Fairness at the Organizational Level: Examining the Effect of Organizational Justice Climate on Collective Turnover Rates and Organizational Performance

Abstract

In the continuing quest to understand public employees’ reactions to fair (or unfair) treatment in the workplace, perceived organizational justice has been conceptualized primarily as an individual-level phenomenon. Although individuals create collective perceptions of the fair treatment of their work unit as a whole, little attention has been paid to consequences of justice climate at the organizational level. Using panel data from the U.S. federal government, this study seeks to fill this gap by examining the effect of four dimensions of organizational justice climate—distributive, procedural, informational, and interpersonal—on collective turnover rates and organizational performance. The findings show the negative association of distributive and interpersonal justice climates with turnover rates and the positive association of distributive, procedural, and interpersonal justice climates with organizational performance. Moreover, further analysis confirms that each dimension of justice climate has relative influence on both outcomes. Implications and contributions of these results for public administration theory and practice are discussed.

Keywords

organizational justice climate social exchange theory two-factor justice model collective turnover rates organizational performance

In the past decade, organizational justice has become a central topic in public administration (Cho & Sai, 2012; Hassan, 2013a; Rubin & Chiqués, 2015; Rubin & Weinberg, 2016). Much attention has been given to justice perceptions because of the important work-related outcomes that have been linked to fair treatment of employees by the organization and its decision-makers. Indeed, empirical research demonstrates that when public employees are fairly treated in terms of reward distribution, the procedures by which rewards are distributed, and interpersonal treatment by managers or supervisors (hereafter managers), they exhibit positive work attitudes, including increased job satisfaction, trust in management, intention to remain, job involvement, and intrinsic work motivation (Cho & Sai, 2012; Choi, 2011; Hassan, 2013a; Kim & Rubianty, 2011; Ko & Hur, 2014; Meng & Wu, 2015; Noblet & Rodwell, 2009; Rubin, 2009). Furthermore, fair treatment encourages employees to engage in positive work behaviors, such as cooperation and organizational citizenship behavior (Chen & Jin, 2014; Cho & Sai, 2012).

More recently, however, many scholars have turned their attention from an individual approach toward organizational justice to a shared perception of justice at the organizational level (Li & Cropanzano, 2009; Whitman, Caleo, Carpenter, Horner, & Bernerth, 2012). Although justice research conducted at the individual level has contributed significantly to theoretical development by explaining the relationships between perceived justice and various types of work-related outcomes, an individualistic approach may fail to take into account the social context in which collective perceptions of fairness result in higher-level outcomes (Mossholder, Bennett, Kemery, & Wesolowski, 1998; Naumann & Bennett, 2000). Individuals do not exist in a vacuum; instead, their judgments about justice are constantly affected by others within the work unit (Li & Cropanzano, 2009; Naumann & Bennett, 2000). In this vein, Lind, Kray, and Thompson (1998) noted that “most of the potential information about the fairness of any given authority or institution lies in collective, not personal, experiences” (p. 19). This implies that individuals interact with one another, exchange their interpretations of work unit treatment, and formulate a collective sense of fairness, which is known as organizational justice climate (Li & Cropanzano, 2009).

Despite the growing attention to employees’ shared perceptions of fair treatment, much of the empirical research on justice in public administration has been conducted at the individual level, with little attention paid to organizational-level investigations (see Rubin & Kellough, 2012, for an exception). This imbalance is potentially problematic because justice climate effects are more powerful when all or most of an organization’s members have been treated fairly (Naumann & Bennett, 2000; Yang, Mossholder, & Peng, 2007). Furthermore, although justice perception is a multifaceted concept that comprises distributive, procedural, informational, and interpersonal justice (Colquitt, 2001), it is surprising that little scholarly effort has been invested in exploring how the four dimensions of justice affect organizational outcomes differently. This gap in the literature suggests that an examination of the relative influence of each justice dimension may offer a more reliable portrait of the processes by which fair treatment of employees in public sector organizations promotes desired organizational outcomes (Roberson & Colquitt, 2005).

The current study aims to advance the previous research in three distinct ways. First, responding to the need for more empirical studies on the relationships between justice climate and organizational outcomes, this study examines the effects of the four dimensions of justice climate on collective turnover rates and organizational performance at the U.S. federal agency level. In particular, based on the norm of reciprocity within social exchange theory, shared perceptions of organizational justice are negatively related to turnover rates, but positively related to goal achievement because employees are more likely to remain in their current organization and show better performance when they are being treated fairly (Cohen-Charash & Spector, 2001; Colquitt et al., 2013). Second, this study explores relative effects of the four dimensions of justice climate on both outcomes. Building on the two-factor justice model, distributive and interpersonal justice climates that are classified as outcome-focused are more influential on person-referenced outcomes, such as employee turnover, whereas procedural and informational justice climates that are categorized as process-focused are more influential on organization-referenced outcomes, such as organizational performance (Cho & Sai, 2012; Sweeney & McFarlin, 1993). Finally, although employees adjust their fairness perceptions as they acquire additional information regarding justice-related events (Ambrose & Cropanzano, 2003; Hausknecht, Sturman, & Roberson, 2011), most previous research in public administration has relied on a cross-sectional research design when examining the relationship between perceived justice and work-related outcomes. Consequently, scholars have provided a good snapshot of justice in any one instance but have failed to draw stronger conclusions about causal processes (Ambrose & Cropanzano, 2003). In this regard, this study takes a longitudinal approach to investigating the underlying effects of justice climate over time using panel data from the U.S. federal government.

The article begins by reviewing the literature on the four dimensions of organizational justice at the individual level and providing a brief overview of how justice research in public administration has evolved over time. Then, I provide theoretical arguments pertaining to organizational justice climate and develop hypotheses about its effect on employee turnover rates and organizational performance. Subsequently, the article presents the statistical data analyses and results. The last section provides the implications of the findings for theory and practice and discusses limitations with suggestions for future research.

