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thesis entitled

TURKISH POLICE PERCEPTIONS AND
ATTITUDES TOWARD USING COMPUTER
TECHNOLOGY IN POLICE CARS

presented by

HAMZA TOSUN

has been accepted towards fulﬁllment
of the requirements for the

Master of Science degree in Criminal Justice

 

 

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TURKISH POLICE PERCEPTIONS AND
ATTITUDES TOWARD USING COMPUTER
TECHNOLOGY IN POLICE CARS

by

Hamza TOSUN

A THESIS

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

MASTER OF SCIENCE

School of Criminal Justice

2006

ABSTRACT

TURKISH POLICE PERCEPTIONS AND
ATTITUDES TOWARD USING COMPUTER
TECHNOLOGY IN POLICE CARS

by

Hamza TOSUN

The present study evaluates Istanbul Police Department ofﬁcers’ attitudes and
perceptions toward the new MOBESE system terminals, which are mounted in police
cars in Istanbul, Turkey. Based on the Technology Acceptance Model (TAM), a
prediction model has been developed. Data gathered from 280 police ofﬁcers by means
of self-report survey. Several signiﬁcant ﬁndings were found. According to bivariate
analysis, MOBESE Usage has positively correlated with perceived usefulness, perceived
ease of use, attitude toward system, and user satisfaction. This result is consistent with
the ﬁndings found by previous research. However, having utilized negative binomial
regression, no variables were found signiﬁcant. In order to test TAM’s variables,
regression was performed for the second time with the variables perceived ease of use,
perceived usefulness, and attitude toward MOBESE. This time, perceived ease of use was
found to be statistically signiﬁcant. The results of the study imply that police ofﬁcers are
inﬂuenced by the ease of use of the MOBESE terminal. Since the working environment
of patrol ofﬁcers are not like regular ofﬁcers, they need to get any information very easily
and quickly when they are patrolling. Therefore any software and hardware equipment
should be designed to be used easily, and heir user interfaces should not be complicated

so that users can understand and use easily.

DEDICATION
I would like to dedicate this thesis to my wife, with her support and her continuous

encouragement, I sustain through this process.

ACKNOWLEDGMENTS
First and foremost I want to thank Turkish National Police for granting me to
pursue graduate education. I also want to thank my thesis committee: Dr Steven
Chermak, Dr. Vincent Hoffman and Dr. Chris Maxwell. They provided me excellent
guidance, helpful suggestions and constructive criticisms to build this work. I also would
like to thank Nick Corsaro and Carol Zimmerman.
I would like to thank the School of Criminal Justice and the Director, Dr. Edmund

McGarrell for their support and never ending encouragement.

TABLE OF CONTENTS

 

 

 

 

 

 

 

 

 

 

LIST OF TABLES - - ...... . .............. vi
LIST OF FIGURES- - - viii
1. INTRODUCTION- - - -- -- - -- -- ................. 1
2. LITERATURE REVIEW - - - - - - - - ........ 3
3. RESEARCH HYPOTHESES - 11
4. RESEARCH DESIGN ................. - . -_ - ...... - - - 13
4.1. DATA ....................................................................................................................... 13
4.2. SAMPLE ................................................................................................................... 13
5. RESULTS - - - -- - . - _ _ - - ..... - - - 16
5.1. DEMOGRAPHIC INFORMATION ................................................................................. 16
5.2. MEASURES ............................................................................................................... 18
5.2.1. Dependent Variable .......................................................................................... 18
5.2.2. Independent Variables ...................................................................................... I8

5. 3. HYPOTHESIS TESTING ............................................................................................. 20
6. DISCUSSION- - - -28
6.1 LIMITATIONS OF THE STUDY ..................................................................................... 31
6.2. FUTURE RESEARCH .................................................................................................. 32
APPENDIX A: THE VARIABLES AND THE ITEMS“ - 33
APPENDIX B: THE MOBESE STUDY QUESTIONNAIRE- - 36
REFERENCES _ -43

 

LIST OF TABLES

 

 

 

 

 

TABLE 1A A - ................................................................. 15
SUMMARY OF RESPONSE FOR EACH DISTRICTS ........................................ 15
TABLE 2____ ------ - ................................................................... 17
DEMOGRAPHIC INFORMATION ......................................................................... 17
TABLE 3A ...................................................................... 20
DESCRIPTIVE STATISTICS OF VARIABLES ...................................................... 20
TABLE 4“,- A A A - ..................................................................... 21
CORRELATION OF PERCEIVED USEFULNESS AND MOBESE USAGE ........ 21
TABLE 5A-AAA A A A ................................................................. 21
CORRELATION OF PERCEIVED EASE OF USE AND MOBESE USAGE ........ 21
TABLE 6A A A ................................................................. 22

 

CORRELATION OF ATTITUDE TOWARD MOBESE AND MOBESE USAGE 22

 

 

 

 

TABLE 7. A _ AA - A ................................................................. 22
CORRELATION OF TENURE AND MOBESE USAGE ........................................ 22
TABLE 8 AA ................................................................. 23
CORRELATION OF AGE AND MOBESE USAGE ................................................ 23
TABLE 9 - ................................................................... 23
CORRELATION OF USER EXPERIENCE AND MOBESE USAGE .................... 23
TABLE 10 A ..................................................................... 24
CORRELATION OF USER SATISFACTION AND MOBESE USAGE ................ 24
TABLE 11 AA A AA - ................................................................. 25

 

vi

CORRELATIONS OF VARIABLES ......................................................................... 25

 

NEGATIVE BINOMINAL REGRESSION RESULT FOR MOBESE USAGE ...... 27
TABLE 13 AAAAAAAAAA A A A .......................... . ........ . ....... . .................... 28
NEGATIVE BINOMINAL REGRESSION RESULT FOR MOBESE USAGE
USING TAM’S
VARIABLES .......................................................................................................... 28

vii

LIST OF FIGURES

FIGURE 1 ..................................................................................... 8
THEORY OF REASONED ACTION .................................................... 8
FIGURE 2 ................................................................................... 9
THECNOLOGY ACCEPTANCE MODEL ............................................. 9
FIGURE 3 ........ . .......... . ............................................................... 10
THE DEVELOPMENT OF A PREDICTION MODEL TO ESTIMATE THE 10
ACTUAL SYSTEM USE OF MOBESE ..............................................
FIGURE 4 ................................................................................... 26
BAR CHART OF MOBESE USAGE VARIABLE .................................... 26

viii

1. INTRODUCTION

Technology has greatly affected police practices over time (Chan, Brereton,
Legosz and Doran, 2001). By introducing new technologies such as the telegraph, two-
way radios, and computer-aided dispatching, police services have been greatly changed.
Technology extends the physical capacity of police officers to see, hear, recognize,
record, remember, match, verify, analyze and communicate (Chan, 2003, p.655). In short,
technology has redeﬁned the knowledge and skills required for doing police work (Chan,
et a1. 2001 ).

While the use of information technology in recent decades has become part of
everyday life for individuals and organizations, police agencies have been one of the
leading public organizations that invest in information technology (IT) for gathering,
storing, and disseminating intelligence. As a result, although police work remains action-
and danger-oriented, a most important recent aspect of police work is “information”
work: today’s police are knowledge workers (Chan 2003, p.657). Thus, police
organizations in many countries now have implemented information technology. The
Istanbul Police Department (IPD) is one of those police agencies.

