“Too Much” Self-Efficacy? Understanding the Curvilinear Consequences of Between-Person Self-Efficacy through a Moderated-Mediation Model of Perceived Proximity and Employee Effort

Abstract

Building from a paradox frame, we argue that at the between-person level the relationship between self-efficacy and effort may be nonlinear. We bound our conceptual model with a focus on perceived proximity, reflecting employees’ perceptions of how close they feel to their organization and colleagues. We test our model in a lagged, multi-source field study, with matching employee survey data from 1502 employees, and archival effort, and performance, metrics collected several months later. The results from our analyses reveal a curvilinear association between self-efficacy and effort, which is moderated by perceived proximity. We also find that the relationship between self-efficacy and performance is mediated by effort for individuals with low self-efficacy (Low SEs), but not for individuals with moderate (Moderate SEs) or high self-efficacy (High SEs). Implications for theory and practice are discussed.

Keywords

self-efficacy effort performance perceived proximity curvilinear paradox

For over 40 years, organizations researchers have recognized the importance of self-efficacy, which refers to individuals’ perception of their capacity to complete specific tasks and reach goals (e.g., Bandura, 1977; 1986; 1997). This construct has its roots in social cognitive theory (SCT; Bandura, 1986), which provides that individuals operate in a dynamic environment that provides insight into—and feedback bearing on—behavior that can impact expectations and confidence associated with one’s abilities to perform work tasks (i.e., self-efficacy). The process is reciprocal, as self-efficacy can enhance performance by increasing the difficulty of self-set goals, as well as directing and escalating efforts and persistence toward goal accomplishment (Bandura, 1977; 2012; Bandura & Locke, 2003). For many years, scholars had widely accepted the position that—at the between-person level of analysis—self-efficacy has systematically positive consequences for a range of organizationally functional outcomes, including both effort and performance (cf. Judge & Bono, 2001; Stajkovic & Luthans, 1998).

Accumulating research evidence, however, has fundamentally challenged this broad expectation. Scholarship bearing on this relationship has been reported from a range of settings (see Table 1), such as education, entrepreneurship, and decision-making. This research indicates that “too” high levels of self-efficacy may engender dysfunctional consequences, such as strategic inflexibility (Audia, Morwitz, & Kumar, 2000), resource misallocation (Stone, 1994; Beck & Schmidt, 2018), commitment to overly risky actions (Simon & Houghton, 2003), as well as diminished motivation (Rodriguez et al., 2014), effort (Vancouver, Gullekson, Morse, & Warren, 2014), and performance (e.g., Hmieleski & Baron, 2008; Tzur, Ganzach, & Pazy, 2016; Vancouver et al., 2014). What this suggests is that a complex set of dynamics underlie the association between self-efficacy and its consequences.

Table 1.

Between-Person Self-Efficacy Paradox Literature Review Table.

Study	Sample	Dependent Variable(s)	Analytical Method	Reported Finding(s)
Audia et al. (2000)	168 undergraduate students completing a business simulation	Strategic persistence	Hierarchical regression	High self-efficacy was associated with greater strategic inflexibility in a computer-based business simulation
Beck & Schmidt (2018)	82 undergraduate students completing a problem-solving task	Resource allocation	Random coefficient modeling; quadratic term	High levels of perceived self-efficacy was negatively associated with resource allocation under scarce time conditions
Ede et al. (2017)	75 undergraduate students participating in an exercise task	Performance	Regression analysis; quadratic term	Self-efficacy exhibited a curvilinear relationship with performance in an exercise endurance task
Gregus et al. (2017)	34 elementary school teachers	Students’ victimization experiences	Hierarchical linear modeling, quadratic term	Teachers’ self-efficacy exhibited a curvilinear (U-shaped) relationship with students bullying experiences
Hayward & Hambrick (1997)	Archival data on 106 firm/CEO acquisition transactions	Inflated firm acquisition premium	Multiple regression analysis	Highly confident CEOs overvalue their businesses
Hmieleski & Baron (2008)	159 CEO/company founders	New venture performance	Moderated hierarchical regression	High levels of entrepreneurial self-efficacy, coupled with high optimism, related negatively to venture performance
Lee et al. (2019)	188 supervisor-subordinate dyads from South Korea	Creativity	Hierarchical regression; quadratic term	Self-efficacy had a curvilinear (inverted U-shaped) relationship with creativity that was moderated by supervisor close monitoring
Leon-Perez et al. (2011)	164 MBA students participating in a simulated negotiation	Relational negotiation outcomes	Hierarchical regression; quadratic term	Self-efficacy had a curvilinear (inverted U-shaped) relationship with subjective relational negotiation outcomes
Mann & Eland (2005)	83 medical students completing an instructional laboratory task	Skill performance	t-tests	Individuals with high self-efficacy spent less time practicing a newly learned task than those with low self-efficacy
Rodriguez et al. (2014)	95 Spanish university teachers	Students’ academic motivation	ANOVA and multiple comparison tests	Teachers with high self-efficacy had students with lower academic motivation, relative to teachers with moderate self-efficacy
Simon & Houghton (2003)	65 executives working in high technology firms	Riskiness of product innovations	Hierarchical multiple regression	Managers who expressed extreme certainty of achieving success were more likely to introduce overly risky product innovations
Stone (1994)	139 undergraduate students participating in a decision task	Cognitive effort; attention	ANOVA; post-hoc comparisons	Self-efficacy was associated with the allocation of less attention and effort toward tasks
Tzur et al. (2016)	114 undergraduate students completing a problem-solving task	Performance	Planned comparisons for simple effects	Self-efficacy exhibited a negative relationship with performance under low reward conditions
Vancouver et al. (2014)	86 undergraduate students completing an anagram task	Effort; performance	Planned comparisons	Self-efficacy exhibited a negative relationship with effort and performance quality
Vancouver & Kendall (2006)	63 undergraduate students	Resource allocation	Hierarchical linear modeling	Self-efficacy exhibited a negative relationship with resource allocation (i.e., study time)
Vancouver et al. (2008)	112 undergraduate students completing a decision-making task	Resource allocation	Hierarchical linear modeling, quadratic term	Self-efficacy exhibited a negative relationship with effort allocated to accepted goals
Whyte et al. (1997)	132 undergraduate students completing a decision-making task	Intentions to escalate commitment	Planned comparisons	Intentions to escalate commitment to a losing course of action were more pronounced among participants in the high self-efficacy condition
Wilson et al. (2014)	38 parents	Parent sensitivity	Qualitative analysis	Parenting self-efficacy exhibited an inverted U-shaped relationship with parental sensitivity
Zacharakis & Shepherd (2001)	53 venture capitalists completing an investment scenario-based task	Decision accuracy	Regression	Venture capitalists with high levels of confidence are more likely to invest in unprofitable ventures

Recognizing that self-efficacy can exhibit both positive (e.g., Bandura & Locke, 2003; Judge & Bono, 2001; Sitzmann & Yeo, 2013; Stajkovic & Luthans, 1998; Yeo & Neal, 2006), and negative relationships with self-regulatory outcomes (Vancouver et al., 2014), scholars have begun investigating the underlying complexity of these relationships by, for example, exploring nonlinearity in the association between self-efficacy and key self-regulatory mechanisms. This framing is consistent with the too-much-of-a-good-thing principle, which suggests that ordinarily beneficial antecedents may be harmful when taken too far (Pierce & Aguinis, 2013). For example, there is evidence that self-efficacy exhibits nonlinear relationships with creativity (Lee, Yun, Lee, & hyun Lee, 2019), entrepreneurial business ownership (Gielnik, Bledow, & Stark, 2020), task performance (Ede, Sullivan, & Feltz, 2017), as well as negotiation, and training outcomes (see Table 1).

