formalization that requires the salesperson to perform a prescribed set of behavioral activities (Kohli et al. 1998; Michaels et al. 1988). While these behavioral directives clarify the sales organization’s role expectation, the constraints brought about by activity control may inhibit the salesperson’s ability to successfully address the different needs between the customer and the sales organization, thus increasing salesperson’s perceived role conflict (Michaels et al. 1988). For example, certain behavioral directives (e.g., selling a new product to all customers) may create role conflict for a customer-oriented salesperson should this directive not result in a good fit with certain customer needs (Jones et al. 2003). In this context, capability control is not likely to be a particularly useful job resource because it cannot address the source of the perceived role conflict. Thus, we do not expect capability control to have an interactive effect with activity control on role conflict. We do expect, however, activity control to weaken the negative effect of outcome control on role conflict because activity requirements can significantly inhibit the salesperson’s flexibility to align the customer’s and the sales organization’s interests.
  1. H5b:

    Activity control weakens the negative effect of outcome control on the salesperson’s role conflict.

     

Other relationships in the model

The hypotheses of this study focus on the interactive effects of outcome control, activity control, and capability control on the salesperson’s job engagement and job stress. We next briefly discuss other relationships we do not hypothesize but are specified in the model. The main effects of selling behaviors on sales performance have been well documented in prior literature, where adaptive selling behavior (e.g., Spiro and Weitz 1990) and selling effort (e.g., Brown and Peterson 1994) were found to positively affect sales performance. To the extent that no single selling approach will work equally well across different customers, adaptive selling enables the salesperson to make adjustments based on distinct customer needs, which leads to better sales performance. The sales literature also demonstrates that by working longer hours or increasing the number of sales calls, selling effort can improve sales performance (Brown and Peterson 1994; Fang et al. 2004).

Role ambiguity and role conflict are expected to have detrimental effects on job engagement because job stress leads to negative job attitudes and discourages salespeople from investing time and energy in the job (Zablah et al. 2012). Moreover, role conflict may increase role ambiguity because the conflicting role expectations (e.g., incompatible company policy and customer requests) can increase the salesperson’s uncertainty about the best way to perform his/her job (Behrman and Perreault 1984; Hartline and Ferrell 1996; Miao and Evans 2007). When role ambiguity is high, salespeople have a difficult time understanding what is expected of them or what courses of action are appropriate to complete the task, thereby resulting in lower levels of performance (Jaworski and Kohli 1991; Miao and Evans 2007; Roman and Iacobucci 2010). The direct relationship of role conflict and sales performance has been inconclusive as positive, negative, or non-significant relationships have been reported in the literature (e.g., Grant et al. 2001; Michaels et al. 1987; Singh 1998; Zablah et al. 2012), which suggests that role conflict may have a negative effect on sales performance mainly through role ambiguity.

Research method

Sample and data collection

The U.S. manufacturing sector (SIC codes 20–39) was selected as the setting for the empirical test of the proposed hypotheses. An important consideration in selecting this empirical setting was sufficient variability in the application of outcome control, activity control, and capability control (e.g., Challagalla and Shervani 1996; Kohli et al. 1998). A random name list of 1,561 sales managers (each from a different firm) within SIC codes 20–39 was obtained from a leading list broker. A marketing research firm solicited from the list sales managers who agreed to provide access to their salespeople to participate in the study. To encourage participation, sales managers were promised a copy of the summary of the study findings; in addition, the participating salespeople were guaranteed confidentiality and were given the chance to win one of ten gift cards. A total of 471 sales managers (for a 30.2% response rate) agreed to participate. They, in turn, provided a total of 1,371 salespeople for this study.3 Envelopes including salesperson cover letters, salesperson surveys, and postage-paid return envelopes were mailed to each of the 471 sales managers for distribution to their salespeople. Three weeks after the first mailing, a reminder package enclosing the same materials was sent to each of the 471 sales managers. This two-wave mailing effort generated 223 completed salesperson surveys (for a response rate of 16.3%). The sample was composed primarily of men (78.6%). The average salesperson had a tenure of 9.6 (SD = 8.2) years at his/her present job and 15.7 (SD = 11.1) years of full-time sales experience, and approximately 42% were between 40 and 55 years of age.

