The importance of velocity,or why speed may matter more than distance

Effects of velocity on affect

Although self-regulation was not their main focus, Hsee and colleagues (Hsee & Abelson, 1991; Hsee, Abelson, & Salovey, 1991) were among the first to explicitly consider relations of velocity with satisfaction. They used choices among hypothetical situations to demonstrate that people have a preference for positive velocity compared to zero velocity, which was in turn preferable to negative velocity, finding that velocity is positively related to satisfaction, even when discrepancies are held constant across conditions. Interestingly, people preferred improving to a high hypothetical outcome (e.g., starting with a salary of $75,000 and receiving a 5% raise each year) over a constant high hypothetical outcome (e.g., a static salary of $100,000), even when the constant high outcome had a larger payout. One way to interpret this finding is that positive emotional responses are generated when people’s current state moves toward their goal state (in this case, their desired salary) independent of the size of the actual–goal discrepancy. In fact, emotional responses were less positive when actual–goal discrepancies were small but static. Due to the nature of Hsee and Abelson’s (1991) experiments however, it is difficult to determine whether the hypothetical situations are reflective of people’s preference for increasing goals (goal setting) or increasing performance relative to a stationary goal (goal striving).

Lawrence, Carver, and Scheier (2002) provided a more direct test of affective reactions to velocity via manipulated feedback on an ambiguous task. They demonstrated a similar pattern of results in the context of task performance relative to a goal, finding that the perceived performance trend was positively related to mood enhancement. Research has also demonstrated a positive relationship between velocity and positive affect as well as a negative relationship between velocity and negative affect using objective measures of velocity that involved tracking the rate of change in goal–performance discrepancies over time (Chang, Johnson, & Lord, 2010; Chang, Johnson, & Rosen, 2009).

More recent work in this area has begun to examine boundary conditions for these relationships. For example, velocity and discrepancy information interact to predict task satisfaction (as well as goal expectancy and commitment). The nature of the interaction is such that discrepancy feedback has a weaker effect on outcomes when velocity is fast versus slow—in other words, fast velocities compensate for large discrepancies (Chang et al., 2010). In addition, there is evidence that task importance may moderate the relationship between velocity and task satisfaction such that the relationship is stronger for more important goals (Elicker et al., 2010). This finding is consistent with affective events theory (Weiss & Cropanzano, 1996), which predicts that affective responses vary in intensity as a function of goal importance, though the theory does not discuss the role of velocity explicitly. As a final example, temporal orientation may influence the importance people place on velocity and discrepancy information, such that people with a strong present orientation care more about current goal–performance discrepancies whereas those with a strong future orientation care more about velocities (Chang et al., 2009).

Research in this area has also attempted to elucidate the difference between velocity and velocity discrepancy. As with performance, it is the discrepancy between perceived and desired velocity that is proposed as the relevant quantity for predicting outcomes (Carver & Scheier, 1990). Considering the rate of change in job characteristic perceptions relative to expectations, Chang et al. (2010) demonstrated that the difference between perceived and expected velocity was predictive of job satisfaction (specifically, smaller velocity discrepancies were associated with higher levels of satisfaction). Thus, over a relatively wide range of samples and methodologies, research in this area lends considerable support to the prediction that velocity impacts affective responses to goal-striving activities.

Effects of velocity on persistence and goal revision

In addition to affective outcomes, researchers have also considered the effects of velocity on success expectancies and goal revision. While they did not discuss velocity explicitly, Campion and Lord (1982) proposed that goal-relevant motivation is likely impacted not only by the size of goal–performance discrepancies but also by the stability of such discrepancies. Consistent with this idea, they found that consecutive performance episodes in which performance levels fell short of the desired goal (i.e., little or no velocity) were associated with more downward goal revision than the total number of episodes with subgoal performance levels. Subsequent longitudinal studies have found that the presence of goal–performance discrepancies near the end of the time allotted for a performance episode are more likely to result in downward goal revision for both distal and proximal goals than when there is more time remaining, providing some additional evidence that a lack of sufficient velocity in the past is associated with downward goal revision (Donovan & Williams, 2003; Williams, Donovan, & Dodge, 2000).