Decades ago, Gecas (1989, p. 311) called for research “…which more clearly determines the parameters of [between-person] self-efficacy’s beneficial consequences, that is under what conditions might high self-efficacy…be dysfunctional.” Since then, little such conditional—or threshold—research has focused on the shape of the relationship between self-efficacy and regulatory outcomes (see Gielnik et al., 2020 for an exception). Given emerging conceptual and empirical tensions, we adopt between-persons theory and design. We explore whether the relationship between self-efficacy and effort reaches a “critical threshold,” perhaps as a consequence of complacency or overconfidence (Gecas, 1989; Gist, 1987; Hmieleski & Baron, 2008) which may emerge due to impaired decision-making, as evidenced by reported cognitive inflexibility (Audia et al., 2000), risk insensitivity (Simon & Houghton, 2003), and situational myopia (Lovallo & Kahneman, 2003). Thus, we contribute to the literature by exploring nonlinearity in the association between self-efficacy and effort, a key self-regulatory outcome.

We also seek to also address questions relating both to the mechanism of translation and boundary conditions associated with downstream consequences of self-efficacy. For example, Sitzmann and Yeo (2013, p. 562) urged researchers to continue “…developing an integrative framework of the mediating and moderating mechanisms” that affect self-efficacy-performance relationships. Thus, we also: (a) examine the mediating role of effort in the relationship between self-efficacy and performance, and (b) introduce perceived proximity (Bandura, 1977) as a moderator of this indirect relationship in an effort to generate more nuanced insight into the mechanics of the association between self-efficacy and key organizational outcomes.

A central notion in SCT is that observational learning impacts behavior, and that the results of behavior drive expectations that impact future behavior. Self-efficacy research embedded in the SCT frame directly implies that perceived proximity may impact relationships between self-efficacy and workplace outcomes (e.g., Bandura, 1977; 1997; Mulki & Jaramillo, 2011; Staples, Hulland, & Higgins, 1998). This idea is exemplified by the dynamics outlined in SCT (e.g., Bandura, 1999), which specifies that learning—here, learning bearing on how much effort to exert within a performance setting—emerges within social contexts as a result of reciprocal interactions between individuals, their behavior, and the environment.

Perceived proximity reflects employees’ perceptions of how close they feel to their organization and colleagues (Wilson, O’Leary, Metiu, & Jett, 2008), which is increasingly salient given the proliferation of remote work arrangements (Wilson et al. 2008). Underscoring the contingency posed by remoteness, Raghuram, Wiesenfeld, & Garud (2003, p. 183) noted that distant employees are less likely to be “exposed to cues that help define and constrain not only the nature of their tasks and the outcomes that are desirable, but also the process by which the work should be done.” The interplay between self-efficacy and perceived proximity for understanding variation in performance outcomes is particularly relevant in sales settings, the context of the current study, as remote work represents a predominate paradigm in modern sales organizations (Mulki & Jaramillo, 2011). SCT provides that perceived proximity should enhance employees’ opportunities to develop insight into the requisite effort necessary to generate functional levels of performance, and should thus impact expectations associated with given levels of effort.

With this focus, we make four key contributions. First, we build deeper understanding of the relationship between self-efficacy and effort, at the between-person level of analysis. Embracing the notion of paradox (Lewis, 2000), we argue the relationship between self-efficacy and effort may be nonlinear, taking the form of an inverted U-shape. Second, building from foundational theory in the domain (Bandura & Locke, 2003), we examine the mediating role of effort in the indirect relationship between self-efficacy and performance. Third, we adopt a rigorous, two-lines analytical approach (Simonsohn, 2018) to test proposed nonlinear relationships; examining whether the indirect relationship among self-efficacy, effort, and performance differs at different levels of self-efficacy. Finally, consistent with core theory (e.g., Bandura, 1997), and in recognition of the increasing prevalence of remote work, we bound our model with a focus on the moderating role of perceived proximity in the indirect relationship between self-efficacy and performance. Our conceptual model is depicted in Figure 1.

Figure 1.

Conceptual model.

Background and Theoretical Development

Self-Efficacy, Effort, and Performance

Considerable research supports the expectation that, at the between-person level of analysis, self-efficacy is predictive of task-related effort and performance (e.g., Judge, Jackson, Shaw, Scott, & Rich, 2007; Stajkovic & Luthans, 1998). Consistent with SCT, this evidence generally suggests that people with higher self-efficacy (High SEs) exert more effort and perform at a higher level, relative to those with lower self-efficacy (Low SEs). For example, Pajares (1997, p. 4) argued that self-efficacy beliefs “…help determine how much effort people will expend on an activity…and how resilient they will prove in the face of adverse situations.” The underlying logic is that, because High SEs devote more resources (e.g., time, energy, and effort) to their tasks, (compared to Low SEs), this facilitates higher downstream performance. Thus, consistent with a voluminous body of conceptual and empirical evidence, we expect that there is a positive, main-effects association between self-efficacy and subsequent performance, leading to the following:

There is a positive relationship between self-efficacy and performance.

SCT provides that self-efficacy is malleable, being subject to “barriers” and “facilitators” including task characteristics (e.g., complexity; resources), interpersonal factors (e.g., modeling; feedback), the work environment (e.g., distractions; risk), and individual-difference factors (e.g., ability; performance strategies; Gist & Mitchell, 1992; Fuchs, Sting, Schlickel, & Alexy, 2019). For example, when a task that depends on analytical skills is embedded in a distracting environment (e.g., intrusive interpersonal traffic), the work environment may diminish employees’ capacity estimates (i.e., because distractions can diminish employees’ ability to leverage analytical skills) relative to a less distracting environment where these skills can be more effectively engaged (e.g., Gist & Mitchell, 1992). However, although the “more is better” self-efficacy perspective has long prevailed, mounting evidence across domains (e.g., organizational behavior; entrepreneurship; education; strategy) suggests that “too-high” levels of self-efficacy can have negative consequences. For example, as regards the potential double-edged nature of situational facilitators, while frequent positive performance feedback can increase employees’ self-efficacy, “too-high” self-efficacy may contribute to a decrease in downstream effort.

A number of between-person experimental studies are illustrative of this “too-high” self-efficacy effect. For example, Whyte, Saks, Hook (1997) manipulated self-efficacy perceptions in a decision-making task, and found that intentions to escalate commitment to a losing course of action were more pronounced among High SEs (i.e., those whose self-efficacy was “too-high”). Also using a decision-making task, Stone (1994) found that self-efficacy was associated with the allocation of less attention and effort toward tasks, arguing that high self-efficacy induces complacency. Audia et al. (2000) reported similar results, that High SEs exhibited strategic inflexibility in a business simulation. More recently, Vancouver et al. (2014) reported that High SEs exhibited less effort and lower performance in an anagram task; while Beck and Schmidt (2018) found that self-efficacy related negatively to resource allocation under scarce time conditions. Contextualized against the “too-much-of-a-good-thing” principle (Pierce & Aguinis, 2013), it becomes clear that, rather than emerging as a result of “high” self-efficacy, these negative downstream consequences are more likely to have emerged as a result of “too” high levels of self-efficacy. Together, these studies offer evidence of negative effects of “too high” (between-person) self-efficacy. It is meaningful to emphasize that the above results all emerged within laboratory settings. This is an important consideration because participants in these studies were unlikely to have had substantial previous experience with the experimental task(s), and against which they would have been in position to base their self-efficacy judgments.

