Next Article in Journal
Exploring the Conceptual Structure of the Research on Innovation in Hotels through Co-Word Analysis
Previous Article in Journal
Impact of Emotional Intelligence on Organisational Performance: An Analysis in the Malaysian Public Administration
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Administrative Sciences
Volume 11
Issue 3
10.3390/admsci11030077
admsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Probing the Links between Workforce Diversity, Goal Clarity, and Employee Job Satisfaction in Public Sector Organizations

by
Edmund C. Stazyk
1,*,
Randall S. Davis
2 and
Jiaqi Liang
3
1
Rockefeller College of Public Affairs & Policy, University at Albany, State University of New York, Albany, NY 12222, USA
2
School of Management and Marketing, Southern Illinois University Carbondale, Carbondale, IL 62901, USA
3
Department of Public Administration, University of Illinois Chicago, Chicago, IL 60607, USA
*
Author to whom correspondence should be addressed.
Adm. Sci. 2021, 11(3), 77; https://doi.org/10.3390/admsci11030077
Submission received: 29 June 2021 / Revised: 22 July 2021 / Accepted: 23 July 2021 / Published: 28 July 2021
Browse Figure
Versions Notes

Abstract

:
A considerable body of research substantiating the importance of workforce diversity to public organizations has accrued over the past two decades. However, research on workforce diversity has also been narrow in scope and frequently fails to link diversity to important individual and organizational outcomes. Using data (n = 1,109,134 employees from 500 sub-agencies) collected in three waves (2010, 2011, and 2012) of the Federal Employee Viewpoint Survey (FEVS), this study examines whether (1) increased diversity influences organizational goal clarity, (2) diversity and goal clarity, in turn, influence employee job satisfaction, and (3) diversity management policies influence job satisfaction by clarifying organizational goals for workers. FEVS is administered yearly by the U.S. Office of Personnel Management (OPM) and is designed to assess whether and to what extent federal employees believe the characteristics of successful organizations are present in their agency. Results from a multi-level structural equation model (MSEM) suggest diversity is associated with greater goal clarity and that diversity management policies, by clarifying organizational goals, positively affect job satisfaction. Findings also indicate that the type of diversity matters.

1. Introduction

The number of women and minorities in the U.S. civilian workforce has increased considerably over the past sixty years. Women participated in the civilian workforce at a rate of 34% in 1950 (Toossi 2002, 2012a); by 2020, this percentage increased to 50.04% (Law 2020). Additionally, large-scale changes in the socio-demographic profile of the U.S. workforce are expected to continue. For example, Hispanics and Latinos (8.5%), African Americans (10.9%), and Asians (3.7%) constituted roughly 23% of the labor force in 1990 (Toossi 2012a, 2012b); by 2019, these figures had increased to 18% for Hispanics and Latinos, 13% for African Americans, and 6% for Asians, totaling roughly 37% of the civilian workforce (U.S. Bureau of Labor Statistics 2020). If these trends continue in the coming decades, the effects of a shifting workforce will likely be pronounced in the public sector for two reasons. First, public organizations already employ a sizable percentage of the civilian and non-civilian workforce—approximately 15% of all workers in 2019 (Hill 2020). Second, relative to the private sector, women and minorities are over-represented in public organizations (Llorens et al. 2008). Collectively, these factors underscore the need for public organizations to embrace diversity management.
Current interest in diversity management emphasizes the “business case” for diversity (Ivancevich and Gilbert 2000; Kochan et al. 2003; Thomas 1990). The business case for diversity originated outside of public administration and is predicated on four assumptions. First, workforce diversification is an organizational reality, driven by a changing global socio-demographic profile. Second, to retain a competitive advantage in an evolving labor market, organizations must leverage diversity (Kochan et al. 2003; Thomas 1990). Third, the purchasing power of diverse groups of consumers is increasing rapidly. Consequently, organizations must offer goods and services that meet the needs and expectations of diverse consumers (Humphreys 2008). Fourth, diverse workforces can improve organizational performance by introducing new and different perspectives into organizations that may be used to enhance decision-making (Ivancevich and Gilbert 2000; Jong 2019; Kochan et al. 2003; Langbein and Stazyk 2013; Moon and Christensen 2020; Park and Liang 2020; Stazyk and Davis 2015; Stazyk et al. 2017).
Assertions that diversity can enhance organizational performance have influenced public administration (Choi and Rainey 2010; Jong 2019; Moon and Christensen 2020; Park and Liang 2020; Pitts and Recascino Wise 2010; Wise and Tschirhart 2000). Since 1990 research has emphasized that diversity can improve the design and implementation of public policies (Choi 2009; Jong 2019; Moon and Christensen 2020; Park and Liang 2020; Pitts and Recascino Wise 2010; Riccucci 2002), as well as employees’ experiences in organizations (Choi and Rainey 2010; Jong 2019; Moon and Christensen 2020; Pitts 2009). Nevertheless, it has become increasingly apparent that the benefits of diversity for organizations are highly contextualized (Jackson et al. 2003; Jong 2019; Kochan et al. 2003; Moon and Christensen 2020; Pitts and Recascino Wise 2010; Williams and O’Reilly 1998; Wise and Tschirhart 2000). Such findings have led several public management scholars to conclude existing research has been overly narrow in scope, and that many of the presumed performance-related benefits of workforce diversity remain largely speculative (Choi 2009; Choi and Rainey 2010; Jong 2019; Langbein and Stazyk 2013; Moon and Christensen 2020; Naff and Kellough 2003; Pitts and Recascino Wise 2010; Selden and Selden 2001; Stazyk et al. 2017). Calls have also been made to incorporate other individual and organizational factors missing in, but presumed relevant to, diversity management (Choi and Rainey 2010; Jong 2019; Langbein and Stazyk 2013; Moon and Christensen 2020; Park and Liang 2020; Pitts and Recascino Wise 2010; Stazyk et al. 2017; Wise and Tschirhart 2000).
We respond to these calls by assessing the relationships between two types of workforce diversity (gender and minority status), goal clarity, diversity management policies, and employee job satisfaction. Using three waves of data from the Federal Employee Viewpoint Survey (U.S. Office of Personnel Management 2010, 2011, 2012), we consider three questions: (1) does increased diversity affect the perceived clarity of organizational goals among a sample of federal employees, (2) in what ways do diversity and goal clarity influence employee job satisfaction, and (3) can diversity management policies influence job satisfaction by clarifying organizational goals? We use a multi-level structural equation model to test hypotheses at the organizational (sub-agency) and individual levels. Results are discussed according to their relevance to theory and practice.