To assess potential non-response bias, comparisons of early and late responses (first 20% versus last 20%) across study variables were made using t- tests, which resulted in non-significant differences (p > .10). Therefore, non-response bias is not likely a serious concern (Armstrong and Overton 1977).

Measurement

All multi-item measures were adapted from existing literature and their standardized loadings, Cronbach’s alpha, and average variance extracted (AVE) are reported in the Appendix. The three sales control constructs were measured with scales from Kohli et al. (1998). Specifically, activity control (α = .86), capability control (α = .92), and outcome control (α = .93) were each measured with five items. Research has shown that the original adaptive selling scale developed by Spiro and Weitz (1990) was not unidimensional as it measured both adaptive selling belief and adaptive selling behavior (Marks et al. 1996). Therefore, following recent sales research involving adaptive selling (e.g., Fang et al. 2004; Roman and Iacobucci 2010), we used a six-item scale that measures only the dimension of adaptive selling behavior (α = .83). Selling effort (α = .73) was measured with three items from Sujan et al. (1994) assessing the total amount of effort salespeople put into their work. Role ambiguity (α = .78) and role conflict (α = .78) were each measured with three items adapted from Netemeyer et al. (2004). Salesperson performance (α = .87) was measured with five items from Cravens et al. (1993) assessing salespeople’s contributions to their company’s objectives such as market share and dollar sales amount.

We also included two covariates—sales experience and education level—in the model, in addition to controlling for the main effects of outcome control, activity control, and capability control. According to JD-R theory, sales experience and education level are considered as individual-level job resources (as opposed to organizational level job resources such as sales control systems) that may have an impact on job engagement and job stress (Bakker and Demerouti 2007). Sales experience was measured using the total number of years of full-time sales jobs. Education level was measured as a categorical variable indicated by the salesperson’s highest degree accomplished. Effects on job engagement, job stress, and salesperson performance of these variables are tested simultaneously in the model.

Measurement model

Confirmatory factor analysis (CFA) was performed using EQS 6.1 to evaluate the validity of the eight latent constructs in the study. Each item was restricted to its a priori factor and each factor was allowed to correlate with all the other factors. The measurement model demonstrated an acceptable fit (χ(532)2 = 937.238, p < .01; NFI = .925, NNFI = .962, CFI = .966, SRMR = .066, RMSEA = .060). All a priori factor loadings of the latent constructs were large, positive, and significant (p < .01), demonstrating convergent validity (Bagozzi et al. 1991). Discriminant validity was evaluated in two ways. First, each pair of constructs was assessed using nested CFA models in which a one-factor model was compared to a two-factor model using chi-square difference tests; in each case the two-factor model had a significantly better fit (p < .01). Second, the average variance extracted (AVE) by each construct was greater than its shared variance with all other constructs (Fornell and Larcker 1981). These analyses established the validity of all the constructs used in this study. The descriptive statistics of the data are reported in Table 1.
Table 1

Descriptive statistics of model variables

 

1

2

3

4

5

6

7

8

9

10

M

SD

1. Activity control

1

         

5.26

1.15

2. Capability control

0.72**

1

        

4.73

1.39

3. Outcome control

0.74**

0.69**

1

       

5.25

1.36

4. Adaptive selling behavior

0.25**

0.16*

0.24**

1

      

5.48

.90

5. Selling effort

0.22**

0.19**

0.26**

0.43**

1

     

5.08

1.08

6. Role conflict

−0.12

−0.14*

−0.13

−0.08

0.00

1

    

3.55

1.44

7. Role ambiguity

−0.35**

−0.20**

−0.34**

−0.30**

−0.32**

0.08

1

   

