Racial Diversity and Task Performance: The Roles of Formalization and Goal Setting in Government Organizations

Abstract

Previous studies on demographic diversity have yielded inconsistent results with respect to several individual and organizational outcomes. The current study proposes that structural characteristics can enhance the positive influence of racial diversity on employee outcomes. Specifically, this study examines the roles of formalization as a moderator and goal setting as a mediator in the relationship between racial diversity and employee task performance. Using a sample of New York state employees from 42 state agencies, this study found that (a) in organizations where tasks are formalized, the effects of racial diversity on employee perception of goal specificity and goal difficulty were positive and (b) some of the interaction effects between racial diversity and formalization were, in turn, positively related to task performance through perception of the goal difficulty. Overall, while the results confirm the complex nature of diversity effects mentioned in previous research, they contribute further evidence regarding the positive impact of racial diversity and the notion that the contextual environment of public organizations—in this case, formalization—contributes to positive performance in demographically diverse workforces.

Keywords

workforce diversity goal setting formalization moderated mediation

Theoretical approaches used in diversity research tend to address the psychological processes by which diversity can influence work outcomes (Van Knippenberg, De Dreu, & Homan, 2004). Williams and O’Reilly (1998), for example, identify two competing perspectives in diversity research: the information-decision-making perspective and the social categorization perspective. On one hand, the idea that demographic diversity may offer some advantages is based on the information-decision-making perspective, which suggests that diverse groups tend to possess “a broader range of task-relevant knowledge, skills, and abilities that are distinct and nonredundant” (Van Knippenberg et al., 2004, p. 1009), allowing individuals to solve complex and nonroutine problems (Chowdhury, 2005) and stimulating increased creativity and innovation (Van Knippenberg et al., 2004). Therefore, diverse groups are regarded as more effective in group decision making and effectiveness because they use broader perspectives and bring together different ideas (Milliken & Martins, 1996).

On the other hand, demographic diversity could have detrimental effects. According to the social categorization perspective, group members categorize self and others on the basis of similarities and differences and form in-group and out-group distinctions whereby group members may favor and cooperate more with members of their in-group more than with members of an out-group (Van Knippenberg et al., 2004). Such “intergroup bias” formed by social categorization negatively influences “affective-evaluation reactions” within diverse groups (Van Knippenberg et al., 2004, p. 1015), which results in the potentially negative effects of diversity. Empirical studies based on the social categorization perspective indicate that demographic diversity may yield negative outcomes such as decreased cooperation and cohesion, communication problems, task/emotional conflict, turnover, and reduced team effectiveness (Milliken & Martins, 1996; Pelled, 1996; Pelled, Eisenhardt, & Xin, 1999; Van Knippenberg et al., 2004; Williams & O’Reilly, 1998).

In line with these two competing perspectives, previous research focusing on the effects of workforce diversity has yielded both positive (e.g., better problem solving) and negative outcomes (e.g., low performance; Riccucci, 2002; Van Knippenberg et al., 2004; Wegge, Roth, Neubach, Schmidt, & Kanfer, 2008). Not surprisingly, the inconsistent patterns obtained in prior research have led to calls for additional investigation into the mechanisms by which diversity influences individual perceptions and attitudes, emphasizing the role of the contextual environment of work groups to fully understand the complexity of diversity in organizations (Joshi & Roh, 2007; Pelled et al., 1999; Schippers, den Hartog, Koopman, & Wienk, 2003). Thus, diversity scholars assume that individuals in diverse organizations have the opportunity to either promote or hinder organizational outcomes, with individuals’ actions being influenced by their perceptions of contextual characteristics of the organization. Diversity scholars have thus expanded the scope of their research, including new types of variables in the search for intervening or contextual variables (i.e., mediators or moderators; Bell, Villado, Lukasik, Belau, & Briggs, 2011) that may play a role in the diversity–work outcomes relationship. Focusing on racial diversity, the current study investigates the process by which racial diversity can affect the public organization workplace.

Current Study

The present study considers the information-decision-making perspective in proposing that racial diversity would be associated with employees’ goal setting—operationalized here as goal specificity and goal difficulty (Locke & Latham, 1990, 2002). In addition, this study investigates formalization as a moderator and goal setting as a mediator in the relationship between racial diversity and employee outcomes (Figure 1).

