EXAMINING THE ANTECEDENTS OF THE WORK MOTIVATION
IN THE CONTEXT OF TURKISH NATIONAL POLICE
By
Ismail Cenk Demirkol

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Criminal Justice
2012

ABSTRACT
EXAMINING THE ANTECEDENTS OF THE WORK MOTIVATION
IN THE CONTEXT OF TURKISH NATIONAL POLICE
By
Ismail Cenk Demirkol
The present study enhances our understanding of police officers’ work motivation in the
context of Turkey. An extensive literature review was devoted in order to develop a revised
model for examining the antecedents of the police officers’ work motivation. The model was
developed based on the existing literature and previous empirical evidence regarding Locke &
Latham’s (1990) goal-setting theory. The model not only helps us to have a better understanding
about antecedents of the work motivation but also portrays the relationship between work
motivation and related independent variables, including goal content, goal commitment, selfefficacy, feedback, rewards, and participatively-set goals.
The data for this study was collected using a self-administrated survey of police officers
who are assigned to formal police duties and as well as guarding the airport security under the
command of the Security Directorate of Istanbul. Each of the study items was measured and
multiple items were derived from previously validated studies. The survey was administered to
the police officers who work at Ataturk Airport, plain clothes, and the public order department.
Multivariate Ordinary Least Square (OLS) regression analyses were conducted to test the
hypothesized relationships between the independent and dependent variables. The results of the
study were mainly consistent with the goal-setting model. The results indicated that goal
difficulty, goal specificity, task significance, commitment, self-efficacy, and rewards were
related to police officers’ sense of motivation. Finally, recommendations for policy implications
and for future research were suggested based on the result of the study.

Copyright by
ISMAIL CENK DEMIRKOL
2012

Dedicated to Him
to my parents
to my wife Nesibe
and to my son Emir Kamil

iv

ACKNOWLEDMENT

I would like to express my appreciation to several people without whom my doctoral
studies and this dissertation would not have been possible. First and foremost, I would like to
thank my chair Dr. Mahesh K. Nalla for his patience, understanding, advice, encouragement and
contribution during my dissertation research and writing. I will never forget your acceptance of
being my chair to my dissertation at first instance. I would also like to acknowledge members of
my guidance and dissertation committee, each of whom contributed to my dissertation in several
ways. I deeply appreciate Dr. Edmund F. McGarrell for saving time for me despite his busy
schedule. I learned a lot from Dr. Charles Corley from sharing his wisdom about the life. Thanks
Dr. Steven Gold for sharing your knowledge and expertise long before serving in my dissertation
committee. I am so very thankful to Dr. Soma Chaudhuri who was always helpful and friendly.
Their supportive style has made the whole process a lot easier than it might have been. I would
also like to thank Dr. Daniel Ilgen who contributed to dissertation and advanced my
understanding on the topic. Very special thanks to the staff of the School of Criminal Justice at
Michigan State University. I always felt that they are very helpful and understanding.
I also need to acknowledge that my colleagues Dr. Ahmet Guler, Yusuf Erkan, and Murat
Hamarat who inspired me intellectually and helped me to collect data for this study. In terms of
valuable financial support and offering such an opportunity, I thank to Turkish National Police.
Without such a support, I could not dream of pursuing an academic degree in the US and afford
all the expenses.
During the graduate program, I had several friends with whom I shared the struggles and
challenges of doctoral education. I would like to acknowledge their constant support and

v

friendship. They will be always my lifetime friends. I also need to acknowledge my wonderful
friends Mustafa Gurkan, Abdullah Korucu, and Mustafa Kaya with whom I have shared most of
my life since the graduation from police academy. I know that they have been and will be always
there.
Finally, and most importantly, I would like to appreciate my parents and family who
always believed in my dream and was supportive to pursue my dream. I always have the support
of my mother and father. I thank my wife Nesibe for supporting my academic inspirations and
career by sharing the burdens and hardships of this journey. Without her help and understanding,
it might have taken more time to finish this journey. She provided me with the encouragement
and freedom to pursue and finish my degree. Thanks to my son Emir Kamil, who has brought
joy, cheer, and smile in my life. I am so thankful for your presence in my life.

vi

TABLE of CONTENTS

LIST OF TABLES ............................................................................................................. ix
LIST OF FIGURES ........................................................................................................... xi
CHAPTER 1 ....................................................................................................................... 1
INTRODUCTION .............................................................................................................. 1
1.1 Statement of the Problem .......................................................................................... 1
1.2 Purpose of the Study ................................................................................................. 4
CHAPTER 2 ....................................................................................................................... 7
LITERATURE REVIEW ................................................................................................... 7
2.1 Theoretical Framework: Goal-Setting Theory .......................................................... 7
2.2 Determinants of Work Motivation .......................................................................... 18
2.2.1 Goal Content ..................................................................................................... 18
2.2.2 Goal Commitment ............................................................................................ 24
2.2.3 Self-Efficacy ..................................................................................................... 29
2.2.4 Feedback ........................................................................................................... 33
2.2.5 Rewards ............................................................................................................ 36
2.2.6 Participatively-Set Goals versus Assigned Goals ............................................. 38
2.3 Conclusion............................................................................................................... 40
CHAPTER 3 ..................................................................................................................... 41
METHODOLOGY ........................................................................................................... 41
3.1 Research Questions ................................................................................................. 41
3.2 Survey Construction ................................................................................................ 43
3.2.1 Work Motivation .............................................................................................. 43
3.2.2 Goal Context ..................................................................................................... 44
3.2.3 Goal Commitment ............................................................................................ 45
3.2.4 Self-Efficacy ..................................................................................................... 45
3.2.5 Task Significance ............................................................................................. 46
3.2.6 Participatively-Set Goals .................................................................................. 46
3.2.7 Feedback ........................................................................................................... 46
3.2.8 Rewards ............................................................................................................ 47
3.2.9 Department Assignment ................................................................................... 47
3.2.10 Demographic Characteristics .......................................................................... 48
3.3 Sampling and Sampling Procedures ........................................................................ 48
3.4 Analytical Part ......................................................................................................... 51
CHAPTER 4 ..................................................................................................................... 53
ANALYSES and FINDINGS ........................................................................................... 53
4.1 Descriptive Statistics of Demographic Characteristics ........................................... 53
4.2 Descriptive Statistics of Police Officers’ Perceptions about the Survey Items....... 55
4.2.1 Work Motivation .............................................................................................. 56
vii

4.2.2 Goal Context ..................................................................................................... 57
4.2.3 Goal Commitment ............................................................................................ 59
4.2.4 Self-Efficacy ..................................................................................................... 60
4.2.5 Task Significance ............................................................................................. 61
4.2.6 Participatively-Set Goals .................................................................................. 62
4.2.7 Feedback ........................................................................................................... 63
4.2.8 Rewards ............................................................................................................ 64
4.2.9 Department Assignment ................................................................................... 66
4.3 Scale Constructions ................................................................................................. 67
4.3.1 Work Motivation Scale ..................................................................................... 67
4.3.2 Goal Context Scales.......................................................................................... 68
4.3.3 Goal Commitment ............................................................................................ 70
4.3.4 Task Significance ............................................................................................. 70
4.3.5 Self-Efficacy ..................................................................................................... 71
4.3.6 Feedback ........................................................................................................... 72
4.3.7 Participatively-Set Goals .................................................................................. 73
4.3.8 Rewards ............................................................................................................ 74
4.3.9 Department Assignment ................................................................................... 74
4.3.10 Descriptive Statistics of Dependent and Independent Variables .................... 75
4.4 Bivariate Analyses................................................................................................... 78
4.4.1 Correlation Analysis ......................................................................................... 78
4.4.2 ANOVA Analysis of Demographic Characteristics ......................................... 83
4.4.3 ANOVA Analysis of Dependent and Independent Variables .......................... 86
4.5 Multivariate Analysis .............................................................................................. 90
4.5.1 Goal Difficulty Model ...................................................................................... 92
4.5.2 Goal Specificity Model ................................................................................... 105
4.5.3 Combined Model ............................................................................................ 112
CHAPTER 5 ................................................................................................................... 120
DISCUSSION and CONCLUSION ............................................................................... 120
5.1 Discussion ............................................................................................................. 121
5.2 Implications of the Results .................................................................................... 133
5.3 Limitations and Future Research........................................................................... 137
5.4 Conclusions ........................................................................................................... 139
APPENDICES ................................................................................................................ 141
APPENDIX A: Consent Form .................................................................................... 142
APPENDIX B: Survey Instrument.............................................................................. 143
BIBLIOGRAPHY ........................................................................................................... 149

viii

LIST OF TABLES

Table 1. Descriptive Statistics of Demographic Characteristics ....................................... 54
Table 2. Descriptive Statistics for Police Officers’ Perceptions of Motivation ................ 57
Table 3. Descriptive Statistics for Police Officers’ Perceptions of Goal Specificity ....... 58
Table 4. Descriptive Statistics for Police Officers’ Perceptions of Goal Difficulty ......... 59
Table 5. Descriptive Statistics for Police Officers’ Perceptions about Commitment ....... 60
Table 6. Descriptive Statistics for Police Officers’ Perceptions about Self-Efficacy....... 61
Table 7. Descriptive Statistics for Police Officers’ Perceptions of Task Significance ..... 62
Table 8. Descriptive Statistics for Police Officers’ Perceptions of Participatively-Set
Goals ................................................................................................................................. 63
Table 9. Descriptive Statistics for Police Officers’ Perceptions about Feedback............. 64
Table 10. Descriptive Statistics for Police Officers’ Perceptions of Rewards ................. 65
Table 11. Descriptive Statistics for Police Officers’ Perceptions of Department
Assignment ....................................................................................................................... 66
Table 12. PCA and Cronbach’s α Scores for Motivation Scale........................................ 68
Table 13. PCA and Cronbach’s α Scores for Goal Context Scales .................................. 69
Table 14. PCA and Cronbach’s α Scores for Goal Commitment Scale ........................... 70
Table 15. PCA and Cronbach’s α Scores for Task Significance Scale............................. 71
Table 16. PCA and Cronbach’s α Scores for Self-Efficacy Scale .................................... 72
Table 17. PCA and Cronbach’s α Scores for Feedback Scale .......................................... 73
Table 18. PCA and Cronbach’s α Scores for Participatively-Set Goals Scale ................. 73
Table 19. PCA and Cronbach’s α Scores for Rewards Scale ........................................... 74
Table 20. PCA and Cronbach’s α Scores for Department Significance Scale ................. 75
Table 21. Descriptive Statistics of Dependent and Independent Variables ...................... 76
Table 22. Bivariate Correlations among all Variables ...................................................... 81
ix

Table 23. Summary of one-way ANOVA Analyses of Demographic Variables ............. 84
Table 24. Summary of Tukey’s HSD analysis of Demographic Variables ...................... 85
Table 25. Summary of one-way ANOVA of Study’s Scales by Department ................... 89
Table 26. Summary of Tukey’s HSD analysis of Study’s Scales by Department ............ 91
Table 27. Summary of Regression Analysis for Variables Predicting Police Officers’
Work Motivation............................................................................................................... 96
Table 28. Summary of Regression Analysis for Variables Predicting Police Officers’
Work Motivation by Department ...................................................................................... 99
Table 29. Summary of One-Way ANOVA Analyses of Reward Scale’s items by
department....................................................................................................................... 100
Table 30. Summary of Tukey’s HSD analysis of Rewards Scale Items by Department 101
Table 31. Summary of Regression Analysis for Work Motivation by Plain Clothes
Department and Public Order Department ..................................................................... 103
Table 32. Summary of Regression Analysis for Variables Predicting Police Officers’
Work Motivation............................................................................................................. 108
Table 33.Summary of Regression Analysis for Variables Predicting Police Officers’
Work Motivation by Department .................................................................................... 111
Table 34. Summary of Regression Analysis for Variables Predicting Police Officers’
Work Motivation............................................................................................................. 115
Table 35. Summary of Regression Analysis for Variables Predicting Police Officers’
Work Motivation by Department .................................................................................... 117
Table 36. Summary of Regression Analysis for Work Motivation by Plainclothes
Department and Public Order Department ..................................................................... 119
Table 37. Summary of Regression Analysis for Work Motivation by Plainclothes
Department ...................................................................................................................... 129
Table 38.Summary of Pearson Correlation Analyses ..................................................... 131

x

LIST OF FIGURES

Figure 1: The High Performance Cycle ............................................................................ 40

xi

CHAPTER 1
INTRODUCTION
1.1 Statement of the Problem
Why is work motivation important? According to Simon (1991), all sectors including
private, public, and nonprofit organizations share a similar and fundamental problem, namely,
“inducing their employees to work toward the organizational goals” (p. 28). Especially in public
administration, researchers have long argued about and focused on how to motivate public
employees “to work energetically and intelligently towards achieving public purposes” (Behn,
1995, p. 315). However, much of the prior research has focused “too heavily on employees
within the industrial and business organizations” (Perry & Porter, 1982, p. 97). Furthermore,
there has been little improvement on work motivation in the field of public policy and public
administration (Wright, 2001).
The current literature on motivational factors in policing is also limited, although policing
is one of the most examined issues in the field of criminal justice. Many of the researchers
focused on police functions and role in a society (Bittner, 1970; Muir, 1977; Lipsky, 1980),
police use of force and brutality (Westley, 1970; Adams, 1995; Klockars, 1995; Worden, 1995;
Milton et al., 1977; Skolnick & Fyfe, 1993; Friedrich, 1980), police socialization and personality
(Bayley & Bittner, 1997; Skolnick, 1975; Van Maanen, 1973, 1975; Herbert, 1998; Fielding,
1984; Wilson, 1978), police deviance and corruption (Kappeler et al., 1998; Barker, 1978;
Barker & Carter, 1986; Shearing, 1981; Sherman, 1974, 1978; Goldstein, 1975; Newburn, 1999;
Punch, 2000), the effectiveness of policing (Kelling & Coles, 1996; Clarke & Hough, 1984;
Skogan, 1976, 2004; McGarrell et al., 2001; Sherman, 1992; Sherman & Weisburd, 1995), police
discretion and its determinants (Black, 1980; Friedrich, 1980; Mastrofski et al., 1987; Sherman,

1

1980; Worden, 1989; Goldstein, 1960, 1963), private policing (Nalla & Newman, 1990;
Johnston, 1992; Shearing & Stenning, 1987; Button, 2002; Wakefield, 2003), and policing
philosophies including community policing (Klockars, 1988; Mastrofski et al., 1995; Skogan &
Hartnett, 1997; Skogan, 2004; Trojanowicz & Bucqueroux, 1990; Greene & Mastrofski, 1988;
Rosenbaum, 1994; Friedmann, 1992), problem-oriented policing (Goldstein, 1990; Bichler &
Gaines, 2005; Eisenberg & Glasscock, 2001; Eck & Spelman, 1987), broken windows policing
(Kelling & Coles, 1996; Harcourt & Ludwing, 2006), and intelligence-led policing (Ratcliffe,
2008; Ratcliffe & Guidetti, 2008; McGarrell et al., 2007; Carter & Carter, 2009; Cope, 2004).
Some researchers (Roberg, 1979; Roberg et al., 2002; Bennett & Hess, 2004; Fyfe et al.
1997; More & Wegener, 1992; Sheehan & Cordner, 1995; Swanson et al., 2005) offered linkages
between motivational theories and policing to improve police management. Moreover, existing
literature on policing examined various aspects of performance of police officers and police
departments, including departmental and personal factors and public evaluation of police
performance (Armeli et al., 1998; Fagan et al., 1998; Mazerolle et al., 2007; Moore & Braga,
2003; Cortina et al., 1992; Mas, 2006; Reisig, 1999; Reisig & Correia, 1997). Researchers and
police departments mostly rely on reported crime rates, overall arrests, clearance rates, and
response times to measure police performance (Alpert & Moore, 1998; Mastrofski, 1996),
although researchers argued over the validity of these performance indicators (Black, 1970;
Kelling, 1996). Thus, researchers and police departments started to use other performance
measures such as fear of crime, criminal victimization, citizen confidence and citizen satisfaction
with police (Stephens, 1996). However, our understanding about the correlation between
motivational factors and individual performance of police officers is still limited.

2

The quality of life in a work place and productivity are related to the performance of the
employees (Rainey, 2009). Although Katzell & Thompson (1990) noted that “empirical studies
on various [motivation] theories typically account for less than 20% of the variance in output” (p.
64), scholars and researchers have examined motivation for decades to improve individuals’ and
organizations’ performance and productivity (Pinder, 1998; Latham, 2007; Rainey, 2009). Aside
from some factors like level of investment, successful innovation, development of new
technology, and political environment, which are beyond the control of most individuals,
performance and productivity can be explained by the motivation and ability of employees
(Pinder, 1998; Latham, 2009; Ambrose & Kulik, 1999). Ability can be defined as aggregation of
natural skills and gained skills through experience and training that one possesses (Pinder, 1998).
The second factor that affects productivity is motivation. In addition to individual ability,
immediate managers and higher level managers in public and private organizations have some
control over the motivation of their employees.
Managers ask their employees to perform to the best of their ability and try to ensure that
they can and do (Ambrose & Kulik, 1999). It is argued that ability is a more important factor
than motivation for job performance (Kanfer & Ackerman, 1989; Pinder, 1998). Higher
motivation alone does not necessarily mean higher performance (Rainey, 2009). In some cases,
such as when an employee does not have adequate ability to perform a job, a greater level of
motivation cannot ensure the individual can perform the job as well as desired. Moreover,
gaining experience during work time may increase the ability of a person, whereas it may
decrease the motivation to work.
The term “motivation” comes from the Latin word movere, which means “movement”
(Latham, 2009). Work motivation can be described as “a person’s desire to work hard and work

3

well to the arousal, direction, and persistence of effort in work settings” (Rainey, 2009, p. 248).
According to Pinder (1998), work motivation refers to “a set of energetic forces that originate
both within as well as beyond an individual’s being, to initiate work-related behavior, and to
determine its form, direction, intensity, and duration” (p. 11). The intensity dimension refers to
the level of motivation at a given moment regardless of the potential available. It is assumed that
people are less likely to be motivated when little effort is needed. However, when the task is
difficult or requires much effort, people show higher levels of motivation. The direction is
related to specific goals, while the duration implies accomplishment. The definition (Pinder,
1998) also considers other factors such as environmental influences and biological features of
work-related behavior. Furthermore, the definition stresses that, since motivation cannot be
observed directly, it is an invisible physical process and internal concept.
Work motivation is one of the core concepts in the field of management to which both
researchers and managers have paid extra attention (Steers et al., 2004). It is a topic studied more
than any other micro factor like work-related attitudes, communication, and decision making and
macro factors like organizational design, organizational change, and organization culture (Baron,
1991) in the organizational behavior field (Pinder, 1998), especially in the micro level focus of
organizational behavior (O’Reilly, 1991). A study conducted by Cascio & Aguinis (2008)
showed that predictors of performance and work motivation are the two most-examined topics in
the top five areas of industrial and organizational behavior. It is believed that although we cannot
measure motivation directly, it exists and affects human productivity (Pinder, 1998).
1.2 Purpose of the Study
The purpose of this paper is to advance understanding of work motivation in the field of
policing. Work motivation and employee performance are two of the most examined topics in

4

the field of organizational psychology, especially in the industrial and business organizations
(Perry & Porter, 1982). Some of the previous research on policing examined job satisfaction and
its determinants among police officers (Greene, 1989; Griffin et al., 1978; Buzawa, 1984;
Dantzker, 1993, 1994, 1997; O’Leary-Kelly & Griffin, 1995; Nalla et al., 2011; Buker & Dolu,
2010). However, some researchers (Pinder, 1998; Vroom, 1964; O’Reilly, 1991; Locke &
Latham, 1990; Latham, 2007; Rainey, 2009) argued that job satisfaction is related to employee
turnover, employee absenteeism, and mental and physical health, rather than employee
productivity.
Another limitation of individual performance of police officers is that existing research
on policing focused on performance of street police work. However, aside from other
responsibilities, such as peace and public tranquility preservation, crime prevention, crime
investigation and offender arrest, and law enforcement, one of the primary functions of the police
is to protect lives and properties (Bouza, 1990; Steverson, 2008; Grieve et al., 2007), including
airports. In many countries, including Turkey, police or federal agencies are responsible for
protecting airports.
Therefore, the present study will contribute to our understanding of work motivation in
the public sector by employing goal-setting theory within the context of a public sector
organization and, more specifically, within the context of policing. This study will utilize
existing literature to develop a model of work motivation and contrast the motivational
differences, if any, between police officers working at airports and police officers working on the
streets. The model will be tested by using data which will be collected by applying a selfadministrated survey. An OLS regression analysis will be conducted to analyze data. Finally, the

5

results of the study will be examined to improve our understanding of work motivation in the
context of policing.

6

CHAPTER 2
LITERATURE REVIEW
2.1 Theoretical Framework: Goal-Setting Theory
Goal-setting theory assumes that the goals related to a task affect people’s performances
and choices (Locke & Latham, 1990a). Moreover, the theory asserts that difficult goals and
specific goals lead to higher task performance than easy or vague goals (Locke, 1966; Locke &
Latham, 1990a; Locke & Latham, 1990b; Locke & Latham, 2006; Latham, 2007; Latham &
Locke, 2007). Pinder (1998) formulated the theory with four tenets: goals affect human effort,
difficult goals result in higher performance, specific goals also lead to higher performance, and
incentives are meaningful except when they are related to specific or hard goals. Goals affect
task performance by four mechanisms: “by directing attention and action (choice), mobilizing
energy expenditure or effort, prolonging effort over time (persistence), and motivating the
individual to develop relevant strategies (cognition)” (Locke et al., 1981, p. 145; Latham, 2007,
p. 53).
Since the concept of goal is identified as an important concept of motivation (Pervin
2003), using goals over needs or external forces as a motivational construct offers many
advantages (Latham 2007). According to Pervin (2003), “goals have cognitive, affective, and
overt behavioral properties with them” (p. 311). The cognitive component of the goals refers to
the mental representation of people’s conscious or unconscious goals. Furthermore, people set
their goals based on a variety of emotional feelings like achieving pleasure or avoiding some
pain. Finally, people make “behavioral plans” including “cognitive representation of activities”
and some assessments (Pervin, 2003, p. 312) to achieve their goals. By doing so, “the employee
is oriented toward the future as far as cognitive capacity permits.” (Latham, 2007, p. 54).

7

According to Locke & Latham (2002), goals work in four ways to affect performance: by
directing functions, energizing functions, enforcing persistence, and leading to the arousal,
discovery, and use of task-relevant knowledge and strategies (p. 706-707).
The origin of the theory goes back to Locke’s (1964) doctoral dissertation (Latham,
2007). Locke & Latham (1990b; 2006) stated that goal-setting theory was developed through
previous research on intentions, task and set, and level of aspiration. The model evolved over a
25-year period, based on nearly 400 experimental and field studies with more than 40,000
subjects and 88 different tasks in the US and seven other countries (Locke & Latham, 2006;
Locke & Latham, 1990b; Latham & Locke, 1991; Latham & Locke, 2007). The generalizability
of the theory to other tasks and countries was provided by those studies (Latham & Locke,
1991).
Goal-setting theory is one of the most examined and most dominant theories in the field
of organization behavior (O’Reilly, 1991; Mitchell & Daniels, 2003). Pinder (1998) referred to
goal-setting theory as “being the most dominant, valid, and useful modern theory of work
motivation” (p. 382). Heslin et al. (2009) noted that goal-setting theory can be used to enhance
the performance of individuals, groups, organizational units, and even an entire organization.
Furthermore, some researchers (Wiese et al., 2005; Brunstein, 1993) argued that goal context is
related to personal well-being. For example, Brunstein (1993) showed in a longitudinal study
that students’ subjective well-being is related to “the intensity of students' commitment to pursue
personal goals” (p. 1067). Finally, researchers argued (Latham et al., 2008; Perry &
Vandenabeele, 2008) and showed (Wright, 2001; 2004) that goal-setting theory can be applicable
to the employees working in public sector organizations.

8

Although the origin of the goal-setting theory dates back to the 1960s, Locke & Latham
(1990a) offered their formal model in 1990. After introducing the full model, researchers
examined other factors within the goal-setting model (Steers et al., 2004; Locke & Latham,
2006). For example, Earley & Erez (1991), Latham & Locke (1991), and Mathieu & Button
(1992) examined the effects of assigned goals and normative information on performance.
Further, researchers (Klein & Mulvey, 1995; O’Leary-Kelly et al., 1994; Mitchell & Silver,
1990; Crown & Rosse, 1995; DeShon et al., 2004) examined the individual and group goals
within the goal-setting context. Moreover, Smith et al. (1990), Baum et al. (2001), and Baum &
Locke (2004) examined the performance of an entire organization. The other new focus areas
can be categorized as goal determinants of goal choice, the effects of learning goals on
performance, the relationship between the framing of a goal as gain versus loss and performance,
and how subconscious goals affect performance (Locke & Latham, 2006). The model tries to
answer why some people perform better than others, if their abilities and knowledge are equal
(Latham & Locke, 1991). According to Latham & Locke (1991), the answer to the question is
that motivational factors cause performance differences among people.
According to Locke & Latham (1990a), the model states that goals and intention, which
are immediate indicators of human actions, affect and direct human behaviors. The term of the
goal refers to something that people want to achieve (Locke & Latham, 1990a). Goals take many
forms such as level of job performance, performance standard, quota, work norm, task, objective,
deadline, and budget (Locke et al., 1981). Goals consist of two main dimensions: content and
intensity (Latham & Locke, 1991). Goal content refers to “the object or result being sought”
(Locke & Latham, 1990a, p. 25). Those objects can be buying a house, pursuing a career in
academia, and feelings like happiness or having a good reputation.