2.46

1.10

8. Salesperson performance

0.39**

0.31**

0.42**

0.41**

0.44**

0.00

−0.44**

1

  

5.38

1.00

9. Sales experience

0.07

0.03

0.09

0.14*

0.18**

−0.15*

−0.11

0.23**

1

 

15.68

10.98

10. Education

−0.09

−0.15*

−0.10

0.03

0.09

−0.03

0.02

0.01

0.07

1

N/A

N/A

*p < .05

**p < .01

Assessment of common method variance

Because the data came from a single respondent, there is a concern of potential common method variance (CMV). To assess the extent to which CMV may exist in the data, we re-estimated the measurement model by including a common method factor (Carson 2007). The common method factor loads on all items and controls for variance and covariance among the items introduced by responses from a single source. The common method factor is not correlated with other trait constructs, reflecting the assumption that the degree of CMV is independent of the true magnitude of trait constructs (Homburg et al. 2011). Variance decompositions and CMV-adjusted AVE for each construct are reported in Table 2. It was found that trait variance (average 83.9%) significantly exceeds both method (average 4.4%) and error variance (average 11.7%) for all scales and that the average magnitude of method variance is comparable with those of other recent marketing studies (e.g., Carson 2007; Kim et al. 2006). Because the method factor accounts for a very small percentage of variance explained and our hypotheses involve complex positive as well as negative interactions that could not be easily explained by CMV, we conclude CMV was not likely a serious threat in this study.
Table 2

Variance decompositions for multi-item scales

Scale

Traita

Methodb

Errorc

Adjusted AVEd

Outcome control

0.859

0.097

0.044

0.798

Activity control

0.858

0.056

0.086

0.669

Capability control

0.834

0.100

0.066

0.717

Adaptive selling behavior

0.884

0.013

0.103

0.594

Selling effort

0.779

0.026

0.195

0.576

Role conflict

0.772

0.022

0.206

0.562

Role ambiguity

0.821

0.036

0.143

0.664

Salesperson performance

0.904

0.005

0.091

0.669

aPercentage of variance explained by trait (construct)

bPercentage of variance explained by common method factor

cPercentage of variance explained by error

dAdjusted for common method variance/covariance in the items

Results

We used a partial least squares (PLS) structural equation model (SEM) to test the hypothesized relationships in Figure 1 for the following reasons. First, PLS is a latent variable modeling approach that allows us not only to simultaneously estimate the entire conceptual model inclusive of all structural paths but also to take into account measurement quality of indicators (Ahearne et al. 2010a; Chin et al. 2003; Mintu-Wimsatt and Graham 2004). Second, PLS is superior to covariance-based SEM when a product-indicator approach for modeling interactions is employed (see Chin et al. 2003 for a detailed comparison of covariance-based SEM versus PLS for modeling product-indicator latent variable interactions). Specifically, model specification for product-indicator interactions in covariance-based SEM is known to create specification tedium, errors, and estimation difficulties due to nonlinear constraints that must be specified, especially in larger SEM models (Ping 1996). Bagozzi and Yi (2012) also note that interaction effects in covariance-based SEM require complex constraints when specifying the model and a very large sample is needed to give correct parameter and standard error estimates. In our model, for example, covariance-based SEM would require a sample size of at least 1,305 cases, which is much larger than our sample size that is otherwise considered adequate in PLS (see Chin et al. 2003 for sample size comparisons).

Following recent sales research (Ahearne et al. 2010a), we multiplied standardized indicators of independent and moderating variables to create latent construct interactions in PLS. In Table 3, we report standardized path coefficients and their t-values (based on 1,000 bootstrapping in PLS), along with the R2 for each endogenous construct. In addition, we plotted significant interaction effects; these plots provide visual depictions of how effects of independent variables differ with high vs. low levels of the moderators. Results appear in Fig. 2.
Table 3

PLS hypothesis testing results

Path

Standardized estimates

t-value

Adaptive selling behavior

0.25a

 

