employee empowerment, employee attitudes, and performance: testing a causal model

Sergio Fernandez is associate

professor in the School of Public and

Environmental Affairs at Indiana University

Bloomington. His research focuses on

privatization and government outsourcing,

organizational change, leadership, and

employee empowerment.

E-mail: [email protected]

Tima Moldogaziev is assistant profes-

sor of public fi nance and public manage-

ment at the University of South Carolina.

His research focuses on organizational

behavior as it relates to empowerment,

innovation, and performance in the public

sector, contracting, and resource manage-

ment. He also publishes on matters of

subnational debt management, fi nancial

intermediation and regulation, and munici-

pal securities pricing and liquidity.

E-mail: [email protected]

490 Public Administration Review • May | June 2013

Public Administration Review,

Vol. 73, Iss. 3, pp. 490–506. © 2013 by

The American Society for Public Administration.

DOI: 10.1111/puar.12049.

Sergio FernandezIndiana University, Bloomington

Tima MoldogazievUniversity of South Carolina

Th e last three decades have witnessed the spread of employee empowerment practices throughout the public and private sectors. A growing body of evidence suggests that employee empowerment can be used to improve job satisfaction, organizational commitment, innovativeness, and performance. Nearly all previous empirical studies have analyzed the direct eff ects of employee empowerment on these outcome variables without taking into account the mediating role of employee attitudes. Th is article contributes to the growing literature on employee empow-erment by proposing and testing a causal model that estimates the direct eff ect of employee empowerment on performance as well as its indirect eff ects as mediated by job satisfaction and innovativeness. Th e empirical analy-sis relies on three years of data from the Federal Human Capital Survey/Federal Employee Viewpoint Survey and a structural equation modeling approach, including the use of lagged variables. Th e results support the hypoth-esized causal structure. Employee empowerment seems to have a direct eff ect on performance and indirect eff ects through its infl uence on job satisfaction and innovative-ness, two key causal pathways by which empowerment practices infl uence behavioral outcomes.

The intellectual roots of employee empower-ment stretch back decades to the advent of the human relations movement in organiza-

tion theory (Herrenkohl, Judson, and Heff ner 1999). From the 1940s through the 1970s, ideas regarding employee empowerment were treated “at best as interesting fodder for academic debates” (Potterfi eld 1999, 30) or at worst as “socialism, democracy gone wild, or worse yet, a form of communism” (Lawler 1986, 9). During the 1980s, however, global compe-tition and strong pressure to continuously improve quality led many American fi rms to adopt employee empowerment programs (Bowen and Lawler 1992, 1995; Conger and Kanungo 1988; Lawler, Mohrman, and Ledford 1995; Potterfi eld 1999; Spreitzer 1995, 1996; Th omas and Velthouse 1990). Empowerment gained currency among government reformers as well, fi guring prominently in the New Public Management reforms undertaken in the United Kingdom,

Australia, Canada, France, Sweden, Norway, and the United States, where it became one of the four guiding principles of the Bill Clinton administration’s National Performance Review (Gore 1993; Kettl 2005; Matheson 2007; Peters 1996; Pollitt 1990; Wise 2002).

A growing literature indicates that employee empow-erment is positively related to performance (Fernandez and Moldogaziev 2011; Kirkman and Rosen 1999; Lawler, Mohrman, and Ledford 1992, 1995; Lee, Cayer, and Lan 2006; Nielsen and Pedersen 2003; Spreitzer 1995). Related empirical studies show a positive link between employee empowerment and several important work-related attitudes, includ-ing innovativeness (Fernandez and Moldogaziev 2013; Spreitzer 1995), job satisfaction (Bowen and Lawler 1992; Davies, Laschinger, and Andrusyszyn 2006; Fulford and Enz 1995; Kim 2002; Klecker and Loadman 1996; Kuokkanen, Leino-Kilpi, and Katajisto 2003; Lawler, Mohrman, and Ledford 1992, 1995; Lee, Cayer, and Lan 2006; Sarmiento, Laschinger, and Iwasiw 2004; Savery and Luks 2001; Seibert, Silver, and Randolph 2004; Ugboro and Obeng 2000; Wright and Kim 2004; Wu and Short 1996), organizational commitment (Guthrie 2001; Kirkman and Rosen 1999; Lawler, Mohrman, and Ledford 1992, 1995), and job involvement (Coye and Belohlav 1995). Nearly all of these empirical studies analyzed the direct eff ects of employee empower-ment on performance and work-related attitudes independently of each other and without consider-ing indirect or mediating eff ects. However, employee empowerment theory points to a more complex causal structure, with employee empowerment practices infl uencing the performance of employees directly as well as indirectly, as mediated by the attitudes of those employees (Bowen and Lawler 1995; Conger and Kanungo 1988; Spreitzer 1995, 1996; Th omas and Velthouse 1990). Other notable areas of research in management, including motivation theory (see Latham 2012) and leadership theory (see Bass and Bass 2008), point to a similar causal structure, with

Employee Empowerment, Employee Attitudes, and Performance: Testing a Causal Model

Employee Empowerment, Employee Attitudes, and Performance: Testing a Causal Model 491

From a managerial perspective, employee empowerment is a relational construct that describes how those with power in organi-zations share power, information, resources, and rewards with those lacking them. Th e intellectual origins of this construct date back to seminal contributions to the human relations movement in organization theory (e.g., Argyris 1957; Follett 1926; Likert 1967; McGregor 1960; Potterfi eld 1999). Kanter (1979) devel-oped a structural theory of organizational power that describes how power is derived from three sources: lines of supply, particularly to essential resources in the external environment; lines of information, including task-related knowledge, performance feedback, and other information about what is going on inside the organization; and lines of support, including top management support and discre-tion to engage in innovative behavior. To the extent that managers

provide employees with access to these three sources of power, they succeed in empower-ing them. More recently, Arnold et al. (2000) and Ahearne, Mathieu, and Rapp (2005) developed multidimensional defi nitions of employee empowerment that treat empow-erment as a leadership approach or style. Ahearne, Mathieu, and Rapp’s empowering leadership style involves leadership behaviors

that enhance the meaningfulness of work, foster participation in decision making, express confi dence in high performance, and pro-vide autonomy from bureaucratic constraints. Arnold et al. defi ned empowerment as an approach to leadership that includes the fol-lowing leadership behaviors: leading by example, involving others in decision making, coaching, informing, and showing concern for others.

Bowen and Lawler’s (1992, 1995) analysis of the burgeoning empowerment trend in the private sector built on Kanter’s notion of empowerment. Th ey acknowledged that a key ingredient of empowerment is sharing power and authority with lower-level employees and allowing them to make decisions about how services are rendered. However, they observed that “many empowerment programs fail when they focus on ‘power’ without also redistribut-ing information, knowledge and rewards” (1992, 32). According to them, employee empowerment is an approach to service deliv-ery that involves having managers share with their employees four organizational ingredients: “(1) information about the organization’s performance, (2) rewards based on the organization’s performance, (3) knowledge that enables employees to understand and contribute to organizational performance, and (4) power to make decisions that infl uence organizational direction and performance” (32).

Th e debate over the meaning of employee empowerment is ongo-ing, and trying to reach a resolution is beyond the scope of this article. Bowen and Lawler’s four-dimensional conceptualization of employee empowerment as a relational construct—or manage-rial approach—is chosen here, for several reasons. First, doing so allows this article’s results to be compared with and contribute to a growing body of evidence of the eff ects of employee empower-ment practices on employee attitudes and behavior (Bowen and Lawler 1992, 1995; Fernandez and Moldogaziev 2011; Kim 2002; Lawler, Mohrman, and Ledford 1992, 1995; Lee, Cayer, and Lan 2006; Mesch, Perry, and Wise 1995; Perry 2004; Pitts 2005; Savery and Luks 2001; Wright and Kim 2004). Second, employee

managerial interventions infl uencing employee attitudes, which, in turn, infl uence their behavior.