Figure 1.

Research model.

The present study builds on and extends previous studies in public administration in several ways. First, it is the first to test the relationship between racial diversity and goal setting, which will help explain how goal setting is influenced by an organization’s racial diversity. Literature suggests that public employees are susceptible to experiencing greater goal ambiguity than their counterparts in the private sector (Chun & Rainey, 2005; Rainey, 2009). Given the importance of goals in public organizations, one could argue that goal setting, which public administration scholars have generally overlooked in their research on the effects of organizational diversity, may be an important variable to consider.

Second, this study assesses the moderating effect of formalization in the relationship between racial diversity and task performance in a state government agency context. A review of public administration research focusing on diversity indicates that there is little research examining the effects of structural characteristics in this domain. Recent work by Langbein and Stazyk (2013), however, emphasizes that diversity research should consider job aspects, indicating that the benefits of diversity may be partly determined by organizational structures and hierarchies. Indeed, much research in public administration has touched on the functions and nature of bureaucratic characteristics in public organizations such as rules, procedures, and hierarchies, arguing for their positive aspects (Adler & Borys, 1996; Portillo & DeHart-Davis, 2009; Stazyk & Goerdel, 2011). In this study, formalization, one of several structural characteristics, is expected to influence the effect of diversity on goal setting. It should be noted here that prior investigations into various potential-intervening variables (e.g., task process, emotional conflict cognitive process, and behavioral processes; Ancona & Caldwell, 1992; Elsass & Graves, 1997; Pelled et al., 1999; Schippers et al., 2003) have yielded important insights into how individuals within diverse groups/organizations interact with each other and perceive the tasks, rules, or processes of their organizations (Pelled, 1996; Pelled et al., 1999). This suggests that it may be particularly critical to explore how public sector employees in diverse groups share their values and goals and communicate among members.

Finally, this study examines the demographic information of 42 state government agencies in conjunction with the participants’ individual perceptions of goals and task performance, taking advantage of existing archival data produced by a State Workforce Management Report.

Literature Review and Hypotheses

Goal Setting Theory

Goals motivate individuals to search for new knowledge as well as to use their existing abilities and “stored” knowledge, and are viewed as the mechanism by which values lead to action (Latham & Pinder, 2005). According to goal setting theory, specific and challenging goals improve task performance by directing attention, mobilizing effort and persistence, and encouraging the development and use of task strategies (Locke & Latham, 1990, 2002). These findings have been supported by numerous empirical studies conducted in both laboratory and field settings (Ambrose & Kulik, 1999; Kleingeld, van Mierlo, & Arends, 2011; Locke & Latham, 1990, 2002). In contrast to general goals that do not specify what is to be accomplished, specific goals help employees decide how much and what type of effort should be exerted to attain the goals and provide employees with a better understanding of what is expected (Bandura, 1989; Schnake & Cochran, 1985). Difficult goals require individuals to exert greater effort and show more persistence than do easy goals, and thus lead to higher levels of performance (Locke & Latham, 1990). As such, setting specific difficult goals has long been recognized as an important motivational mechanism for enhancing task performance (Locke & Latham, 1990, 2002).

Link Between Racial Diversity and Goal Setting

Given that goal setting research has consistently found that specific difficult goals enhance performance, it would be interesting to investigate the role of diversity in this relationship. In particular, both information-decision-making and resource-based perspectives would suggest that diverse groups are likely to facilitate the information-processing impacts of specific and difficult goals on performance.

Information-decision-making perspective

Literature suggests that individuals in diverse groups can engage in the process of elaborating task-relevant information, where unique and nonredundant information is shared and the exchange, discussion, and integration of ideas, knowledge, and insights pertaining to assigned tasks are facilitated (Kearney, Gebert, & Voelpel, 2009; Van Knippenberg et al., 2004). Hence, it is assumed that diverse groups in which the elaboration of task-relevant information is fostered are more likely to make use of the variety of resources in the group to understand ambiguous goals and set higher goals, which may be positively related to their perception of the goals.