The IPD has invested in IT infrastructure, which it uses daily in police work.
Speciﬁcally, to improve the safety of Istanbul residents, the IPD has developed a new
complex information system called Mobile Information System, MOBESE. Using the
new MOBESE, the IPD more capably manages communications with police ofﬁcers
across the entire region. Chan (2003) stated that, “The acceptance or rejection of
technological change in any speciﬁc setting can depend on its design and

implementation, the political issues it raises and its degree of congruence with the

technological frames of users.”(p. 674). Therefore, in order to implement effective
technology in police work, the adaptation and acceptance of new technology by police
ofﬁcers is essential.

The present study evaluates IPD ofﬁcers’ attitudes and perceptions toward the
new MOBESE system terminals, which are mounted in police cars. A literature review
shows that the “technology acceptance model,” based on the theory of reasoned action
(Davis, 1989), is a useful model for assessing police ofﬁcers’ adaptation to the use the
MOBESE system. In this thesis, an empirical study has been conducted to evaluate the
IPD police ofﬁcers’ attitudes, perceptions, and acceptance of the MOBESE system.

The main purpose of the study is to understand to what extent the perceptions and
attitudes of TNP ofﬁcers predict their use of MOBESE. Another purpose is to evaluate
the relationship between user-technology and user-acceptance so as to identify potential
barriers to police ofﬁcers’ use of MOBESE. There are several reasons why this study
makes a worthwhile contribution. First, although the use of technology in police
agencies (and throughout societies) have expanded dramatically, there is little empirical
research that examines this important organizational change. Second, the theoretical
framework for this study has been applied to many different ﬁelds of study, but primarily
in the management and business sectors. This is the ﬁrst study to apply this framework
to the study of police use of technology. Third, cross-cultural research has increased in
importance, but little research exists that examines important policing issues in Turkey.
This study ﬁlls these gaps, and it may provide valuable information for the current

implementation and future developmental stages of the MOBESE system.

2. LITERATURE REVIEW

The Technology Acceptance Model (TAM). Since many organizations, both
private and public, have invested heavily in information systems, the measurement of
users’ attitudes and perceptions toward information technology systems is an important
one for researchers. To examine this issue, many studies have been conducted using
relevant models to evaluate system usage; one such model is the technology acceptance
model (Davis, Bagozzi, and Warsaw,1989). TAM has been used by a variety of scholars
in different academic ﬁelds.

For example, Schillewaert, Aheame, Frambach, and Moenaert (2000) studied the
acceptance of information technology in the sales force. Schillewaert et a1. assessed the
acceptance of sales technology of 224 sales representatives working at various
organizations such as manufacturing, services, trade, ﬁnance, and information. In
addition to individual characteristics such as computer self-efﬁcacy and personal
innovativeness, the researchers studied the acceptance of sales technology using external
variables such as “organizational facilitators including user training, technical support,
organizational implementation, and social inﬂuence including supervisor inﬂuence, peer
usage, costumer inﬂuence, and competitive pressure” (p. 5). Using cross-sectional survey
design, they selected subjects from the subscription list of a magazine relating sales and
automation. The results of the study revealed several important ﬁndings: 1) perceived
usefulness was the fundamental driver for sales technology acceptance, 2) perceived ease
of use was a secondary driver of acceptance, and 3) the personal innovativeness of the
salesperson with respect to using the information technology also played a key role in the

acceptance of sales technology. Schillewaert et al. also found that peer usage also had a

direct connection to the extent that the personnel would use the new technology. That is,
colleagues may assist and show one another the advantages of using the sales technology.
In short, organizational engagement such as “user training, technical user support and
management commitment” is very crucial in terms of the acceptance of a new system by
employees.

In another study, Evers (1997) identiﬁed cultural preferences for interface design
features that inﬂuence a user’s interface acceptance. Evers conducted a survey of 200
international students and an Australian control group of 38 students at the University of
New South Wales in Sydney, Australia. The results revealed that the culture played an
important factor on the process of interface acceptance among three Asian cultures (Hong
Kongese, Indonesians and Chinese). The elements that were found to correlate highly
with culture were the colors of the screen, pop-up menus, touch screen, data glove,
sounds, and multimedia (p. 30). Noting that system design preferences impact on attitude
towards using attitude of satisfaction, Evers argued that culture can be used as a
motivational construct blending familiarity and experience besides task characteristics (p.
32).

In yet another study, Brown (2002) examined the user’s technology acceptance in
the developing country context, where the language of instruction is not the mother
tongue for most students. By administrating a survey to 78 ﬁrst year South African
University students with little prior experience of Internet technologies, Brown found that
“the individual characteristics of self-efﬁcacy” and “computer anxiety signiﬁcantly

inﬂuenced perceived ease of use” (p.1). According to the ﬁndings, perceived usefulness

might not predict adoption of the system, so perceived ease of use would be the main
predictor of usage and perceived usefulness (p.12).

According to other literature, a user’s psychological situation also is a factor in
terms of technology acceptance (Rhee 2004). Conducting a survey of employees of four
companies and collecting data from 236 participants whose jobs were categorized in ten
different classes, Rhee implemented the technology acceptance model to assess the
impact of the stress on the technology acceptance. Rhee’s results suggested that 1)
supervision of top manager is the important aspect affecting new technology acceptance,
and 2) organizations should take necessary steps such as training, users, sharing

knowledge, and redesigning tasks in order to implement a new technology.

Although there is a relatively large and growing body of research applying the
technology acceptance model, researchers have not used in to examine the attitudes of
police ofﬁcers. There is, however, a few related studies in policing that are important.
First, Martin J. Zaworski (2005) conducted a quantitative and qualitative study to analyze
“the impact of the Automated Regional Justice Information System (ARJIS) on the
performance of law enforcement ofﬁcers” (p. 1). Zaworski conducted a survey of police
ofﬁcers working in the San Diego County Sheriff’s Ofﬁce (SDSO), and selected ofﬁcers
from another agency who were not using ARJ IS as a comparison group. The study’s
objective was to “determine whether information sharing technology makes a difference
in the ofﬁcers’ assessment of the value of technology in the following areas: individual
effectiveness, job performance, productivity, investigative support, arrests, and
clearances. The study also examined the effects of potential intervening variables such as

computer training” (p. 4). The research ﬁndings revealed that the impact of information

technology on individual effectiveness, job performance, and individual productivity
were signiﬁcant between group scores (p. 36). In terms of making arrests using
information technology, two groups showed no signiﬁcant difference. In the study,
Zaworski used the crime data including the crime and arrests per 1000 population for
both agencies from 2000 through 2002, inclusive. Clearance cases were also classiﬁed
using UCR as “Crimes against persons” and “Crimes against property” (p.17). In clearing
cases, “the info-sharing group scores were signiﬁcantly higher than the comparison
group, suggesting that information technology plays a role in clearing more cases” (p.
36). From a user-satisfaction perspective, Zaworski found that although the relation
between computer training and user-satisfaction seemed to be weak, nevertheless it was
signiﬁcant (p. 47) In other words, while each training variables including ‘training
hours’, ‘amount of training’, ‘training timing’, ‘training quality’ and ‘training frequency’
differed signiﬁcantly from user satisfaction score; “the strength of the relationships
between the training variables and scores for ‘user satisfaction measures’ were weak” (p.

34).

In another study, Northop (1993) examined the use of computers by patrol
ofﬁcers and detectives. Evaluating data derived from Urban Information Systems
(U RBIS) project, which is “a multi-year study of computerization in US. local
governments,” Northop concluded that “the user friendliness of the systems themselves”
and “training of police professionals in use of the systems” greatly inﬂuenced “the

utilization of computer search capabilities.” (p. 21)

The Theory of Reasoned Action.