Th is article contributes to the literature on employee empowerment in the public sector in several ways. First, public management scholars have typically treated employee empowerment as a unidimensional construct and measured it using a single indicator (e.g., Lee, Cayer, and Lan 2006; Mesch, Perry, and Wise 1995; Perry 2004; Pitts 2005; Wright and Kim 2004). In this article, however, a multidimensional measure of employee empowerment based on Bowen and Lawler’s (1992, 1995) conceptualization of empowerment as a multifaceted managerial approach is developed and validated using data from the Federal Human Capital Survey/Federal Employee Viewpoint Survey (FHCS/FEVS). Th is represents an important step forward in the conceptualization and measurement of employee empowerment as a theoretical construct in the public management literature. Second, this article develops and tests a causal model that estimates the direct as well as the indirect eff ects of employee empowerment on performance. Th is allows for exploring the role of two mediating variables or causal path-ways—innovativeness and job satisfaction—by which empowerment practices lead to improvements in performance (Bowen and Lawler 1995; Spreitzer 1995). Previous contributions to the public management literature have only analyzed the direct eff ects of employee empowerment on performance (Fernandez and Moldogaziev 2011; Lee, Cayer, and Lan 2006), job satisfaction (Lee, Cayer, and Lan 2006; Wright and Kim 2004), and innovativeness (Fernandez and Moldogaziev 2013), overlooking the mediating role played by employee cognition and aff ect. Finally, the article employs an innovative approach to structural equation modeling (SEM) along with FHCS/FEVS data from multiple points in time to create time lags that allow for more accurate estimates of causal eff ects (see Biddle and Marlin 1987; Gollob and Reichardt 1987).

The Employee Empowerment ConstructScholars have made signifi cant headway in developing the construct of employee empowerment; however, they continue to disagree about what employee empowerment actually means (Conger and Kanungo 1988; Potterfi eld 1999; Th omas and Velthouse 1990). Two distinct theoretical perspectives have emerged in the litera-ture: a psychological and a managerial one. From a psychological perspective, empowerment is a motivational construct akin to a state of mind or a set of cognitions. Conger and Kanungo (1988) described empowerment as heightened belief in the ability to per-form, echoing Bandura’s (1986) notion of a self-effi cacy expectation (see also Lawler 1973). Building on that conceptualization, Th omas and Velthouse (1990) defi ned empowerment as a heightened level of intrinsic task motivation or internalized commitment to a task as evident in four assessments of that task: impact, competence, meaningfulness, and choice. To the extent that an employee makes positive assessments of these four aspects of the task, he or she will feel greater intrinsic task motivation and become empowered. Spreitzer (1995, 1996) described employee empowerment as a four-dimensional motivational construct composed of four cognitions—meaning, competence, self-determination (Th omas and Velthouse’s “choice”) and impact—that refl ect an active rather than a passive orientation toward a work role.

Th is article develops and tests a causal model that estimates

the direct as well as the indirect eff ects of employee empower-

ment on performance.


innovativeness, including both encouragement to innovate and actual innovative behavior. Granting discretion to employees is particularly important for initiation of innovation, as it provides autonomy to act in new and creative ways that depart from stand-ard operating procedures (Kanter 1982; Pierce and Delbecq 1977). Training and development can serve as channels for the diff usion of innovations as employees learn about and introduce ideas applied successfully in other organizations. Training and development improve an employee’s ability to diagnose and solve technical prob-lems (Damanpour 1991; Hurley and Hult 1998; Katz and Tushman 1981; Th ompson 1965), thus increasing the odds that innovative proposals will be designed and implemented successfully (Dewar and Dutton 1986; McGinnis and Ackelsberg 1983). Goal setting conveys organizational priorities and encourages achievement-ori-ented employees to seek out new strategies and tactics for attaining those goals. Importantly, negative feedback indicative of failure can induce a search for innovative solutions to problems (Cyert and March 1963; Fernandez and Wise 2010; Manns and March 1978). Spreitzer, De Janasz, and Quinn (1999) showed that empowered managers are more likely to be perceived as innovative and inspira-tional by employees than other managers.

Hypothesis 2: Employee empowerment will have a positive eff ect on innovativeness.

Empowerment and Job SatisfactionLarge longitudinal studies conducted during the 1990s showed that empowerment and high-involvement management practices are

eff ective at improving job satisfaction (Lawler, Mohrman, and Ledford 1992, 1995). Several studies on the use of empowerment in public organizations indicate that an employee empowerment approach is among the strong-est predictors of job satisfaction for public employees (Lee, Cayer, and Lan 2006; Wright and Kim 2004). Empowerment practices are designed to incentivize employees through their infl uence on diff erent intrinsic (feed-back, autonomy) and extrinsic (merit-based

pay, training opportunities) job characteristics. Research based on the job characteristics model (Hackman and Oldham 1976) has shown consistently strong correlations between intrinsic job characteristics and job satisfaction and other employee attitudes, particularly when subjective measures of intrinsic job characteristics are used (Fried and Ferris 1987; Glick, Jenkins, and Gupta 1986; Glisson and Durick 1988). Th e general argument is that jobs that are intrinsically rewarding increase employee satisfaction by enrich-ing work, making it more challenging and fulfi lling (Hackman and Oldham 1976). Over the course of several decades, eff orts to improve quality of life in the workplace in both the public and private sectors have emphasized employee participation, feedback, and other empowering interventions to promote job satisfaction and overall well-being (Berg 1997; Davis 1977; Davis and Churns 1975). More recent empirical research grounded in self-determi-nation theory suggests that factors such as feedback, training and development opportunities, and delegation promote satisfaction of the psychological needs for autonomy and competence, in turn increasing satisfaction and well-being (Deci, Connell, and Ryan 1989; Deci et al. 2001; Gagne and Deci 2005; Illardi et al. 1993).

empowerment conceived of as a managerial approach resonates with normative theory in public administration calling for democratization of the workplace, sharing of power with employ-ees, and fl attening of bureaucratic hierarchies (Denhardt 1984; Golembiewski 1965, 1972; Kirkhart 1971). Th ird, empowerment as a relational construct—or managerial approach—directly points to a set of recognizable and widely understood practices or levers that managers can pull to infl uence employee behavior. Finally, even though the relational construct was chosen over the psycho-logical one, this article still draws critical insight from research on employee empowerment as a psychological construct, particu-larly the notion that cognitive and aff ective responses mediate the relationship between empowerment practices and perform-ance (Conger and Kanungo 1988; Spreitzer 1995; Th omas and Velthouse 1990).

The Direct and Indirect Effects of Employee Empowerment on PerformanceA causal model of employee empowerment’s direct and indirect eff ects on performance is presented in this section. An employee empowerment approach, as conceptualized by Bowen and Lawler, is hypothesized to have a direct eff ect on performance as well as indirect eff ects, as mediated by innovativeness and job satisfaction, two related but separate constructs that have been widely studied by management and organizational behavior scholars. Each of the links in this hypothesized causal structure is described here.

Empowerment and PerformanceAn employee empowerment approach com-posed of practices aimed at sharing information, rewards, job-related knowledge, and authority with employees is expected to be positively related to performance. Th e added discretion granted to empowered employees provides them with the fl exibility to adapt to unfore-seen circumstances, improve the quality of interactions with service recipients, and make more productive use of their time (Bowen and Lawler 1992, 1995; Langbein 2000). Discretion is particularly important for performance as task complexity and environmental turbulence increase (see Burns and Stalker 1961; Landau and Stoudt 1979), as these conditions place a premium on the ability to adapt. Empowerment also enhances the technical knowledge and capabilities of employees, enabling them to perform tasks more eff ectively (Bowen and Lawler 1992, 1995; Lawler, Mohrman, and Ledford 1995). Goal setting and feedback, activities emphasized in an empowerment approach, also have a signifi cant bearing on employee eff ort and performance. A wide range of studies have shown that setting goals, especially more challenging and specifi c ones, results in increased eff ort and persistence in the face of setbacks (Latham and Locke 1991; Locke and Latham 1990). Finally, performance feedback alerts employees of errors and provides suggestions for correcting them.