Theories and Hypothesis Development

Four Dimensions of Organizational Justice at the Individual Level

Organizational justice is concerned with the extent to which employees perceive the treatment received from an organization or managers to be fair (Colquitt, Greenberg, & Zapata-Phelan, 2005). Four distinct dimensions of justice perceptions—distributive, procedural, informational, and interpersonal—have traditionally been conceptualized as the individual-level phenomena in the literature (Colquitt, 2001).

First, distributive justice refers to an individual’s perceptions of the extent to which the outcomes obtained are fair (Byrne & Cropanzano, 2001; Folger & Cropanzano, 1998). Early organizational justice research was based on Adams’s (1965) equity theory, in which an individual formulates justice perception by performing a comparative calculation of one’s contributions and rewards from a decision-making system (Crawshaw, Cropanzano, Bell, & Nadisic, 2013). The theory predicts that employees who feel that outcome distributions are unfair will attempt to restore balance to the ratio of input and output of rewards by modifying their inputs (Colquitt et al., 2005). The second dimension of organizational justice is procedural justice, which refers to the fairness of the allocation decisions that assign outcomes (Colquitt, 2001; Folger & Cropanzano, 1998). Leventhal (1980) proposed that individuals use various criteria to assess decision-making procedures to judge whether the procedures are fair or unfair. He postulated specific procedural rules for fair procedures, including consistency (i.e., consistent procedures across time and persons), bias suppression (i.e., the exclusion of self-interest), accuracy (i.e., the provision of accurate information), correctability (i.e., allowing for appeals and grievances), representativeness (i.e., the basic concern of all individuals being represented), and ethicality (i.e., moral and ethical standards). Allocation procedures that satisfy these criteria persuade individuals to believe that they are treated by the organization fairly. More recently, scholars have argued that individuals formulate justice perceptions about not only the formally institutionalized structures and procedures for outcome allocation, but also the quality of interactions with authority figures (Colquitt, 2001; Colquitt et al., 2005; Crawshaw et al., 2013). Greenberg (1993) suggested another two dimensions of justice that are characterized by interpersonal communication and treatment that individuals receive from decision-makers with authority over them. Specifically, informational justice is defined as providing accurate information in timely communications, being transparent in communication, and providing reasonable explanations for events at work, whereas interpersonal justice is shaped by interpersonal experience of being treated with respect and politeness by the authorities (Colquitt et al., 2005).

To sum up, the justice research has clearly established that individuals care about four sets of factors, including the allocation of rewards (distributive justice), the procedures or policies used to determine outcomes (procedural justice), adequate and sufficient information about decision making (informational justice), and interpersonal treatment with respect and dignity (interpersonal justice).

Justice Research in Public Administration

Organizational justice has received increasing attention in recent years in the public administration literature. Table 1 summarizes previous empirical studies on the effect of organizational justice on various work-related outcomes that have been published in a core set of public administration journals since 2000.

Table 1.

Summary of Previous Studies on Organizational Justice in Public Administration.

Author(s)	Independent variable(s)	Dependent variable(s)	Data	Unit of analysis	Method
Rubin (2009)	Procedural justice	Trust Job satisfaction Turnover intention	2002 FHCS	Individual	Cross-sectional
Noblet and Rodwell (2009)	Distributive justice Procedural justice Informational justice Interpersonal justice	Job satisfaction Well-being	Australian Government Survey	Individual	Cross-sectional
De Schrijver, Delbeke, Maesschalck, and Pleysier (2010)	Organizational fairness	Organizational misbehavior	Flemish Government Survey (Dutch)	Individual	Cross-sectional
Choi (2011)	Distributive justice Procedural justice Interpersonal justice	Job satisfaction Trust in manager Trust in management Intention to leave	2005 MPS	Individual	Cross-sectional
Rubin (2011)	Procedural justice	Member of union	2005 MPS	Individual	Cross-sectional
Kim and Rubianty (2011)	Distributive justice Procedural justice Interactional justice	Intrinsic motivation	2005 MPS	Individual	Cross-sectional
Cho and Sai (2012)	Distributive justice Procedural justice Informational justice	Career development Employee satisfaction Loyalty to senior leadership Cooperation	2008 FHCS	Individual	Cross-sectional
Hassan (2013a)	Distributive justice Procedural justice	Organizational identification Job involvement Turnover intention	Local Government Survey (U.S.)	Individual	Cross-sectional
Meng and Wu (2015)	Distributive justice Procedural justice	Leader-Member-Exchange Job engagement	Public School Survey (China)	Individual	Cross-sectional
Rubin and Kellough (2012)	Procedural justice	Employee complaint rates	2005 MPS	Organization	Cross-sectional
Choi and Rainey (2014)	Organizational fairness	Job satisfaction	2006 FHCS	Individual	Cross-sectional
Ko and Hur (2014)	Procedural justice	Job satisfaction Turnover intention	2008 FHCS	Individual	Cross-sectional
Chen and Jin (2014)	Procedural justice Informational justice Interpersonal justice Leadership justice	Organizational citizenship behavior	Employee survey in 16 companies (China)	Individual	Cross-sectional
Rubin and Chiqués (2015)	Procedural justice	Union membership	2005 MPS	Individual	Cross-sectional

Note. FHCS = Federal Human Capital Survey; MPS = Merit Principle Survey.

Overall, fairness seems to be the main principle of human resource management shaping employees’ positive job attitudes and behaviors in public sector organizations.¹ This literature has significantly contributed to the theoretical development of how justice perceptions affect individual outcomes and practical implications in terms of implementing managerial practices that aim to enhance employees’ fairness perceptions.