 Outcome control × Capability control ➝ Adaptive selling behavior

0.29*

2.16

 Activity control × Capability control ➝ Adaptive selling behavior

10

0.93

 Outcome control × Activity control ➝ Adaptive selling behavior

35**

2.64

 Outcome control ➝ Adaptive selling behavior

0.16

1.53

 Activity control ➝ Adaptive selling behavior

0.09

0.94

 Capability control ➝ Adaptive selling behavior

06

0.98

 Role conflict ➝ Adaptive selling behavior

02

0.34

 Role ambiguity ➝ Adaptive selling behavior

25**

3.24

 Sales experience ➝ Adaptive selling behavior

0.10*

1.79

 Education ➝ Adaptive selling behavior

0.05

1.15

Selling effort

0.24a

 

 Outcome control × Capability control ➝ Selling effort

0.21*

1.99

 Activity control × Capability control ➝ Selling effort

−0.12

1.26

 Outcome control × Activity control ➝ Selling effort

0.19*

2.03

 Outcome control ➝ Selling effort

0.34**

2.77

 Activity control ➝ Selling effort

−0.09

1.33

 Capability control ➝ Selling effort

0.04

0.56

 Role conflict ➝ Selling effort

0.15**

2.45

 Role ambiguity ➝ Selling effort

26**

3.40

 Sales experience ⟶ Selling effort

0.12*

2.27

 Education ➝ Selling effort

0.08

1.43

Role ambiguity

0.34a

 

 Outcome control × Capability control ➝ Role ambiguity

−0.16

1.35

 Activity control × Capability control ➝ Role ambiguity

−0.22*

1.79

 Outcome control × Activity control ➝ Role ambiguity

0.28*

2.28

 Outcome control ➝ Role ambiguity

39**

3.71

 Activity control ➝ Role ambiguity

34**

3.47

 Capability control ➝ Role ambiguity

0.26**

3.23

 Role conflict ➝ Role ambiguity

0.09*

1.67

 Sales experience ➝ Role ambiguity

−0.02

0.52

 Education ➝ Role ambiguity

0.01

0.30

Role conflict

0.16a

 

 Outcome control × Capability control ➝ Role conflict

−0.22*

1.72

 Activity control × Capability control ➝ Role conflict

0.04

0.39

 Outcome control × Activity control ➝ Role conflict

−0.10

1.03

 Outcome control ➝ Role conflict

−0.26*

2.12

 Activity control ➝ Role conflict

−0.09

0.93

 Capability control ➝ Role conflict

−0.06

0.82

 Sales experience ➝ Role conflict

−0.12*

2.00

 Education ➝ Role conflict

−0.06

1.16

*Significant at p < .05 (one-tailed)

**Significant at p < .01 (one-tailed)

aVariance explained in endogenous variables

https://static-content.springer.com/image/art%3A10.1007%2Fs11747-012-0315-4/MediaObjects/11747_2012_315_Fig2_HTML.gif
Fig. 2

Analysis of interaction effects. a Effects on adaptive selling behavior. b Effects on selling effort. c Effects on role ambiguity. d Effect on role conflict

H1a states that outcome control and capability control have a positive interactive effect on adaptive selling behavior, which is supported (β = .29, p < .05).4 The simple slope analysis (Fig. 2, panel A) shows that at a low level of capability control, outcome control has no effect on adaptive selling behavior (β = .08, ns); at a high level of capability control, outcome control is positively related to adaptive selling behavior (β = .30, p < .05). H1b is also supported because outcome control and capability control have a positive interactive effect on selling effort (β = .21, p < .05). The simple slope analysis (Fig. 2, panel B) shows that at a low level of capability control, outcome control has no effect on selling effort (β = .09, ns); at a high level of capability control, outcome control is positively related to selling effort (β = .33, p < .01).

H2 states that activity control and capability control have a negative interactive effect on selling effort. It only received directional support but it is not significant (β = −.12, ns); hence, H2 is not supported. As expected, activity control and capability control have no interactive effect on adaptive selling behavior (β = −.10, ns).