2. Diversity in Organizations

Diversity management research has historically fallen into two camps (Kochan et al. 2003; Williams and O’Reilly 1998). The first asserts diversity introduces new and different perspectives into organizations (Cox and Blake 1991; Cox 1993; Ely 2004). Organizations can harness these diverse perspectives to improve decision-making, which is expected to generate performance gains (Cox et al. 1991; Watson et al. 1993). The second camp, conversely, employs social identity, self-categorization, and attraction/similarity theories (Byrne 1971; Tajfel and Turner 1985) to argue heterogeneous workforces often cause diminished individual and organizational performance. In this case, the assumption is that employees prefer a homogeneous workforce because homogeneity legitimizes and reinforces individuals’ positive self-assessments (Long and Spears 1997). As an organization’s workforce diversifies, it becomes harder to understand how one fits within a group or organizational setting. Conflict becomes more common, communication breakdowns happen more frequently, it becomes more difficult to integrate and coordinate workers, and employee dissatisfaction and turnover become more prevalent (Jackson et al. 2003; O’Reilly et al. 1989; Pelled et al. 1999).
Numerous attempts have been made to test the veracity of these competing perspectives. Initially, research was conducted in laboratory settings with small groups of workers and top management teams. Early research confirmed claims that different forms and types of diversity (e.g., sex, race, education, skills) could enhance organizational performance (Ely 2004; Jackson et al. 2003; Williams and O’Reilly 1998). However, when scholars conducted diversity studies in the field and included larger groups of workers, it became apparent the performance-related benefits of diversity are often contingent upon the type of diversity present in the organization along with numerous contextual factors. Contextual factors exist at the social (e.g., laws, economic conditions, political events) and organizational levels (e.g., team and organizational size, organizational climate, diversity policies) and can change over time (Jackson et al. 2003; Kochan et al. 2003; Williams and O’Reilly 1998).
Consequently, diversity management scholars across research traditions have sought to determine how and when various diversity dimensions and contextual factors matter. Many of these efforts support the notion that diversity is a multifaceted concept. In the public management setting, diversity management programs can generate greater job satisfaction and higher levels of perceived organizational performance among people of color, whereas the effects of such programs may be less significant for white males (Pitts 2009). Similarly, Choi and Rainey (2010) found the relationship between diversity (i.e., age, sex, and race) and perceived organizational performance was moderated by several factors, including one’s organizational tenure, team processes, and a results-oriented culture. In this sense, Choi and Rainey’s results echo research outside of public administration indicating the associations between different diversity dimensions, performance, and key contextual variables vary. For example, in the case of organizational tenure, Choi and Rainey found gender diversity was associated with stronger performance while racial diversity was linked to lower performance. Conversely, racial diversity was associated with improved performance when organizations encouraged teamwork, and age diversity was linked to higher performance in organizations with results-oriented cultures. In general, these findings have been supported in subsequent research. For instance, Moon and Christensen (2020) found that racial and tenure diversity were positively associated with organizational performance, whereas functional diversity was associated with lower performance.
Despite having made substantial progress, public sector diversity management research has been criticized for its narrow focus (Choi and Rainey 2010; Jong 2019; Moon and Christensen 2020; Naff and Kellough 2003; Park and Liang 2020; Pitts and Recascino Wise 2010). Three weaknesses appear to exist. First, as Pitts and Recascino Wise (2010) note, public management scholarship often focuses on particular types of diversity—mainly sex and race and ethnicity. Other diversity dimensions, such as age, disability status, religion, language, and sexual orientation, have received little attention. Second, diversity research is subject to methodological and empirical challenges. For instance, existing research operationalizes diversity in disparate ways (Pitts and Recascino Wise 2010). Similarly, much of the research has been limited to federal datasets and fails to examine diversity at the sub-agency level (Choi and Rainey 2010; Pitts and Recascino Wise 2010). Finally, relatively few contextual factors have been considered (Choi and Rainey 2010; Pitts and Recascino Wise 2010). These shortcomings compromise understanding the relationships between diversity and performance and performance-related outcomes (Choi and Rainey 2010; Naff and Kellough 2003; Pitts and Recascino Wise 2010; Wise and Tschirhart 2000).
Although we utilize federal data and focus on gender and racial and ethnic (minority status) diversity, our paper addresses existing shortcomings in two ways. First, we employ a multilevel structural equation model to examine the effects of gender diversity and minority status on employee job satisfaction. As such, we can assess the impact of diversity on job satisfaction at both the individual and sub-agency levels. Second, and more importantly, we add a new contextual factor to diversity studies: the concept of goal clarity (or, conversely, goal ambiguity). Research across academic traditions consistently finds clear, specific organizational goals improve employee, and organizational performance (see, e.g., Jong 2019). We argue workforce diversity generally reduces the perceived clarity and specificity of goals for employees, which, in turn, is associated with diminished job satisfaction. However, we also contend strong diversity management policies establish clear organizational goals that convey the value of diversity to workers. In this sense, adding the goal clarity concept to existing diversity management studies represents an important advance—one that sheds new light on the relationships between workforce diversity, job satisfaction, and diversity management policies.