Surprisingly, however, most scholars have focused primarily on the effects of individual-level justice perceptions but paid little attention to the unit-level cognition of how a work unit is treated as a whole. According to Kozlowski and Klein (2000), “many phenomena in organizations have their theoretical foundation in the cognition, affect, behavior, and characteristics of individuals, which—through social interaction, exchange, and amplification—have emergent properties that manifest at higher levels” (p. 15). Along the same lines, Konovsky (2000) noted that “almost wholly absent from consideration has been the influence of higher-order factors on procedural justice including culture and organization or group-level procedural justice antecedents and consequences” (p. 504). That is to say, justice perceptions are not formed in isolation but rather in the context of specific relationships with multiple individuals and groups. In addition, given that an employee’s fairness perceptions vary over time, previous justice research using the cross-sectional analysis may reveal a weak predictive validity of organizational justice in explaining work-related outcomes (Hausknecht et al., 2011). Therefore, more empirical studies on justice climate using longitudinal research designs will lead to a better understanding of how the shared perceptions about fair treatment are linked to a broad range of work-related outcomes over time.

Justice as an Organizational-Level Phenomenon

Two theories, social information processing (SIP) theory and the attraction-selection-attrition (ASA) model, provide the background of organizational justice climate research (Li & Cropanzano, 2009). SIP theory asserts that employee attitudes and behaviors are the results of active interaction with each other, which creates a sense of managerial practices and events in their workplace (Salancik & Pfeffer, 1978). Indeed, in a laboratory experiment examining how justice climates form, Roberson (2006) found that members of the same work unit share their experiences and information about why certain procedures are used to distribute outcomes and exchange interpretations of fairness-related events. As a result, this collective sense-making process leads to the convergence of justice perceptions within the work unit (Li & Cropanzano, 2009; Naumann & Bennett, 2000).

Similarly, the ASA model also serves as a foundation for organizational justice climate. The model posits that groups or organizations become homogeneous entities of individuals by integrating their personalities, attitudes, and beliefs through three sets of processes (Schneider, 1987). First, the ASA framework proposes that individuals are more likely to be attracted to organizations with which they share similar perceptions. Second, organizational authorities are prone to select applicants with similar characteristics. Finally, individuals who differ from other organizational members are expected to leave either voluntarily or involuntarily, as they do not harmonize with the other members. Hence, the members of the same organization exhibit a strong homogeneity in their values, attitudes, and personalities (Naumann & Bennett, 2000; Schneider, Goldstiein, & Smith, 1995).

Overall, the two theories provide useful frameworks for understanding why justice judgments are likely to be affected by the responses of others. That is, employees develop a collective sense of fairness by sharing information and experience of work unit treatment. Building on these theories of organizational climate, scholars have distinguished four facets of justice climate: (a) distributive justice climate (i.e., shared fairness perceptions of rewards); (b) procedural justice climate (i.e., shared fairness perceptions of organizational procedures); (c) informational justice climate (i.e., shared fairness perceptions of the credibility and adequacy of information given to employees); and (d) interpersonal justice climate (i.e., shared fairness perceptions of the respect and courtesy that employees receive from their authorities; Li & Cropanzano, 2009; Liao & Rupp, 2005).

Justice Climate, Collective Turnover Rates, and Organizational Performance

Social exchange theory has been widely used as a primary lens to explain how organizational justice perceptions influence work-related outcomes (Cho & Sai, 2012; Cohen-Charash & Spector, 2001; Colquitt et al., 2013). Social exchange is defined as “voluntary actions of individuals that are motivated by the returns they are expected to bring and typically do in fact bring from others” (Blau, 1964, p. 91). Specifically, the central tenet of the theory is that the norm of reciprocity inherent in social exchange relationships encourages individuals to feel the need to repay those who have helped them (Blau, 1964; Scott & Colquitt, 2007).

Given that employees regard fair treatment from their organization or managers as a benefit deserving reciprocation, the concepts of social exchange can be applied to predict the way in which organizational justice affects employee turnover. Because fair treatment indicates the extent to which their organizations and managers value their employees’ work efforts, employees are less likely to leave the organization (Choi, 2011; Hassan, 2013a). To be specific, when employees perceive that they are treated fairly in terms of outcome distributions, the administrative procedures by which those outcomes are allocated, and interpersonal elements of exchange, they repay the fair treatment by remaining loyal and continuing to work in their current organizations (Bal, de Lange, Ybema, Jansen, & van der Velde, 2011). In contrast, if employees are treated unfairly, the norm of reciprocity is harmed (Colquitt et al., 2005). In such context, it is possible that perceived injustice undermines employees’ beliefs in the benefits of the exchange relationship with the organization and managers; based on this perception, employees might consider leaving the organization.

Organizational justice also enhances employees’ feelings of obligation to their organization in the form of improved performance. For instance, fair distribution of outcomes incites employees to fulfill a contractual agreement that characterizes their exchange relationships (Scott & Colquitt, 2007). Performance is generally viewed as a critical responsibility that employees are hired to perform in exchange for extrinsic rewards (Colquitt et al., 2005). Thus, it seems reasonable to predict that public employees who feel underpaid will make fewer contributions to the organization. Similarly, employees who believe that their organization has fair decision-making procedures are likely to reciprocate those fair employee–organization exchange relationships with beneficial actions that improve work performance (Konovsky & Cropanzano, 1991). Justice researchers have also argued that the quality of interpersonal and informational interaction between individuals might influence job performance (Otto & Mamatoglu, 2015). If employees perceive that their managers demonstrate concern and respect and provide adequate information regarding decision-making procedures, the employees will reciprocate managers through improved performance (Wang, Liao, Xia, & Chang, 2010).

Similar relationships to those revealed at the individual level have also been supported at the unit level as consequences of justice climate. Scholars argued that individual- and unit-level organizational justice both include an isomorphic cognitive process of the norm of reciprocity because justice perceptions at both levels create symbolic information about social exchange relationships between employees, the organization, and managers (Naumann & Bennett, 2000; Yang et al., 2007). In support of this rationale, previous studies reported that the shared perceptions of organizational justice enhance employees’ collective loyalty and emotional attachment to the organization and thus reduce organizational-level turnover intention (Simons & Roberson, 2003; Whitman et al., 2012). Furthermore, there is some evidence that justice climate is a significant factor of improving perceived organizational performance such as profitability (Shin, Du, & Choi, 2015) and perceived group-level performance (Colquitt, Noe, & Jackson, 2002; Cropanzano, Li, & Benson, 2011; Naumann & Bennett, 2000; Whitman et al., 2012). These results suggest that the mechanisms of social exchange are applicable to the organizational level. Consequently, reciprocal obligations derived from collective perceptions of fair treatment encourage employees as a whole to remain in the current organization and increase organizational performance.