Thus, it is also important to note here that a similar pattern of results also emerges within field studies of incumbents, where participants are more likely to have hands-on task experience thus enabling them to develop more functionally anchored judgments of self-efficacy. Despite this backdrop, field studies also offer evidence of the negative (between-person) consequences of “too high” self-efficacy. For example, Hmieleski and Baron (2008) found that high entrepreneurial self-efficacy, coupled with high optimism, related negatively to venture performance. Likewise, highly efficacious executives tend to overvalue and pay excessive premiums for acquisitions (Hayward & Hambrick, 1997), introduce overly risky innovations (Simon & Houghton, 2003), and ignore useful information (Lovallo & Kahneman, 2003). Similarly, highly efficacious venture capitalists tend to invest in unprofitable ventures (Zacharakis & Shepherd, 2001). As above, the too-much-of-a-good thing principle suggests that it was likely “too high” (rather than “high”) self-efficacy that was responsible for this pattern of results. We surmise these consequences emerged because “expectations” (e.g., Bandura, 1986) based on experience provided unrealistically optimistic assessments of necessary levels of effort.

A review of the accumulated evidence bearing on negative outcomes associated with self-efficacy reveals several themes. First, “too” high levels of self-efficacy clearly have the potential to impair decision-making, as evidenced by reported cognitive inflexibility, dysfunctional escalation of commitment, dysfunctional ex-ante and ex-poste risk insensitivity, and situational myopia. Second, “too” high levels of self-efficacy appear to have operational implications bearing on resource allocation, as reflected by evidence of misallocation of time to study, practice, and problem solving. A third theme that emerges is that “too” high self-efficacy is associated with lower performance across a range of outcomes, as evidenced by reported deficits in problem-solving, negotiation, and task performance (lab), and lower creativity and new venture performance (field).

These results, and the three underlying themes we identified through our review of this research, raise a question; namely, whether, at the between-person level of analysis, is more self-efficacy (i.e., “too much” self-efficacy) a good thing, or “too much” of a good thing (i.e., Pierce & Aguinis, 2013)? Rather than resign this tension the “theoretical disagreements” category, we propose that a paradox (Lewis, 2000) underlies the relationship between self-efficacy and key self-regulatory outcomes, such as outcome expectations derived from experience (e.g., Bandura, 1986). We propose a framework that integrates these seemingly incompatible findings, and argue the relationship between self-efficacy and effort may be characterized by an inverted U-shape (Grant & Schwartz, 2011). We leverage the notion of a critical threshold to argue that, while high self-efficacy may produce functional levels of effort, and high subsequent performance, when self-efficacy becomes “too high” (i.e., exceeds a critical threshold between “high” and “too high”), decision-making and resource allocations may become dysfunctional, leading to negative downstream consequences.

Consistent with SCT, we expect that self-efficacy falling below a critical threshold (i.e., tipping point) will lead to less effort. Low SEs tend to doubt they have the behavioral capability to accomplish tasks. They also tend to develop expectations of difficulty or failure, an inclination to give up, and attributions of failure due to inadequate knowledge, skills or abilities (KSAs). Consistent with the tenets of SCT and evidence in the domain, we expect that low self-efficacy is associated with lower effort (Bandura, 1986; Stajkovic & Luthans, 1998).

At the other end of the self-efficacy spectrum, a fundamentally different set of perceptions emerges, but with similar consequences – less effort. Confidence in one’s ability is typically associated with higher performance expectations (Bandura, 1989), envisioned (and anticipated) success (Shipman & Mumfurd, 2011), task-related effort and persistence (Bandura, 1997). However, the decisive question is whether “too” high levels of this otherwise beneficial characteristic result in lower-than-necessary effort. Here, we anticipate that “too much” self-efficacy can foster complacency, and the expectation that little (or less than necessary) effort is required for goal achievement.

This notion is not new, and is echoed in the success-induced complacency phenomenon (Miller, 1994). Research reveals that (too) high self-efficacy can lead to decreased effort because of a reliance on (overly) optimistic ability judgments. For example, Margolis and McCabe (2004) argued that High SEs may devote less time and effort preparing than necessary. Similarly, Fang, Palmatier, & Evans (2004) reported that, when faced with achievable performance goals, (too) high SEs may believe less-than-necessary effort is needed to reach those goals. Salespeople, for example, may be assigned sales goals requiring that many sales calls be made, given historically low conversion rates (Armstrong, Morwitz, & Kumar, 2000). When self-efficacy is “too” high, salespeople may overestimate likely conversion rates, and thus execute fewer sales calls (i.e., effort). Or, they may fail to prepare sufficiently to achieve their goals, viewing preparation as necessary only for those with lesser KSAs. Self-efficacy exceeding a critical threshold (“too” high self-efficacy) may thus lead to neglect of otherwise essential activities, viewed as unnecessary given perceived KSAs. While lower-than-necessary effort is more likely to emerge at a higher level of self-efficacy for some individuals than others, “too” high self-efficacy may clearly induce complacency that constrains the allocation of resources needed for goal accomplishment.

The above suggests that some self-doubt regarding KSAs may be beneficial, and result in the highest level of effort. We offer that employees with moderately high self-efficacy (Moderate/High SEs) are likely to believe they have the KSAs to effectively accomplish their tasks, while also recognizing that doing so requires preparation and hard work. In light of observed negative consequences of polar self-efficacy, its benefits may be most evident at “moderate/high” levels, where debilitating self-doubt and complacency are less likely. Thus:

Self-efficacy has an inverted U-shape relationship with effort.

Among the key, recognized mechanisms responsible for self-efficacy’s performance benefits are the perseverance and effort exerted toward goal accomplishment in the face of obstacles. Indeed, effort and perseverance are established consequences of self-efficacy, and are characteristics by which self-efficacy is defined. Self-efficacy is a recognized predictor of task-related effort, which is a factor that contributes to variation in performance outcomes (e.g., Judge et al., 2007; Stajkovic & Luthans, 1998). For example, Pajares (1997) argued that self-efficacy beliefs contribute to the effort expended to achieve task goals, and resilience in the face of obstacles. It is ultimately because High SEs devote more resources to their work that they are able to achieve higher performance, and thus we predict that effort mediates the relationship between self-efficacy and performance (cf. Beck & Schmidt, 2018).

Effort has a positive relationship with performance.

Effort mediates the relationship between self-efficacy and performance.