The relationship between velocity and goal revision has been explicitly tested as well using an objective velocity measure (calculated via differences in goal–performance discrepancies over time). In line with previous findings, objective velocity was negatively related to goal revision (Chang et al., 2009). One explanation as to why velocity affects goal revision is because people infer success expectancies from velocity feedback, such that fast versus slow velocities communicate that goal attainment is likely versus unlikely, respectively (Carver & Scheier, 1998; Chang et al., 2010; Johnson et al., 2006). Thus, in response to slow or negative velocities and thus the low probability of success, people revise their goals downward.

Effects of velocity on commitment in multiple goal contexts

A research stream considers the impact of velocity on goal striving in multiple goal contexts. Across a variety of conditions, when given multiple tasks to complete in a limited amount of time, Schmidt and DeShon (2007) found that participants initially focused on the more difficult goal. However, as the time allotted to the performance episode diminished, participants switched their focus to the task that they previously had more success in completing. In a subsequent study, these results were found to hold when goal progress was influenced by environmental factors; however, when goal progress was solely a function of individual effort, this pattern of results was reversed with participants initially focusing on the easier task and then switching to the more difficult task during the later parts of the episode (Schmidt, Dolis, & Tolli, 2009). Given our previous discussion on goal progress and velocity, it should be noted that the focus of the measures used in these studies is on relative goal–performance discrepancies across tasks. It is only because of the structured and relatively frequent progress assessment inherent in the experimental design that one can infer the role of velocity in the findings. In addition, while not conducted in an explicit multiple goal context, other research in this domain has found that explicitly measured velocity perceptions are positively associated with the mental focus devoted to a particular task, presumably at the expense of other tasks competing for limited attentional resources (Elicker et al., 2010). While the research in this domain is less mature compared to the other streams, initial findings indicate that velocity influences goal striving in multiple goal contexts, but the nature of these effects may vary as a function of time and environmental constraints.

Measurement of velocity

Prior to discussing the implications of velocity for employees in organizations, we first clarify how velocity has been operationalized in the empirical research described before. In general, researchers have developed three ways to measure or manipulate velocity. The first operationalization is based upon Carver and Scheier’s (1998) proposal that velocity is regulated by a separate feedback system (the metasystem), which compares actual velocity against ideal velocity. Along these lines, Chang et al. (2010, Study 1) measured participants’ subjective appraisal of their actual velocity and their ideal velocity. Both ideal and perceived velocity were then compared to examine their relative contribution to employees’ job satisfaction. This approach is akin to the subjective fit assessment in the person–environment fit literature (French, Rogers, & Cobb, 1974), where the individuals provide information about both the person and the environment (i.e., standards) for the comparison. The advantage of this approach is that it is theoretically driven and provides a direct test of the metasystem (Carver & Scheier, 1998). In addition, it takes individual differences in velocity standards into consideration. However, because this approach relies on individuals to provide information concerning both actual and desired velocity levels, cognitive biases that affect subject fit perceptions (e.g., consistency, dissonance; French et al., 1974) may also influence reports of perceived and ideal velocity.

A second approach involves providing participants with direct, objective progress rate as feedback. For example, Hsee and Abelson (1991) manipulated the velocity feedback by informing their participants that the rate of the salary change is 5% a year. This approach has the primary benefit of drawing causal inferences between velocity feedback and the outcome variables (e.g., efforts, goal commitment). However, the objective manipulation does not take individuals’ desired level of velocity into consideration. A final approach involves objective calculation of individuals’ changes in ideal–actual discrepancies over time. For example, Chang et al. (2009) measured students’ goal level and actual performance in class every 3 weeks over the course of the semester (15 weeks), and calculated the differences between discrepancies across two time points to indicate velocity. Donovan and colleagues (Donovan & Williams, 2003; Williams et al., 2000) used a similar approach when examining discrepancy changes over time regarding college athletes’ performance. The advantage of this approach is that velocity information is objectively generated rather than relying on individuals’ self-reports, which may be subjective to biases. In addition, researchers may space the delivery of measures across temporal intervals that is appropriate for the task under investigation. Unfortunately, similar to objective manipulation, objectively calculated velocity does not consider individuals’ preferred level of velocity. Lastly, it is possible that different measurements of velocity may represent conceptually different aspects of velocity. Given the infancy of the velocity literature, further research is needed to better assess the appropriateness of these measurement approaches for different research questions.