Notwithstanding the abundance of empirical work on the issue of justice perceptions and their consequences at the individual level, little attention has been paid to the impact of the four types of shared justice perceptions on employee turnover and performance at the organizational level in public administration. The sparse existing research is limited to examinations of one or two types of justice climates. This gap in the research means that we know little about the associations of the four dimensions of justice climates with actual turnover rates and organizational performance in public sector organizations. Hence, the following hypotheses are formulated:

Hypothesis 1: Distributive, procedural, informational, and interpersonal justice climates will be negatively associated with collective turnover rates in U.S. federal agencies.

Hypothesis 2: Distributive, procedural, informational, and interpersonal justice climates will be positively associated with organizational performance in U.S. federal agencies.

In addition to social exchange theory, a two-factor justice model provides a theoretical framework to examine varying effects of the four dimensions of justice climate on organizational outcomes (Cho & Sai, 2012; Colquitt, Conlon, Wesson, Porter, & Ng, 2001; Colquitt et al., 2013; Sweeney & McFarlin, 1993). The fundamental idea of the model is that outcome-focused justice climates (i.e., distributive and interpersonal) exert greater influence on person-related outcomes (i.e., job satisfaction and turnover), whereas process-focused justice climates (i.e., procedural justice and informational) exert greater influence on organization-related outcomes (i.e., affective commitment and organizational performance). According to Greenberg (1993), the four dimensions of organizational justice can be categorized by content of justice judgment. For example, individuals’ expectations of a fair distribution of economic outcomes are primary factors in creating distributive justice judgments; in comparison, the fairness of decision-making procedures or rules is concerned with formulating procedural justice judgment (Cho & Sai, 2012; Sweeney & McFarlin, 1993). Similarly, informational justice reflects the social aspects of procedural justice because it offers information about decision-making procedures that demonstrate regard for employees’ concerns, whereas interpersonal justice involves social aspects of distributive justice in the sense that it is characterized by expressions of remorse for the outcomes themselves (Ambrose, Hess, & Ganesan, 2007; Greenberg, 1993). Similar to distributive justice, interactional justice is a stronger predictor of person-related outcomes than informational justice. On the contrary, like procedural justice, informational justice is a stronger predictor of organization-related outcomes than interpersonal justice.

Indeed, in an early examination of these differential effects, Folger and Konovsky (1989) found that distributive justice has greater leverage on satisfaction with pay, whereas procedural justice exerts a greater influence on organizational commitment and trust in leadership. In the same vein, Konovsky and Cropanzano (1991) reported that procedural justice is more predictive of affective commitment, management trust, and performance than distributive justice. In the more recent public administration literature, scholars offer somewhat mixed empirical evidence about the two-factor model. To be specific, Choi (2011) revealed that distributive justice is more influential on turnover intention and job satisfaction in U.S. federal agencies than are procedural and interactional justice. Conversely, Cho and Sai (2012) found that informational justice has larger leverage on job satisfaction and loyalty to senior leadership of federal employees than do distributive and procedural justice.

In sum, although some studies yield empirical evidence partially supporting the differential predictions, the two-factor justice model is still widely used to explain the relative effects of the four dimensions of organizational justice on organizational outcomes. Surprisingly, however, little research has been undertaken on the relative impact of all four dimensions of perceived justice. Even though previous studies have tested ideas of the two-factor justice model at the individual level, it is expected that varying effects of the four justice climates of collective turnover rates and organizational performance will also exist at the organizational level. This discussion leads to the following hypotheses:

Hypothesis 3: Distributive and interpersonal justice climates will be more strongly associated with collective turnover rates than will the procedural and informational justice climates in U.S. federal agencies.

Hypothesis 4: Procedural and informational justice climates will be more strongly associated with organizational performance than will the distributive and interpersonal justice climates in U.S. federal agencies.

Model Specification

Data and Sample

To test these hypotheses, this study draws on three different sources of data collected from 2010 to 2013.² The first data source comes from four waves of the Federal Employee Viewpoint Survey (FEVS) conducted by the U.S. Office of Personnel Management (OPM). OPM published the FEVS annually, using a stratified random sampling method to obtain generalizable results representing the federal government. The number of respondents and response rates (by year) of the FEVS were 263,475 and 52% (2010); 266,376 and 49.3% (2011); 687,687 and 46.1% (2012); and 376,577 and 48.2% (2013). The responses to the survey questions were measured on a 5-point Likert-type scale that ranged from 1 (strongly disagree or very dissatisfied) to 5 (strongly agree or very satisfied). It was impossible, however, to perform a panel analysis at the individual level because the surveys were anonymous. The current study, therefore, aggregated individual responses with the survey weights to the organizational-level construct to conduct a longitudinal analysis.³ That is, federal agencies (i.e., departments and independent agencies) were the primary units of analysis in this study.

The second data source was drawn from the Enterprise Human Resources Integration Statistical Data Mart (EHRI-SDM), which includes federal workforce information. OPM established the EHRI-SDM in 2010 to provide access to personnel data for 96% of nonpostal federal employees in the U.S. executive branches. For instance, the data excludes agencies working on tasks related to national security, intelligence, and postal service. Despite these exclusions, the EHRI-SDM is widely regarded as a comprehensive data warehouse comprising statistical information about demographic characteristics of the federal workforce.