The Moderating Impact of Perceived Proximity

Finally, while we expect Moderate/High SEs to exhibit the most effort, this relationship likely depends on employees’ perceived proximity to their coworkers and organization. SCT offers that there are reciprocal, deterministic relationships between the behaviors people engage in, the behaviors they observe in their environment, and the consequences of these actions for understanding and predicting future behavior (Bandura, 1986). People develop expectations bearing on the outcomes their behaviors are likely to engender based on social observation and learning, and then engage in behaviors based on anticipated and experienced consequences. The more extensive the opportunities to build realistic expectations about the consequences of their behavior, the more likely individuals are to develop patterns that coincide with functional expectations. Our focus on perceived proximity is informed by these expected patterns.

Perceived proximity, a recognized driver of key work outcomes beyond variation accounted for by objective proximity (O’Leary, Wilson, & Metiu, 2014), is associated with several cognitive and behavioral workplace outcomes, including perceptions of (and reactions to) the environment (Mathieu, 1991), certainty (Hollander, 2009), and commitment (Monge & Contractor, 2003). Thus, it offers employees the opportunity to observe, learn, and develop insight into normative or expected levels of effort associated with functional performance outcomes (Bandura, 1989).

The allocation of requisite task-specific effort is a known consequence of self-efficacy (e.g., Kanfer & Heggestad, 1997). In forming efficacy beliefs, individuals assess their task-specific resources and the potential constraints that apply when performing a task (Gist & Mitchell, 1992). As Stajkovic and Luthans (1998, p. 255) argued, “…unless the definitions of the task and task circumstances are provided in a clear and concise manner, employees may not be able to accurately assess complex task demands, may not fully know what they have to do, and thus will lack accurate information for regulating their effort.” A range of factors are likely to substantively impact ambiguity within the sales role specifically. These can include uncertainty regarding autonomy orbiting tasks and activities, task priority, advancement criteria, sales manager support, procedures relating to ethical situations associated with partners, expectations and demands bearing on sales interactions, how to address customers’ objections, among others (see Rhoads, Singh, & Goodell, 1994). Thus, proximity offers employees opportunities to gain insight into normative standards against which to benchmark their efforts within performance settings where role ambiguity may be pervasive (e.g., Goolsby, 1992).

Absent these opportunities to develop normative effort benchmarks, employees may misinterpret or misjudge requisite or functional levels of effort (i.e., those necessary to achieve goal-anchored standards of performance). Such misperceptions may engender faulty assessments of the effort required to achieve work-related goals, which both theory and empirical research suggest is likely to have negative performance implications. As Bandura (1997, p. 66) argued, “…efficacy beliefs cannot operate as a relative influence in an information vacuum.” Scholars agree that self-efficacy is subject to the influence of a range of contextual factors (e.g., Gist & Mitchell, 1992). Consistent with this position, Fuchs et al. (2019) noted that the same individual may exhibit different levels of confidence in their abilities, depending on contextual cues. For example, Hambrick, Finkelstein, & Mooney (2005) argued that CEOs’ decision-making and overconfidence may be subject to variation in task environment intensity, and speculated as to whether trait-dependent attributions would be less frequent in less intense task environments.

At higher levels of perceived proximity, Moderate/High SEs should have greater opportunity to benchmark their own levels of effort against normative or ambient levels of effort present within their organization broadly, and among their coworkers specifically. As a consequence, they are less likely to experience the complacency that can lead to lower (i.e., dysfunctional) levels of effort. We anticipate that high perceived proximity is likely to help clarify employees’ effort-related expectations (Kiesler & Cummings, 2002). Perceived proximity also may engender a sense of surveillance; that one’s effort is being monitored by coworkers and supervisors (Kiesler & Cummings, 2002; Wilson et al., 2008). Thus, in addition to providing employees with opportunities to learn about and develop a sense of what functional levels of effort look like as a consequence of exposure to their coworkers, perceived proximity also may systematically influence employees’ conformity to normative expectations of effort though experienced social pressure (Forsyth, 1998). Perceived proximity thus should provide employees with opportunities to develop a clearer understanding of effort necessary to accomplish role-responsibilities and achieve performance goals, as well as instill feelings of social pressure to approximate these normative expectations.

These ideas are embedded in the underlying theoretical framing anchoring self-efficacy research, which provides that contextual factors that define employees’ operating spaces have the potential to impact relationships between self-efficacy, effort, and performance (Bandura, 1997; Gist & Mitchell, 1992). Importantly, these influences include cues and prompts that employees derive from the social and interpersonal aspects of their work environment. For example, observing or being aware of coworkers can provide information about relative levels of ability (Bandura, 1986), “correct” performance strategies (Gist & Mitchell, 1992), and the levels of effort and persistence required for effective task performance (Kanfer & Ackerman, 1989).

Thus, when perceived proximity is high, employees with otherwise “too” high self-efficacy may have more insight into the levels of effort necessary to achieve performance goals, and may feel more pressure to exert the necessary effort to accomplish them. As a result, when perceived proximity is high, employees with otherwise “too” high self-efficacy may be less vulnerable to self-efficacy driven complacency. Thus, we expect that high perceived proximity will flatten the curve for employees with “too” high self-efficacy. In contrast, when perceived proximity is low, “too” high self-efficacy may lead to the (potentially inaccurate) conclusion that sufficient effort has been exerted to achieve satisfactory performance. Consistent with the too-much-of-a-good thing principle (Pierce & Aguinis, 2013), when perceived proximity is low, employees with “too” high self-efficacy may invest less-than-necessary effort, contributing to an inverted U-shaped relationship, and the following:

Perceived proximity moderates the relationship between self-efficacy and effort, such that there is: a) an inverted U-shaped relationship when perceived proximity is low, and b) a positive relationship when perceived proximity is high.

Methods

Participants and Data Collection

Our sample consisted of salespeople in a U.S. information technology solutions firm offering cloud services to corporate clients. Our multi-source, temporally lagged data consisted of: (a) written salesperson surveys (collected at Time 1) and (b) archival effort and job performance data (collected 3 months later at Time 2). Employees were informed that their participation in this survey was intended to help the firm better understand factors relating to sales performance. Respondents, whose primary duties included selling to and servicing customers, were assigned to a specific geographic area and product portfolio. On average, approximately 80% of their compensation was derived from salary, and 20% from performance-based commissions. Survey data were collected as the first step in a consulting initiative to provide training recommendations to top management¹. We distributed surveys to all 2012 sales representatives in the firm. After removing incomplete responses and collecting objective data at Time 2 (end of fiscal quarter), our resulting sample was 1502 (74.7%) usable, matched responses. The average age of respondents was 36.7, and 62% of our sample was male.

Measures

Unless otherwise noted, employees responded to measures using a 7-point scale, with responses ranging from 1 (strongly disagree) to 7 (strongly agree).

Self-efficacy . We indexed self-efficacy with Jones’ (1986) eight-item scale (α = .77; Mean = 5.3, SD = .90), which we modified to reference the current (rather than a future) job. Items focused on the extent that employees feel confident about their job skills and capabilities. A sample item is, “My job is within the scope of my abilities.” In a review of how self-efficacy has been measured in sales research, Peterson (2020) noted that most studies set within a sales context using a rating scale to index self-efficacy leveraged a modified version of the Jones (1986) scale. There is considerable evidence bearing on the reliability and validity of a modified version of the scale in sales (Renn & Fedor, 2001; Ahearne, Mathieu, & Rapp, 2005; Rapp, Baker, Bachrach, Ogilvie, & Beitelspacher, 2015) and other contexts (e.g., Jex & Bliese, 1999; Chen & Bliese, 2002). While the scale measures only the strength (i.e., confidence) aspect of self-efficacy, Lee and Bobko (1994) noted the strength-based approach is the most common method for measuring self-efficacy, and showed that strength-based measures correlate highly with magnitude, confidence, and composite measures.

