Maastricht School of Management
FHR Lim A Po Institute
MBA VI
Master of Business Administration Program
2009 – 2011
Maastricht School of Management
What Are The Main Determinants for the Attitude to use
Mobile phone Application in Suriname
BY
Lloyd S. Banda
SURINAME
December 2011
Supervised by: Dr. Elstak Mirdita PhD
This thesis was prepared under the conditions and requirements for the degree of Master of
Business Administration (MBA) from Maastricht School of Management (MSM), Maastricht, the
Netherlands and the FHR Institute for Social Studies (FHR)
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2
ACKNOWLEDGEMENTS
First of all, I would like to give thanks to God. His grace made it possible for me to start and
finish this MBA program.
Secondly of all I would like to thank my employer, Telesur for giving me the opportunity and
support to attend this MBA program.
I am very grateful to my supervisor, Elstak Mirdita, who agreed to supervise my thesis without
any hesitation. Her critical comments, suggestions, insight, strategy, tactics and motivational
words have inspired me to perform well.
Special thanks are expressed to Dirk Currie, the CEO of Telesur for allowing me to do this study.
His support has kept my spirits up. Also, I am grateful to Steven Tjitrotaroeno for her insights
and comments which improved my thesis.
I also want to express my gratitude to all my colleagues of Telesur who have supported me
throughout the program.
A special thanks to Hans Lim A Po and Olly Chin A Sen and their team for giving Surinamese
citizens the opportunity to develop their knowledge amongst professionals.
Most of all I would like to dedicate this thesis to my loving wife, Elvy Enny and daughter, Fariel
and sons Miguel, Serchinio and Shaqualle who have supported me in countless ways.
All these people have helped to make this MBA program a valuable and unforgettable learning
experience.
Lloyd S Banda
Paramaribo, December 2011
3
ABSTRACT
Where the mobile phone used to be offered solution for business people with a busy schedule,
this device is difficult to imagine today's society. Yet the function of the mobile phone over the
years been subject to considerable change. Where the device was originally intended as
communication, there are plenty of options added.
One of the most recent developments is the emergence of mobile application. Rapidly
infrastructures are built and under high competitive pressure from various angles content is
developed. The expectations from the market are high. Nevertheless, there are also comments on
the demand for this type of product / application. Is there enough interest in this product /
application to make a success? If there is no market for mobile applications, there can be little
earned from the investments made.
This research focuses on aspects of mobile application users, with the idea that the product will
never succeed when there is no demand. How does the Surinamese consumers across mobile
applications and what factors determined this attitude? And how would the current market
situation can react so that the chances of success be increased? These are questions that this
research will focus on.
Whether the alleged relationships as shown in the model match the reality is tested using a
survey research. 132 respondents completed the online survey. Through this survey the model
could not only be tested for reliability, but could also be based on the model conclusions about
the current perception of the users, on various determinants. The investigation revealed that only
three of the supposed relationships of the model actually present. The statistical analysis showed
that this was due in large part by high correlations between certain variables. This has
consequences for the model as it was drawn from the theory. Besides this theoretical conclusion
could also answer to the question of how the adoption of mobile application could be promoted.
The study showed that the current use intention are very poor and that this largely due to a
neutral perception of the product and the negative perception of the image. These aspects should
be further boosted by providers by informing consumers about the benefits and possibilities of
mobile application.
4
TABLE OF CONTENTS
Contents
ACKNOWLEDGEMENTS .......................................................................................................................... 3
ABSTRACT.................................................................................................................................................. 4
TABLE OF CONTENTS .............................................................................................................................. 5
Introduction ............................................................................................................................... 8
Chapter 1
1.1
General view ............................................................................................................................... 8
1.2
Importance and Relevance of the subject area ............................................................................. 9
1.3
The Telecommunication market in Suriname ............................................................................ 10
1.3
Research Objective .................................................................................................................... 10
1.4
Research Questions .................................................................................................................... 10
1.5
Research strategy and methods ................................................................................................. 11
Literature Review................................................................................................................... 12
Chapter 2
2.1
Introduction ................................................................................................................................. 12
2.2
Factors that affecting attitude .................................................................................................... 14
2.2.1
Perceived Enjoyment .......................................................................................................... 14
2.2.2
Perceived Ease to Use ......................................................................................................... 15
2.2.3
Compatibility ...................................................................................................................... 15
2.2.4
Image................................................................................................................................... 15
2.2.5
Perceived Usefulness .......................................................................................................... 16
2.2.6
Attitude and Intention to use ............................................................................................... 16
2.3
The Research model and hypotheses ..................................................................................... 17
2.3.1
Perceived enjoyment ........................................................................................................... 17
2.3.2
Perceived ease of use .......................................................................................................... 18
2.3.3
Compatibility ...................................................................................................................... 18
2.3.4
Image................................................................................................................................... 19
2.3.5
Perceived usefulness ........................................................................................................... 19
2.3.6
Attitude .............................................................................................................................. 20
2.3.7
The Conceptual Framework and Hypotheses ..................................................................... 21
Chapter 3
Methodology .......................................................................................................................... 22
5
3.1
Introduction ................................................................................................................................ 22
3.2
Questionnaire Design ................................................................................................................. 22
3.3
Data collection ........................................................................................................................... 23
3.3.1
Pilot test ............................................................................................................................. 23
3.3.2
Main survey ........................................................................................................................ 24
3.4
Measures used in this study ........................................................................................................ 24
3.5
Data analysis procedure .............................................................................................................. 27
3.6
Reliability and validity ................................................................................................................ 28
Findings................................................................................................................................... 29
Chapter 4
4.1 Introduction ....................................................................................................................................... 29
4.2
Preparation of the data ............................................................................................................... 29
4.3
Demographic profile of respondents ........................................................................................... 29
4.4
Descriptive statistics .................................................................................................................. 31
Substantive analysis of the questionnaire ........................................................................... 32
4.4.1
Inferential statistics ................................................................................................................... 36
4.5
4.5.1
Hypotheses Testing with Regression analysis ................................................................. 37
4.5.2
Regression analysis for intention to use ............................................................................. 37
4.5.3
Regression analysis for attitude ....................................................................................... 39
4.5.4
Regression analysis for all independent variables with intention to use as dependent
variables 40
4.5.5
Summary of Results on Hypotheses Testing ...................................................................... 42
Chapter 5
Conclusion ............................................................................................................................. 44
5.1
Answer the Research question ................................................................................................... 44
5.2
Theoretical implication .............................................................................................................. 46
5.3
Consequences for the practice.................................................................................................... 47
Discussion & Recommendation ............................................................................................. 49
Chapter 6
6.1
Chapter overview ...................................................................................................................... 49
6.2
Research progress and issues ...................................................................................................... 49
6.3
Theoretical Recommendations ................................................................................................... 52
6.4
Managerial Recommendations ................................................................................................... 52
Bibliography ................................................................................................................................................ 54
Appendix A.
Pre-test questionnaire- Dutch language .......................................................................... 57
6
Appendix B.
Main questionnaire- English language ............................................................................ 61
APPENDIX C: Substantive analysis of the determinants ........................................................................ 65
APPENDIX D: Regression analysis ........................................................................................................ 79
APPENDIX E.
Reliability of all variables .......................................................................................... 83
APPENDIX F: Respondent frequency profile ........................................................................................ 84
APPENDIX G: Regression analysis all variables on intention to use, including demographics ............... 86
7
Chapter 1
1.1
Introduction
General view
Do you know how many people in Suriname are without a mobile telephone? Almost everyone
has some kind of mobile device like a mobile phone or smart phones and to use it in the
everyday life is understood. The concept of the mobile application has lately become a trend and
has a great impact on our daily lives. The way we are using our mobile devices has changed and
the expectations on those devices have increased. In a fairly rapid tempo has this small device
convinced consumer it's impossible to imagine life today without the application on the mobile
phone.