Finally, the third data source was an agency’s annual Performance and Accountability Report (PAR), which contains the agency’s performance information for the fiscal year. In accordance with the Government Performance and Results Act of 1993, federal agencies are required to announce what they are seeking to accomplish and how they will attain strategic goals. Specifically, the reports describe progress in reaching the agency’s performance indicators using five rating criteria: (a) exceeded, (b) met, (c) unmet, (d) deferred, and (e) not available. Consequently, PARs are a reliable data source for understanding the agencies’ achievement of goals for each fiscal year (Lee & Whitford, 2013). However, as not all agencies were listed in PARs across time, the data are unbalanced panel data of 32 agencies over the time period.⁴

Measures

Four dimensions of justice climate

Twelve items from FEVS were used to measure four dimensions of justice climate (Cho & Sai, 2012; Choi, 2011; Choi & Rainey, 2014; Hassan, 2013a; Rubin, 2009). Distributive justice climate—shared perceptions of the fairness of outcomes—was measured using three items: (a) “Promotions in my work unit are based on merit,” (b) “Awards in my work unit depend on how well employees perform their jobs,” and (c) “Pay raises depend on how well employees perform their jobs.” Four items were used to assess procedural justice climate, which focuses on shared perceptions regarding four of Leventhal’s (1980) six criteria: (a) “My performance appraisal is a fair reflection of my performance” (i.e., accuracy), (b) “I can disclose a suspected violation of any law, rule, or regulation without fear of reprisal” (i.e., correctability), (c) “Arbitrary action, personal favoritism, and coercion for partisan political purposes are not tolerated” (i.e., bias suppression), and (d) “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” (i.e., representativeness). Informational justice climate—shared perceptions of the fairness of the information and the explanation provided to group members regarding the decisions made by the organization—is constructed using three items: (a) “Managers communicate the goals and priorities of the organization,” (b) “Managers promote communication among different work units (for example, about projects, goals, and needed resources),” and (c) “How satisfied are you with the information you receive from management on what’s going on in your organization?” Finally, interpersonal justice climate, which is concerned with the shared perceptions of managers’ respectful treatment of group members and willingness to listen their concerns, was measured by two items: (a) “My supervisor/team leader listens to what I have to say” and (b) “My supervisor/team leader treats me with respect.” Confirmatory factor analysis (CFA) was conducted to examine convergent and discriminant validity of the constructs of the hypothesized measurement model, and assumed individual scale items loaded on four latent variables—distributive, procedural, informational, and interpersonal justice. CFA results across each year of the FEVS indicated that the root mean square error of approximation (RMSEA), which should be lower than .080, ranged from .067 to .070; the comparative fit index (CFI) and Tucker–Lewis Index (TLI), which should both generally be above .900, ranged from .970 to .972 and from .958 to .962, respectively. Thus, the results justify the use of distinct measures for each of the latent variables. The values of the standardized factor loadings (λs) ranged from .62 to .94, and only one out of the 12 items had a loading below .70. All factor loadings for the latent constructs were statistically significant at the .01 level. These results justify the use of distinct measures for each of the latent variables (see Appendix).

Finally, the coefficient alphas for each dimension of justice climate exceeded the typical threshold (.70) across all years of the study. This study averaged individual responses of each justice dimension to create justice climate at the organizational level.⁵

Collective turnover rates

Using EHRI-SDM, turnover behavior is measured as an actual turnover rate by dividing the number of employees who voluntarily quit the organization during a certain period by the total workforce size. As with most turnover research, involuntary separations (e.g., termination and reduction-in-force) and voluntary separations for other causes (e.g., retirements and transfer-outs) are excluded (Cohen, Blake, & Goodman, 2016). Because the turnover rate is a proportion that is bounded by 0 and 1, logit transformation was performed prior to the estimation of the linear models. As a result, the transformed turnover rates could theoretically assume any value between plus and minus infinity.⁶

Organizational performance

Although organizational performance in public administration is a difficult concept to define and measure (Lee & Whitford, 2013; Rainey, 2014), Rainey and Steinbauer (1999) proposed a goal approach that focuses on the assessment of how many and how well the organization attains a set of goals. Similarly, Boyne (2003) argued that using the degree of actual goal attainment as the key criterion of governmental performance has “a powerful resonance with current debates about the effectiveness improvement” (p. 216). Furthermore, Rainey (2014) noted that “when organization theorists first began to develop models of organizational effectiveness, it appeared obvious that one should determine the goals of one’s organization and assess whether it achieves them” (p. 155). As a result, the goal approach was deemed appropriate for evaluating a federal agency’s performance. Using annual PARs, this study used levels of an agency’s goal achievement as proxies for organizational performance by dividing the number of “met” or “exceeded” ratings by the total number of performance ratings (Lee & Whitford, 2013). The “not assessed” or “deferred” ratings were excluded from the total performance ratings for the dependent variable because these ratings indicate that the data for evaluation of goal achievement are not available in a given fiscal year. Similar to turnover rates, goal attainment is a proportion that is bounded by 0 and 1. Thus, logit transformation was performed prior to model estimation.

Control variables

In addition to the four dimensions of justice climate, this study controlled for several correlates of turnover behavior and organizational performance. First, various job resources (i.e., administrative and financial resources) offer a sustainable competitive advantage for an agency and lead to better performance (Lee & Whitford, 2013). The survey item was “I have sufficient resources (for example, people, materials, budget) to get my job done.” Second, goal clarity was analyzed because agencies’ annual goals with clear targets contribute to an improvement in organizational performance (Chun & Rainey, 2005). The survey item was “I know how my work relates to the agency’s goals and priorities.” In addition, this study included policy problem complexity (i.e., the proportion of employees who are lawyers, engineers, psychologists, and scientists), demographic diversity (i.e., gender, racial, and age diversity), and workforce size (i.e., the number of personnel in an agency), which may influence employee turnover and organizational performance (Choi & Rainey, 2010; Chun & Rainey, 2005).⁷ Finally, year and agency dummy variables were also added to each model to control for year- and agency-fixed effects in the data.

Findings

Table 2 provides descriptive statistics and correlation coefficients among variables. Regarding the summary statistics of dependent variables, the mean value of voluntary turnover rates was .03 (SD = .02) and ranged from .01 to .14, whereas organizational performance had the mean value of .28 (SD = .13) with a minimum of .02 and a maximum of .67. In addition, the correlation analysis showed that the two dimensions of interactional justice climate—informational and interpersonal justice climates—correlated negatively with turnover rates. In addition, the four dimensions of justice climate were found to be positively correlated with organizational performance. However, because the correlations among justice climate variables were strong, the variance inflation factor (VIF) tests were conducted to check the multicollinearity. The results indicated that the average VIF score for the independent variables in the statistical models was about 3.26, suggesting no multicollinearity problems (Bowerman & O’Connell, 1990).