H3a predicts that outcome control and activity control have a negative interactive effect on adaptive selling behavior, which is supported (β = −.35, p < .01). The simple slope analysis (Fig. 2, panel A) shows that at a high level of activity control, outcome control has no effect on adaptive selling behavior (β = .06, ns); at a low level of activity control, outcome control is positively related to adaptive selling behavior (β = .65, p < .01). H3b is also supported because outcome control and activity control have a positive interactive effect on selling effort (β = .19, p < .05). The simple slope analysis (Fig. 2, panel B) shows that at a high level of activity control, outcome control is positively related to selling effort (β = .27, p < .01); however, at a low level of activity control, outcome control has a positive but non-significant effect on selling effort (β = .11, ns).

H4a received only directional support as outcome control and capability control have a negative but non-significant interactive effect on role ambiguity (β = −.16, ns); therefore, H4a is not supported. H4b is supported because activity control and capability control have a negative interactive effect on role ambiguity (β = −.22, p < .05). The simple slope analysis (Fig. 2, panel C) shows that at a low level of capability control, activity control has no effect on role ambiguity (β = −.08, ns); however, at a high level of capability control, activity control has a negative effect on role ambiguity (β = −.59, p < .01). It was also found that outcome control and activity control have a positive interactive effect on role ambiguity (β = .28, p < .05), in support of H4c. Figure 2 (panel C) indicates that at a low level of activity control, outcome control has a negative effect on role ambiguity (β = −.22, p < .05), yet at a high level of activity control, outcome control has no effect on role ambiguity (β = .03, ns).

H5a states that outcome control and capability control have a negative interactive effect on role conflict, which is supported (β = −.22, p < .05). The simple slope analysis (Fig. 2, panel D) shows that at a low level of capability control, outcome control has no effect on role conflict (β = −.07, ns); however, at a high level of capability control, outcome control has a negative effect on role conflict (β = −.36, p < .01). H5b is not supported because outcome control does not appear to have an interactive effect with activity control on role conflict (β = −.10, ns). Finally, as we anticipated, activity control does not have an interactive effect with capability control on role conflict (β = .04, ns).

We next report results related to the sales performance portion of the model that were tested but not specifically hypothesized. As expected, both adaptive selling behavior (β = .16, p < .01) and selling effort (β = .22, p < .01) have positive effects on salesperson performance. Job stressors were found to have a detrimental effect on sales performance, albeit through different mechanisms. Role ambiguity has a direct negative effect on performance (β = −.27, p < .01), whereas role conflict has a negative indirect effect on salesperson performance via role ambiguity (β = .09, p < .05) as role conflict is not directly associated with performance (β = .05, ns). In addition, role ambiguity has negative effects on adaptive selling behavior (β = −.25, p < .01) and selling effort (β = −.26, p < .01), whereas role conflict has, unexpectedly, a positive effect on selling effort (β = .15, p < .01). It appears that a moderate level of role conflict may actually “stimulate the salesperson to exert higher levels of selling effort” (Miao and Evans 2007, p. 93).

As for the main effects of sales control, outcome control directly enhances selling effort (β = .34, p < .01), whereas activity control does not (β = −.09, ns). Meanwhile, although neither outcome control (β = .16, ns) nor activity control (β = .09, ns) seems to have a direct influence on adaptive selling behavior, the positive effect of outcome control on adaptive selling behavior is borne out only when capability control is employed concurrently. Both outcome control (β = −.39, p < .01) and activity control (β = −.34, p < .01) suppress role ambiguity, and outcome control also has a negative effect on role conflict (β = −.26, p < .05). Noteworthy is that these mitigating effects on job stress are salient only when outcome control and activity control are complemented by a high level of capability control, which highlights the value of considering sales control interactive effects. The only unexpected finding is that capability control has a positive effect on role ambiguity (β = .26, p < .01), which we will discuss in the subsequent section. It was also found that activity control (β = .10, ns), outcome control (β = .13, ns), and capability control (β = .03, ns) do not have direct effects on salesperson performance. Finally, the covariate—sales experience—was found to have positive effects on adaptive selling behavior (β = .10, p < .05), selling effort (β = .12, p < .05), salesperson performance (β = .16, p < .01), and a negative effect on role conflict (β = −.12, p < .05).