Mobile applications are a rapidly developing segment of the global mobile market. They consist
of software that runs on a mobile device and performs certain tasks for the user of the mobile
phone. Mobile application, referred to as system operating on mobile device, are evolving
rapidly, making ubiquitous information access at anytime and anywhere a true reality [ Kaasinen,
Aaltonen, Kolari, Melakoski, & Laakko, 2000].
Mobile applications are common on most mobile phones today. They are key to providing user
interfaces for the basic telephony and messaging services, as well as for more advanced and
entertaining experiences such as playing games, browsing and watching video on mobile phones.
Many mobile applications come pre-installed on mobile phones e.g. SMS/MMS client, browser,
music player, whereas other may be provisioned and configured post sales by means of
maintenance management in shop of over the air. Application on mobile phone are a natural
extension to the current wired infrastructure. A variety of applications targeting the consumer is
now available in the market. The mobile applications enabling Business to Business (B2B) and
Business to Consumer (B2C) transactions are rapidly becoming mainstream along with other
shrink-wrap software products. In the business, a variety of people including road warriors, sales,
and service professionals, are being equipped with on-the-go computing capabilities using
mobile technologies for the entertainment, education, communication, work, and other sectors.
Mobile phones nowadays have grown beyond their fundamental role as more than a
communication tool and now thanks to the new development a personal bond between the user
8
and his mobile has emerge. We are witnessing an era when users buy mobile phones not only to
be in contact with others, but to express themselves, their attitudes, feelings and interests to
express. Customers demand more from their Mobile. They use their mobile phones to play
games, read news, browse the Internet, keep a tab on astrology, and listen to music, others use it
to make music, or check their bank balance.
1.2
Importance and Relevance of the subject area
Most cell phone users don’t know much about their operating system and its potential. That is
one of the main problem in my opinion. The most common applications like telephone, SMS and
in the meantime camera functionalities are widely used. But a cell phone in today’s society is not
only a tool for telephoning and writing SMS. It is a personal item which provides entertainment
and information. The application on the mobile phones have changed the style of live of all kinds
of consumers, especially those in younger generations. The most youth and school children have
owned at least one mobile phone. The mobile phones offer a range of applications, from
telephone conversation and simple text messages (SMS), to multimedia messaging services
(MMS) and Internet access, depending on the capability of individual mobile phones and the
services available. According to [Abdul Karim, Darus and Hussin, 2006] these applications have
been made possible through various developments in mobile telephone technology such as
GPRS, WAP and the 3G standard.
The importance of the study stems mainly from the importance of the technology itself, because
it is found that the application on the mobile phone are the most attractive and rapidly growing
technologies of our time. The study on how and why consumers focus on (new) technology and
mobile application can be relevant and important for both providers and customers. Increased use
of mobile technology is also an important reason for studying the adoption.
Some of the new information and commercial technologies and applications are examined in the
past and mobile technology is one of them. Mobile applications have been developed and used in
different areas. Pervasive and ubiquitous mobile technology has penetrated both the personal and
business domains. Mobile application has an impact on consumers because they offer all
universal access to information and services , as well as an opportunity for a unique and personal
exchange of information (Watson et al., 2002). As a result, for using mobile devices in their
9
daily life, a way have been created to stay in touch with the rest of the world to communicate and
network. New technology is said to be inseparable from everyday life [Weiser,1991]. According
to [Dahlberg et al., 2008] consumers power is undoubtedly one of the more important factors
that makes adoption a success, it is also a research area that receives much attention
The study can be useful for organizations that are planning to introduce mobile application and
are faced with lack of comprehensive understanding of mobile application user environment.
1.3
The Telecommunication market in Suriname
The government owned company the Telecommunication Company Suriname [Telesur] had a
monopoly since the April 2007 when the mobile market was liberalised. From this moment
onwards and with the introduction of prepaid telephone the number of users of a mobile phone
grew rapidly. The total population of Suriname consist of 492,829 people [ABS 2005].
According to [IMWO, 2007] in 2007, 76% of the Surinamese population had a mobile phone
The recent developments of the mobile industry in the Surinamese market and the rapid changes
and developments of mobile phones worldwide make it necessary for the telecom providers to
effectively reach their target groups.
1.3
Research Objective
The objectives of this study are the following: To identify what the main factors are for the
intention to use mobile application.
1.4
Research Questions
The intent to acceptance in this investigation is measured on the basis of the intent of the
consumers in order to use the product (behavioral intention to use). The intention to use the
product can be treated as equivalent to the intent to acceptance of the application by the
consumer. If the consumer the intention to make the application to be used, then it is assuming
that this new application which has accepted and thereby intent has developed. This intent to
acceptance is measured on the basis of a compound model in which the technology acceptance
10
model and the innovation or diffusion theory these are the two main. A key variable in the
whole, the attitude, which can influence all the construct
The following main research question was formulated:
What are the main determinants of the attitude towards mobile apps and how does this attitude
impact the use of mobile apps.
1.5
Research strategy and methods
In this research model to investigate the different variables will use the Technology Acceptance
Model (TAM) by Davis (1989) as a basis. The TAM was specifically designed to explain
computer usage behavior, and it can also be used in the context of e-commerce, with websites on
the Internet. Since Davis et al. (1989) state that when using TAM, other variables can only have
an influence on intention through these variables, we hypothesize in our conceptual framework
several variables that have not an indirect influence on Intention to use application on mobile
phone through these Attitude. All the variable will also be hypothesized as having an influence
on Intention to use through Attitude. These variables will lead to the final conceptual framework
and a model that will be tested in this research.
The empirical research will be done with a survey on the Internet. The snowball sampling
technique will be used, and respondents will receive an email with a link to the questionnaire. In
the questionnaire we will operationalize all the variable. The data obtained from the participants
will be analyzed with the use of the statistical program SPSS version 19.
From the answers of the respondents to the questions a dataset will be drawn up, which is
suitable for data analysis. Cronbach’s alpha will be used to examine the validity and reliability
of the different measures, and regression analysis will be used to evaluate the hypotheses and the
causal model. This analysis will lead to conclusions about our hypotheses, whether they are
supported, or need to be rejected. This leads to an overall conclusion which describes what
variables are of direct and/or indirect influence on Intention to use mobile application.
11
Chapter 2
2.1
Literature Review
Introduction
The reason for using an adoption model is to understand the adoption requirements of the
endusers, analyse of their context-specific and role-specific behaviour. The research model that
was used for these study is based on the Technology Acceptance Model (TAM) that was
proposed by Davis [1989]. The Technology Acceptance Model is a behavioural model that
describes the antecedents of the adoption of information technology (IT) and is considered a
robust tool for measuring the adoption of new technologies by the users.
The technology adoption and service models are used as conceptual instruments of the central
issues for the mobile domain (Vatanparast, 2009) to identify. They highlight factors that
influence consumers who are willing to use mobile services. It is relevant to a thorough
knowledge of the theories underlying technology to and service adoption in order to be
successful in mobile service and advertising business. Robey (1979) was one of the first
researchers who managed to combine previous work done by Vertinksky, Barth, and Mitchell
(1975) and Schultz and Slevin (1975) create a model that gives a forecast on the development of
the a significant connection between the user attitudes (or observations) and the use of the
system.
Perceived
Usefulness
External
Variables
Attitude
Intention to use
Actual to use
Perceived
Ease of Use
Figure 1: The Original Technology Acceptance Model
(Source: Davis, 1989, p.985)
12
Fishbein (1967) multi-attribute model indicates that the attitude of the sum of cognitive beliefs
indicates, if the positions are determined by the relevant cognitive beliefs and evaluation of those
beliefs. Fishbein and Middletown City (1995), suggest that the use of cognitive factors that are
inappropriate or incorrectly measured, only non-cognitive factors can influence the attitude.
Schwartz (1997) considers other feelings, such as mood and cognitive actually serve as sources
of information, just as beliefs were assessed and evaluated in the model of Fishbein's. Fishbein
and Ajzen (1975) developed an adaptive behavioral theory and model called the Theory of
Reasoned Action (TRA). It extends Fishbein's multi-attribute model by clarifying the
relationship between attitudes and behavior. Two new contributions to the model are Fisbein
attitude toward behavior and subjective norms that influence the intention to perform a behavior.