Table 2.

Descriptive Statistics and Correlation Matrix.

		1	2	3	4	5	6	7	8	9	10	11	12	13
1	Collective turnover rates	1
2	Organizational performance	−.14^a
3	Distributive justice climate	−.08^a	.32	1
4	Procedural justice climate	−.08^a	.32	.71	1
5	Informational justice climate	−.23	.30	.73	.81	1
6	Interpersonal justice climate	−.18	.28	.68	.83	.67	1
7	Job resources	−.17	.25	.69	.53	.67	.54	1
8	Goal clarity	−.17	.13^a	.61	.54	.74	.50	.60	1
9	Policy complexity	−.20	.03^a	.22	.26	.15	.26	.11^a	.35	1
10	Gender diversity	.23	.03^a	.38	.24	.18	.43	.23	.30	.10^a	1
11	Racial diversity	−.03^a	−.23	−.12^a	−.28	.00^a	−.23	−.04^a	.18	−.17	−.01^a	1
12	Age diversity	.16	−.09^a	.06^a	.05^a	.07^a	.04^a	.13^a	−.04^a	−.23	.02^a	.17	1
13	Workforce size (1,000)	.15^a	−.05^a	−.34	−.23	−.14^a	−.37	−.11^a	−.17	−.36	−.15^a	.16	.04^a	1
	M	.03 (−3.69)	.28 (1.08)	2.91	3.60	3.41	4.03	3.12	4.08	12.04	.48	.51	.75	45.18
	SD	.02 (0.68)	.13 (0.74)	.23	.15	.19	.12	.25	.12	15.34	.03	.07	.02	66.76
	Minimum	.01 (−5.38)	.02 (−0.70)	2.28	3.13	2.78	3.33	2.15	3.57	.58	.27	.38	.67	.03
	Maximum	.14 (−1.85)	.67 (3.68)	3.45	4.00	3.90	4.33	3.94	4.46	61.29	.50	.66	.79	337.68

Not significant at 95% confidence level. Logit transformed values are in parentheses.

Although panel data provide more valid inferences by tracking spatial units over time, rather than cross-sectional and time series data (Lee & Whitford, 2013; Oberfield, 2014), potential problems associated with heteroscedasticity and serial autocorrelation still remain. The Wooldridge test for autocorrelation in the panel data yielded an F statistic of 16.08 (p < .001) and 11.23 (p < .001) for the models, which indicated that the errors were serially correlated. The likelihood ratio test for heteroscedasticity yielded chi-squares of 4.90 (p < .05) and 15.27 (p < .001) for the models. Due to the presence of heteroscedasticity and serial autocorrelation in the panel data, the proposed hypotheses were tested using the feasible generalized least squares (FGLS) regression method with a heteroskedastic error structure and AR(1) autocorrelation. The results of the FGLS regression analyses of collective turnover rates and organizational performance are shown in Table 3. The Wald chi-squared statistics present that all models perform well and are significant (p < .001).

Table 3.

FGLS Regression Results.

	Model 1			Model 2
	Collective turnover rates			Organizational performance
	β	b	SE	β	b	SE
Distributive justice climate	−1.410***	−0.468	0.361	1.064†	0.324	0.614
Procedural justice climate	−0.038	−0.008	0.260	1.258*	0.250	0.513
Informational justice climate	0.208	0.057	0.412	−0.853	−0.215	0.951
Interpersonal justice climate	−.794†	−0.146	0.410	1.331*	0.224	0.557
Job resources	0.300	0.110	0.199	0.670**	0.225	0.199
Goal clarity	0.553	0.093	0.602	−1.876	−0.291	1.179
Policy problem complexity	−0.099***	−2.222	0.019	−0.012	−0.253	0.033
Gender diversity	3.994	0.183	3.588	2.341	0.098	6.267
Racial diversity	6.565***	0.697	1.335	8.080**	0.788	2.862
Age diversity	7.713***	0.233	2.087	−0.649	−0.018	3.874
Workforce size (log)	0.432**	1.381	0.166	0.400	1.172	0.296
Constant	−15.276***	—	3.487	−11.893*	—	5.640
Wald $χ^{2}$	5,894.93***			28,978.17***
N	109			109

Note. b represents standardized coefficients. The two dependent variables are gathered using data from fiscal year from 2011 to 2014 (at time t + 1) to ensure that the control and independent variables precede the dependent variables by one time interval.

†

p < .10. *p < .05. **p < .01. ***p < .001.

Model 1 in Table 3 presents the relationship between the four dimensions of justice climate and employee turnover rates. As predicted, the results indicate that there is a negative relationship between distributive justice climate and turnover rates (β = −1.410, p < .001). That is, agencies are expected to have lower levels of employee turnover rates when the majority of employees perceive the reward distribution as fair. Similarly, interpersonal justice climate is significantly and negatively associated with turnover rates (β = −.794, p < .10). This suggests that shared perceptions of treatment with dignity and respect by managers are more likely to make employees, as a whole, stay in the current agency. However, according to the results, procedural justice climate and informational justice climate do not have statistically significant relationships with turnover rates. Model 2 tests the relationship between the four dimensions of justice climate and organizational performance. The results indicate that distributive justice climate (β = 1.064, p < .10), procedural justice climate (β = 1.258, p < .05), and interpersonal justice climate (β = 1.331, p < .05) are significantly and positively related to organizational performance. This result implies that an agency shows better performance when employees share consensual perceptions of fairness of outcome allocations, procedures that lead to outcomes, and interpersonal treatment with social sensitivity and respect by managers. However, the results showed no significant relationship between informational justice climate and organizational performance.