9

Goal content may vary from person to person and include few or many goals, short-term
or long-term goals, easy or difficult goals, specific or value goals, and consistent and conflicting
goals (Locke & Latham, 1990a). However, the goal-setting model mostly focuses on difficult
goals versus easy goals and specific goals versus “do your best” goals. Specific and difficult
goals increase one’s level of performance more than vague, non-quantitative goals like “do your
best” and other unspecific goals (Locke & Latham, 1990a). Since vague goals give people a huge
amount of discretion, people at any point may think that they do their best for a task in a range
from minimum to maximum. However, people feel accomplishment when they finish or reach
the specific goal. On the other hand, difficult goals stimulate people’s attention and lead to
higher levels of persistence to accomplish the goals.
Another concept that should be explained clearly is task. Task refers to “a piece of work
to be accomplished” (Locke & Latham, 1990a, p. 25). The distinction is important since
finishing a task could be a goal for many people. There is also a distinction between the goal
difficulty and task difficulty (Locke & Latham, 1990a). A task can be hard for a person based on
the task’s complexity and skill and knowledge level required to accomplish it. Harder tasks
demand a higher performance level but may yield a lower performance score than easier tasks.
For example, it is easier for people to write a letter than a scientific paper, since the second one
requires more knowledge and skills than the first one.
However, Locke & Latham (1990a) noted that in some instances task difficulty may lead
to a higher performance score as in the study of Campbell & Ilgen (1976). Campbell & Ilgen’s
(1976) study is unique, since it is the only study that considered goal and task difficulty
separately (Locke & Latham, 1990a). The result (Campbell & Ilgen, 1976) showed that task
difficulty affects task performance. Although task difficulty does not affect the motivational

10

process, people gain more information and more insight while doing difficult tasks. Even if a
person fails to accomplish the task, the task knowledge gained in the attempt is useful for the
performance of future tasks that require similar skills or knowledge. Similar to hard tasks, hard
goals also require more knowledge and skills than easier goals. A person may want to finish a
specific task in a shorter period than usual. To be able to do that, he/she needs more skills and
knowledge. Harder goals result in more performance. It is found that there is a consistent linear
and positive relationship between the performance and goal difficulty (Latham & Locke, 1991).
In sum, goal difficulty refers to the level of task proficiency, whereas task performance refers to
the nature of a task. Latham & Locke (1991) noted that “knowing task difficulty, however, does
not reveal the person’s goals and thus makes it difficult to predict how well a person will
perform the task” (p. 214).
Goal intensity refers to several factors that operate together and combine within the goalsetting process. Those factors are: the effort necessary to form a goal; its importance relative to a
person’s other goals; the level of commitment; and, the value of the goal. The term intention
refers to people’s determination to accomplish one goal or task by taking certain actions (Locke
& Latham, 1990a). One of the major aspects of intensity that has been studied extensively is
commitment (Latham & Locke, 1991). Goal 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” (Latham & Locke, 1991, p. 217). The sources
of the goal —whether set by an individual, assigned by an external source, or decided jointly—
do not affect goal commitment (Locke & Latham, 1990a).
The literature on goal commitment shows that there is a direct and moderate effect of
goal commitment on performance (Locke & Latham, 1990a; Latham & Locke, 1991). Existing

11

research indicates that when goal difficulty is held constant, people who commit themselves
strongly to a goal perform better than those who are less committed to a goal. People who do not
commit themselves to a goal tend to give up their hard goals in favor of finding easier ones. The
moderator effect of goal commitment works with the performance. Locke & Latham (1990a)
suggest that high goal commitment and high performance increase people’s goal level. Latham &
Locke (1991) noted the ultimate goal proof as “the action taken to attain it which in turn reflects
the thinking which preceded it and the choice to act on that thinking” (p. 217). According to
Latham (2007), goal commitment increases job performance, since “the goal provides a
regulatory mechanism that allows the employee to observe, monitor, subjectively evaluate, and
adjust job behavior in order to attain the goal” (p. 53). It should be also noted that Locke et al.
(1988) define goal commitment and goal acceptance differently. Goal commitment refers to a
more inclusive concept, which can be applied to any goal regardless of whether it is self-set,
participative set, or assigned. However, goal acceptance refers to a goal which is assigned to a
person.
Since there is a relationship between goal commitment and performance, it is worthwhile
to mention determinants of goal commitment. The determinants of goal commitment are
authority, peer influence, publicness, rewards and incentives, punishment, general valance and
instrumentality, and expectancy of success and self-efficacy (Locke & Latham, 1990a). People
consider their immediate supervisors or managers in the public or private sector as legitimate
authorities. Supervisors and managers tell people their tasks, observe their performance, and give
warnings when necessary. Although further research is needed to examine the correlation
between authority and goal commitment, existing research suggests that a legitimate authority
affects goal commitment in these conditions: when an authority is physically present, is

12

supportive, is trustworthy, convinces an employee of the rationality of the goal, exerts reasonable
pressure, and is knowledgeable and likable (1990a, p. 136). When employees feel that there is no
conflict between the organizational goals and given tasks and they are capable to do the given
tasks, employees follow the orders given by a legitimate authority.
Another factor which affects goal commitment is peer influence. It was found that people
who are assigned to a group have a higher commitment level for individual and group goals than
do people who work alone. Moreover, research suggests that if a person observes a strong role
model, he/she tends to have a higher goal commitment level. However, when people observe a
negative role model, they show a low goal commitment level. Finally, peer groups exert pressure
on people to perform better and this leads to a high level of goal commitment. Literature on task
performance indicates that competition has direct and indirect effects on employee performance.
However, none of the research directly tested how competition affects goal commitment. Locke
& Latham (1990a) proposed that since there could be a link between competition and goal
commitment, further research should examine the relationship between two.
Researchers also examined the effects of public and private work environments on goal
commitment. It was found that although research suggests inconsistent findings on performance
differences in work performed in a public or private environment, publicness significantly affects
people’s goal commitment level. Some other work motivation theories like expectancy and
social learning theories suggest that incentives and rewards work as an alternative to goal
commitment. However, few studies examine directly the relationship between the incentives and
rewards and goal commitment. The findings are inconsistent and researchers found insignificant
relations between the incentives and rewards and goal commitment in most cases. Locke &
Latham (1990a) argued that incentives and rewards affect performance in various ways, which

13

also affects goal commitment. They (1990a) suggest that researchers should examine the effects
of incentives and rewards on goal commitment with better study designs and measurement. In
contrast to research on incentives and rewards, some studies examined how punishment affects
goal commitment. The research indicates that the effects of punishment on goal commitment
depend upon how it is used and perceived.
Valance and instrumentality refers to “how individuals process and respond to
incentives” (Locke & Latham, 1990a, p. 145) in goal-setting theory. Research indicates that goal
instrument is significantly related to goal commitment. Moreover, perceived rewards lead to
higher commitments and subsequently to higher performance as well. However, existing
research suggests that a number of personal factors such as ego and goal conflict may affect
valance and, subsequently, goal commitment. Expectation of success and self-efficacy are other
factors that affect goal commitment. People tend to have a higher level of goal commitment if
they believe the goal is achievable. If people believe the goal is too difficult to accomplish, they
are more likely to have a lower goal commitment level. Related to the expectation of success,
self-efficacy has effects on goal commitment and on performance. Self-efficacy refers to a
person’s judgment about his/her ability to accomplish a task (Locke & Latham, 1990a). Selfefficacy is especially useful for maintaining goal commitment level when a person encounters a
challenge or obstacle in performing a task. Moreover, the information provided at the beginning
of a task or during the task as feedback affects people’s self-efficacy. However, providing
information at the beginning or during the task does not always lead to a higher performance
level.
As indicated before, a goal can be set in three ways. Someone may assign a goal to an
individual, the goal can be assigned mutually, or an individual may set the goal himself. Many

14

researchers have examined whether employees should participate in the goal decision-making
process (Pinder, 1998). Two theoretical perspectives approach the question differently (Locke &
Latham, 1990a). Classical management theories state that managers and leaders are responsible
for goal assignments and employees’ commitments to the goal. However, humanistic
organizational theories argue that employees should participate in the goal decision-making
process. Participating in the goal decision-making process causes employee attachment and
higher levels of commitment to the goals.
These two different theoretical perspectives cause considerable argument in the literature
concerning the effects of assigned goals versus participatively-set goals on goal commitment and
performance (Latham & Locke, 1991). Based on a series of 11 studies conducted by Latham and
his colleagues (Locke & Latham, 1990a; Latham & Locke, 1991), the researchers concluded that
there are no or few differences between the effects of assigned goals and participatively-set goals
on goal commitment and performance. However, another set of seven studies conducted by
Latham and his colleagues indicated that participatively-set goals lead to higher commitment
levels than assigned goals (Latham & Locke, 1991; Locke & Latham, 1990a; Pinder, 1998).
These inconsistent findings between two studies led to an unusual solution, namely, that
“Latham and Erez decided to place their differing views head to head by collaborating on the
design of four experiments using a mutually respected third party, Edwin Locke, as mediator.”
(Pinder, 1998, p. 373-374). However, this unusual study showed that assigned goals affect
motivation and goal commitment as much as participatively-set goals do. Nevertheless,
participation may lead to higher performance levels through two cognitive mechanisms (Pinder,
1998). Employee participation affects self-efficacy and the quality of the strategies that
employees develop and use. Finally, it was found that self-set goals also do not have significant

15

and consistent effects on goal commitment and performance (Locke & Latham, 1990a). In sum,
research indicates that none of the methods of setting goals perform better than others or affect
goal commitment and performance.
The goal-setting model examines the relationship between goals and feedback (Locke &
Latham, 1990a; Latham & Locke, 1991; Pinder, 1998). Latham & Locke (1991) noted that
studies that indicated the positive effects of feedback on performance were not conducted
properly. Feedback was used to set improvement goals for the study subjects by comparing their
past performance. When subjects are provided with feedback that cannot be used to set goals,
research indicates that feedback or goal-setting alone do not affect performance. In other words,
feedback and goal-setting have effects on performance when they exist together. Locke &
Latham (1990a) examined 33 studies to show the effectiveness of goals plus feedback versus
goals or feedback alone. They concluded that the effectiveness of goals plus feedback is more
consistent and has a more positive effect on performance than goals or feedback alone. The joint
effect of goals and feedback affect performance, because of the distinction between goals and
performance (Locke & Latham, 1990a). Goals provide information about the level of
performance that is to be attained. However, feedback refers to information, while a goal refers
to evaluation. People use feedback to track their performances by comparing standards and set
goals as needed. Two possible outcomes occur based on the provided feedback (Latham &
Locke, 1991):
1. If performance meets or exceeds the standard, performance is typically
maintained.
2. If performance falls below the standard, subsequent improvement will occur
to the degree that: (a) the individual is dissatisfied with that level of

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performance and, more importantly, expects to be dissatisfied with it in the
future; (b) the individual has high self-efficacy, that is, confidence in her ability
to improve; and (c) the individual sets a goal to improve over her past
performance ( p. 226).
The model also considers other variables such as ability, demographic variables such as
gender, age, and race, and situational constraints (Locke & Latham, 1990a; Latham & Locke,
1991). Research shows an inconsistent and curvilinear relationship between performance and
ability. Moreover, the effect of ability on performance is related to goal difficulty. When goal
difficulty is above the ability of a person, goal difficulty limits the ability of a person and
prevents high performance. Locke & Latham (1990a) argued that education level has no effect
on goal-setting, and there is no logical reason why there should be one. Besides ability, since
there are very few studies conducted on race, age, and gender, we need further studies to
examine how those variables effect goal-setting and performance. Finally, research on situational
constraints and performance indicates that when situational constraints are low, goal level is
significantly related to performance. In particular, when people have high self-efficacy and high
commitment in low-level situational constraints, they perform better than others.
The final topic in the model is the consequences of the goals (Locke & Latham, 1990a;
Latham & Locke, 1991). Based on satisfaction theory, Latham & Locke (1991) state that
“emotional responses are the result of automatic, subconscious value appraisals” (p. 231). People
try to reach their goals, which are desired or valued outcomes. It is obvious that when people
accomplish a goal, they feel satisfaction with their performance. Therefore, it can be expected
that the level of satisfaction is increased based on the degree of success experienced. Based on
16 studies, Locke & Latham (1990a) found a strong correlation between the degree of success

17

and satisfaction. Satisfaction level moves in either direction, positive or negative, according to
the level of success or failure. The importance of the goal leads to a greater negative or positive
satisfaction level. In addition to satisfaction, the importance of the goals prevents boredom and
reduces the role of conflict and ambiguity.
2.2 Determinants of Work Motivation
As discussed in the previous section, goal-setting theory was formulated and developed
based on extensive field research and experimental studies. However, to provide a better
overview, research regarding the determinants of work motivation within goal-setting theory will
be examined. This chapter will examine in detail five factors-goal content, goal commitment,
self-efficacy, feedback, and money-as incentives that affect performance the most.
2.2.1 Goal Content
Goal content refers to characteristics of goals like goal difficulty, specificity, and
vagueness. Much of the previous research focused on how specific goals and difficult goals are
related to the performance of employees and people (Locke et al., 1981; McCaul & Kopp, 1982;
Locke & Latham, 1990a; Klein et al., 1990). Locke et al. (1981) examined studies conducted
between 1969 and 1980 to see the relationship between goals and performance. Regarding goal
difficulty, Locke et al. (1981) found that 29 experimental laboratory designs showed a linear
relation between, and four experimental studies provided conditional support for, goal difficulty
and task performance, whereas only six experimental designs showed no relation between goal
difficulty and performance. Furthermore, 15 field studies showed the goal effects on
performance in varying degrees, while only three field studies resulted in negative associative
effects between goals and performance.

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Regarding goal specificity, the study (Locke et al., 1981) showed that 20 experimental
studies and 31 field studies, including 7 field studies that concurred partially, supported the
hypothesis that specific hard goals lead to better performance. Only one experimental and one
field study showed no relationship between goal specificity and performance. Overall, it was
found that 99 of 110 experimental and field studies partly or wholly supported the hypothesis
that difficult goals and specific goals result in higher performance. Later, Locke & Latham
(1990a) conducted a more comprehensive and detailed meta-analysis of the relationship between
goal difficulty and performance and specific goals and performance based on 393 field and
experimental studies. The results of their study showed that 140 of 192 studies of goal difficulty
and performance and 152 of 201 studies of specific and hard goals as opposed to vague goals and
performance, a total of 292 out of 393 studies, revealed significant effects of goal content on
performance.
Before Locke & Latham (1990a) offered their formal model, several other researchers
conducted meta-analyses on goal difficulty and performance and goal specificity and
performance. For example, Guzzo et al. (1985) found in their meta-analysis that goal-setting was
one of the most significant and effective psychologically-based intervention programs on worker
productivity. Mento et al. (1987) conducted a meta-analysis by analyzing research conducted
between 1966 and 1984. They examined 70 studies of goal difficulty and performance and 49
studies of goal specificity and performance. The results of the study showed similar patterns
regarding the relationship between goal difficulty and performance and goal specificity and
performance. They found strong support for the effects of goal difficulty and goal specificity on
performance. Another meta-analysis study conducted by Tubbs (1986) and involving 56 studies
of goal difficulty and 48 studies of goal specificity also showed strong support for their main

19

hypothesis that difficult goals and specific goals are related to performance. Both researchers,
Mento et al. (1987) and Tubbs (1986), argued that experimental studies show stronger support
for the relationship between the goal difficulty-specificity than the field studies do.
Wood et al. (1987) extended Mento’s et al. (1987) meta-analysis by including additional
field and experimental studies and by employing more careful analysis of task complexity. The
results of the meta-analysis showed that goal difficulty and goal specificity are strongly related to
task performance. Furthermore, they found that “the magnitude of goal effects on performance
was greater on simple tasks than on complex tasks” (1987, p. 420). Zetik & Stuhlmacher (2002)
conducted a meta-analysis involving 22 research reports that examined the effects of goals and
their attributes on the performance of negotiators. Their findings (2002) can be summarized as:
(a) negotiators who set specific goals consistently made higher profits than the negotiators who
did not pursue specific goals; and (b) difficult goals lead to higher performance and,
subsequently, to higher profits. They (2002) concluded that goal-setting theory can be used for
negotiations and “negotiators are at a great advantage for earning profit when they go into
negotiation prepared with a goal, especially when the goal is specific and difficult” (p. 47).
Aside from meta-analysis, the existing literature provides a huge list of experimental and
field studies of goal content and performance (Locke & Latham, 1990a; Locke & Latham, 2002).
For example, one of the earliest studies was conducted by Bryan & Locke (1967). They
conducted a two-phase experimental design with two groups consisting of a total of 48
undergraduate students and five trials to test the effect of time on attitudes. Two groups were
provided the same task and procedures but the number of assigned tasks was lower for the
second group. The students were asked to solve problems within a designated time allotment (12
minutes and 6 minutes) in the first three trials, in a self-paced time allotment in trial four, and in

20

the fastest time possible in the last trial. They found that students who were provided more time
slowed their pace and took longer than the students who were provided less time to solve the
problems. In other words, subjects who were given shorter time limits increased their
performance to solve the problem. However, the results showed no significant differences for
trial four and trial five for both groups.
Latham & Locke (1975) replicated Bryan & Locke’s (1967) experimental design within a
real-life setting. They examined 379 independent wood-harvesting crews. The first group was
free to sell as much wood as they could in a given month, whereas the second group had quotas
for one or more weeks during each month. Latham & Locke (1975) followed each group
consecutively for three months, from April to June. The results showed that aside from April,
the crews who experienced quotas were more productive than the crews who were free to sell as
much as they could harvest.
Another experimental design was conducted by Latham & Baldes (1975) to test how
specific hard goals as opposed to “do your best” goals affected the performance of 36 logging
drivers. The logging drivers were observed July to September, when weather is most suitable for
logging, without being assigned specific goals, and they were observed again during the nine
consecutive months of less suitable weather with specific assigned goals that required logging
drivers to load their trucks up to the maximum legal weight. The results indicated that although
there were seasonal differences among the months, logging drivers performed much better after
they were assigned a specific goal. Smith et al. (1990) examined goal-setting in a macro-level
study to test the effects of goal-setting on organizations. They assigned 296 undergraduate
students in 16 simulated organizations. Researchers used Miles & Randolph’s (1979)
“organizational game” and observed these 16 simulated organizations running at different times

21

for 12 months. The results indicated that specific organizational goals affected organizational
performance. Furthermore, the results also suggested that specific goals were related to planning
quality.
Brown & Latham (2000) tested goal-setting theory with 32 unionized employees working
at a telecommunications company. These 32 subjects were divided into three groups: goalsetting, self-instruction plus goal-setting, and “do your best”. The findings of the study showed
that employees who set specific and difficult goals performed more than employees who were
allowed to do their best. In other words, the main hypothesis of the goal-setting theory that
difficult and specific goals lead to higher performances, was achieved. Brown & Latham (2000)
found that there was a linear relationship between the goal level and performance. However, the
results suggested that there was no correlation between the self-instruction and performance.
Finally, one of the most recent experimental studies was conducted by Morisano et al. (2010).
They tested goal-setting theory with 85 college students who did not perform well in a real-life
setting. The importance of their study is that students were instructed and trained through a webbased program. Researchers used a revised version of Peterson & Mar’s (2004) program
consisting of eight steps to allow students to set specific personal goals. After a four-month
period, students who participated in the goal-setting experiment showed significant differences
and increased their overall grade-point average (GPA).
When it comes to field studies, Andrews & Farris (1972) conducted one of the very
earliest field studies. They examined the relationship between time pressure and scientists’
performance. They used a panel study to collect data from scientists and engineers working at a
NASA research division where scientists and engineers were subjected to extreme time and
physical pressures. The first phase was conducted in 1965 and involved 117 scientists and

22

engineers; the second phase of the study was administrated after five years and involved 118
scientist and engineers, 78 of whom participated in the first phase as well. The results of the
study suggested that contrary to the idea that scientists should be relaxed in order to perform
better and to be productive, the challenge of greater time pressure was positively related to
higher performance after controlling for supervisory status, education, and seniority.
Furthermore, it was found that scientists and engineers who performed at higher levels demanded
more pressure. The researchers also argued that time pressure is related to several other aspects
of performance including usefulness, innovation, and productivity.
A meta-analytic study (Lepine et al., 2005) regarding stressors’ effects on performance
revealed the same findings. Lepine et al. (2005) examined 82 articles and manuscripts to see how
challenge stressors (measures of job role demands, pressure, time urgency, and workload) and
hindrance stressors (measures of constraints, hassles, resources, inadequacy, role ambiguity, role
and interpersonal conflict) affect peoples’ performances. The results of the study indicated that
while hindrance stressors are negatively related to performance, challenge stressors have a
positive effect on performance.
Wright (2004) examined goal-setting in a public organization. He conducted a survey
with 385 New York State employees working at several different state agencies. Wright (2004)
examined how the elements of goal-setting theory, including goal difficulty, goal specificity,
feedback, and self-efficacy, affect state employees’ work motivation. The results of the study
indicated that both goal difficulty and goal specificity were related to state employees’ work
motivation. Wright (2004) noted that the goal-setting model provides a useful framework to
understand public sector employees’ task, mission, and motivation. One of the most recent field
studies was conducted by Webb et al. (2010). They examined the factors that affect employees’

23

goal choices and performance. To test their hypothesis, they collected data from employees at
four call centers with a final sample size of 476. Additionally, the companies provided
employees’ actual performance data to the researchers. The researchers found that employees
who performed better in the past set more difficult goals. Furthermore, the results indicated that
prior performance and goal difficulty are positively related to their future performance.
Researchers have tested how goal context has affected employees’ performance since the
origin of the theory. The above-mentioned meta-analyses, experimental studies, and field studies
showed that goal difficulty and goal specificity are two of the most significant predictors of
employee performance within the goal-setting context. As demonstrated, (a) setting a goal leads
to higher performance than a no-goal situation, (b) specific high goals result in higher
performance than vague goals, and (c) difficult goals lead to higher performance than easy goals.
The research showed that goal context is not only related to employee performance but also
related to quality of work. Furthermore, in some cases, goal context also results in innovative
ideas and products. Moreover, the theory can be used for employees working at public sector
organizations to increase their performance. Therefore, it can be concluded that setting specific
and difficult goals in both the private and public sectors leads to higher employee, group, and
department performances.
2.2.2 Goal Commitment
The second important component of the goal-setting framework is commitment.
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” (Latham & Locke, 1991). Locke (2000), Locke & Latham (1990a; 1990b)
argued that unless employees committed to a goal including any specific or challenging

24

objective, employees did not show higher level performance within the goal-setting model. Goal
commitment has a direct and indirect effect on performance (Latham & Locke, 1991). People
who show higher level commitment to difficult goals are more likely to perform better, since less
committed people are more likely to abandon hard goals and pursue easier ones. However,
Latham & Locke (1991) noted that “when goals are low, on the other hand, high
commitment may restrict performance because committed people will be loathe to raise
their goals, whereas uncommitted people may set higher goals” (p. 217).
A meta-analysis conducted by Donovan & Radosevich (1998) showed that the interaction
of goal difficulty and goal commitment accounted for only 3% of the variance in task
performance. In other words, the 12 studies included in the meta-analysis revealed inconsistent
findings regarding the relationship between goal commitment and performance. According to
Klein et al. (1999), the negative result of Donovan & Radosevich’s (1998) study was caused by
including only a small sample of studies in their own study. Furthermore, other researchers
examined why research failed to show a significant relationship between goal commitment and
performance. Hollenbeck & Klein (1987) noted that previous research failed to show a
relationship effect of goal commitment on performance because many previous studies ignored
goal commitment as a variable, even though it was one of the central concepts in the goal-setting
model. Furthermore, many of the previous studies showed inconsistencies in their
conceptualization of goal commitment. Locke et al. (1981) examined why studies showed
inconsistent findings regarding goal commitment and performance relationship. According to
Locke et al. (1981), the possible reasons for inconsistent findings are (a) problems with
measuring goal acceptance; (b) problems with limited variation among the subjects caused by the

25

scales used to measure goal commitment; and (c) problems with subjects who “may not be able
to discriminate small differences in psychological commitment” (p. 143).
Similar to Locke et al. (1981) and Hollenbeck & Klein (1987), Tubbs (1993) noted that
researchers failed to test the effect of goal commitment on performance because of the
application of different measures and concepts. Hollenbeck et al. (1988a) constructed a Likerttype scale consisting of nine items to measure goal commitment. However, after conducting a
principal axis factor analysis, Hollenbeck et al. (1989a) excluded two items from the scale and
used a seven-item Likert-type scale instead of the nine-item likert-type scale. They conducted an
experimental study with 190 college students to examine the antecedents and results of goal
commitment. The results of the study indicated that (a) goal commitment was significantly
related to performance; (b) goal commitment was related to publicness, locus control, and need
for achievement but not goal origin had an effect on goal commitment; (c) goal commitment was
partially related to goal level; (d) the constructed goal commitment scale was reliable.
Hollenbeck et al. (1989b) examined the construct validity of the self-report measure of
goal commitment which was applied in Hollenbeck et al.’s (1989a) study. They (1989b)
combined three previous studies to develop an efficient and valid measure of goal commitment.
They offered a four-item unidimensional scale to measure goal commitment after examining the
studies. Hollenbeck et al. (1989b) noted that the four-item goal commitment scale has internal
consistency, is related to performance, and covers three alternative measures of the same
construct, namely, force to attain the goal, self-set goal-assigned goal discrepancy, and actual
goal change. Further, Klein et al. (2001) conducted a more comprehensive analysis by combining
meta-analytic and multi-sample confirmatory factor analytic technique. They examined 15
studies consisting of 17 independent samples and 2,918 subjects. The result of the study

26

suggested that the five-item scale is “unidimensional and equivalent across measurement timing,
goal origin, and task complexity” of goal commitment (Klein et al., 2001, p. 33).
Furthermore, researchers argued (Locke & Latham, 1990a; 1990b; 2006; 2009; Locke et
al., 1988; Pinder, 1998; Mitchell & Daniels, 2003; Heslin et al., 2009; Seijts & Latham, 2000a;
Porter, 2005) and showed (Erez & Zidon, 1984; McCaul et al. 1987; Locke et al. 1984; Klein,
1991; Wright, 1992; Wright et al., 1993; Porter, 2005) goal commitment is related to
performance. According to Locke et al. (1988), “if there is no commitment to goals, then goal
setting does not work” (p. 23). Wofford et al. (1992) conducted a meta-analysis with 78 goalsetting studies to examine the antecedents and consequences of goal commitment. They found
that goal commitment was related to goal achievement. According to Wofford et al. (1992), goal
achievement was a more important factor than the performance within the goal-setting model.
Furthermore, they found that self-efficacy, expectation of goal attainment, and task difficulty
were significantly related to goal commitment.
Another and more comprehensive meta-analysis was conducted by Klein et al. (1999)
based on 74 studies with 83 independent samples which ranged in size from 20 to 406. They
(1999) found that (a) goal commitment is positively related to performance across studies; (b) the
relationship between the goal commitment and performance is robust; (c) goal level moderates
the relationship between goal commitment and performance such that difficult goals lead to a
stronger relationship between goal commitment and performance than easy goals do; (d) goal
origin, task complexity, and incentives do not have an effect on the relationship between goal
commitment and performance; and, (e) there is a positive relationship between goal commitment
and expectancy, attractiveness, and motivational forces as antecedents of goal commitment (p.
889-890).

27

Both field and experimental studies showed that goal commitment is a vital component of
the goal-setting model that affects performances. For example, Seijts et al. (1997) examined how
task importance and publicness affect the relationship between goal difficulty and performance.
To do so, they conducted an experimental design with 200 undergraduate students. They used a
seven-point Likert-type item (such as, how important is this task to you?) to measure perceived
task importance. The results of the study showed that (a) perceived task importance moderates
the relationship between the goal level and performance; (b) more difficult goals lead to a higher
level of perceived task importance; (c) publicness increases the perceived task importance of
subjects more than an anonymous condition; and (d) subjects who committed themselves to more
difficult goals performed better than subjects who did not commit themselves to difficult goals.
Seijts et al. (1997) noted that “the goal difficulty-performance function appears to hold only
when participants worked under public conditions on a task that was perceived as important” (p.
58-59). Klein & Kim (1998) examined the effects of goal commitment on performance in a retail
organization. The study’s sample consisted of salespersons. The results of the study indicated
that goal commitment was significantly related to salespersons’ performance. Furthermore, the
results also suggested that while managerial support had a positive effect on salespersons’
commitment, situational constraints were negatively related to goal commitment.
The existing literature showed that although goal commitment is one of the central
concepts in the goal-setting model, most of the early studies in goal-setting context either
ignored it or did not measure it appropriately. However, since the late 1980s when Hollenbeck et
al. (1989a) offered a Likert-type goal commitment scale, it has been used in much of the research
performed. It is suggested that goal-setting theory does not work without goal commitment. In
other words, if a person does not have goal commitment, goal-setting, regardless of whether the

28

goals are difficult or specific, does not affect performance. The above-mentioned meta-analyses
and research showed that goal commitment has a moderating and direct effect on performance.
2.2.3 Self-Efficacy
Locke (2000) and Locke & Latham (2002) suggested that two factors are critical to goal
commitment. The first one is the relevant or significant value of a goal to a person. The second
one is self-efficacy, of the quality of “being capable or attaining or making substantial progress
toward the goal” (Locke, 2000, p. 46). In other words, people should believe that they can
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). Self-efficacy is related to people’s thought patterns, actions, and emotional
responses (Bandura, 1982). Bandura (1982) noted, “In causal tests the higher the level of induced
self-efficacy, the higher the performance accomplishments and the lower the emotional arousal”
(p. 122).
According to Bandura (1977), self-efficacy has a direct effect on goal choices. People
choose their behaviors based on their expectations for the outcomes and their perceived selfefficacy. If they believe they can not accomplish a goal in a given situation because they
convince themselves that the given situation is beyond their skills and capabilities, they do not
pursue such goals. Otherwise, they engage and pursue the goals. For example, Lee & Bobko
(1992) found in their experimental design with 92 undergraduate students that self-efficacy was
negatively related to the perceived difficulty of a task. Furthermore, perceived self-efficacy
affects behaviors by fostering an expectation of success. That is, when people choose their
behaviors, their sense of self-efficacy determines “how much effort people will expend and how
long they will persist in the face of obstacles and aversive experiences” (Bandura, 1977, p. 194).

29

Within the goal-setting model, self-efficacy leads to (a) setting higher goals, (b) being more
committed to difficult goals, (c) being more resilient and persistent despite the possibility of
failure, (d) having the possibility of developing better task strategies, and (e) being more likely to
perform better (Locke, 2000, p. 46).
Many of the researchers examined the effects of self-efficacy on performance
theoretically and empirically. Bandura & Cervone (1983) examined the effects of self-evaluation
and self-efficacy on motivation within the goal-setting framework. They conducted an
experimental design with four experimental groups consisting of 20 subjects and with one
control group consisting of 10 subjects. The results of the experimental study indicated that
subjects who had goals performed better than subjects who did not have goals. Moreover, it was
found that subjects who were dissatisfied with their performance in a situation were more likely
to improve their performance in future challenges. Finally, it was proven that when subjects had
goals and were provided feedback, self-efficacy was significantly related to their performances.
Bandura & Wood (1989) conducted an experimental design with 60 subjects who were
assigned to participate in a simulated organization. They examined how perceived controllability
and performance standards affect people’s self-efficacy and goal choices. The results of their
study (1989) showed that belief in controllability is positively related to self-efficacy. Subjects
who did not believe that they could control the simulated organization showed a low sense of
self-efficacy. The results also suggested that subjects tended to set more difficult goals when
they believed they could control the simulated organization. In addition, the study indicated that
prior performance of the subjects affected perceived self-efficacy, personal goal-setting, and
subsequent performances. Moreover, the researchers found that self-efficacy was related to
performance directly and indirectly by developing analytic strategies to achieve their goals.