Hypothesis 1: Employee empowerment will have a positive eff ect on performance.

Empowerment and InnovativenessEmpirical evidence points to a strong relationship between the empowerment practices described by Bowen and Lawler and

An employee empowerment approach composed of practices aimed at sharing information,

rewards, job-related knowledge, and authority with employees is expected to be positively related

to performance.


of an innovation (Bolton 1993) and for problem-oriented innova-tions, which are justifi able in the short run and linked directly to a problem (Cyert and March 1963).

Hypothesis 5: Performance will have a negative eff ect on innovativeness.

Job Satisfaction and PerformanceFor many decades, job satisfaction and morale have been studied as antecedents of individual performance. Th e most comprehensive meta-analysis of empirical studies on this subject shows that job sat-isfaction and performance correlate at about the r = .30 level, with higher correlations for more complex jobs (Judge et al. 2001). An earlier meta-analysis indicated that the strength of the job satisfac-tion–performance relationship varies by aspect of job, with much lower correlations for satisfaction with pay and higher correlations with intrinsic job characteristics (Iaff aldano and Muchinsky 1985). Job satisfaction can directly improve performance by improving levels of energy, activity, and creativity and by enhancing memory and analytical abilities (Brief and Weiss 2002; Isen and Baron 1991; Judge et al. 2001). It can also improve performance indirectly by increasing organizational commitment and organizational citizen-ship behavior and reducing turnover and absenteeism (Cooper-Hakim and Viswesvaran 2005; Dalal 2005; Harrison, Newman, and Roth 2006; Judge et al. 2001; LePine, Erez, and Johnson 2002; Meyer et al. 2002).

Hypothesis 6: Job satisfaction will have a positive eff ect on performance.

Measurement, Data, and MethodsTh is section describes the variables, data, and statistical techniques used in the analysis.

VariablesTh e four main variables in the analysis are empowerment, innovative-ness, job satisfaction, and performance. All four variables are treated as latent variables measured using multiple observable indicators.

Th e latent variable empowerment represents a multifaceted manage-ment approach composed of four practices: providing information about goals and performance (practice 1), off ering rewards based on performance (practice 2), providing access to job related knowledge and skills (practice 3), and granting discretion to change work proc-esses (practice 4) (see Bowen and Lawler 1992, 1995). It is measured using four observable indicators representing the four practices listed earlier. Each of these observable indicators is in the form of a summated scale created from multiple survey items from the 2010 FEVS. Th e Cronbach’s alphas for these scales range from .74 for practice 3 to .88 for practice 2. A higher-order confi rmatory factor analysis (CFA) of this four-dimensional defi nition of an employee empowerment approach provides sound evidence of both conver-gent and discriminant validity.1 All observable indicators used to measure latent variables are shown in appendix 1. Th e results of the higher-order CFA appear in appendix 2.

Th e latent variable innovativeness is measured using two observable indicators from the FHCS/FEVS: encouragement to innovate and innovative behavior. Th ese two indicators capture both a feeling

Intrinsic job characteristics appear to have a stronger impact on job satisfaction than extrinsic ones (Deci 1972; Judge et al. 1998; Mottaz 1985; O’Reilly and Caldwell 1980). Th is is not to suggest, however, that introducing extrinsic rewards such as merit-based pay, a key empowerment practice, will not improve job satisfaction. Indeed, a large body of research shows that pay and other extrinsic rewards can be used eff ectively to increase eff ort, performance, and job satisfaction (Green and Haywood 2008; Lawler, Mohrman, and Ledford 1992, 1995; Mottaz 1985; O’Reilly and Caldwell 1980), even among public sector employees with higher levels of public service motivation (Alonso and Lewis 2001; Perry, Mesch, and Paarlberg 2006; Rainey 1982; Wittmer 1991; Wright 2007).

Hypothesis 3: Employee empowerment will have a positive eff ect on job satisfaction.

Innovativeness and PerformanceIn a dynamic external environment, more adaptable organizations capable of undergoing changes in function and form tend to perform better and are more likely to survive. Innovation represents a vital form of organizational learning and adaptation (Simon 1997). Product and technological innovations continue to be key sources of performance improvement and competitive advantage for private sector fi rms (Christensen 1997; Fagerberg, Mowery, and Nelson 2006; Porter 1985). New Public Management reforms, especially those undertaken in the United States, Australia, and the United Kingdom, have stressed innovation as a way to improve public sector performance (Australian National Audit Offi ce 2009; Bartos 2003; Breul and Kamensky 2008; Gore 1993; Kamensky 1996; Kettl 2005; O’Flynn 2007; Pollitt and Bouckaert 2004). Th e importance of innovation has prompted governments in those and other countries to develop separate appraisal and reward systems that operate parallel to annual performance appraisal and merit pay systems in order to evaluate the effi cacy of innovative ideas and reward employees for developing them (Fernandez and Pitts 2011). Th e benefi ts of organi-zational change and innovation are not always realized, however, as many innovative ideas are poorly conceived and implemented (Hartley 2005). Organizational change can be very disruptive, adversely aff ecting performance to the point of organizational decline and death (Amburgey, Kelly, and Barnett 1993). In short, the impact of innovativeness on performance should be positive in the long term but marginal or even negative in the short term until new processes can be learned and institutionalized (Fernandez and Rainey 2006).

Hypothesis 4: Innovativeness will have a positive eff ect on performance.

Th e relationship between performance and innovativeness may be simultaneous, with poor or substandard performance encouraging innovative behavior. As Cyert and March argued, “Failure induces search and search ordinarily results in solutions. Consequently, we would predict that, everything else being equal, relatively unsuc-cessful fi rms would be more likely to innovate [that is, come up with new solutions to a problem] than relatively successful fi rms” (1963, 188). Research by others supports the notion that necessity is the mother of invention (Borins 2012; Chandler 1977; Fernandez and Wise 2010; Manns and March 1978; March and Simon 1993; Williamson 1975). Th e link between poor performance and inno-vativeness appears to be particularly strong among early adopters


subagency level of analysis and estimating relationships among vari-ables across three points in time (2008, 2010, and 2011). Doing this signifi cantly diminishes the sample size from approximately 200,000 observations (individuals) to just 228 observations (subagencies), but it also reduces bias in estimating causal eff ects.

Th e nonrecursive model of the eff ects of empowerment on innova-tiveness, job satisfaction, and performance has the following structure:

Four empowerment practice equations:

xpractice 1 = λpractice 1 ξempowerment + δ1 (1)




Innovativeness equations:

yencouragement to innovate = λencouragement to innovate ηinnovatiness + �1 (5)

yinnovative behavior= λinnovative behavior ηinnovatiness + �2 (6)

Job satisfaction equations:

ysatisfaction with organization = λsatisfaction with organization ηjob satisfaction + �3 (7)

ysatisfaction with job = λsatisfaction with job ηjob satisfaction + �4 (8)

Performance equations:

ywork unit performance = λwork unit performance ηperformance + �5 (9)

yagency performance = λagency performance ηperformance + �6 (10)

Measurement models for outcomes:

ηinnovatiness = γempowerment ξempowerment + βperformance ηperformance + ζ1 (11)

ηjob satisfaction = γempowerment ξempowerment + ζ2 (12)

ηperformance = γempowerment ξempowerment + βinnovativenessηinnovativeness

+ βjob satisfaction ηjob satisfaction + ζ3 (13)

In matrix notation, the set of equations is restated as,

(14)

(15)

(16)

Model Covariance/Correlation Matrix and the Fitting Function:2

Th e implied covariance matrix in the model has the following components:

(17)

of encouragement to innovate (see Locke and Latham 2004) and innovative behavior on the part of the employee.