2.1. Goals, Diversity, and Job Satisfaction

Considerable research exists highlighting the importance of goals for individual and organizational performance. Much of this research is grounded in Locke and Latham’s (1990, 2002) goal setting theory, which asserts goals and goal setting strategies are significant for two reasons. First, goals operate as the basis for employee motivation (Locke and Latham 2002). Second, goals direct employee action and behavior in ways that can improve performance (Locke and Latham 2002). Goals, and particularly high-level goals, (1) focus employee effort and attention on specific goal-relevant behaviors, (2) energize workers in ways that result in greater and prolonged effort, and (3) produce behaviors that “indirectly…lead to the arousal, discovery, and/or use of task-relevant knowledge and strategies” (Latham and Locke 2006, p. 707; Latham and Locke 2007; Locke and Latham 2002; Wood and Locke 1990). These factors enhance performance when goals are specific, challenging but attainable, viewed as legitimate by employees, communicated to workers, and supported by managers (Locke and Latham 1990, 2002; Wright 2004).
Goal setting theory coalesces around the notion that clear, specific goals signal what an organization values and expects from workers—often by specifying or directing (e.g., through performance appraisal systems) how employee action relates to individual rewards and the organization’s broader mission (Davis and Stazyk 2015; Locke and Latham 1990, 2002; Milkovich and Wigdor 1991; Stazyk 2016; Stazyk and Goerdel 2011). Motivation and performance gains result partly from the tendency and desire of employees to accomplish pre-determined goals and tasks (e.g., because an employee values an organization’s mission, for reasons of self-efficacy, or to maximize extrinsic incentives) (Locke and Latham 1990, 2002; Wood and Locke 1990). However, clear goals also provide purpose and direction to employees’ jobs, thereby helping workers realize where effort should be exerted (Davis and Stazyk 2015; Jong 2019; Stazyk 2016; Wright 2001, 2004).
The underlying logic inherent in goal setting theory is also relevant to public administration scholarship and practice. In the public sector, the motivational prospects of goals are generally assessed using the organizational goal clarity concept. Organizational goal ambiguity has been defined as “the extent to which an organizational goal or set of goals allows leeway for interpretation, when the organizational goal represents the desired future state of the organization” (Chun and Rainey 2005b, p. 531). When goals require less interpretation on the part of employees, they are understood to be more certain and specific (i.e., goal clarity). Conversely, goals that entail greater interpretive leeway are characterized as more ambiguous (i.e., goal ambiguity). Although ambiguous goals may advantage organizations in some circumstances, existing public management research draws on the fundamental tenets of goal setting theory to argue goal ambiguity usually harms individual and organizational performance (Davis and Stazyk 2015; Chun and Rainey 2005a, 2005b; Stazyk and Goerdel 2011).
When employees fail to understand an organization’s goals or, by extension work roles, several negative outcomes are likely (Chun and Rainey 2005a, 2005b; House and Rizzo 1972; Ivancevich and Gilbert 2000; Jackson et al. 2003; Jong 2019; Rizzo et al. 1970). In such cases, employees exhibit higher levels of occupational stress and anxiety, job absence, and turnover, as well as lower levels of physical and emotional health, motivation, and commitment (House and Rizzo 1972; Rizzo et al. 1970). Furthermore, employees are unlikely to believe organizational decisions are fair and legitimate (Milkovich and Wigdor 1991; Rubin 2009). Most notably, goal ambiguity also translates into lower employee job satisfaction (Chun and Rainey 2005a, 2005b; Wright 2001, 2004; Wright and Davis 2003). Employee job satisfaction has been defined as a “pleasurable or positive emotional state resulting from the appraisal of one’s job…” (Locke 1976, p. 1304). Job satisfaction directly and indirectly affects important individual and organizational outcomes, including work motivation, turnover, productivity, and commitment (Locke 1976; Mobley et al. 1978; Mobley et al. 1979; Wright 2001, 2004; Wright and Davis 2003).
We contend increased workforce diversity can lead to higher levels of goal ambiguity and lower levels of employee job satisfaction. Although the relationship between goal ambiguity and job satisfaction is firmly established, we are unaware of any efforts to link workforce diversity with goal theories with the notable exception of Jong (2019). Jong drew extensively on Stazyk and colleague’s earlier work (e.g., Langbein and Stazyk 2013; Stazyk and Goerdel 2011) to examine whether diversity and formalization can clarify goals and goal setting processes for employees. Consistent with findings from Stazyk and colleagues, Jong finds that racial diversity and formalization had a positive impact on how employees perceive goal specificity and difficulty.
Given the dearth of research on this topic, we draw on social identity, self-categorization, and attraction/similarity theories to begin bridging this gap. Jointly, these theories demonstrate that group homogeneity promotes cohesion and communication between individuals, whereas heterogeneity has the opposite effect. As organizations become more heterogeneous, individuals and groups may be less trusting of one another. In such cases, lower levels of social cohesion and integration are likely (Ely 2004; Lott and Lott 1961; O’Reilly et al. 1989; Smith et al. 1994; Williams and O’Reilly 1998). High levels of heterogeneity reduce interpersonal communication and generate more individual and group conflict (Pelled et al. 1999). These factors shape employees’ affective reactions to an organization, resulting in diminished job satisfaction (Jackson et al. 2003; Pfeffer 1983).
Furthermore, as proponents of workforce diversity have argued, diversity introduces new and different perspectives into organizations. Although greater workforce diversity may increase the number of “intellectual toolkits” available within an organization to address problems (Jong 2019; Langbein and Stazyk 2013; Moon and Christensen 2020; Park and Liang 2020; Stazyk et al. 2017), we believe a diversity of perspectives may also engender greater goal ambiguity for three reasons. First, adding new and different perspectives into an organization may expand the number of goals considered relevant and legitimate by organizational members. In other words, an organization’s goal-set is likely to enlarge as its workforce diversifies. Second, as both goal orientation and goal setting theories have demonstrated, individuals have their own goals that may or may not align with those of the organization (Locke and Latham 2006; Pieterse et al. 2011). In homogenous work environments, organizations and employees find it easier to develop a “shared mental model” (Pieterse et al. 2011). However, in heterogeneous work environments, it becomes more difficult to convince employees to work toward a common goal set (Locke and Latham 2006; Pieterse et al. 2011). Third, as additional perspectives and goals are introduced into the organization, conflict between organizational members is likely to increase insofar as individual and group-level goals may seem incompatible (Locke and Latham 2006; Pieterse et al. 2011). Conflict may also reflect legitimate disputes over the domains and content of goals (Choi and Rainey 2010; Davis and Stazyk 2016; Foldy 2004; Pitts 2005). Such factors provide strong reasons to anticipate that workforce diversity increases goal ambiguity. The goal ambiguity concept is predicated on the notion that more numerous and conflicting goals generate greater ambiguity for workers (i.e., employees perceive goals as less certain and specific). Both are likely to occur as an organization diversifies. Unfortunately, higher levels of goal ambiguity often translate into lower levels of employee job satisfaction (Wright and Davis 2003). Therefore, we test the following hypotheses:
Hypothesis 1a (H1a).
Higher levels of racial and ethnic diversity within an organization will decrease the perceived clarity of organizational goals among employees.
Hypothesis 1b (H1b).
Higher levels of gender diversity within an organization will decrease the perceived clarity of organizational goals among employees.
Hypothesis 2.
As organizational goal clarity decreases employees will become less satisfied with their jobs.

2.2. The Role of Diversity Management Policies

Much of the discussion above leads to the, seemingly, inevitable conclusion that public organizations have minimal control over the possible negative effects of workforce diversity on organizational goal ambiguity and employee job satisfaction. However, diversity management research is grounded in the assertion that organizations must proactively “manage for diversity” to benefit from it (Cox 1993; Thomas and Ely 1996; Thomas 1990). As Olsen and Martins (2012) argue, managing for diversity entails the “utilization of human resource (HR) management practices to (i) increase or maintain the variation in human capital on some given dimension(s), and/or (ii) ensure that variation in human capital on some given dimension(s) does not hinder the achievement of organizational objectives, and/or (iii) ensure that variation in human capital on some given dimension(s) facilitates the achievement of organizational objectives” (p. 1169). Diversity management initiatives often positively affect individual and/or organizational outcomes (Olsen and Martins 2012; Pitts 2009).
Diversity management research also coalesces around two points relevant to the relationships posited. First, a high degree of variation exists in the types of diversity management policies pursued by organizations (Ely and Thomas 2001; Olsen and Martins 2012). No single approach is inherently superior, so long as it strives to integrate rather than segregate or assimilate diverse employees (Olsen and Martins 2012). Second, the specific format and content of a diversity management policy or program is far less important than having an organizational environment that values “diversity-to-work-outcomes” (Olsen and Martins 2012). Simply, the benefits of workforce diversity and of any given diversity management policy are a function of an organization’s overall diversity management stance relative to the needs, wants, and expectations of its workers. Therefore, we assume most federal-level diversity management policies recognize the value of workforce diversity and strive to integrate diverse employees.
Importantly, diversity management policies may alter how employees view the clarity of organizational goals. Diversity management policies set boundaries around what constitutes acceptable employee behavior, while establishing legally grounded safeguards for workers. Further, diversity management policies may also shift the culture of organizations such that employees more readily appreciate and capitalize on the benefits of increased diversity (Foldy 2004; van Knippenberg et al. 2004). In this case, diversity management policies signal that the organization values diversity and expects workers will tap diverse perspectives to improve decision-making and outcomes (Cox 1993; Olsen and Martins 2012; Thomas and Ely 1996; Thomas 1990). Essentially, integrative diversity management policies may make employees more tolerant and accepting of any ambiguity associated with workforce diversity. Additionally, incorporating diverse perspectives into the decision-making process becomes an important goal to be realized by employees—a goal that tips employees’ “shared mental model” in favor of workforce diversity and away from the tendency to believe diverse perspectives produce irreconcilable individual differences and organizational level goals (Olsen and Martins 2012).
We assume diversity management policies work to clarify goals by changing how employees’ view any uncertainty that arises as organizations diversify (Jong 2019; Stevens and Campion 1994; van Knippenberg et al. 2004; Wright 2001). Without such policies, employees may view increased workforce diversity as producing harmful degrees of ambiguity; with integrative policies, employees may believe diversity produces strategic benefits that result in better outcomes. In the latter case, employees’ affective responses to diversity and ambiguity are likely to be more favorable and less likely to reduce employee job satisfaction. Drawing on this logic, we assume diversity management policies are likely to have direct and indirect effects on the relationships between workforce diversity, goal ambiguity, and employee job satisfaction (readers interested in a conceptual representation of our theoretical and empirical model, should see Figure 1 below), such that the following hypotheses are in order:
Hypothesis 3a (H3a).
Higher levels of racial and ethnic diversity within an organization will increase the perceived efficacy of diversity management policies.
Hypothesis 3b (H3b).
Higher levels of gender diversity within an organization will increase the perceived efficacy of diversity management policies.
Hypothesis 4 (H4).
Diversity management policies directly increase the perceived clarity of organizational goals.
Hypothesis 5 (H5).
Diversity management policies directly increase employee job satisfaction.
Hypothesis 6 (H6).
Diversity management policies indirectly increase employee job satisfaction by clarifying organizational goals.