Discussion and implications

This research investigates the interactive effects of outcome control, activity control, and capability control on the salesperson’s job engagement (i.e., adaptive selling and selling effort) and job stress (i.e., role ambiguity and role conflict) above and beyond their main effects through the lens of JD-R theory (Bakker and Demerouti 2007). While prior sales control research suggests that outcome control and activity control may motivate adaptive selling behavior and selling effort (Kohli et al. 1998) while reducing job stress (Challagalla and Shervani 1996), results of this study demonstrate that capability control is a crucial job resource salespeople can draw on in the sales control context when they are faced with challenging job demands. In fact, the positive effects of outcome control and activity control will be especially pronounced only when capability control is concurrently employed.

A particularly interesting finding is that outcome control can enhance the salesperson’s adaptive selling behavior only when capability coaching and feedback are provided in the selling process. When capability control is not provided concurrently, outcome control does not seem to motivate salespeople’s initiative to be adaptive during customer interactions. According to JD-R theory, adaptive selling will likely tax the salesperson’s valuable resources (e.g., time and mental energy) because it involves actively collecting, interpreting, and responding to distinct customer information across selling situations. Therefore, the extent to which the salesperson will actively engage in adaptive selling depends on whether relevant job resources are available to reinforce the salesperson’s cognitive capabilities and to facilitate adaptation (Bakker and Demerouti 2007; Hobfoll 2001).

Capability coaching and diagnostic feedback can help salespeople hone in on their adaptive knowledge and skills. For example, capability feedback may point out to the salesperson that the relative effectiveness of a particular type of influence strategy (e.g., inspirational appeals) is contingent on the buyer’s interaction orientation (e.g., relational vs. transactional) as opposed to the communication style per se (McFarland et al. 2006). As such, salespeople faced with demanding sales outcome goals are able to draw on capability control as a valuable job resource by modifying their selling strategies more effectively, thereby more likely to be engaged in the practice of adaptive selling. When capability coaching and feedback are not available, salespeople may be more reluctant to invest resources in adaptive strategies due to lower perceived competence and/or higher perceived risks. Although outcome control has a direct positive effect on selling effort, moderation analysis indicates that such an effect is especially pronounced when there is a high level of capability control. As salespeople receive capability feedback and see improvement in their selling skills/knowledge, they will be more confident in the direction they channel their effort, thereby motivating them to expend a greater amount of overall effort in attaining outcome goals due to higher levels of expectancy (Vroom 1964). Activity control does not have an interactive effect with capability control on either adaptive selling behavior or selling effort. JD-R theory states that which job resource and job demand may have an interactive effect depends on the specific job characteristics under consideration (Bakker and Demerouti 2007). Therefore, capability control does not appear to be a particularly useful resource in engaging salespeople when they have to perform activity-based tasks.

Although not predicted by JD-R theory, we also found significant interaction effects on adaptive selling behavior and selling effort of outcome control and activity control, where activity control appears to be a double-edged sword in that it enhances the positive effect of outcome control on selling effort but at the same time dampens outcome control’s positive effect on adaptive selling behavior. Activity control prescribes a combination of activities salespeople must perform in the selling process, which can be at odds with optimal resource allocation leading to suboptimal sales performance (Ahearne et al. 2010b). When fulfilling activity demands may compromise sales outcomes (e.g., sales volume), salespeople may have to use “performance-protection strategies” by increasing overall effort (e.g., longer working hours) in order to meet these dual demands (Bakker and Demerouti 2007). Meanwhile, activity control can mitigate the positive effect of outcome control on adaptive selling behavior. Adaptive selling behavior requires salespeople to vary selling procedures and/approaches across different selling situations, thereby risking not fulfilling tasks required by activity control. For example, activity control may require salespeople to focus on a particular product during the sales calls, whereas adaptive selling may suggest that different products be emphasized contingent on customer types and selling situations (Spiro and Weitz 1990). Therefore, activity control will discourage adaptive selling behavior when it is concurrently employed with outcome control.