Thus far, studies relevant to the user’s acceptance of technology have been
presented. Most of this research used the technology acceptance model to examine a
user’s acceptance of information technology. The TAM is based on the theory of
reasoned action (TRA) proposed by Fishbein and Ajzen (1975) where, as Leong (2003)
stated, intentions, which are “determined by the person’s attitude and subjective norm
concerning behavior,” predict a person’s behavior. The theory has three components: 1)

behavioral intention, 2) attitude, and 3) subjective norm.

Behavioral intention refers to “the strength of one’s intention to perform a
speciﬁed behavior” (Fishbein and Ajzen 1975, p. 288), where attitude is deﬁned as “an
individual’s positive or negative feeling about performing the target behavior” (p. 216).
Subjective norm is “the person’s perception that most people who are important to him
think he should or should not perform the behavior” (p. 302). As demonstrated in Figure
1, while attitude is inﬂuenced by behavioral beliefs and the evaluation of the major
outcomes, the subject’s norm is “determined by normative beliefs and motivation to

comply with salient referents” (Eagly and Chaiken 1993, p. 172)

Figure 1 Theory of Reasoned Action

 

 

BELIEFS AND
EVALUATIONS

 

 

 

 

 

 

 

 

NORMATIVE
BELIEFS AND
MOTIVATION
TO COMPLY

 

 

 

 

 

BEHAVIOR

 

 

 

 

 

 

ATTITUDE
BEHAVIORAL
INTENTION
SUBJECTIVE
NORM

 

 

 

 

Source: Eagly, H. A., Chaiken, S., (1993) The Psychology of Attitudes.
Harcourt Brace Jovanovich, Inc. p. 172

Thus, as Figure 2 shows, the theories of reasoned action (TRA) and the

acceptance of technology (TAM) underlie the prediction of the acceptance of technology
by showing the links between beliefs (usefulness of an information system) the use of

such a system, and users’ attitudes, intentions, and actual usage of the system” (Leong

2003, p. 4) According to the model, perceived usefulness is “the degree to which a

person believes that using a particular system would enhance his or her job performance'
(Davis 1989, p.320), and perceived ease of use is “the degree to which a person believes

that using a particular system would be free of effort” (p. 320). Therefore, based on the

theory of reasoned action and the acceptance technology model and the integration of

these

 

Figure 2 Technology Acceptance Model

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PERCEIVED
' USEFULNESS
EXTERNAL ATTITUDE BEHAVIORAL ACTUAL
VARIABLES INTENTION SYSTEM USE
PERCEIVED
EASE OF USE

 

 

 

 

 

 

 

Source: Rhee, Y., (2004) A Case Study of Employees ’ Perception of Organizational
Characteristics, Subsequent Stress, and New Technology Acceptance Ph. D
Dissertation, Department of Industrial Engineering, University of Wisconsin,
Madison. P. 36

theories into the “causal” model as shown in Figure 2, and on previously conducted
research showing the relevance of these models for estimating user acceptance of
information technology for some groups with some technologies, I propose to test a
“prediction” model to estimate the extent to which the above independent variables are

related to the use by Istanbul Police Ofﬁcers of a new, unique system called Mobile

Information System (MOBESE).

System Use (Y) = Perceived Usefulness (X1) + Perceived Ease of Use (X2) + Attitude

(X3) + Intention to Use (X4) + Tenure on the Job (X5) + Age (X6) + Experience (X7)
+ User Satisfaction (X8), where: the dependent variable (Y), “system use,” is a measure
of the extent to which ofﬁcers have used the MOBESE technology and the independent

variables (Xs) are perceived usefulness, perceived ease of use, attitude toward using the

MOBESE, intention to use MOBESE, tenure with the police department, level of
education, age (as a measure of life experience), and ofﬁcers’ satisfaction with the

system.

Figure 3. The Development of a Prediction Model to Estimate the Actual System Use of

MOBESE by Istanbul Police Ofﬁcers

 

 

 

 

 

SU=PU+PEU+AT+IU+TJ+AGE+EXP+US

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I know of no previous study that has examined the variables (ﬁgure 3) together to
determine the relative importance of each for predicting behavior, in this case police
behavior, speciﬁcally as it pertains to the use of the new MOBESE. The results of the
estimation of this mathematical model can be practically applied to the prediction of
police ofﬁcer behavior.

For example, based on the TRA and TAM theories, individuals who have higher
perceived usefulness and ease of use and greater attitudes and therefore intentions toward
the system would be predicted to use the system to a greater extent.

This model can also be used for developmental purposes, such as for training of
individuals whose prediction scores are lower, relative to others, based on their
perceptions and attitudes. That is, some individuals may have unwarranted
misperceptions that could be modiﬁed with accurate information, and behavioral
intentions can change if attitudes also are changed (Fishbein & Ajzen, 1975).

This research is, to a great extent, exploratory: no other study has tested these

variables together in a prediction model. There is, therefore, no way to estimate a prior

which variables will produce the greatest prediction estimates (values or coefﬁcients).
The following hypothesis, however, based on the above reported research and causal
models, will be tested using simple regressions.

3. RESEARCH HYPOTHESES
Based on the above reported literature review, I propose the following hypotheses which
will be tested using simple regressions (i.e., from the correlation matrix):

Iﬁpothesis 1: Perceived usefulness is positively related to MOBESE use.

 

Hypothesis 2: Perceived ease of use is positively related to MOBESE use.

Hypothesis 3: Attitude toward the system is positively related to MOBESE use.

 

Hypothesis 4: Intention to use MOBESE is positively related to the actual use of

 

MOBESE.

Hypothesis 5: Tenure in the Turkish National Police is related to MOBESE use.
Hypothesis 6: Age is related to MOBESE use.

Hypothesis-7: Ofﬁcers who have used any kind of computer system will have greater
positive attitudes toward using MOBESE terminals, relative to ofﬁcers who have had no
previous computer experience.

Hypothesis-8: User satisfaction is positively related to system (MOBESE) use.

Tenure with the organization and age are related to experience, both work and
life, and past experience is a strong predictor of future behavior (Muchinsky, 1990).
Also, Eagly and Chaiken (1993) stated that “the consistency between attitudes and
behaviors is also increased by prior knowledge about attitude objects, another factor that

may be increased by direct experience” (p. 201).

II

I therefore propose that individuals with greater life and work experience would
be more amenable to adopting a new system, relative to others with lesser experiences.

As Rhee (2004) stated in the literature review, user satisfaction and system usage
is positively related (p. 46). Rhee deﬁned user satisfaction as “the extent to which
technology application meets the end user’s needs.” (p. 63). Dillon (2001) reported that,
“satisfaction is inﬂuenced by such factors as personal experience with other technologies,
preferred working style, the manner of introduction, and the aesthetics of the product.”
(p. 3). Dillon explained user experience at three levels: process, outcome, and affect.
While process and outcome refer to user-device interactivity, affect covers attitudes,
emotions, and other mood-related elements involved in the experience. Xiao and
Dasgupta (2002) tried to measure user satisfaction by using a 12-item End-User
Computing Satisfaction (EUCS) developed by Doll and Torkzadeh (1988). Conducting a
survey of 340 end users about their satisfaction/dissatisfaction with Internet portals, they
found that EUCS scale was reliable and valid and that this instrument could be applied to
evaluate end-user applications. Therefore, I will use the Xiao and Dasgupta instrument to
measure ofﬁcers’ satisfaction with MOBESE terminals.