Resource-based perspective

In supporting the argument above, the resource-based view suggests that organizations with diverse perspectives should possess more resources to draw on and create value that would otherwise be scarce or difficult to duplicate, which should enable diverse groups to be more creative and innovative (Richard, 2000). As such, the variety of insights and perspectives that diverse groups bring to organizations improves their ability to reach different and challenging goals and become more successful because they have the resources needed to be competitive (Richard, 2000). Employees who believe they have high levels of skill should be confident enough to set challenging goals. Moreover, one would expect that individuals who have resource skills will recognize their competency and hold beliefs about their ability to create and guide their organization to perform well.

As such, both information-decision-making and resource-based perspectives provide theoretical justifications that explain how racial diversity can be expected to yield a motivational climate that can enhance positive goal setting.

Formalization as a Moderator in the Racial Diversity-Goal Setting Relationship

Formalization refers to the extent to which rules, procedures, instructions, and communications are written down and followed (Pugh, Hickson, Hinings, & Turner, 1968). Following Pugh and Hickson’s (2007) notion of structure, Bunderson and Boumgarden (2010) conceptualize the term bureaucratic as meaning “more highly structured.” They maintain that a team that has clearer procedures specifying how work is to be performed is regarded as more highly structured. With respect to structural characteristics, public administration literature has highlighted positive aspects of bureaucratic attributes such as formal rules (Portillo, 2012; Portillo & DeHart-Davis, 2009). Moreover, prior literature has noted that employees who believe that their goals are the same as those of their organization welcome formal work procedures that are appropriately designed and implemented (Adler & Borys, 1996). Well-designed procedures clarify goals (Organ & Greene, 1981) and specify employees’ work roles and the procedures that they must follow (Griffin, Neal, & Parker, 2007).

Formalization may be particularly valuable in diverse organizations in which individuals feel they need to convince colleagues or supervisors that they do not base actions on prejudicial or stereotypical views of others. Individuals in organizations that are high, rather than low, in formalization are less likely to form stereotypes based on social categorization processes because formalization urges employees to follow mechanisms such as procedures and rules that they can utilize in performing their tasks. As such, employees in diverse organizations may be protected by formalized authority and contexts where individuals are encouraged to perform their tasks based on “authority in position rather than in person” (Langbein & Stazyk, 2013, p. 471).

One can expect that as organizational structures become more bureaucratic (i.e., as employees are expected to follow specific rules or procedures in performing tasks), the positive effects of demographic diversity on goal setting might be enhanced. Similarly, any negative effects might be less pronounced in the sense that the interaction between structural attributes and diversity may create situational constraints in public organizations that decrease the likelihood that individuals will act based on stereotypes or biases. Thus, a high level of structural attributes reduces the likelihood of adverse effects of demographic diversity that impair interaction and coordination among employees. This argument is consistent with Langbein and Stazyk’s (2013) perspective that hierarchical attributes in public organizations are positively related to diversity effects by allowing individuals to understand and align with organizational values and goals.

Taken together, it is expected that racially diverse organizations that have high levels of formalization will benefit from processes, coordination, and cooperation that positively influence goal setting, and that some of these effects will, in turn, influence task performance (Locke & Latham, 1990, 2002). Based on these insights from the literature, this study proposes that formalization positively interacts with racial diversity to predict goal setting (i.e., goal specificity and goal difficulty), which in turn increases task performance.

Hypothesis 1: The indirect effect of racial diversity on task performance through goal specificity is stronger when formalization is high.

Hypothesis 2: The indirect effect of racial diversity on task performance through goal difficulty is stronger when formalization is high.

Method

Sample

Data for this study were collected from full-time professional workers in New York state government via a self-administered online survey. The survey assured respondents that participation in the study was voluntary and that all information collected from participants would be kept anonymous. Out of the workers who received the survey, 602 employees from 42 agencies completed the survey and reported their agency.¹ Because this study measured racial diversity at the agency level, only those employees who reported their agency were included in the analysis. Survey participants represent a variety of job areas.² The majority of participants (87%) were White/Caucasian, 52% of the participants were female, and 45% had supervisory/managerial status. Over 80% had at least a bachelor’s degree and 46% had professional certification or licensure. Respondents’ age was on average 51 years, and their average tenure at their employing organization was 18 years. Survey items were rated using a 5-point Likert-type scale from 1 = strongly disagree to 5 = strongly agree (see Appendix A).