Perceived proximity . We indexed perceived proximity with eight-items (α = .93, Mean = 5.2, SD = 1.3) from O’Leary et al.’s (2014) twelve-item scale. We adapted the scale by changing the referent from specific colleagues (i.e., [distal colleague]) to reference proximity to the organization and colleagues. Sample items include, “Psychologically, my organization and colleagues feel close” and “Even when working remotely, I still feel close to my organization and colleagues.” Due to survey length constraints imposed by the focal organization, we shortened the scale by dropping four items in advance of the survey. To preserve the scale’s intended factor structure, which includes affective and cognitive components (O’Leary et al., 2014; Wilson et al., 2008), we retained four items reflecting each of these components. We dropped three items that did not reference proximity (i.e., close, far, distance), such as “I have a warm feeling about…” and “I often think about…” We also dropped one item for which the wording would have become confusing/unclear if we changed the referent to the organization and colleagues (rather than specific colleagues) for the purpose of the focal study.

Effort . We collected an archival measure of effort from company records. Following the approach from previous research which has operationalized effort in terms of sales call activity (e.g., Ahearne, Jones, Rapp, & Mathieu, 2008), we operationalized effort as average weekly activities aimed at attracting or retaining customers (i.e., the number of sales calls) over the post-survey fiscal quarter (Mean = 33.8, SD = 16.5). Firm managers use this behavior-based metric to track sales representatives’ efforts toward achieving their performance goals (Anderson & Oliver, 1987).

Performance . Three months after the survey, we collected an objective, archival performance measure. Following previous research using sales samples (e.g., Ahearne et al., 2005; 2008) we indexed performance as achieved monthly sales relative to quota (percentage-of-quota), which we averaged to reflect job performance across the post-survey fiscal quarter. Average percentage-of-quota achieved in the sample was 101.69 (SD = 8.90). The firm uses this metric to make key employee decisions (e.g., bonuses, evaluations, and promotions). Salespersons’ quotas are established annually by an external consulting firm, with input from management, and are based on relevant factors including employees’ past performance, as well as market and territory information. The quota measure controls possible confounding factors (e.g., customer potential/density, territory size) which can impact sales performance (Churchill, Ford, Hartley, & Walker, 1985). While performance expectations vary by industry, it is typical to expect most salespeople (∼60%) to meet/exceed quota. Not surprisingly, this suggests that the mean of our performance measure is reflective of “average” sales performance, with achieved performance one standard deviation above/below the average reflective of high/low performance.

Covariate . Because experience can impact task performance (e.g., Quinones, Ford, & Teachout, 1995), we control for experience with an index score calculated as an average of employees’ experience in sales, the industry, and the company (α = .86, Mean = 10.1, SD = 7.96). Table 2 contains correlations, descriptive statistics, and Cronbach’s alpha for latent measures.

Table 2.

Correlations between Study Variables.

Variable	Mean	SD	1	2	3	4	5	6
1. Self-efficacy (SE)	5.3	.90	.77
2. Perceived proximity (PP)	5.2	1.3	.15**	.93
3. Effort	33.8	16.5	.06*	−.06*	––
4. Performance	101.7	8.90	.19**	−.01	.05**	––
5. Experience	10.1	7.96	−.01	.03	.07**	.01	.86
6. SE x PP	––	––	−.18**	−.07**	−.05	−.09**	.03
7. SE² x PP	––	––	.29**	.46**	.05*	.15**	−.04	−.60**

Note. n = 1502; * p < .05; ** p <. 01. Cronbach’s alpha for latent variables is reported on the diagonal.

Analytical Approach

The results from univariate tests of normality (Shapiro-Wilk statistics) demonstrate non-normal distributions for our two outcome variables [(effort = .75; p < .01); performance = .98 (p < . 01)]. A lack of univariate normality is often present with real data (Micceri, 1989; DeCarlo, 1997), as these statistical tests tend to be overly sensitive to large sample sizes. Normally distributed data is a long-recognized challenge in the field (Micceri, 1989), but statistical tests with robust estimators provide relevant alternatives (DeCarlo, 1997).

In addition, because effort is known to be conditionally skewed (toward zero, with a greater number of participants giving minimal vs. maximum effort), we accounted for this tendency in our study design. Following recommendations that in research focused on effort-related outcomes, it is important to leverage large sample sizes that tend to be robust to normality assumptions (Schmidt & Finan, 2018). The underlying logic here is that, when N is large enough, the sampling distribution begins to approximate normality due to constraints implied by the Central Limit Theorem. In large samples, the sampling distribution tends toward normal, regardless of the shape of the data (Denis, 2018; Rovai, Baker, & Ponton, 2013). However, because our data violated the assumption of multivariate normality, we leveraged the Satorra-Bentler scaled chi-squared test, which is the appropriate assessment for goodness of fit testing in this case because it provides chi-square testing for continuous, non-normal outcomes. Using a scaling correction, this approach is robust to nonnormality (Satorra & Bentler, 2010). Using R (Version 4.1.1) we examined model fit using a Satorra-Bentler scaled chi-square test. With robust model estimates (scaling correction factor = 1.22), the results confirmed that our model demonstrated good fit (χ² (148) = 868.80, p < .01; CFI = .93; RMSEA = .057; CI [.054, .060]; SRMR =.058).² All items loaded significantly on their intended latent factor (p < .01).

To provide a rigorous and robust test of the presence of an inverted U-shaped association between self-efficacy and effort, we leveraged Simonsohn’s (2018) “two-line” approach. First, we estimated a flexible (quadratic) model of effort that included only self-efficacy and self-efficacy². Initial regression results exhibited a positive slope (7.382) at the lowest observed value of X (self-efficacy_min), supporting our expectation of an inverted U-shape. Next, we estimated a cubic spline regression to obtain fitted values of y (effort), and specifically identify the most extreme fitted y-value (effort_max). Given our expectation of a U- rather than a V-shaped relationship, we identified the set of most extreme y-values, by considering y_flat, (all y-values within one standard error of y_max) (Simonsohn, 2018). The corresponding set of x-values provided candidates for x_c (the breakpoint in our U-shaped relationship). Using the median x-value in this range as our interim breakpoint (e.g. 5.5), we ran an interrupted regression, including this breakpoint in the first and second segment. The resulting Z-values for the slopes of the two lines were Z₁ = 4.519 and Z₂ = 1.063. Using the ratio Z₁/(Z₁+Z₂), we computed the percentile of the x-value within x_flat, representing the ultimate breakpoint required for the final interrupted regression. We used this breakpoint (x_c = −5.13) to test significance in the first and second regression segments. Results were supportive, with a significant positive slope for the first segment (b = 2.19, p < .01), and a significant negative slope for the second segment (b = −2.93, p < .01). Given that the quadratic regression approach to testing U-shaped relationships has an unacceptably high false-positive rate (Simonsohn, 2018), the two-line analysis provided initial substantiation for our curvilinear expectations. The test shows the severity of the slope of the curve’s positive and negative areas, providing insight into the impact that additional units of self-efficacy (x) have on effort (y).