The model links individual beliefs, attitudes, intentions and behavior to the psychological
process that the observed relationship between attitudes and behavior (Fishbein & Ajzen, 1975)
mediates describe. TRA can predict consumer’s behavior to perform or not perform in a situation
where the customer is solely and directly responsible for his own behavior, and he is considerate.
The relationship between the attitude and the behavior in regard to mobile services and
advertising can be studied using Fishbein and Ajzen (1975) theory of reasoned action (TRA).
Theory of Acceptance Model (TAM) is related to the Theory of Reasoned Action (Fishbein &
Ajzen, 1975) and Theory of Planned Behavior (Ajzen, 1991). TAM was originally developed by
Davis (1989) to play for the user acceptance of information technology and use at work. TAM is
based on intention to use technology and defines two important concepts, "perceived usefulness"
and
"perceived
ease
of
use"
(Davis,
1989).
TAM
has
been
used
in
studies
focus on users. Studies on adoption of mobile services, the results met the central
factors in TAM: perceived ease of use and perceived utility. In a comparison with TAM and TPB
conducted by Mathieson (1991), he found TAM easier to implementate and have a slight
empirical advantage over TPB, but distributed only general information information on user’s
opinion about a system. On the other hand, TPB provides more detailed information that can be
to promote the on user’s opinion about a system. Usefulness and ease of mobile service can be
studied by technology accepted model . Based on technology accepted model , it is expected that
consumers with more experience and a positive attitude towards mobile phone technology should
have more positive attitudes towards mobile application.
13
2.2
Factors that affecting attitude
In order to carry out the research purpose, factors that affecting the consumer attitude toward
mobile application is described. Factors that effecting consumer attitude presented by [Davis et
al., 1989] and factors that presented by Nysveen et al [2005a; Karahanna et al 1999; Barnes and
Huff, 2003] will be considered as main factors that play a role in attitude formation. To better
predict adoption of mobile services, and suggest several extensions that may be relevant in
explaining customer’s intention to use mobile services.
2.2.1 Perceived Enjoyment
According to researcher they view perceived enjoyment as an intrinsic source of motivation,
referring to the performance of an activity for no apparent reason other than the process of
performance itself van der Heijden [2003] and Moon and Kim [2001]. Their research
demonstrates that perceived enjoyment has an effect on both attitude and consumers’ behavioral
intention toward using application on a mobile phone.
According to Kumar and Bruner II [2005] and Van der Heijden [2003] they incorporate a nice
factor TAM and treated as an endogenous variable for a more predictive. The correlation
provides significant validation of the extended TAM. Scientist on the use of cellular devices and
internet sites to find them both useful and enjoyable for users. According to Van der Heijden
[2003] he explicitly building the role of intrinsic motivation in TAM, announced the concept of
experienced pleasure to explain how consumers should make use of the websites. The
observation may be indicated to the extent to which consumers seductive pleasure is perceived
by the activity of using a particular product or service, rather than being distracted by one of his
own performance implications. Similar to the definition of perceived playfulness, experience
pleasure, or fun, is also treated as a major factor hedonistic ahead of consumers' attitudes towards
the use of a system [Bruner and Kumar II, 2005, Moon and Kim, 2001]. Previous studies of
WWW and mobile commerce to take pleasure in the view of the TAM for a more accurate
prediction of acceptance toward a specific source to get, especially because a product or service
and is associated with pleasure, it contributes to the causal relationship [(Bruner II and Kumar,
2005, Moon and Kim, 2001, van der Heijden, 2003]. According to [Davis, 1989, Igbaria,
14
Schiffman and Wieckowski, 1994] an individual can experience immediate enjoyment or
pleasure of using a particular system, and observe any active involvement in the use of new
technologies to remember in its own right
2.2.2 Perceived Ease to Use
According to [Davis et al., 1989] Perceived ease of use is the degree to which a user believes that
using a technology will be free of effort. The Technology Acceptance Model (TAM) suggests
that perceived ease of use has a direct effect on positive attitude.
The perceived ease-of-use it is the evaluation of the user of how easy for him or her to interact
with technology or particular information system [Zhiping, 2009]. Perceived ease of use, in
contrast, refers to "the degree to which a person believes that using a particular system would be
free of effort" [Davis, et al, 1989]. It is also claimed by Davis, 1989 easier applications have
bigger potential to be accepted by users.
2.2.3 Compatibility
Compatibility is the degree to which the innovation fits with the potential adopter’s existing
values, previous experiences and current needs [Rogers 1983]. In the context of WAP-enabled
mobile phone, a person’s lifestyle will strongly infuenced his/her decision to adopt the
technology. Mobile device must work well with the user’ s already existing computing
environment like for example the possibility to synchronize the mobile device to the user’ s
stationary computer.
2.2.4 Image
Image can be defined as the degree to which the use of an innovation is perceived to enhance
one’s image or status in one’s social system [Rogers 1983]. It is likely that mobile phone may be,
at present, more of a lifestyle product than a product of necessity.
Given the TAM concept that Davis [1989] develops to predict and explain consumers’ intentions
toward adopting information systems, we incorporate three underlying constructs of perceived
15
enjoyment, compatibility and image into the original TAM model in an attempt to add our
knowledge by undertaking an in-depth conceptual and empirical examination. These constructs
may significantly affect consumers’ attitudes or behaviors, and may provide a more effective means
to predict consumer intentions when adopting mobile application.
2.2.5 Perceived Usefulness
According to TAM, perceived usefulness is expected to have a direct effect on positive attitude.
The perceived usefulness is defined by Davis, 1989 as "The degree to which a person believes
that using a particular system would enhance his or her job performance." (p.319-339). There is a
positive correlation between perceived usefulness of mobile services and consumer satisfaction
in mobile services environment [Lee et al., 2007,]. According to Soura-Koury et al., (2010) the
study conducted by also found that perceived usefulness is one of the key variable for prediction
consumer attitude toward using mobile application.
2.2.6 Attitude and Intention to use
The Atiitude –Behavarioral intention relationship represented in TAM implies that, all else being
equal, people form intentions to perform behaviors toward which they have positive affect.
Furthermore based on [Davis, 1989] the technology acceptance model posits that actual use of a
specified system will be determined by an individual’s behavioral intention, which is jointly
determined by an individual’s attitude toward using a system. Previous empirical studies show
that an individual’s attitude is influenced by various antecedent factors or external variables,
which may be system features, training, documentation, compatibility, and user support [Davis,
1989; Lucas and Spitler, 1999; Vijayasarathy, 2004]. According to the Theory of Reasoned
Action, a person’s behavioral intention, which is the strength of one’s intention to perform a
specific behavior, is a function of two basic determinants, one personal in nature and the other
reflecting social influence. Personal factor is the individual’s negative or positive evaluation of
performing the behavior, this factor is termed attitude toward the behavior. Secondly the
16
determinant of intention is the person’s perception of the social pressures put on him to perform
the behavior in question. This factor is termed subjective norm (Ajzen and Fishbein, 1980).
The technology accepted model TAM posits that the actual usage of a technology is determined
by behavioral intention (BI), which is jointly affected by an individual’s attitude (AT).
The application features on the mobile phone can be viewed as a hybrid of TV and computer
technologies. The people’s who frequently watch live TV and video discs easily form a favorable
attitude toward using them. According to [Lederer, Maupin, Sena, and Zhuang, 2000; Moon and
Kim, 2001; O’Css and Fenech, 2003; van der Heijden, 2003] a stronger relationship occurs
between attitude and behavior on the evaluation of an innovative technology in similar situations.
Attitude is defined as an individual’s favorable or unfavorable feeling on using a given
technology [Ajzen and Fishbein, 1980], and it is suggested that attitude has positive effect on
behavioral intention.
2.3
The Research model and hypotheses
Given the TAM concept that Davis [1989] develops to predict and explain consumers’ intentions
toward adopting information systems, we incorporate three underlying constructs of perceived
enjoyment, compatibility and image into the original TAM model in an attempt to add our
knowledge by undertaking depth conceptual and empirical examination. These constructs
significantly affect consumers’ attitudes or behaviors, and may provide effective means to
predict consumer intentions when adopting application on the mobile phone.