The fact that outcome control enhances adaptive selling behavior only when there is a low level of activity control and especially when there is a high level of capability control challenges prior studies touting virtues of behavioral controls (without clarification as to whether activity or capability control is being deployed), which are often measured as activity control (e.g., Cravens et al. 2004; Jaworski et al. 1993; Oliver and Anderson 1994; Ramaswami 1996).5 Results of this study suggest that activity control can, in fact, be counterproductive and that researchers should distinguish between activity control and capability control when investigating sales control interactive effects.

Consistent with prior sales control research (Challagalla and Shervani 1996; Jaworski and Kohli 1991), we found that outcome control and activity control can reduce role ambiguity. However, a major departure of our study from the extant literature is that our results suggest that the main effects of outcome control and activity control should be interpreted carefully due to their significant interactions with each other or with capability control. Specifically, activity control is found to be associated with lower levels of role ambiguity only when there is a high level of capability control; when capability control is minimal, activity control does not reduce role ambiguity. It appears that capability control is a useful job resource in providing additional role information for salespeople to perform activity tasks more effectively. Activity control usually does not provide detailed diagnostic feedback beyond a pre-determined set of activities that salespeople must perform (Mallin and Pullins 2009). For example, a salesperson may be required to visit a certain number of new accounts every month. Activity control may not inform the salesperson why some new accounts declined the salesperson’s visit. Therefore, capability control can fill the void by providing additional role information for salespeople to successfully fulfill required activity goals. In contrast, when activity control is combined with outcome control, role ambiguity will increase.

Although outcome control can reduce the salesperson’s role ambiguity because it clearly specifies expectations of the sales organization (Jaworski and Kohli 1991), the simultaneous requirements of activity control can lead to lower outcome performance expectancy and role uncertainty because some activity goals may be accomplished at the expense of sales outcomes (Ahearne et al. 2010b). While we did not find that capability control amplifies the negative effect of outcome control on role ambiguity, researchers should be cautious in interpreting this non-significant finding as the sign is in the predicted direction which is marginally significant (i.e., p < .10). With a larger sample size it may become significant.

An unexpected finding is that capability control has a positive effect on role ambiguity, which resonates with Challagalla and Shervani’s (1996) caution that capability appraisal can be perceived by salespeople as highly subjective and prone to variability in interpretation. Our results indicate that capability control can effectively enhance role clarity only when it is employed together with activity control. That is, activity requirements may serve as a more objective reference point for capability appraisal and feedback to be considered an objective and helpful job resource by salespeople, thereby reducing salesperson’s role ambiguity in performing activity-based tasks. This finding also reveals that, although well intended, capability control as a standalone control tool may have its limits. Moreover, outcome control was found to suppress role conflict especially when there is a high level of capability control. As a valuable job resource, capability control also reflects management’s commitment and support in customer-focused selling, which makes salespeople better able to cope with role conflict arising from satisfying customers’ needs (Jones et al. 2003). Finally, activity control does not interfere with outcome control’s suppressing effect on role conflict; it appears that the detrimental effect on job stress of activity-outcome hybrid control is mainly through role ambiguity. Taken together, these results provide compelling evidence that the interactive effects of outcome control, activity control, and capability control must be considered beyond their main effects to better understand the complex nature and consequences of sales control systems.