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Finally, the results indicated that self-efficacy had an effect on subjects’ goal-setting as well.
Subjects who had strong self-efficacy set more challenging goals than subjects who did not have
as high a sense of self-efficacy.
A meta-analysis conducted by Stajkovich & Luthans (1998) based on 114 studies with a
total sample size of 21,616 showed a significant and positive relationship between self-efficacy
and performance. Furthermore, they (1998) found that self-efficacy was significantly related to
performance for low, medium, and high levels of task complexity. Additionally, the power of the
relationship between self-efficacy and work performance was higher in the studies involving a
low level of task complexity than in the other two task complexity groups. Barling & Beattie
(1983) found that self-efficacy was related to sales performance. Bouffard-Bouchard (1990)
examined the influence of self-efficacy judgments on cognitive performance. The results showed
that self-efficacy was related to the number of completed problems, the efficiency of subjects’
problem-solving strategies, and the accuracy of their self-evaluation of their responses. The
researcher (1990) referred to self-efficacy as “a viable construct for comprehending
performance” (p. 353).
The study conducted by Cervone et al. (1991) indicated that self-efficacy was positively
related to task performance. Furthermore, they (1991) argued that “[s]elf-efficacy judgments
should most strongly regulate action on challenging, valued activities for which people have
enough information to assess adequately their performance capabilities” (p. 265). Zimmerman et
al. (1992) found that self-efficacy for academic achievement was positively and significantly
related to setting more difficult goals and higher performance. In their experimental design
Brown & Latham (2000) found that employees with a higher level of self-efficacy perform better
than employees with a lower level of self efficacy. Earley & Erez (1991) conducted an

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experimental design involving 174 college students. They found that self-efficacy was positively
related to performance.
Silver & Bufanio (1996) examined the effects of group-efficacy on performance in an
experimental study with 75 students who participated in two task trials. They found that groupefficacy was significantly and positively related to group goals and subsequent task performance.
Furthermore, the study indicated that group-efficacy was a more significant predictor than group
past performance. Later, Seijts & Latham (2000b) conducted a more comprehensive
experimental design study on the relationship between group-efficacy and performance. They
argued that the size of the group is related to group commitment, group self-efficacy, and their
subsequent performances. Furthermore, it was expected that the members of the small group
committed themselves more than the members of the large group. The results of the study
suggested that the members of the small group had a significantly higher sense of commitment
and self-efficacy than the large-group members. Furthermore, the small-group members
subsequently performed better than the large-group members.
In sum, self-efficacy is an important concept within the goal-setting model. Researchers
argued and showed that self-efficacy has a positive, direct and moderate effect on performance.
People with a higher sense of self-efficacy tend to set more difficult goals, develop analytic
strategies to accomplish a task, and commit themselves to difficult goals. Self-efficacy is also an
important factor for groups, although small groups foster a higher sense of self-efficacy and
subsequently more commitment and better performance. Finally, existing research suggests that
self-efficacy is an important factor that affects people in various situations and organizations.

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2.2.4 Feedback
Researchers argued (Ilgen et al., 1979; Pritchard et al., 1988) and found (Guzzo et al.,
1985; Ilgen & Moore, 1987) that feedback has a positive effect on performance. Feedback in a
goal-setting framework refers to “information to the individual as to the degree to which the
standard is being met” (Latham & Locke, 1991, p. 226). Providing feedback to people improves
work performance in a couple of ways (Latham & Locke, 1991). First, when individuals
know/learn that their performance is below the standard, they become dissatisfied and tend to
improve their performance. Second, individuals with a higher sense of self-efficacy are more
likely to improve their performance to reach or surpass the standard. Finally, individuals might
set more difficult goals to improve upon their past performance when they are aware of their past
performance level.
However, researchers (Locke & Latham, 1990a; Latham & Locke, 1991) argued that
providing feedback alone does not always lead to higher performances. For example, Kluger &
DeNisi (1996) conducted a meta-analysis on the effects of feedback on performance; the study
used 131 papers, 607 effect sizes, and 23,663 observations. The results of the study indicated that
although feedback is significantly and positively related to performance, nearly one-third of the
studies included in the meta-analysis showed an inverse relationship between feedback and
performance. According to Ilgen et al. (1979), several factors like source (formal performance
appraisal, the supervisor, co-workers, the task, and self), message (timing, sign, and frequency),
and the characteristic of the recipients may affect how people perceive and accept feedback (p.
353-358). Therefore, providing feedback does not always lead to positive outcomes or work as a
reinforcement of people’s behavior. Existing literature suggests that offering feedback in some
cases, for example, when the feedback is negative, may lead to anger, tension, annoyance, and

33

frustration (Baron, 1988; Geddes & Baron, 1997). Providing negative feedback might result in
“adopting ineffective techniques for dealing with poor performances, intensifying subsequent
conflict between the source and recipients, and reducing self-set goals and feelings of self
efficacy” (Baron, 1990, p. 235).
Similar to goal commitment, goal-setting is ineffective and has little effect on
performance without feedback (Locke & Latham, 1990a; Latham & Locke, 1991), and feedback
is also ineffective without goal-setting (Locke & Latham, 1990a; Locke, 2001). DeNisi & Kluger
(2000), who developed their own theory regarding feedback titled “feedback intervention
theory”, argued that one of the basic and most straightforward assumptions is that “behavior is
regulated by a comparison of feedback with a goal or standard” (p. 131). A meta-analysis on
goals and feedback together versus either one alone showed that “17 of 18 studies found the
combination of goals and feedback to be better than goals alone, and 21 of 22 studies found it to
be better than feedback alone” (Locke & Latham, 1990a, p. 192). Similarly, Mento et al. (1987)
found that feedback plus goal-setting was a stronger predictor of performance than only a goalsetting framework. Another meta-analysis conducted by Neubert (1998) on 11 studies with 16
effect sizes supports similar findings. Neubert (1998) found that adding feedback to goal-setting
showed a stronger effect on performance than only goal-setting. The results indicated that adding
feedback to goal-setting was especially significant for complex tasks as compared to relatively
simple tasks.
Erez (1977), who was the first to examine the moderating effect of feedback on
performance in a goal-setting model (Locke & Latham, 1990a), conducted a two-stage
experimental design to examine the relationship between feedback and performance within the
goal-setting framework. The results of the study suggested that the interaction effect of feedback

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and goal was a stronger predictor of performance than the two other main factor, feedback and
goal, on its own. Bandura & Cervone (1983) found that subjects who had goals and who were
provided feedback performed significantly better than either subjects who had goals or subjects
who were provided feedback alone. Cervone & Wood (1995), based on an experimental design
that studied the relationship among goals, feedback, self-regulatory processes, and performance,
suggested that people do not perform at higher levels unless they are given specific goals and
feedback on their efforts.
Pritchard et al. (1988) conducted an experimental design in which they introduced
“Productivity Measurement and Enhancement System” (ProMES) to test the relationship among
group feedback, goal-setting, incentives, and organizational productivity with military personnel
working at an air force base. Before they applied the feedback, goal-setting, and incentives
treatments to the subjects, they had developed a productivity measurement system and examined
the study site for nine months. Next, the subjects were subjected to feedback for five months,
feedback plus goal-setting for another consecutive five months, and finally feedback plus goalsetting plus incentives for another consecutive five months. The results of the study indicated
that feedback is strongly and positively related to performance. The results also suggested that
group-level feedback has a positive effect on performance even for complex tasks.
Existing literature suggests that feedback has a positive effect on performance. However,
it should be noted that feedback and goal-setting should be set at the same time to increase work
performance. The source, the message, and the nature of the feedback recipients receive are
factors that affect employees’ behavior in either direction, positive or negative. Therefore, work
performance requires both goal-setting and appropriate feedback. Employees should be aware of
their performance level compared to goals. Subsequent to such feedback, people and groups are

35

expected to consider their performances and adjust their behaviors to reach goals when
necessary.
2.2.5 Rewards
The relationship between incentives and performance has long been studied by
researchers both in a goal-setting framework and in other models (Pritchard et al., 1988). Locke
et al. (1981) refer to money as a “powerful motivator of performance” (p. 136). However, one of
the first studies examining the effects of incentives on performance in a goal-setting model failed
to show such a relationship (Locke et al., 1968). Furthermore, Guzzo et al. (1985) found that
although the variance of financial compensation was the greatest among all other intervention
programs (including recruitment and selection, training and instruction, appraisal and feedback,
management by objectives, goal-setting, and work redesign), financial compensation was not
significantly related to performance. Additionally, providing rewards to people sometimes leads
to negative behavioral attitudes. When people see rewards as “too general and non-personal”,
which means people who do not deserve recognition are also rewarded, “routine or noncontingent”, “insulting or degrading”, and as “punishment”, they may feel “anger, betrayal, or
insulted” (Doherty, 1998, p. 998). Such feelings may lead to a decrease in job satisfaction and
goal commitment. Guzzo et al. (1985) and Pritchard et al. (1988) argued that the positive effect
of incentives on performance depends on circumstances and the methods by which incentives are
delivered. It should also be mentioned that personality and situational factors like leadership,
wage-policy, and organizational characteristics may moderate the effect of incentives on
performance (Terborg & Miller, 1978; Thierry, 1987). However, it is not the purpose of this
study to examine such relationships.

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Bandura (1997) noted that incentives affect people’s behaviors if they believe they can
accomplish a task successfully. Lee et al. (1997) found that subjects tend not to pursue difficult
goals when they believed they could not achieve the rewards. Further, existing research suggests
that rewards are related to performance in a goal-setting framework. For example, Pritchard &
Curts (1973) examined the effects of incentives and goal-setting on performance separately with
a two-phase experimental design. They found that monetary incentives were significantly and
positively related to performance in both goal-setting and no-goal-setting conditions.
Furthermore, the results suggested that subjects showed higher performance in goal-setting
conditions than no–goal-setting conditions. The results indicated that there were no significant
differences between the no incentives and small incentives conditions in the goal-setting design.
Subjects who were paid more performed better than the other two groups in the goal-setting
design. According to Pritchard & Curts (1973), one of the reasons that might have led Locke et
al. (1968) to find no significant relationship between rewards and performance could be that the
incentives offered to subjects were too small. Locke (1968) and Locke & Latham (1990a) argued
that if the incentives offered do not meet employees’ expectations, workers tend to perform at the
same level or even lower.
Latham et al. (1978) conducted an experimental design with 132 engineers and scientists.
The results of the study indicated that incentives were an important factor in performance in the
goal-setting model. In a study conducted by Terborg & Miller (1978), it was found that both
manipulation of performance-pay and performance-goal affected subjects’ performance
positively. The results also suggested that while pay-performance had a positive effect on
quantity performance, it was found that goal-setting affected both the quantity and quality of
performances. Finally, the results indicated that rewards and goal-setting were not dependent

37

upon each other and affected performances independently. Similarly, London & Oldham (1976)
found that incentives and goal-setting affected performance independently. Overall, existing
literature suggests that incentives can have a positive effect on performance with and without
goal-setting.
2.2.6 Participatively-Set Goals versus Assigned Goals
Researchers (Locke & Latham, 1990a; Locke, 2000; Locke et al., 1981) argued that
participation in goal-setting does not lead to higher performance and goal commitment. In other
words, there is no difference in performance levels and goal commitment whether the goals are
assigned or participatively-set. However, existing research suggests inconsistent findings on the
relationship between participatively-set goals and employee performance. For example, Locke &
Latham (1990a) examined prior studies including experimental and field studies, different tasks,
and studies conducted by different researchers on the subject and concluded that participation in
goal-setting is not a significant predictor of performance in a goal-setting model. The study
conducted by Dossett et al. (1979) indicated that when the effect of goal difficulty is removed,
participatively-set goals are not related to performance and goal acceptance. In an experimental
design conducted by Kernan & Lord (1988), it was found that assigned goals led to higher levels
of goal commitment than the participatively-set goals. The results indicated that there were no
differences between assigned goals and participatively-set goals with regard to goal acceptance
and performance.
Latham & Marshall (1982) found that regardless of employee age, education, position
level, years as a supervisor, and time employed in the public sector, public sector employees did
not show any significant differences in goal acceptance and performance among the three goalsetting conditions, which were self-set, participatively-set, and assigned goals in the goal-setting

38

model. An experimental study of the effect of participatively-set goals on performance (Latham
& Saari, 1979) indicated that there is no significant difference between participatively-set goals
and assigned goals and goal acceptance and performance. Latham & Steele (1983) examined the
relationship between participation and performance in an experimental study and found that
participation was not related to performance. Latham & Yukl (1976) found that there were no
significant differences between assigned goal-setting and participative goal-setting on
performance, goal attainment, and job satisfaction. Latham & Yukl (1976) argued that when
assigned goals are reasonable and not threatening, people tend to accept such goals easily. The
results suggested that individual differences in personality, education, and experience did not
moderate the relationship between participation and performance in a goal-setting framework.
However, the positive effect of participation on performance in the goal-setting model is
found by several other researchers. For example, Campbell & Gingrich (1986) found that
participation affects performance significantly and positively for complex tasks in the goalsetting model. An experimental study of 96 white-collar employees indicated that participation
affected performance quantity, incidental learning, goal acceptance, group commitment, and
satisfaction (Erez & Arad, 1986). Erez & Earley (1987) showed that “participative strategies led
higher levels of goal acceptance and performance than the assigned strategies” in a goal-setting
model (p. 658). Another study conducted by Erez et al. (1985) revealed that participation
affected goal acceptance and subsequent performance. Latham & Yukl (1975) found that
participatively-set goals led to higher levels of performance, goal difficulty, and goal acceptance
for uneducated logging crews. A longitudinal field study conducted by Pearson (1987) on
railway trackmasters and trackmen showed that participation is related to performance and job
satisfaction. Wagner (1994) conducted a meta-analysis based on 52 studies and found that

39

participation has a significant and positive effect on performance and job satisfaction. Contrary
to the widely-held belief that participation is related to goal attainment, goal commitment, and
performance, the existing literature suggests inconsistent findings on this issue.
2.3 Conclusion
Figure 1 shows the adapted version of high performance cycle (Locke & Latham, 1990a;
Latham & Locke, 1991). It summarizes the previous paragraphs into a diagram. As Figure 1
shows, the model starts with the high challenge, specific and difficult goals. If people have
commitment to goals, it leads to higher performance depending on feedback and self-efficacy. If
people are satisfied with the experienced rewards, rewards also lead to higher performance.
When people are allowed to participate in goal setting model, they are expected to show better
performances. If people are satisfied with the outcomes of their performance, they become more
committed to their organizations and ready to perform future tasks. Therefore, the cycle goes to
the starting point and repeats itself.
Figure 1: The High Performance Cycle
*Goal Content
Difficult Goals
Specific Goals
Commitment
Feedback
Self Efficacy
Rewards

Performance

Satisfaction

Participatively set
goals
Adapted from “A Theory of Goal Setting & Task Performance” by E. A Locke, & G. P.
Latham, 1990a, p. 253.

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CHAPTER 3
METHODOLOGY
This chapter presents the methodology of the study in four parts. First, the hypothesis of
the study will be presented. Second, the research area and procedures will be examined. Third,
the study measures will be reviewed. Fourth, the analysis will be introduced.
3.1 Research Questions
Existing literature suggests that goal context is related to job performance (Locke et al.,
1981; Locke & Latham, 1990a, Klein et al., 1990). Laboratory studies (Bryan & Locke, 1967;
Latham & Locke, 1975; Latham & Baldes, 1975; Brown & Latham, 2000), field studies (Andrew
& Ferris, 1972; Wright, 2004), and meta-analysis (Locke et al., 1981; Locke & Latham, 1990a;
Guzzo et al., 1985; Mento et al., 1987; Tubbs, 1986) have shown that goal difficulty and goal
specificity are related to performance. Therefore, consistent with the previous studies, the first
hypotheses are:
H1: Goal difficulty is positively related to motivation of police officers.
H2: Goal specificity is positively related to motivation of police officers.
Further, researchers (Locke & Latham, 1990a, 1990b; Locke, 2000; Locke et al., 1988)
argued that without goal commitment, goal-setting does not result in performance. Although
some researchers found inconsistent results in their studies of the relationship between goal
commitment and performance (Donovan & Radosevich, 1998), other researchers found that goal
commitment was a significant factor for employee performance (Locke et al., 1988; Pinder,
1998; Mitchell & Daniels, 2003; Erez & Zidon, 1984; McCaul et al., 1987; Locke et al., 1984;
Klein, 1991; Wright, 1992). Existing literature also suggests that perceived task significance is

41

also related to task performance (Latham & Locke, 1991; Wright, 2004; Seijts et al., 1997).
Therefore, the hypotheses of the study regarding goal commitment are:
H3: Goal commitment is positively related to motivation of police officers.
H4: Task significance is positively related to motivation of police officers.
Similar to goal commitment, Locke (2000) argued that self-efficacy has a direct and
moderator effect on goal commitment and work motivation. People who have a greater sense of
self-efficacy choose higher goals and show higher performance levels than people who have a
lower sense of self-efficacy. Researchers showed that (Bandura & Cervone, 1983; Bandura &
Wood, 1989; Stajkovich & Luthans, 1998, Barling & Beattie, 1983; Cervone et al., 1991) selfefficacy is positively related to task performance. As indicated in the abovementioned studies,
the hypothesis regarding self-efficacy is:
H5: Self-efficacy is positively related to motivation of police officers.
Like goal commitment, it is suggested that feedback and goal-setting lead to higher levels
of performance when they exist in a model or program together (Locke & Latham, 1990a;
Latham & Locke, 1991; Locke, 2001). Existing literature found that goal-setting plus feedback
is a stronger predictor of performance than a goal-setting-only or feedback-only framework for
both individual level and group level (Locke & Latham, 1990a; Mento et al., 1987; Neubert,
1998; Pritchard et al., 1988). Therefore, the following hypothesis is identified for the feedback
and performance relationship:
H6: Feedback is positively related to motivation of police officers.
Available research also indicates the positive relationship between rewards and
performance (Bandura, 1997) in a goal-setting model (Pritchard et al., 1988; Locke et al., 1968).
Researchers found that goal-setting and rewards may affect employees’ performance

42

independently (Terborg & Miller, 1978; London & Oldham, 1976). Therefore, in accordance
with the existing literature, the relationship between performance and rewards is indicated as
below:
H7: Rewards are positively related to motivation of police officers.
Finally, researchers reported inconsistent findings regarding the effects of participativelyset goals versus assigned goals on job performance. Some researchers argued for (Locke &
Latham, 1990a; Locke, 2000; Locke et al., 1981) and found (Dossett et al., 1979; Kernan &
Lord, 1988; Latham & Marshall, 1982; Latham & Steele; 1983) an insignificant relationship
between participatively-set goals and assigned goals. However, other researchers found that
participatively-set goals have an effect on performance, goal acceptance, and goal commitment
in a goal-setting model (Campbell & Gingrich, 1986; Erez & Arad, 1986; Erez & Earley, 1987;
Erez et al., 1985). Therefore, it is hypothesized that:
H8: Participatively-set goals are positively related to motivation of police officers.
3.2 Survey Construction
A survey instrument was developed to measure work attitudes of police officers working
in various departments in Istanbul. To develop the survey instrument, previously used and
validated scales were used. Scale items were measured on a five-point (coded 1 through 5)
strength-of-agreement scale (i.e., strongly disagree, disagree, neither disagree nor agree, agree,
and strongly agree).
3.2.1 Work Motivation
A work motivation scale (Wright, 2004) was used to measure police officers’
motivational behavior. The scale originally was developed on Patchen’s (1970) four-item scale
and Baldwin’s (1984, 1987, 1990) five-item scale. Wright (2004) added another item to this

43

scale. These items were included to measure the direction, intensity, and persistence of police
officers’ motivational behaviors. The motivation scale consists of the following six items:
1. I put my best effort to get my job done regardless of the difficulties.
2. I am willing to start work early or stay late to finish a job.
3. It has been hard for me to get very involved in my current assignments. (R)
4. I usually do not work as hard as others who do the same type of work. (R)
5. I do extra work for my job that isn’t really expected of me.
6. Time seems to drag while I am on the job. (R)
3.2.2 Goal Context
Goal context refers to both goal difficulty and goal specificity. To measure goal
specificity and difficulty, Wright’s (2004) goal specificity and goal difficulty scales, which are
adapted versions of Locke & Latham’s (1990a) scale, were included in the study. The goal
specificity scale comprised the following five items:
1. My responsibilities at work are very clear and specific.
2. I understand fully which of my job duties are more important than others.
3. It is difficult to evaluate success or failure on my job. (R)
4. I know exactly what I am supposed to do on my job.
5. My supervisor clearly explains to me what my goals are.
Goal difficulty scale items are:
1. The work objectives in my job require a great deal of effort.
2. A high degree of skill and know-how is necessary to do my job well.
3. Jobs like mine are quite demanding day after day.
4. My work is very challenging.

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5. I have new and interesting things to do in my work.
3.2.3 Goal Commitment
Police officers’ behavior regarding goal commitment was measured by using Hollenbeck
et al.’s (1989b) goal commitment scale. However, instead of using the originally developed nineitem scale, only five items on the scale were included, as suggested by Klein et al. (2001). An
additional item from Locke & Latham (1990a) was added to the scale. Therefore, the final scale
consists of six items:
1. It’s hard for me to take the kinds of things I must do in my position. (R)
2. Quite frankly, I don’t care if I achieve my responsibilities or not. (R)
3. I am strongly committed to pursuing assignments given to me.
4. It wouldn’t take much to make me just get by assignments given to me. (R)
5. I am very committed to doing my assignments well.
6. I sometimes fail to accomplish my assignments. (R)
3.2.4 Self-Efficacy
To measure police officers’ sense of self-efficacy, Wright’s (2004) self-efficacy scale,
developed from Sims et al. (1976), was used. The scale consists of four items. The items are:
1. I am confident that I can successfully perform any tasks assigned to me on my current
job.
2. I am not as well prepared as I could be to meet all the demands of my job. (R)
3. I can’t get my work done on time even when I try very hard. (R)
4. Doing my work as well as I am able to leads to high quality results.

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3.2.5 Task Significance
Related to goal commitment, a task significance scale is used to measure importance of
tasks. To measure task significance, Hackman & Oldham’s (1980), Mottaz’s (1981), and
Wright’s (2004) task significance scales were utilized. The adapted version of the task
significance scale consists of the following seven items:
1. A lot of people will be affected by how I do my job in this department.
2. The work I do in this department is extremely meaningful to me.
3. I understand the importance of accomplishing my work objectives.
4. I work on assignments that seem useless or unnecessary. (R)
5. My assignment is really important and worthwhile.
6. Sometimes, I am not sure I completely understand the purpose of what I am doing.
7. I often wonder the importance of my assignment really is. (R)
3.2.6 Participatively-Set Goals
To test whether police officers set their goals participatively or not, Locke & Latham’s
(1990a) goal-setting questionnaire is used. The scale comprised two items:
1. My supervisor lets me participate in the setting of my goals.
2. My supervisor lets me have some say in deciding how I will go about implementing
my goals.
3.2.7 Feedback
Police officers’ perceived sense of received feedback was measured by Locke &
Latham’s (1990a) and Wright’s (2004) four-item scale. The scale items are:
1. I get regular feedback indicating how I am performing.
2. I get coaching from my supervisor to help me do a better job.

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3. I get helpful information from others about how well I am performing at my job.
4. I receive useful evaluations of my strengths and weaknesses at work.
3.2.8 Rewards
Police officers’ perceived sense of work-related rewards was measured by six items
adapted from Locke & Latham (1990a) and Wright (2004). The scale items are:
1. When I improve my performance, my accomplishments are recognized by my
supervisors.
2. I have seen good job performance rewarded in my work unit.
3. If I accomplish my work objectives, it increases my chances to get extra monetary
rewards or letter of commendation.
4. If I accomplish my work objectives, it increases my chances to choose the people I
work with.
5. If I accomplish my work objectives, it increases my chances to choose the shift I
work.
6. If I accomplish my work objectives, it increases my chances to be assigned a better
department.
3.2.9 Department Assignment
Since the sampling in this study consisted of three departments, it could be assumed that
police officers’ perceived sense of department significance might vary based on their assigned
departments. Existing literature on policing (Skolnick, 1975; Paoline, 2003; Goodman, 1997;
Gaines et al., 2003) suggests that police officers prefer to work in crime-oriented departments
rather than departments in which they engage in administrative issues. Therefore, an adapted

47

version of Crank’s (1993) service scale was used to measure police officers’ perceived level of
task significance by assigned department. The scale items are:
1. Some other departments are actually more important to society than mine is.
2. I think that my assignment here is more important than any assignment in another
department for society.
3. The importance of being a police officer in this department is sometimes
overstressed.
3.2.10 Demographic Characteristics
Aside from the abovementioned variables and scales, this study included the
demographic characteristics of the police officers. These demographic variables are age,
education, department, seniority in TNP, salary, whether police officers were given monetary
rewards or not during the year 2011, and whether police officers earned letters of commendation
during the year 2011. The categorical control variables with more than two categories were
recoded into dummy variables for the correlation and OLS regression analyses to see the
differences, if any, between the categories. For example, department variables were recoded into
dummy variables like 0=other vs. 1=airport, 0=other vs. 1=plain clothes, and 0=other vs.
1=public order department.
3.3 Sampling and Sampling Procedures
The study’s survey was conducted through a self-administered, paper-based
questionnaire. The survey was self-administrated, because there was no interaction between the
data collector and respondents. Each of the subjects was given the questionnaire in their
departments by their supervisors. However, a cover letter and thank-you letter as suggested by

48

Dillman (2007) were provided to subjects. The importance of the study and the importance of
their participation in the study were explained in the cover letter.
All participants were informed about how their identities would be protected and about
other ethical issues related to the study. Additionally, before police officers started to fill out the
survey, a consent form, which indicated that their participation was voluntary, their responses
might be recorded, and their identity would be kept confidential, was distributed at the beginning
of the questionnaire. The research questions and the questionnaire were prepared based on a
review of the literature. However, to improve the questionnaire and to eliminate translation
mistakes, a cognitive interview was conducted before the study. The aim of the cognitive
interview is “achieving the best possible questionnaire” (Dilman, 2007, p. 81). A cognitive
interview can identify whether respondents understand the questions as the surveyor intended
and whether there are any vague and/or confusing questions for respondents. After the survey
instrument was fully developed, the survey was translated into Turkish and was translated back
into English. Doing so allowed the researcher to see any possible mistakes in translation. Finally,
the survey was administrated by several people prior to the data collection phase to remove any
remaining problems completely.
The sampling of the study consisted of police officers who were members of the Turkish
National Police. The TNP shows different characteristics from that of US police forces. TNP,
along with the Turkish Gendarmerie and Coastal Security, operates across Turkey under the
authority of the Ministry of Internal Affairs. However, its main jurisdiction areas are cities and
border gates, including 81 cities. Its jurisdiction in rural areas, where the gendarmerie is
responsible, is limited and requires permission from either the governor or the courts. TNP has a
very centralized structure under the administration of a governor who is appointed by the Turkish

49

government. The governor is responsible for the administration of TNP across Turkey. TNP is
represented by a high command called the General Directorate of Security, consisting of 25
departments.
As one of the provincial departments, Istanbul Security Directorate was chosen as the
research site. Istanbul Security Directorate was chosen intentionally, since it is the biggest police
department and serves the biggest population in Turkey. All departments, including stations and
border gates, and all police officers are under the command of the Istanbul Security Directorate.
However, to be able to see variances among police officers and their departments and to be able
to increase the response rate, a purposive sampling procedure was applied. The survey was
administered to the police officers who worked at Ataturk Airport, the plain clothes department,
and the public order department, which were under the command of the Istanbul Security
Directorate. Therefore, the sampling of the study consisted overall of 10,000 police officers
working in these departments.
Subjects were given the questionnaire in their departments and asked to participate in the
study. A consent form was provided to the subjects with the questionnaire. The consent form
indicated who the researcher was, the aim of the study, and any possible risks to subjects;
expressed appreciation for their willingness to participate; explained their rights regarding the
study, such as the right to withdraw from the study at any time; and, described how their
identities would be protected and how the results would be used. Each department was asked to
provide a collection box in an arranged room for completed surveys. Therefore, it was expected
that any possible pressure on subjects from their supervisors and the researcher would be
eliminated. Finally, before conducting the study, the Michigan State University Institutional
Review Boards (MSU IRB) procedure was followed to protect subjects’ rights and welfare.