2.3.1 Perceived enjoyment
Perceived enjoyment defined "the degree to which an individual believes that the activity of
using a product or service is perceived as pleasant in its own right, apart from any performance
consequences that can be predicted" [Davis, Bagozzi, & Warshaw, 1992 ]. [Rössler & Hoflich
2001] declaring that include enjoying an important factor that affects the attitude toward mobile
messaging. The main idea behind this linkage is that when the mobile service leads to an
intrinsic reward will encourage fun, and so there will be more likely that the person will use this
application. This claim is also confirmed by the empirical results of the study of Nysveen et al
[2005a], which experienced that enjoyment was expressed as a significant history of use and
17
attitudes had a significant effect on all applications that were examined (SMS, contact contact
the application, payment application and gaming application). Thus it is reasonable to assume:
H1. Perceived enjoyment has a positive effect on Attitude towards using mobile application
2.3.2 Perceived ease of use
Perceived ease of use is described as the degree to which a person believes that using a particular
system would be free of effort [Davis 1989]. Based on the definition of the perceived ease of use,
a person will have a service / application as easy to use if no additional effort is required of him.
Davis [1989] indicates that complexity, defined by Rogers and Shoemaker [1971] as "the degree
to which an innovation is perceived as relatively difficult to understand and use 'parallels
perceived ease somewhat closely. This is the reason that the less complex innovation is, the
easier it will be to use. In addition to this, Davis [1989] claims that an application that is
perceived to be easier to use than the other is more likely to be accepted by users. it follows that
the less complex system or application, the easier it will be for the user to an application, the less
effort it requires, and thus the greater the chance for the user to run the program. observed ease
of use, in addition to an antecedent of perceived to be useful, predicts the end of faith in a
technology it has been established and therefore his attitude toward the technology, which in turn
accepting it has been established [eg Ma & Liu 2004, Davis et al 1989, Venkatesh & Davis,
2000]. Hung et al [2003] showed that perceived ease of use influences attitudes to positive use
mobile application. Therefore, I postulate the following hypothesis.
H2. Peceived Ease to use has a positive effect on Attitude towards using mobile
application
2.3.3 Compatibility
Compatibility is define as the degree to which an innovation is perceived as consistent with
the existing values, past experiences and needs of potential adopters [Rogers 1983]. According to
Rogers (1983), he splits the compatibility into the three following categories: Compatibility with
18
values and beliefs, compatibility with previously introduced ideas, and compatibility with needs.
The compatibility was meant to be measured by each of these components. However, a problem
concerned this was encountered. It became clear that it will not be possible to test the effect of all
three compatibility variables. From here, it would use notion of compatibility with life style as
the approach in this study. Rogers [1983], he considers compatibility to be one of the
determinants which influence the adoption of innovations, such as for example new mobile
application. He claims that the idea that is more compatible is less uncertain to the potential
adopter. Incongruence or dissonance in the mind of a consumer, which can occur when ideas are
incompatible, would be smaller and thus the probability to use a service would be higher.
Therefore the direct link between compatibility and attitude to use mobile services is positive.
H3. Compatibility has a positive effect on Attitude towards using mobile application
2.3.4 Image
[Karahanna et al 1999; Barnes and Huff, 2003] defined Image as the degree to which the use of
an innovation is perceived to enhance one’s image or status in one’s social system.
It is likely that mobile phone may be, at present, more of a lifestyle product than a product of
necessity. This fact has not been lost to mobile manufacturers. For example, HTC has identifed
some user groups when designing its mobile phones. This group they are the trend-setters,
the time manager, the technology enthusiasts and the social connectors [Dawson 2003] . The
company Nokia, in the same capacity, has phones that target either young users or fashion- and
image-conscious users.The use of application on mobile phone is often associated with certain
social image. It is believed that early adopters of application on mobile phone are either trendy .
Thus, if one wants to be associated with the above groups, the following hypothesis will apply:
H4. Image has a positive effect on Attitude towards using mobile application
2.3.5 Perceived usefulness
Perceived usefulness defined as the degree to which a person believes that using a particular
system would enhance his or her job performance [Davis 1989]. These follows from the
definition of perceived usefulness that people will consider a system to be useful when it
19
enhances their job performance. According to [Davis 1989] a system high in perceived
usefulness, in turn, is one for which a user believes in the existence of a positive useperformance relationship. Within an organizational context, people are generally reinforced for
good performance by raises, promotions, bonuses, and other rewards [Pfeffer, 1982; Schein,
1980; Vroom, 1964].
According to Davis [1989] the hypothesis empower that perceived usefulness is a direct
determinant of customers who 'intention to use technology. The rationale behind this is that if the
system or application is to promote one's work that will be seen as useful and will incentive a
person more to use the system or application as it helps to use a positive relationship to gain
performance . The argument for the direct effect of the perceived benefits of using intentions for
non-work related situations can be summarized as follows: While the system, or mobile
applications, contributes to achieving the consumer of a particular purpose, or if the customer
feels to perform the same task, the system or service will give a higher value or satisfaction, then
the system or service construed for being useful. In this way, a person will have a higher
ambition for the system or application to use as it helps to task and to achieve positive value and
satisfaction . Hence, the observed ease of use influences positive attitude to mobile application .
In accordance with TAM, I postulate the following:
H5. Perceived usefulness has a positive effect on Attitude towards using mobile application
2.3.6
Attitude
The Attitude is defined as “an individual’s positive or negative feelings about performing the
target behaviour” [Fishbein & Ajzen 1975: 216]. Pavlou and Fygenson [2006], Chen et al.
(2007), and Kim et al. (2009) believed user’s attitude towards mobile devices was that the
devices are good and entertaining, thus affecting their intention of use. Chang et al. [2008] and
Lu and Ling (2009) believed that attitude is important when participating in activities and that it
brings about the intention of use in regard to its users. Thus, this study presents Hypothesis 6:
H6. Attitude has a positive effect on Intention towards using mobile application
20
2.3.7 The Conceptual Framework and Hypotheses
Perceived
Enjoyment
H1
Perceived
Ease of Use
H2
H3
Compatibility
H6
Attitude
Intention toUse
Mobile Apllication
H4
Image
H5
Perceived
Usefulness
Figure 2 shows the hypothesized model for the current study.
Hypotheses
H1.
Perceived enjoyment has a positive effect on Attitude towards using mobile application
H2.
Perceived Ease to use has a positive effect on Attitude towards using mobile application
H3.
Compatibility has a positive effect on Attitude towards using mobile application
H4.
Image has a positive effect on Attitude towards using mobile application
H5.
Perceived usefulness has a positive effect on Attitude towards using mobile application
H6. Attitude has a positive effect on Intention towards using mobile application
Table 1. Overview of hypotheses
21
Chapter 3
3.1
Methodology
Introduction
In this chapter, even more depth to the nature of survey research and the manner in which the
method in this study shape. This will come to the fore how the assumptions, formulated in the
previous chapter, in the light will be in practice. In the first paragraph, the methodology will be
explained. Finally, in the last paragraph the establishment of the questionnaire explained and the
Validity and Reliability.
3.2
Questionnaire Design
This research was accomplished by conducting a questionnaire survey. The questionnaire
consists of two distinct parts. The first part focused on gathering demographic information from
the respondents. Age, gender, education level are the variables that helped us to identify the users
participating to our study. The age groups were five and chosen to be 0-18, 19-25, 26-34,35-49
and over 50 years old. For the highest education level, the respondents had to choose between
lower technical degree, more expanded degree, high general degree, secondary vocational
degree, high school degree, and university degree
In the same part, we tried to get a picture of the users usage and their mobile device because the
use of mobile application is depended by the compatibility of the device.