Measures

Racial diversity

This study measured racial diversity using Blau’s (1977) index of heterogeneity, $1 - \sum {p_{i}}^{2}$ where p is the proportion of an agency in the respective diversity category and i is the number of different categories represented in the agency. The index varies from 0, indicating no diversity, to a maximum of 1. This study calculated racial diversity on the basis of archival data found in the State Workforce Management Report (https://www.cs.ny.gov/businesssuite/docs/workforceplans/2014.pdf) and used the 42 state agencies listed in the Appendix B. For racial diversity, there were six different race categories represented in the report. For each agency, this study recorded the percent that was in the following groups: White, Black, Hispanic, Asian/Pacific, American Indian, and Unknown. The mean state agency racial diversity score was .41, with a range from .21 to .70.

Goal difficulty and goal specificity were measured based on Wright’s (2004) adaptation of Locke and Latham’s (1990) Goal Setting Questionnaire. The Cronbach’s alpha estimate for goal difficulty (three item) and goal specificity (three item) were .73 and .77, respectively. Goal difficulty measured the extent to which individuals’ jobs require high levels of effort and skill and how challenging their jobs are, and goal specificity measured the degree to which their job requirements are specific and well understood. These measures focused on employees’ jobs, in general, rather than on any task-specific goal (Wright, 2004). Scale items of three items were averaged into single indicators for each measure.

Formalization evaluated the extent to which procedures or job duties are clearly specified or written down (Pugh et al., 1968). The measure of formalization in this study captured a positive aspect of formalization (Adler & Borys, 1996). Thus, higher scores indicate that work unit procedures or rules were perceived as being more clearly specified and defined. This three-item scale has a reliability of .53. The three items were averaged to create a single indicator.

Task performance was assessed using Williams and Anderson’s (1991) three-item measure, which asks individuals to rate their own task performance, for example, “I always complete assigned duties in a timely fashion.” Based on a factor analysis, one item with a low factor loading (less than 0.5) was excluded from further analysis. This scale (two items) had a reliability of .59, and the two items were averaged to create a single indicator.

This study controlled for employees’ demographic characteristics that may influence goal specificity and goal difficulty. Respondents’ supervisory status, sex, and race (where 0 = White/Caucasian to 1 = Non-White/Caucasian) were coded as dummy variables, and years of service was reported in years. In addition, job areas and agency size were included as control variables. Following previous studies (Choi, 2009; Choi & Rainey, 2014), agency size was operationalized as the natural logarithm of the number of employees.

Analysis

Following Preacher and Hayes’ (2008) and Aiken and West’s (1991) suggestions, racial diversity and formalization were mean-centered to avoid multicollinearity between the predictors and the interaction terms. To test the indirect effects of racial diversity and formalization on task performance via goal specificity and goal difficulty, the current study uses PROCESS, a moderated mediation approach recommended by Preacher and Hayes (2008). This approach was used because it allows the testing of both moderation and mediation effects in a single model and examines conditional indirect effects at various levels of the moderator variable (Preacher & Hayes, 2008). The PROCESS program generates coefficients of the conditional indirect effects of the moderator variable (i.e., when a moderator is high and low) and an index of the moderated mediation effect with confidential intervals, as well as coefficients for each variable and interaction term included in the analysis. As this approach allows for the testing of the significance of the indirect effects using bootstrapping and estimated bias-corrected confidence intervals for the indirect effects, it is considered a superior approach to the more traditional mediation tests (e.g., Sobel test) as an inferential procedure (see Cole et al., 2008; Hayes, 2009; Preacher, Kristopher & Hayes, 2008).³

Results

The means, standard deviations, correlations, and scale reliabilities for the variables in the study are reported in Tables 1 and 2.⁴ The diversity variable was not significantly correlated with either goal specificity or goal difficulty. Formalization was positively associated with both goal specificity and goal difficulty.

Table 1.

Descriptive Statistics.