3. Methods

In the following subsections, we review our (1) sample and the data collection process used to produce the sample, (2) study methods, and (3) study controls.

3.1. Data Collection Instruments and Sample

This paper utilizes data from three waves (2010, 2011, and 2012) of the Federal Employee Viewpoint Survey (FEVS) to test hypotheses. FEVS is administered yearly by the Office of Personnel Management (OPM) and is designed to assess whether and to what extent federal employees believe the characteristics of successful organizations are present in their agency. Each year, OPM distributes surveys to full-time, permanent employees in several federal government agencies. Data are available for respondents in 572 sub-agencies across all three survey years. After excluding respondents with missing information on control variables and those who did not answer any items used in this analysis, data for 500 sub-agencies remained. Overall, the current analysis includes responses from 1,109,134 federal government employees, and the average number of respondents per sub-agency was 2218. Unfortunately, FEVS does not make data on the specific racial or ethnic category of each respondent publicly available; instead, data are aggregated to reflect whether respondents have minority or non-minority status. Demographic characteristics of respondents are provided in Table 1.

3.2. Methodology

To test study hypotheses, a multi-level structural equation model (MSEM) is employed. This technique affords the ability to decompose the variation in latent variables into individual level and organizational level components, providing more accurate tests of hypotheses at multiple levels of measurement (Snijders and Bosker 1999). The hypothesized effects of diversity are modeled at the organizational level whereas remaining hypotheses are modeled at the individual level. Although many of the variables in this paper originate from perceptual items, measures of diversity have been calculated using the Blau index (Blau 1977) to capture sub-agency diversity. To ensure the accuracy of conclusions and parameter estimates, we evaluated two models: one using un-weighted data and one using a weighting variable included in the data. The results were nearly substantively and statistically identical. As such, the results reported here are from the un-weighted data. See Appendix A for a full description of study measures.

3.3. Model Controls

Although this paper focuses on the relationships between workforce diversity, goals, and job satisfaction, it is important to rule out plausible alternative explanations. First, as one climbs the organizational hierarchy, individuals’ perspectives on diversity might change. Consequently, we control for supervisory status, modeled such that 1 = non-supervisor or team leader, 2 = supervisor, and 3 = manager or executive. Second, employee age is also included and scaled such that 1 = 29 and under, 2 = 30–39, 3 = 40–49, 4 = 50–59, and 5 = 60 and over. Finally, we include control variables for pay category and time spent working in the federal government. Pay category is scaled such that 1 = federal wage system, 2 = GS 1 through 12, 3 = GS 13 through 15, and 4 = SES or other. Tenure is measured as a categorical variable with seven categories: 1 = Less than 1 year, 2 = 1 to 3 years, 3 = 4 to 5 years, 4 = 6 to 10 years, 5 = 11 to 14 years, 6 = 15 to 20 years, and 7 = More than 20 years. The Federal Employee Viewpoint Surveys lack any indicators capturing employees’ education. As such, we are unable to control for the potentially important role education may play in this paper.

4. Analysis and Results

Figure 1 provides standardized parameter estimates and fit statistics for the individual and organizational levels. At the individual level, latent variables are defined by observed survey items. The black circles at the end of the arrows from the individual level latent variables illustrate that the intercepts of the observed variables are allowed to vary across sub-agencies. Indicators of latent variables at the organizational level are depicted as circles because they are comprised of the random intercepts of observed variables at the individual level. Finally, gender and minority status are depicted as boxes because they are observed variables measured at the organizational level. In other words, diversity measures vary across sub-agency, but are identical for all individuals within the same sub-agency.
All four of the individual level hypotheses (Hypotheses 2, 3, 5, and 6) are supported. Employees who believe their organizations have well-defined, effective diversity management policies report greater goal clarity and job satisfaction. Furthermore, employees who believe organizational goals are clearer tend to be more satisfied with their jobs. However, results also indicate the relationship between diversity management policies and job satisfaction is complex. The benefits of diversity management policies on job satisfaction appear to arise, in part, by working to clarify organizational goals for employees. In this case, the indirect effect of having well-defined diversity management policies on job satisfaction is 0.324 (p < 0.001). The total effect of diversity management policies on job satisfaction can be calculated by adding the indirect effect to the direct effect (Kline 2005). The total effect of diversity management policies on job satisfaction is 0.584, confirming Hypothesis 6. The direct effect of diversity management policies, however, is less pronounced than the indirect pathway through goal clarity, suggesting that clarifying goals is a critical outcome of diversity management policies. In sum, results from the individual level model suggest employees will be significantly more satisfied with their work when they believe their organization has established diversity management policies, and they view diversity management policies as a mechanism that clarifies goals. Standardized parameter estimates, standard errors, and associated significance levels are provided in Table 2.
Although study findings suggest that employees benefit substantially from diversity management policies, the organizational picture is somewhat less clear. Only one of the organization level hypotheses is wholly supported. First, contrary to the expected direction in Hypothesis 1a, as racial and ethnic diversity within the sub-agency increases, goal clarity also increases. However, gender diversity does not significantly influence goal clarity, failing to support Hypothesis 1b. Second, increases in racial and ethnic diversity lead to a decrease in the perceived efficacy of diversity management policies, which runs contrary to the direction posited in Hypothesis 3a. However, increases in gender diversity lead to increases in the perceived efficacy of diversity management policies, supporting Hypothesis 3b. Consistent with previous research, these findings provide evidence that different forms of diversity disparately influence organizations. See Table 2 for standardized parameter estimates and significance levels.
In addition to supporting 5 of 8 hypotheses, the model presented has reasonable explanatory capacity. Unlike traditional regression models, structural equation models produce several R2 values—one corresponding to each endogenous variable. Additionally, in MSEM models, R2 values are generated for both individual and organization level variables. First, at the individual level, the model controls explain 3.6% of the variation in diversity management policies. Second, the model controls and the perceived efficacy of diversity management policies explain 34.0% of the variation in goal clarity. Finally, the model controls, diversity management policies, and goal clarity account for 56.0% of the variation in job satisfaction. At the organizational level, gender and racial and ethnic diversity account for 2.8% of the variation in the perceived efficacy of diversity management policies. Next, gender and racial and ethnic diversity with diversity management policies account for 68.5% of the variation in goal clarity. Last, gender and racial and ethnic diversity, diversity management policies, and goal clarity account for 80.2% of the variation in job satisfaction. Although the amount of variation in the perceived efficacy of diversity management policies, at both the individual and organizational levels, is relatively modest, the remaining R2 values indicate this model explains a large proportion of variation in model variables.
Notably, several model controls are also significant. First, individuals higher in the organizational hierarchy (measured by supervisory status) have greater job satisfaction and report having clearer goals and a more favorable diversity management environment. Second, increases in employee age are associated with greater job satisfaction and goal clarity. Third, increases in salary (measured by pay category) are associated with reductions in goal clarity, but increases in the perceived efficacy of diversity management policies. Finally, increases in tenure are associated with decreases in job satisfaction, goal clarity, and the perceived efficacy of diversity management policies. Table 3 provides the standardized parameter estimates and significance levels for all individual level controls.