Th e latent variable job satisfaction is measured using two observ-able indicators from the FHCS/FEVS: satisfaction with job and satisfaction with organization. Th ese are global measures of overall job satisfaction and do not capture satisfaction with particular aspects of work (e.g., pay, benefi ts, and promotional opportunities). Summated rating scales created from indicators of satisfaction with diff erent aspects of work do not correlate very highly with global measures of overall job satisfaction, causing some scholars to ques-tion the validity of using such scales to measure overall satisfaction (Judge and Church 2000).

Th e latent variable performance is measured using two observable indicators from the FHCS/FEVS: work unit performance and agency performance. Th ese are perceptual and internal measures of work unit performance and agency performance. Th e limitations to using perceptual and internal measures of performance are discussed later.

DataTh e data for the analysis are from the 2008 Federal Human Capital Survey and the 2010 and 2011 Federal Employee Viewpoint Surveys conducted by the U.S. Offi ce of Personnel Management (OPM). Th ese surveys were administered electronically via the Internet (with limited distribution of paper surveys to those without Internet access) to 540,727, 504,609, and 417,128 federal employees across fi ve supervisory levels ranging from nonsupervi-sors to senior executives in 2008, 2010, and 2011, respectively. Th e response rates were 51 percent (212,223 respondents), 52 percent (263,475 respondents), and 49 percent (266,376 respondents) in 2008, 2010, and 2011, respectively. Respondents worked for more than 80 cabinet-level and smaller independent agencies representing approximately 97 percent of the executive branch workforce. Th e OPM used a stratifi ed sampling technique to produce generaliz-able results for each agency as well as for the entire federal govern-ment; in some of the smaller agencies, all employees were surveyed. Approximately a quarter of the respondents were dropped each year because of missing data on one or more variables. No meaning-ful diff erences were found between observations dropped from the analysis and those that were included.

ModelA series of structural equation models with the set of equations appearing later are developed and tested at two levels of analysis: the individual and subagency levels. Th e fi rst model, model 1, is tested using cross-sectional data from the 2010 FEVS at the individual level of analysis. Models 2, 3, and 4 are tested using lagged variables and data from the 2008, 2010, and 2011 FHCS/FEVS at the subagency level of analysis. One of the limitations of the structural equation modeling approach is that this kind of analysis is typically based on cross-sectional data that do not allow for time lags and, as a result, often produce biased estimates of causal eff ects (Biddle and Marlin 1987; Gollob and Reichardt 1987). Because the FHCS/FEVS sur-veys do not identify individual respondents, it is not possible to track them over time in order to test a model with lagged variables at the individual level of analysis. However, a model with time lags—with outcome variables measured at points in time after the independent variables—can be tested by aggregating individual respondents to the


not have origins or units of measurement. Means, variances, and covariances of ordinal variables have no meaning. To use ordinal variables in structural equations models requires other techniques than [the latent continuous approach requires]” (1994, 303). Moreover, Bollen (1989) specifi cally warns that the model covari-ance structure assumptions produce inconsistent estimates of true parameter values when categorical variables are involved. Hence, the structural measurement model is extended to accommodate categorical variables and to be able to report meaningful parameter values.

Consequently, because the covariance structure hypothesis is often violated with ordered variables (Bollen 1989), instead of the usual Pearson correlation matrix, a polychoric and polyserial correlation matrix is employed to fi t the model to the data. Th e polychoric correlation scores between the ordered variables in the model and the polyserial correlation scores between the ordered and continuous variables in the model are indeed greater than the Pearson counter-parts, as statistical theory predicts. Table 1 reports these correlation scores for all the observed variables in the model. In every instance in which an ordered variable is involved, attenuation in Pearson cor-relation scores is observed. Th us, there is indication that the covari-ance structure hypothesis may be violated in the data. Furthermore, ordered variables are more likely to be accompanied by skewness

(18)

(19)

Bollen (1989, 323–26) and Jöreskog (1994, 298–99) derive math-ematically the covariance matrix components in detail. Th e resulting covariance matrix is then,

(20)

Generally, estimations of the general structural model for continu-ous and normally distributed variables are then conducted by the maximum likelihood method (ML):3

(21)

However, the structural model utilizes a set of ordered variables that violate the basic assumption of continuous and normal distri-bution. Th erefore, to obtain unbiased estimates, one must correct for the defi ciencies that the linear structural models may not solve. Jöreskog argues that “[o]rdinal variables are not continuous vari-ables and should not be treated as if they are. Ordinal variables do

Table 1 Polychoric/Polyserial Correlation Matrix versus Pearson Correlation Matrix, and Descriptive Statistics (N = 197,446)

Polychoric/Polyserial Correlation Scores

Variables/Parameters 1 2 3 4 5 6 7 8 9 10

Empowerment 1 Practice 1 1.002 Practice 2 0.72 1.003 Practice 3 0.76 0.72 1.004 Practice 4 0.77 0.72 0.74 1.00Innovativeness 5 Encouragement to innovate 0.67 0.64 0.72 0.71 1.006 Innovative behavior 0.30 0.26 0.32 0.31 0.41 1.00Job satisfaction 7 Satisfaction with job 0.55 0.57 0.57 0.56 0.51 0.29 1.008 Satisfaction with organization 0.64 0.59 0.62 0.62 0.56 0.35 0.60 1.00performance 9 Work unit performance 0.69 0.63 0.70 0.73 0.66 0.38 0.58 0.62 1.0010 Agency performance 0.75 0.68 0.70 0.75 0.66 0.33 0.58 0.70 0.85 1.00Mean 0.00 0.00 0.00 0.00 3.67 4.42 3.87 3.68 4.29 3.98Standard deviation 0.85 0.92 0.83 0.79 1.14 0.67 1.01 1.06 0.78 0.87Variance 0.73 0.84 0.70 0.63 1.31 0.45 1.03 1.12 0.61 0.76Skewness –0.59 –0.41 –0.87 –0.54 –0.71 –1.13 –0.97 –0.79 –1.08 –1.09Kurtosis 0.03 –0.46 0.54 –0.29 –0.33 1.96 0.60 0.08 1.30 1.67

Pearson Correlation Scores

Variables/Parameters 1 2 3 4 5 6 7 8 9 10

Empowerment1 Practice 1 1.002 Practice 2 0.72 1.003 Practice 3 0.76 0.72 1.004 Practice 4 0.77 0.72 0.74 1.00Innovativeness5 Encouragement to innovate 0.64 0.62 0.70 0.69 1.006 Innovative behavior 0.26 0.23 0.28 0.27 0.31 1.00Job satisfaction7 Satisfaction with job 0.51 0.53 0.55 0.51 0.44 0.22 1.008 Satisfaction with organization 0.59 0.54 0.59 0.58 0.49 0.26 0.51 1.00performance9 Work unit performance 0.66 0.60 0.68 0.70 0.60 0.29 0.50 0.54 1.0010 Agency performance 0.72 0.65 0.68 0.73 0.60 0.25 0.49 0.61 0.77 1.00


(22)

With this function in place and the polychoric/polyserial correlation matrix computed, a categorical structural model can be fi tted to the data using equation 21.