Velocity and task-based self-regulation

As mentioned earlier, nearly all empirical studies concerning velocity have been conducted in achievement settings where participants must complete an assigned task or attain a specific performance level. As such, their results carry clear implications for employees’ task-based self-regulatory activities in organizational settings. In the case of task and job satisfaction, prior research (e.g., Chang et al., 2010; Hsee & Abelson, 1991; Hsee et al., 1991; Lawrence et al., 2002) has suggested that in addition to the discrepancy feedback, velocity, especially when it is faster than expected, is positively related to employees’ satisfaction at work. Fast velocity also encourages employees to sustain their goal pursuit efforts by enhancing the expectancy of successful goal attainment (Chang et al., 2010; Johnson et al., 2006). Lastly, velocity feedback may be particularly helpful for increasing employees’ persistence in goal pursuit when they experience large discrepancies between their current performance and the goal level (Chang et al., 2010; Chang et al., 2009). Interestingly, one byproduct of especially fast velocities may be reduced effort, owing to people’s belief that goal attainment is certain (Carver & Scheier, 1998). Such coasting effects have been observed in the case of high self-efficacy, which has negative relations with subsequent effort and performance (e.g., Vancouver & Kendall, 2006; Vancouver, Thompson, Tischner, & Putka, 2002). There is a need, then, for research that examines possible negative relations of velocity with performance, particularly when discrepancies are small.

While the aforementioned examples point to the possible direct generalization of effects associated with velocity, organizations may present some unique contextual characteristics that can either constrain or enhance these effects compared to those observed in the experimental setting. For example, while multiple studies have demonstrated the effects of velocity on downward goal revision (e.g., Chang et al., 2009; Donovan & Williams, 2003; Williams et al., 2000), these findings may be less applicable for employees in an organization. One of the objectives for the performance management system in the organization is to set standards for employees’ task performance. In this case, unless employees’ personal goal after their downward revision is still above the organizational standard, it is difficult to imagine that organizations will tolerate the continuous downward revision and not intervene to uphold a minimum level of task performance for employees.

On the other hand, there are other organizational settings where the effects of velocity are likely to be heightened. In particular, Chang et al. (2010) found that fast velocities compensate for large discrepancies, such that individuals maintain their task satisfaction, goal commitment, and success expectancy so long as velocities are fast (regardless of whether discrepancies are small or large). Interestingly, employees in organizations are often faced with situations where there are large discrepancies between their current standing and goal levels. For example, new employees face the daunting task of acquiring knowledge and attitudes that are necessary to function as effective organizational members (Chao, O’Leary-Kelly, Wolf, Klein, & Gardner, 1994). Similarly, employees in training may need to learn new and complex skills that involve a steep learning curve. In these situations, the satisfaction of employees who receive velocity feedback should be quite high owing to their rapid learning, despite large discrepancies between their current state and goal state. Over time, however, satisfaction may decline as employees become more expert and their rate of learning slows, consistent with the power law of practice (Newell & Rosenbloom, 1981). This suggests that the relative importance of velocity is greater during the early versus later stages of skill acquisition. Once employees have developed adequate skill, then the small discrepancy between employees’ current state and goal state may be sufficient for maintaining their satisfaction and motivation. This is consistent with Chang et al.’s (2010) finding that fast velocities and small discrepancies have compensatory effects.

There may also be cases where employees’ work environments are continually subject to changes in complexity, such that workers must handle higher levels of demands (i.e., component complexity), interact with constituents more frequently (i.e., coordinative complexity), or relearn how their behaviors translate into effective task performance (i.e., dynamic complexity; Wood, 1986). All of these changes in complexity result in sudden decrements in performance (Bell & Kozlowski, 2008; LePine, Colquitt, & Erez, 2000), requiring employees to adapt their behaviors in order to achieve task goals. Given that many employees respond to change with active or passive resistance (Kotter, 1995; Lawrence, 1954), communicating feedback that indicates positive velocity may encourage greater self-efficacy and persistence from employees, thereby enhancing their adaptive performance in response to environmental changes.