50

A total of 2,500 surveys were delivered to police officers working in these three
departments. The data collection process was conducted between January 30, 2012, and February
20, 2012. The number of returned surveys was 2,132, a response rate of 85%. The response rate
was in the expected range, since prior research conducted on TNP showed similar results. After
the data cleaning process, however, the study sample consisted of 1,970 cases. Among those
1,970 cases, some variables had missing values. As a general rule, “variables containing missing
data on 5% or fewer of the cases can be ignored” (Meyers et al., 2005, p.59) and can be included
in the analyses. None of the variables had greater than 5% of missing values and were therefore
included in the analyses. A mean substitution approach was used to assign a value for the
missing values in these variables. Mean substitution is a process by which the researcher assigns
the mean of that variable for all missing values of a variable (Meyers et al., 2005). Although
there are some reservations about using mean substitution, and there are other methods for
dealing with such data problems, such as multiple regression imputation and mixed models, the
mean substitution is “the most common and most conservative of the imputation practices”
(Meyers et al., 2005, p. 63). The mean substitution procedure was used to assign values for all
variables having missing cases.
3.4 Analytical Part
Once the data was gathered, SPSS software was used to analyze the data. Factor analysis
was conducted to reduce the large number of variables into scales. Reliability analysis was done
to check for Cronbach’s Alfa. Once scales were determined, general characteristics of the study
participants were demonstrated. Further, descriptive statistics of dependent and independent
variables were examined as well. After univariate level analysis, bivariate analysis was
conducted through correlation analysis and one-way ANOVA analysis to find the relationship

51

between the variables and the variation among the police departments. Finally, to test the
hypothesis of the study, ordinary least square regressions were conducted to examine the
influence of predictor variables on the dependent variable.

52

CHAPTER 4
ANALYSES and FINDINGS
In this chapter major research findings about the police officers’ attitudes towards work
motivation are presented. First, descriptive statistics, demographic characteristics, and control
variables are presented. Second, descriptive statistics of participants’ perceptions about scale
items are presented. Third, principle component analyses are conducted and scale reliabilities are
controlled. Fourth, the results of the bivariate relationships between the variables are examined.
Finally, the results of multivariate analyses are revealed by conducting OLS regression analysis.
4.1 Descriptive Statistics of Demographic Characteristics
Table 1 demonstrates the general characteristics of study participants. As seen in this
table, the age of the police officers ranged from 20 to 55. The average age of the participants was
27, and 97.4% of the participants were male. Therefore, a gender variable was not included in the
study. The education level of police officers ranged from high school diploma to baccalaureate
degree. However, as can be seen in Table 16, only 3% of the police officers have only a high
school education. Approximately 35% of the police officers have a two-year college degree, and
62% of have a baccalaureate degree. Among the police officers who participated in the study,
580 were airport police officers, 651 were plainclothes officers, and 739 were officers in the
public order department. Officers’ monthly salaries ranged from 1900 TL to 2700 TL (the
exchange rate is approximately 1 US Dollar to 1.75 TL). The average salary of the police officers
was 2219 TL.

53

Table 1. Descriptive Statistics of Demographic Characteristics
Airport
(N=580)
N
Mean
(%)

Public
(N=739)
N
Mean
(%)

All
(N=1970)
N
Mean
(%)

26.81

Variable Name

Plain Clothes
(N=651)
N
Mean
(%)
27.04

27.42

Age
1=Female
Gender
2=Male

34
(5.9)
546
(94.1)

1.94

13
(2.0)
638
(98.0)

1.98

4
(.5)
735
(99.5)

1.99

1=High School
2=Two Year College

199
(34.3)

178
(27.3)

318
(43.0)

368
(63.4)

457
(70.2)

390
(52.8)

20

55

1.97

1

2

2.59

1

3

695
(35.3)

3=College

Education

13
(2.2)

Max

27.11
51
(2.6)
1919
(97.4)

Min

1215
(61.7)

2.61

16
(2.5)

2.68

31
(4.2)

2.49

60
(3.0)

Year at TNP

4.43

3.71

5.04

4.42

1

35

Income

2251

2204

2206

2219

1900

2700

.41

0

1

.38

0

1

Monetary
Rewards
Letter of
Commendation

0=No
1=Yes
0=No
1=Yes

115
(19.8)
465
(80.2)
496
(85.5)
84
(14.5)

.80

.14

82
(12.6)
569
(87.4)
479
(73.6)
172
(26.4)

.87

.26

54

224
(30.3)
515
(69.7)
647
(87.6)
92
(12.4)

.70

.13

421
(21.4)
1549
(78.6)
1622
(348)
348
(17.7)

Seventy-eight percent of the police officers stated that they received monetary rewards
during the 2011.Only 17.6% of the police officers were entitled to a letter of commendation.
Monetary rewards are given to police officers who achieve exceptional results, like solving a
homicide crime or capturing heroin and other substances, and to those with consistently excellent
performance ratings. Generally, when a police officer solves a tough case, the other group
members are also given monetary rewards. The letters of commendation are given to police
officers when they participate in extra duties. However, as might be expected, police officers
prefer to be rewarded with money than with letters of commendation.
Table 1 suggests variation among the departments regarding the demographic
characteristics of the police officers. For example, although the percentage of the female police
officers was very low for each police department, the airport had the highest percentage of
female officers. Among the three police departments, the plainclothes department had the highest
percentage of police officers holding a college degree. The public order department had the
highest percentage of officers with seniority in TNP and the greatest percentage of older officers.
The percentages indicating monetary rewards and letters of commendation awarded to police
officers in 2011 were highest for the plainclothes department.
4.2 Descriptive Statistics of Police Officers’ Perceptions about the Survey Items
In this section, police officers’ perceptions about the survey items were presented. As
mentioned in the methodology section, all scales were measured based on the five-point Likerttype scale (1=Strongly Disagree to 5=Strongly Agree). To prevent confusion, however, items
phrased the opposite way around the other items’ scores were reversed, such that 1=Strongly
Agree and 5=Strongly Disagree. Therefore, participants who originally scored 1=Strongly

55

Disagree for a reverse item now got 5=Strongly Agree. The mean values of the items were also
represented in this manner.
The interpretations of the scores were made by focusing on all participants. Additionally,
tables presenting police officers’ perceptions show only scores for the combination of Agree and
Strongly Agree to make a comparison among the three police departments. However, instead of
conducting a one-way ANOVA analysis for each scale item to examine possible differences
among the departments statistically, one-way ANOVA analyses were conducted only for scales
considering the number of scale items. Results of the one-way ANOVA analyses were presented
after conducting principal component factor analyses (PCA) and examining scale reliabilities.
4.2.1 Work Motivation
The results indicated in Table 2 suggested that the majority of the police officers (84%)
stated that they try to do their best for their job. Sixty percent of the police officers were willing
to work early and stay late to finish their job. The results showed 73% of the police officer stated
that they could engage in their current assignments easily. Three out of every four police officers
claimed that they work as hard as other police officers doing similar tasks. Item five had the
smallest percentage: only 53% of the police officers stated that they do extra work that isn’t
expected of them. Finally, 64% of the police officers indicated that time passed quickly while
they were on the job.
The results shown in Table 2 indicate that aside from item five, all items’ mean scores
and percentages indicating the Agree/Strongly Agree (A/SA) scores were higher for the police
officers working in the plainclothes department (respectively M=4.37, SD=.94 for Item 1;
M=3.95, SD=1.13 for Item 2; M=4.17, SD=1.02 for Item 3; M=4.10, SD=1.15 for Item 4;

56

M=3.92, SD=1.16 for Item 6) than for the police officers working in the other two police
departments.
Table 2. Descriptive Statistics for Police Officers’ Perceptions of Motivation
All
(N=1970)

Airport
(N=580)

Plain Clothes
(N=651)

Public Order
(N=739)

A/SA
%

Items
I put 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 (R)
I usually do not work as hard
as others who do the same
type of work (R)
I do extra work for my job that
isn’t really expected of me
Time seems to drag while I am
on the job (R)

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

1660
84

4.18
1.07

485
84

4.12
1.08

579
89

4.37
.94

596
81

4.04
1.14

1177
60

3.53
1.23

296
51

3.29
1.25

493
76

3.95
1.13

388
53

3.36
1.22

1435
73

3.90
1.12

405
70

3.81
1.15

540
83

4.17
1.02

490
66

3.73
1.13

1490
76

4.02
1.22

431
74

3.99
1.23

514
79

4.10
1.15

545
74

3.96
1.27

1025
52
1267
64

3.39
1.20
3.64
1.23

297
51
345
59

3.63
1.23
3.49
1.25

322
49
491
75

3.31
1.21
3.92
1.16

406
55
431
58

3.47
1.15
3.51
1.23

A/SA= Agree/Strongly Agree; M=Mean, SD= Standard Deviation; R=Reversed item
4.2.2 Goal Context
Table 3 shows the percentage scores for each statement. Almost 70% of the police officers

stated that they had clear and specific responsibilities at work. Seventy-one percent of the police
officers indicated they knew the priority order of their job duties. However, they expressed
confusion about evaluating success and failure in their jobs. Only 40% of the officers disagreed
with the statement in item three. Table 3indicated that 72% of the police officers knew what they
were supposed to do in their jobs. Finally, 56% of the police officers stated that their supervisors
explained their goals to them. Similar to the pattern in motivation, police officers working in the
plainclothes department had the highest mean scores for each item (respectively M=4.01,
57

SD=1.06 for Item 1; M=4.07, SD=1.03 for Item 2; M=3.05, SD=1.28 for Item 3; M=3.98,
SD=1.05 for Item 4; M=3.68, SD=1.14 for Item 5)
Table 3. Descriptive Statistics for Police Officers’ Perceptions of Goal Specificity
All
(N=1970)
Items
My responsibilities at work
are very clear and specific.
I understand fully which of my
job duties are more important
than others
It is difficult to evaluate
success or failure on my job
(R).
I know exactly what I am
supposed to do on my job.
My supervisor clearly explains
to me what my goals are.

Airport
(N=580)

Plain Clothes
(N=651)

Public Order
(N=739)

A/SA
%
1364
69

M
SD
3.73
1.19

A/SA
%
390
67

M
SD
3.66
1.18

A/SA
%
511
78

M
SD
4.01
1.06

A/SA
%
463
63

M
SD
3.53
1.26

1527
78

3.92
1.11

432
74

3.85
1.12

544
84

4.07
1.03

551
75

3.83
1.16

797
40

3.00
1.29

237
41

3.05
1.29

275
42

3.05
1.28

285
39

2.93
1.29

1428
72
1097
56

3.83
1.13
3.37
1.23

414
71
260
45

3.81
1.14
3.12
1.24

512
79
448
69

3.98
1.05
3.68
1.14

502
68
389
53

3.72
1.16
3.30
1.25

A/SA= Agree/Strongly Agree; M=Mean, SD= Standard Deviation; R=Reversed item
As Table 4 shows, 67% of the officers perceived their work objectives to require a great
deal of effort. Fifty-seven of the police officers agreed that their jobs required a high degree of
skill and know-how. Fifty-four percent of the police officers stated that their jobs required more
effort day after day. According to the results, 63% of the officers saw their jobs as challenging.
Finally, only 51 percent of the police officers agreed with the statement that they had new and
interesting things to do in their work. Table 4 suggests that item mean scores and percentages
were lower for police officers working at the airport than for the other two departments
(respectively M=3.37, SD=1.22 for Item 1; M=3.14, SD=1.24 for Item 2; M=3.07, SD=1.17 for
Item 3; M=3.42, SD=1.26 for Item 4; M=2.88, SD=1.25 for Item 5). As might be expected, most

58

of the police officers working at the airport indicated their jobs were not difficult and did not
offer new and interesting things.
Table 4. Descriptive Statistics for Police Officers’ Perceptions of Goal Difficulty
All
(N=1970)
Items
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.
I have new and interesting
things to do in my work

Airport
(N=580)

Plain Clothes
(N=651)

Public Order
(N=739)

A/SA
%
1324
67

M
SD
3.68
1.14

A/SA
%
313
54

M
SD
3.37
1.22

A/SA
%
492
76

M
SD
3.88
1.00

A/SA
%
519
70

M
SD
3.74
1.16

1122
57

3.40
1.18

278
48

3.14
1.24

426
65

3.62
1.05

418
57

3.41
1.21

1073
54
1243
63
1009
51

3.39
1.13
3.65
1.21
3.29
1.21

243
42
313
54
210
36

3.07
1.17
3.42
1.26
2.88
1.25

393
60
412
63
433
67

3.54
1.04
3.62
1.16
3.69
1.08

437
59
518
70
366
50

3.50
1.13
3.87
1.17
3.25
1.17

A/SA= Agree/Strongly Agree; M=Mean, SD= Standard Deviation; R=Reversed item
4.2.3 Goal Commitment
Table 5 indicates that 72% of the police officers embraced their assigned tasks. The
majority of the police officers, 80%, cared about fulfilling their responsibilities. Similarly, 84%
of the officers stated that they were committed to pursuing their assignments. Fifty-seven percent
disagreed that they gave up their assignments easily. The results showed that 84% of the officers
stated that they were committed to doing their assignments well. Finally, 65% of the police
officers disagreed that they sometimes could not complete their assignments. The descriptive
results presented in Table 5 suggest that officers working at the plainclothes department had the
highest mean values on all items but item three (M=4.16, SD=1.03).

59

Table 5. Descriptive Statistics for Police Officers’ Perceptions about Commitment
All
(N=1970)

Airport
(N=580)

Plain Clothes
(N=651)

Public Order
(N=739)

A/SA
%

Items
It’s hard for me to take the
kinds of things I must do in
my position. (R)
Quite frankly, I don’t care if I
achieve my responsibilities or
not. (R)
I am strongly committed to
pursuing assignments given to
me
It wouldn’t take much to make
me just get by assignments
given to me. (R)
I am very committed to doing
my assignments well.
I sometimes fail to accomplish
my assignments(R)

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

1409
72

3.78
1.11

418
72

3.83
1.06

498
76

3.90
1.11

493
67

3.64
1.13

1567
80

4.16
1.20

465
80

4.20
1.13

525
81

4.22
1.24

577
78

4.08
1.22

1652
84

4.15
.99

485
84

4.22
.94

557
86

4.16
1.03

610
83

4.09
1.00

1115
57

3.34
1.34

322
56

3.27
1.36

400
61

3.44
1.38

393
53

3.29
1.29

1650
84
1275
65

4.24
1.04
3.67
1.21

491
85
378
65

4.27
1.02
3.68
1.19

558
86
438
67

4.31
1.02
3.75
1.19

601
81
459
62

4.14
1.07
3.60
1.22

A/SA= Agree/Strongly Agree; M=Mean, SD= Standard Deviation; R=Reversed item
4.2.4 Self-Efficacy
Table 6 indicates that 81% of the police officers claimed they had the self-confidence to
perform any task. Sixty-four percent rejected the statement about being unprepared to meet the
demands of their jobs. Seventy-six percent of the police officers disagreed with the statement
that they could not get their work done even when they tried hard. Finally, a majority of the
police officers, 79%, stated that their performances led to high quality results. Table 6 suggests
that items’ scores were lower for the public order department compared to the other two police
departments (respectively M=4.05, SD=1.06 for Item 1; M=3.56, SD=1.14 for Item 2; M=3.90,
SD=1.13 for Item 3; M=3.88, SD=1.06 for Item 4).

60

Table 6. Descriptive Statistics for Police Officers’ Perceptions about Self-Efficacy
All
(N=1970)

Airport
(N=580)

Plain Clothes
(N=651)

Public Order
(N=739)

A/SA
%

Items
I am confident that I can
successfully perform any tasks
assigned to me on my job
I am not as well prepared as I
could be to meet all the
demands of my job(R)
I can’t get my work done on
time even when I try very hard
(R)
Doing my work as well as I
am able to leads to high
quality results

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

1602
81

4.14
1.04

478
82

4.18
.99

547
84

4.21
1.06

577
78

4.05
1.06

1268
64

3.66
1.14

378
65

3.63
1.18

445
68

3.80
1.10

445
60

3.56
1.14

1493
76

3.95
1.13

456
79

4.02
1.08

495
76

3.94
1.17

542
73

3.90
1.13

1552
79

3.99
1.05

464
80

3.99
1.03

542
83

4.12
1.07

546
74

3.88
1.06

A/SA= Agree/Strongly Agree; M=Mean, SD= Standard Deviation; R=Reversed item
4.2.5 Task Significance
The results show that 63% of the officers stated that how they do their jobs affects
people’s lives. Sixty-seven percent indicated that they found the work they did to be meaningful.
A majority of the police officers, 82%, expressed an understanding of the importance of fulfilling
their assigned duties. Sixty-one percent of the police officers did not agree with the statement
indicating they worked on unnecessary tasks. When the same question was asked in a positive
manner, 74% of the police officers agreed that their assignments were important. Forty-nine
percent of the police officers indicated that they understood the purpose of their work. However,
only 27% of the police officers rejected the statement that they wondered about the real
importance of their assignment. A comparison of departments indicates that police officers
working in the plainclothes department (M=4.07, SD=1.03) had the highest score for finding

61

their jobs very meaningful, with the airport police (M=3.53, SD=1.18) and those in the public
order department (M=3.53, SD=1.16).
Table 7. Descriptive Statistics for Police Officers’ Perceptions of Task Significance
All
(N=1970)

Airport
(N=580)

Plain Clothes
(N=651)

Public Order
(N=739)

A/SA
%

Items
A lot of people will be
affected by how I do my job in
this department
The work I do in this
department is extremely
meaningful to me
I understand the importance of
accomplishing my work
objectives
I work on assignments that
seem useless or unnecessary
(R)
My assignment is really
important and worthwhile
Sometimes, I am not sure I
completely understand the
purpose of what I am doing (R)
I often wonder the importance
of my assignment really is (R)

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

1234
63

3.59
1.17

381
66

3.64
1.18

403
67

3.67
1.15

420
57

3.48
1.16

1313
67

3.71
1.15

347
60

3.53
1.18

528
81

4.07
1.03

438
59

3.53
1.16

1617
82

4.10
.99

480
83

4.13
.92

555
85

4.21
.99

582
79

3.97
1.04

1201
61

3.60
1.27

366
63

3.66
1.21

436
67

3.77
1.29

399
54

3.41
1.27

1461
74

3.93
1.11

411
71

3.86
1.11

505
78

4.00
1.18

545
74

3.93
1.06

969
49

3.26
1.34

248
43

3.08
1.33

384
59

3.54
1.30

337
46

3.17
1.35

534
27

2.70
1.36

157
27

2.71
1.13

198
30

2.77
1.16

179
24

2.61
1.12

A/SA= Agree/Strongly Agree; M=Mean, SD= Standard Deviation; R=Reversed item
4.2.6 Participatively-Set Goals
The results presented in Table 8 show that only 28% of the police officers stated that their
supervisors let them participate in the setting of their goals. However, 43% of the police officers
indicated that their supervisors allowed them to express their ideas about how they would
implement their assigned goals. The results also indicate that the mean scores were highest for

62

the plainclothes department (respectively M=2.92, SD=1.29 for item 1; M=3.25, SD=1.25 for
item 2) compared to the other two police department.
Table 8. Descriptive Statistics for Police Officers’ Perceptions of Participatively-Set Goals
All
(N=1970)

Airport
(N=580)

Plain Clothes
(N=651)

Public Order
(N=739)

A/SA
%

Items
My supervisor lets me
participate in the setting of my
goals.
My supervisor lets me have
some say in deciding how I
will go about implementing
my goals.

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

560
28

2.63
1.25

142
24

2.48
1.22

258
40

2.92
1.29

160
22

2.50
1.19

854
43

2.99
1.28

229
39

2.89
1.29

341
52

3.25
1.25

284
38

2.84
1.27

A/SA= Agree/Strongly Agree; M=Mean, SD= Standard Deviation; R=Reversed item
4.2.7 Feedback
Table 9 shows that only 42% of the police officers believed that they got regular
feedback from their supervisors about their job performance. Similarly, 45% of the police
officers said that their supervisors coached them to help them do a better job. However, when the
questions were asked in a more general manner, 65% of the police officers indicated that they
received helpful information from others. These people could be their supervisors, team leaders,
or colleagues. Again, 56% of the police officers stated that they received useful evaluations of
their strengths and weaknesses at work. The results suggest that when more general questions
were asked about the source of feedback, the mean scores became higher for the items. The
items’ mean scores were higher for the plainclothes department (respectively M=3.33, SD=1.13
for item 1; M=3.38, SD=1.20 for item 2; M=3.79, SD=1.04 for item 3; and M=3.66, SD=.99 for
item 4) than the mean scores of the other two police departments.

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Table 9. Descriptive Statistics for Police Officers’ Perceptions about Feedback
All
(N=1970)

Airport
(N=580)

Plain Clothes
(N=651)

Public Order
(N=739)

A/SA
%

Items
I get regular feedback
indicating how I am
performing
I get coaching from my
supervisor to help me do a
better job.
I get helpful information from
others about how well I am
performing at my job.
I receive useful evaluations of
my strengths and weaknesses
at work.

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

822
42

3.05
1.20

180
31

2.74
1.22

345
53

3.33
1.13

297
40

3.05
1.21

888
45

3.07
1.25

210
36

2.86
1.24

368
57

3.38
1.20

310
42

2.97
1.25

1280
65

3.60
1.10

334
58

3.41
1.14

476
73

3.79
1.04

470
64

3.58
1.10

1098
56

3.40
1.08

272
47

3.18
1.13

442
68

3.66
.99

384
52

3.35
1.07

A/SA= Agree/Strongly Agree; M=Mean, SD= Standard Deviation; R=Reversed item
4.2.8 Rewards
Table 10 shows that no more than 50% of the officers agreed or strongly agreed with any
one of the items. Forty-five percent of the police officers indicated that their accomplishments
were recognized by their supervisors when they increased their performance. Forty-nine percent
of the police officers stated that they saw good job performance rewarded in their work unit. As
can be seen in Table 10, 50% of the officers believed they could get monetary rewards or letters
of commendation if they accomplished their work objectives. Only 19% of the police officers
agreed with the statement that they could choose the shift they work if they accomplished their
work objectives. Finally, 42% of the police officers believed that accomplishing their work
objectives increased their chance to be assigned to a better department. According to Table 10,
the mean scores of the items, except item 2, were lower for airport police (respectively M=2.70,
SD=1.31 for Item 1; M=2.86, SD=1.36 for Item 3; M=2.36, SD=1.25 for Item 4; M=1.99,
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SD=1.20 for Item 5; M=2.33, SD=1.33 for Item 6) than for the plainclothes department
(respectively M=3.47, SD=1.25 for Item 1; M=3.63, SD=1.19 for Item 3; M=3.10, SD=1.26 for
Item 4; M=2.61, SD=1.32 for Item 5; M=3.40, SD=1.29 for Item 6) and the public order
department (respectively M=3.06, SD=1.29 for Item 1; M=3.01, SD=1.32 for Item 3; M=2.86,
SD=1.27 for Item 4; M=2.13, SD=1.21 for Item 5; M=2.98, SD=1.33 for Item 6).
Table 10. Descriptive Statistics for Police Officers’ Perceptions of Rewards
All
(N=1970)
Items
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
changes 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.
If I accomplish my work
objectives, it increases my
chances to choose shift I work
If I accomplish my work
objectives, it increases my
chances to be assigned a better
department

Airport
(N=580)

Plain Clothes
(N=651)

Public Order
(N=739)

A/SA
%

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

894
45

3.09
1.32

189
33

2.70
1.31

368
57

3.47
1.25

337
46

3.06
1.29

967
49

3.20
1.29

267
46

3.14
1.28

405
62

3.57
1.18

295
40

2.92
1.31

980
50

3.17
1.33

224
39

2.86
1.36

436
67

3.63
1.19

320
43

3.01
1.32

701
36

2.79
1.97

137
24

2.36
1.25

286
44

3.10
1.26

278
38

2.86
1.27

381
19

2.25
1.27

79
14

1.99
1.20

184
28

2.61
1.32

118
16

2.13
1.21

824
42

2.93
1.38

135
23

2.33
1.33

374
57

3.40
1.29

315
43

2.98
1.33

A/SA= Agree/Strongly Agree; M=Mean, SD= Standard Deviation; R=Reversed item

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4.2.9 Department Assignment
Table 11 shows the frequencies and mean scores of the items. Only 30% of the police
officers thought that their departments were more important to society than other police
departments. Forty-two percent indicated that their assignments were more important to society
than any assignments in another department. Finally, 41% of the police officers rejected the
statement that the importance of being a police officer in this department was sometimes
overstressed. A comparison of the three police departments indicates that the mean scores of the
items were lower for airport police (respectively M=2.47, SD=1.19 for Item 1; M=2.82, SD=1.12
for Item 2; M=2.97, SD=1.18 for Item 3), and higher for the plainclothes department
(respectively M=2.80, SD=1.24 for Item 1; M=3.40, SD=1.09 for Item 2; M=3.14, SD=1.16 for
Item 3).
Table 11. Descriptive Statistics for Police Officers’ Perceptions of Department Assignment
All
(N=1970)
Items
Some other departments are
actually more important to
society than mine is. (R)
I think that my assignment
here is more important than
any assignment in another
department for society.
The importance of being a
police officer in this
department is sometimes
overstressed. (R)

Airport
(N=580)

Plain Clothes Public Order
(N=651)
(N=739)

A/SA
%

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

A/SA
%

M
SD

586
30

2.70
1.25

132
23

2.47
1.19

216
33

2.80
1.24

238
32

2.80
1.28

833
42

3.18
1.15

166
29

2.82
1.12

327
50

3.40
1.09

340
46

3.26
1.15

807
41

3.06
1.17

222
38

2.97
1.18

286
44

3.14
1.16

299
40

3.05
1.18

A/SA= Agree/Strongly Agree; M=Mean, SD= Standard Deviation; R=Reversed item

66

4.3 Scale Constructions
Since some things cannot be measured directly, researchers measure different aspects of
their underlying constructs (Field, 2009). However, when the question arises about whether
“these different variables [are] driven by the same underlying variable,” principal component
analysis (PCA) is used “to identify groups or clusters of variables” (Field, 2000, p. 628). PCA
“aims to account for the variance in the observed measures rather than explain the correlations
among them” (Brown, 2006, p. 22). Therefore, PCA can be used as a tool (a) to understand the
structure of variables; (b) to construct a questionnaire; (c) as a data reduction technique to reduce
a larger set of measures to a smaller set (Field, 2009; Brown, 2006; Raykov & Marcoulides,
2008). After conducting PCA for each scale, the scales’ reliabilities were examined by
Cronbach’s alpha test.
4.3.1 Work Motivation Scale
A principal component analysis (PCA) was conducted on the six items with orthogonal
rotation (varimax). The Kaiser–Meyer–Olkin measure verified the sampling adequacy for the
analysis, KMO = .73, and except for one item whose value was equal to .41, KMO values for
individual items were > .72, which is well above the acceptable limit of .5 (Field, 2009).
Bartlett’s test of sphericity, χ² (15) = 1297.62 p< .001, indicated that correlations between items
were sufficiently large for PCA. An initial analysis was run to obtain eigenvalues for each
component in the data. Two components had eigenvalues over Kaiser’s criterion of 1 and in
combination explained a total of 53% of the variance. Given the convergence of the scree plot
and Kaiser’s criterion on two components, these two components were retained in the final
analysis. Table 12 shows the factor loadings after rotation. Based on the factor loadings, item

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four and item five were excluded from the scale. The reliability of the motivation scale
consisting of four items is .67.
Table 12. PCA and Cronbach’s α Scores for Motivation Scale (N = 1970)
Factor
Loading

Variables
Motivation
I put my best effort to get my job done regardless of the difficulties

.70

I am willing to start work early or stay late to finish a job

.67

It has been hard for me to get very involved in my current assignments (R)

.74

I usually do not work as hard as others who do the same type of work (R)

.43

I do extra work for my job that isn’t really expected of me

.10

Time seems to drag while I am on the job (R)

.67

Cronbach’s Alpha

.67

4.3.2 Goal Context Scales
Table 13 indicates the results of the PCA analysis for the items of goal specificity.PCA
results show that the Kaiser–Meyer–Olkin measure verified the sampling adequacy for the
analysis, KMO = .73, and all KMO values for individual items were > .69, which is well above
the acceptable limit of .5. Bartlett’s test of sphericity, χ² (10) =1523.03, p < .001, indicated that
correlations between items were sufficiently large for PCA. Only one component had
eigenvalues over Kaiser’s criterion of 1 and explained 44% of the variance. Since only one
component was extracted, there was no rotation for the scales. Based on the factor scores, item
three was dropped from the scale. The reliability analysis for the scale is .71. The total scores
were expected to range from 4 to 20.