ResultsTh e empirical analysis begins with a test of model 1, a nonrecur-sive structural equation model with cross-sectional data at the individual level of analysis (see fi gure 1). Th e results indicate that the data have a very strong fi t to the proposed causal model.5

Most of the hypothesized relationships are supported and statistically signifi cant (see table 2). Th e variable empowerment has sizable and highly signifi cant relationships with all three outcomes variables: innovative-ness, job satisfaction, and performance. When empowerment goes up by one standard devia-tion, job satisfaction goes up by 0.89 standard deviation, all else being equal (z = 444.50, p < .001). Similarly, a one standard deviation

increase in empowerment is associated with a 0.76 standard devia-tion increase in innovativeness, all else held constant (z = 76.25, p < .001). Th e relationship between empowerment and performance is the third largest in terms of magnitude of the eff ect, yet it is still rather sizable. A one standard deviation increase in empowerment results in a 0.55 standard deviation increase in performance, all else held constant (z = 66.39, p < .001).

and kurtosis in distribution. Byrne (2009), Muthen and Kaplan (1985), and Bollen (1989) suggest that skewness and kurtosis greater than the absolute value of unity tend to distort the results produced by FML using the usual Pearson coeffi cients, which is the standard method utilized for the continuous and normally distrib-uted variables. Skewness and kurtosis greater than ±1 are indeed present in some of the ordered variables. Descriptive statistics for the variables in the model are also provided in table 1.

A two-step procedure is followed to fi t the nonrecursive structural model of the eff ects of empowerment on innovativeness, job satisfac-tion, and performance, as discussed in Muthen and Kaplan (1985), Bollen (1989), Jöreskog (1994), Byrne (2009), and Kline (2011). Th e fi rst step involves estimating a polychoric/polyserial correlation matrix. Th is corrected matrix (correction is from the default Pearson scores) is then used in the maximum likelihood estimation of the model fi t in the second step. Bollen (1989) warns that the standard errors produced by the maximum likelihood method tend to be biased; therefore, we employ bootstrapped standard errors instead, as suggested by Fox (2009).

Because there are six ordered variables (observable indicators) in the model that are measured on a fi ve-point scale, the thresholds corresponding to the categorical distribution of these variables are also estimated (Bollen 1989; Jöreskog 1994).4 Th ese thresholds produce a nonlinear function that relates the categorical variables to the latent implied variables. Hence, for the y variables, the nonlinear function is,

Figure 1 Model 1: Nonrecursive Structural Model of the Effects of Empowerment on Innovativeness, Job Satisfaction, and Performance; Standardized Coeffi cients; Individual Level of Analysis (N = 197,446) (all variables from FEVS 2010)

Innovativeness(2010)

Job Satisfaction(2010)

Empowerment(2010)

Performance(2010)

Practice 2

Practice 1

Practice 3

Practice 4

Encouragementto Innovate

InnovativeBehavior

Satisfaction withJob

Satisfaction withOrganization

AgencyPerformance

Work UnitPerformance

.87

.82

.87

.88

.76

.89

.55

.90 .94

.09

.44

–.08

.95 .44

.73

.82

Th e variable empowerment has sizable and highly signifi cant relationships with all three outcomes variables: innova-tiveness, job satisfaction, and

performance.


Th e results suggest that when performance goes up by one standard deviation, innovativeness increases by 0.09 standard deviation, all else held constant (z = 8.19, p < .001). In short, hypotheses 1, 2, 3, and 6 are supported, while hypotheses 4 and 5 are rejected.

Th e correlation residual and standardized residual matrices are examined next to assess whether the model explains the cor-responding sample correlation suffi ciently well. Table 3 reports these estimated scores for the variables in the model. None of the unstandardized covariance residuals (absolute values of fi tted residu-als) is greater than the widely accepted threshold level of 0.10.6 Th us, the model implied correlation matrix explains suffi ciently well the sample correlation matrix. Th e choice of utilizing a polychoric/polyserial correlation matrix (for ordered-ordered/ordered-continu-ous variables) is justifi ed.

Th e various fi t statistics for the nonrecursive structural model, model 1, indicate fairly solid model fi t (see table 4). Th e Jöreskog-Sörbom goodness-of-fi t index (GFI) and the adjusted GFI are greater than the conventional 0.90 threshold, suggesting a strong model fi t. Similarly, the Bentler comparative fi t index (CFI), the Bentler-Bonner normalized fi t index (NFI), and the Tucker-Lewis index (TLI) all achieve the suggested values of greater than 0.90 (see Kline 2011; Maruyama 1998; Schumacker and Lomax 2004). Furthermore, the value of the root mean square residuals is equal to 0.08, which comfortably passes the 0.10 cutoff point for large samples (see Jaccard and Wan 1996; Kline 2011). Th e standardized root mean square residual index is 0.02, which is also indicative of a strong model fi t. Other fi t scalars such as the chi-squared statistic or the Bayesian information criterion (BIC) are highly signifi cant, thus suggestive of poor fi t (33,807 and 33,453, respectively). However, both of these values are a function of sample size. Given the extremely large sample size of almost 200,000 observations used in this article, these last two scalars should not diminish the fi t of the model.

Th is nonrecursive structural model, model 1, was selected over two other competing recursive models, all with cross-sectional data at the individual level of analysis. Th e fi rst was a recursive model, similar to model 1, but without the feedback loop from perform-ance to innovativeness. Th e second was a recursive model that had innovativeness, job satisfaction, and performance as outcomes of empowerment, where the outcomes were assumed to have no asso-ciations among themselves. Likelihood ratio tests were performed to evaluate these competing models.7 Th e results indicate that model 1, a nonrecursive structural model, is the preferred specifi ca-tion. In addition to likelihood ratio tests, the BICs for these models suggest very strongly that the recursive specifi cations are inferior to the nonrecursive model (see Burnham and Anderson 2004; Long 1997 for BIC criterion decisions). Th e absolute value of diff erence of BIC measures between the nonrecursive model and the other two models is signifi cant at p < .001, thus off ering support in favor of model 1.

Th e discussion now turns to the results of models 2, 3, and 4, recursive structural equation models with lagged variables at the subagency level of analysis. Th e use of lagged variables in SEM helps produce more accurate estimates of causal eff ects by allow-ing suffi cient time for causes to exert their eff ects and enabling the

Table 2 Categorical ML Estimates and Disturbance Variances for a Nonrecursive Model of the Effects of Empowerment on Innovativeness, Job Satisfaction, and Performance (Model 1) (N = 197,446)

ParameterUnstandardized

Coeffi cient SD CR pStandardizedCoeffi cient

Regression weightsPractice 1

empowerment1.06 0.00 474.15 *** 0.87

Practice 2 empowerment

1.00 0.82


1.06 0.00 470.09 *** 0.87


1.07 0.00 480.69 *** 0.88

Encouragement to innovate innovativeness

1.00 0.95

Innovative behavior innovativeness

0.46 0.00 170.86 *** 0.44

Satisfaction with job job satisfaction

0.96 0.00 643.28 *** 0.90

Satisfaction with organization job satisfaction

1.00 0.94

Work unit performance performance

0.89 0.00 335.59 *** 0.73

Agency performance performance

1.00 0.82

Innovativeness empowerment

0.88 0.01 76.25 *** 0.76

Job satisfaction empowerment

1.03 0.00 444.50 *** 0.89

Performance empowerment

0.56 0.01 66.39 *** 0.55

Performance innovativeness

–0.07 0.01 –9.74 *** –0.08

Performance job satisfaction

0.38 0.00 86.74 *** 0.44

Innovativeness performance

0.10 0.01 8.19 *** 0.09

Disturbance Variances

Empowerment 0.67 0.00 218.88 *** 1.000Practice 1 0.24 0.00 251.70 *** 0.24Practice 2 0.33 0.00 274.54 *** 0.33Practice 3 0.25 0.00 254.87 *** 0.25Practice 4 0.23 0.00 247.49 *** 0.23Encouragement to

innovate0.11 0.00 30.65 *** 0.11

Innovative behavior 0.81 0.00 302.45 *** 0.81Innovativeness 0.27 0.00 75.41 *** 0.30Satisfaction with job 0.19 0.00 209.03 *** 0.19Satisfaction with

organization0.12 0.00 141.63 *** 0.12

Job satisfaction 0.18 0.00 162.30 *** 0.20Work unit performance 0.47 0.00 253.85 *** 0.47Agency performance 0.32 0.00 187.29 *** 0.32Performance 0.13 0.00 88.08 *** 0.19

* p < .05; ** p < .01; *** p <.001.