Variables	M	SD	Minimum	Maximum
Goal specificity	3.85	0.81	1	5
Goal difficulty	3.91	0.75	1	5
Formalization	3.30	0.78	1	5
Racial diversity	0.41	0.13	0.21	0.70
Task performance	4.18	0.70	1	5
Agency size (natural log of the number of employees)	8.48	1.19	3.43	10.29
Supervisory status (1 = nonsupervisor, 2 = supervisor)	1.43	0.50	1	2
Sex (1 = female; 2 = male)	1.44	0.52	1	2
Service years in the state agency	18.10	11.15	0	43
Race (0 = White/Caucasian, 1 = Non-White)	0.13	0.34	0	1

Table 2.

Correlations.

Variables	Racial diversity	Goal specificity	Goal difficulty	Formalization	Task performance
Racial diversity	—
Goal specificity	.06	(.77)
Goal difficulty	.07	.22*	(.73)
Formalization	.03	.50*	.14*	(.53)
Task performance	.06	.28*	.22*	.13*	(.59)

Note. Reliability (Cronbach’s alpha) scores are shown in parentheses on the diagonal.

p < .05.

Tables 3 and 4 present the moderated mediation estimates used to test the hypotheses for goal specificity and goal difficulty, respectively.⁵ Agency size, job area, supervisory status, sex, race, and service years were entered as control variables in the models. In addition, agency effects are controlled in the fixed-effect models.

Table 3.

Moderated Mediation Effect of Racial Diversity and Goal Specificity (n = 595).

	Goal specificity	Task performance
Agency size	.001 (.003)	.004 (.023)
Supervisory status	.036 (.063)	.088 (.054)
Male	−.065 (.064)	−.244* (.055)
Service years	.002 (.003)	−.008* (.002)
Non-White	.105 (.097)	.243* (.083)
Racial diversity	.193 (.244)	.131 (.209)
Formalization	.448* (.033)	.000 (.003)
Formalization × Racial diversity	.507* (.252)	.156 (.217)
Goal specificity		.186* (.038)
R ²	.280 (F = 21.83)	.140 (F = 8.47)
ΔR²	.006* (F = 4.31)
Moderated mediation effect Coefficients (Boot SE)LL 95% CI, UL 95% CI	.095 (.064)[–.015, .244]
Conditional Effect of Diversity on Goal Specificity at Values of Moderator (Formalization):
Formalization	Effect (SE)	LL 95% CI, UL 95% CI
Low	−.267 (.331)	[–0.917, 0.383]
High	.722 (.347)	[0.039, 1.404]
Conditional Indirect Effect(s) of Diversity on Performance Through Mediator (Goal Specificity) at Values of Moderator (Formalization):
Formalization	Effect (SE)	LL 95% CI, UL 95% CI
Low	−.053 (.084)	[–0.239, 0.105]
High	.125 (.066)	[0.017, 0.282]

Note. Unstandardized regression coefficients are reported. Standard errors are shown in parentheses. Values for quantitative moderator (formalization) are ± 1 SD from mean. Controlled for agency-fixed effect. Controlled for job areas. Bootstrap sample size = 1,000. LL = lower limit; UL = upper limit.

p < .05.

Table 4.

Moderated Mediation Effect of Racial Diversity and Goal Difficulty.

	Goal difficulty	Task performance
Agency size	.041 (.028)	−.003 (.024)
Supervisory status	.220* (.066)	.063 (.055)
Male	−.067 (.067)	−.244* (.056)
Service years	.002 (.003)	−.008* (.002)
Non-White	−.039 (.102)	.255* (.085)
Racial diversity	.374 (.254)	.089 (.212)
Formalization	.081* (.034)	.060* (.028)
Formalization × Racial diversity	.575* (.262)	.191 (.219)
Goal difficulty		.158* (.037)
R ²	.057 (F = 3.391)	.130 (F = 7.51)
ΔR²	.010*(F = 5.05)
Moderated mediation effect Coefficients (Boot SE)LL 95% CI, UL 95% CI	.091* (.054)[.004, .216]
Conditional Effect of Diversity on Goal Difficulty at Values of Moderator (Formalization):
Formalization	Effect (SE)	LL 95% CI, UL 95% CI
Low	−.168 (.346)	[–0.848, 0.511]
High	.948 (.361)	[0.237, 1.659]
Conditional Indirect Effect(s) of Diversity on Performance Through Mediator (Goal Difficulty) at Values of Moderator (Formalization)
Formalization	Effect (SE)	LL 95% CI, UL 95% CI
Low	−.027 (.059)	[–0.160, 0.084]
High	.146 (.069)	[0.037, 0.334]

p < .05.