All of the aforementioned examples represent scenarios where large discrepancies are present, in which case velocity feedback may be particularly crucial in maintaining employees’ success expectancies, goal-directed efforts, and persistence. As such, organizations should assist employees’ self-regulatory activities by directing their attention to velocity, rather than discrepancy feedback. For example, goals should be set at the velocity level (i.e., standard for progress rate) for employees experiencing a large discrepancy. In addition, rather than providing employees with information that highlights the differences between their current states and the goal state, feedback should inform employees how much progress they have made towards the goal state. The increasing prevalence of computer-based management systems (e.g., enterprise resource planning and point of sale systems) and electronic record keeping means that in many cases the performance history information necessary to provide employees with velocity feedback is readily available. Doing so will likely offset the negative effects of discrepancy on employees’ task satisfaction, success expectancies, and goal persistence.

Interestingly, recent work by Reb and colleagues (e.g., Reb & Cropanzano, 2007; Reb & Greguras, 2010) pointed out that fast velocity may not only maintain individual employees’ own self-regulatory activities when they face large discrepancies, it may influence how others evaluate the focal employees’ performance. Reb and Cropanzano (2007) showed that holding average performance constant, when individual’s performance showed an improvement trend over time, participants rated the overall performance of such individual more positively than the performance of those who exhibited a flat or deteriorating trend over time. In this case, because the average performance was the same across different trend manipulations, an improvement trend can be considered as someone who started out with a large discrepancy from the goal (i.e., performance level was below the average), and demonstrated fast progress rate at reducing the size of the discrepancy. In a follow-up study, Reb and Greguras (2010) manipulated both average performance levels (i.e., below-average performance, average performance, above-average performance) and performance trends over time (i.e., improving, flat, deteriorating). They found that participants judged the overall performance to be more positive when an individual had a below-average performance but an improving trend, compared to one who had below-average performance and a deteriorating trend. These findings suggested that when employees experience large discrepancy but fast velocity, not only are they more likely to maintain their internal self-regulatory activities, but they may also experience external reinforcement (i.e., positive overall performance ratings) that may help maintain their motivation for goal pursuit.

A final implication is that providing velocity feedback may avoid some of the harmful effects of discrepancy feedback. Although task feedback may satisfy employees’ sense of competence (Gagné & Deci, 2005), in many cases it has weak or negative effects (Kluger & DeNisi, 1996). Detrimental effects occur because discrepancy feedback at high levels in the goal hierarchy invoke the self and indicate enduring person-based deficiencies, which elicit defensive responses and directs attentional resources to the self rather than tasks. Velocity feedback is different because it represents the aggregation of discrepancy feedback at lower task levels over time, which is then communicated to higher levels (Johnson et al., 2006; Lord et al., 2010). Although it is meaningful at higher levels in the goal hierarchy, velocity information is still about the task and not about the self. By providing information that is interpretable at higher levels yet information that is still task-focused, velocity feedback may promote the self-less focus that is needed to become sufficiently engaged in complex tasks (Gusnard, 2005).

Velocity and belonging-based self-regulation

One way that belonging and relatedness needs are satisfied at work is via person–environment fit, or when compatibility exists between ideal internal aspects (e.g., values and abilities) and experienced aspects of the external environment (Edwards, 1991; Kristof, 1996). Indeed, employees who perceive fit with their organization, supervisor, and/or coworkers report less strain and withdrawal cognitions, and are less likely to turnover than employees who perceive misfit (Hoffman & Woehr, 2006; Kristof-Brown, Zimmerman, & Johnson, 2005). Employees who perceive fit with their work environment also report more trust, group cohesion, and satisfaction, and they develop higher quality relationships and engage in more prosocial behaviors than employees who perceive misfit (Hoffman & Woehr, 2006; Kristof-Brown et al., 2005; Verquer, Beehr, & Wagner, 2003). These favorable consequences of person–environment fit owe to the successful regulation of belonging and relatedness needs.