The items measuring perceived usefulness and perceived ease of use will be
adapted from Davis (1989). The questionnaires measuring attitude and intentional
behaviors adapted from Taylor and Todd (1995). Appendix A lists all of the items and
the variable that each item measures. Appendix B shows the survey that will be
administered to the police ofﬁcers.

To summarize, the TAM model, derived from the theory of reasoned action, has

been used in various studies with different groups of individuals and different systems of

12

technology, however, no study has yet applied the proposed prediction model to the job
of police ofﬁcer. No study has examined the model’s variables in terms of relative
predictability for the extent to which an ofﬁcer would actually use a speciﬁc technology,
such as the MOBESE. Based on the above ﬁndings using causal modeling, the theory of
reasoned action and the technology acceptance models seem appropriate theoretical bases
to now estimate a prediction model (Figure 3) and for testing related hypotheses using

simple regression analysis.

4. RESEARCH DESIGN

4.1. Data

Data were collected using a survey developed by this author for the present study.
The survey measured the attitudes and perceptions of police ofﬁcers who are currently
using the MOBESE technology to perform their job tasks. The survey items were adapted
from the study conducted by Rhee (2004), and Xiao and Dasgupta (2002); however, to
perform better quantitative analysis, and to be able to develop the prediction model, the
survey responses were ordered on a Likert scale ranging from (1) low to (5) high. The

survey items are presented in Appendix A.

4.2. Sample

The study subjects are police ofﬁcers currently working as patrol ofﬁcers for
Istanbul Police Department. MOBESE terminals mounted in 1000 police cars are used by
approximately 2500 police ofﬁcers. Several issues raised while distributing the surveys.
The ﬁrst concern was geography. Considering Istanbul has 32 districts, almost half of
which were located in Europe and others in Asia, the decision was originally made that

the survey would be distributed to 15 districts in European side for convenience. The plan

was for twenty ofﬁcers to be randomly selected from each district and questionnaires
would be sent them according to the initial decision. Another issue that inﬂuenced
sampling was a series of external events. At the time the study began there were several
bombing incidents existing in several places of Istanbul and most police ofﬁcers had to
work more intensely than the regular time because Istanbul Police were alarmed. The last
issue was the huge differences in the number of ofﬁcers using MOBESE system. In other
words, while some districts have more than 100 ofﬁcers, others have around 20 or 30.
When surveys distributed the districts that have a large number of ofﬁcers, it could be
biased because of that the questionnaires would be distributed to those who are known by
district manager as computer expert or those who are thought by their superiors as they
give answers in favor of system. As a result, instead of distributing the surveys to twenty
ofﬁcers in 15 districts, distributing them to all ofﬁcers working four districts (two big and

two small) is more appropriate for achieving unbiased results.

Surveys were distributed to these designated four District’s Police Departments
which are mainly located in European side of Istanbul City. Then, District Police
Managers were noticed about selecting ofﬁcers randomly. From 4 districts, a total 280
ofﬁcers completed questionnaires. Table 1 is the summary of response rate for each

district.

14

Table 1
Summary of resmnse for each districts

 

 

 

Districts Total Survey Total survey Response Rate
distributed returned

Bahcelievler 120 106 88%

Bakirkoy 1 1 1 1 11 100%

Bayrampasa 40 40 l 00%

Zeytinburnu 28 23 82%

Total 299 280 93%

 

 

Comparing general response rate of survey studies which are mostly at 50 percent or
lower, 93 percent response rate is unexpectedly high and surprising. Studies like this
conducted in Turkey are very uncommon, especially compared to the frequency of
surveys done in the United States, where almost all of this type of research has been
done. Therefore, individuals participating in the study were willing to answer questions
because they have not been overwhelmed with survey requests. This may be a reason of
high response rate. Another reason of this high response rate may be that it was the ﬁrst
request for police ofﬁcers to provide their thoughts about a new system launched almost
ten months ago. Third, since the author of the survey is a police chief in Turkey, and this
study had to be approved by the director of TNP, respondents might have felt obligated to
participate. If this third issue is true, then many ofﬁcers might not have responded
truthfully about the system. It is difﬁcult to know whether this was the case. The
respondents were ensured of their protections as required by the IRB, and encouraged to

speak truthfully and honestly. Moreover, there was variations in their reactions to the

15

system, and thus one can be fairly conﬁdent with these results accurately reﬂect their

position on this new technology.

5. RESULTS

5.1. Demographic Information

Demographic information is presented in Table 2. According to Table 2, only 1.4
percent of the participants are female, 98.6 percent of them are male. 32.2 percent of the
ofﬁcers were in the age group of 21 to 30, 37.7 percent were in the age group of 31 to 40
and 27.9 percent were in the age group of 41 to 50. As for ofﬁcers’ tenure on their job
13.7 percent of the respondents were in their ﬁrst three years, 26.4 percent were in their 4
to 7 years, 14.4 percent were in 8 to 11 years, 15.2 percent were 12 to 15 years, and
interestingly 30.3 percent of the respondents had been working as a police ofﬁcer for
more than 15 years. Most users ﬁnished ordinary high school (67.9 %) prior to police
school. Only 5.8 percent of the respondents graduated from technical high school, while
26.3 ﬁnished vocational high school. 7.5 percent of the participants have extended their
degree to undergraduate, while 92.1 percent of them only ﬁnished police school.
According to ofﬁcers’ jurisdiction type, 87.5 percent were working in residential areas,
7.4 percent were patrolling in business district, and 5.5 percent were working in night

clubs district.

16

Table 2
Demographic Information
Variables
Gender (279)
Male
Female
District (280*
BE
BK
BP
ZB
Age (276)
Under 20 years
21-30 years
31-40 years
41-50 years
51 years or older
School type prior to Police School (274)*
Ordinary High School
Technical High School
Vocational High
School
Education Level (267)
Police School
PS+Undergraduate D.
PS+UD+Masters
Tenure (277)
0-3 years
4-7
8-11
12-15
More than 15 years

Jurisdiction Type (256)**
Business District
Night Clubs
Residential and other
Dist.

17

275

106
111
40
23

89
104
77

186
16
72

246
20

38
73
40
42
84

19
14
223

Percent

98.6
1.4

37.9
39.6
14.3
8.2

0.4
32.2
37.7
27.9
1.8

67.9
5.8
26.3

92.1
7.5
0.4

13.7
26.4
14.4
15.2
30.3

7.4
5.5
87.1

5.2. Measures

5.2.1 . Dependent Variable

In this study, a ten-item scale was developed to measure MOBESE usage. This
scale measured whether ofﬁcer used terminal or not (0 times), or if he or she had used
terminal, how many times a respondent had used it during an average day. As seen in
Table 3, the response rate for this variable 76 % (n=214), which is considerably lower
than total response rate. Nevertheless, Bachman and Paternoster (2004) deﬁned “a good
rule of thumb” for the sample size (p. 234). According to them, “the assumption of
normality” is met when the sample size is 100 or more. Hence, n=214 is enough to make
any data analysis. Table 3 also presents its mean and standard deviation values.
Considering the mean value, 15.88, it can be said that terminals were rarely used by

ofﬁcers.