Racial Diversity and Goal Specificity

As shown in Table 3, the interaction between racial diversity and formalization was positively related to goal specificity (β = .507, p < .05). The R² increase due to interactions (ΔR² = .006, F = 4.31, p < .05) indicates a small, but significant effect. The (interaction) effect of race diversity on goal specificity is significantly positive, β = .722, SE = .347, p < .05, confidence interval (CI) = [.039, 1.404], when formalization is high (i.e., plus one standard deviation from mean), whereas the interaction effect is not significant (β = −.267, SE = .331, p < .05, CI = [–.917, .383]) when formalization is low (i.e., minus one standard deviation from mean).

The effect of goal specificity on task performance was significantly positive (β = .186, p < .05). However, the indirect effect of racial diversity on task performance through goal specificity was not significant (moderated mediation coefficient = .095, ns), that is, the lower and upper levels of the CI include zero (CI = [–.015, .244]). The analysis also indicates that the conditional indirect effects of racial diversity on task performance through goal specificity was significantly positive (β = .125, p < .05, CI = [.017, .282]) when formalization was high (i.e., plus one standard deviation from mean). Thus, while the effect of the interaction between racial diversity and formalization on goal specificity indicates a significant effect, the moderated mediation effect on task performance was not significant, thus partially supporting the Hypothesis 1.

Racial Diversity and Goal Difficulty

As shown in Table 4, the interaction between racial diversity and formalization was positively related to goal difficulty (β = .575, p < .05). The R² increase due to interactions (ΔR² = .010, F = 5.05, p < .05) indicates a small, but significant effect. The (interaction) effect of race diversity on goal difficulty is significantly positive (β = .948, SE = .361, p < .05, CI = [.237, 1.659]) when formalization is high (i.e., plus one standard deviation from mean), whereas the interaction effect is not significant (β = −.168, SE = .346, ns, CI = [–.848, .511]) when formalization is low (i.e., minus one standard deviation from mean).

The effect of goal difficulty on task performance was positively associated with task performance (β = .158, p < .05). Table 4 also shows a moderated mediation effect for racial diversity and goal difficulty. The effect of the interaction of racial diversity and formalization on task performance through goal difficulty was positive and significant (moderated mediation coefficient = .091, p < .05); that is, the lower and upper levels of the CI did not include zero (CI = [.004, .216]). Therefore, the findings support Hypothesis 2. Furthermore, the analysis indicates that the conditional indirect effects of racial diversity through goal difficulty on task performance was significantly positive (β = .146, p < .05) when formalization was high.

Discussion

Responding to calls for further exploration of diversity in the context of public organizations (e.g., Pitts & Wise, 2010), the current study examined the claim that formalization in public organizations helps to realize favorable outcomes, focusing particularly on whether and when racial diversity positively relates to individual goal setting and task performance. Results confirm that (a) formalization strengthened the positive impact of racial diversity on perceptions of goal specificity and goal difficulty and (b) some of the effect of the interaction between racial diversity and formalization on goal difficulty, in turn, was associated with increased task performance. This pattern of results is consistent with Langbein and Stazyk’s (2013) claim that the benefits of diversity might be determined by public organizations’ structural characteristics, and thus contributes to the argument that bureaucratic characteristics can lead to positive organizational outcomes (Stazyk & Goerdel, 2011).

Implications

The findings have several theoretical implications for both the diversity and goal-setting literatures. With respect to diversity, it is believed that demographic diversity can have either positive or negative effects depending on contextual conditions. Given that research on the effects of demographic diversity is replete with inconsistent findings—i.e., both positive and negative consequences—(Joshi & Roh, 2007), the findings of this study help to clarify this issue by identifying important moderating and mediating variables and providing empirical support for the approach. In particular, the current study focuses on a structural characteristic (i.e., formalization) that triggers the benefit of demographic diversity, suggesting that more attention should be devoted to identifying contextual factors through which diversity attributes influence goal setting and other outcome variables.