Because salespeople were nested within managers, we tested for manager effects by running a MANOVA test. This MANOVA model included manager ID as the independent variable, with effort and performance as the dependent variables. We found a nonsignificant manager effect for both effort [F= .974, p = .615] and performance [F= .976, p = .603]. These results suggest that neither effort nor performance evidenced manager-idiosyncratic variance, and that modeling nested effects was unwarranted. Thus, we tested the moderated curvilinear relationship using hierarchical regression (De Stobbeleir, Ashford, & Buyens, 2011; Lam, Huang, & Snape, 2007), rather than hierarchical linear modeling.

To enhance interpretability and reduce multicollinearity, variables were mean centered prior to creating the interaction and quadratic terms (Aiken & West 1991; Dawson & Richter 2006). These results appear in Table 3. We employed six steps to examine the moderated quadratic effects in our model. We first entered our covariate (i.e., experience), followed by the linear effect of self-efficacy (Steps 1 and 2). To assess curvilinearity, we added a quadratic self-efficacy term (Step 3). In Step 4, we added the moderator term (i.e., perceived proximity), followed by the self-efficacy-perceived proximity interaction (Step 5). Finally, in Step 6, we produced the final model, retained all previous terms and which included perceived proximity, and its interaction with the self-efficacy quadratic.

Table 3.

Summary of Six-Step Regression Analysis with Effort Criterion.

	Step 1 and 2			Step 3 H2: Quadratic Effect			Step 4 and 5			Step 6 H5: Interaction Effects
Variable	B	β	p	B	β	p	B	β	p	B	β	p
Constant	32.18	––	––	32.88	––	––	32.84	––	––	32.67	––	––
Experience	.16	.08	.003	.16	.08	.002	.17	.08	.002	.17	.08	.001
Self-Efficacy (SE)	.99	.06	.031	.36	.02	.479	.51	.03	.313	.42	.02	.410
Self-Efficacy²				−.83	−.08	.003	−.82	−.08	.006	−.67	−.07	.028
Perceived proximity (PP)							−1.03	−.08	.002	−1.44	−.11	.000
SE X PP							−.24	−.02	.422	.19	.02	.591
SE² X PPP										.33	.08	.037
df		1499			1498			1496			1495
F		6.74			7.45			6.47			6.13
ΔF		––			.71			.98			.34
R ²		.009			.015			.021			.024

Notes. N = 1502. Linear effects are not directly hypothesized but tested prior to curvilinear and interaction models.

Results

The experience covariate related significantly to effort (β = .08, p < .01), and self-efficacy exhibited a positive linear relationship with effort (β = .06, p < .05). This pattern of results aligns with research bearing on the relationship between self-efficacy and effort at the between-person level of analysis (e.g., Bandura, 1977; 1997; Locke & Latham, 1990); as well as the positive influence of experience on self-efficacy (e.g., Ahearne et al., 2005). These findings underscore the validity of our baseline model. Step 3 identified a significant, negative quadratic relationship between self-efficacy and effort (β = −.08, p < .01). While this significant quadratic term alone does not signal an inverted U-shape relationship, coupled with the results from the two-lines analysis, this pattern substantiates the curvilinear prediction we make in H2. The inverted U-shaped pattern suggests that, beyond a critical threshold, self-efficacy may be negatively associated with effort. Finally, perceived proximity significantly interacted with the curvilinear self-efficacy term (β = .08, p < .05), supporting H5. We plotted this interaction to enhance interpretation of the results (Dawson & Richter 2006). As depicted in Figure 2, the curve begins to “flatten” at high levels of perceived proximity. This suggests that when employees feel less proximal to their organization and colleagues, the curvilinear relationship remains, yet high levels of perceived proximity appear to mitigate the negative effects of “too much” self-efficacy.

Figure 2.

Moderating effect of perceived proximity on the relationship between self-efficacy and effort.

While the central limit theory provides that as samples become very large, estimates tend to converge (Denis, 2018), we confirm and expand on our initial findings by using robust bootstrapping and confidence interval testing. First, to estimate the causal paths and instantaneous indirect effects for a mediation model in which the relationship between the independent and mediating variables are quadratic (curvilinear), we used the Hayes and Preacher (2010) MEDCURVE macro in SPSS 27.0. This allowed us to simultaneously model the quadratic effect of self-efficacy on effort (b = −.78, SE = .28, p < .01) and the linear effect of effort on performance while controlling for self-efficacy (b = .03, SE = .01, p < .05). These results provide support for H1 and H3, respectively. Variance in performance explained by the mediation model was significant (R² = .07, p < .01), indicating that effort plays a significant mediating role (p < .05) in the relationship between self-efficacy and performance. This finding, given the curvilinear relationship between self-efficacy and performance suggests the need to parse out mediation effects at varying levels of self-efficacy.

The SPSS MEDCURVE macro (Hayes & Preacher, 2010) estimates simultaneous indirect effects of a causal driver through a proposed mediator (Hayes & Preacher, 2010) at high, average, and below average values of self-efficacy (± 1 SD from the mean). Bootstrapping analysis (sample = 1000) revealed that, among Low SEs, the quadratic self-efficacy term significantly increases performance through effects on effort (θ [4.3] = .06; 95% CI [.004, .124]). Among Moderate SEs (θ [5.3] = .01; 95% CI [-.006, .063]) and High SEs (θ [6.2] = −.03; 95% CI [-.110, .008]), increasing self-efficacy had no effect on performance via effort. This finding suggests that the relationship between self-efficacy, effort, and performance depends on self-efficacy. These results align with accumulating evidence bearing on the importance of accounting for paradox in the relationship between self-efficacy and self-regulatory outcomes.

Discussion

For many decades, organizations researchers broadly assumed that self-efficacy has uniformly positive associations with self-regulatory outcomes (e.g., effort; performance). However, accumulating evidence over the past 15 years, from laboratory and field studies alike, offers substantial evidence that self-efficacy does not generate uniformly positive outcomes. Rather than having a straightforward linear association, results from a growing body of research reinforce the need to adopt a nuanced approach to explaining outcomes associated with self-efficacy, which may be more complex than previously assumed. In the current study, we address paradoxical findings relating to the downstream consequences of self-efficacy, and argue that the indirect relationship between self-efficacy and objective performance is both in part carried through effort, and also is nonlinear at the between-person level of analysis. Our model coincides with the implications of the too-much-of-a-good-thing principle (Pierce & Aguinis, 2013), which stipulates that otherwise ordinarily beneficial antecedents may generate unproductive, dysfunctional outcomes when taken too far; and can account for an apparently paradoxical pattern of relationships which have, to-date, lacked a coherent explanation.

The current field study, which leverages a temporally lagged, multi-source, between-person design, represents an effort to reconcile the paradoxical record bearing on self-regulatory consequences of self-efficacy. With this focus, we make three primary contributions relating to: (a) potential nonlinearity in the relationship between self-efficacy and effort, (b) the mediating role played by effort in the relationship between self-efficacy and downstream performance outcomes, and (c) boundary conditions bearing on the character of this indirect relationship.