The respondent were asked how many hours a day you use your mobile phone and how many
hours a day you use your mobile application. The respondents had to choose for the last question
between “less than a hour:, “1 hour to 2 hours”, “2 to 3 hours”, “3 to 4 years” and “more than 5
hours”. To whom have you recommended the application, you use?” also included. Lastly, a
helpful question for understanding the second part of the questionnaire was, “how important are
the following factors (image, compatibility, enjoyment, usefulness, attitude, intention for using
mobile application.
The second part asked questions relating to customers’ perceptions and attitude towards using
22
mobile application. Respondents were asked to express their agreement or disagreement with a
statement on a five-point Likert-type scale with anchors ranging from “1 = “strongly disagree” to
“5 = “strongly agree”. This scale has been used in previous TAM related researches [ Igbaria et.
al., 1995; Teo et al., 1999].
However, for all of these constructs, items was adopted in the context of use of mobile
application for the sake of simplicity to measure appropriately, which was developed by the
previous researchers. For all of these variables, the previous researchers had used 5 points Likert
scale starting from 1 for strongly disagree to 5 for strongly agree. Therefore, the researcher of
present study also used 5 point Likert scale to measure all these variables.
3.3
Data collection
For this study we use two type of surveys . The first one, pilot-test, this means that you must test
it out to see if it is obtaining the result you require. The second survey was meant to test the
specific hypotheses suggested in the chapter 2.
3.3.1
Pilot test
On behalf of Cooper and Schindler [2003] a pilot test was done to identify weaknesses in the
design and instrument and proxy data for selection of probability samples. The researchers will
do a pre-test to re-checken all aspects of this questionnaire , including clarity, question content,
order, form, layout, ambiguous question, difficulty and relevance of the question for this study
variable. Through the pre-test, the researcher can also examine the reliability and validity of the
questionnaires. According to the researcher he intended to conduct a pretest to evaluate the
questionnaire for clarity, bias, ambiguous and relevance to the use of application on the mobile
phone. A group size of pilot testing may vary from 5-50 respondents Burns and Bush [1998].
Therefore, the researcher selected 8 users from a convenient way to conduct the pilot survey. For
this study the researcher employed only 8 respondents in the pilot survey because of time
limitations.
23
3.3.2 Main survey
The data for the main research of this study were collected through a questionnaire. The
questionnaire consisted of two parts. The first part of the questionnaire shall be used to the
question of how adoption may be encouraged to answer. It sets out the values of the different
variables in the new model tested. In the second part, some general questions, which can provide
background information for the marketer.
The application were measured against the 25 items that measured the variables used in this
study (i.e. usefulness, ease of use, image, enjoyment, compatibility, attitude and intention to
use). Participants indicated the agreement towards the items using a 5-point Likert- type scale
that ranged from strongly disagree to strongly agree.
The questionnaire was distributed personally and on e-mail in Dutch language to insure as high
number of respondents as possible. After a two-week survey period, a total of 148 respondents
had filled out the questionnaire. Of these 148 samples, 132 were valid and 16 were rejected due
to missing data or found to have declared fake and impossible to happen information.
Questionnaires were filled in different places, at universities classes of Suriname, at the high
school, at secondary school, at company of Telesur and at several department. This variety of
places, strengthen the validity of our results, since the respondents do not belong to a specific
environment in which they can be influenced by each other.
3.4
Measures used in this study
To gather data, the researcher used questionnaires. The questionnaire survey was the most
effective method for this study to collect the data for the following reasons-
Respondents anonymity could be maintained
-
The researcher conducted survey on 132 respondents. Therefore, a questionnaire survey
was the most appropriate one for the current study.
-
The data gathered through questionnaire was easy to put in quantitative analysis.
-
It takes less time to fill up a questionnaire. Therefore, the customers were not reluctant in
providing accurate data.
24
A Structured questionnaire was used in this study to collect data from customers. The researcher
utilized 25 different type of question to measure the variables. In the questionnaire, there were 7
variable, which was perceived usefulness, perceived ease of use, perceived enjoyment,
compatibility, attitude, image and finally intention to use mobile application.
Items used to operalitionalize the constructs were adopted from relevant previous researches.
The adopted items were validated, and the changes in wording were made to tailor the instrument
for this research.
The questionnaire items were selected from previous research and the results of focus group
sessions, and were rephrased to suit the context of the study and to represent the variables in the
research model.
The Items measured on a scale for perceived usefulness, perceived ease of use, attitude and
intention to use were adapted from the original TAM instrument (Davis, 1989) and their
subsequent applications on mobile banking or other technology applications (Luarn & Lin, 2004;
Davis et al., 1989; Agarwal & Prasad, 1997; Taylor and Todd et al.,1995; Lederer et al., 2000;
Tan & Teo, 2000; Venkatesh & Davis, 2000; Wang et al., 2003).
Items for Image were adapted from prior studies [Moore and Benbasat ,2001; Agarwal &
Prasad, 1997; Plouffe et al, 2001; Tan & Teo, 2000] and the focus group discussions.
The Items for perceived enjoyment were adapted from the original instrument (Nysveen et al
[2005a] and from other studies on mobile banking or other technology applications (Luarn &
Lin, 2004; Tan & Teo, 2000; Wang et al., 2003).
Items for compatibility were adapted from the studies of, Slyke at al [2004] Taylor and Todd
(1995), Moore and Benbasat (2001), and Agarwal and Prasad (1997).According to them a high
compatibility will lead to more desirable adoption.
A five-point Likert scale, with anchors ranging from “strongly disagree” to “strongly agree”, was
used for all questions except the demographic ones. Results of confirmatory factor analysis
indicated that a priory assumption was substantiated with a 8-factor solution, and the loading of
the ten components are presented in Table 1. Based on the extensive examination of the
psychometric properties of the scales, the study concludes that each variable represents a reliable
and valid construct. All developed items were relevantly matched to the use of mobile
application (Appendix F and Table 2).
25
Variable
Item
Perceived
Usefulness (PU)
Q8= PU1 Using the mobile application on the mobile phone is for
me time-saving
Q9= PU2 Using the mobile application on the mobile phone is an
efficient for me
Q10 PU3 The mobile application on mobile phone is useful to me
Peceived Ease of
use (PEU)
Q11 = PEU1  Learning to use the mobile application on the mobile
phone is easy to me
Q12 = PUE2  it is easy to make the mobile application on the
mobile do what I want it do
Q13 = PEU3 My interaction with the mobile application on the
mobile phone is clear and understandable
Q14 = PEU4 It is easy to use the mobile application on the mobile
phone
Perceived
Enjoyment
(PENJ)
Compatibility
(COMP)
Image (IM)
Behavioral
intention (BI)
Q15 = PENJ1  I find the mobile application on the mobile phone
entertaining
Q16 = PUNJ2 I find the mobile application on the mobile phone
pleasant
Q17 = PENJ3 I find the mobile application on the mobile phone
exciting
Q18 = PENJ4 I find the mobile application on the mobile phone
fun
Q19 = COMP1 I think that using the mobile application on the
mobile phone, fits the way I would like
Q20 = COMP2 Using the mobile application on the mobile phone
fits my style
Q21 = COMP3 Using the mobile application on the phone fits the
way I like to do things
Q22 = IM1  Only young people use mobile application on the
mobile phone
Q23 = IM2 Using a mobile application on my mobile phone i
distinguish from others
Q24 = IM3 People who use mobile application on the mobile phone
are trendy
Q25 = IM4 Using mobile application on the mobile phone
improves my image
Q26 = BI1 I think the chances are that within 6 months i will use
another type of mobile application on my mobile phone
Q27 = BI2 I think the chances are that within 12 months I will use
Source
Davis [1989]
Davis [1989]
Nysveen et al
[2005a]
Slyke et al [2004]
Moore and
Benbasat et al
[1991]
Taylor and Todd
et al [1995]
26
another type of mobile application on my mobile phone
Q28 = BI3 I think the chances are that within 18 months I will use
another type of mobile application on my mobile phone
Attitude (ATT)
Q29 = ATT1 The idea for using a mobile application on a mobile
phone makes sense
Q30 = ATT2 The idea of a mobile application on a mobile phone
appeals to me.