To compare the relative impact of each justice climate, I also calculated standardized coefficients, which are presented in Table 3. The results indicate that the two outcome-focused justice climates (i.e., distributive and interpersonal) are more influential for employee turnover rates than those of the two process-focused justice climates (i.e., procedural and informational). With each standard deviation increase of distributive justice climate, agencies’ predicted turnover rates decrease by 46.8% (i.e., 4.68 times less likely to turnover). A standard deviation increase in distributive justice climate yields a turnover rate decrease of 14.6%. This confirms the two-factor justice model, which insists that employees’ collective perceptions of fair treatment by their organization and managers with respect to distribution of rewards are more influential for person-related outcomes at the organizational level.

In terms of the relative leverage of the four dimensions of justice climate on organizational performance, the results show that the impact of procedural justice climate on organizational performance is greater than that of interpersonal justice climate but weaker than that of distributive justice climate. Specifically, distributive justice climate has the largest impact on organizational performance: a standard deviation increase of distributive justice climate corresponds to a predicted 32.4% increase in goal achievement. While a standard deviation increase of procedural justice climate generates an organizational performance increase of 25.0%, a standard deviation increase of interpersonal justice climate causes organizational performance to increase by about 22.4%. These findings were counterintuitive because the two-factor justice model suggests that process-focused justice climates are better predictors of organization-related outcomes than outcome-focused justice climates. It seems that shared fairness perceptions of decision-making procedures leading to decision outcomes are slightly more influential for organizational performance than shared perceptions of the quality of the interpersonal treatment that employees receive from managers. Interestingly, distributive justice climate is the most powerful predictor of organizational performance among the four dimensions of justice climates: Employees’ shared fairness perceptions of outcome distributions are critical for agencies in reaching high levels of goal achievement.

Regarding the control variables, policy problem complexity is negatively related to collective turnover rates, suggesting that federal agencies with high proportions of professional employees experience greater personnel stability. It is plausible that employees who perform more complex tasks than do administrative and clerical staffs have high levels of role clarification that reduce actual turnover (Hassan, 2013b). In addition, the results indicate that racial diversity and age diversity have positive relationships with turnover rates. Interpersonal conflicts caused by perceived similarities and dissimilarities between employees who have different demographic backgrounds may increase employees’ turnover behaviors (Moon, 2016). In addition, there is a positive relationship between workforce size and turnover rates. One possible explanation is that employees are more likely to leave a large organization because they engage in fewer interpersonal connections with others (Andrews, 2017), which increases turnover rates. Finally, racial diversity is a strong predictor of organizational performance. Racial heterogeneity not only provokes emotional conflicts and social identity among employees, but also provides a broad range of information, knowledge, and perspectives that help an organization achieve goals (Cunningham, 2009).

Discussion

The main purpose of this study was to explore how four dimensions of justice climate at the organizational level influence collective turnover rates and organizational performance in the federal government as well as their relative effects on the two organizational consequences. Based on social exchange theory, this study predicts that the four justice climates are negatively related to employee turnover behavior, but positively related to organizational performance at the organizational level. Moreover, drawing from a two-factor justice model, while the two dimensions of outcome-focused justice climate have a stronger impact on turnover rates, the two dimensions of process-focused justice climate have a stronger impact on organizational performance.

Overall, the evidence is encouraging. First, distributive and interpersonal justice climates are significantly and negatively related to employee turnover rates, whereas procedural and informational justice climates have no significant impact. These findings are somewhat inconsistent with previous empirical research at the individual level (Choi, 2011; Hassan, 2013a; Ko & Hur, 2014; Rubin, 2009) revealing that procedural and informational justice reduce employee turnover intention in public sector organizations. Given that turnover intention is not always strongly correlated with actual turnover behavior (Cohen et al., 2016), the mixed findings may result from distinctions between subjective and objective measures of turnover. Another possible explanation is that a shared consensus of fair treatment emerges within an organization and may exert unique influences on work-related outcomes above and beyond individual-level justice perceptions (Li & Cropanzano, 2009; Naumann & Bennett, 2000). In addition, the findings indicate that distributive, procedural, and interpersonal justice climates are significantly and positively associated with organizational performance. Interestingly, however, informational justice climate is not significantly related to organizational performance. These results suggest that when employees collectively perceive their work environment to be fair with respect to outcome distribution, decision-making processes, and the quality of interpersonal treatment by managers, they are likely to repay fair treatment with high levels of goal achievement. Second, further analysis of their relative effects confirms that distributive and interpersonal justice climates have the greater impact on turnover rates. Additionally, the impact of procedural justice climate on organizational performance is greater than interpersonal justice climate but less than distributive justice climate. Although somewhat inconsistent with the two-factor model, these findings suggest that the shared perceptions of fair treatment provided by the organization in terms of extrinsic reward distributions play a substantial role in promoting desired organizational consequences in federal agencies. A plausible interpretation is that federal spending cuts and economic recession during the study period (2010-2013) may make employees place a high value on fair distribution of extrinsic rewards such as pay, promotion, and recognition.

Theoretical Implications

This study makes several significant contributions to the field of public administration and public management. First, it extends organizational justice research at the individual level by expanding our knowledge about the consequences of collective perceptions of fairness at the organizational level. Public administration scholars have largely examined the relationship between perceived justice and individual-oriented outcomes (Cho & Sai, 2012; Choi, 2011; De Schrijver, Delbeke, Maesschalck, & Pleysier, 2010; Hassan, 2013a; Kim & Rubianty, 2011; Ko & Hur, 2014; Meng & Wu, 2015; Noblet & Rodwell, 2009; Rubin, 2009). Although such individual-level studies contribute to the understanding of the effect of organizational justice on employee outcomes, such relationships need to be explored within the broader organizational context. Given that employees tend to share similar views and experiences regarding fairness in the workplace (Naumann & Bennett, 2000), understanding organizational justice as an organizational-level phenomenon may provide a more complete picture of how employees collectively respond to fair treatment of the work unit (Li & Cropanzano, 2009).