Implications for Theory and Practice

First, to address an apparent paradox bearing on the association between self-efficacy and performance, we advance and test the proposition that the relationship between self-efficacy and effort may have an inverted U-shape, when self-efficacy becomes “too” high. Our focus on nonlinearities in the self-efficacy ecosystem aligns with previous research that examines the nature of relationships between self-efficacy and proximal self-regulatory outcomes. For example, scholars have reported that self-efficacy may exhibit a curvilinear (inverted U-shaped) relationship with resource allocation (i.e., effort) at the within-person level of analysis (cf. Vancouver et al., 2014), and that team efficacy exhibits a similar curvilinear relationship with team-level effort and performance (Rapp, Bachrach, Rapp, & Mullins, 2014), and problem-solving outcomes (Tasa & Whyte, 2005). Notably, in a departure from the growing body of work in the domain, our model is among the first efforts describing curvilinearity in the relationship between self-efficacy and effort at the between-person level of analysis (see Gielnik et al., 2020 for evidence of an inverted U-shaped relationship between self-efficacy and business ownership). Of the research reporting nonlinear self-efficacy outcomes (see Table 1), our study is the first to test such effects with the robust, analytically conservative approach suggested by Simonsohn (2018).

Consistent with the implications of foundational SCT conceptual framing in the domain (e.g., Bandura, 1999), we theorized that Low SEs are likely to doubt their abilities to accomplish their work tasks (Bandura, 1977; 1986), and are thus likely to exhibit less effort toward the achievement of those tasks. We also argued that High SEs exceeding a critical threshold (i.e., “too” high self-efficacy; Pierce & Aguinis, 2013) exert less-than-necessary effort, and thus achieve lower downstream performance. In light of negative consequences of these polar levels of self-efficacy, we proposed that the benefits of self-efficacy are likely most apparent at moderate/high levels, where debilitating self-doubt (i.e., a consequence of low self-efficacy) or complacency (i.e., “too” high self-efficacy) are less likely. We predicted that Moderate/High SEs exhibit the highest levels of effort. Such individuals likely believe they have the KSAs needed to accomplish their work tasks, while also recognizing that doing so requires preparation and effort. Consistent with these expectations, we find support for the prediction that self-efficacy exhibits an inverted U-shaped relationship with effort.

In line with results from between-person research, that self-efficacy can have both positive (e.g., Bandura, 1977) and negative consequences for effort (Vancouver et al., 2014), our findings speak to the importance of modeling a point of inflection in the relationship between self-efficacy and effort. Like recent research exploring paradox in organizational contexts (e.g., Rapp, Bachrach, & Rapp, 2013; Rubin, Dierdorff, & Bachrach, 2013), our findings suggest that, as relates to exertion of task-related effort, there may be greater utility in moderate-to-high levels of self-efficacy (Grant & Schwartz, 2011). Self-efficacy that is “too” high (i.e., above a critical threshold) may lead to complacency, prompting inadequate preparation and focus on achieving task-related goals.

In contrast, self-efficacy that is “too” low (i.e., below a critical threshold) may be both demotivating, as employees do not believe that they have sufficient capacity to achieve critical outcomes (Vroom, 1964), and debilitating as employees feel incapacitated in their pursuit of work goals (Bandura, 1997). If the benefits of self-efficacy are most likely to materialize at moderate-to-high levels, where debilitating self-doubt (i.e., at low self-efficacy) and self-assurance driven complacency (i.e., at “too” high levels of self-efficacy) are less likely, the nonlinearity we theorize characterizes the relationship between self-efficacy and effort provides a conceptual framework to help integrate discrepant evidence from the literature. It will be important for future research to further explore the critical threshold hypothesis we offer, and to provide evidence of the generalizability of our results in contexts beyond sales.

Our second contribution concerns the mediating role of effort in the indirect relationship between self-efficacy and performance. To date, research has speculated as to the mediating role of effort in the linear between-person self-efficacy to performance relationship. Several studies have reported that resource allocation (i.e., effort) can mediate this relationship at the between-person level of analysis (Vancouver et al., 2014; Beck & Schmidt, 2018). Building from this empirical foundation, we examine the mediating role of effort, explicitly accounting for nonlinearity in the relationship between self-efficacy and effort.

Our results indicate that effort plays a mediating role in this relationship at low levels of self-efficacy, but not at “too” high levels of self-efficacy. At polar levels, self-efficacy reveals a direct relationship with downstream performance outcomes. This introduces several questions about what underlies this direct effect, and what else may be happening at “high” levels of self-efficacy that facilitates performance, despite the absence of the mediating role played by effort. It may be that High SEs, who have accumulated insight and experience, may be positioned to “work smarter” (Sujan, Weitz, & Sujan, 1988) by developing strategies enabling them to generate performance outcomes, without exerting the same levels of otherwise necessary effort (i.e., the levels of effort necessary among Moderate/High SEs). Considering the study’s sales context, it may be that salespeople imbued with high self-efficacy approach their sales calls feeling more capable and confident (e.g., Bandura, 1989), allowing them to achieve larger sales from fewer high-value customers, rather than smaller sales from more, lower-value customers. Further, speculatively, although we adopt a between-person approach, the dependent measure in our model explicitly incorporates employees’ past performance. The direct relationship we find between self-efficacy and performance may (in part) be accounted for by the fact that employees’ past performance is associated with future self-efficacy, which coincides with the within-person portrayal of the performance-to-self-efficacy relationship (Vancouver et al., 2014).

Our third contribution relates to the moderating role played by perceived proximity in the relationship between self-efficacy and effort. We build our arguments from SCT, which maintains that social observation and learning generate expectations about likely outcomes associated with behavior, and that the more extensive the opportunities to develop realistic expectations the more likely it is that functional patterns of behavior will develop. Recognizing that employees’ perceived proximity to their organization and colleagues can lend accuracy to ability judgments (Bandura, 1997; Staples et al., 1998), normative effort expectations, and social pressures to conform to such expectations (Forsyth, 1998), we tested the moderating effect of perceived proximity in the nonlinear relationship between self-efficacy and effort. Our findings generally support the expectation that perceived proximity moderates this nonlinear relationship by flatting the curve, which suggests perceived proximity can diminish the potentially negative effects of “too” high self-efficacy (Pierce & Aguinis, 2013).

We also uncover what may appear to be a counter-intuitive result; that the average level of effort among employees reporting low perceived proximity is higher than among employees reporting high perceived proximity. However, an economics-based perspective leveraging ambiguity aversion (Epstein, 1999) offers an explanation. Simply, people who are ambiguity averse tend to make choices where the probabilities associated with the outcomes of their choices are known. For employees who perceive themselves to be less proximal to their organization and colleagues, the level of effort necessary to achieve specified outcomes is likely less clear to them, relative to more proximal colleagues with more opportunities for social learning. These employees may exert more effort toward performance goals because the probabilities associated with the consequences of their choices are more certain (i.e., they are more likely to achieve higher performance by exerting more effort). Employees are less likely to make choices where the probabilities associated with the consequences of their choices are unknown (i.e., it is unclear whether a lower level of effort will generate desired levels of performance). For employees reporting higher perceived proximity, who thus have access to more normative information about the levels of effort likely to generate positive returns, ambiguity is likely to be lower, and so they are less likely to experience this ambiguity aversion.