Q31 = ATT3 I like the idea of using a mobile application on the
mobile phone
Q32 = ATT4 Using a mobile application on a mobile phone has its
advantages
Taylor and Todd
et al [1995]
Table 2: Variable list with prior studies
3.5
Data analysis procedure
The data collected from the questionnaires is completely summarized and analyzed by using
Statistical Package for Social Science (SPSS) version 18.0. SPSS enables accuracy and makes it
relatively easy to interpret data. The following analysis statistics were undertaken in SPSS for
further analysis:
Descriptive:
1. Frequencies;
2. Descriptives
Associative:
1. Reliability;
2. Regression;
3. Correlations
Firstly, frequencies and descriptive statistics summarize some main data of respondents to get an
overview and to provide guidance for conducting further analysis. Secondly, regression and
correlations amongst dependent and independent variables are carried out to identify the possible
relationships.
27
3.6
Reliability and validity
Each questionnaire must be reliable and valid results for the right to use for further action.
Reliability is the consistency of your measurement. It is the ability of an instrument measured the
same way and on behalf of the same conditions with the same subject. In short, it is the
repeatability of the measurement. A measure is considered to be as reliable as a person's score on
the same test twice in a similar state performs, and can demonstrate that the findings are equal
and reliable [Cooper and Schindler, 2003]. Reliability is assessed by looking at Cronbach's
alpha. This measure of reliability refers to the consistency with which each item represents the
construct of interest, as described in chapter 2. We found the following Cronbach’s alpha’s for
all scales in our research, as can be seen in Table 3.
Cronbach's
Cronbach's Alpha if
Alpha
Item Deleted
PU
0.674
0.754
PEU
0,838
0.760
PENJ
0,836
0.722
COMP
0,761
0.719
IM
0.716
0.810
BI
0,886
0.790
ATT
0,819
0.740
Table 3: Cronbach’s alpha
We also tested whether Crobach’s alpha would go up when an item was deleted from a scale.
With all scales Cronbach’s alpha went down if an item was deleted [see appendix C], so one
items PU1 had to be deleted to increase reliability of the scale. In conclusion we can state that all
scales have a very high internal consistency reliability.
The concept of validity or internal validity represents how accurately the questions measure
corresponding components. Validity refers to how well a measurement truly represents
characteristics that exist in the phenomenon being investigated [ Malhotra & Birks, 2006 p. 737].
In order to guarantee external validity, measures were taken to collect a sample that is as
representative as possible.
28
Chapter 4
Findings
4.1 Introduction
This chapter illustrates the results of the data collected. Section 4.1 is the introduction to the
chapter, while section 4.2 describes how the data is prepared for analysis. Section 4.3 illustrates a
demographic profile of the respondent data, where after section 4.4 and 4.5 outlines this data
profile by respectively descriptive, correlation and regression. These results are obtained after
transfer and edit of the data in SPSS.
4.2
Preparation of the data
In order to make all the collected data suitable for the analysis, all questionnaires are screened to
be complete. All returned incomplete questionnaires are therefore considered as errors and
removed from the survey data. Out of the 148 distributed questionnaires, 16 are incomplete.
Hence, in total 132 of the 148 complete questionnaires are being used for this research.
Each question and possible answer of the questionnaire has a code, since coding of data is
necessary for transferring and editing data in SPSS. The questions and possible answers are
corresponded in the order of the actual questionnaire.
4.3
Demographic profile of respondents
Table 4 and 5 provides data about participants’ demographic profiles. The data shows that the
number of female respondents is slightly higher than the number of male respondents, with
female accounting for 57.6% and male 42.4% of the responses. One possible explanation for
more female respondents could be that female are more likely to be interested in the usage and
adoption of technology such as mobile phones; Singh [2000] found that more females used
mobile phone than males.
29
Number of
Respondents
56
Percent
42.4
Cumulative
Percent
42.4
female
76
57.6
57.6
0-18
years
19-25
years
26
19.7
19.7
54
40.9
60.6
26-34
years
18
13.6
74.2
35-49
years
24
18.2
92.4
> 50
years
LTO
10
7.6
100
10
7.6
7.6
MULO
19
14.4
22
HAVO
13
9.8
31.8
VWO
8
6.1
37.9
MBO
9
6.8
44.7
HBO
19
14.4
59.1
University
54
40.9
100
male
1
Gender
2
3
AGE
Education
*Note: N=132 (sample size)
Table 4: Frequency table of respondents’ profile
/ source developed from survey data
It is striking that a large majority of respondents a age has Under the 25 years. This has perhaps
to do with the scope of the Internet. The survey is by something more female completed, 57,6%.
The level of training of the respondents is fairly distributed within the secondary education and
dhigher education. It is striking that almost 65% of the respondents higher trained.
At the gender side there were 57% women and 42% men. 49,2% of the respondents use
Mobile application and 46,2 % of the respondent use mobile phone. While 40% no one
recommend to use the mobile application. More detail about the respondent frequency profile see
appendix E
30
Valid
Most used
mobile
application
using your
mobile
phone
using your
mobile
application
Recommended
the mobile
service
Number of
Respondents
30
Percent
22.7
Cumulative
Percent
22.7
SMS
65
49.2
72
Game
10
7.6
79.5
Facebook
14
10.6
90.2
other
13
9.8
100
< 1 hour
15
11.4
11.4
1-2 hours
30
22.7
34.1
email, web,
browsing
2-3 hours
8
6.1
40.2
3-4 hours
18
13.6
53.8
> 5 hours
61
46.2
100
< 1 hour
39
29.5
29.5
1-2 hours
29
22
51.5
2-3 hours
20
15.2
66.7
3-4 hours
11
8.3
75
> 5 hours
33
25
100
parents
28
21.2
21.2
colleague's
14
10.6
31.8
friends
36
27.3
59.1
school
8
6.1
65.2
children
6
4.5
69.7
no one
40
30.3
100
*Note: N=132 (sample size)
Table 5: Frequency table of respondents’ profile
4.4
/ source developed from survey data
Descriptive statistics
This section shows the results from the independent variables perceived usefulness, perceived
ease of use, perceived enjoyment, compatibility, image and the mediating variable attitude and
the dependent variable intention to use mobile application. The results of the factors which
possibly influence the attitude toward using mobile application, are also part of this section.
31
Question 8 till 32 of the questionnaire are related to these variables. Table 6 shows the results of
the reliability, alpha, the mean, the standard deviation of for all these variables. The
Confirmatory factor should be > 0.5 and ideal 0.7: Hair et al (2005). The reliability values based
on Cronbach’s alpha are all greater than the recommended minimum of 0.5 (Hair et al. 2006).
Perceived usefulness (PU)
Perceived ease of use (PEU)
Perceived enjoyment
(PENJ)
Comptability (COMP)
Image (IM)
Behavorial Intention (BI)
Attitude (ATT)
Cronbach's
alpha
N
132
Minimum
1.00
Maximum
5.00
Mean
3.7121
Std.
Deviation
.89979
N of Items
3
132
1.00
5.00
3.8125
.78474
4
.760
132
1.00
5.00
3.5511
.78920
4
.722
132
1.00
5.00
3.5000
.80341
3
.719
132
1.00
4.25
2.3864
.73578
4
.810
132
1.00
5.00
2.9545
1.02287
3
.790
132
1.00
5.00
3.8390
.68318
4
.740
.754
Table 6: Descriptive results of the reliability alpha, mean and std deviation of all variables
Source: developed from survey data
Minimum and maximum values show that all the variables are consistently within the points on
the scale that they had been measured on (from 1 to 5). Out of all variables, IMAGE had the
lowest mean score (2,95) and ATTITUDE scored highest (3,83). The mean of the variables is
between 2.38 and 3.83 that’s mean that the respondents neutral scored on all variable. For more
detail about the variables reliability, mean and std deviation see Appendix C.