5. Discussion and Conclusions

This manuscript addresses three overarching questions. First, does increased gender and racial and ethnic diversity affect the perceived clarity of organizational goals? Contrary to expectations, greater racial and ethnic diversity is associated with higher degrees of goal clarity. Interestingly, gender diversity fails to influence goal clarity, suggesting managers may wish to emphasize racial and ethnic diversity in goal setting processes. Second, do diversity and goal clarity influence job satisfaction? Consistent with past research, results suggest that the perceived efficacy of diversity management policies and goal clarity improve job satisfaction. Third, can diversity management policies influence job satisfaction by clarifying goals? Our findings support the notion that diversity management policies can enhance job satisfaction by clarifying organizational goals.
That gender diversity and minority status have different relationships with goal clarity is interesting. On one hand, public management research has often found the relationship between gender diversity and other important individual and organizational constructs is rarely unidirectional (e.g., Choi and Rainey 2010; Moon and Christensen 2020). Similarly, research also finds minorities tend to be less satisfied than whites with the efficacy and prevalence of diversity management policies in federal agencies (e.g., Pitts 2009). In this sense, study results comport with existing research, providing support for our proposed model. On the other hand, it is surprising that greater racial and ethnic diversity is associated with higher goal clarity whereas gender diversity has no such effect. Because we rely on cross-sectional data, we are unable to assess causality in this paper. However, it may be that individuals in racially and ethnically diverse sub-agencies have already experienced the benefits of diversity management policies and are, therefore, more inclined to believe racial and ethnic diversity comes with strategic benefits. Time series data would allow for a better test of Hypothesis 1a.
That certain forms of diversity are associated with greater goal clarity than others raises important theoretical and practical issues. Theoretically, results suggest diversity can improve individual and organizational outcomes. However, the benefits of diversity are not necessarily analogous across different forms and types of diversity. This suggests aspects of both the “managing for diversity” perspective and social identity theories have merit. Scholars should be careful to account for the possible benefits and costs of increased workforce diversity in their research. Likewise, results confirm claims that diversity management scholars should avoid conflating different forms of diversity in their research, and point to the real need to determine when various forms of diversity produce distinct individual and organizational outcomes.
This study also provides insight into the theoretical ties between diversity management policies, goal clarity, and job satisfaction. Results suggest that diversity management policies not only establish legal safeguards for workers, but also likely function at a deeper social level. Diversity management policies signal to employees that an organization values diversity and expects its workers to do the same. In the face of well-designed, integrative policies, employees are likely to develop a shared understanding of organizational goals even when new, possibly divergent expectations are introduced into the organization as a consequence of increased workforce diversification.
Practically, our findings suggest managers should avoid treating diversity as a generic concept. Managers aiming to tap the benefits of diversity may want to consider designing and implementing narrowly tailored policies and initiatives that target specific types of diversity. Results also highlight the importance of designing and implementing effective goal setting strategies in diverse organizations. Public managers can often take steps to clarify goals for employees, and it is possible that managers confronted with a diverse workforce recognize the necessity of taking additional steps to clarify goals for workers.
If scholars and practitioners are willing to address various forms of diversity with different initiatives, findings suggest practitioners might particularly benefit from increased emphasis on racial and ethnic diversity. Racial and ethnic diversity—unlike gender diversity—appear to influence goal clarity. Thus, managers might profit more immediately by directing greater attention to the challenges associated with racial and ethnic diversity. One should not assume, however, emphasizing racial and ethnic diversity must come at the expense of gender diversity. Perhaps managers could focus more heavily on communicating the benefits of diversity policies to all employees, thereby clarifying the purpose of these rules and regulations, as well as signaling commitment to diversity.
Finally, several research limitations exist. First, this study assumes gender and racial and ethnic diversity are mutually exclusive categories. Inevitably, race and ethnicity and gender overlap in meaningful ways (e.g., African-American woman). It would be fruitful to seek ways to accurately analyze groups with overlapping memberships. Second, this study assesses diversity management policies from employees’ perspectives. Future research could examine more objective data regarding the actual enforcement of diversity management policies. Third, because we use data from FEVS, we are unable to assess a number of constructs, such as education, conflict, and communication, which may affect study findings. Similarly, the data used here do not track employee responses over time, limiting our ability to examine lagged effects or changes in attitudes. Future research might profit from assessing how the relationships proposed in this paper evolve.
This study also leaves open several important questions that should be addressed in future research. First, research would likely profit by examining which goal setting strategies produce effective outcomes for diverse workforces and under what conditions. Second, organizations vary in terms of their relative homogeneity and heterogeneity. It would be useful to ascertain whether different goal setting strategies should be employed in highly homogeneous and heterogeneous organizations. Third, this study assumes that diversity management policies are integrative. However, future research should assess the attributes of effective diversity management policies in relation to their ability to clarify goals.
Despite any limitations, this paper takes a first step toward assessing the relationship between workforce diversity and organizational goal clarity. Workforce diversity is bound to favorably and unfavorably influence organizations, and practical recommendations likely require a better understanding of the benefits and challenges of diversity management. Echoing others, further research examining individual and organizational outcomes are desperately needed to meaningfully advance diversity management scholarship and practice.

Author Contributions

Conceptualization, E.C.S., R.S.D. and J.L.; methodology, R.S.D., E.C.S. and J.L.; formal analysis, R.S.D. and E.C.S.; resources, E.C.S. and R.S.D.; data curation, E.C.S. and R.S.D.; writing—original draft preparation, E.C.S., R.S.D. and J.L.; writing—review and editing, E.C.S., R.S.D. and J.L.; visualization, R.S.D.; supervision, E.C.S.; project administration, E.C.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data for this study are publicly available through the U.S. Office of Personnel Management’s Federal Employee Viewpoint Surveys. Links to each wave of data can be found at https://www.opm.gov/fevs/public-data-file/ (accessed on 27 July 2021).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Operational Definitions

Job Satisfaction (Cronbach’s Alpha = 0.773)
Similar to the work of Pitts (2009) and Yang and Kassekert (2010), job satisfaction is assessed using three items on five-point scales, ranging from strongly disagree to strongly agree. Items are scaled such that higher values reflect greater satisfaction:
  • Considering everything how satisfied are you with your job?
  • Considering everything how satisfied are you with your pay?
  • Considering everything how satisfied are you with your organization?
Goal Clarity (Cronbach’s Alpha = 0.814)
Goal clarity is assessed using three items drawn from Cho and Perry (2012) and Whitford et al. (2010). The first two items capture “goal directedness” whereas the third is a direct measure of goal clarity. For goals to be deemed clear, they must be specific but attainable, communicated to workers (goal directedness), and viewed as legitimate by employees (Locke and Latham 1990). As such, it is appropriate to assess goal clarity using items that tap clarity and directedness. Each item was rated on a five-point scale, ranging from strongly disagree to strongly agree. Higher values reflect greater goal clarity:
  • Managers communicate the goals and priorities of the organization.
  • Managers review and evaluate the organization’s progress toward meeting its goals and objectives.
  • I know how my work relates to the agency’s goals and priorities.
Diversity Policy (Cronbach’s Alpha = 0.773)
Diversity policy is assessed using three items rated on a five-point scale, ranging from strongly disagree to strongly agree. Higher values reflect a more favorable diversity policy environment:
  • Policies and programs promote diversity in the workplace (for example, recruiting minorities and women, training in awareness of diversity issues, mentoring).
  • Prohibited Personnel Practices (for example, illegally discriminating for or against any employee/applicant, obstructing a person’s right to compete for employment, knowingly violating veterans’ preference requirements) are not tolerated.
  • My supervisor/team leader is committed to a workforce representative of all segments of society.
Measure of Workforce Diversity
This study employs Blau’s index (Blau 1977) to generate the measure of sub-agency workforce diversity. Specifically, D = 1 − ΣPi2, where D denotes the agency overall diversity, and Pi is the proportion of group members in a particular category i. Two categories (i.e., male and female) are used to produce the agency gender diversity. We also use two categories (i.e., minority and non-minority) to develop the measure of racial and ethnic diversity. Blau index values were computed using available information from the dataset.