This study also offers important managerial implications for sales organizations. Managers need to understand that hybrid sales control systems affect salesperson performance through their effects on salesperson’s job engagement and job stress. Managers are advised to employ outcome-capability hybrid control because capability control is a job resource capable of motivating both working smarter (i.e., adaptive selling) and working harder (i.e., selling effort) behaviors and mitigating role conflict when salespeople are facing challenging job demands such as a high sales quota. Managers should also recognize that activity control and capability control are two distinct types of behavioral control that have a positive synergistic effect on role clarity. Simply prescribing a set of behavioral activities in the selling process may not provide adequate task information for salespeople who encounter problems; capability coaching and feedback can complement activity control by providing necessary additional information for salespeople to better perform activity requirements. Moreover, outcome-activity hybrid control entails a tradeoff in that it can increase selling effort but decrease adaptive selling behavior and role clarity at the same time. The cost of the dual demands of outcome and activity goals may prove to outweigh the benefit. Therefore, managers should use caution if outcome control and activity control are to be employed together. Overall, these findings suggest that investigating outcome control, activity control, and capability control in isolation from one another may compromise researchers’ and managers’ ability to understand the complex nature of sales control systems due to their significant and differential interactive effects.

Limitations and future research directions

Our findings are subject to some limitations that also inform future research directions. One limitation of this study is the cross-sectional nature of the data used and a relatively low response rate. Although caution was used in the design of the questionnaire (Podsakoff et al. 2003) and post hoc statistical analysis indicated that common method bias is not likely a serious concern, future research could collect longitudinal performance data from a larger representative sample to verify hypothesized relationships in this study.

Second, this study considers adaptive selling behavior as a global construct. Research has suggested that there may be a variety of influence tactics for effective adaptive selling such as information exchange, promises, or inspirational appeals (McFarland et al. 2006). Future research could investigate how outcome control, activity control, and capability control may interact to enhance or detract from each of those influence tactics when salespeople practice adaptive selling behavior.

Third, as with the great majority of sales control research, this study focuses on sales performance as the key criterion variable. As the selling environment becomes more relational in nature, investigating differential interactive effects of sales control strategies on customer relationship-forging tasks or relationship-building performance as key outcome variables (e.g., Hunter and Perreault Jr. 2007) are warranted. Perhaps more interestingly, the extent to which relational customers may provide diagnostic feedback to the salesperson in particular selling tasks (e.g., key account management) and how such customer-based feedback may have interactive effects with formal sales control systems provide valuable direction for future research.

Footnotes
1

Although some activity-based tasks (e.g., entering call data into a CRM system) can be considered by salespeople as time consuming and even counterproductive, activity control nonetheless prescribes a combination of instrumental behavior-based requirements (e.g., selling a new product to a predetermined client list) that challenge salespeople in terms of cognitive demands, workload, and time pressure. We thank an anonymous reviewer for making this observation.

 
2

We do not claim that adaptive selling behavior and selling effort have been the only selling behaviors addressed in the sales literature; instead, we consider these two types of selling behaviors because they represent levels of salespeople’s investment in the selling process that have been frequently investigated by sales researchers.

 
3

Because multiple salespeople in the same company may have provided data, responses from the salespeople working for the same company may not be independent, which can affect the results derived from the PLS structural equation model. To test the extent to which this clustering effect may be present, we followed Wieseke et al. (2012) by calculating intraclass correlation coefficients (ICC) and design effects by multiplying the ICC by (average cluster size −1) and adding 1. Obtained design effects range from 1.02 to 1.37, which are all well below 2, suggesting bias due to clustering can be considered negligible (Wieseke et al. 2012).

 
4

Per reviewers’ suggestion, we also tested the three-way interactive effects of outcome control, activity control, and capability control on job engagement (i.e., adaptive selling behavior and selling effort) and job stress (i.e., role ambiguity and role conflict) using the two-step latent score construction procedure in PLS (Ahearne et al. 2010b; Chin et al. 2003). None of the results were significant, and the inclusion of the three-way interactive effects did not change the significance of other results. Therefore, in the interest of parsimony we do not include the three-way interaction term in the model.

 
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We thank an anonymous reviewer for this insight.

 

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© Academy of Marketing Science 2012