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Table 13 shows the PCA’s results for the items of goal difficulty as well.PCA results
showed that the Kaiser–Meyer–Olkin measure verified the sampling adequacy for the analysis,
KMO = .74, and all KMO values for individual items were > .72, which is well above the
acceptable limit of .5. Bartlett’s test of sphericity, χ² (10) =1490.11, p < .001, indicated that
correlations between items were sufficiently large for PCA. Since only one component
explaining 44% variance was extracted, there was no rotation for the scales. The factor loadings
of the scale items were presented in Table 13. The reliability score for the scale is .70.
Table 13. PCA and Cronbach’s α Scores for Goal Context Scales (N = 1970)
Factor
Loading

Variables
Goal Content Specificity
My responsibilities at work are very clear and specific.

.77

I understand fully which of my job duties are more important than others

.71

It is difficult to evaluate success or failure on my job (R).

.23

I know exactly what I am supposed to do on my job.

.79

My supervisor clearly explains to me what my goals are.

.64

Cronbach’s Alpha

.71

Goal Content Difficulty
The work objectives in my job require a great deal of effort.

.71

A high degree of skill and know-how is necessary to do my job well.

.73

Jobs like mine are quite demanding day after day.

.77

My work is very challenging.

.63

I have new and interesting things to do in my work

.44

Cronbach’s Alpha

.70

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4.3.3 Goal Commitment
PCA results with orthogonal rotation (varimax) showed that the Kaiser–Meyer–Olkin
measure verified the sampling adequacy for the analysis, KMO = .78, and all KMO values for
individual items were > .74, which is well above the acceptable limit of .5. Bartlett’s test of
sphericity, χ² (15) =1821.38, p < .001, indicated that correlations between items were sufficiently
large for PCA. Since only one component explaining 41 % variance was extracted, there was no
rotation for the scales. Table 14 shows the factor loadings of items. Item four was excluded from
the scale to increase scale reliability. The reliability score for the scale is .70.
Table 14. PCA and Cronbach’s α Scores for Goal Commitment Scale (N = 1970)
Factor
Loading

Variables
Goal Commitment
It’s hard for me to take the kinds of things I must do in my position. (R)

.57

Quite frankly, I don’t care if I achieve my responsibilities or not. (R)

.74

I am strongly committed to pursuing assignments given to me

.69

It wouldn’t take much to make me just get by assignments given to me. (R)

.50

I am very committed to doing my assignments well.

.68

I sometimes fail to accomplish my assignments(R)

.63

Cronbach’s Alpha

.70

4.3.4 Task Significance
PCA result with orthogonal rotation (varimax) for the task significance scale showed that
the Kaiser–Meyer–Olkin measure verified the sampling adequacy for the analysis, KMO = .75,
and all KMO values for individual items were > .57, which is equal to or above the acceptable
limit of .5. Bartlett’s test of sphericity, χ² (21) =2500.15, p < .001, indicated that correlations

70

between items were sufficiently large for PCA. Two components had eigenvalues over Kaiser’s
criterion of 1 and explained a total of 55% of the variance. Item six and item seven were
excluded from the scale, since their factor scores fell below the value of .40. Additionally, item
one was dropped from the scale to increase scale reliability. The task significance scale
ultimately consists of four items with a .72 reliability score.
Table 15. PCA and Cronbach’s α Scores for Task Significance Scale (N = 1970)
Factor
Loading

Variables
Task Significance
A lot of people will be affected by how I do my job in this department

.61

The work I do in this department is extremely meaningful to me

.74

I understand the importance of accomplishing my work objectives

.73

I work on assignments that seem useless or unnecessary (R)

.49

My assignment is really important and worthwhile

.70

Sometimes, I am not sure I completely understand the purpose of what I am
doing

.30

I often wonder the importance of my assignment really is (R)

.26

Cronbach’s Alpha

.72

4.3.5 Self-Efficacy
Table 16 shows the results of PCA with orthogonal rotation (varimax). The Kaiser–
Meyer–Olkin measure verified the sampling adequacy for the analysis, KMO = .69, and all
KMO values for individual items were > .68, which is well above the acceptable limit of .5.
Bartlett’s test of sphericity, χ² (6) =1116.99, p < .001, indicated that correlations between items
were sufficiently large for PCA. Since only one component explaining 50% variance was

71

extracted, there was no rotation for the scales. Table 16 shows the scale items’ factor loadings
and scale reliability score, which is .67.
Table 16. PCA and Cronbach’s α Scores for Self-Efficacy Scale (N = 1970)
Factor
Loading

Variables
Self Efficacy
I am confident that I can successfully perform any tasks assigned to me on my
current job

.71

I am not as well prepared as I could be to meet all the demands of my job(R)

.72

I can’t get my work done on time even when I try very hard (R)

.74

Doing my work as well as I am able to leads to high quality results

.67

Cronbach’s Alpha

.67

4.3.6 Feedback
Table 17 reveals the results of PCA analysis with orthogonal rotation (varimax). The
Kaiser–Meyer–Olkin measure verified the sampling adequacy for the analysis, KMO = .70, and
all KMO values for individual items were > .69, which is well above the acceptable limit of .5.
Bartlett’s test of sphericity, χ² (6) =1587.75, p < .001, indicated that correlations between items
were sufficiently large for PCA. Since only one component explaining 54% variance was
extracted, there was no rotation for the scales. Item one and item two were dropped from the
scales before conducting reliability analysis. These items are related to feedback from the
supervisors. However, the nature of police work, especially for the public order department and
the plainclothes department, prevents a constant dialogue between the supervisors and police
officers. Therefore, the last two items, which ask police officers’ perceived sense of received
feedback in a more general sense, were retained in the scale. The reliability score for these two
items was .69.
72

Table 17. PCA and Cronbach’s α Scores for Feedback Scale (N = 1970)
Factor
Loading

Variables
Feedback
I get regular feedback indicating how I am performing

.69

I get coaching from my supervisor to help me do a better job.

.75

I get helpful information from others about how well I am performing at my job.

.75

I receive useful evaluations of my strengths and weaknesses at work.

.77

Cronbach’s Alpha

.69

4.3.7 Participatively-Set Goals
Table 18 reveals the results of PCA with orthogonal rotation (varimax). The Kaiser–
Meyer–Olkin measure verified the sampling adequacy for the analysis, KMO = .50, and all
KMO values for individual items were > .50, which is well above the acceptable limit of .5.
Bartlett’s test of sphericity, χ² (1) =941.51, p < .001, indicated that correlations between items
were sufficiently large for PCA. Since only one component explaining 81% variance was
extracted, there was no rotation for the scales. The score of the reliability analysis is equal to .76.
The range of the scale was expected to range from 2 to 10. Higher values mean police officers
are more likely to participate in the process of assigning tasks.
Table 18. PCA and Cronbach’s α Scores for Participatively-Set Goals Scale (N = 1970)
Factor
Loading

Variables
Participatively-Set Goals
My supervisor lets me participate in the setting of my goals.

.90

My supervisor lets me have some say in deciding how I will go about
implementing my goals.

.90

Cronbach’s Alpha

.76
73

4.3.8 Rewards
PCA analysis with orthogonal rotation (varimax) for the police officers’ perceived sense
of rewards scale showed that the Kaiser–Meyer–Olkin measure verified the sampling adequacy
for the analysis, KMO = .85, and all KMO values for individual items were > .82, which is equal
to or above the acceptable limit of .5. Bartlett’s test of sphericity, χ² (15) =4371.90, p < .001,
indicated that correlations between items were sufficiently large for PCA. Only one component
had eigenvalues over Kaiser’s criterion of 1 and explained 56% of the variance. Table 19 shows
the outcomes of the items and the reliability of the scale which was equal to .84.
Table 19. PCA and Cronbach’s α Scores for Rewards Scale (N = 1970)
Factor
Loading

Variables
Rewards
When I improve my performance, my accomplishments are recognized by my
supervisors.

.76

I have seen good job performance rewarded in my work unit

.72

If I accomplish my work objectives, it increases my changes 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.
If I accomplish my work objectives, it increases my chances to choose shift I
work
If I accomplish my work objectives, it increases my chances to be assigned a
better department

.80
.78
.66
.76
.84

Cronbach’s Alpha
4.3.9 Department Assignment

Table 20 shows the result of PCA analysis with orthogonal rotation (varimax) for the
scale. Kaiser–Meyer–Olkin measure verified the sampling adequacy for the analysis, KMO =
.49, and all KMO values for individual items were > .50, which is below the acceptable limit of
.5. Furthermore, the reliability analysis for the combination of item one and item three was also
74

very low. Therefore, instead of a creating a scale, these items were treated as single item.
However, item one and item three were ignored and item two was used in the study. Item two
examined police officers’ perception of the importance of their assigned tasks in a given
department compared to any other department.
Table 20. PCA and Cronbach’s α Scores for Department Significance Scale (N = 1970)
Factor
Loading

Variables
Department Significance
Some other departments are actually more important to society than mine is.
I think that my assignment here is more important than any assignments in
another department for society.
The importance of being a police officer in this department is sometimes
overstressed.

.75
.02
.80
.34

Cronbach’s Alpha
4.3.10 Descriptive Statistics of Dependent and Independent Variables

The main focus of this study is to measure police officers’ attitudes towards work
motivation. As mentioned earlier, a motivation scale was used to measure police officers’
motivational attitudes. Table 21 reveals descriptive statistics of the scale. The total scores were
expected to range from 4 to 20. Since the motivation scale was based on a 5-point Likert scale,
higher values mean police officers were more motivated to perform their assigned tasks. When
the scale distribution is divided into approximate thirds (4-9, 10-15, and 16-20), the mean score indicates
that police officers had an upper mid- to high-level sense of task motivation towards their assigned tasks.

The results of the study indicate that the mean of the goal difficulty scale was 17.4 with a
standard deviation of 3.9. The scale ranges from 5 to 25. Higher values mean the police officers
perceived their assigned tasks to be difficult. Dividing the scale distribution into approximate
thirds (5-11, 12-18, and 19-25) indicates that police officers had an upper mid- to high level
75

sense of perceived difficulty in their assigned tasks. Table 21 shows that the mean of the goal
specificity scale is 14.8, with a standard deviation of 3.4. The range of the scale goes from 4 to
20. Higher values mean the officers had more specific goals. The approximate thirds of the scale
(4-9, 10-15, and 16-20) indicate that the perceived goal specificity of police officers towards their
assigned tasks fall into the upper mid-level range.

Table 21. Descriptive Statistics of Dependent and Independent Variables (N = 1970)
Variable Name

Mean

SD

Min

Max

Motivation Scale

15.3

3.3

4

20

Goal Difficulty Scale

17.4

3.9

5

25

Goal Specificity Scale

14.8

3.4

4

20

Commitment Scale

20.0

3.8

5

25

Task Significance Scale

15.3

3.3

4

20

Self-Efficacy Scale

15.7

3.1

4

20

Feedback Scale

7.0

1.9

2

10

Participatively Set Goals Scale

5.6

2.3

2

10

Rewards Scale

17.4

5.9

6

30

Department Significance

3.2

1.1

1

5

Another predictor of police officers’ attitudes towards work motivation is commitment. The
average score on the commitment scale was 20.0, with a standard deviation of3.8. The range of the scale
goes from 5 to 25. Higher values mean police officers had higher levels of commitment towards

their assigned tasks. The approximate third scores (5-11, 12-18, and 19-25) indicate that police
officers had a higher level of commitment to their assigned tasks. The mean score of the task significance
scale is15.3, with a standard deviation of 3.3. The dispersion of the scale goes from 4 to 20. Higher

76

values mean a higher perceived significance of assigned tasks. When divided into approximate
thirds (4-9, 10-15, and 16-20), the mean score of the scale indicates that police officers had an upper midto high level perception of the significance of the task they were assigned.

The mean value of the self-efficacy scale, which ranges from 4 to 20, is 15.7 with a 3.1
standard deviation. Higher values mean police officers had higher levels of perceived selfefficacy. When the scale distribution is divided into approximate thirds (4-9, 10-15, and 16-20), the
results indicate that police officers might have had “not strong” but rather an upper mid-level
sense of self-efficacy. The feedback scale ranges from 2 to 10, with a mean of 7.0 and a standard
deviation of 1.9. The mean score indicates that the police officers had a mild to strong sense of
receiving feedback from the people with whom they work (approximate thirds 2-4, 5-7, and 810).
The mean score of the participatively-set goals scale, which is 5.6 with the standard
deviation 2.3, suggests that police officers had mid-level feelings of participating in the decision
making about their assigned tasks. The rewards scale ranges from 6 to 30 with a mean of 17.4 and a
standard deviation of 5.9. The mean score indicates that the police officers had a mid-level sense of
getting rewards (approximate thirds of6-14, 15-22, and 23-30). Finally, the mean of the department

significance item is equal to 3.2, with a standard deviation of 1.1.The mean score suggests that
police officers had a mid-level feeling that their assignments in a given department were
important. To summarize, descriptive statistics presented general characteristics of dependent
and independent variables. Furthermore, these descriptive statistics allow one to see central
tendencies and dispersion of the variables.

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4.4 Bivariate Analyses
4.4.1 Correlation Analysis
A correlation analysis was conducted to see if there was a relationship between the
dependent and independent variables. Since the main aim of the study was to test the relationship
between police officers’ work motivation and independent variables, the result of correlation
analysis will be explained by focusing on this main concern. Table 22 shows the results of the
analyses. The results indicate that all eight variables of the goal-setting model are significantly
and positively related to police officers’ work motivation.
The results of the Pearson Correlation Coefficients between the motivation and these
eight variables are goal difficulty (r=.20, p<.001), goal specificity (r=.45, p<.001), commitment
(r=.43, p<.001), task significance (r=.48, p<.001), self-efficacy (r=.44, p<.001), feedback (r=.29,
p<.001), participatively-set goals (r=.24, p<.001), and rewards (r=.26, p<.001). These scores
suggest that the increase in the police officers’ sense of any of these variables results in police
officers having more motivation to perform their assigned tasks. For example, if the police
officers feel that their assigned tasks are difficult and/or specific, they are more likely to perform
better. In addition to these variables, police officers’ perceived significance of task in assigned
departments is also positively and significantly related to police officers’ motivation. Keeping in
mind the limitation of bivariate analyses, like omitting the effect of a third variable on the
dependent variable, it can be concluded that these results verified all of the research hypotheses
at the bivariate level.
With respect to control variables, all control variables but gender and high school versus
other variables are significantly related to the motivation of police officers. Actual monetary
rewards (r=.12, p<.001) and letters of commendation (r=.06, p<.001) that police officers earned

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during 2011, working in the plainclothes department (r=.25, p<.001), and holding a college
degree (r=.07, p<.01) were positively related to police officers’ motivation, but age (r=-.05,
p<.05), seniority at TNP (r=-.09, p<.001), working at the airport (r=-.11, p<.001) or in the public
order department (r=-.14, p<.001), and holding a two-year college degree (r=-.06, p<.05) had a
negative and significant relationship with police officers’ work motivation.
The results suggest that actual rewards including money and letters of commendation
police officers earned during 2011 had a positive effect on their work motivation. Furthermore,
holding a college degree, as compared to other types of degrees, and working at the plainclothes
department, rather than either of the other two departments, had a positive effect on motivation
as well. However, as police officers age and gain seniority, they are more likely to lose their
motivation to perform their assigned tasks.
Table 22 indicates relations among other variables as well. For example, the results
suggest that goal difficulty (r=.13, p<.001), goal specificity (r=.36, p<.001), task significance
(r=.52, p<.001), self-efficacy (r=.65, p<.001), and feedback (r=.21, p<.001) were significantly
related to goal commitment. Furthermore, monetary rewards (r=.13, p<.001) and letters of
commendation (r=.07, p<.01) had a positive and significant relation to goal commitment as well.
The results suggest that police officers working in the plainclothes department (r=.06, p<.01) had
a higher sense of goal commitment compared to those in other departments. Police officers
holding a college degree (r=.13, p<.001) were more committed to their assigned tasks as well.
The results imply that feedback (r=.28, p<.001) and past experiences with monetary
rewards (r=.13, p<.001) and letters of commendation (r=.08, p<.001) were related to police
officers’ perceived level of self-efficacy. Similar to goal commitment, the results also suggest the
same patterns regarding the relationship between self-efficacy, assigned department, and

79

education level. Police officers working in the plainclothes department (r=.07, p<.001) had a
higher sense of self-efficacy than did those in the other two departments, airport and public
order. Similarly, police officers holding a college degree (r=.13, p<.001) had a higher sense of
self-efficacy than did those officers with a high school degree or two-year college degree.
Finally, Table 22 reflects the relationship between the departments and independent
variables. For example, while police officers working at the airport (r=-.25, p<.001) perceived
their assigned goals to be less difficult than in the other two departments, police officers working
in the plainclothes department (r=.17, p<.001) and public order department (r=.07, p<.01) had a
sense of having more difficult goals. However, the results suggest police officers working at the
airport (r=-.08, p<.001) and in the public order department (r=-.11, p<.001) perceived their
assigned tasks to be less specific, compared to those officers in the plain clothes department
(r=.18, p<.001).
Aside from showing the relations among the variables, Pearson correlations are a very
useful tool to identify possible multicollinearity problems. Multicollinearity exists when “two
predictors correlate very strongly” (Meyers et al., 2005, p. 180) and it is one of the factors that
violates the assumption of OLS regression analyses. Although there is not any single suggestion
for solving this potential problem, researchers (Meyerson et al., 2005; Berry & Fieldman, 1985)
argued that any Pearson correlations value that is higher than .70 or .80 between two variables is
an indicator of possible multicollinearity. Although there were some cases of a high Pearson
correlations value like commitment and self-efficacy (r=.65, p<.001), or age and year at TNP
(r=.68, p<.001), the only Pearson Correlations value that was higher than.70 was the relation
between the two-year college and college variables (r=-.94, p<.001).

80

Table 22. Bivariate Correlations among all Variables (N = 1970)
Variables
Motivation

1
1

2

Goal Difficulty

.20***

1

Goal Specificity

.45***

.25***

1

Commitment

.43***

.13***

.36***

1

Task Significance

.48***

.25***

.46***

.52***

1

Self-Efficacy

.44***

.17***

.37***

.65***

.52***

1

Feedback

.29***

.28***

.32***

.21***

.32***

.28***

Participatively Set Goals

.24***

.15***

.33***

.03

.26***

.04

.36***

1

Rewards

.26***

.16***

.35***

.02

.28***

.05*

.34***

.61***

1

Department Significance

.17***

.22***

.17***

.01

.17***

.05*

.19***

.17***

.24***

Age

-.06*

-.01

.00

.02

.05*

.00

-.01

.04

.04

-.04

Gender
Year at TNP

.02

.05*

.01

-.03

.00

-.03

.02

.01

.03

.07**

.03

-.09*** -.02

-.02

-.01

.01

-.00

-.02

.02

.03

-.03

.68***

Money Rewards

.12***

-.02

.08***

.13***

.14***

.13***

.06**

.02

-.02

.01

.13***

Letter of Commendation

.06**

.04

.06*

.07**

.09**

.08***

.06**

.07***

.07**

.04

.10***

Airport

-.11*** -.25*** -.08*** .03

-.03

.02

-.14*** -.07*** -.22*** -.20*** -.04

Plain Clothes

.25***

.15***

.07***

.16***

Public Order

-.14*** .07**

-.11*** -.09*** -.12*** -.09*** -.03

High School

-.04

.02

-.03

-.03

Two Year College

-.06*

.01

-.05*

College

.07**

-.01

.06**

.17***

3

.18***

4

.06**

5

6

7

8

9

10

11

1

-.01

-.10*** -.06**

.06*

.05*

.05*

.06**

.00

.32***

-.12*** -.10*** -.12*** -.01

.00

.03

.03

-.22***

.13***

-.02

-.05*

-.03

.10***

81

-.04
.13***

.00
.01

.28***

1

.14***

.00

.17***

1

.10***
(continued)

Table 22. (continued)
Variables
Motivation

12

13

14

15

16

17

18

19

20

21

Goal Difficulty
Goal Specificity
Commitment
Task Significance
Self-Efficacy
Feedback
Participatively Set Goals
Rewards
Department Significance
Age
1

Gender
Year at TNP

.02

Money Rewards

.00

.01

Letter of Commendation

-.02

.12***

.15***

Airport

-.13***

.00

.02

-.05*

1

Plain Clothes

.03

-.12***

.15***

.16***

-.45***

1

Public Order

.10***

.11***

-.17***

-.11***

-.50***

-.54***

High School

-.01

.35***

.01

.03

-.03

-.02

.05*

Two Year College

.02

.06*

-.18***

-.01

-.01

-.12***

.13***

-.13***

1

College

-.02

-.18***

.17***

-.01

.02

.12***

-.14***

-.22***

-.94***

1
1
1

Note: Entries are Pearson Correlation Coefficients; *p<.05, ** p<.01, *** p<.001

82

1
1
1

4.4.2 ANOVA Analysis of Demographic Characteristics
A series of one-way ANOVA analyses were carried out to test variation on control
variables by department. Table 23indicates that the average age of the police officers did not
differ significantly among the departments: F(2,1967)=2.91, at p<.05 level. Therefore, it can be
concluded that the average age of the police officers was not significantly different among the
three police departments.
Controlling possible mean differences regarding police officers’ average working year at
TNP by department revealed that there was a significant variation among the departments:
F(2,1967)=16.66, p<.001. A Tukey’s honestly significant difference (HSD) analysis indicated
that police officers working in the public order department (M=5.04, SD=5.48) had higher
average seniority scores than police officers working at the airport (M=4.43, SD=3.62) or in the
plainclothes department (M=3.72, SD=3.05). Comparing airport police to those in the
plainclothes department suggested that the average working year was significantly lower for the
plainclothes department than for the airport officers.
A one-way ANOVA showed that actual monetary rewards earned by police officers in
2011 significantly varied among the three police department: F(2,1967)= 33.98, p<.001.
Tukey’s HSD post hoc analysis suggested that police officers working in the plainclothes
department (M=.87, SD=.33) earned more monetary rewards more than police officers working
at the airport (M=.80, SD=.40) or in the public order department (M=.70, SD=.46). The results
indicated that the average monetary rewards were significantly higher for the airport officers
than for the public order department officers.
The results of a one-way ANOVA analysis showed that the average score for letters of
commendation received by police officers in 2011 varied significantly among the three police

83

departments: F(2,1967)=26.76, p<.001. Post hoc analysis using Tukey’s HSD test indicated that
police officers working in the plainclothes department (M=.26, SD=.44) had a higher average
score for receiving letters of commendation than the police officers working at the airport
(M=.14, SD=.35) or in the public order department (M=.12, SD=.33). The results suggested a
non-significant difference between the airport and public order department.
Finally, a one-way ANOVA analysis indicated that police officers’ average education
level varied significantly among the departments F(2,1967)=22.30, p<.001. Tukey’s HSD
analysis indicated that the average education score for police officers working in the public order
department (M=2.49, SD=.58) was higher than the average score of police officers working at
the airport (M=2.61, SD=.53) or in the plainclothes department (M=2.68, SD=.52). However, the
results indicated that the average education scores did not differ significantly between the airport
and plainclothes officers.
Table 23. Summary of one-way ANOVA Analyses of Demographic Variables (N = 1970)
Airport
(N=580)
Variable Name

Plain Clothes
(N=651)

Public Order
(N=739)

Mean

SD

Mean

SD

Mean

SD

F Score

Age

26.81

4.49

27.04

3.23

27.42

5.68

2.91

Year at TNP

4.43

3.62

3.72

3.05

5.04

5.48

16.66***

Monetary Rewardsa

.80

.40

.87

.33

.70

.46

33.98***

Letter of Commendationa

.14

.35

.26

.44

.12

.33

26.76***

Educationb

2.61

.53

2.68

.52

2.49

.58

22.30***

a= 0=No and 1=Yes; b= 1= High School, 2= Two Year College, 3= College
*p<.05, ***p<.001

84

Table 24. Summary of Tukey’s HSD analysis of Demographic Variables (N = 1970)
Variable Name
Department

Age

Year at TNP

Monetary
Rewards

Letter of
Commendation

Education

SE

Mean Dif.

SE

Mean Dif.

SE

Mean Dif.

SE

Plain Clothes

-.22

.26

.71**

.24

-.08**

.02

-.12***

.02

-.07

.03

-.61*

.26

-.62*

.24

.10***

.02

.02

.02

.12***

.03

Airport

.22

.26

-.71**

.24

.08**

.02

.12***

.02

.07

.03

-.39

.25

-1.33***

.23

.18***

.02

.14***

.02

.19***

.03

Airport

.61*

.26

.62*

.24

-.10***

.02

-.02

.02

-.12***

.03

Plain Clothes

Public Order

Mean Dif.

Public Order

Plain Clothes

SE

Public Order

Airport

Mean Dif.

.39

.25

1.33***

.23

-.18***

.02

-.14***

.02

-.19***

.03

*p<.05, ** p<.01, *** p<.001

85

4.4.3 ANOVA Analysis of Dependent and Independent Variables
A one-way ANOVA analysis was conducted to compare the relations between the police
officers’ working department and dependent, independent, and control variables. Since ANOVA
analyses indicate whether the means differ or not but not how they differ, a post-hoc test,
Tukey’s honestly significant difference (HSD), was conducted. Table 25 suggests that there are
significant effects of departments on the variables.
Table 25 shows that police officers working at the airport had an average motivation
value of 14.71 (SD=3.18); police officers working in the plainclothes department had an average
motivation score of 16.42 (SD=3.13); and, police officers working in the public order department
had an average motivation value of 14.65 (SD=3.26). The results suggest that police officers’
motivation varied significantly based on their assigned departments: F(2,1967)=64.46, p<.001.
Post hoc analyses using Tukey’s HSD for differences indicated that police officers working in
the plainclothes department had a higher level of motivation for their assigned goals compared to
police officers working at the airport or in the public order department. However, the results
suggest that there was no difference regarding motivation towards assigned tasks between police
officers working at the airport and those working in the public order department.
A one-way ANOVA was carried out to test for police officers’ sense of perceived goal
difficulty differences among the departments. Perceived goal difficulty differed significantly
across the three departments: F(2,1967)=71.91, p<.001. Tukey’s post-hoc comparisons of the
three departments indicate that police officers working at in the plainclothes department
(M=18.350, SD=3.13) perceived their assigned tasks to be more difficult than the police officers
working at the airport (M=15.89, SD=4.13) or in the public order department (M=17.77,
SD=3.73). A comparison between the airport and public order department results suggests that

86

police officers working in the latter perceived their assigned tasks to be more difficult than did
the police officers working at the airport.
Another test utilizing one-way ANOVA results indicated that police officers’ perceived
level of goal specificity varied significantly among the three departments: F(2,1967)=34.11,
p<.001. Tukey’s HSD post hoc analysis suggests that perceived sense of goal specificity was
significantly higher for police officers working in the plainclothes department (M=15.74,
SD=3.10) than for the police officers working at the airport (M=14.44, SD=3.34) or in the public
order department (M=14.39, SD=3.58). However, there was no significant difference between
the police officers working at the airport and those in the public order department regarding
perceived sense of goal specificity.
Table 25 reveals that there was a significant variation among the three police departments
regarding police officers’ sense of commitment to their assigned tasks: F(2,1967)=8.26, p<.001.
Tukey’s HSD post hoc test indicated that police officers working in the public order department
(M=19.57, SD=3.72) had a lower sense of goal commitment than the police officers working at
the airport (M=20.20, SD=3.66) or the plainclothes department (M=20.33, SD=3.83). Tukey’s
HSD scores did not suggest any differences between airport and plainclothes officers.
Another one-way ANOVA analysis showed that the police officers’ perceived sense of
task significance varied significantly based on police officers’ assigned departments:
F(2,1967)=24.18, p<.001. Post hoc analysis using Tukey’s HSD criterion indicated that police
officers working in the plainclothes department (M=16.05, SD=3.39) had a higher level of
perceived task significance than the police officers working at the airport (M=15.18, SD=3.26)
and in the public order department (M=14.84, SD=3.26).The comparison between the

87

departments, airport and public order, suggests that there was no significant differences between
these two departments regarding police officers’ perceived sense of task significance.
Controlling for possible mean differences regarding police officers’ sense of self-efficacy
by department revealed that there was a significant variation among the departments:
F(2,1967)=8.58, p<.001. Tukey’s HSD analysis indicated that police officers working at the
airport (M=15.82, SD=2.94) and in the plainclothes department (M=16.07, SD=3.24) had a
higher sense of self-efficacy than those working in the public order department (M=15.40,
SD=3.04). However, the results showed two groups, airport and plainclothes, were not
statistically significant regarding police officers’ sense of self-efficacy.
With respect to feedback, there was a statistically significant difference between groups
as determined by one-way ANOVA: F(2,1967)=32.59, p<.001. A Tukey’s HSD post hoc test
revealed that police officers working in the plainclothes department (M=7.45, SD=1.79) had a
higher sense of receiving feedback in general than the two other groups, police officers working
at the airport (M=6.59, SD=1.99) and officers working in the public order department (M=6.93,
SD=1.86). Police officers working in the public order department had a statistically significant
higher sense of receiving feedback than those officers working at the airport.
The results of a one-way ANOVA analysis showed that police officers’ sense of
participating in decision making regarding their assigned tasks varied significantly among the
departments: F(2,1967)=28.81, p<.001. Post hoc analysis using Tukey’s HSD test indicated that
police officers working in the plainclothes department (M=6.17, SD=2.29) had a higher sense of
participating in the decision making about their assigned tasks than the police officers working at
the airport (M=5.37, SD=2.27) or in the public order department (M=5.34, SD=2.19). The results
suggested a non-significant difference between the airport and public order officers.