Th e results also show that there is a signifi cant and sizeable posi-tive relationship between job satisfaction and performance. When job satisfaction goes up by one standard deviation, performance goes up by 0.44 standard deviations all else being equal (z = 86.74, p <≈.001). Innovativeness and performance are found to have statisti-cally signifi cant but substantively small reciprocal eff ects. A one standard deviation increase in innovativeness is associated with a 0.08 standard deviation drop in performance, all else being equal (z = –9.74, p < .001). At the same time, the eff ect of performance on innovativeness appears to be positive albeit still not very sizable.


time interval for the direct eff ect on empowerment on performance from three years to two. Finally, in model 4, the time interval for the direct eff ect of empowerment on performance is reduced to one year by measuring empowerment in 2010 and the three outcome vari-ables—innovativeness, job satisfaction, and performance—in 2011. It is expected that empowerment will continue to have positive eff ects on innovativeness, job satisfaction, and performance in these three models. However, the magnitude of the direct eff ect of empowerment on performance should vary according to the length of the interval between the two variables.

Th e results from models 2, 3, and 4 are identical to those from model 1 in terms of direction and statistical signifi cance when it comes to the relationship between empowerment and performance (positive and statistically signifi cant at the p < .001 level); the relationship between empowerment and innovativeness (positive and statistically signifi cant at the p < .001 level); the relationship between empowerment and job satisfaction (positive and statistically signifi cant at the p < .001 level); and the relationship between job satisfaction and performance (positive and statistically signifi cant at the p < .001 level). Th ese results provide additional support for hypotheses 1, 2, 3, and 6, respectively. Eff ect sizes do appear to vary, however, according to the time interval between variables. Importantly, the eff ect of empowerment on performance grows as the time interval between the variables shortens. When the interval is three years (model 2), the coeffi cient for the direct eff ect of empowerment on performance is 0.10. Reducing the interval to two years (model 3) increases the coeffi cient to 0.19. A further

Table 3 Categorical ML Residual Covariance Matrix for a Nonrecursive Model of the Effects of Empowerment on Innovativeness, Job Satisfaction, and Performance (Model 1) (N = 197,446)

Unstandardized Covariance Residuals

Variables 1 2 3 4 5 6 7 8 9 10

Empowerment1 Practice 1 0.002 Practice 2 0.00 0.003 Practice 3 0.01 0.01 0.0004 Practice 4 0.00 0.00 –0.02 0.00Innovativeness5 Encouragement to innovate –0.03 –0.01 0.03 0.01 0.006 Innovative behavior –0.02 –0.04 –0.00 –0.02 0.00 0.00Job satisfaction7 Satisfaction with job –0.01 –0.03 0.00 0.03 0.02 0.08 0.008 Satisfaction with organization 0.01 –0.01 –0.03 0.01 –0.01 0.02 0.00 0.00Performance9 Work unit performance –0.01 0.05 0.02 –0.01 0.00 0.06 0.01 –0.02 0.0010 Agency performance 0.01 –0.00 –0.00 –0.01 –0.01 0.08 –0.02 0.02 0.00 0.00

Standardized Covariance Residuals

Variables 1 2 3 4 5 6 7 8 9 10

Empowerment1 Practice 1 0.002 Practice 2 0.94 0.003 Practice 3 3.01 3.24 0.004 Practice 4 0.62 0.69 –6.57 0.00Innovativeness5 Encouragement to innovate –10.01 –4.73 11.65 3.32 0.006 Innovative behavior –9.66 –17.71 –0.79 –6.26 0.00 0.00Job satisfaction7 Satisfaction with job –2.85 –9.51 0.52 9.65 6.41 33.49 0.008 Satisfaction with organization 5.12 –2.80 –9.80 4.80 –5.26 9.56 0.00 0.00Performance9 Work unit performance –3.69 19.33 7.33 –3.39 1.73 24.34 3.49 –8.47 0.0010 Agency performance 1.84 –1.20 –3.49 –4.88 –3.74 35.79 –8.73 8.89 0.00 0.00

Table 4 Categorical ML Model Fit Statistics for Models of the Effects of Empowerment on Innovativeness, Job Satisfaction, and Performance

Fit Scalar

Model 1Individual Level, Nonrecursive,

FEVS 2010

Model 2Subagency Level, Recursive, FHCS

2008, FEVS 2010, FEVS 2011

Model 3Subagency

Level, Recursive, FHCS 2008,FEVS 2010

Model 4Subagency

Level, Recursive, FEVS 2010,FEVS 2011

χ² 33,807.00 204.20 204.20 224.40df 29 30 30 30p 0.00 0.00 0.00 0.00χ²/df 1,165.76 6.81 6.82 7.48BIC 33,450.00 339.97 338.43 360.11RMSEA 0.08 0.16 0.16 0.17SRMR 0.02 0.01 0.01 0.01GFI 0.97 0.90 0.90 0.91CFI 0.98 0.92 0.93 0.92NFI 0.98 0.91 0.92 0.91IFI 0.97 0.91 0.92 0.92TLI 0.97 0.88 0.89 0.88

researcher to determine how the size of an eff ect may depend on the length of the interval between independent and dependent variables (Gollob and Reichardt 1987). Th ese three models vary in terms of the length of the time intervals between variables. In model 2, empowerment is measured in 2008, the variables innovativeness and job satisfaction in 2010, and performance in 2011, thereby creating a time interval of three years for the direct eff ect of empowerment on performance. In model 3, empowerment is again measured in 2008, innovativeness and job satisfaction continue to be measured in 2010, but performance is also measured in 2010, thereby shortening the


Figure 3 Model 3: Structural Model of the Effects of Empowerment on Innovativeness, Job Satisfaction, and Performance; Standardized Coeffi cients; Subagency Level of Analysis (N = 228) (empowerment practices from FHCS 2008; innovativeness, job satisfaction, and performance from FEVS 2010)



Empowerment(2008)

Performance(2010)

Practice 2

Practice 1

Practice 3

Practice 4


InnovativeBehavior



AgencyPerformance


.90

.89

.91

.92

.76

.81

.19

.16

.69

1.05 .58

.82

.84

.93 .98

Figure 2 Model 2: Structural Model of the Effects of Empowerment on Innovativeness, Job Satisfaction, and Performance; Standardized Coeffi cients; Subagency Level of Analysis (N = 228) (empowerment practices from FHCS 2008; innovativeness and job satisfaction from FEVS 2010; performance from FEVS 2011)



Empowerment(2008)

Performance(2011)

Practice 2

Practice 1

Practice 3

Practice 4


InnovativeBehavior



AgencyPerformance


.90

.89

.91

.92

.74

.81

.10

.93 .98

.06

.76

1.08 .55

.75

.79

one-year decrease in the interval increases the coeffi cient to 0.28. Th is pattern of results suggests that the direct eff ect of empower-ment on performance is felt most in the short term and diminishes

in its intensity as time goes by. Th is is a reasonable fi nding insofar as one might expect the eff ectiveness of a managerial action to fade over time as resources are expended, conditions change,


and performance. Th ese important contribu-tions to the literature, together with those conducted in private sector organizations, shed light on pieces of the complex puz-zle that is employee empowerment. Th is is the fi rst study, however, to develop and test a model of employee empowerment in the public sector that accounts for the direct eff ect of an employee empowerment approach on performance as well as its indirect eff ects on

performance as mediated by employee job satisfaction and innova-tiveness. Th e empirical results generally support the causal model proposed here, including fi ve of the six hypotheses that were tested.

Th e results indicate that an employee empowerment approach com-posed of various practices aimed at sharing information, resources, rewards, and authority with employees has a direct and sizable positive eff ect on performance as perceived by employees. Th is fi nd-ing is in line with previous research from the private (Bowen and Lawler 1992; 1995; Lawler, Mohrman, and Ledford 1992; 1995) and public sectors (Fernandez and Moldogaziev 2011) showing that gains in performance derived from empowerment practices. As proposed, it is also found that an employee empowerment approach appears to indirectly aff ect performance through its infl uence on job satisfaction and innovativeness. Th e results suggest that the eff ect of employee empowerment on job satisfaction is positive and even stronger than empowerment’s direct eff ect on performance. Job sat-isfaction, in turn, has a positive eff ect on performance of a magni-tude similar to that shown in previous meta-analyses (see Judge et al. 2001). By increasing job satisfaction, then, the use of employee

and additional actions begin to exert their infl uence on performance.

Th e eff ects of empowerment on the mediating variables innovativeness and job satisfaction do not appear to vary much by the length of the time interval. No clear pattern emerges when it comes to the eff ect of job satisfaction on performance, although the eff ect is smaller when cross-sectional data are used (model 1) instead of lagged variables (models 2, 3, and 4). Previously, it was found that the results from model 1 rejected hypothesis 4, which states that innovativeness will have a positive eff ect on performance. However, the results from the three additional models all seem to support this hypothesis, as they show a small but positive relation-ship between these two variables. Th us, greater innovativeness on the part of empowered employees may indeed be a causal pathway by which empowerment practices increase performance, although job satisfaction appears to be the more important mediating variable.

Finally, the fi t statistics for models 2, 3, and 4 at the subagency level of analysis are generally in line with those for model 1 at the individual level of analysis, except for the root mean square error of approximate (RMSEA) (see table 4). Th us, aggregating the data does not appear to substantially aff ect model fi t despite the enormous reduction in sample size.

Discussion and ConclusionPrevious public management studies have examined the direct eff ects of employee empowerment on various work-related attitudes

Figure 4 Model 4: Structural Model of the Effects of Empowerment on Innovativeness, Job Satisfaction, and Performance; Standardized Coeffi cients; Subagency Level of Analysis (N = 228) (empowerment practices from FHCS 2010; innovativeness, job satisfaction, and performance from FEVS 2011)



Empowerment(2010)

Performance(2011)

Practice 2

Practice 1

Practice 3

Practice 4




AgencyPerformance


.91

.88

.90

.95

.70

.81

.28

.06

.66

1.13

InnovativeBehavior

.49

.74

.80

.92 .99

Th is pattern of results sug-gests that the direct eff ect of

empowerment on performance is felt most in the short term and diminishes in its intensity as

time goes by.


Another limitation of the study is the limited number of observable indicators used to measure some of the latent constructs. While most of the conventional fi t statistics point to a good model fi t, they also suggest room for improvement. In particular, the measurement model could be improved by using additional indicators to measure the latent constructs of innovativeness and performance.

Finally, the decision to use a perceptual and internal measure of per-formance in this study raises questions about its comparability with other measures of performance. Heneman’s (1986) meta-analysis of studies focusing mostly on private forms found a relatively weak correlation (r = .27, not statistically signifi cant) between perceptual and archival/behavioral measures, leading him to conclude that the two are not interchangeable. Bommer et al.’s (1995) meta-analysis found an overall higher but still modest correlation between the two types of measures (r = .30, statistically signifi cant), although the cor-relations were much higher (r = .71) when the same dimension of performance was considered (e.g., output). Conversely, other stud-

ies, including Nathan and Alexander (1988), produced little if any evidence of a signifi cant distinction between perceptual and archi-val/behavioral measures (in Bommer et al. 1995, 588–89). Among public management scholars, Brewer argued that studies show that “measures of perceived organizational performance correlate positively with moder-

ate to strong associations with objective measures of organizational performance” (2005, 511). Lewis (2008) found that politiciza-tion had a similarly negative eff ect on PART (Program Assessment Rating Tool) scores and FHCS indicators of performance like the ones used in this study.

Th e nature of the relationship between internal performance meas-ures (those developed by organizational members) and external ones (those developed by external stakeholders) must also be con-sidered. Walker and Boyne stated that “a range of evidence dem-onstrates that there are positive statistically signifi cant correlations between external and internal measures of overall performance, some in the region of r = .8” (2006, 378). Th ey noted, however, that those fi ndings are only achieved when measures of the same dimension of performance are used. In their own empirical analy-sis, they found that external and internal measures of performance had correlations ranging from .46 to .60, with the highest correla-tions among variables measuring technical aspects of organizational performance, such as outputs and effi ciency. In short, it is fair to conclude that although not interchangeable, perceptual and internal measures of performance are at least moderately correlated with archival/behavioral and external ones, respectively, and can serve as reasonable proxies for them, especially when the same dimension of performance is measured. Nevertheless, the fi ndings of this study would be strengthened by testing a similar model but with measures of performance that are archival/behavioral and/or external. Th e Barack Obama administration’s delay in developing a substitute for PART scores may prevent this from happening in the foreseeable future.

Notes1. A higher-order confi rmatory factor analysis was performed to assess the measure-

ment of Bowen and Lawler’s four-dimensional empowerment construct using

empowerment practices can also result in improved performance, in addition to these practices’ direct infl uence on performance.

Th e empirical results also show that an employee empowerment approach has a positive and sizeable eff ect on innovativeness, as hypothesized. Th is eff ect is larger than employee empowerment’s direct eff ect on performance. Innovativeness, in turn, seems to have a small positive eff ect on performance, as observed in most of the models that were tested. Th e eff ect appears to be quite small dur-ing the same year or even a year later. Th is suggests that the use of empowerment practices to stimulate innovation will not result in immediate gains in performance and that managers adopting such an approach must be patient for the organizational changes sparked by empowerment to bear fruit in the form of performance improve-ments (Fernandez and Rainey 2006).

Th e empirical analysis also off ered some evidence of a simultane-ous relationship between innovativeness and performance, with the latter having a small positive eff ect on the former. Th is suggests that it is success rather than failure that encourages one to become more innovative. Th ere are several possible explanations for this unexpected fi nding. It is important to note that nearly 70 percent of the survey respondents were federal employ-ees low on the organizational hierarchy (i.e., nonsupervisors and team leaders). At senior management levels, fail-ure may indeed induce a search for solutions, as Cyert and March (1963) argued. At the front lines, however, a mere sign of declining performance may not be enough to induce a search, as employ-ees wait for directives from above before undertaking meaningful changes. Also, performance problems may need to be acute before innovation is encouraged. Th e measures of performance used in this article, however, do not allow one to gauge the seriousness of prob-lems perceived by employees. Finally, the indicators used to measure performance capture work unit and organizational performance and not individual performance. For employees to feel the urge to innovate, their own performance may have to be inadequate, not just that of others around them.

Several limitations to this study point to the need for additional research on employee empowerment in the public sector. First, the models tested here represent an important step forward in that they go beyond testing for simple main eff ects by analyzing how employee attitudes mediate the relationship between employee empowerment practices and performance. However, because of the limitations posed by the data, only two mediating variables—inno-vativeness and job satisfaction—could be measured and included in the models. Further research is needed to examine how other employee attitudes such as self-effi cacy, organizational commitment, and public service motivation might mediate the eff ects of employee empowerment practices on performance. Importantly, the analysis did not account for the mediating role of psychological empower-ment, which some empowerment theorists argue is a direct conse-quence of relational empowerment (Conger and Kanungo 1988; Th omas and Velthouse 1990; Spreitzer 1995). In short, the models tested in this article represent a simplifi ed version of the employee empowerment process, and analysis of more comprehensive causal structures is warranted.

Th is suggests that it is success rather than failure that encour-

ages one to become more innovative.