References

  1. Blau, Peter M. 1977. Inequality and Heterogeneity: A Primitive Theory of Social Structure. New York: The Free Press. [Google Scholar]
  2. Byrne, Donn E. 1971. The Attraction of Paradigm. New York: Academic Press. [Google Scholar]
  3. Cho, Yoon Jik, and James L. Perry. 2012. Intrinsic motivation and employee attitudes: Role of managerial trustworthiness, goal directedness, and extrinsic reward expectancy. Review of Public Personnel Administration 32: 382–406. [Google Scholar] [CrossRef]
  4. Choi, Sungjoo. 2009. Diversity in the US federal government: Diversity management and employee turnover in federal agencies. Journal of Public Administration Research & Theory 19: 603–30. [Google Scholar]
  5. Choi, Sungjoo, and Hal G. Rainey. 2010. Managing diversity in U.S. federal agencies: Effects of diversity and diversity management on employee perceptions of organizational performance. Public Administration Review 70: 109–21. [Google Scholar] [CrossRef]
  6. Chun, Young Han, and Hal G. Rainey. 2005a. Goal ambiguity in U.S. federal agencies. Journal of Public Administration Research and Theory 15: 1–30. [Google Scholar] [CrossRef]
  7. Chun, Young Han, and Hal G. Rainey. 2005b. Goal ambiguity and organizational performance in U.S. federal agencies. Journal of Public Administration Research and Theory 15: 529–57. [Google Scholar] [CrossRef]
  8. Cox, Taylor. 1993. Cultural Diversity in Organizations: Theory, Research, and Practice. San Francisco: Berrett-Koehler. [Google Scholar]
  9. Cox, Taylor H., and Stacy Blake. 1991. Managing cultural diversity: Implications for organizational competitiveness. The Executive 5: 45–56. [Google Scholar] [CrossRef]
  10. Cox, Taylor H., Sharon A. Lobel, and Poppy Lauretta McLeod. 1991. Effects of ethnic group cultural differences on cooperative and competitive behavior on a group task. Academy of Management Journal 34: 827–47. [Google Scholar]
  11. Davis, Randall S., and Edmund C. Stazyk. 2015. Developing and testing a new goal taxonomy: Accounting for the complexity of ambiguity and political support. Journal of Public Administration Research and Theory 25: 751–75. [Google Scholar] [CrossRef]
  12. Davis, Randall S., and Edmund C. Stazyk. 2016. Examining the links between senior managers’ engagement in networked environments and goal and role ambiguity. Journal of Public Administration Research and Theory 26: 433–47. [Google Scholar] [CrossRef]
  13. Ely, Robin J. 2004. A field study of group diversity, participation in diversity education programs, and performance. Journal of Organizational Behavior 25: 755–80. [Google Scholar] [CrossRef]
  14. Ely, Robin J., and David A. Thomas. 2001. Cultural diversity at work: The effects of diversity perspectives on work group processes and outcomes. Administrative Science Quarterly 46: 229–73. [Google Scholar] [CrossRef]
  15. Foldy, Erica Gabrielle. 2004. Learning from diversity: A theoretical exploration. Public Administration Review 64: 529–38. [Google Scholar] [CrossRef]
  16. Hill, Fiona. 2020. Public Service and the Federal Government. Brookings. May 27. Available online: https://www.brookings.edu/policy2020/votervital/public-service-and-the-federal-government/ (accessed on 28 June 2021).
  17. House, Robert J., and John R. Rizzo. 1972. Toward the measurement of organizational practices: Scale development and validation. Journal of Applied Psychology 56: 388–96. [Google Scholar] [CrossRef]
  18. Humphreys, Jeffrey M. 2008. The Multicultural Economy 2003: America’s Minority Buying Power. Washington, DC: The Hispanic Association on Corporate Responsibility. [Google Scholar]
  19. Ivancevich, John M., and Jacqueline A. Gilbert. 2000. Diversity management: Time for a new approach. Public Personnel Management 29: 75–92. [Google Scholar] [CrossRef]
  20. Jackson, Susan E., Aparna Joshi, and Niclas L. Erhardt. 2003. Recent research on team and organizational diversity: SWOT analysis and implications. Journal of Management 29: 801–30. [Google Scholar] [CrossRef]
  21. Jong, Jaehee. 2019. Racial diversity and task performance: The roles of formalization and goal setting in government organizations. Public Personnel Management 48: 493–512. [Google Scholar] [CrossRef]
  22. Kline, Rex B. 2005. Principles and Practice of Structural Equation Modeling. New York: Guilford Press. [Google Scholar]
  23. Kochan, Thomas, Katerina Bezrukova, Robin Ely, Susan Jackson, Aparna Joshi, Karen Jehn, Jonathan Leonard, David Levine, and David Thomas. 2003. The effects of diversity on business performance: Report of the diversity research network. Human Resource Management 42: 3–21. [Google Scholar] [CrossRef]
  24. Langbein, Laura, and Edmund C. Stazyk. 2013. Vive la différence? The impact of diversity on the turnover intention of public employees and performance of public agencies. International Public Management Journal 16: 465–503. [Google Scholar] [CrossRef]
  25. Latham, Gary P., and Edwin A. Locke. 2006. Enhancing the benefits and overcoming the pitfalls of goal setting. Organizational Dynamics 35: 332–40. [Google Scholar] [CrossRef]
  26. Latham, Gary P., and Edwin A. Locke. 2007. New developments in and directions for goal setting. European Psychologist 12: 290–300. [Google Scholar] [CrossRef] [Green Version]
  27. Law, Tara. 2020. Women Are Now the Majority of the U.S. Workforce–But Working Women Still Face Serious Challenges. Time. January 16. Available online: https://time.com/5766787/women-workforce/ (accessed on 28 June 2021).
  28. Llorens, Jared J., Jeffrey B. Wenger, and J. Edward Kellough. 2008. Choosing public sector employment: The impact of wages on the representation of women and minorities in state bureaucracies. Journal of Public Administration Research & Theory 18: 397–413. [Google Scholar]
  29. Locke, Edwin A. 1976. The nature and causes of job satisfaction. In Handbook of Industrial and Organizational Psychology. Edited by Marvin D. Dunnette. New York: Wiley, pp. 1297–350. [Google Scholar]
  30. Locke, Edwin A., and Gary P. Latham. 1990. A Theory of Goal Setting and Task Performance. Englewood Cliffs: Prentice-Hall. [Google Scholar]
  31. Locke, Edwin A., and Gary P. Latham. 2002. Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist 57: 705–17. [Google Scholar] [CrossRef] [Green Version]
  32. Locke, Edwin A., and Gary P. Latham. 2006. New directions in goal-setting theory. Current Directions in Psychological Science 15: 265–68. [Google Scholar] [CrossRef]