5.2.2. Independent Variables

Independent variables were determined based on Technology Acceptance Model
and other previous research. Descriptive statistics for these variables are presented in
Table 3. In order to determine whether the survey items captured the constructs of intent,
varimax rotation factor analysis was used. According to Leong (2003), “the decision rule
was that each item had to Show a loading of greater than 0.50 on one underlying
dimension”. The results of factor analysis are shown in Appendix-A. The items having
less than .5 have been written in italic. After excluding the items having less than .5,
components were restructured and their alpha scores have shown in Table 3. According

to factor analysis, since the components of attitude towards MOBESE and Behavioral

18

intention have loaded on same factor, these items have been merged into one component
called attitude toward MOBESE.

Perceived usefulness is a scale of six items (appendix-A). Their cronbach alpha
value is .96, which means that the reliability of this variable is very high. Its mean value
(17.93) implies that MOBESE system is moderately perceived as useful by most ofﬁcers.

Another variable, perceived ease of use, is a scale of six items. These items were
adapted from TAM. The reliability of this scale is .87 which demonstrates that it is
acceptable for use in testing the hypothesis. The factor analysis also indicates that these
six items belong exclusively to this variable.

The variable attitude toward MOBESE system also is an index created by four
items; however, when the factor analysis was performed, attitude toward MOBESE and
behavioral intention were loaded on the same factor. Therefore four items measuring
attitude toward MOBESE and two items measuring behavioral intention was combined
and named attitude toward MOBESE. This modiﬁed new scale, therefore, has six items.
As seen in Table 3, Cronbach’s alpha value of these items is .94.

The scale of user satisfaction had originally fourteen items. However, according
to factor analysis result, items 11. and 12. were dropped from the scale. User satisfaction
was constructed with the remaining 12 items. The cronbach’s alpha score of these items
is .95.

In order to measure previous computer experience of an MOBESE user, a 7-item
scale was developed. After performing factor analysis, two items were dropped.
Cronbach’s alpha for the other 5 items are .74 which is acceptable for use in testing the

hypothesis. The scores of the variable are between 5 and 10. Five indicates that user had

19

considerable experience on computers and use them a lot. Ten demonstrates that user has

no previous computer knowledge. Its mean score is presented in Table-3.

Table 3
Descriptive statistics of variables

 

 

N Missing Missing Items Mean Std. Cronbach ’s
% Dev. Alpha

Perceived Usefulness 260 23 8.13 6 17.93 7.37 .96
Perceived Ease of Use 246 37 13.07 6 19.99 5.60 .87
Attitude toward 261 22 7.77 6 20.37 6.52 .94
MOBESE

User Satisfaction 236 44 16.61 12 32.94 12.96 .95
User Experience 217 66 23.32 5 7.75 1.63 .74
MOBESE Use 214 69 24.38 10 15.88 7.12 .91

 

 

5. 3. Hypothesis Testing

In order to test hypotheses, bivariate correlation was utilized. First, alpha level
was determined as .05 and .01. To accept any research hypothesis stated in this study, test
should be signiﬁcant at alpha either .05 or 01. level. Since the variables are at the interval
level, the Pearson correlation coefﬁcient is appropriate to test correlation between two
variables (Norusis 2002, p.482).

Hypothesis 1: Table 4 shows the correlation between perceived usefulness and
MOBESE usage. As the correlation analysis indicated, there is a positive relationship
between usefulness and usage at alpha .01 level. That is to say, ofﬁcers who think that the
MOBESE system is useful tend to use MOBESE terminals in the police cars. This result
supports research hypothesis that Perceived useﬁIlness is positively related to MOBESE

use.

20

Table 4
Correlation of Perceived Usefulness and MOBESE Usage

 

 

MOBESE Usage
Perceived Usefulness Pearson Correlation .269**
Sig. (1 -tailed) .000
N 203

 

** Correlation is signiﬁcant at the 0.01 level (l-tailed).

Hypothesis 2: The correlation between perceived ease of use and MOBESE usage

 

was examined to test if there is any positively correlation between these two variables. As
seen in Table 5, the correlation analysis showed that perceived ease of use and MOBESE
usage are positively correlated. The more that the terminals are case to use, the more they
are used intensively by police ofﬁcers. This result found signiﬁcant at alpha level .01

supported research hypothesis that Perceived ease of use is positively related to

 

 

MOBESE use.
Table 5
Correlation of Perceived Ease of Use and MOBESE Usage
MOBESE Usage
Perceived Ease of Use Pearson Correlation .257**
Sig. (l-tailed) .000
N 194

 

** Correlation is signiﬁcant at the 0.01 level (1 -tailed).

Hypothesis 3 and 4: Based on literature, research hypotheses asserted that
attitudes toward the system should be positively related to MOBESE use. The correlation
analysis was examined the factors of attitude toward MOBESE and MOBESE Usage. A
Signiﬁcant relationship was found between these two variables (Table 6). That is, users

who have positive attitude toward the system are apt to use terminals more than those

21

who have lower attitude toward the system. In sum, this result supported the research

 

 

hypothesis 3.
Table 6
Correlation of Attitude Toward MOBESE and MOBESE Usage
MOBESE Usage
Attitude Toward MOBESE Pearson Correlation .221 **
Sig. (l-tailed) .000
N 199

 

** Correlation is signiﬁcant at the 0.01 level (l-tailed).

 

Hypothesis 5: Correlation between tenure and MOBESE usage was examined by
using the correlation analysis. The result indicated that there is no correlation between
tenure and system usage at alpha level .05 (Table 7). Therefore, the research hypothesis

that Tenure in the Turkish National Police is related to MOBESE use is rejected.

 

 

Table 7
Correlation of Tenure and MOBESE Usage
MOBESE Usage
Tenure on the TNP Pearson Correlation -.012
Sig. (l-tailed) .123
N 213

 

Hypothesis 6: Similar to previous result, it was not found relationship between
age and MOBESE Usage at .05 alpha level (Table 8). Based on the correlation analysis,

the research hypothesis was rejected.

22

Table 8
Correlation of Age and MOBESE Usage

 

 

MOBESE Usage
Ofﬁcer’s Age Pearson Correlation -.083
Sig. (l-tailed) .114
N 212

 

Hypothesis 7: In order to test the correlation user experience and MOBESE usage

 

a correlation matrix was utilized. According to the results of correlation analysis, having
computer knowledge prior to launch MOBESE system was not signiﬁcantly correlated
MOBESE Usage at .05 alpha level (Table 9). According to this result, the research
hypothesis is not supported that ofﬁcers who have used any kind of computer system will
have greater positive attitudes toward using MOBESE terminals, relative to ofﬁcers who

have had no previous computer experience.

 

 

Table 9
Correlation of User Experience and MOBESE Usage
MOBESE Usage
User Experience Scale Pearson Correlation .037
Sig. ( 1-tailed) .315
N 172

 

Hypothesis 8: This study proposed that user satisfaction is positively correlated to
MOBESE usage. In order to test this hypothesis, the correlation analysis was utilized.
The result indicated that user satisfaction is positively correlated MOBESE usage at .01
alpha level (Table 10). The more users were satisﬁed MOBESE terminal, the more they

used the system. This result supported the research hypothesis.

23

Table 10
Correlation of User Satisfaction and MOBESE Usage

 

 

MOBESE Usage
User Satisfaction Pearson Correlation .284"
Sig. (l-tailed) .000
N 184

 

** Correlation is signiﬁcant at the 0.01 level (l-tailed).

In conclusion, the correlation analysis showed that Perceived Usefulness,
Perceived Ease of Use, Attitude toward MOBESE, and User Satisfaction are signiﬁcant
at .01 alpha level, while ofﬁcers’ age tenure, and user experience are not related with
MOBESE Usage.