4.4.1 Substantive analysis of the questionnaire
In this section of the chapter we elaborate on the answers given on the various propositions. The
emphasis is on the average responses for each variable from the model. The specific responses
for each statement are not included in the analysis but are found in Annex C. This will be a
picture of the views of respondents on the various propositions. apparently, will be examined to
what extent there are differences between different age groups, educational levels, use of mobile
application, mobile application most commonly used, use of mobile phone or gender. All items
are measured on a five point scale. The first point on the scale is assigned a value of one, the
32
highest score a five. The averages vary between here and the extent to which respondents
indicate whether or not to agree with the statements. The extensive tables can be found in
Appendix C.
Attitude
The average position amounts to 3.83. On average, respondents scored neutral on the whole
variable. Remarkable is the small difference between men and women. Men and women scored
an average of 3.82/ 3.85. Women and men have a slightly positive attitude towards mobile
application. The age groups between 35-49 years are enthusiastic than the younger age groups. In
education there are few striking differences as the average 3.6944. The most commonly used
mobile application games with an average of 4.12 while the attitude to the use of mobile phone
between 1-2 hours has an average of 3.95. The attitude used to create mobile application from 34 hour is the most common and has an average of 4.02. To whom is recommended mobile
service have parents and children an average of 3.91 and repectievelijk 4.10. See Appendix C
Intention to use mobile application
Use Intention consisted of three statements where the respondent had to indicate how big he
thought the chances that in 6, 12 or 18 months would make use of mobile application.
Average of all respondents considered the likelihood that they make use of mobile application
within one of these three terms 'small' to 'very small' Table 7A and 7B. The chance will
sometime increase as the periods increase, the average was 3.05 at 18 months. Nevertheless,
based on these figures it be said that there is no shortage of intention to use at this time and that
there is enthusiasm for mobile applications. For all three periods Of course, the chance of using
slightly larger in males an average of 3.2143, women consider the likelihood that they will soon
use mobile application on their mobile phone has a smaller average of 2.7632. Higher education
(HBO, WO) score averaged 3.4386 and 3.2083 for all three positions slightly higher than the
respondents with a different level of education. The differences are not so large (see Appendix
C). The age groups over 50 years are slightly more enthusiastic than the younger age groups. The
most commonly used mobile application is Email, Web browsing with an average of 3.4444
33
while the use of mobile phone between 3-4 hours has an average of 3.2778. The use of mobile
application <1 hour is the most common and has an average of 2.99. To whom is recommended
mobile service have children and friends repectievelijk an average of 3.38 and 3.22.
N
Chances are within 6 months I will use another type
Minimum
Maximum
Mean
Std. Deviation
132
1
5
2.91
1.108
132
1
5
2.90
1.152
132
1
5
3.05
1.141
of mobile application
Chances are within 12 months I will use another type
of mobile application
Chances are within 18 months I will use another type
of mobile application
Valid N (listwise)
Table 7A
132
Mean of intension to use mobile application
Demographic variables
High score group
Gender
Men
Mean score on intention to use
3.2143
 50 year
Age
3.3000
Education
HBO and VWO
3.4386 and 3.2083
Most used mobile application
email, web, browsing
3.4444
Using your mobile phone
3-4 hours
3.2778
Using your mobile application
< 1 hour
2.9915
Recommended the mobile service
children and friends
3.38 and 3.22
Table 7B high score group on intention to use mobile application
Perceived Ease to Use
Where many were expected to ease of use of mobile services would pose an obstacle, it appears
that the respondents have no problems seeing. In statements like: "Using mobile application
seems not difficult 'respondents scored relatively high (3.81). The lowest was scored by the
fifties, but with a score of 3.42 would be for them to constitute a threat device operation. Men
score averaged 3.84. This they score again a few tenths higher than the women.
34
Imago
Mobile application that would carry the image of the respondents in this survey is not confirmed
by them. The average total score on the variable is 2.31. Interestingly, the two youngest age
groups the lowest scores. They are generally disagreed with the statements that mobile
application is good for the image and that they are able to distinguish them from others. These
results go against the theoretical expectations and this should therefore be considered a distortion
due to socially desirable answers. See Appendix C
Perceived Usefulness
The total variable relative benefit is valued at an average of 3.712. Again, this is against the
neutral score of three seated. The lowest is scored on the statements that imply that life would be
more pleasant mobile application and the use of mobile application could have a relaxing effect.
The age of 50 years scored on average slightly higher than 4.2. They appreciate the benefits set
slightly higher. Again notice that men scored higher on average 3.75 against 3.67 of the female
respondents. Higher education (HBO, WO) 4.1 and 3.8 scored a few tenths lower than the other
educational levels. See Appendix C
Perceived Enjoyment
The average position amounts to 3.55. On average, respondents scored neutral on the whole
variable. Remarkable is the small difference between men and women. Men and women scored
an average of 3.62 and 3.50. Women and men have a slightly positive attitude towards mobile
application. The age groups between 35-49 years are enthusiastic than the younger age groups. In
education there are few striking differences as the average 3.72 for VWO group. The most
commonly used mobile application games with an average of 3.80 while the attitude to the use of
mobile phone between 1-2 hours has an average of 3.65. The attitude used to create mobile
application from 2-3 hour is the most common and has an average of 3.72. To whom is
recommended mobile service have children and friends an average of 3.70 and repectievely 3.62.
See Appendix C
35
4.5
Inferential statistics
This section tests the relationships between the different dependent and independent variable(s)
in order to get an answer to the research questions. The hypotheses of the research (section 2.2)
form the basis. In order to know to what extent there is a relationship between the different
variables, the correlation coefficient is examined. This coefficient is a measure which indicates
the strength and direction of a linear relationship between two random variables. It can vary from
-1 (perfect negative correlation) through 0 (no correlation) to +1 (perfect positive correlation)
and is also known as the Pearson Correlation Coefficient (r-value). In other words, if the
correlation coefficient value is close to either -1.0 or 1.0, it means that there is a strong negative
or a strong positive relationship between the two variables. Generally the strengths of the
correlations are determined on the basis of the following standard:
Correlation range
Small
Medium
Large
Size of correlation
.10 - .29
.30 - .49
.50 – 1.0
Table 8: Correlation size on the basis of the range
Source: J. Pallant (2007). SPSS Survival Manual (3rd ed.). England: Open University
Press.
The Sig. (1-tailed/ 2-tailed) value expresses a value to accept or reject the (nul) hypotheses. It is
also called the p-value. The p-value is the probability that the correlation is one just by chance.
Therefore the smaller the p-value, the better. Generally the rule is: reject H0 if p ≤ .05 and accept
H0 if p ≥ .05. J. Pallant [2007].
The descriptive of these 7 variables are shown in appendix se table 8. The variables perceived
usefulness, perceived ease of use, perceived enjoyment, compatibility, image, attitude and
intention to use mobile application are carried out through correlations to identify the
relationships.
36
4.5.1
Hypotheses Testing with Regression analysis
In this Chapter we will perform 3 different regression analyses, based on our conceptual
framework. The first regression analysis will be with Intention to use as the dependent variable
(include the mediator attitude), the second will be with Attitude (mediator) as the dependent
variable (without the intention to use variable), and the last one will be with Intention as the
dependent variable (without the mediator).
The regression analysis is used to see how far the dependent variable can be explained from the
various independent variables. To test our hypotheses set in the conceptual framework, we want
to find out if the independent variables are significant predictors of the dependent variables. To
test these relationships and in that way our hypotheses, the regression analysis will be used in
this chapter. The regression analysis is a statistical technique that can be used to analyze the
relationship between several independent variables and a single dependent variable. According to
[Hair et al., 2006] the objective of regression analysis is to use the independent variables whose
values are known to predict the single dependent value selected by the researcher. Also with a
regression analysis you can determine whether the independent variables explain a significant
variation in the dependent variable, whether a relationship exists. Also you can determine how
much of the variation in the dependent variable can be explained by the independent variables:
the strength of the relationship [Malhotra and Peterson, 2006]. In regression analysis this is
measured by Adjusted R Square, R², for example if R² is 0,73 then the independent variables
explain 73% of the variation in the dependent variable.