Interestingly, the phenomenon of person–environment fit resembles the self-regulation processes we described earlier. That is, person–environment fit can be conceptualized as a discrepancy between people’s ideal conditions, which are derived from internal standards, and experienced conditions, which are derived from the external environment (Johnson et al., in press). Examples of ideal standards used to evaluate the environment include employees’ needs, values, traits, and task-based goals, knowledge, and skills (Kristof, 1996). These ideal standards are then used to judge the extent to which targets in one’s environment (e.g., supervisors, colleagues, and the organization) are compatible. Note that person–environment fit is not all-or-none; instead, employees vary in the extent to which they fit in with their environment (Edwards, Cable, Williamson, Lambert, & Shipp, 2006). Only when there are little or no ideal–actual discrepancies, then, do people experience a sense of belonging. When discrepancies are detected, however, it disrupts the system and motivates employees to alter their behavior (e.g., act in ways to reduce group conflict) or their cognition (e.g., deemphasize the ideal standard that is the source of misfit) in order to restore a sense of belonging. Thus, ideal–actual discrepancies play a central role in both self-regulation theories and person–environment fit theories.

If satisfying the needs for belonging and relatedness involves regulating ideal–actual discrepancies, then velocity feedback likely plays an important role (Johnson et al., in press). This is especially true because person–environment fit is not static, but rather it is a dynamic phenomenon that changes over time (Jansen & Shipp, in press; Shipp & Jansen, 2011). This suggests, then, that the extent to which employees fit with others changes over time, especially for new hires while being socialized (Cable & Parsons, 2001; Cooper-Thomas, van Vianen, & Anderson, 2004). During the socialization process, new hires are exposed to the values and beliefs of the organization and slowly begin to adopt them as they transition from outsiders to embedded members. An important part of this transition is how quickly new hires’ values and beliefs align with those of the organization. We therefore expect that person–environment velocity will predict newcomer adjustment and belonging outcomes (e.g., social acceptance, role clarity) incremental to person–environment discrepancy (Johnson et al., in press). In line with previous findings (Chang et al., 2010), discrepancy by velocity interactions are also expected, such that large person–environment discrepancies have little impact on adjustment and belonging outcomes so long as person–environment velocities are sufficiently fast.

Based on theoretical (Carver & Scheier, 1998) and empirical evidence (Hsee & Abelson, 1991), we expect that person–environment velocities will have stronger effects than person–environment discrepancies on affective reactions towards the targets of fit, such as liking for and satisfaction with supervisors, coworkers, or organizations. While not conducted in an organizational context, a recent experience sampling study by Laurenceau, Troy, and Carver (2005) found that the perceived velocity of interpersonal fit predicted affective responses in romantic couples above and beyond mean levels of perceived fit. Consistent with the current discussion, changes in intimacy, which reflects person–person emotion fit, were positively related to positive feelings about the relationship incremental to average intimacy level. There was also evidence that for some participants changes in relational conflict owing to values or goals misfit led to increased relationship anxiety incremental to average misfit level.

To the extent that positive reactions occur, they may spillover and cultivate a greater sense of loyalty and obligation to the other party, leading to the emergence of high-quality social exchanges rather than low-quality economic ones. For example, Jackson and Johnson (2012) found that supervisor–subordinate fit based on relational and collective values led to high-quality leader–member exchange, which in turn related to high levels of task and citizenship behaviors. Although the authors limited their focus to person–environment discrepancies, we suspect that fast person–environment velocities would have similar (and perhaps stronger) effects.

In addition to affective reactions, velocity also has stronger ties to persistence relative to discrepancy (Carver & Scheier, 1998; Chang et al., 2010). In terms of social contexts and belonging outcomes, this suggests that person–environment velocities will be important antecedents of commitment to the other party and, ultimately, continuation of the relationship with the other party. Given the affective undertones of velocity, fast person–environment velocities are likely to give rise to affective forms of commitment to one’s supervisors, coworkers, and organizations (see Meyer & Allen, 1997). When employees show affective commitment to another person or group, they internalize the values and goals of the other party and develop a strong emotional attachment to them (Johnson, 1991; Johnson, Chang, & Yang, 2010). This type of commitment creates a stronger bond in relationships than does commitment based on instrumental, quid pro quo exchanges. Thus, it is not surprising that employees who show affective commitment are unlikely to discontinue their working relationships with supervisors, coworkers, and companies (Meyer, Stanley, Herscovitch, & Topolnytsky, 2002; Taing, Jackson, Poteat, & Johnson, 2009). We therefore expect that person–environment velocities will have strong positive relations with affective commitment to partners and groups and negative relations with withdrawing from and quitting relationships and groups.