5.3. Regression Analysis for Prediction of MOBESE Usage

Before implementing regression analysis, a correlation matrix was performed to
check whether multicollinearity would cause a problem. As seen the correlation results in
Table 11, there is a high correlation between age and tenure (.88)--therefore age was
dropped from the regression analysis. Thus, six independent variables including
perceived ease of use, perceived usefulness, attitudes toward MOBESE, user experience,
user satisfaction, and tenure on the job was regressed on the dependent variable,

MOBESE Usage.

24

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25

Ordinary Least Squares regression and logistic regression were used to predict
MOBESE usage. As seen in Figure 4, the distribution of the dependent variable,
MOBESE Usage, is not normally distributed. Therefore, the linear regression can not be
applied due to the violated the assumption that cases should be normally distributed
(Bachman and Paternoster 2004, p. 511). It is important to note, however, that the
results using OLS regression and logistic regression are similar to the negative binomial

regression results.

 

 

Figure 4. Bar chart of MOBESE Usage variable

 

 

 

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60%

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3
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10101; 1 2 [GA 14. l0J1 6. I01111 8. IOJZOIOOTZZ. 00l24. 00'26. 00'28. 00130. 00'37 T.00|40 l00144|00
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USAGE

 

 

 

 

 

 

 

 

 

When the variance is larger than the mean and the distribution of the dependent

variable is overdispersed, the most appropriate statistical technique to be applied is

26

negative binomial regression (N BR). According to the descriptive statistics the variance
of usage is 50.2, its mean value is 15.8, and the chibar(2) value is 108.05; this is
signiﬁcance evidence of overdispersion. These values suggest that the NBR is most
appropriate statistic procedure for this study. STATA 9.1 software has been used for
conducting NBR. Table 11 shows the result.

Table 12 shows that no variables in the regression analysis are statistically
signiﬁcant. In other words, the variables utilized by this study were not found signiﬁcant

at alpha .05 level.

 

 

Table 12

Negative Binomial Regression Result for MOBESE Usage

Independent Variables b 2 P>z e"b %
Perceived Ease of Use 0.01129 1.440 0.150 1.0114 1.1
Perceived Usefulness 0.00847 1.300 0.194 1.0085 0.9
Attitude toward MOBESE -0.00615 -0.798 0.425 0.9939 -0.6
User Experience 0.01368 0.630 0.529 1.0138 1.4
User Satisfaction 0.00446 1.143 0.253 1.0045 0.4
Tenure on Job 0.00105 0.011 0.991 1.0010 0.1
Constant 2.29505 9.97 0.000

 

 

b = raw coefﬁcient

2 = z—score for test of b=O

P>|z| = p-value for z-test

e"b = exp(b) = factor change in expected count for unit increase in MOBESE Usage
% = percent change in expected count for unit increase in MOBESE Usage
Chibar(2)=108.05 (p <001)

However, when the three variables that are main constructs of the Technology
Acceptance Model were regressed on usage, perceived ease of use was obtained as the
only variable that is found statistically signiﬁcant at .05 alpha level. The result is shown

in Table 13.

27

Table 13
Negative Binomial Regression Result for MOBESE Usage using TAM’S variables

 

 

 

Independent Variables B 2 P>z e"b %
Perceived Ease of Use 0.01339 2.027 0.043 1.0135 1.3
Perceived Usefulness 0.00712 1.336 0.182 1.0071 0.7
Attitude toward MOBESE 0.00347 0.565 0.572 1.0035 0.3
Constant 2.307872 20.00 0.000

 

b = raw coefﬁcient
2 = z-score for test of b=0
P>|z| = p-value for z-test
e"b = exp(b) = factor change in expected count for unit increase in MOBESE Usage
% = percent change in expected count for unit increase in MOBESE Usage
Chibar(2)=127.83 Q) <001)
According to this result, perceived ease of use increased the expected number of
usage by a factor of 1.01, holding all other variables constant. Consistently, perceived

ease of use increases the expected number of usage by 1.3 percent, holding the variables

perceived usefulness and attitude toward MOBESE constant.

6. DISCUSSION
One of the main goals of the present study was to evaluate IPD ofﬁcers’ attitudes
and perceptions toward the new MOBESE system terminals, which are mounted in police
cars. As shown in the literature part, the Technology Acceptance Model (TAM), based on
the theory of reasoned action (Davis, 1989), has been selected to assess police ofﬁcers’
adaptation to the use the MOBESE system. In this thesis, an empirical study has been
conducted to evaluate the IPD police ofﬁcers’ attitudes, perceptions, and acceptance of

the MOBESE system.

28

Having examined the hypothesis, this study revealed that perceived usefulness,
perceived ease of use, attitude toward MOBESE, and user satisfaction are positively
related to MOBESE usage. This result partially supports previous research.

This study has examined the model’s variables in terms of relative predictability
for the extent to which an ofﬁcer would actually use a speciﬁc technology, such as the
MOBESE. Based on the literature examined, a prediction model has been created to test
related hypotheses using regression analysis. On behalf of the model, it has been tried to
ﬁnd to what extent and how each independent variables affect the MOBESE Usage.
Based on previous research, MOBESE Usage was regressed by six independent variables
including perceived usefulness, perceived ease of use, attitude toward MOBESE, their
tenure on the job, user experience on computers, and user satisfaction. In the previous
chapter, these variables were analyzed by using STATA software.

Several signiﬁcant ﬁndings were found by examining both the bivariate and
multivariate relationship between independent and dependent variables in the model.
According to bivariate analysis, as seen in Tables 4-10, MOBESE Usage has positively
correlated with perceived usefulness, perceived ease of use, attitude toward system, and
user satisfaction. This result is consistent with the ﬁndings found by previous research.

However, having utilized negative binomial regression, no variables were found
signiﬁcant. In order to test TAM’S variables, regression was performed for the second
time with the variables perceived ease of use, perceived usefulness, and attitude toward
MOBESE. This time, perceived ease of use was found to be statistically signiﬁcant. This

ﬁnding is consistent with Brown’s study (2002). According to the ﬁndings of Brown’s

29

study, perceived usefulness might not predict adoption of the system, so perceived ease
of use would be the main predictor of usage and perceived usefulness (p. l 2).

Consistently, this study also found that perceived ease of use is the only predictor
that was found statistically signiﬁcant. In other words, perceived usefulness, behavioral
intention to use MOBESE, ofﬁcers’ age, their tenure, attitude toward MOBESE, and user
satisfaction were not important in predicting MOBESE use.

One explanation for the ﬁndings of no signiﬁcance of the independent variables
except for perceived ease of use is that MOBESE system has been launched almost ten
months before the survey conducted. Due to several technological and administrative
factors such as the frequency in the breakdown of the system, loss of connection, and
closed inquiry services because of lacking legitimate approval, MOBESE system in the
police cars could not be implemented properly by users. As seen in Table 4, only 48,4%
of participants have used MOBESE system at least one time, which means that half of
respondents did not (or could not) use the system.

This study has contributed to the literature in various ways. First, while many
researchers have been conducted on university students’ or employers of private
companies, little research has been conducted in the police settings. Moreover, this study
results will provide valuable information for future research. This study also has
increased our understanding of critical factors in patrol ofﬁcers’ technology acceptance
such as perceived ease of use .

There are several implications of this study for departmental decision making.
First, decision-makers should focus on ease usability of any information system during

decision process. The results of the study imply that police ofﬁcers are inﬂuenced by the

30

ease of use of the MOBESE terminal. Since the working environment of patrol ofﬁcers
are not like regular ofﬁcers, they need to get any information very easily and quickly
when they are patrolling. Therefore any software and hardware equipment should be
designed to be used easily, and heir user interfaces should not be complicated so that

users can understand and use easily.