The study’s findings also offer insights for managerial practice. First, our results suggest that simply fostering higher (or “too” high) self-efficacy may have adverse downstream performance consequences. It will be important for managers to offer interventions that promote moderate-to-high (i.e., functional) self-efficacy. Doing so should help Lower SEs to open the functional self-efficacy window and help employees with “too” high levels of self-efficacy to avoid breaking it. Realistic job previews (Phillips, 1998) offer one approach with potential to activate moderate-to-high levels of self-efficacy, positioning employees’ expectations against realities, engendering higher job performance. During onboarding, employees exposed to accurate portrayals of the complexities associated with achieving desired outcomes (i.e., sales, in this study) may more effectively temper inflated/deflated ability self-assessments, and approach tasks with a more realistic understanding of the effort necessary to perform at a functional level vis-à-vis goals. It is also important to recognize that many companies do not assess employees’ self-efficacy. Managers may not be directly aware of the concept of self-efficacy, and may not be in a position to accurately gauge self-efficacy or changes in self-efficacy over time. Thus, another practical implication of our results is that it is important to build awareness of the concept of self-efficacy, and for organizations to regularly conduct assessments of self-efficacy. Multiple validated self-efficacy measures are available (e.g., Sherer et al., 1982; Schwarzer & Jerusalem, 1995; Rosenberg, 1965; Chen et al., 2001).

Our findings also point to the relevance of identifying contextual variables with potential to influence relationships between self-efficacy and productive outcomes. Here, we find that perceived proximity can mitigate potentially negative downstream consequences for employees with “too” high self-efficacy, which suggests that organizations should retain closer contact with “too” high SEs, but devote fewer resources to Moderate/High SEs. This understanding can help inform management activities to best meet employees’ needs, and encourage effort (e.g., spending more (less) time with “too” high (low) SEs). Thus, to maximize self-efficacy’s performance benefits, managers should be vigilant of employees’ proximity perceptions. There are numerous tools (e.g., employee support systems; interaction enabling technologies) managers can leverage to influence employees’ perceptions of proximity (Wilson et al. 2008).

Study Limitations

Our results should be considered in light of limitations in our study design. First, while we tested our model in a lagged field setting, employees likely were aware of their previous effort and performance, which may have affected their self-efficacy ratings (Bachrach, Bendoly, and Podsakoff, 2001; Sitzmann & Yeo, 2013). Employees who achieved high (low) outcomes at an earlier point may have reported higher (lower) self-efficacy as a result. Thus, while within-person effects may be present, we seek to control the extent of their impact by using a performance measure that explicitly incorporates past-performance. Future, longitudinal research will help to establish causal inferences bearing on relationships between self-efficacy, effort, and performance. The most we can conclude is that our results align with our causal arguments, although the effect sizes we uncover are relatively small given the large sample size. These effect sizes may be attributable to the industrial ecosystem of our study setting, and the objective, archival nature of our dependent variable. The business-to-business cloud services technology context may reflect a somewhat abstract performance setting, compared with more conventional industrial sales settings. It will be important for future research to generalize our model in other contexts to establish a foundation for determining normative effect sizes.

Second, the sales sample we utilized may limit the generalizability of our results. While sales settings provide employees with frequent assessments of job-related ability (e.g., daily sales closings), employees in other settings may be less aware of their performance. There also are limitations in our dataset that preclude developing definitive explanations for why we uncover differential relationships between self-efficacy and effort. For example, it will be important for future research to explore the role played by perceptions of one’s abilities (e.g., Bandura, 1999), such as the role of doubt for Low SEs, complacency for “too” high SEs, and the sense of being surveilled bearing on the moderating role of perceived proximity in our focal relationships.

Further, there may be limitations related to our study measures. Our self-efficacy measure captures the degree respondents feel confident about their job skills and capabilities. Because we did not capture actual ability to ascertain whether self-efficacy ratings reflected accurate or exaggerated ability assessments (Moore & Healy, 2008), we could not distinguish between highly efficacious and capable employees from the merely overconfident. Future research should examine actual versus perceived ability in the relationships between self-efficacy, effort, and productive outcomes. It is also important to note that, while the measure we leveraged has seen extensive use and validation in field studies within sales contexts, there may be some construct validity challenges associated with the Jones (1986) scale worth noting. Specifically, the Jones (1986) measure may conflate concepts of strength of capacity/efficacy beliefs with normative comparisons (i.e., comparisons to colleagues); with beliefs regarding the source of efficacy beliefs (i.e., past experiences); job satisfaction and/or instrumentality (i.e., satisfying self-expectations); and adjustment to the job (i.e., anticipating problems). While the Jones (1986) measure has been widely adopted in the literature, these issues are not trivial from the standpoint of conceptual clarity. For example, as Vancouver & Day (2005) noted, conflation of these constructs with self-efficacy may increase predictive validity at the expense of construct validity.

Finally, there may be unique effects related to perceived proximity from colleagues and the organization. Recent evidence suggests that perceived proximity from the organization may be associated with factors such organizational rules, routines, and shared belief systems; whereas perceived proximity with colleagues may involve social factors such as trust, identification, and relationship quality (e.g., Harush, Lisak, & Glikson, 2018; Ruiller, Van Der Heijden, Chedotel & Dumas, 2019). While our measure precluded our ability to examine these unique effects, it represents an interesting future research area.

Conclusion

There is emergent recognition that the consequences of self-efficacy may not always be positive. To reconcile apparently paradoxical relationships from the literature, we tested the proposition that rather than being linear, the relationship between self-efficacy and effort may be nonlinear. Our results offer support for what we refer to as the “critical threshold” hypothesis and align with recent insights suggesting that the downstream consequences of self-efficacy are more complex than previously assumed. Further, consistent with the implications of SCT, we find that the nature of the nonlinear relationship between self-efficacy and effort depends on perceived proximity. This finding offers insight into boundary conditions impacting the nature of the nonlinear relationship between self-efficacy and effort. Finally, we find that the relationship between self-efficacy and performance (at the between-person level of analysis) emerges through effort. Our hope is that these results provide a foundation for further exploration of the complex relationships between self-efficacy and critical workplace outcomes.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Daniel G. Bachrach

Tammy L. Rapp

Notes

Associate Editor: Thomas Zagenczyk

Author Biographies

Dr. Dan Bachrach is a Morrow Faculty Excellence Fellow at the University of Alabama. He is an Elected Fellow of the American Psychological Association (APA), Society for Industrial and Organizational Psychology (SIOP), and the Association for Psychological Science (APS). His research focuses on drivers of sales team performance. dbachrac@cba.ua.edu

Dr. Tammy Rapp is the Robert H. Freeman Associate Professor of Management at Ohio University. Her research examines the factors that support team effectiveness and has been published in outlets such as the Journal of Applied Psychology and Journal of Management. She holds a Ph.D. from the University of Connecticut. rapp@ohio.edu

Dr. Jessica Ogilvie is an assistant professor of Marketing at Marquette University. Jessica earned her PhD from the University of Alabama and her award-winning research has been published in outlets including the Journal of Marketing and the Journal of Personal Selling and Sales Management. jessica.ogilvie@marquette.edu

Dr. Adam Rapp is the Schey Professor of Sales at Ohio University. He has published three books and over sixty-five articles in outlets such as Journal of Applied Psychology, Management Science, and Journal of Marketing Research, among others. His research examines factors influencing performance among front-line service and sales personnel. rappa@ohio.edu

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