88

Table 25. Summary of one-way ANOVA of Study’s Scales by Department (N = 1970)
Airport
(N=580)
Variable Name

Plain Clothes
(N=651)

Public Order
(N=739)

Mean

SD

Mean

SD

Mean

SD

F Score

Min

Max

Motivation

14.71

3.18

16.42

3.13

14.65

3.26

64.46***

4

20

Goal Difficulty

15.89

4.13

18.35

3.36

17.77

3.73

71.91***

5

25

Goal Specificity

14.44

3.34

15.74

3.10

14.39

3.58

34.11***

4

20

Commitment

20.20

3.66

20.33

3.83

19.57

3.72

8.26***

5

25

Task Significance

15.18

3.26

16.05

3.39

14.84

3.26

24.18***

4

20

Self-Efficacy

15.82

2.94

16.07

3.24

15.40

3.04

8.58***

4

20

Feedback

6.59

1.99

7.45

1.79

6.93

1.86

32.59***

2

10

Participatively Set
Goals

5.37

2.27

6.17

2.29

5.34

2.19

28.81***

2

10

Rewards

15.39

5.83

19.77

5.39

16.96

5.69

96.69***

6

30

2.82

1.12

3.40

1.09

3.26

1.15

43.53***

1

5

Department
Significance
***p<.001

With a one-way ANOVA, it was found that police officers’ perceived sense of the
possibility of receiving rewards based on their assigned tasks varied significantly among the
three police departments: F(2,1967)=96.69, p<.001. Tukey’s HSD post hoc analysis suggested
that the perceived sense of the possibility of receiving rewards was significantly higher for police
officers working in the plainclothes department (M=19.77, SD=5.39) than for those working at
the airport (M=15.39, SD=5.83) or in the public order department (M=16.96, SD=5.69).
Furthermore, police officers working in the public order department had a statistically significant
greater perception than their airport peers of the possibility of receiving rewards.
Finally, a one-way ANOVA analysis indicated that police officers’ perceived sense of
their assigned tasks in a given department varied significantly among the departments:
89

F(2,1967)=43.53, p<.001. Tukey’s HSD analysis indicated that police officers working in the
plainclothes department (M=3.40, SD=1.09) assigned greater importance to their department
tasks than did the police officers working at the airport (M=2.82, SD=1.12) or in the public order
department (M=3.26, SD=1.15). Furthermore, the results indicated that two groups, airport and
public order officers, differed significantly in that public order police officers gave greater
importance to their department tasks than airport officers assigned to their own departmental
tasks.
4.5 Multivariate Analysis
Locke (2000) suggested that (a) the more difficult the goals, the higher the performance
achieved; (b) the more specific the goals, the more explicitly performance is regulated; and (c)
the more specific and difficult the goals, the highest the performance achieved (p. 44-45).
Therefore, in this part of the study, three main regression models were conducted: a goal
difficulty model, a goal specificity model, and a combined model of goal difficulty and goal
specificity. For each of the main models, four different models were utilized to see the effect of
independent variables on the police officers’ motivation separately. After conducting four
models, the full models were carried out by department to test and see how independent and
control variables worked for each of the police departments.

90

Table 26. Summary of Tukey’s HSD analysis of Study’s Scales by Department (N = 1970)
Variable Name
Department

Motivation

Goal Difficulty

Goal Specificity

Commitment

Task Significance

SE

Mean Dif.

SE

Mean Dif.

SE

Mean Dif.

SE

Plain Clothes

-1.70***

.18

-2.46***

.21

-1.30***

.19

-.13

.21

-.87***

.19

.06

.18

-1.88***

.21

.05

.19

.63**

.21

.33

.18

Airport

1.70***

.18

2.46***

.21

1.30***

.19

.13

.21

.87***

.19

1.76***

.17

.58*

.20

1.35***

.18

.76***

.20

1.21***

.18

-.06

.18

1.88***

.21

-.05

.19

-.63**

.21

-.33

.18

Plain Clothes

-1.76***

.17

-.58*

.20

-1.35***

.18

-.76***

.20

-1.21***

.18

Variable Name

Public Order

Mean Dif.

Public Order

Plain Clothes

SE

Public Order

Airport

Mean Dif.

Self Efficacy

Airport

Department

Feedback

Participatively
Set Goals

Department
Significance

Rewards

Public Order

Mean Dif.

SE

Mean Dif.

SE

Mean Dif.

SE

Mean Dif.

SE

Plain Clothes

-.25

.18

-.85***

.11

-.80***

.13

-4.38***

.32

-.58***

.06

.43*

.17

-.34**

.10

.03

.12

-1.57***

.31

-.43***

.06

Airport

.25

.18

.85***

.11

.80***

.13

4.38***

.32

.58***

.06

Public Order

Plain Clothes

SE

Public Order

Airport

Mean Dif.

.67***

.17

.51***

.10

.83***

.12

2.81***

.30

.14*

.06

Airport

-.43*

.17

.34**

.10

-.03

.12

1.57***

.31

.43***

.06

-.67***

.17

-.51***

.10

-.83***

.12

-2.81***

.30

-.14*

.06

Plain Clothes
*p<.05, ** p<.01, *** p<.00

91

4.5.1 Goal Difficulty Model
Table 27 shows the results of the multivariate OLS regression analyses that were
conducted based on the theoretical model and existing literature. Four different models were
examined with the ordinary least square (OLS) regression analyses. The first model included
variables only from the goal-setting model. Perceived task significance by the given department
was added into the second model. The third model was composed of goal-setting model
variables, perceived task significance in a given department, and control variables, including
police officers’ seniority and the actual monetary rewards and letters of commendation police
officers earned in 2011. The final model combined these variables with the departments, which
were coded as other vs. plain clothes and other vs. public order.
Before interpreting the findings, OLS regression diagnostics were mentioned briefly for
the full model. A regression diagnostic was performed to find influential cases, if any, in the
models. Although there were some outliers, Cook’s D values suggested that only one case was
found that was different than the rest. The case had a Cook’s D value greater than .02. Another
OLS regression analysis was conducted without the case. The results suggested that there was no
major change regarding the strength and significance level of the variables. Furthermore, it is
suggested that cases having Cook’s D values higher than 1.0 are problematic. However, none of
the cases had a Cook’s value greater than .02. Finally, the case was inspected carefully by the
researcher and no value was found lower or more than the possible minimum and maximum
values. Therefore, the case was kept for the study.
In addition to examining influential cases, possible multicollinearity problems within the
study were examined. According to Weisburd & Britt (2007), a tolerance level of less than .20
indicates a serious multicollinearity problem. However, none of the models had a tolerance value

92

less than .20. The smallest tolerance value of each model was equal to .51. Finally, a DurbinWatson test was conducted to see whether the study violated the assumption of independent
errors. According to Field (2009), the test statistic can vary from 0 to 4. Values lower than 2
mean positive correlation, while values higher than 2 mean negative correlation between the
residuals. The value 2 means no correlation between the residuals. Although there is not a clear
cut point, it is suggested that values less than 1 and more than 3 are problematic (Field, 2009).
However, the result of the Durbin-Watson test showed that the value was equal to 1.96, which is
very close to 2 and within the acceptable limit. Therefore, it can be concluded that the study did
not violate the assumption of independent errors.
According to Table 27, the F value is equal to 139.10, and it is significant at p< .001
level. Therefore, it can be concluded that 33% (R2=.33) of the variance in police officers’ work
motivation was significantly explained by the first model. All independent variables, respectively
goal difficulty (B=.04, p<.05), commitment (B=.16, p<.001), task significance (B=.20, p<.001),
self-efficacy (B=.19, p<.001), feedback (B=.10, p<.01), participatively-set goals (B=.11,
p<.001), and rewards (B=.07, p<.001), significantly and positively contributed to the model.
Therefore, all hypotheses were verified by Model 1.
As shown in Model 1, when all other variables were held constant, a unit increase in
police officers’ sense of goal difficulty led to a .04-unit increase in police officer motivation. A
one-unit increase in police officers’ sense of commitment resulted in a .16-unit increase in
officer motivation. Furthermore, for each additional increase in police officers’ sense of task
significance, police officers’ motivation increased by .20 unit. One-unit increases in police
officers’ sense of self-efficacy led to a .19-unit increase in police officers’ sense of motivation.
The results also indicated that a unit increase in feedback led to a .10-unit increase in motivation.

93

A one-unit increase in participatively-set goals resulted in a .11-unit increase in motivation.
Finally, a one-unit increase in police officers’ sense of rewards led to a .07-unit increase in
motivation.
It is possible to compare the effect of the independent variables on the dependent variable
based on the β values of the independent variables. According to Table 27, task significance (β=
.20) had the strongest effect on the dependent variable, followed by commitment (β= .18) and
self-efficacy (β= .18). Police officers’ sense of rewards (β= .12) had the fourth strongest effect on
the dependent variable. Finally, participatively-set goals (β= .08), feedback (β= .06), and goal
difficulty (β= .05) had the weakest effect on the dependent variable.
In terms of Model 2, the R2 value (R2=.34) indicates that 34% of the variance in police
officers’ sense of work motivation was significantly explained by the model (F=124.06, p<.001).
Although police officers’ perception of task significance in a given department was significant
(B=.20, p<.001), the goal difficulty variable lost its strength and was not significant at the p<.05
level. Therefore, it can be concluded that a unit increase in police officers’ sense of task
significance by the given department led to a .20-unit increase in the dependent variable.
Table 27 presents that the F value is equal to 86.19, and it is significant at p< .001 level
for the Model 3. Thirty-five percent of the variance (R2=.35) was explained significantly by the
model. All variables except letters of commendation and goal difficulty significantly contributed
to Model 3. However, while the monetary rewards variable (B=.34, p<.05) had a positive
significant effect on police officers’ work motivation, seniority in TNP (B=-.07, p<.001) was
significantly and negatively related to the dependent variable. Therefore, it can be suggested that
police officers who earned monetary rewards in 2011 had a .34-unit greater sense of work
motivation compared to police officers who did not receive monetary rewards that year.

94

Additionally, a unit increase in seniority in TNP led to a .07-point decrease in police officers’
work motivation.
Table 27 shows the results of multivariate regression analyses for the full model.
According to Table 27, the F value is equal to 78.65, and it is significant at p< .001 level. The R2
value (R2=.36) indicates that 36% of the variation in police officers’ motivation is explained by
the model. In addition to goal difficulty and letters of commendation, adding two police
departments into the model caused monetary rewards to be not significant at p<.05 level. The
results suggest that while working in the plainclothes department is positively and significantly
related to work motivation compared to working at the airport, the public order department did
not contribute significantly to the model. Therefore, it can be concluded that working in the
plainclothes department led to a .94-unit increase in police officers’ work motivation compared
to working at the airport. These results suggest that possible variations exist among the
departments. To see the variations, if any, a full model was conducted for each police department
separately.
Table 28 shows the regression analyses by the departments. According to the results
revealed in Table 28, all models had significant F values, respectively, F=24.94, p<.001 for the
airport, F=25.08, p<.001 for the plainclothes department, and F=34.35, p<.001for the public
order department. The R2 values indicate that 35% of the variance (R2=.35) was explained by the
airport model; 32% of the variance (R2=.32) was explained by the plain clothes model; and 36%
of the variance (R2=.36) was explained by the public order model. However, the results indicated
that the effect and the strength of the independent variables on the dependent variable varied
among departments.

95

Table 27. Summary of Regression Analysis for Variables Predicting Police Officers’ Work Motivation (N=1970)
Goal Difficulty Model

Model 1

Variable

B

SE B

Constant

2.74***

.44

.04*

.02

Commitment

.16***

Task Significance

Model 2
β

B

SE B

2.44***
.03а

.02

.02

.18

.17***

.20***

.02

.20

Self-Efficacy

.19***

.03

Feedback

.10**

Participatively-Set Goals
Rewards

β

.45

.05

Model 3
SE B

2.63***

.46

.04

.03 а

.02

.02

.19

.16***

.19***

.02

.19

.18

.20***

.03

.04

.06

.09*

.11***

.04

.08

.07***

.01

.12

β

β

B

SE B

3.06***

.46

.04

.02

.02

.02

.02

.19

.17***

.02

.19

.19***

.02

.19

.19***

.02

.19

.18

.19***

.03

.18

.19***

.03

.18

.04

.05

.08*

.04

.05

.07*

.04

.04

.11***

.03

.08

.11***

.03

.08

.12***

.03

.08

.06***

.01

.11

.06***

.01

.11

.04***

.01

.08

.20***

.06

.07

.19***

.06

.07

.17**

.06

.06

-.07***

.01

-.09

-.06***

.01

-.07

Monetary Rewards

.34*

.15

.04

.22

.15

.03

Letter of Commendation

.04

.16

.01

-.11

.16

-.01

-.08

.13

-.01

-.16

.13

-.02

Plain Clothes

.94***

.17

.13

Public Order

.03

.15

.01

Goal Difficulty

B

Model 4

Department Importance
Year in TNP

Education

R2
F

b

.33

.34

.35

.36

139.10***

124.06***

86.19***

78.65***

b= 0=other, 1=college degree; *p<.05, ** p<.01, *** p<.001, ap<.10

96

Table 28 shows that while the independent variables of commitment (B=.14, p<.001),
task significance (B=.22, p<.001), self-efficacy (B=.27, p<.001), feedback (B=.15, p<.05), and
participatively-set goals (B=.15, p<.05) were positively and significantly related to motivation,
goal difficulty (B=-.09, p<.01) was negatively and significantly related to motivation in the
airport model. Police officers’ sense of rewards (B=.05, p<.10), actual monetary rewards earned
in 2011 (B=.53, p<.10), and education (B=-.43, p<.10) were significantly related to work
motivation at p<.10 level. However, perceived sense of task significance by a given department,
seniority in TNP, and letters of commendation variables did not contribute to the model
significantly.
For the plainclothes department, the results suggest that goal difficulty (B=.12, p<.001),
commitment (B=.15, p<.001), task significance (B=.23, p<.001), and participatively-set goals
(B=.14, p<.05) had a positive and significant effect on police officers’ sense of work motivation.
However, seniority (B=.08, p<.05) affected dependent variables significantly and negatively. The
variable of police officers’ perceived sense of task significance by assigned department (B=.17,
p<.10) and monetary rewards (B=.62, p<.10) contributed to the model positively at p<.10 level.
The variables of self-efficacy, feedback, police officers’ sense of rewards, and letters of
commendation did not have a significant effect on police officers’ sense of work motivation.
Table 28 reveals that while the variables of goal difficulty (B=.06, p<.05), commitment
(B=.20, p<.001), task significance (B=.15, p<.001), self-efficacy (B=.28, p<.05), police officers’
perceived sense of rewards (B=.06, p<.01), and perceived sense of task importance in assigned
department (B=.20, p<.01) were significantly and positively related to dependent variables,
seniority in TNP (B=-.06, p<.001) and education (B=-.45, p<.10) had a negative and significant
effect on work motivation in the public order department. The participatively-set goal (B=.10,

97

p<.10) variable was positively related to work motivation at p<.10 level. However, the variables
of feedback, actual monetary rewards, and letters of commendation did not affect police officers’
motivation significantly.
The results showed that goal difficulty had an adverse effect on police officers’
motivation in the airport model. This result is surprising and unexpected, since the goal-setting
model assumes that difficult goals lead to higher performance. Therefore, a close examination
was conducted to identify possible reasons for such an adverse relation between goal difficulty
and motivation. The goal-setting model suggests that the level of satisfaction with rewards is
related to accepting future difficult goals (see Figure 1). A comparison of the three departments
regarding police officers’ sense of receiving rewards (see Table 25 and Table 26) demonstrates
significant variation among the departments. Police officers working at the airport had a lower
sense of the possibility of receiving rewards (M=15.39, SD=5.83) than officers working in the
plainclothes department (M=19.77, SD=5.39) and public order department (M=16.96, SD=5.69),
even if they worked hard and accomplished their tasks. Additional one-way ANOVA analyses
were carried out to examine the effects of the reward scale’s items individually.
Looking at Table 29 and Table 30 suggests that police officers’ sense varied significantly
for each item (respectively F= 55.23, p<.001; F= 45.96, p<.001; F= 63.20, p<.001; F= 52.69,
p<.001; F= 42.74, p<.001; and F= 102.88, p<.001). Further, the results show that perceived
sense of receiving rewards was generally lower for airport police officers than for other police
officers. In particular, Item 1 (respectively M=2.70, SD=1.32; M=3.47, SD=1.25; M=3.10,
SD=1.29) and Item 6 (respectively M=2.32, SD=1.33; M=3.40, SD=1.29; M=3.10, SD=1.38)
varied significantly among the departments.

98

Table 28. Summary of Regression Analysis for Variables Predicting Police Officers’ Work Motivation by Department
Goal Difficulty Model

Airport (N=580)

Variable

B

SE B

Constant

2.82***

.87

Goal Difficulty

-.09***

.03

Commitment

.14***

Task Significance
Self-Efficacy

Plain Clothes (N=651)
β

B

SE B

3.83***

.85

-.12

.12***

.03

.04

.16

.15***

.22***

.04

.23

.27***

.05

Feedback

.15*

Participatively-Set Goals

β

Public Order (N=739)
β

B

SE B

2.03**

.76

.12

.06*

.03

.07

.04

.19

.20***

.03

.23

.23***

.04

.25

.15***

.04

.15

.25

.04

.05

.04

.28***

.04

.26

.06

.10

.08

.07

.05

-.05

.06

-.03

.15*

.06

.10

.14*

.06

.10

.10 а

.06

.07

Rewards

.04

.03

.07

.04

.03

.06

.06**

.02

.11

Department Importance

.17

.11

.06

.17 а

.10

.06

.20*

.09

.07

Year in TNP

-.04

.03

-.04

-.08*

.04

-.08

-.06***

.02

-.11

Monetary Rewards

.63*

.29

.08

.57 a

.32

.06

-.18

.22

-.03

Letter of Commendation

-.05

.31

-.01

-.26

.24

-.04

.10

.30

.01

Educationb

-.43a

.24

-.07

.34

.23

.05

-.45*

.20

-.07

R2
F

.35

.32

.36

24.94***

25.08***

34.35***

b= 0=other, 1=college degree; *p<.05, ** p<.01, *** p<.001, ap<.10

99

Table 29. Summary of One-Way ANOVA Analyses of Reward Scale’s items by department
(N = 1970)
Airport
(N=580)
Variable Name

Plain Clothes
(N=651)

Public Order
(N=739)

Mean

SD

Mean

SD

Mean

SD

F Score

Min

Max

Item 1

2.70

1.32

3.47

1.25

3.10

1.29

55.23***

1

5

Item 2

3.13

1.28

3.57

1.18

2.93

1.31

45.96***

1

5

Item 3

2.86

1.36

3.63

1.19

3.01

1.32

63.20***

1

5

Item 4

2.36

1.25

3.10

1.26

2.86

1.27

52.69***

1

5

Item 5

1.99

1.20

2.61

1.32

2.13

1.21

42.74***

1

5

Item 6

2.32

1.33

3.40

1.29

3.00

1.38

102.88***

1

5

Item 1= When I improve my performance, my accomplishments are recognized by my
supervisors; Item 2= I have seen good job performance rewarded in my work unit; Item 3= If I
accomplish my work objectives, it increases my changes to get extra money rewards or letter of
commendation; Item 4= If I accomplish my work objectives, it increases my chances to choose
people I work with; Item 5= If I accomplish my work objectives, it increases my chances to
choose shift I work; Item 6= If I accomplish my work objectives, it increases my chances to be
assigned a better department
***p<.001

100

Table 30. Summary of Tukey’s HSD analysis of Rewards Scale Items by Department (N = 1970)
Variable Name
Department

Item 1

Item 2

Item 3
SE

Mean Dif.

SE

Plain Clothes

-.77***

.07

-.43***

.07

-.77***

.07

-.36***

.07

.21**

.07

-.15

.07

Airport

.77***

.07

.43***

.07

.77***

.07

.41***

.07

.64***

.07

.62***

.07

Airport

.36***

.07

-.21**

.07

.15

.07

Plain Clothes

Public Order

Mean Dif.

Public Order

Plain Clothes

SE

Public Order

Airport

Mean Dif.

-.41***

.07

-.64***

.07

-.62***

.07

Variable Name
Department

Item 4

Item 5

Item 6
SE

Mean Dif.

SE

Plain Clothes

-.73***

.07

-.61***

.07

-1.07***

.08

-.50***

.07

-.13

.07

-.65***

.07

Airport

.73***

.07

.61***

.07

1.07***

.08

.23**

.07

.48***

.07

.42***

.07

Airport

.50***

.07

.13

.07

.65***

.07

Plain Clothes

Public Order

Mean Dif.

Public Order

Plain Clothes

SE

Public Order

Airport

Mean Dif.

-.23**

.07

-.48***

.07

-.42***

.07

*p<.05, ** p<.01, *** p<.001

101

This is an important finding since, as a rule, the most desirable departments to work in,
like narcotics and organized crime, choose their new members from the plainclothes and public
order departments. However, police officers should perform consistently in the top 20% of their
departments to be interviewed and chosen for these more desirable departmental assignments.
Furthermore, if they do not perform satisfactorily and fall into the lowest 20% of their
department’s performance ranking, they are assigned to other departments where police officers
do not prefer to work. Therefore, police officers perform at higher levels in order to be assigned
to better departments. Since this process was implemented a couple of years ago, officers have a
greater belief that if they perform well and accomplish their tasks, they are likely to be assigned
to better departments. However, unless they perform very poorly or are involved in an
administrative or criminal investigation, airport police officers are less likely to be assigned to a
less desirable position. Similarly, they are less likely to be assigned to a better department,
whatever their level of performance. Therefore, as the model suggests, since airport police
officers have less job satisfaction and a lower expectation of the possibility of rewards, they are
less likely to accept future difficult goals.
Therefore an additional regression analysis was conducted by including only the
plainclothes department and public order department. Table 31 shows the result of the regression
analyses. The results suggest that all main variables except feedback significantly and positively
contributed to the goal difficulty model. The variables having an effect on the police officers’
sense of motivation were goal difficulty (B=.09, p<.001), commitment (B=.18, p<.001), task
significance (B=.20, p<.001), self-efficacy (B=.16, p<.001), participatively-set goals (B=.13,
p<.01), and perceived sense of rewards (B=.06, p<.001). Additionally, while perceived sense of
importance of task in a given department (B=.20, p<.01) is related positively and significantly,

102

seniority in TNP (B=-.08, p<.001) is negatively and significantly related to police officers’ sense
of motivation. Finally, monetary rewards, letters of commendation earned in 2011, and education
did not have a significant effect on the dependent variable.
Table 31. Summary of Regression Analysis for Work Motivation by Plain Clothes Department
and Public Order Department (N=1390)
Goal Difficulty Model
Variable

B

SE B

Constant

2.48***

.56

Goal Difficulty

.09***

.02

.09

Commitment

.18***

.03

.20

Task Significance

.20***

.03

.19

Self-Efficacy

.16***

.03

.15

.03

.05

.01

Participatively-Set Goals

.13**

.04

.09

Rewards

.06***

.02

.11

Department Importance

.20**

.07

.07

-.08***

.02

-.11

Monetary Rewards

.26

.18

.03

Letter of Commendation

.07

.19

.01

Educationb

.04

.15

.01

Feedback

Year in TNP

R2

β

.36

F

63.61***
a

b= 0=other, 1=college degree; *p<.05, ** p<.01, *** p<.001, p<.10
As discussed previously, valance and instrumentality refer to “how individuals process
and respond to incentives” (Locke & Latham, 1990a, p. 145) in a goal-setting model. Therefore,
it can be concluded that police officers’ attitudes differ regarding their actual and expected
rewards. Finally, as the results suggest, when police officers have a sense that their performances
103

and accomplishments are recognized and subsequently lead to rewards, they are most likely to
accept difficult goals and perform better.
Monetary rewards were significant for airport (B=63, p<.05) and plainclothes officers
(B=.57, p<.10), but an independent sample t-test was conducted to examine why monetary
rewards did not contribute to the model even a minimum of p<.10 for the public order officers.
The results of the independent sample t-test analysis compared the effect of receiving monetary
rewards (did not receive=0, received=1) in 2011 on officers’ motivation. The results suggest that
there was not any significant difference between the two groups regarding motivation and scale
items and these two scales. Therefore, it can be argued that since the sense of being rewarded for
the police officers who did not receive monetary rewards was as high as the police officers who
were entitled to monetary rewards, they have a sense of motivation as high as police officers who
earned monetary rewards.
Table 28 indicates that commitment (respectively B=.14, p<.001; B=.15, p<.001; B=.20,
p<.001) and task significance (respectively B=.22, p<.001; B=.23, p<.001; B=.15, p<.001)
significantly contributed to all three sub-models. However, while self-efficacy affected
motivation in the airport (B=.27, p<.001) and public order departments (B=.28, p<.001), it did
not have a significant effect on motivation in the plainclothes department. Police officers’ sense
of receiving feedback had a significant and positive effect on motivation only in the airport
model (B=.15, p<.05). Participatively-set goals was significantly and positively related to
motivation in the airport (B=15, p<.05), plainclothes (B=.14, p<.05), and public order (B=.10,
p<.10) departments. Finally, police officers’ perceived sense of possibility of receiving rewards
significantly affected their sense of work motivation in the public order department (B=.06,

104

p<.01). It did not have any effect on the dependent variable in the airport and plainclothes
department.
4.5.2 Goal Specificity Model
Table 32 indicates the results of the multivariate OLS regression analyses for the goal
specificity model. Similar to the goal difficulty model, four models and three sub-models by
department were tested. OLS regression diagnostics were also examined for the goal specificity
model. The results indicated that none of the cases has a Cook’s D value greater than 1.
However, two cases were found to be different than the rest. The OLS regression analyses
conducted without these two cases only affected R2.05 point and the significance level of the
feedback from p<.10 to p<.05 level. Close examination of these two cases suggested no reason to
drop these two variables from the model. Therefore, these two cases were retained in the model.
Additionally, the results suggested that the smallest tolerance value for the model is .50, which is
well above the critical limit of .20. Finally, Durbin-Watson analysis showed that the value is
equal to 1.82, which is close to the accepted limit of 2. Based on the regression diagnostic
analyses, it can be concluded that the goal specificity model did not violate no perfect
multicollinearity and independent errors assumptions.
Table 32 indicates that the F value is equal to 153.96, and it is significant at p< .001
level. Therefore, it can be concluded that 36% (R2=.36) of the variance in police officers’ work
motivation was significantly explained by the first model. All independent variables, goal
specificity (B=.19, p<.001), commitment (B=.14, p<.001), task significance (B=.17, p<.001),
self-efficacy (B=.17, p<.001), feedback (B=.09, p<.01), participatively-set goals (B=.07,
p<.001), and rewards (B=.05, p<.001), significantly and positively contributed to Model 1.
Therefore, it can be concluded that Model 1 confirmed all hypotheses of the study.