6.1 Limitations of the Study

Two points can be focused as being the most important limitations of the study.
First, this study is designed as cross-sectional style. Like conducted previous research,
data in this study were gathered from a particular population at one point in time.
Therefore, as stated Singleton and Straits (1999), cross-sectional designs “do not show
clearly the direction of causal relationships” between variables (p. 248). Second, the
information system was not working properly during the time the study was done as
mentioned above. In other words, if ofﬁcers had a chance to use all features of the
system, the results may have been different. In order to acquire more reliable information
on ofﬁcers’ attitude and perceptions toward the system in question, a follow-up study is
needed which will be done after the system is completely ready to be operated by users.

Another limitation of this study is related to its sampling design. Due to the
several factors mentioned in previous sections, this study selected only four districts from
thirty-two districts. If all ofﬁcers were participated this study, the result would be more

reliable.

31

6.2. Future Research

This study is a starting point for further research on measuring user acceptance of
mobile information systems in the police setting. There are several areas that are
remained uncovered in this study which include the comparison between ofﬁcers and
their superiors and the comparison between Istanbul and other cities utilizing similar
information systems. Future research should focus on these uncovered areas to be able to

get better information about the model used.

32

APPENDIX A: THE VARIABLES AND THE ITEMS

33

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Items Questions Factor
Loadings
Perceived 1. Using MOBESE terminal in my job enables me to .870
Usefulness accomplish task more quickly.
2. Using MOBESE terminal improves my job performance. .879
3. Using MOBESE terminal in my job increases my .859
productivity.
4. Using MOBESE terminal enhances my effectiveness on .838
thejob.
5. Using MOBESE terminal makes it easier to do my job. .865
6. I ﬁnd MOBESE terminal useful in myjob. .623
Perceived Ease of 1. Learning to operate MOBESE terminal is easy for me. .674
Use 2. I ﬁnd it easy to get MOBESE terminal to do what I want .671
it to do.
3. My interaction with MOBESE terminal is clear and .687
understandable.
4. I ﬁnd MOBESE terminal to be ﬂexible to interact with. .640
5. It is easy for me to become skillful at using MOBESE .755
terminal.
6. I ﬁnd MOBESE terminal easy to use. .771
User Satisfaction 1. The MOBESE system is successful. .640
2. 1 am satisﬁed with the MOBESE system. .665
3. The MOBESE system provides the precise information I .730
need.
4. The information content meets my needs. .823
5. The MOBESE system provides reports that seem to be .766
just about exactly what I need.
6. The MOBESE system provides sufﬁcient information. .848
7. The MOBESE system is accurate. .788
8. I am satisﬁed with the accuracy of the MOBESE system. .767
9. The output is presented in a useful format. .886
10. The information is clear. .847
11. The MOBESE system is user friendly. .127
12. The MOBESE system is easy to use. .132
13. I get the information I need in time. .814
14. The MOBESE system provides up-to-date information. .734
Attitude toward 1. Using MOBESE terminal is a good idea. .799
MOBESE + 2. Using MOBESE terminal is a wise idea. .846
Behavioral 3. I like the idea of using MOBESE terminal. .834
Intention 4. Using MOBESE terminal would be pleasant. .816
5. I intend to use MOBESE terminal frequently on my .600
patrol duty.
6. I intend to use MOBESE terminal in future. .656
Attitude toward 1. Working with a computer makes me nervous. .867
Computer 2. Computers make me feel uncomfortable. .857
3. Computers make me feel uneasy. .428
4. Computers scare me. .455
User Experience LWhat age did you first use computer? .067
2. Do you have computer in your home? .591

 

34

 

 

 

 

 

 

 

related MOBESE system?

Items Questions Factor
Loadings
User Experience 3. Do you use computer outside of work? .842
4. Did you have any computer knowledge before MOBESE .762
system launched?
5. Before patrol duty, had you been working bureau .684
requiring computer use such as secretaryship,
documentation archive, etc.?
6. Do you have Internet banking account? .628
7 .Have you ever participated in in-service training .031

 

MOBESE Usage

 

1. During an average day, how many times do you use the

MOBESE terminal in order to communicate to dispatcher
?

 

2. During an average day, how many times do you use the
MOBESE terminal in order to communicate to police
stations?

 

3. During an average day, how many times do you use the
MOBESE terminal in order to communicate to other
patrols?

 

4. During an average day, how many times do you use the
MOBESE terminal to get address information that you do
not know?

 

5. During an average day, how many times do you use the
MOBESE terminal to get distance information between
locations?

 

6. During an average day, how many times do you use the
MOBESE terminal to get jurisdiction information that you
are?

 

7. During an average day, how many times do you use the
MOBESE terminal to inquire plate numbers?

 

8. During an average day, how many times do you use the
MOBESE terminal to inquire personal information?

 

9. During an average day, how many times do you use the
MOBESE terminal to inquire the criminal records?

 

 

10. During an average day, how many tirnes do you use the
MOBESE terminal in order to inquire individual’s ID
Number?

 

 

* The items in bold has been excluded due to their low factor loadings.

35

 

APPENDIX B: THE MOBESE STUDY QUESTIONNAIRE

36

THE MOBESE QUESTIONNAIRE

Survey conducted by
Hamza TOSUN
Under the direction of
Dr. Steve M. Chermak
School of Criminal Justice
Michigan State University

Received IRB Approval: App#i024185

37

Dear Participant:

1 seek your expert Opinion on the MOBESE system. In this study, you will complete a
questionnaire concerning various factors that may or may not encourage the use of MOBESE by Turkish
police. I am interested in your opinions about ease of use, user satisfaction, prior experience with
technology, usefulness, and attitudes about technology. The questionnaire contains 69 items which I
estimate will take 10-15 minutes to complte.

There are several reasons why participation would be beneﬁcial. There currently are no data from
the users’ perspective to indicate what factors do or do not promote usage of MOBESE. First, the results
will identify ofﬁcer concerns about the use of this new technology. Second, it might also provide valuable
information on training gaps about the use of this system. Third, the results of this study may be of interest
to police managers as they continue to upgrade and expand this system.

There are some risks to participating in this study. You may be anxious about answering some of
the questions related to your supervisor’s attitudes toward MOBESE. This risk is minimized because the
completion of the survey is anonymous. 1 am not asking for your name, identiﬁcation, or position. Only my
thesis committee and 1 will have access to these data. Data will only be presented in the aggregate in the
thesis and any other research reports.

Your completion of this survey is completely voluntary. You are free to not answer any question
or to stop participating at any time without penalty. All questionnaires are anonymous, and the forms will
be kept conﬁdential by me to the maximum extent allowable by law. By completing this survey, you
indicate your voluntary consent to participate in this study and to have your answers included in the
research data set. You may submit the survey to me at the address below, using the self-addressed and
stamped enveIOpe enclosed for your convenience.

If you have any question about this study, you may contact me at the email address below, or you
may contact my thesis advisor, Dr. Steve M. Chermak, at . If you have any questions or
concerns regarding your rights as study participant, or are dissatisﬁed at any time with any aspect of this
study, you may contact- anonymously, if you wish- Peter Vasilenko, PHD, Director of Human Research
Protections, (517)355-2180, fax (517)-432-4503, e-mail , mail 202 Olds Hall, Michigan State
University, East Lansing, MI 48824-1047.