  33. Long, Karen, and Russell Spears. 1997. The self-esteem hypothesis revisited: Differentiation and the disaffected. In The Social Psychology of Stereotyping and Group Life. Edited by Russell Spears, Penelope J. Oakes, Naomi Ellemers and S. Alexander Haslam. Oxford: Blackwell, pp. 296–317. [Google Scholar]
  34. Lott, Albert J., and Bernice E. Lott. 1961. Group cohesiveness, communication level, and conformity. The Journal of Abnormal and Social Psychology 62: 408–12. [Google Scholar] [CrossRef]
  35. Milkovich, George T., and Alexandra K. Wigdor. 1991. Pay for Performance: Evaluating Performance Appraisal and Merit Pay. Washington, DC: National Academy Press. [Google Scholar]
  36. Mobley, William H., Stanley O. Horner, and Andrew T. Hollingsworth. 1978. An evaluation of precursors of hospital employee turnover. Journal of Applied Psychology 63: 408–14. [Google Scholar] [CrossRef]
  37. Mobley, William H., Rodger W. Griffeth, Herbert H. Hand, and Bruce M. Meglino. 1979. Review and conceptual analysis of the employee turnover process. Psychological Bulletin 86: 493–522. [Google Scholar] [CrossRef]
  38. Moon, Kuk-Kyoung, and Robert K. Christensen. 2020. Realizing the performance benefits of workforce diversity in the U.S. federal government: The moderating role of diversity climate. Public Personnel Management 49: 141–65. [Google Scholar] [CrossRef]
  39. Naff, Katherine C., and J. Edward Kellough. 2003. Ensuring employment equity: Are federal diversity programs making a difference? International Journal of Public Administration 26: 1307–36. [Google Scholar] [CrossRef]
  40. O’Reilly, Charles A., III, David F. Caldwell, and William P. Barnett. 1989. Work group demography, social integration, and turnover. Administrative Science Quarterly 34: 21–37. [Google Scholar]
  41. Olsen, Jesse E., and Luis L. Martins. 2012. Understanding organizational diversity management programs: A theoretical framework and directions for future research. Journal of Organizational Behavior 33: 1168–87. [Google Scholar] [CrossRef]
  42. Park, Sanghee, and Jiaqi Liang. 2020. Merit, diversity, and performance: Does diversity management moderate the effect of merit principles on governmental performance? Public Personnel Management 49: 83–110. [Google Scholar] [CrossRef]
  43. Pelled, Lisa Hope, Kathleen M. Eisenhardt, and Katherine R. Xin. 1999. Exploring the black box: An analysis of work group diversity, conflict and performance. Administrative Science Quarterly 44: 1–28. [Google Scholar] [CrossRef] [Green Version]
  44. Pfeffer, Jeffrey. 1983. Organizational demography. In Research in Organizational Behavior. Edited by Larry L. Cummings and Barry M. Staw. Greenwich: JAI Press. [Google Scholar]
  45. Pieterse, Anne Nederveen, Daan van Knippenberg, and Wendy P. van Ginkel. 2011. Diversity in goal orientation, team reflexivity, and team performance. Organizational Behavior and Human Decision Processes 114: 153–64. [Google Scholar] [CrossRef]
  46. Pitts, David W. 2005. Diversity, representation, and performance: Evidence about race and ethnicity in public organizations. Journal of Public Administration Research and Theory 15: 615–31. [Google Scholar] [CrossRef]
  47. Pitts, David W. 2009. Diversity management, job satisfaction, and performance: Evidence from U.S. federal agencies. Public Administration Review 69: 328–38. [Google Scholar] [CrossRef]
  48. Pitts, David W., and Lois Recascino Wise. 2010. Workforce diversity in the new millennium: Prospects for research. Review of Public Personnel Administration 30: 44–69. [Google Scholar] [CrossRef]
  49. Riccucci, Norma M. 2002. Managing Diversity in Public Sector Workforces. Boulder: Westview Press. [Google Scholar]
  50. Rizzo, John R., Robert J. House, and Sidney I. Lirtzman. 1970. Role conflict and ambiguity in complex organizations. Administrative Science Quarterly 15: 150–63. [Google Scholar] [CrossRef]
  51. Rubin, Ellen V. 2009. The role of procedural justice in public personnel management: Empirical results from the Department of Defense. Journal of Public Administration Research and Theory 19: 125–43. [Google Scholar] [CrossRef]
  52. Selden, Sally Coleman, and Frank Selden. 2001. Rethinking diversity in public organizations for the 21st century: Moving toward a multicultural model. Administration & Society 33: 303–29. [Google Scholar]
  53. Smith, Ken G., Ken A. Smith, Judy D. Olian, Henry P. Sims Jr., Douglas P. O’Bannon, and Judith A. Scully. 1994. Top management team demography and process: The role of social integration and communication. Administrative Science Quarterly 39: 412–38. [Google Scholar] [CrossRef]
  54. Snijders, Tom A. B., and Roel J. Bosker. 1999. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Thousand Oaks: Sage. [Google Scholar]
  55. Stazyk, Edmund C. 2016. The prevalence of reinvention reforms in local governments and their relationship with organizational goal clarity and employee job satisfaction. Public Performance & Management Review 39: 701–27. [Google Scholar]
  56. Stazyk, Edmund C., and Randall S. Davis. 2015. Taking the ‘high road’: Does public service motivation alter ethical decision making processes? Public Administration 93: 627–45. [Google Scholar] [CrossRef]
  57. Stazyk, Edmund C., and Holly T. Goerdel. 2011. The benefits of bureaucracy: Public managers’ perceptions of political support, goal ambiguity, and organizational effectiveness. Journal of Public Administration Research and Theory 21: 645–72. [Google Scholar] [CrossRef]
  58. Stazyk, Edmund C., Randall S. Davis, and Shannon Portillo. 2017. More dissimilar than alike? Public values preferences across U.S. minority and white managers. Public Administration 95: 605–22. [Google Scholar] [CrossRef]
  59. Stevens, Michael J., and Michael A. Campion. 1994. The knowledge, skill, and ability requirements for teamwork: Implications for human resource management. Journal of Management 20: 503–30. [Google Scholar] [CrossRef]
  60. Tajfel, Henri, and John C. Turner. 1985. The social identity theory of intergroup behaviour. In Psychology of Intergroup Relations. Edited by Stephen Worchel and William G. Austin. Chicago: Nelson-Hall, pp. 7–24. [Google Scholar]
  61. Thomas, R. Roosevelt, Jr. 1990. From affirmative action to affirming diversity. Harvard Business Review 68: 107–17. [Google Scholar] [PubMed]
  62. Thomas, David A., and Robin J. Ely. 1996. Making differences matter: A new paradigm for managing diversity. Harvard Business Review 74: 79–90. [Google Scholar]
  63. Toossi, Mitra. 2002. A Century of Change: The U.S. Labor Force, 1950–2050. Available online: http://www.bls.gov/opub/mlr/2002/05/art2full.pdf (accessed on 27 July 2021).