The final implications of person–environment velocity that we discuss involve the hierarchical organization of goals and feedback loops (as shown in Figure 2). This hierarchical organization also extends to person–environment fit, such that fit based on enduring person-level characteristics like needs, values, and traits exist at higher levels of the hierarchy whereas fit based on task-level characteristics like task-specific knowledge, skills, abilities, and goals exist at lower levels of the hierarchy (Johnson et al., in press). This suggests that not all types of person–environment fit are equally important given that the meaning and significance of standards decrease as you move down the hierarchy (Carver & Scheier, 1998; Johnson et al., 2006). Thus, person–environment discrepancies and velocities involving person-level characteristics will have stronger effects than discrepancies and velocities involving task-level characteristics. This idea is consistent with Kluger and DeNisi’s (1996) feedback intervention theory, which proposes that feedback elicits stronger reactions when provided at the person versus task level because the former invokes people’s sense of self. Although task feedback at the person level is harmful because it shifts people’s focus from the task to the self (Kluger & DeNisi, 1996), thus limiting the attentional resources needed to perform tasks effectively (Gusnard, 2005), fit feedback is more meaningful at the person level because it primes people’s defining values and goals. Fit based on these high-level, defining qualities should engender a stronger sense of belonging. Consistent with this prediction, empirical evidence suggests that needs–supplies fit tends to have stronger relationships with outcomes compared to task demands–abilities fit (Cable & Edwards, 2004). For example, the results of a meta-analysis conducted by Kristof-Brown et al. (2005) revealed that needs–supplies fit had stronger relations than demands–abilities fit with satisfaction (ρ = .61 vs. .41), commitment (ρ = .37 vs. .31), job performance (ρ = .20 vs. .12), and turnover intentions (ρ = −.50 vs. −.23).

Not only do the meaning and significance of standards differ across the goal hierarchy, but the importance of feedback may differ as well. Specifically, person–environment velocities likely have greater impact at higher (vs. lower) levels in the goal hierarchy because discrepancy information is aggregated over time at lower levels and is then fed back to higher levels in the form of velocity information (Johnson et al., 2006; Lord et al., 2010). In other words, velocity is the principal form of informational currency at the upper levels in goal hierarchies. Thus, person–environment velocities will be particularly influential when employees judge their fit with supervisors, coworkers, and companies on person-level characteristics like needs, values, and traits. Also, velocity feedback may not be as damaging as discrepancy feedback at higher levels because velocity is forward looking and emphasizes movement rather than people’s current states and degree of misfit. Taken together, velocity potentially plays an important role in the self-regulation of belonging and relatedness needs.

Velocity and esteem-based self-regulation

Self-esteem is a set of beliefs, attitudes, and emotional reactions that people hold about themselves, particularly about their self-worth and how others appraise them (Leary & Baumeister, 2000). Given the central role that work plays in many people’s lives, organization-based self-esteem (i.e., seeing oneself as an important, meaningful, effectual, and worthwhile member) contributes significantly to overall self-esteem (Pierce, Gardner, Cummings, & Dunham, 1989). Interestingly, just as people compare their current task performance to their ideal or goal level of performance, several scholars (Barkow, 1980; Bednar, Wells, & Peterson, 1989; Brockner, 1988; Korman, 1970; Leary & Baumeister, 2000; Swann, Rentfrow, & Guinn, 2002) have proposed that people compare information concerning their personal worth, which is gleaned from the environment, to their self-esteem level, which serves as the standard within a feedback system. Although there is debate regarding whether people desire positive, self-enhancing feedback (Shrauger, 1975) versus accurate, self-verifying feedback (Swann et al., 2002), both perspectives agree that “people want to avoid losses of self-esteem and so are loath to receive feedback that is more negative than their current self-appraisal” (Leary & Baumeister, 2000, p. 4). Nevertheless, the crucial detail is that self-esteem operates within a feedback system, which means velocity feedback is relevant.

The need for esteem is satisfied when instrumental and relational feedback from the environment is equal to or greater than a person’s ideal level of self-worth (i.e., his or her self-esteem). In terms of instrumental concerns, self-worth is used to judge the adequacy of one’s tangible and intangible economic outcomes, such as pay, recognition, and status symbols like job titles and offices. Equity theory (Adams, 1965) can be used to understand how this process unfolds, which posits that people compare their own input–outcome ratios to those of others. Inputs such as knowledge, expertise, effort, and academic credentials all contribute to employees’ perceived value to their companies (in other words, their organization-based self-esteem), and this perceived worth is judged against the outcomes that their company provides them. If a person’s own outcomes are not commensurate with his or her perceived worth, relative to the outcomes and worth of other employees, then the employee will take either behavioral or cognitive action aimed at eliminating the discrepancy. Usually employees deal with discrepancies first by altering their behavior (changing their own inputs or outcomes, or those of others), followed by altering their cognition (changing the referent standard or their perceptions of inequity) if initial attempts at reducing discrepancies are unsuccessful (Adams, 1965).

To date, research on equity theory has focused on discrepancies between inputs and outcomes and between one’s own input–outcome ratios and other people’s input–outcome ratios. Velocity, however, may also play a role. For example, given its unique ties to affect, velocity feedback may temper negative emotional reactions to perceived inequity if input–outcome discrepancies or own–other ratio discrepancies are shrinking at acceptable rates. In this vein, detecting a discrepancy is akin to a primary appraisal, whereas velocity information functions like a secondary appraisal that indicates whether or not a person is able to cope with the unfavorable discrepancy (see Lazarus, 2001). Although discrepancies produce an immediate negative emotional response, this initial reaction will subside if velocity feedback is favorable. Example actions that companies can take to increase employees’ outcomes and thereby increase the perceived velocity at which input–outcome discrepancies are shrinking include traditional means like pay raises and promotions, but also alternative means like training and development opportunities, and enhancing job autonomy and skill variety. Even low-cost means like increased recognition and opportunities for voice may create more favorable input–outcome discrepancies. So long as employees perceive these discrepancies are becoming smaller (i.e., positive velocity), employees’ affective reactions toward their job and themselves will be favorable.

Given its ties to withdrawal, velocity feedback may also prove valuable for predicting whether or not employees exit exchange relationships owing to perceptions of inequity. That is, if the rate at which input–outcome discrepancies or own–other ratio discrepancies are reducing is acceptable, then employees will be more likely to remain in employment relationships with their company or supervisor. In contrast, slow changing or stagnant discrepancies communicate that the self-worth of employees as members of their current organization is questionable, which may constitute a job-related shock that precipitates thoughts of quitting and external job searches. Thus, fast velocities may help employees to preserve their perceptions of self-worth in organizations, even in the face of current economic inequities.

Self-worth is also used to judge the adequacy of one’s relational outcomes, such as attention, deference, and eligibility for social inclusion (Barkow, 1989; Leary & Baumeister, 2000). According to sociometer theory (Leary & Baumeister, 2000), the feedback system monitors other people’s language and behavior and responds to discrepancies between incoming social inclusion information and self-esteem levels. Threats of social exclusion motivate people to take action in order to repair interpersonal relations. What drives emotional responses during this self-regulatory process, however, are changes in social inclusion status, such that increases and decreases in relational evaluations elicit positive and aversive emotions, respectively (Leary & Baumeister, 2000). A decrease in social inclusion status will push esteem-based regulation to the forefront of employees’ attention, with the goal to slow and ultimately reverse the decrease. Doing so would entail influencing the emergence of belonging-based goals at lower levels in the goal hierarchy in order to strengthen interpersonal ties. Unfortunately, research to date on the process of seeking feedback from others regarding how one is perceived and valued at work and comparing this feedback to internal standards (i.e., self-esteem) has been limited to discrepancy information (Swann, Johnson, & Bosson, 2009). As we discussed in the previous section though, velocity also influences interpersonal relations between people (e.g., Laurenceau et al., 2005), which has implications for both esteem and belonging needs.