With respect to goal setting research, this study suggests that racial diversity can be regarded as an important contextual antecedent of effective goal setting. To date, diversity research has provided few insights into the linkage between workforce diversity and goal setting. Given the finding that goal setting plays an important role in the relationship between racial diversity and task performance, the current study proposed goal setting as a mechanism for furthering our understanding of diversity effects, thus taking an important first step in investigating the link between these important constructs.

The results of this study indicate the important moderating role of formalization in the relationship between race diversity and goal setting by highlighting interaction effects. Although the effect size of the interaction effects is not large (i.e., the R-square increase due to the interaction effect is less than .20), the findings have potentially important implications for practitioners. As discussed above, when there is a low level of formalization, the interaction between racial diversity and goal setting is not significant. This means that unless formalization policies are sufficiently established, it might be difficult for a diverse workforce to generate positive perceptions of goal setting, which suggests the need to establish formalization-relevant policies / programs for task performance in diverse organizations.

Furthermore, the results suggest that a diverse workforce itself does not automatically entail favorable or harmful outcomes. Rather, diversity dynamics must be viewed within specific organizational contexts, which may realize benefits of diversity or mitigate its dysfunctions. The findings are, therefore, highly important for public managers who seek ways of working with a diverse workforce. In a similar vein, this research suggests that to stimulate a motivational force such as a positive goal setting, intentionally employing a demographically diverse workforce has benefits under certain work situations. For example, diversity management practices may involve fostering work environments where well-designed procedures or rules are effectively implemented and formalization practices are well defined. In addition, one can expect that negative consequences resulting from employees’ perceptions of ambiguous goals can be minimized or prevented in advance through effective diversity management programs.

Limitations and Future Research

Some limitations must be considered. First, this study relied on subjective ratings of the key structural variable—formalization. Perceptions of structure may not adequately reflect actual structural practices. Future research could assess structural features using both perceptual indicators and more objective measures of organizational features such as the number of formal policies or the number of layers in the organizational hierarchy (Hempel, Zhang, & Han, 2012). Replicating the findings with more objective measures would add to our confidence in the findings in this study.

Another limitation of this study relates to the measurement of variables. In particular, some variables included in the present study were measured with only a limited number of items (e.g., task performance). Related to this, some of the measures showed marginal reliabilities (e.g., .59 for the task performance scale), which optimally would have surpassed the commonly accepted reliability standard of .70 (Nunnally, 1978). Although lower reliability levels are considered acceptable for measures that utilize a small number of items (Cortina, 1993), future research should consider measuring these variables using multiitem scales that capture more aspects of the constructs to increase their precision and the validity of the findings.

Finally, most of the respondents included in this study were White (87%). Although the majority of New York state employees are White (74.2%; New York State Workforce Management Report, 2014), the difference in percentage may be problematic because the sample may not be representative of each agency. Whereas the diversity measure in this study is calculated based on agency workforce information, major variables (formalization, goal setting, task performance) are measured based on survey respondents. Thus, the survey sample included in this study represents more White respondents’ perception, although the analyses in this study controlled for individual race using a dummy variable (i.e., White = 0, non-White = 1). To deal with this issue, future research could examine the proportion of racial minorities in each agency. For example, Choi (2017) defines different demographic compositional settings according to the proportion of racial/ethnic minorities: predominantly White (0%-5% racial/ethnic minorities), White-dominated (5%-15% racial/ethnic minorities), White-majority (15%-30% racial/ethnic minorities), White minority mixed (30%-50% racial/ethnic minorities), and minority-majority (50%-85% racial/ethnic minorities).