  64. Toossi, Mitra. 2012a. Labor Force Projections to 2020: A More Slowly Growing Workforce. Available online: http://www.bls.gov/opub/mlr/2012/01/art3full.pdf (accessed on 27 July 2021).
  65. Toossi, Mitra. 2012b. Labor Force Projections to 2020: A More Slowly Growing Workforce. Available online: http://www.bls.gov/opub/mlr/2012/01/errata.pdf (accessed on 27 July 2021).
  66. U.S. Bureau of Labor Statistics. 2020. Labor Force Characteristics by Race and Ethnicity. 2019. Available online: https://www.bls.gov/opub/reports/race-and-ethnicity/2019/home.htm (accessed on 27 July 2021).
  67. U.S. Office of Personnel Management. 2010. Federal Employee Viewpoint Survey. 2010. Available online: https://www.opm.gov/fevs/public-data-file/ (accessed on 27 July 2021).
  68. U.S. Office of Personnel Management. 2011. Federal Employee Viewpoint Survey. 2011. Available online: https://www.opm.gov/fevs/public-data-file/ (accessed on 27 July 2021).
  69. U.S. Office of Personnel Management. 2012. Federal Employee Viewpoint Survey. 2012. Available online: https://www.opm.gov/fevs/public-data-file/ (accessed on 27 July 2021).
  70. van Knippenberg, Daan, Carsten K. W. De Dreu, and Astrid C. Homan. 2004. Work group diversity and group performance: An integrative model and research agenda. Journal of Applied Psychology 89: 1008–22. [Google Scholar] [CrossRef]
  71. Watson, Warren E., Kamalesh Kumar, and Larry K. Michaelsen. 1993. Cultural diversity’s impact on interaction process and performance: Comparing homogeneous and diverse task groups. Academy of Management Journal 36: 590–602. [Google Scholar]
  72. Whitford, Andrew B., Soo-Young Lee, Taesik Yun, and Chan Su Jung. 2010. Collaborative behavior and the performance of government agencies. International Public Management Journal 13: 321–49. [Google Scholar] [CrossRef]
  73. Williams, Katherine Y., and Charles A. O’Reilly III. 1998. Demography and diversity in organizations: A review of 40 years of research. Research in Organizational Behavior 20: 77–140. [Google Scholar]
  74. Wise, Lois Recasino, and Mary Tschirhart. 2000. Examining empirical evidence on diversity effects: How useful is diversity research for public-sector managers? Public Administration Review 60: 386–94. [Google Scholar] [CrossRef]
  75. Wood, Robert E., and Edwin A. Locke. 1990. Goal setting and strategy effects on complex tasks. In Research in Organizational Behavior. Edited by Barry M. Staw and Larry L. Cummings. Greenwich: JAI Press. [Google Scholar]
  76. Wright, Bradley E. 2001. Public-sector work motivation: A review of the current literature and a revised conceptual model. Journal of Public Administration Research and Theory 11: 559–86. [Google Scholar] [CrossRef]
  77. Wright, Bradley E. 2004. The role of work context in work motivation: A public sector application of goal and social cognitive theories. Journal of Public Administration Research and Theory 14: 59–78. [Google Scholar] [CrossRef]
  78. Wright, Bradley E., and Brian S. Davis. 2003. Job satisfaction in the public sector: The role of the work environment. American Review of Public Administration 33: 70–90. [Google Scholar] [CrossRef]
  79. Yang, Kaifeng, and Anthony Kassekert. 2010. Linking management reform with employee job satisfaction: Evidence from federal agencies. Journal of Public Administration Research and Theory 20: 413–36. [Google Scholar] [CrossRef]
Admsci 11 00077 g001 550
Figure 1. Multi-level SEM.
Figure 1. Multi-level SEM.
Admsci 11 00077 g001
Table
Table 1. Demographic characteristics.
Table 1. Demographic characteristics.
n%
Sex
Male615,08451.4
Female519,37843.4
Missing62,7735.2
Race/Ethnicity
Minority375,03431.3
Non-Minority734,36761.3
Missing87,8347.3
Age
29 and Under63,5065.3
30–39183,02215.3
40–49328,60127.4
50–59407,89134.1
60 and Older143,10712.0
Missing71,1085.9
Table
Table 2. Standardized parameter estimates.
Table 2. Standardized parameter estimates.
Individual Level Model
Job Satisfaction
ESTS.E.EST\S.E.p
Goal Clarity0.5630.01440.1350.000
Diversity Management Policies0.2600.01517.7700.000
Goal Clarity
ESTS.E.EST\S.E.p
Diversity Management Policies0.5750.01734.5090.000
Organization Level Model
Job Satisfaction
ESTS.E.EST\S.E.p
Goal Clarity0.6360.05711.1720.000
Diversity Management Policies0.3010.0614.9080.000
Goal Clarity
ESTS.E.EST\S.E.p
Diversity Management Policies0.8250.02434.8140.000
Goal Clarity
ESTS.E.EST\S.E.p
Racial/Ethnic Diversity0.2060.0385.4190.000
Gender Diversity0.0320.0301.0550.291
Diversity Management Policies
ESTS.E.EST\S.E.p
Racial/Ethnic Diversity−0.1550.065−2.3560.018
Gender Diversity0.1040.0512.0460.041
Job Satisfaction
ESTS.E.EST\S.E.p
Racial/Ethnic Diversity−0.0860.032−2.6770.007
Gender Diversity−0.0150.021−0.6840.291
Table
Table 3. Standardized parameters for control variables.
Table 3. Standardized parameters for control variables.
Job Satisfaction
ESTS.E.EST/S.E.p
Supervisory Status0.0110.0033.8510.000
Age Group0.0300.00213.7140.000
Pay Category0.0030.0030.9150.360
Federal Tenure−0.0270.002−11.3160.000
Goal Clarity
ESTS.E.EST/S.E.p
Supervisory Status0.0550.00511.3580.000
Age Group0.0280.00310.2100.000
Pay Category−0.0420.004−11.0200.000
Federal Tenure−0.0260.004−6.5040.000
Diversity Management Policies
ESTS.E.EST/S.E.p
Supervisory Status0.1500.00624.9210.000
Age Group−0.0010.003−0.3030.762
Pay Category0.0740.0116.6090.000
Federal Tenure−0.1030.004−25.8780.000
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Stazyk, E.C.; Davis, R.S.; Liang, J. Probing the Links between Workforce Diversity, Goal Clarity, and Employee Job Satisfaction in Public Sector Organizations. Adm. Sci. 2021, 11, 77. https://doi.org/10.3390/admsci11030077

AMA Style

Stazyk EC, Davis RS, Liang J. Probing the Links between Workforce Diversity, Goal Clarity, and Employee Job Satisfaction in Public Sector Organizations. Administrative Sciences. 2021; 11(3):77. https://doi.org/10.3390/admsci11030077

Chicago/Turabian Style

Stazyk, Edmund C., Randall S. Davis, and Jiaqi Liang. 2021. "Probing the Links between Workforce Diversity, Goal Clarity, and Employee Job Satisfaction in Public Sector Organizations" Administrative Sciences 11, no. 3: 77. https://doi.org/10.3390/admsci11030077

APA Style

Stazyk, E. C., Davis, R. S., & Liang, J. (2021). Probing the Links between Workforce Diversity, Goal Clarity, and Employee Job Satisfaction in Public Sector Organizations. Administrative Sciences, 11(3), 77. https://doi.org/10.3390/admsci11030077

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop