Enhancing Motivation and Job Satisfaction of Police Officers: A Test of High Performance Cycle Theory

Abstract

Although goal-setting theory is one of the most examined motivation theories, few studies examine a theoretical framework of the high performance cycle (HPC) offered by Locke and Latham. Thus, the aim of this article is to examine the causes of job motivation and satisfaction within the framework of HPC. The data were gathered from 1,970 police officers working in various police departments in Turkey. Overall, the results of the study were consistent with the tenets of HPC. Results suggest that specific goals, self-efficacy, and feedback increase police officers’ job motivation, which leads to rewards and subsequently, job satisfaction among police officers. The results also suggest that job motivation has direct and indirect effects on job satisfaction. The practical implications of this study are to show that HPC is an effective and applicable framework to increase police officers’ job motivation and satisfaction.

Keywords

high performance cycle job satisfaction motivation goal commitment

Introduction

Work motivation (O’Reilly, 1991; Pinder, 1998; Steers, Mowday, & Shapiro, 2004) and job satisfaction (Rainey, 2014; Spector, 1997) are two of the most researched topics in the field of organizational studies. Work motivation is described as “a person’s desire to work hard and work well to the arousal, direction, and persistence of effort in work settings” (Rainey, 2014, p. 263). One of the major goals of public administration research is the focus on how to motivate public employees and enable them “to work energetically and intelligently towards achieving public purposes” (Behn, 1995, p. 315). The existing research on policing indicates that motivation is related to positive outcomes among police officers. For example, it was found that higher levels of motivation were related to the intention to stay in the police force and lower levels of reported daily hassles (Otis & Pelletier, 2005). In another study, higher levels of self-determined motivation were related to vigor (high levels of energy and mental resilience), dedication (being strongly involved in one’s work), and absorption (being fully concentrated and happily engrossed in one’s work; Gillet, Huart, Colombat, & Fouquereau, 2013).

Locke (1976) defined job satisfaction as a “pleasurable or positive emotional state resulting from the appraisal of one’s job or job experiences” (p. 1300). The existing research suggests that job satisfaction is related to job performance, employee turnover, absenteeism, and the health and well-being of employees (Landy & Conte, 2013; Spector, 2012). Research in policing shows that job satisfaction is related to turnover intention (Allisey, Noblet, Lamontagne, & Houdmont, 2014; Brough & Frame, 2004), emotional exhaustion and efficiency (Manzoni & Eisner, 2006), alcohol use disorders (Davey, Obst, & Sheehan, 2001), and engagement with work (Brunetto, Teo, Shacklock, & Farr-Wharton, 2012). The end result is that officers’ job satisfaction offers practical guidelines for improving effectiveness and well-being of individuals (Judge & Klinger, 2008). Given the importance of these two factors, our understanding is still limited about the predictors of job satisfaction in policing (Miller, Mire, & Kim, 2009; Zhao, Thurman, & He, 1999).

Accordingly, the purpose of the present study is to test a model that explains the causes of work motivation and satisfaction among police officers. Specifically, we intend to examine and provide insight into the relationship between work motivation and job satisfaction. In this article, we introduce our guiding theoretical model and arguments that support it. Then, we apply the theoretical framework to the data. Finally, we present the results and conclusions that emerge from this study.

Theoretical Framework

Prior research identifies “hundreds of motivational theories” (Landy & Conte, 2013, p. 320) that explain the antecedents of work motivation and job satisfaction. Researchers used Herzberg’s (1968) two-factor theory of motivation and Hackman and Oldham’s (1980) job characteristics model to examine the predictors of job satisfaction in policing (Zhao et al., 1999). Another modern motivation theory that explains motivation and job satisfaction is Locke and Latham’s (1990a) goal-setting theory. Although goal-setting theory did not receive much attention in the field of policing, in the field of organizational behavior, it is one of the most researched theories (Mitchell & Daniels, 2003; O’Reilly, 1991). Pinder (1998) referred to goal-setting theory as one of the major, most effective, and suitable modern theories of work motivation.

Although the goal-setting theory first emerged in the 1960s, Locke and Latham’s (1990a) contribution with their formal model did not occur until 1990. Locke and Latham (1990a; Locke, 1991) combined several motivation theories to connect and explain the interrelation between work motivation and job satisfaction in the high performance cycle (HPC). According to Latham and Locke (2007, p. 291), HPC offers a solution to “century-old debate” regarding the relationship between job satisfaction and performance—in other words, whether motivation causes job satisfaction or vice versa. The assumption of the theory is that the goals related to a task influence people’s performances and choices (Locke & Latham, 1990a). Furthermore, the theory stresses that difficult goals and specific goals lead to higher task performance relative to laidback or imprecise goals (Locke, 1966; Locke & Latham, 1990a, 1990b). Higher task performance results in higher job satisfaction if it is perceived as meaningful and is rewarded either internally or externally (Latham, Locke, & Fassina, 2002).

HPC propositions begin with demands consisting of the specificity of the goal, goal difficulty, and self-efficacy, which affect employees’ motivation (Latham & Locke, 2007; Locke & Latham, 1990a). Five factors—namely, ability, commitment, feedback, task complexity, and situational constraints—moderate the relationship between demands and motivation. In addition, four mediators of goal setting—choice, effort, persistence, and strategy—explain why or how goals increase job performances (see Latham et al., 2002; Locke & Latham, 1990a). In sum, HPC proposes that specific, difficult goals with self-efficacy produce higher performance, resulting in contingent and noncontingent rewards and, subsequently, satisfaction and commitment to the organization and its goals (Locke & Latham, 1990b). Furthermore, satisfaction loops back to employees accepting future challenging goals; thus, the name “high performance cycle” (Latham, Borgogni, & Petitta, 2008, p. 389).

Prior Research

Although goal-setting theory is one of the most studied motivation theories, as mentioned earlier, only a couple of studies examine HPC empirically. The first study examining HPC was conducted by Selden and Brewer (2000). They made a cross-sectional design with a sample of 2,474 senior executives working for the federal government in the United States. They used the Office of Personnel Management survey to examine HPC and chose items among the survey measures that reflect the description of HPC variables. In other words, they used approximate measures for each variable in HPC. The results provide empirical support for HPC. However, Latham and Locke (2007) noted that the findings must be viewed with caution because of the abovementioned limitations of the study.

The second study intended to examine HPC was conducted by Borgogni and Russo (2013), who administered a survey to 322 middle managers in a telecommunications organization in Italy. In this effort, Borgogni and Russo tested HPC only partially, the motivation/performance sequence, with a subsample of the same group. However, they did not examine job satisfaction and organizational commitment in their study. Thus, the results support the validity of HPC for the first part. Aside from the abovementioned studies, the remaining studies on goal-setting theory examine individual facets of HPC (Latham & Locke, 2007; Latham et al., 2002).

Since the origins of the theory, researchers have tested how goal difficulty and goal specificity have affected employees’ performance. The meta-analyses (Guzzo, Jette, & Katzell, 1985; Mento, Steel, & Karren, 1987; Tubbs, 1986; Wood, Mento, & Locke, 1987) and experimental and field studies (Brown & Latham, 2000; Bryan & Locke, 1967; Latham & Baldes, 1975; Latham & Locke, 1975; Morisano, Shore, Hirsh, Peterson, & Pihl, 2010; Smith, Locke, & Barry, 1990; Webb, Jeffrey, & Schulz, 2010; Wright, 2004) point out that within the goal-setting context, two of the most significant predictors of employee performance are goal difficulty and goal specificity.

According to Latham and Locke (1991), commitment refers to “the degree to which the individual is attached to the goal, considers it significant or important, is determined to reach it, and keeps it in the face of setbacks and obstacles” (p. 217). Locke (2001) and Locke and Latham (1990a, 1990b) argued that in the absence of employee commitment, whether the goal is a specific or challenging objective, employees do not show a higher level of performance within the goal-setting model. Locke (2001) and Locke and Latham (2002) suggested that two factors are critical to goal commitment. The first factor relates to the relevant or significant value of a goal to a person. The second factor is self-efficacy, meaning “being capable or attaining or making substantial progress toward the goal” (Locke, 2001, p. 46). In other words, people should be convinced that they have the ability to accomplish the goal fully or at least partially. Bandura (1995) defined self-efficacy as “beliefs in one’s capabilities to organize and execute the courses of action required to manage prospective situations” (p. 2). The existing literature indicates that self-efficacy is related to motivation and performance (Bandura & Cervone, 1983; Bandura & Wood, 1989; Stajkovic & Luthans, 1998).

Within the goal-setting framework, “feedback” refers to evidence employees have about “the degree to which the standard is being met,” as such feedback improves work performance in a couple of ways (Latham & Locke, 1991, p. 226). First, when individuals recognize that their performance is below par, they become dissatisfied and this, in turn, pushes them to improve their performance. Second, individuals with a higher sense of self-efficacy are more likely to increase their performance to meet or surpass standards. Finally, if individuals are aware of their past performance level, they might set more difficult goals to improve upon their past performance. The existing research shows that feedback has a positive effect on performance (Guzzo et al., 1985; Ilgen & Moore, 1987).

In sum, very little research has examined HPC comprehensively with variables that were originally created to measure the constructs of the perspective. Given the limited contributions to HPC literature, it could be argued that our understanding of HPC requires further inquiry. Thus, this article fills the void and contributes to the literature by examining HPC to demonstrate the precise nature of the relationships among motivation, rewards, and job satisfaction. However, because of the limitation of data, this study examines only the contribution of goal specificity, goal difficulty, self-efficacy, commitment, and feedback to HPC but prevents us from testing the impact mediators have on HPC. Although the HPC framework was developed to understand the means to enhance employee performance (Latham et al., 2008; Locke & Latham, 1990a, 1990b), this perspective “provides a framework for understanding motivation in the work place based on goal theory and, in addition, provides a basis for making interventions” (Latham & Locke, 2007, p. 291). This is especially important for employees of the public sector, where they mostly produce services (Perry & Porter, 1982). Constraints like “(1) jobs for which performance criteria cannot be readily defined or measured and (2) conflicting criteria for superior performance” prevent managers and researchers from defining and measuring public sector employees’ performance (Perry & Porter, 1982, p. 91). Police organizations are no different, and the same arguments are relevant to this sector as they are part of public sector organizations. Thus, this article also will contribute to public sector literature. This study uses police officers’ self-reported motivation rather than their actual performances to examine HPC.

Present Study

Method

Survey Construction and Sample

A survey instrument was developed and administered to assess the proposed HPC perspective to measure the direct and indirect effects of various factors on motivation, rewards, and satisfaction. The survey instrument was developed based on previously used and validated scales from prior research. The survey questionnaire was first developed in English and it was translated into Turkish and then translated back into English to check for conceptual and content equivalence. Researchers (Harkness, 2008) have noted that there could be some limitations with back-and-forth translation methodology. However, Cha, Kim, and Erlen (2007) have emphasized that back translation of an instrument is essential for the validation of translated survey items and is used widely for cross-cultural studies. We measured the attitudinal scale items on a 5-point Likert-type scale from 1 (strongly disagree) through 5 (strongly agree).

The sample for the study consisted of Turkish National Police (TNP) officers who, for the most part, work in 81 cities and border gates. In rural areas where the gendarmerie is responsible, the access to those officers for research purposes is limited and requires permission from either the governor or the courts. The chief administrator of the TNP is the governor who is appointed by the Turkish government. The administrator oversees a very centralized police bureaucracy and is responsible for its administration across the country.

Istanbul Security Directorate (ISD), one of the provincial and the biggest police departments serving the largest segment of the population in Turkey, was chosen as the research site. The ISD oversees all departments, including stations and border gates, and all police officers. A purposive sample of 2,500 from more than 10,000 police officers who worked in various departments was drawn for the purposes of this study. Officers assigned to plain clothes, public order, and the airport were among the departments where the survey was administered.

The questionnaire was administered to the officers in their respective departments with prior notice seeking their participation in the study between January 30, 2012, and February 20, 2012. Questionnaires with a consent form were given to the participants to fill out under the guidance of representatives from the research department, instead of their respective supervisors. A locked collection box for completed surveys was placed in each room in which the survey was administered to ensure that the participants did not perceive possible pressure from their supervisors. The total number of returned surveys was 2,132, representing a response rate of 85%.

From among these, a total of 1,970 usable cases were left after the data-cleaning process. From among these, there were a few of these surveys containing missing values. As a general rule, “variables containing missing data on 5% or fewer of the cases can be ignored” (Meyers, Gamst, & Guarino, 2005, p. 59) and can be included in the analyses. None of the variables in our data had greater than 5% of missing values and they were therefore included in the analyses. We employed the mean substitution approach, a process by which the researcher assigns the mean value of the variable for all missing values of that specific variable and that is also one of “the most common and most conservative of the imputation practices” (Meyers et al., 2005, p. 63). The age of the police officers in the sample ranged from 20 to 55 years (M = 27 years). Overall, 97% of the participants were male. The officers’ education levels ranged from a high school diploma to a baccalaureate degree: only 3% had a high school education; approximately 35% had a 2-year college degree; and 62% of them had a baccalaureate degree.

Measures

Job Satisfaction

Job satisfaction was measured with two items (Boke & Nalla, 2009; Brayfield & Rothe, 1951). The scale assesses the degree to which respondents agree or disagree with the following statements: “If I had the opportunity to go back to the day I decided to become a police officer, I would choose to become a police officer again” and “I really look forward to coming to work every day.” Both items in the scale had loadings of .86 and had a Cronbach’s alpha of .64.

Work Motivation

Participants indicated how involved they are and how hard they work toward their job using a work motivation scale employed and validated by Wright (2004). The work motivation scale consists of four items. Questions included officers’ perceptions of the effort that goes into getting the job done regardless of the level of difficulty, willingness to start the work day early and/or stay late to finish a job, whether it is hard to get very involved in the current assignment, and if time seems to drag while on the job, which was reverse coded. The four items in the motivation scale were, “I put in my best effort to get my job done regardless of the difficulties,” “I am willing to start work early or stay late to finish a job,” “It has been hard for me to get very involved in my current assignments” (reverse coded), and “Time seems to drag while I am on the job” (reverse coded). The items in the scale loaded in the range of .66 to .74 and had a Cronbach’s alpha of .67.

Rewards

Drawing from Locke and Latham’s (1990a) prior research, we measure police officers’ sense of work-related rewards with five items. Participants were asked to indicate the possibility of being rewarded when they perform well or accomplish their work objectives. The five items are, “When I improve my performance, my accomplishments are recognized by my supervisors”; “I have seen good job performance rewarded in my work unit”; “If I accomplish my work objectives, it increases my chances to get extra money rewards or letter of commendation”; “If I accomplish my work objectives, it increases my chances to choose people I work with”; and “If I accomplish my work objectives, it increases my chances to be assigned a better department.” The items in the scale loaded in the range of .75 to .83 and had a Cronbach’s alpha of .84.

Goal Difficulty

Goal difficulty and goal specificity were measured with scales that were developed and validated by Wright (2004). Goal difficulty scales consisted of five items to measure participants’ sense of how difficult their jobs are in general. This variable included responses to questions such as if work objectives in their job require a great deal of effort, a high degree of skill, and know-how, and if the job is demanding or challenging and the officers have new and interesting things to do at work. The five items in this scale are, “The work objectives in my job require a great deal of effort,” “A high degree of skill and know-how is necessary to do my job well,” “Jobs like mine are quite demanding day after day,” “My work is very challenging,” and “I have new and interesting things to do in my work.” The items in the scale loaded in the range of .63 to .77 with one item with .43. The Cronbach’s alpha for this scale was .67.

Goal Specificity

Goal specificity sought responses on four questions, such as if responsibilities are clear and specific, understanding which job duties are more important than others, a comprehension of what exactly they are supposed to do on the job, and how the job is done. In addition, the variables also contained officers’ perceptions of whether the supervisor clearly explains goals. The four items in this scale were, “My responsibilities at work are very clear and specific,” “I understand fully which of my job duties are more important than others,” “I know exactly what I am supposed to do on my job,” and “My supervisor clearly explains to me what my goals are.” The items in the scale loaded in the range of .64 to .80. The Cronbach’s alpha for this scale was .71.

Self-Efficacy

Police officers’ sense of self-efficacy was measured drawing from the work of Wright’s (2004) research on self-efficacy built on the contributions of Sims, Szilagyi, and McKemey (1976). The scale was composed of four items. The questions tapped into officers’ responses relating to their confidence in successfully performing tasks assigned to them on the job, how well they are prepared to handle the job, if they get their work done on time, and if doing their job well leads to high quality results. The four items measuring participants’ self-efficacy were, “I am confident that I can successfully perform any tasks assigned to me on my current job,” “I am not as well prepared as I could be to meet all the demands of my job” (reverse coded), “I can’t get my work done on time even when I try very hard” (reverse coded), and “Doing my work as well as I am able to leads to high quality results.” The items in the scale loaded in the range of .67 to .74 and had a Cronbach’s alpha of .67.

Goal Commitment

Participants’ behavior regarding goal commitment was measured using Hollenbeck, Klein, O’Leary, and Wright’s (1989) goal-commitment scale and Locke and Latham’s (1990a) goal-setting questionnaire. However, instead of using the originally developed nine-item scale, only five items on the scale were included, as suggested by Klein, Wesson, Hollenbeck, Wright, and DeShon (2001). Questions tap into how strongly officers feel committed to pursuing and doing their assignments well. In addition, three questions, which were reverse coded, tap into domains that gather their views on if they care to achieve their responsibilities, if they fail in their responsibilities, and if it is hard to be involved in things that a task requires. The specific questions included in this scale were, “It’s hard for me to take the kinds of things I must do in my position” (reverse coded); “Quite frankly, I don’t care if I achieve my responsibilities or not” (reverse coded); “I am strongly committed to pursuing assignments given to me”; “I am very committed to doing my assignments well”; and “I sometimes fail to accomplish my assignments” (reverse coded). The items in the scale loaded in the range of .58 to .74 and had a Cronbach’s alpha of .70.

Feedback

Whether participants were provided feedback was measured with two items from Wright (2004). The scale consisted of the following items: “I get helpful information from others about how well I am performing at my job” and “I receive useful evaluations of my strengths and weaknesses at work.” Both items in the scale had loadings of .87 and had a Cronbach’s alpha of .69.

We employed structural equation modeling (SEM) to test our hypothesized model. SEM is a useful tool to combine other analyses like factor analysis, canonical correlation, and multiple regression (Tabachnick & Fidell, 2013). It also allows researchers to examine whether the model provides a reasonable fit to data; contributions of direct and independent effects of independent variables on multiple dependent variables at the same time; and comparisons of alternative models (Tabachnick & Fidell, 2013). In addition, researchers can have a comprehensive picture of the entire model (Gefen, Straub, & Boudreau, 2000). In sum, SEM provides a richer and more complete understanding of complex models and theories (Bullock, Harlow, & Mulaik, 1994) and offers a better understanding of HPC.

Results

Table 1 represents the reliability estimates and zero-order correlation among the study items. Reliability estimates for the study items (Cronbach’s alpha), as noted above, ranged from .64 to .84. Only three of the items in this study have higher Cronbach’s alpha values than the conventional acceptance level of .70. The values for the other five study items range between .64 and .69, which are below this level. However, Cortina (1993) noted that the number of the items in the factor affects Cronbach’s alpha value depending on the items used. The lowest Cronbach’s alpha value of .64 belongs to job satisfaction scale consisting of only two measures. Furthermore, Kline (2016) noted that when sample size is large, lower levels of score reliability can be tolerated in latent variable models. All the correlations among the study items are significant at p < .001. Furthermore, all correlations are positive, as expected. From the framework of HPC, the moderator variables are positively and significantly related to work motivation (r = .20 for goal difficulty, r = .45 for goal specificity, r = .44 for self-efficacy, r = .43 for commitment, and r = .29 for feedback, which are significant at p < .001). The results also indicate a positive and significant relationship between motivation and rewards (r = .29, p < .001) and rewards and job satisfaction (r = .39, p < .001).

Table 1:

Means, Standard Deviations, Reliabilities,^a and Intercorrelations^b Among High Performance Cycle (HPC) Dimensions (N = 1,970)

No.	Scales	M	SD	1	2	3	4	5	6	7	8
1	Job Satisfaction	6.45	2.08	(.64)
2	Motivation	15.25	3.29	.36	(.67)
3	Rewards	15.18	5.14	.39	.29	(.84)
4	Goal Difficulty	17.41	3.87	.16	.20	.18	(.67)
5	Goal Specificity	14.85	3.41	.32	.45	.38	.25	(.71)
6	Self-Efficacy	15.75	3.09	.17	.44	.10	.17	.37	(.67)
7	Commitment	20.00	3.75	.15	.43	.07	.13	.36	.65	(.70)
8	Feedback	7.00	1.91	.30	.29	.35	.28	.32	.28	.21	(.69)

Values in parentheses indicate the reliability score for the scale. ^bAll intercorrelations are significant at p < .001.

Before testing the relationship among the factors and HPC, the measurement model was evaluated. Kline (2016) suggested using a model chi-square (χ²), root mean square error of approximation (RMSEA), Bentler comparative fit index (CFI), and standardized root mean square residuals (SRMR) as the minimum set of statistics to be reported for evaluation of model fits. In addition to these statistics, the adjusted goodness of fit index (AGFI) is also provided. It is suggested that values below .10 for RMSEA, values below .08 for SRMR, and values equal to or above .90 for CFI and AGFI indicate a good fit for the data (Kelloway, 2015).

The measurement model demonstrated that it did not have a good fit, χ²(406) = 2,523.60, p < .001; CFI = .87; AGFI = .90; RMSEA = .05; SRMR = .06. A review of the standardized estimates revealed a correlation value of .95 between self-efficacy and goal commitment, which is an indication of multicollinearity. Kline (2016) offered two basic options to address multicollinearity. One method is to eliminate one of the variables or combine the measures into one factor. As previously validated measures were used in this study and the two concepts are distinct, we prefer to eliminate goal commitment from the study. Self-efficacy was kept in the model, as Latham et al. (2008, p. 388) indicated that specific, difficult goals and self-efficacy contribute significantly to high performance.

The results of the alternative model without goal commitment indicate a substantial decrease in χ², as expected, and suggest the data fit the model well, χ²(278) = 1,911.46, p < .001; CFI = .88; AGFI = .91; RMSEA = .06; SRMR = .06. However, the model was modified by adding three additional covariances between the error terms of measurement items (specifically Items 2 and 3 of self-efficacy, Items 4 and 5 of rewards, and Items 3 and 4 of goal difficulty) based on the modification indices. The results of the modified model suggest an improvement on fit indices and adequate fit for the model, χ²(275) = 1,640.25, p < .001; CFI = .90; AGFI = .92; RMSEA = .05; SRMR = .06. The χ² difference test between the third model and the first model, Δχ²(131) = 883.35, p < .001, and the third model and the second model, Δχ²(3) = 271.21, p < .001, showed that eliminating goal commitment and adding three additional covariances among the error terms resulted in a significant and substantial increase in model fit. Hence, we selected the third model to examine HPC.

Next, we examined the structural model of HPC. The results suggest that the model overall fit the data, χ²(284) = 1,990.48, p < .001; CFI = .87; AGFI = .90; RMSEA = .06; SRMR = .07. The only fit index not consistent with a good model fit is CFI (.87). Wang and Wang (2012, p. 36) noted that using a large number of indicators per factor has advantages and disadvantages. The advantage of using more indicators per factor is to provide a more precise estimate. However, using more indicators has negative effects on some model fit indices, including CFI. As noted earlier, previously validated scales designed to measure HPC constructs were used in this study and, thus, we kept them in the analyses of HPC in the study.

However, a close examination of the modification indices suggests a need for model respecification. Based on the theoretical arguments, we added a direct path between motivation and job satisfaction. The path is consistent with the theoretical framework of HPC. As noted earlier, job satisfaction is the subsequent result of performance and motivation (Latham & Locke, 2007; Locke & Latham, 1990a). The result of the respecified model achieved a better model fit, χ²(283) = 1,870.16, p < .001; CFI = .88; AGFI = .91; RMSEA = .05; SRMR = .06. The χ² difference test between the respecified model and original model, Δχ²(1) = 120.32, p < .001, also confirm the model improvement significantly.

The respecified structural model suggests that aside from the path of goal difficulty to motivation, all structural parameters in the model consistent with our hypotheses were statistically significant (see Figure 2). In the first part of HPC, goal specificity (β = .40, p < .001), feedback (β = .16, p < .001), and self-efficacy (β = .33, p < .001) had a positive and significant effect on motivation. In the second part of HPC, motivation was significantly related to rewards (β = .47, p < .001). Finally, motivation (β = .40, p < .001) and rewards (β = .36, p < .001) have a positive and significant effect on job satisfaction. However, goal difficulty had no significant effect on police officers’ work motivation (β = .05). The model, overall, explains 59% of the variance in motivation (R² = .59), 22% of the variance in rewards (R² = .22), and 42% of the variance in job satisfaction (R² = .42).

Figure 1.

Hypothesized Model of High Performance Cycle (HPC)

Figure 2.

Results of the Respecified Model

Discussion

Prior research studies on HPC are limited in a couple of ways. First, researchers used different constructs to test HPC, and, second, they examined HPC partially. As a result, no prior research has been conducted to date that was specifically designed to test HPC directly. The research presented here contributes to the existing literature on HPC and enhances our understanding of the relationship between motivation, rewards, and job satisfaction. Despite the goal to test the model according to Locke and Latham’s (1990a) suggestion, limitation in data prevented us from including all the variables in this study. Notwithstanding, this study is one of the few studies designed to measure HPC directly and holistically as well as to measure its application to police organizations. Findings from this study suggest that nearly 60% of the variance in police officers’ motivation and 42% of the variance in job satisfaction were explained by HPC. As expected and hypothesized, job motivation was found to be significantly related to rewards and rewards were found to be significantly related to job satisfaction. These findings have a number of important implications for our theoretical understanding of HPC and for police managers to find ways to improve officers’ job satisfaction and motivation.

From a theoretical point of view, the results provided here support HPC. Few research studies have examined HPC and limited evidence was evident regarding the validity of HPC from these studies. In addition, these studies suffer from several methodological limitations, as discussed earlier. The present study extends our understanding related to the utility and validity of HPC. Our study explicitly shows that enhancing police officers’ work motivation and job satisfaction are possible within the HPC framework. This is very important, because the model used here offers insight into the possible processes that enhance police officers’ work motivation and job satisfaction. Relatedly, to date, most of the studies conducted within the goal-setting framework focus on performances of employees (Latham & Brown, 2006). Thus, another theoretical contribution of this study to both goal-setting theory and HPC is to provide evidence that HPC can be used to enhance job satisfaction of employees.

A noteworthy conclusion from this study is the additional finding of the link between work motivation and job satisfaction. That is, in addition to HPC (Locke & Latham, 1990a) framework hypothesizing work motivation affects employees’ job satisfaction through rewards, findings from this study indicate that work motivation has a direct effect on job satisfaction as well. Expectancy-based theories argue that “satisfaction follows from the rewards produced by performance” (Judge, Thoresen, Bono, & Patton, 2001, p. 378). This argument is consistent with HPC, as this perspective is developed from several motivation theories, including expectancy theory (Locke, 1991). Thus, the findings presented here also contribute to expectancy-based motivation theories’ argument of the relationship between job satisfaction and work motivation in general.

From a pragmatic perspective, the results reported here are encouraging in that HPC is a straightforward and strong intervention tool for police managers to enhance their officers’ work motivation and job satisfaction. This study provides evidence that HPC is related to police officers’ work motivation and job satisfaction. This is especially important as job satisfaction as a research topic relative to other fields has received less attention in policing (Miller et al., 2009; Zhao et al., 1999). In addition, researchers who study police officers’ performances mostly rely on aggregate data from variables such as reported crime rates, overall arrests, and clearance rates (Alpert & Moore, 1998; Mastrofski, 1996). Furthermore, most of this prior research has examined either job satisfaction or police performances and motivation exclusively. However, findings from this study suggest that police managers might benefit from using HPC to maximize police officers’ work motivation and job satisfaction simultaneously.

The findings presented here suggest that police managers should aim their intervention efforts at redesigning work by (a) giving especially specific tasks to police officers, (b) helping police officers to gain self-efficacy, and (c) providing an environment where police officers can get regular feedback about their performance. While most police agencies have mechanisms in place for providing evaluation procedures and protocols, offering accuracy as well as frequency of feedback rituals can be tweaked to enhance the intervention efforts. In doing so, it would improve police officers’ motivation levels and subsequently result in greater job satisfaction.

Considering that the nature of police work is not easily measurable, like patrolling streets or surveilling people, specific goals lead police officers to know exactly what they are supposed to do during their work time. Specific tasks allow police officers to organize and regulate their time and resources. Specific goals help police officers to “eliminate ambiguity and reduce the leeway for idiosyncratic interpretations” (Locke, 2001, p. 45). In addition to giving specific goals, providing feedback allows managers to convey information to the police officers about desired outcomes of their performance. Employees may not know whether they are on target without useful and helpful feedback (Locke & Latham, 2002). Feedback allows employees to adjust their performance, and police managers can play an important role in improving police officers’ motivation.

The results from this study suggest that self-efficacy, consistent with the HPC perspective, had a positive effect on police officers’ motivation. Locke and Latham (2002, p. 708) listed three items to improve subordinates’ self-efficacy: ensuring adequate training, being or showing a role model, and communicating managers’ confidence in their subordinates. It is argued that one of the tools that managers can use is training to change attitudes of police officers (Palmiotto, Bizer, & Prabha Unnithan, 2000). Thus, police managers should provide and design in-service training to improve their officers’ skills and knowledge. Second, police managers can show officers their recognition of things that work well and those that did not. Setting such a communication channel could also lead managers to earn police officers’ trust and respect.

However, the present study failed to show that goal difficulty increases employee motivation. The findings presented here suggest that goal difficulty was not significantly related to work motivation in the structural model. Findings presented in Table 1 indicate that goal difficulty was significantly related to work motivation of police officers. It could be argued that goal difficulty lost its power at the multivariate analyses because of goal specificity. However, Locke (2001) suggested combining goal specificity and goal difficulty to maximize performance and motivation. If a task is very specific but very easy, employee performance and motivation will be low (Locke, 2001). Even though the results indicate that goal difficulty is not significant in the model, police managers give specific, difficult goals that are attainable to police officers to maximize their performance and motivation.

There are some limitations that should be addressed. First, Latham and Locke (2007) noted that HPC requires longitudinal design rather than cross-sectional design to examine a causal motivational cycle. As the study was cross-sectional in design, we were unable to test the motivational cycle of HPC. Second, data limitation from the present study prevented us from assessing one of the outcomes of HPC—that is, commitment to the organization and acceptance of future challenging goals. Third, the present study did not include mediator variables in addition to task complexity to examine HPC, again, in tune with the data limitation. Thus, a longitudinal design should be conducted with all variables to examine HPC fully. Fourth, instead of using police officers’ actual performances, this study used a motivation scale representing police officers’ perceptions of given tasks. Thus, it could be argued that it was subject to bias. Future research may use and measure police officers’ actual performances. Finally, as we noted earlier, some of the scales employed in this study have slightly lower Cronbach’s alpha values below the conventional threshold levels. Thus, future research may be cognizant of this limitation. Despite these limitations, our study is the first to test the expanded model of HPC perspectives relative to those available to date and its application to policing organizations.

In sum, the findings are consistent with the originally proposed HPC model overall and offer support to the theoretical framework as outlined by Locke and Latham (1990a). This study shows that HPC is a useful framework in showing police managers’ specific processes by which police officers’ job satisfaction and motivation can be increased. The results revealed that HPC, although originally designed for application in the private sector, can be applied to the public sector as well. This line of reasoning is important, as it is argued that public organizations are often beset with vague, complex, and conflicting goals (Rainey & Bozeman, 2000). Furthermore, public sector organizations are expected to improve their productivity and reduce their costs (Wright, 2001). As discussed by Latham et al. (2008), goal-setting theory and HPC could be a good starting point to increase public sector employee performances and job satisfaction.

Footnotes

Ismail C. Demirkol, PhD, was a major at Turkish National Police. Since earning a doctorate degree in criminal justice, he has conducted research on policing and police culture. He has also conducted research on migration and xenophobia.

Mahesh K. Nalla is professor at the School of Criminal Justice at Michigan State University in East Lansing, USA. His current cross-national research projects are centered on (a) regulation and legitimacy of public and private police, and (b) gendered spaces and victimization.

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