105

As shown in Table 32, when all other variables were held constant, it can be seen that a
unit increase in police officers’ sense of goal specificity led to a .19unit increase in police
officers’ sense of motivation. A one-unit increase in police officers’ sense of commitment
resulted in a .14-unit increase in their motivation. Furthermore, for each additional increase in
police officers’ sense of task significance, police officers’ motivation increased by .17 unit. A
one-unit increase in police officers’ sense of self-efficacy led to a .17-unit increase in police
officers’ sense of motivation.
The results also indicate that a unit increase in feedback led to a .09-unit increase in the
dependent variable. A one-unit increase in participatively-set goals resulted in a .07-unit increase
in motivation. Finally, a one-unit increase in police officers’ sense of rewards led to a .05-unit
increase in police officers’ motivation. Comparing the effect of the independent variables based
on the β values suggested that goal specificity (β= .19) had the strongest effect on the dependent
variable, followed by task significance (β= .17), commitment (β= .16) and self-efficacy (β= .16).
However, rewards (β= .09), participatively-set goals (β= .05), and feedback (β= .05) had the
weakest effect on the dependent variable.
Regarding Model 2, The R2 value (R2=.36) indicated that 36% of the variance in police
officers’ sense of work motivation was significantly explained by the model (F=137.00, p<.001).
The results suggested that police officers’ sense of goal specificity held its strength and
significance level in Model 2. Furthermore, none of the other variables related to the goal-setting
model lost their strength and significance level, either. The results indicated that police officers’
perceived sense of task significance in a given department (B=19, p<.001) was positively and
significantly related to the dependent variable. Therefore, it can be concluded that a unit increase

106

in police officers’ sense of task significance by the given department led to a .19-unit increase in
the dependent variable.
Table 32 presents that the F value is equal to 94.66, and it is significant at p< .001 level
for the Model 3. Thirty-seven percent of the variance (R2=.37) was explained significantly by the
model. Aside from the letter of commendation and education variables, all variables significantly
contributed to Model 3. Similar to the goal difficulty model, while the monetary rewards variable
(B=.31, p<.05) was significantly related to police officers’ sense of work motivation, seniority in
TNP (B=-.07, p<.001) had a significant and negative effect on the dependent variable.
Therefore, it can be suggested that police officers who earned monetary rewards in 2011 had a
.31-unit greater sense of work motivation than police officers who did not receive monetary
rewards that year. Finally, it can be concluded that a unit increase in seniority in TNP led to a
.07-point decrease in police officers’ sense of work motivation.
According to Table 32, the F value is equal to 85.85, and it is significant at p< .001 level
for the full model. The R2 value (R2=.38) indicated that 38% of the variation in police officers’
sense of motivation was explained by the full model. Comparing the effects of two police
departments on the model caused monetary rewards to become no more significant in the model
and feedback to become significant at B=.06, p<.10 level. The results suggested that working in
the plainclothes department (B=.92, p<.001) had a positive and significant effect on the
dependent variable. Therefore, it can be concluded that working in the plainclothes department
led to a .92-unit increase in the officers’ sense of work motivation, as compared to working at the
airport. However, the results indicated that working in the public order department was not
significantly related to police officers’ sense of motivation compared to working at the airport.

107

Table 32. Summary of Regression Analysis for Variables Predicting Police Officers’ Work Motivation (N=1970)
Goal Specificity Model

Model 1

Variable

B

SE B

Constant

2.65***

.40

Goal Specificity

.19***

.02

Commitment

.14***

Task Significance
Self-Efficacy

Model 2
β

B

SE B

2.29***
.18***

.02

.02

.16

.14***

.17***

.02

.17

.17***

.03

Feedback

.09*

Participatively-Set Goals

β

.41

.19

Model 3
SE B

2.52***

.42

.19

.18***

.02

.02

.16

.14***

.16***

.02

.16

.16

.17***

.03

.04

.05

.08*

.07*

.03

.05

.05***

.01

.09

β

β

B

SE B

2.79***

.43

.19

.17***

.02

.18

.02

.16

.14***

.02

.16

.16***

.02

.16

.16***

.02

.16

.16

.17***

.03

.16

.17***

.03

.16

.04

.05

.07*

.04

.04

.06 а

.04

.04

.08*

.03

.05

.08*

.03

.05

.09*

.03

.06

.04**

.01

.08

.05***

.01

.08

.03*

.01

.05

.19***

.05

.07

.18***

.05

.06

.16**

.05

.05

-.07***

.01

-.09

-.06***

.01

-.07

Monetary Rewards

.31*

.15

.04

.20

.15

.03

Letter of Commendation

.06

.16

.01

-.09

.16

-.01

Educationb

-.09

.13

-.01

-.17

.13

-.03

Plain Clothes

.92***

.16

.13

Public Order

.07

.15

.01

Rewards

B

Model 4

Department Importance
Year in TNP

R2
F

.36

.36

.37

.38

153.96***

137.00***

94.66***

85.85***

b= 0=other, 1= college; *p<.05, ** p<.01, *** p<.001, ap<.10
108

Table 33 shows the results of the OLS regression analyses by department. According to
the results in Table 33, all models have significant F values, respectively F=24.50, p<.001 for
the airport, F=30.56, p<.001 for the plainclothes department, and F=35.56, p<.001for the public
order department. The R2 values indicated that 34% of the variance (R2=.34) was explained by
the airport model, 37% of the variance (R2=.37) by the plain clothes model, and 37% of the
variance (R2=.37) by the public order model. However, the results indicated that the effect and
the strength of the independent variables on the dependent variable varied among departments.
According to Table 33, the variables of goal specificity (B=.11, p<.01), commitment
(B=.12, p<.01), task significance (B=.19, p<.001), self-efficacy (B=.24, p<.001), and monetary
rewards (B=.74, p<.05) had a positive and significant effect on the police officers’ sense of
motivation in the airport model. Additionally, the variables of feedback (B=.11, p<.10) was
significantly and positively related to the dependent variable at p<.10. However, the results
indicated that participatively-set goals, rewards, police officers’ sense of task importance in a
given department, seniority in TNP, letters of commendation, and education were not
significantly related to police officers’ sense of motivation in the airport model.
Table 33 indicated that while goal specificity (B=.30, p<.001), commitment (B=.13,
p<.001), and task significance (B=.19, p<.001) were positively and significantly related to the
dependent variable, years in TNP (B=-.07, p<.05) affected police officers’ sense of motivation
negatively and significantly in the plain clothes model. Participatively-set goals (B=.10, p<.10)
had a positive effect on the dependent variable at p<.10. However, the rest of the variables,
including self-efficacy, feedback, rewards, police officers’ sense task importance in a given
department, monetary rewards, letters of commendation, and education did not have a significant
effect on police officers’ sense of motivation.

109

Regarding the public order model, Table 33 shows that goal specificity (B=.13, p<.001),
commitment (B=.18, p<.001), task significance (B=.12, p<.01), self-efficacy (B=.27, p<.001),
rewards (B=.05, p<.05), and task significance in a given department (B=.22, p<.05) had a
positive and significant effect on the police officers’ sense of motivation. However, year in TNP
(B=-.05, p<.01) and education (B=-.45, p<.05) had a negative and significant effect on the
dependent variable. The variables of feedback, participatively-set goals, monetary rewards, and
letters of commendation were not related significantly to police officers’ motivation.
Table 33 indicates that specific goals (respectively B=.11, p<.01; B=.30, p<.001; B=.13,
p<.001), commitment (respectively B=.12, p<.01; B=.13, p<.001; B=.18, p<.001) and task
significance (respectively B=.19, p<.001; B=.19, p<.001; B=.12, p<.01) had a positive and
significant effect on the dependent variable for all models. Similar to the goal difficulty model,
while self-efficacy affected motivation in airport officers (B=.24, p<.001) and public order
officers (B=.27, p<.001), it was not significantly related to the dependent variable for
plainclothes officers. Police officers’ sense of receiving feedback was related to police officers’
sense of motivation in the airport model (B=.11, p<.10) at p<.10. Similarly, participatively-set
goals (B=10, p<.10) was found to be significant only in the plainclothes department at p<.10.
Police officers’ sense of rewards had a positive effect on the dependent variable in the
public order department (B=.05, p<.05). Police officers’ sense of task significance (B=.25,
p<.01) in a given department had a significant effect only in the public order department.

110

Table 33.Summary of Regression Analysis for Variables Predicting Police Officers’ Work Motivation by Department
Goal Specificity Model

Airport (N=580)

Variable

B

SE B

Constant

1.86*

.84

Goal Specificity

.11**

.04

Commitment

.12**

Task Significance
Self-Efficacy

Plain Clothes (N=651)
β

B

SE B

4.12***

.75

.11

.30***

.04

.04

.14

.13***

.19***

.04

.20

.24***

.05

Feedback

.11 а

Participatively-Set Goals

β

Public Order (N=739)
β

B

SE B

2.48***

.68

.29

.13***

.03

.14

.04

.16

.18***

.03

.20

.19***

.04

.20

.12**

.04

.12

.22

.02

.05

.02

.27***

.04

.26

.06

.07

.08

.06

.04

-.04

.06

-.02

.09

.06

.07

.10 а

.06

.07

.06

.06

.04

Rewards

.04

.03

.07

.00

.02

.00

.05*

.02

.09

Department Importance

.09

.11

.03

.10

.10

.04

.22*

.09

.08

Year in TNP

-.04

.03

-.04

-.07*

.03

-.07

-.06***

.02

-.11

Monetary Rewards

.74*

.29

.09

.29

.31

.03

-.22

.21

-.03

Letter of Commendation

-.07

.31

-.01

-.18

.24

-.03

.11

.30

.01

Educationb

-.35

.24

-.05

.19

.22

.03

-.45*

.20

-.07

R2
F

.34

.37

.37

24.50***

30.56***

35.56***

b= 0=other, 1= college; *p<.05, ** p<.01, *** p<.001, ap<.10

111

Seniority in TNP affected dependent variable negatively and significantly in the plainclothes (B
=-.07, p<.05) and public order departments (B=-.05, p<.001). Monetary rewards (B=.74, p<.05)
was a significant contributor only to the airport model. Education (B=-.45, p<.05) affected police
officers’ sense of motivation negatively in the public order department. Finally, the results
indicated that letter of commendation was not related to the dependent variable in any model.
4.5.3 Combined Model
The results of the OLS regression analyses for the combined model, including both goal
difficulty and goal specificity variables, are presented in Table 34. OLS regression diagnostics
were carried out for the final model as well. The results suggested that only two cases had a
Cook’s D value greater than .02. However, these two cases had values lower than the critical
point of 1. Similar to previous models, an additional OLS regression analysis was carried out to
test the effect of these two variables on the final model. The results indicated that the same two
cases caused the same effects on the model as mentioned for the goal specificity model. The
OLS regression analyses conducted without these two cases had a .05 increase in R2.
Additionally, the feedback variable was found to be significant (B= .07, p<. 05) in the regression
model. However, for the same reasons, these two cases were retained in the analyses. In addition
to influential cases, the multicollinearity analyses indicated that the smallest tolerance value was
equal to .50, which is above the cutoff point of .20. Finally, the Durbin-Watson value for the
final model was equal to 1.81, which is close to the accepted limit of 2.
Table 34 indicates that the F value is equal to 135.11, and it is significant at p< .001
level. Therefore, it can be concluded that 36% (R2=.36) of the variance in police officers’ work
motivation was significantly explained by the first model. Aside from goal difficulty, all
independent variables, goal specificity (B=.18, p<.001), commitment (B=.14, p<.001), task

112

significance (B=.16, p<.001), self-efficacy (B=.17, p<.001), feedback (B=.08, p<.01),
participatively-set goals (B=.07, p<.001), and rewards (B=.05, p<.001),had a significant and
positive effect on the dependent variable in Model 1. Therefore, it can be concluded that all
hypotheses except hypothesis one are confirmed by the final model.
Table 34 shows that, when all other variables were held constant, a unit increases in
police officers’ sense of goal specificity led to a .18-unit increase in police officers’ sense of
motivation. A one-unit increase in police officers’ sense of commitment resulted in a .14-unit
increase in the motivation of police officers. Furthermore, for each additional increase in police
officers’ sense of task significance, their motivation increased by .16unit. A one-unit increase in
police officers’ sense of self-efficacy led to a.17-unit increase in their sense of motivation. The
results also indicated that a unit increase in feedback led to a .08-unit increase in the dependent
variable. A one-unit increase in participatively-set goals resulted in a .07-unit increase in
motivation. Finally, a one-unit increase in police officers’ sense of rewards led to a .05-unit
increase in their motivation. The β values indicated that the effects of independent variables on
the dependent variables can be listed in order from highest to lowest as goal specificity (β= .19),
task significance (β= .17), commitment (β= .16), self-efficacy (β= .16), rewards (β= .09)
participatively-set goals (β= .05), and feedback (β= .05).
As shown in Table 34, the R2 value (R2=.36) indicated that 36% of the variance in police
officers’ sense of work motivation was significantly explained by the Model 2 (F=121.91,
p<.001). Adding police officers’ sense of task significance by the given department caused
feedback to drop in strength and significance level from B=.08, p<.05 to B=.07, p<.10. However,
the variable significantly and positively contributed to the model (B=.18, p<.001). Therefore, it

113

can be said that a one-unit increase in police officers’ sense of task significance in a given
department led to a .18unit increase in the dependent variable.
Regarding Model 3, Table 34 indicates that the F value of the model equals to F=87.49,
which is significant at p<.001 level. Adding control variables into the model did not cause a
major change in the model. The results indicated that year in TNP (B=-.07, p<.001) had a
negative and significant effect on the dependent variable. However, it was found that police
officers who earned monetary rewards in 2011 had a .32-unit greater sense of motivation than
officers who did not receive monetary rewards. Finally, the letters of commendation and
education variables were not significantly related to police officers’ sense of motivation in
Model 3.
As shown in Table 34, 38% (R2=38) of the variance in police officers’ sense of
motivation was explained significantly by Model 4 (F=80.09, p<.001). In this model the effect
of departments was examined. The results indicated that the major change was in the monetary
rewards and feedback, which were not significantly related to the dependent variable in this
model. With respect to the effect of working in a specific department, Table 34 shows that police
officers’ sense of motivation was .91 unit greater for police officers working in the plainclothes
department, compared to those working at the airport. However, no significant difference in
officers’ motivation was seen between working at the airport and working in the public order
department.

114

Table 34. Summary of Regression Analysis for Variables Predicting Police Officers’ Work Motivation (N=1970)
Combined Model

Model 1

Variable

B

SE B

Constant

2.39***

.44

Goal Difficulty

.03

.02

Goal Specificity

.18***

Commitment

Model 2
β

B

SE B

2.13***
.02

.02

.02

.19

.18***

.14***

.02

.16

Task Significance

.16***

.02

Self-Efficacy

.17***

Feedback

β

.44

.03

Model 3
B

SE B

2.33***
.02

.02

.02

.19

.18***

.14***

.02

.16

.17

.16***

.02

.03

.16

.17***

.08*

.04

.05

Participatively-Set Goals

.07*

.03

Rewards

.05*

.01

β

.45

.02

Model 4
β

B

SE B

2.73***

.46

.02

.01

.02

.01

.02

.18

.17***

.02

.18

.14***

.02

.16

.14***

.02

.16

.16

.16***

.02

.16

.16***

.02

.16

.03

.16

.17***

.03

.16

.17***

.03

.16

.07 a

.04

.04

.06a

.04

.04

.06

.04

.03

.05

.08*

.03

.05

.08*

.03

.05

.09*

.03

.06

.09

.04**

.01

.08

.05***

.01

.08

.03*

.01

.05

.18***

.06

.06

.17**

.06

.06

.15**

.06

.05

-.07***

.01

-.09

-.06***

.01

-.07

Monetary Rewards

.32*

.15

.04

.20

.15

.03

Letter of Commendation

.06

.16

.01

-.09

.16

-.01

Educationb

-.09

.13

-.01

-.17

.13

-.03

Plain Clothes

.91***

.16

.13

Public Order

.07

.15

.01

Department Importance
Year in TNP

R2
F

.36

.36

.38

121.91***

135.11***

.37
87.49***

80.09***

a

b= 0=other, 1= college; *p<.05, ** p<.01, *** p<.001, p<.10
115

Table 35 shows that OLS regression analysis by department indicated that all three submodels had significant F values (respectively F=23.97, p<.001; F=28.66, p<.001; and F=33.25,
p<.001). Therefore, it can be concluded that the variance explained in the police officers’ sense
of motivation by the departments was, respectively, 36% (R2=.36 for airport), 37% (R2=.37 for
plainclothes), and 37% (R2=.37 for public order). Regarding the airport model, the results
indicated that goal specificity (B=.11, p<.01), commitment (B=.12, p<.01), task significance
(B=.22, p<.001), self-efficacy (B=.24, p<.001), feedback (B=.14, p<.05), participatively-set
goals (B=.13, p<.05), and monetary rewards (B=.68, p<.05) were positively and significantly
related to police officers’ sense of motivation. However, similar to the goal difficulty model, the
results indicated a negative and significant relation between the goal difficulty (B=-.10, p<.001)
and motivation. The other variables, including rewards, police officers’ sense of task significance
in a given department, year in TNP, letter of commendation, and education were not significantly
related to police officers’ sense of motivation in the airport model.
Table 35 expresses that goal difficulty (B=.07, p<.05), goal specificity (B=.28, p<.001),
commitment (B=.13, p<.001), and task significance (B=.19, p<.001) had a positive and
significant effect on the dependent variable. Additionally, participatively-set goals affected
motivation (B=.10) at p<.10 level. However, year in TNP (B=-.07, p<.05) was related to the
dependent variable negatively and significantly. The results suggested an insignificant relation
between the other independent and control variables and motivation.

116

Table 35. Summary of Regression Analysis for Variables Predicting Police Officers’ Work Motivation by Department
Combined Model

Airport (N=580)

Variable

B

SE B

Constant

2.69**

.86

Goal Difficulty

-.10***

.03

Goal Specificity

.11**

Commitment

Plain Clothes (N=651)
β

B

SE B

3.45***

.82

-.13

.07*

.03

.04

.12

.28***

.12**

.04

.13

Task Significance

.22***

.04

Self-Efficacy

.24***

Feedback

β

Public Order (N=739)
β

B

SE B

1.83*

.75

.07

.05*

.03

.06

.04

.28

.12***

.03

.13

.13***

.04

.16

.18***

.03

.21

.23

.19***

.04

.20

.11**

.04

.11

.05

.22

.02

.05

.02

.27***

.04

.25

.14*

.06

.09

.05

.06

.03

-.05

.06

-.03

Participatively-Set Goals

.13*

.06

.09

.10a

.06

.07

.07

.06

.04

Rewards

.03

.03

.05

.01

.02

.01

.05*

.02

.09

Department Importance

.15

.11

.05

.08

.10

.03

.20*

.09

.07

Year in TNP

-.04

.03

-.05

-.07*

.03

-.07

-.06***

.02

-.10

Monetary Rewards

.68*

.29

.09

.35

.31

.04

-.20

.21

-.03

Letter of Commendation

-.06

.31

-.01

-.19

.24

-.03

.13

.30

.01

Educationb

-.40

.24

-.06

.20

.22

.03

-.44*

.20

-.07

R2
F

.36

.37

.37

23.97***

28.66***

33.25***

b= 0=other, 1= college; *p<.05, ** p<.01, *** p<.001, ap<.10

117

Table 35 indicates that goal difficulty (B=.05, p<.05), goal specificity (B=.12, p<.001),
commitment (B=.18, p<.001), task significance (B=.11, p<.01), self-efficacy (B=.27, p<.001),
rewards (B=.05, p<.05), and perceived sense of task significance in a given department (B=.20,
p<.01) affected the dependent variable positively and significantly in the public order model. The
variables which had a significant adverse effect on the dependent variable were year in TNP
(B=-.05, p<.01) and education (B=-.44, p<.05). All other variables, including feedback,
participatively-set goals, monetary rewards, and letter of commendation, did not contribute to the
public order model significantly.
Finally, another OLS regression analysis was conducted by excluding police officers
working at the airport in order to test the theory, since the findings regarding the goal difficulty
were similar to the goal difficulty model. Table 36 indicates that 38% (R2=.38) of the variance in
police officers’ sense of motivation was significantly explained by the model (F=65.64, p<.001).
Aside from the feedback variable, all core variables of the goal-setting model had a positive and
significant effect on the dependent variable, respectively, goal difficulty (B=.07, p<.01), goal
specificity (B=.20, p<.001), commitment (B=.15, p<.001), task significance (B=.14, p<.001),
self-efficacy (B=.14, p<.001), participatively-set goals (B=.08, p<.05), and rewards (B=.04,
p<.05). Based on the comparison of the models presented in Table 34 and Table 36, it can be
concluded that the differences in the airport model altered the strength and significance level of
the goal difficulty model, when the model was combined with the other two models.

118

Table 36. Summary of Regression Analysis for Work Motivation by Plainclothes Department
and Public Order Department (N=1390)
Combined Model
Variable

B

SE B

Constant

2.18***

.55

Goal Difficulty

.07**

.02

.07

Goal Specificity

.20***

.03

.20

Commitment

.15***

.03

.18

Task Significance

.14***

.03

.14

Self-Efficacy

.14***

.03

.13

Feedback

.01

.04

.01

Participatively-Set Goals

.08*

.04

.06

Rewards

.04*

.02

.07

Department Importance

.17**

.07

.06

-.07***

.02

-.10

Monetary Rewards

.18

.18

.02

Letter of Commendation

.10

.18

.01

Educationb

-.00

.15

.00

Year in TNP

R2

.38

F

65.64***

b= 0=other, 1= college; *p<.05, *p<.05, ** p<.01, *** p<.001, ap<.10

119

β

CHAPTER 5
DISCUSSION and CONCLUSION
This study reviews the literature on goal-setting models and examines the predictors of
police officers’ sense of motivation. Although it was found that motivation and the goal-setting
model have been examined extensively, the goal-setting model has received very little attention
from public organizations, especially police departments. Additionally, there has been little
research done regarding policing and motivation in Turkey. Therefore, the main interest of this
study was to examine predictors of police officers’ sense of motivation in Turkey. Based on the
goal-setting model, eight hypotheses were suggested to acquire a more general picture of the
motivational behaviors of police officers.
The main research question of the study was that whether goal difficulty and goal
specificity influences police officers’ motivation or not. Additionally, it was argued that goalcommitment, task significance, self-efficacy, feedback, rewards, and participatively-set goals
have a positive effect on the police officers’ motivation. Existing literature on goal-setting
suggests that goal difficulty and goal specificity are not dependent to each other and effect
employees’ motivation independently (Locke, 2000). Therefore, three main regression models
were conducted: goal difficulty model, goal specificity model, and a combined model of goal
difficulty and goal specificity. By doing so, the individual effects of goal difficulty and goal
specificity on the police officers’ motivation were assessed. The following is a discussion of the
main findings of the research, implication of the results, limitations of the study, and
recommendations of future research.

120

5.1 Discussion
As discussed in the literature review and methodology sections, this study focused on the
relationship between Turkish police officers’ perceived sense of goal difficulty and goal
specificity and their motivational attitudes. Existing literature on the goal-setting theory (Locke
et al., 1981; Locke & Latham, 1990a) suggested that difficult goals and specific goals lead to
higher performance. Based on the theoretical model, three different OLS regression analyses
were conducted. These were goal difficulty, goal specificity, and a combined model of goal
difficulty and goal specificity.
With the first regression model, only the effect of police officers’ sense of goal difficulty
on their motivation was examined. Four different OLS regression models were examined by
adding additional variables and demographic characteristics of the police officers into the goalsetting model. In the first model, the goal-setting model was examined with variables including
goal difficulty, commitment, task significance, self-efficacy, feedback, and participatively-set
goals. The results indicated that all variables were related significantly and positively to police
officers’ sense of motivation. From the focus of the study, police officers’ sense of goal
difficulty (B=.04, p<.05) had a positive and significant effect on motivation.
In the second model, police officers’ sense of task significance in a given department was
added to the model. This variable was added into the model since, as mentioned earlier, police
officers’ motivational attitudes might differ based on their assigned department. As Table 25 and
Table 26 indicated, police officers’ perceptions varied on the task significance scale (F=24.18,
p<.001) and task significance in a given department (F=43.53, p<.001). The tables showed that
while airport police officers’ sense of task significance (M=15.18, SD=3.26) differed
significantly and negatively from the police officers working in the plainclothes department

121

(M=16.05, SD=3.39), there was no significant difference between the police officers working at
the airport and police officers working in the public order department.
However, when police officers were asked whether they perceive their given task to be
significant compared to any task in another department, the tables revealed that the perceived
task significance in a given department was significantly lower for airport officers (M=2.82,
SD=1.12) than for plainclothes (M=3.40, SD=1.09) or public order (M=3.26, SD=1.15) officers.
Therefore, the variable of police officers’ sense of task significance in a given department was
held in the models. The variable (B=.20, p<.001) was significantly and positively related to
motivation. However, adding the variable into the goal-setting model caused goal difficulty to
lose its strength. It is no longer significant at p<.05 level (B=.03, p<.10).
In the third model, year in TNP, monetary rewards, letter of commendation, and
education variables were added to the model. While the year in TNP model had a significant and
negative effect on motivation (B=-.07, p<.001), monetary rewards affected police officers’ sense
of motivation positively (B=.34, p<.05). However, goal difficulty was no longer related to the
police officers’ sense of motivation in the model. Similarly, goal difficulty was found to be
insignificant in the full model, in which the effects of departments were examined. However, the
results of the full model indicated that there could be variation among the police departments,
showing that goal difficulty had no effect on the police officers’ sense of motivation. Therefore,
another OLS regression analysis was carried out by department to test the goal difficulty model.
The OLS regression analysis for departments indicated an unexpected result (see Table
28). While goal difficulty was positively and significantly related to motivation in the models of
the plainclothes department (B=.12, p<.001) and public order department (B=.06, p<.05), it
affected police officers’ sense of motivation negatively and significantly in the airport model

122

(B=-.09, p<.001). Therefore, additional analyses were conducted to examine and find possible
reasons for the negative relations between the goal difficulty and motivation among airport
officers.
A one-way ANOVA analysis (Table 29 and Table 30) showed that aside from item two,
the scores of police officers’ sense of rewards were lower for the airport than for the plainclothes
and public order departments. Airport police officers had a lower sense of being recognized by
their supervisors (M=2.70, SD=1.32), having a chance to choose the people they work with
(M=2.36, SD=1.25), and having a chance to be assigned to a better department (M=2.32,
SD=1.33). The model (see Figure 1) by Locke & Latham (1990a; 2002) suggested that
satisfaction with performance and rewards are related to accepting new/future challenges.
Therefore, as consistent with the model, it is argued that since airport police officers had a lower
sense of being rewarded even if they accomplished their tasks and performed better, their sense
of goal difficulty was negatively related to their sense of motivation. The OLS regression
analysis (see Table 31) conducted by excluding airport officers indicated that goal difficulty
(B=.09, p<.001) was significantly and positively related to police officers’ sense of motivation.
Regarding goal specificity, Table 32 indicates that goal specificity was related to police
officers’ sense of motivation in all OLS regression models. Adding other independent and
demographic variables into the goal-setting model did not lead to any significant change in the
relation between the police officers’ sense of goal specificity and their sense of motivation
(respectively B=.19, p<.001 for model 1; B=.18, p<.001 for model 2; B=.18, p<.001 for model 3;
and B=.17, p<.001 for model 4). Additionally, as revealed in Table 33, goal specificity had a
positive and significant effect on the police officers’ sense of motivation in the model that

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included all police departments (respectively B=.11, p<.01 for airport; B=.30, p<.001 plain
clothes department; and B=.13, p<.001 for public order department).
In the final model, the combined model of goal difficulty and goal specificity, aside from
goal difficulty, all other variables, goal specificity (B=.18, p<.001), commitment (B=.14,
p<.001), task significance (B=.16, p<.001), self-efficacy (B=.17, p<.001), feedback (B=.08,
p<.05), participatively-set goals (B=.07, p<.05), and rewards (B=.05, p<.05), had a positive and
significant effect on the police officers’ sense of motivation in Model 1 (see Table 34). Adding
other independent and demographic variables into the model removed the effect of feedback on
the dependent variable. Feedback was no longer significant in the full model. The other
variables, goal specificity (B=.17, p<.001), commitment (B=.14, p<.001), task significance
(B=.16, p<.001), self-efficacy (B=.17, p<.001), participatively-set goals (B=.09, p<.05), and
rewards (B=.03, p<.05), still affected the dependent variable positively and significantly in the
full model.
The combined model of OLS regression was carried out for each police department
separately (see Table 35). The results of the OLS regression analyses were consistent with
previous models. While goal difficulty (B=-.10, p<.001) had a negative and significant effect on
the police officers’ sense of motivation in the airport model, it was positively and significantly
related to the dependent variable in the plainclothes model (B=.07, p<.05) and public order
model (B=.05, p<.05). Additionally, the goal specificity model was a significant and positive
contributor in all models (respectively, B=.11, p<.01; B=.28, p<.001; and B=.12, p<.001).
Table 36 indicates that both goal difficulty (B=.07, p<.01) and goal specificity (B=.20,
p<.001) affected police officers’ sense of motivation positively and significantly in the model in
which an OLS regression analysis was conducted by including only the plainclothes department

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and public order department. Therefore, it can be concluded that consistent with the goal-setting
model, if police officers believe that their performances and accomplishments are recognized by
their supervisors and lead rewards, difficult goals lead to a higher sense of motivation.
Additionally, when specific goals are regulated in police departments, it leads police officers to
have a higher sense of motivation towards their assigned tasks.
Finally, Table 27 indicates that the explained variance in the goal difficulty model was
36% (R2=.36, p<.001), whereas it was 38% (R2=.38, p<.001) in the goal specificity model (see
Table 32) and 38% (R2=.38, p<.001) in the combined model, as well (see Table 34). Therefore,
it could be argued that since there was no change in R2 between goal specificity and the
combined model, researchers could use only the goal specificity model instead of the combined
model. However, one of the regression assumptions asserts that no relevant variable is excluded,
or no irrelevant variable is included, as a cause in the regression model (Lewis-Beck, 1980).
When a relevant variable is omitted from the regression equation, the slopes of the
variables in the model are unreliable, since their slopes’ value would be overestimated or
underestimated based on the value of the excluded relevant variable (Schroder et. al., 1986).
However, including an irrelevant variable into a regression model causes an increase of the
standard error and a decrease of t ratios of the relevant variables if the irrelevant variable is
correlated with the relevant variables (Schroder et. al., 1980). To prevent the misspecification of
the model for this study, the independent variables were chosen based on goal-setting theory and
existing literature.
In addition to these theoretical grounds, researchers (Allen, 1997; McDaniel, 2009)
argued that the contribution of an independent variable to the model can be examined by
comparing the full model and a nested or restricted model through conducting an F test.