Related to the issue of the discrepancy between agency data (diversity measure) and the actual composition of the sample, one additional limitation should be recognized. Although the analysis in the current study controls for individuals’ job area and supervisory status, agency-level diversity data do not describe the level of diversity by different job areas or level of position. Thus, if racial minorities are concentrated in a limited number of job areas or in lower-level positions, an agency could appear to have a high level of diversity even though the diversity is not fully reflected across the agency. If it was possible to obtain agency data that were stratified by job area and supervisory status, the data analyses might, in fact, provide more precise findings that would allow for implications that were tailored to different groups. This may be a particular concern in the current study, considering that the sample includes an overrepresentation from White respondents and White people may hold particular jobs or positions. Thus, caution should be taken in generalizing the findings of the current study to different settings.

There are a variety of directions for future research to pursue. First, future research may consider gender diversity in the model in addition to racial/ethnic diversity. Both gender and racial/ethnic diversity can be categorized as readily detectable, more observable, and less job-related diversity in contrast to less observable, highly job-related diversity (Pelled, 1996; Webber & Donahue, 2001). In line with the diversity classification, it would be interesting to examine whether gender and racial/ethnic diversity have similar patterns or if each has its own distinct effects under certain situations. It should be noted that gender-related studies of public administration indicate that women rely more on formal rules than do men as a way to compensate for lack of organizational power (Portillo, 2012; Portillo & DeHart-Davis, 2009). Future research should investigate the interaction effects of individuals’ demographic differences (e.g., White male, White female, non-White male, and non-White female) and workforce diversity, along with structural characteristics (e.g., formalization), on employee perceptions.

Finally, future research might extend the model used in this research by including other frequently studied variables, which would provide more inclusive and thus more theoretically and practically useful models of the diversity effects. For example, agency size could be considered in the diversity research. Krause and Douglas (2013) claim that benefits of diversity are only effective in smaller groups because coordination problems occur in larger groups. Although the current study included agency size as a control variable, examining the interaction effects between agency size and racial diversity would be worthwhile. Future research on this topic should seek to provide a better understanding regarding how organizational size and diversity influence employee work outcomes. Another promising avenue for future research could involve examining task-related diversity variables such as tenure and education diversity, in addition to race/ethnicity. It would be particularly interesting to investigate whether there are differences between the influence of less job-related diversity dimensions (e.g., racial diversity) and more job-related diversity dimensions (e.g., tenure and education diversity) on perception of goals and other outcome variables.

Conclusion

Given that past diversity research indicated inconclusive findings regarding the impact of workforce diversity on organizational effectiveness, the current study proposed two important factors—formalization and goal setting—to explain the relationship between racial diversity and task performance and tested the models. Although prior diversity research has focused mainly on the private sector, this research is one of the few to investigate the relationship between demographic diversity and goal setting using public sector employees working in state government agencies (e.g., Bowling, Kelleher, Jones, & Wright, 2006; Jacobson, Palus, & Bowling, 2010). In proposing and testing a theoretical model that draws on two established theoretical frameworks—diversity and goal setting theory—this research contributes to the literature by providing a better understanding of diversity effects and highlighting a key structural characteristic (i.e., formalization) that can enhance both goal setting and task performance in diverse organizations.

Footnotes

Appendix A

Appendix B

List of State Agencies

Agency
1. Adirondack park agency
2. Aging
3. Agriculture and markets
4. Alcoholism and substance abuse services
5. Attorney general
6. Banking
7. Children and family services
8. Civil service
9. Correctional services
10. Criminal justice services
11. Developmental disabilities
12. Education
13. Emergency management
14. Environmental conservation
15. Financial services
16. General services
17. Health
18. Higher education services corporation
19. Homeland security and emergency service
20. Housing finance agency
21. Human rights
22. Information technology services
23. Inspector general
24. Insurance fund
25. Justice center for the protection of people with special needs
26. Labor
27. Mental health
28. Motor vehicles
29. Parks, recreation, and historic preservation
30. Parole
31. People with development disabilities
32. Probation and correctional alternatives
33. Public service
34. State
35. State comptroller
36. State university of New York
37. Taxation and finance
38. Technology
39. Temporary and disability assistance
40. Transportation
41. Veterans’ affairs
42. Workers compensation board

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.

Notes

Author Biography

Jaehee Jong is an assistant professor at the Department of Public Administration in Northern Illinois University. Her research focuses on human resource management and organizational behavior in the public sector.

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