Hamza TOSUN

Master’s Degree Candidate
Michigan State University
Criminal Justice Department
00-1-(5 I 7)~547-0797

38

DEMOGRAPHICS For each items below, please circle the number that is representative for you.

 

 

 

LGENDER 2. AGE
1. Male 1. under 20 years
2. Female 2. 21-30 years
3. 31-40 years
4. 41-50 years
5. 51 years or older
3. TENURE

 

38. Length oftime being as a
police officer

1. 0-3 years

2. 4-7 years

3. 8-11 years

4. 12-15 years

5. more than 15 years

 

 

3b. Length of time employed as a
by the Istanbul Police Department

1.0-3 years

2.4-7 years

3.8-“ years

4. 12-15 years

5. more than 15 years

 

3c. Time in the same position

1. Less than 1 year
2. 1-2 years
3. 3-5 years
4. 6 or more years

 

4. EDUCATION

1. Type of high school completed prior to attending police school;

1. Ordinary High School

2. Technical High School (including computer, electric, electronic high schools)

3. Vocational High School (excluding computer, electric, electronic high schools)
2. Type of post-high school education completed;

1. Police School

2. Police School + Undergraduate Degree
3. Police School + Undergraduate Degree + Master’s Degree

 

 

 

 

 

 

 

 

 

 

 

 

 

MOBESE QUESTIONNAIRE
Please respond to each statement below by circling the number that indicates the extent to which you agree
or disagree.
Strongly Disagree Uncertain Agree Strongly
Disagree Agree
1. Using MOBESE terminal in my 1 2 3 4
job enables me to accomplish task
more quickly.
2. Using MOBESE terminal 1 2 3 4
improves my job performance.
3. Using MOBESE terminal in my I 2 3 4
job increases my productivity.
4. Using MOBESE terminal I 2 3 4
enhances my effectiveness on the
job.
5 Using MOBESE terminal make it 1 2 3 4
easier to do my job.
6. I ﬁnd MOBESE terminal useful I 2 3 4
in my job.
7. Learning to operate MOBESE l 2 3 4
terminal is easy for me.
8. I ﬁnd it easy to get MOBESE l 2 3 4
terminal to do what I want it to do.

 

 

 

39

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Strongly Disagree Uncertain Agree Strongly
Disagree Agree
9. My interaction with MOBESE l 2 3 4 5
terminal is clear and understandable.
10. 1 ﬁnd MOBESE terminal to be
ﬂexible to interact with. I 2 3 4 5
l 1. It is easy for me to become 1 2 3 4 5
skillful at using MOBESE terminal.
12. I ﬁnd MOBESE terminal easy to 1 2 3 4 5
use.
13. The MOBESE system is l 2 3 4 5
successful.
14. I am satisﬁed with MOBESE I 2 3 4 5
system.
15. The MOBESE system provides 1 2 3 4 5
the precise information 1 need.
16. The information content meets 1 2 3 4 5
my needs.
17. The MOBESE system provides 1 2 3 4 5
reports that seem to be just about
exactly what I need.
18. The MOBESE system provides 1 2 3 4 5
sufﬁcient information.
19. The MOBESE system is 1 2 3 4 5
accurate.
20. I am satisﬁed with the accuracy 1 2 3 4 5
of the MOBESE system.
21. The output is presented in a l 2 3 4 5
useful format.
22. The information is clear. 1 2 3 4 5
23. The MOBESE system rs user 1 2 3 4 5
frrendly.
24. The MOBESE system is easy to l 2 3 4 5
use.
25. I get the information I need in I 2 3 4 5
time.
26. The MOBESE system provides 1 2 3 4 5
upto-date information.
27 Using MOBESE terminal is a 1 2 3 4 5
_good idea.
28. Using MOBESE terminal is a I 2 3 4 5
wise idea.
29. 1 like the idea of using I 2 3 4 5
MOBESE terminal.
30. Using MOBESE terminal would 1 2 3 4 5
be pleasant.
31. l rntend to use MOBESE I 2 3 4 5

terminal frequently on my patrol

duty.

 

 

 

 

 

 

 

40

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Strongly Disagree Uncertain Agree Strongly
Disagree Agree
32. I intend to use MOBESE l 2 3 4 5
terminal in future.
33. Workmg With a computer makes 1 2 3 4 5
me nervous.
34. Computers make me feel 1 2 3 4 5
uncomfortable.
35. Computers make me feel I 2 3 4 5
uneasy.
36. Computers scare me. 1 2 3 4 5
37. Administration of Istanbul Polis l 2 3 4 5
Department supports MOBESE
system
38. My superiors support MOBESE l 2 3 4 5
system
USER EXPERIENCE
I. What age did you ﬁrst use computer?

1. Below 15

2. l6-20

3. 21-25

4. 26-30

5. 31-35

6. above 35

2. Do you have computer in your home?

1. Yes

2. No
3. Do you use computer outside of 4. Please indicate how many hours do you spent outside

work? of work on computer?
I. Yes (Please answer question 4) 1. 1 hours
2. No (Go to question 5. Please skip 2. 2-3 hours

question 4)

3. More than 3 hours

 

5. Did you have any computer
knowledge before MOBESE system
launched?

1. Yes (Please answer question 6)
2. No (Go to question 7. Please skip
question 6)

 

 

If yes please indicate level of your knowledge

I. Basic (Only typing and editing documents)

2. Intermediate (with office programs, using Internet
functions web, e-mail etc.)

3. Advanced (developing computer programs, writing
codes)

 

7. Before patrol duty, had you been working bureau requiring computer use such as secretaryship,

documentation archive, etc.?
1. Yes
2. No

 

8. Do you have Internet banking
account?

 

9.

 

If yes Which application do you use Internet banking?
I.Credit Card Payment

 

4l

 

 

 

 

 

l.Yes (Please answer question 9)
2. No (Go to next section. Please

skip question 9)

 

2. Bil payment
3. Other

 

10. Have you ever participated in in-service training related MOBESE system?

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

l.Yes
2. No
MOBESE USAGE

During an average day, how 0 times 1-10 times 1 1-20 times 21-30 More
many times do you use the Times than 30
MOBESE terminal in order times
I. To communicate with l 2 3 4 5
dispatcher

2. To communicate to police I 2 3 4 5
stations

3. To communicate to other 1 2 3 4 5
patrols

4. To get address information that l 2 3 4 5
you do not know

5. To get distance information 1 2 3 4 5
between locations

6. To get jurisdiction information 1 2 3 4 5
that you are?

7. To inquire plate numbers I 2 3 4 5

8. To inquire personal I 2 3 4 5
information?

9. To incurine the criminal records 1 2 3 4 5

10. To inquire individual’s 1D I 2 3 4 5
Number

JURISDICTION INFORMATION

 

l. Please select one of the following
items to describe your jurisdiction

1. Business district

2. Night Clubs

3. Residential District
4. Other

2. Comparing average crime rate in Istanbul, please
indicate crime rate of your jurisdiction.

I.Very High (Almost every day)

2. High

3. Moderate

4. Low

5. Very Low (Almost one in a month)

 

 

3. Please select the crime type that is
seen the most frequently in your

jurisdiction.
l.Pr0perty Crimes
2.Violent Crimes
3.Drug use

4. Other

 

If you know, please indicate crime rate of your
jurisdiction per month.

l. [-20

2. 21-40

3. 41-60

4. more than 61

5. I do not know

 

Thank you for your participation!

42

 

 

 

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