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According to Allen (1997), “Nested hypotheses arise whenever we are interested in comparing
two regression equations that are identical except that one contains restrictions that are not
imposed on the other” (p. 113).Full model refers to a regression model in which all the variables
are kept, whereas restricted/nested models refer to a regression model in which at least one
independent variable is omitted from the model.
The result of the F-test analysis (McDaniel, 2009) comparing the goal specificity model
(only the goal difficulty variable was omitted) and combined model indicated that there were no
statistical differences between the models. However, another nested F-test analysis was
performed, since the airport officer results affected the strength and significance level of the goal
difficulty model adversely. This time, a nested F-test analysis was conducted including only the
plainclothes and public order departments in the goal specificity and combined models. The
result of the nested F-test was found to be significant between the two models (F1, 1377=9.51,
p<.05). Therefore, it can be concluded that adding goal difficulty to the model increased the
explained variances significantly in the full model. Since it contributed to the full model
significantly in a given set of independent variables within the multiple regression equation, it
should not be dropped from the model.
Regarding other findings, consistent with the goal-setting model, commitment and task
significance had a positive and significant effect on the police officers’ sense of motivation in all
models and for all police departments. However, the findings suggested that while self-efficacy
was a significant and positive predictor for the airport and public order departments, it was found
to be insignificant for the plainclothes department. However, the goal-setting model assumes that
self-efficacy has a positive, direct and moderate effect on performance. One-way ANOVA
analysis (F=8.58, p<.001) suggested that police officers working in the plainclothes department

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(M=16.07, SD=3.24) had higher scores on self-efficacy than police officers working at the
airport (M=15.82, SD=2.94) and those working in the public order department (M=15.40,
SD=3.04). Therefore, further analyses were conducted to examine possible factors that led to
such an insignificant relation between the self-efficacy and motivation of the plainclothes
officers.
Locke & Latham (1990a; 2002) argued that self-efficacy is highly and significantly
related to goal commitment. Table 22 shows that the correlation analysis among all variables
indicated a positive and strong correlation between self-efficacy and goal commitment (r=.65,
p<.001). However, since the plainclothes department had the highest mean values of selfefficacy (M=16.07, SD=3.24) and goal commitment (M=20.33, SD=3.83), the correlation
between self-efficacy and goal commitment should be highest for the plainclothes department.
The Pearson correlation analysis indicated that goal commitment and self-efficacy were highly
and significantly related to each other (r=.72, p<.001) for the plainclothes department. Further,
another correlation analysis indicated that there was a positive and significant relation between
self-efficacy and motivation (r=.38, p<.001) and commitment and motivation (r=.41, p<.001) in
the plainclothes department.
Therefore, a close examination for a possible multicollinearity problem in the OLS
regression analyses of the combined model for the plainclothes department was conducted.
Multicollinearity exists “whenever an independent variable is highly correlated with one or more
of the other independent variables in a multiple regression equation” (Allen, 1997, p. 176). High
multicollinearity results in unstable coefficients and higher standard errors for the slope
coefficients. Subsequently, the slope t values will decrease and the slope coefficients will be seen
as less significant. The collieanarity diagnostics of the combined model for the plainclothes

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department indicated that none of the independent variables had smaller tolerance values than
.42, which was higher than the accepted level of .20 (Field, 2009). However, commitment and
self-efficacy had the smallest tolerance values, respectively, .44 and .42. Therefore, another
regression analysis was conducted by omitting the commitment variable from the model.
Table 37 shows that self-efficacy (B=.10, p<.01) had a positive and significant effect on
the police officers’ sense of motivation, when the variable commitment was excluded from the
model. Therefore, it can be concluded that since goal commitment and self-efficacy were highly
correlated, the standard error of the self-efficacy variable was inflated and became insignificant
in the model. Although self-efficacy (B=.17, p<.001) and goal commitment (B=.14, p<.001)
were significant in the full model of the combined model including all departments (see Table
34), the nested F-test was carried out to examine the contribution of self-efficacy to the model.
The result of the nested F-test procedure suggested that adding self-efficacy to the model led to a
significant increase in the explained variance for the model (F1,637=10.72, p<.05). Therefore, it
could be suggested that even self-efficacy and goal commitment were highly and significantly
correlated, so both variable should be kept in the model.
Regarding feedback, participatively-set goals, and rewards, results indicated that they had
a significant effect on the police officers’ sense of motivation in Model 1. However, adding other
variables into the model caused them to lose their strength and significance level in the model. In
the full model of the combined model, while participatively-set goals (B=.09, p<.05) and rewards
(B=.03, p<.05) were related to the dependent variable positively and significantly, feedback was
insignificant in the full model (see Table 34). Similar to goal difficulty and self-efficacy, each of
these three departments had different effects on the dependent variable in the given department
model. For example, feedback (B=.14, p<.05) and participatively-set goals (B=.13, p<.05)

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affected police officers’ sense of motivation significantly only in the airport model. Police
officers’ sense of rewards (B=.05, p<.05) was found to be a significant contributor only in the
public order department.
Table 37. Summary of Regression Analysis for Work Motivation by Plainclothes Department
(N=651)
Combined Model
β

Variable

B

SE B

Constant

4.02***

.81

Goal Difficulty

.07*

.03

.08

Goal Specificity

.29***

.04

.29

Task Significance

.22***

.04

.24

Self-Efficacy

.10**

.04

.11

Feedback

.05

.07

.03

Participatively-Set Goals

.09

.06

.07

Rewards

.01

.03

.01

Department Importance

.05

.10

.02

-.08*

.03

-.07

Monetary Rewards

.32

.31

.03

Letter of Commendation

-.15

.24

-.02

Educationb

.22

.22

.03

Year in TNP

R2

.36

F

29.70***

*p<.05, ** p<.01, *** p<.001

As discussed in the literature review section, existing literature suggests inconsistent
findings regarding feedback, participatively-set goals, and rewards. First, although feedback has
a positive effect on performance, the effect of feedback on performance depends on the source,
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the message, and the nature of the feedback. Since the nature of work varies among the police
departments, police officers might get feedback differently. Subsequently, their workplace might
affect how police officers evaluate feedback they get from others. For example, police officers
working at the airport work within the airport perimeter. They work closely with their
supervisors and their colleagues. Therefore, it was not difficult for them to get any feedback,
positive or negative, from their supervisors or colleagues. However, police officers in the public
order department work in their assigned city divisions as a team. It makes it difficult for police
officers to be controlled or contacted by their supervisors. The plainclothes officers work alone
and in plainclothes. Each group has its own working conditions and feedback process. Therefore,
further analyses should be conducted on how police officers perceive the source, the message,
and the nature of feedback in their assigned department.
Similar to feedback, existing research on the effect of participatively-set goals on
employee motivation suggests inconsistent finding. Although participatively-set goals was found
to be a significant contributor to the combined and full model (see Table 34), participatively-set
goals affected police officers’ sense of motivation only in the airport model. It was related to the
dependent variable at p<.10 level in the plainclothes model and had no effect on motivation in
the public order model. Similarly, additional analyses should be performed on the factors that
make participatively-set goals a significant contributor to the goal-setting model.
In the literature review section, it was argued that rewards work independently from the
goal-setting model. In other words, existing literature suggests that earned rewards is an
important factor in performance when the employees believe the distributions fair, not too
general, and routine (Doherty, 1998). Although it was found to be a significant predictor of
police officers’ sense of motivation in the combined model, it was an insignificant factor in the

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airport and plainclothes departments. Based on the argument raised above, Pearson correlation
analyses were conducted for each police department to examine the relationship between rewards
and other variables.
Table 38.Summary of Pearson Correlation Analyses
Variable Name

1

2

3

4

5

6

7

8

Rewards
.14*** .16*** .25*** -.16*** .18*** -.11** .33*** .57***
Airport (N=580)
Rewards
.25***
.09*
.36*** .13*** .28*** .12** .34*** .62***
Plain Clothes (N=651)
Rewards
.23***
.07
.35***
.05
.32***
.07
.26*** .60***
Public Order (N=739)
1= Motivation; 2= Goal Difficulty; 3= Goal Specificity; 4= Commitment; 5= Task Significance
6=Self-Efficacy; 7=Feedback; 8=Participatively-Set Goals
*p<.05, ** p<.01, *** p<.001
Table 38 shows the results of the correlation analyses between rewards and other
variables. According to Table 38, there was a negative correlation between rewards and
commitment (r=-.16, p<.001) and rewards and self-efficacy (r=-.11, p<.01) in the airport model.
As discussed earlier, the one-way ANOVA analyses (see Table 25 and Table 26) indicated that
while police officers’ sense of the possibility of being rewarded was lower for those working at
the airport (M=15.39, SD= 5.83) than for the other two groups, it was highest for the police
officers working in the plainclothes department (M=19.77, SD=5.39) comparing to the other
police department (F=96.69, p<.001). The results suggest that further analyses should be
conducted on police officers’ perceptions of the fairness of rewards. Additionally, Table 38 also
indicates that police officers’ sense of rewards was correlated to other variables in the
plainclothes model. Although the highest correlation score was .60, p<.001 between rewards and
participatively-set goals in the plainclothes model, the correlation between the rewards and other

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independent variables might cause rewards to become insignificant in the multivariate regression
analyses of the plainclothes model.
Locke & Latham (1990a) argued that age is not related to goal-setting or performance.
However, the result of the study indicated that age had a negative effect on the police officers’
sense of motivation in the full model. The OLS regression analyses by department suggest that
year at TNP affected the dependent variable in the plainclothes and public order departments.
However, it was insignificant in the airport model. It can be assumed that since working in the
plainclothes and public order departments requires more physical challenges, those police
officers might prefer to engage in more routine and ordinary work rather than working on the
streets. Further, these two groups of officers deal with people who are in trouble or involved in a
crime. Therefore, working on the streets also requires more emotional strength compared to other
police department jobs offering desk work.
It was found that the monetary rewards variable was a significant predictor of police
officers’ sense of motivation in the combined model for the airport. Additionally, monetary
rewards was significantly related to police officers’ sense of motivation for the plainclothes
department in the difficult goal models. However, the monetary rewards variable was
insignificant in all models for the public order department. The rotation of the police officers into
and from the public order department occurred more frequently than in the other police
departments. The independent sample t-test analyses showed that there were no significant
differences between the police officers who earned monetary rewards and police officers who
did not get any monetary rewards with respect to motivation and the possibility of being
recognized and rewarded. Therefore, it was argued that, since police officers who were not

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entitled to monetary rewards in 2011 had as great a sense of being rewarded as police officers
given monetary rewards, they showed the same level of motivation as the other police officers.
Although the letter of commendation variable was found significant at the bivariate level,
none of the models indicated that it was a significant predictor of motivation in the OLS
regression analyses. Finally, it was found that police officers’ attitudes might differ towards their
tasks by their assigned departments. The combined model indicated that police officers’ sense of
task significance in a given department was a significant contributor to the model. Similarly, the
contribution of the task significance by a given department varied among the departments. While
it affected the dependent variable in the public order department, it was insignificant in the
airport and plainclothes department. Therefore, further analyses should be conducted on the
effect of task significance by a given department.
5.2 Implications of the Results
This study enhances our understanding about work motivation and its predictors in the
context of Turkey. Although private policing has grown steadily during the last couple of
decades (Nalla & Newman, 1990), the government still largely accommodates public policing to
maintain order, protect people’s lives and property, and investigate crimes. TNP also employs
more than 200,000 police officers around the country. The findings of this study indicate a
positive relationship between the goal-setting model and police officers’ sense of motivation.
Therefore, these findings can be used to develop a number of important implications for
enhancing police officers’ sense of motivation.
This study indicates that goal context, goal difficulty and goal specificity have a positive
effect on police officers’ sense of motivation. Therefore, the security directors of cities in general
and supervisors of police officers use a goal-setting model in the police departments under their

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command. As suggested by Locke (2001), they should make causal maps. On such a map,
supervisors should show how individuals’ performances lead to personal and departmental goals
or outcomes. For example, police officers working in the public order and plainclothes
departments know how their performances and accomplishments are recognized and rewarded.
They try to place in the top 20% of their department to be rewarded or assigned to another
department. This model can be extended to other police departments.
For example, a private organization responsible for airport security uses software to
measure and increase the performances of airport staff. The software program shows restricted
and prohibited items, which are fake items on the screen of x-ray machines. The security official
who is responsible for following the screen should identify 90% of the fake images appearing on
the screen. If he/she fails, he/she should be required to participate in a training program.
Similarly, supervisors control the quality of the products in departments where police officers
work at desk jobs. By doing so, they set a difficult goal for their subordinates which they can
attain and see the results.
Regarding goal specificity, this study shows that goal specificity works in all models and
all police departments. When police officers have a clear understanding of their assigned tasks,
their sense of motivation increases. As mentioned earlier, specific goals regulate the performance
of employees. Supervisors should explain clearly to police officers their assigned tasks.
Supervisors should collect feedback from the officers about whether they have a clear
understanding of their assigned tasks. Further, they should set specific goals for police officers.
For example, supervisors could ask police officers to focus on certain crimes or dealing with a
certain number of crimes instead of setting more abstract goals like solving all kinds of crimes
and as many crimes as possible. Related to specific goals, Locke (2001) suggested that

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supervisors should assign many goals to their employees. Similarly, supervisors in police
departments should assign a certain number of tasks to police officers. By doing so, police
officers can focus on the top one or two of their assigned tasks.
This study also indicates that goal commitment is a significant predictor of police
officers’ sense of motivation. Supervisors should take necessary steps to increase police officers’
sense of goal commitment. Regarding goal commitment, Locke (2001) argued that goal
commitment requires two steps, goal importance and self-efficacy in the first place. Similarly,
the results indicated that goal commitment was related to police officers’ sense of self-efficacy
and task significance in the bivariate level. Therefore, supervisors augment police officers’ sense
of goal commitment by enhancing their sense of self-efficacy and task significance.
Supervisors should convince police officers about the importance of their tasks in two
ways. First, supervisors can persuade police officers that their assigned tasks are very important
from a more general perspective. For example, they can argue that their tasks and
accomplishments serve the interest of TNP, people’s lives, and ultimately, the whole country.
Second, supervisors should assure that the results of officers’ tasks and accomplishments are in
the interest of the police officers themselves.
According to Locke (2001), self-efficacy can be gained through training, practice, and
suitable role models. Supervisors should ensure that police officers get appropriate training based
on their assigned departments. Although police officers had similar trainings and education
programs in police vocational schools and police education centers, the nature of police work
requires different skills and practices based on the assignments. For example, it can be argued
that street work requires police officers to have physical strength, skills of self-defense, and
knowledge of human psychology. However, desk jobs require police officers to have good

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communication skills, good knowledge of the procedures and laws, and so on. Finally,
supervisors may assign police officers who can be a role model to others as a team or group
leader under their command.
According to the results, police officers’ sense of the possibility of being rewarded
affected work motivation positively. Supervisors can make police officers feel confident that (a)
their accomplishments and performances are recognized, (b) the allocation of the rewards is fair,
(c) and the allocation of rewards is dependent on the goals of the department. According to
Locke (2001), any accomplishment, especially at the nonsupervisory level, should be celebrated
and granted in some way, including personal notes, badges, pins, pictures (p. 53). Supervisors
should recognize all accomplishments of their police officers and celebrate them. However,
Locke (2001) noted that it was important to “motivate by goals but reward by performance” (p.
52).
Supervisors can ask police officers to participate in setting goals as an “information
exchange device for developing task strategies” (Locke, 2001, p. 47). It would serve as a
feedback mechanism between the police officers and their supervisors. Additionally, supervisors
should provide feedback to police officers regarding their performances relevant to their assigned
tasks and goals. However, it should be kept in mind that police officers are ready to accept
feedback based on who provides the feedback and how and when feedback is presented.
Therefore, supervisors also may consider participating in in-service training in which human
psychology and communication skills are presented.
Existing literature on policing indicates that police officers’ attitudes differ towards their
assigned departments. This study indicated that police officers’ sense of task significance in a
given department had a significant effect on the police officers’ sense of motivation. Although it

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could be very difficult in practice, supervisors can consider police officers’ opinions about their
assignment at the very beginning. Additionally, they can work to make police officers proud of
their assigned department. Finally, the results indicated negative relations between age and work
motivation. As discussed earlier, street work requires physical and emotional strengths.
Therefore, when police officers become older, they may lose their motivation for their assigned
tasks, especially those who work on the streets. Therefore, older police officers can be assigned
to departments where police officers engage in routine work.
5.3 Limitations and Future Research
Similar to any scientific study, this study also has several limitations. One of the
limitations is related to research in that translated scales were used in the study. The scales that
were included in the study were translated from English into Turkish. Although a cognitive
interview was conducted before the study, several problems could remain regarding the use of
translated questionnaires and scales. For example, cultural differences and linguistic differences
between the populations may cause problems. Second, OLS regression analyses were conducted
to examine the relations between the dependent and independent variables. OLS regression
analysis examines the relations between the variables in an additive manner. However, the
Pearson correlation analysis and the model indicated relations among the independent variables.
Therefore, it can be assumed that independent variables might have moderating effects on the
dependent variable as well. Therefore, additional analyses and statistical techniques can be used.
Third, the police officers’ motivation was measured by the motivation scale rather than by their
actual performances. Although a principal component analysis and reliability analyses were
conducted for the motivation scale, the motivation scale represents police officers’ own
perceptions, which were subject to bias.

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Finally, since this study was conducted on police officers working in three departments,
the generalizability of the study is limited for several reasons. First, it is obvious that there could
be differences among these three departments and other police departments like police stations,
or the public order, counter-terrorism, and intelligence departments. Regarding the sampling
strategy, it could be argued that there could be variation among the cities as well. Second, the
mean scores of the police officers’ age (M=27.11, SD=4.63) and year at TNP (M=4.42,
SD=4.30) indicated that participants of the study consisted of relatively new police officers.
Therefore, the results cannot be generalized to police officers who are older. Finally, since the
gender variable was omitted from the study because of the low number of women police officers
(N=51), the findings cannot be generalized to women police officers in TNP. Finally, the study
used a purposive sampling strategy. Although the researchers’ intentions were explained earlier,
it should be noted that a purposive sampling strategy is not free from sampling bias.
The limitations and results of this study suggest some recommendations for future
research. First, researchers may use and measure police officers’ actual performances. Further,
an experimental research design which includes a comparison group can be used to examine the
goal-setting model in policing. Second, further analyses and statistical techniques can be carried
out to examine the direct and moderating effects of independent variables on the dependent
variables. Third, researchers may include other cities and police departments in their studies to
examine possible variations among them. Regarding sampling strategy, researchers can use a
purposive sampling strategy to collect information about the women police officers’ work
motivation behaviors. Fourth, a qualitative research design can be conducted to get a better
picture and greater depth of understanding about the predictors of work motivation. Since police
officers answered what they were asked in this study, some possible explanations and

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understandings might have been missed. However, with a qualitative study, students may explain
what they think and provide different information on the study topic.
Finally, the results indicated variations among the police departments. For example,
police officers working in the plain clothes department had the highest motivation, followed by
public order department, and airport. It can be speculated that police officers’ motivational
behaviors might be related to their specific work environment and work conditions. The variation
among the departments like being rewarded or punished, working alone or with groups, wearing
uniform or not might affect their attitudes. Therefore, researchers may combine the goal-setting
model with other theoretical frameworks like organizational culture to examine the possible
reasons that caused variation among the police departments. Furthermore, the Hawthorne effect
referring to “subjects' knowledge that they are in an experiment modifies their behavior from
what it would have been without the knowledge” (Adair, 1984, p. 334) might affect police
officers’ behaviors as well. In other words, police officers might have thought that the way they
answer the question might affect their situations or future. To prevent such problems,
participants were informed that although their responses might be recorded their identity would
be kept confidential at the beginning of the study. Further, they filled out the questionnaires on
their own and returned and returned completed surveys to collection box in an arranged room.
Therefore, any possible pressure on subjects from their supervisors and the researcher was
eliminated. However, researchers may consider using other possible steps to remove the
Hawthorne effect to a greater extent in future research on this subject.
5.4 Conclusions
This study enhanced our understanding of police officers’ behaviors on work motivation
by examining existing literature and empirical evidence and analyzing the relationship between

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the dependent and independent variables. Goal-setting theory was used in this study to examine
predictors of work motivation. The results of the study were mainly consistent with the goalsetting model. The results indicated that goal difficulty, goal specificity, task significance,
commitment, self-efficacy, and rewards were related to police officers’ sense of motivation.
However, the results indicated variations among police departments. Further analyses were
conducted to examine possible factors that might lead to variations among the police
departments.
Additionally, although the results indicate that police officers’ sense of work motivation
was explained by goal-setting model variables, it should be noted that the quality of the results
mostly depends on the ability of the police officers. Police officers’ motivational behaviors
influence the quality of their performance to some degree. To reach the best product and
performance, police officers’ abilities and motivational behaviors should be improved. This
study helps researchers to understand work motivation better. Further, this study offers policy
implications to increase police officers’ sense of motivation, goal commitment, self-efficacy, and
task significance.

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APPENDICES

141

APPENDIX A: Consent Form

ASESSING POLICE OFFICERS’ MOTIVATIONAL BEHAVIOURS
Thank you for participating in this research study. The aim of this research study is to
broaden the scope of existing knowledge regarding a range of police officers’ attitudes and
behaviors about work motivation. The survey administrators do not know the names of the
respondents who choose to participate in the research study nor do the surveys have any
identification marks. All responses are completely anonymous and will not be used in any way
that may identify the participant. Your privacy will be protected to the maximum extent
allowable by law.
Participation in this survey is voluntary. This paper based survey will take about 25
minutes to complete. If you have agreed to respond to the survey, you may refuse to participate
in certain procedures, answer certain questions, or discontinue your participation at any time
without penalty or loss of benefits. You indicate your voluntary agreement to participate by
completing and returning this questionnaire. We do not foresee any identifiable risks or benefits
to you for responding to this survey.

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APPENDIX B: Survey Instrument

Section I. In the following sections we would like your views about some issues. Please
answer the following questions by placing an “X” in the corresponding box that best expresses
your personal view: SD (Strongly disagree-1), D (Disagree - 2), C (Not sure or Unclear - 3) A
(Agree - 4), and SA (Strongly Agree - 5).
1
1. Work Motivation
1.1 I put my best effort to get my job done regardless of the difficulties
1.2 I am willing to start work early or stay late to finish a job
1.3 It has been hard for me to get very involved in my current assignments (R)
1.4 I usually do not work as hard as others who do the same type of work (R)
1.5 I do extra work for my job that isn’t really expected of me
1.6 Time seems to drag while I am on the job (R)
2. Goal Content Specificity
2.1 My responsibilities at work are very clear and specific.
2.2 I understand fully which of my job duties are more important than others
2.3 It is difficult to evaluate success or failure on my job (R).
2.4 I know exactly what I am supposed to do on my job.
2.5 My supervisor clearly explains to me what my goals are.
3. Goal Content Difficulty
3.1 The work objectives in my job require a great deal of effort.
3.2 A high degree of skill and know-how is necessary to do my job well.
143

2 3 4

5

3.3 Jobs like mine are quite demanding day after day.
3.4 My work is very challenging.
3.5 I have new and interesting things to do in my work
4. Goal Commitment
4.1 It’s hard for me to take the kinds of things I must do in my position. (R)
4.2 Quite frankly, I don’t care if I achieve my responsibilities or not. (R)
4.3 I am strongly committed to pursuing assignments given to me
4.4 It wouldn’t take much to make me just get by assignments given to me. (R)
4.5 I am very committed to doing my assignments well.
4.6 I sometimes fail to accomplish my assignments
5. Task Significance
5.1 A lot of people will be affected by how I do my job in this department
5.2 The work I do in this department is extremely meaningful to me
5.3 I understand the importance of accomplishing my work objectives
5.4 I work on assignments that seem useless or unnecessary (R)
5.5 My assignment is really important and worthwhile
5.6 Sometimes, I am not sure I completely understand the purpose of what I am doing
5.7 I often wonder the importance of my assignment really is
6. Self Efficacy
6.1 I am confident that I can successfully perform any tasks assigned to me on my current job.
6.2 I am not as well prepared as I could be to meet all the demands of my job (R).
6.3 I can’t get my work done on time even when I try very hard (R).
6.4 Doing my work as well as I am able to leads to high quality results.

144

7. Feedback
7.1 I get regular feedback indicating how I am performing
7.2 I get coaching from my supervisor to help me do a better job.
7.3 I get helpful information from others about how well I am performing at my job.
7.4 I receive useful evaluations of my strengths and weaknesses at work.
8. Participatively-Set Goals
8.1 My supervisor lets me participate in the setting of my goals.
8.2 My supervisor lets me have some say in deciding how I will go about implementing my
goals.
9. Rewards
9.1 When I improve my performance, my accomplishments are recognized by my supervisors.
9.2 I have seen good job performance rewarded in my work unit
9.3 If I accomplish my work objectives, it increases my changes to get extra money rewards or
letter of commendation.
9.4 If I accomplish my work objectives, it increases my chances to choose people I work with.
9.5 If I accomplish my work objectives, it increases my chances to choose shift I work
9.6 If I accomplish my work objectives, it increases my chances to be assigned a better
department
15. Public Service
15.1 Some other departments are actually more important to society than mine is.
15.2 I think that my assignments here are more important than any assignments in another
department for society.

145

15.3 The importance of being a police officer in this department is sometimes overstressed.
Section II. Background Information
1. How old you are on January 1st 2012
2. Gender

A. Female

__________old

B. Male

3. Your highest education:
A. High School

B. Two Year College C. College Degree and above

4. What is the best estimate of your monthly salary?
TL _________________
5. How many years have you worked? (If you work less than one year in your current
department and current position, please indicate how many months have you worked in your
current department and position?)
in Turkish National Police

________years

in Istanbul

________years

in your current department

________years

________months

in your current position

________years

________months

6. Before being assigned to your current position, have you worked in public order departments
(including police stations, public order department, and riot police department, and etc).
A. Yes

B. No

7. Please name top three departments where you would like to be assigned including your current
position
1____________

2____________

8. Did you get monetary rewards during 2011?
A. Yes

B. No

146

3____________

If you received, how many times

________times

9. Did you get letter of commendation during 2011?
A. Yes

B. No

If you received, how many times

________times

THANK YOU FOR YOUR TIME AND CONTRIBUTION TO THIS RESEARCH

147

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