Appendix 1 Variables and Measures

Employee empowermentPractice 1 (information about goals and performance). Measured using a summated rating scale created from the following three ordinal survey items: “Managers

review and evaluate the organization’s progress toward meeting its goals and objectives” (1 = strongly disagree to 5 = strongly agree); “Supervisors/team leaders provide employees with constructive suggestions to improve their job performance” (1 = strongly disagree to 5 = strongly agree); and “How satisfi ed are you with the information you receive from management on what’s going on in your organization?” (1 = very dissatisfi ed to 5 = very satisfi ed)?

Practice 2 (rewards based on performance). Measured using a summated rating scale created from the following four ordinal survey items: “Promotions in my work unit are based on merit” (1 = strongly disagree to 5 = strongly agree); “Employees are rewarded for providing high quality products and services to customers” (1 = strongly disagree to 5 = strongly agree); “Pay raises depend on how well employees perform their jobs” (1 = strongly disagree to 5 = strongly agree); and “Awards in my work unit depend on how well employees perform their jobs” (1 = strongly disagree to 5 = strongly agree).

Practice 3 (access to job related knowledge and skills). Measured using a summated rating scale created from the following three ordinal survey items: “I am given a real opportunity to improve my skills in my organization” (1 = strongly disagree to 5 = strongly agree); “The workforce has the job-relevant knowledge and skills nec-essary to accomplish organizational goals” (1 = strongly disagree to 5 = strongly agree); and “Supervisors/team leaders in my work unit support employee develop-ment” (1 = strongly disagree to 5 = strongly agree).

Practice 4 (discretion to change work processes). Measured using a summated rating scale created from the following ordinal survey items: “Employees have a feeling of personal empowerment with respect to work processes” (1 = strongly disagree to 5 = strongly agree); and “How satisfi ed are you with your involvement in decisions that affect your work?” (1 = very dissatisfi ed to 5 = very satisfi ed)?

InnovativenessEncouragement to innovate. Measured using the following ordinal survey item: “I feel encouraged to come up with new and better ways of doing things” (1 =

strongly disagree to 5 = strongly agree).Innovative behavior. Measured using the following ordinal survey item: “I am constantly looking for ways to do my job better” (1 = strongly disagree to 5 = strongly

agree).job satisfactionSatisfaction with organization. Measured using the following ordinal survey items: “Considering everything, how satisfi ed are you with your organization?” (1 =

very dissatisfi ed to 5 = very satisfi ed).Satisfaction with job. Measured using the following ordinal survey item: “Considering everything, how satisfi ed are you with your job?” (1 = very dissatisfi ed to 5 =

very satisfi ed).PerformanceWork unit performance. Measured using the following ordinal survey item: “How would you rate the overall quality of work done by your work unit?” ( 1 = very

poor to 5 = very good).Agency performance. Measured using the following ordinal survey item: “My agency is successful at accomplishing its mission” (1 = strongly disagree to 5 = strongly

agree).

data from the 2010 FEVS. Multiple ordinal survey items shown in appendix 1 were used to measure the four empowerment practices. In the four-factor model, each of the survey items loaded strongly and in the anticipated direction with the corresponding factor (i.e., empowerment practice) (p < .001). Th ose four factors, in turn, have very strong positive correlations with a second-order factor representing the underlying construct of employee empowerment (p < .001). Th e statistics for several goodness-of-fi t indices support the four-factor model of empowerment. Th e statistics for CFI, which is minimally aff ected by sample size, is 0.94, indicating a good fi t for the four-factor model (Fan, Th ompson, and Wang 1999). Th e Jöreskog and Sörbom goodness-of-fi t index of 0.93 also suggests a good model fi t. Th e NFI statistic of 0.94 and the RMSEA of 0.09 both point to an acceptable fi t for the four-factor model (Schumacker and Lomax 2004). Complex models are more likely to generate better fi t statistics than parsimonious ones. Th erefore, it is recommended that models be subjected to goodness-of-fi t measures that penalize for lack of parsimony. Th e model with a four-factor structure has parsimony ratio (PRATIO) and parsimony normed fi t index (PNFI) statistics of 0.76 and 0.71, respectively, both of which are indicative of a reasonably parsimonious fi t. It should be noted that the chi-squared test results reject the four-factor model (67,091, n = 154,793, df = 50) at the p < .01 level. Large sample sizes such as the one used in this CFA are much more likely to result in Type II errors. Garson (2009) suggests, therefore, discounting the chi-squared results if other fi t statistics support a model with such a large sample size. In contrast to the evidence favoring a four-factor model of employee empower-ment, the higher-order CFA results reject a model with a one-factor structure. Th e CFI and NFI statistics for a one-factor model fail to reach the 0.90 cutoff point; both are only 0.89. And the RMSEA statistic (0.12) is above the conven-tional cutoff for even an adequate model fi t (Schumacker and Lomax 2004). In addition, a comparison of the four-factor and one-factor models, in terms of their Akaike information criterion (AIC) statistics, favors the former over the latter. Th e lower AIC statistic for the four-factor model (67,147.25) is considerably lower than the AIC statistic for the one-factor model (125,414.95), indicating a

signifi cantly better model fi t (Burnham and Anderson 2004; Long 1997). Finally, the absolute value of the diff erence in chi-squared statistics between the four-factor model (chi-squared = 67,091, n = 154,793, df = 50) and one-factor model (chi-squared = 125,367, n = 154,793, df = 54) is 58,276. Th is is indicative of a statistically signifi cant diff erence (p < .001) in support of the four-factor model. According to Fornell and Larcker (1981a, 1981b), average variance explained (AVE) statistics greater than 0.50 are indicative of convergent validity. Th e four empowerment practices have AVEs between 0.74 and 0.96. Discriminant validity is assessed by comparing the square root of the AVE of an empowerment practice to the correlations between that practice and the remaining practices. A square root of an AVE greater than the correlations between an empowerment practice and the remaining practices is indicative of divergent validity. Th e results show that the square root of AVE is greater than all the relevant correlations for all four empowerment practices, with diff erences ranging from 0.24 to 0.12.

2. Bollen (1989) states that the model must meet the t-rule for identifi cation. Th e t-rule, , is the necessary but not suffi cient condition for identifi cation. As further described by Bollen (1989) and Kline (2011), a two-step rule is employed in fi tting the model. In the fi rst step, the model is treated as a confi rmatory factor analysis to establish that all parameters are identifi ed. In the second step, the latent variable structures are assessed using a polychoric/polyserial matrix by an estimation method. If both steps show model parameter identifi cation, then the suffi cient condition that the entire model is identifi ed is met.

3. Alternatively, for continuous and normally distributed variables, a generalized least squares method may be employed, where

. However, it is generally said that the ML method is more effi cient than the generalized least squares approach with producing the estimates of standard errors.

4. Th ese thresholds are easily set/estimated by every SEM program given the distribution and characteristics in the data. [R] estimates the thresholds and the


polychoric correlations jointly from the bivariate marginal distribution. Jöreskog (1994) argues this is the most often used practice.

5. All SEM analyses are conducted in [R]. See Fox (2009) and Revelle (2007) for a discussion of the software and applications. Similar estimation methods with poly-choric/polyserial corrected correlation scores are present in LISREL and M-plus.

6. Please note that in table 3, the standardized residual covariance matrix contains a few signifi cant scores (ratios of covariance residuals over standard errors). Kline (2011, 171) argued that these results are quite normal for very large data sets. Th us, the focus remains on the unstandardized fi tted values.

7. For reasons of brevity, these model fi t results are omitted but are available from the authors upon request.

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Appendix 2. Higher-Order Confi rmatory Factor Analysis, Employee Empowerment

Empowerment

EmpowermentPractice 1


I1 I2 I3 I6 I5 I4 I7 I9 I10 I11 I12

.97.97.86.99

.73 .85 .80 .81 .88 .78 .88 .79 .59 .83 .84

I8

.87

CFI = 0.92 NFI = 0.92 GFI = 0.90RMSEA = 0.12 PRATIO = 0.76 PNFI = 0.71

Stand RMR = 0.04




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