Another contribution is that it improves our knowledge of the effects of the four dimensions of justice climate on employee turnover behavior and organizational performance. Although scholars agree that employees differentiate among distributive, procedural, informational, and interpersonal justice (Colquitt, 2001; Colquitt et al., 2005), the justice literature in public administration has focused primarily on the impacts of only two or three of the dimensions on employee outcomes (see Noblet & Rodwell, 2009, for an exception)—and even then, has only studied the impact on employee work attitudes as dependent variables (e.g., job satisfaction, trust, and turnover intention). This study advances the current scholarship in justice research by exploring the mechanisms by which the four dimensions of justice climate influence employees’ actual turnover behavior and the objective performance of public sector organizations. Furthermore, empirical evidence on the relatively strong impact of outcome-focused justice climates implies that fair distributions of extrinsic rewards should not be underestimated in public administration. For instance, some scholars argue that public employees who are primarily motivated by altruistic interests and intrinsic work values tend to negatively respond to a performance appraisal system or pay for performance (Langbein, 2010; Oh & Lewis, 2009). Yet, current findings suggest that when collective interpretation of distributive justice emerge at the workplace, employees with high public service motivation (PSM) as a collective may be more likely to focus on generating better outcomes for the organization (Liu & Tang, 2011; Stazyk, 2013). Thus, more scholarly effort should be devoted to exploring the linkages between organizational justice and PSM in the public sector work context.

Finally, the longitudinal research design offers a more valid conclusion of causal processes than cross-sectional research (Oberfield, 2014). Most previous studies used a cross-sectional research design that examined variation across cases at a single moment by exploring the linkages between justice climates and different types of outcome variables. However, given that employees alter their fairness perceptions as they experience justice-triggering events and share common views regarding fairness (Hausknecht et al., 2011), a longitudinal examination may capture unique information that is not explained by single-wave research designs.

Practical Implications

This study also offers several managerial implications. Given the importance of fairness perceived by the work unit as a whole, agencies should pay considerable attention to exploring various ways of fostering justice climates at the organizational level. To enhance distributive justice climate, key activities may include implementing fair systemwide policies and practices, such as team-based reward programs (Cho & Sai, 2012; Choi, 2011; Meng & Wu, 2015). Additionally, providing units with more empowerment and decision-making authority in terms of policymaking, recruiting, and promotion may encourage the emergence of procedural justice climates (Schminke, Cropanzano, & Rupp, 2002). Yet, simply creating distributive and procedural justice climates may not be sufficient. Given the prominent role of managers in creating a supportive environment in a work group, public managers should treat their subordinates more politely and honestly to reduce turnover behavior and improve organizational performance (Chen & Jin, 2014). For example, rigorous leadership programs aimed at developing managers’ ethical behavior may enhance subordinates’ shared sense of interpersonal justice (Whitman et al., 2012). Even more importantly, regarding the relative impacts of the four dimensions of justice climate on turnover and performance, federal agencies may develop more fine-grained strategies to achieve target outcomes, especially in the highly limited resource environment (Cho & Sai, 2012; Whitman et al., 2012). For instance, given that distributive justice climate outperforms other dimensions of justice climate in improving desirable organizational consequences, agencies with high rates of employee turnover and low levels of organizational performance need to prioritize allocating rewards equitably in proportion to employees’ inputs and contributions.

Limitations and Future Research Directions

This study is not without limitations. First, it must be acknowledged that the results cannot be universally applicable because they emerge from the analysis conducted in the working environment of the U.S. federal government during specific periods. In future research, it is essential that scholars continue to examine the effect of justice climate on work-related outcomes at the unit level in other nations, at different levels of government (e.g., subagencies and local government), and in different sectors at different points in time; such scholarly efforts would help increase the external validity of the findings of this study.

Another limitation is that the measures of the four justice perceptions are not constructed by using the identical items that previous research has typically used; hence, the study may be criticized in terms of its construct validity. This problem is often encountered in the field of public administration when utilizing the existing data such as FEVS, which mainly aim to evaluate federal employees’ job attitudes toward human resource management but not specific dimensions of perceived justice. Future research can improve the measures of organizational justice in fruitful directions by using more items that accurately capture the four justice dimensions.

Moreover, there is growing attention to exploring contextual unit-level factors that moderate the influence of justice climate on organizational consequences. For example, demographic diversity may prevent employees to share their experience on fair treatment with others because of affective conflicts caused by dissimilarity in gender and race between employees (Roberson & Colquitt, 2005). Additionally, some justice scholars suggest that the relationships between justice climate levels and organizational outcomes depend on justice climate strengths—variability in group members’ climate ratings (Colquitt et al., 2002). Specifically, the effects of justice climate levels would be stronger when climate strength is higher than when climate strength is lower. Future empirical studies should take into account the role of such contextual moderators in the justice climate–outcomes relationships at the collective level.

Last, this study requires caution when interpreting the results. In particular, the use of organization-level data may create potential ecological fallacy by yielding an erroneous inference regarding individual-based findings pertaining to the group—that is, based on the analysis conducted at an aggregate level. Because the unit of analysis in the current study was the U.S. federal agency, the interpretations of findings are not applicable to individual-level units of analysis.

Footnotes

Appendix

Standardized Factor Loadings (λ) From CFA.

Justice dimensions	Items	2010	2011	2012	2013
Distributive Justice	1	.84	.84	.84	.84
	2	.82	.83	.83	.83
	3	.72	.70	.70	.70
Procedural Justice	4	.62	.63	.62	.62
	5	.75	.75	.75	.76
	6	.82	.83	.82	.83
	7	.81	.80	.80	.82
Informational Justice	8	.84	.84	.85	.85
	9	.85	.86	.87	.87
	10	.80	.79	.80	.80
Interpersonal Justice	11	.93	.94	.94	.94
Interpersonal Justice	12	.91	.91	.91	.91

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

Kuk-Kyoung Moon is a research fellow at the Korea Institute of Public Administration in Korea. He earned his PhD degree in public administration and policy from the University of Georgia. His research interests include public organization theory, human resource management, and performance management.

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