4.5.2
Regression analysis for intention to use
The regression coefficients, known as the beta coefficients, range from -1 to 1. There is a
measured influence of various independent variables on a dependent variable. The used α
significance level that has been set at 0,05. By significant relationships the size of the impact be
gauged from the beta coefficients. To check whether this hypothesis may be approved, the usage
intent is taken as dependent variable and the other variables as independent informed.
37
Perceived
Enjoyment
Attitude
H1
H6
Perceived
Ease of Use
H2
H3
Compatibility
Intention to use
Mobile application
H4
Image
H5
Perceived
Usefulness
Figure 3
Regression analysis for intention to use as dependent variable.
With this regression analysis hypotheses H1, H2, H3, H4, H5 and H6 will be tested see figure 3.
The R² of this regression model is 0,193, which means that 19,3% of variance in Intention to use
is explained by this model. In Table 9 the coefficients of this regression, and their significance
can be found.
Table 9 Regression Coefficients for intention
Unstandardized
Coefficients
Variables
Significance.
B
(Constant)
.552
.324
GPENJ
.351
.016
GPEU
-.208
.114
GCOMP
.112
.459
GIM
.288
.013
GPU
.201
.071
GATT
.031
.851
a. Dependent Variable: GBI
b. Adjusted R Square 0.193
As we can see in Table 9, three variables have a direct significant positive influence on Intention
to use, respectively perceived enjoyment (B = 0,351, p= 0,016), image (B = 0,288, p = 0,013).
38
Perceived usefulness also has a positive significant influence on Intention to use (B = 0,201, p
= 0,071). This means that hypothesis H1, H4 and H5 are supported. It is also found that
compatibility not have significant direct influence on Intention to use
(B = - 0,208, p=0,114),
which leads to the rejection hypothesis H3. The Ease of Use does has a negative effect on
Intention to use, but this effect was not significant (B= - 0,208, p=0,114), so hypothesis H2,
where we stated that the perceived Ease to use has a positive effect on Attitude towards using
mobile application, should be rejected. We also found that Attitude not have significant direct
influence on Intention to use (B = 0,031, p=0,8815), which leads to the rejection hypothesis H6.
As the model is correct, would the only independent variable attitudes (GATT) which are significant
advancing rapidly in the test on usage intent (GBI). For more details of the result see Appendix D.
4.5.3
Regression analysis for attitude
When we apply the regression analysis on the front part of the model, with attitudes as dependent
variables, we see the following significant results (see Figure 4 and table 10). More detail
regarding the regression analysis see Appendix D
Perceived
Enjoyment
H1
Perceived
Ease of Use
H2
H3
Compatibility
Attitude
H4
Image
H5
Perceived
Usefulness
Figure 4 Regression analysis for attitude as dependent variable
With this regression analysis hypotheses H1, H2, H3, H4, and H5 will be tested see figure 4. The
R² of this regression model is 0,473, which means that 47,3% of variance in Attitude is explained
by this model. In Table 10 the coefficients of this regression, and their significance can be found.
39
Table 10 Regression Coefficients for attitude
Unstandardized
Coefficients
Model
1
Significance
B
(Constant)
1.337
0.000
GPENJ
0.250
0.001
GPEU
0.095
0.175
GCOMP
0.234
0.004
GIM
- 0.060
0.334
GPU
0.155
0.009
a.
Dependent Variable: GATT
b.
Adjusted R Square 0. 473
As can be seen from Table 10, Perceived enjoyment has a significant positive influence
(B=0,250, p=0,001) on Attitude, while perceived Ease of Use did not have a significant
influence (B= 0,095, p=0,175). So, H2 is rejected, while H1 is supported. Hypothesis H3 is also
supported, since Compatibility had a positive significant influence on Attitude (B=0,234, p=
0,004). We can see from table 10 that the Image has a negative effect on Attitude (B= -0,060, p=
0,334), so hypothesis H4 should be rejected. We can also see that Perceived usefulness has a
significant positive influence (B=0,155, p= 0,009) on Attitude. So , H5 should be supported.
More detail regarding the regression analysis see Appendix D.
4.5.4
Regression analysis for all independent variables with intention
to use as dependent variables
With this regression analysis hypotheses H1, H2, H3, H4, and H5 will be tested see figure 5. The
R² of this regression model is 0,200, which means that 20% of variance in Intention to use
isexplained by this model. In Table 9 the coefficients of this regression, and their significance
can be found.
40
Perceived
Enjoyment
H1
Perceived
Ease of Use
H2
H3
Intention to use
Mobile application
Compatibility
H4
Image
H5
Perceived
Usefulness
Figure 5 Regression analysis for all independent variables with intention to use as
dependent variable
As can be seen from Table 11, Perceived enjoyment has a significant positive influence
(B=0,359, p=0,010) on Attitude, while compatibility did not have a significant influence (B=
0,119, p=0,413). So, H3 is rejected, while H1 is supported. Hypothesis H4 is also supported,
since Image had a positive significant influence on Attitude (B=0,287, p= 0,013). We can see
from table 8 that the perceived Ease of Use has a negative effect on Attitude (B= -0,205, p=
0,115), so hypothesis H2 should be rejected. We can also see that Perceived usefulness did not
have a significant influence (B=0,206, p= 0,057) on Attitude. So , H5 should be rejected. More
detail regarding the regression analysis see Appendix D.
Table 11
Regression Coefficients for intention with all variables
Unstandardized
Coefficients
Model
1
Significance
B
(Constant)
.593
.247
GPENJ
.359
.010
GPEU
-.205
.115
GCOMP
.119
.413
GIM
.287
.013
GPU
.206
.057
a.
Dependent Variable: GBI
b.
Adjusted R Square 0.200
41
4.5.5 Summary of Results on Hypotheses Testing
In the above regression analyses we tested our hypotheses and found several variables that had a
direct or indirect effect on Intention to use. In table 12 and 13 the direct and nodirect effects are
summarized. In table 14 an overview of the results of the hypotheses tested can be found
Variable
Effect
Direct
nodirect
Perceived enjoyment (PENJ)
Perceived ease of use (PEU)
0.351
-0.208
X
Compatibility (COMP)
Image (IM)
0.112
0.288
X
Perceived usefulness (PU)
0.201
x
Attitude (ATT)
0.031
X
X
X
Table 12. : Direct and nodirect effects in Intention to Use mobile application
Variable
Effect
Direct
nodirect
Perceived enjoyment (PENJ)
Perceived ease of use (PEU)
Compatibility (COMP)
Image (IM)
0.250
0.095
0.234
- 0.060
X
Perceived usefulness (PU)
0.155
x
X
X
X
Table 13. : Direct and nodirect effects in attitude to Use mobile application
42
Hypotheses
Result
H1.
Perceived enjoyment has a positive effect on Attitude towards using mobile application
Supported
H2.
Peceived Ease to use has a positive effect on Attitude towards using mobile application
Rejected
H3.
Compatibility has a positive effect on Attitude towards using mobile application
Supported
H4.
Image has a positive effect on Attitude towards using mobile application
Rejected
H5.
Perceived usefulness has a positive effect on Attitude towards using mobile application
Supported
H6. Attitude has a positive effect on Intention towards using mobile application
Rejected
Table 14: Results for Hypotheses
The result of the test in section 4.5.2 and 4.5.3 are shown in figure 6
Perceived
Enjoyment
0.250 (0.001)
Perceived
Ease of Use
0.095 (0.175)
0.031(0.851)
Compatibility
0.234 (0.004)
Attitude
Intention toUse
Mobile Apllication
- 0.060 (0.334)
Image
Perceived
Usefulness
0.155 (0.009)
Figure 6 Regression analysis of the entire model
Note: All solid-line arrows indicate positive relationships; the dashed-line arrows indicate no
relationships are fond in the regression analysis
43
Chapter 5
Conclusion
This study sought an answer to the question of how the user adoption of mobile applicatie can be
promoted. For this purpose we first looked at the useful theories of user adoption, and a
theoretical model has been prepared containing a possible course of the adoption process and the
main determinants. To the research question must therefore be first looked at the results of the
investigation regarding the model. Therefore, this chapter first the theoretical conclusions and the
implications for broader theoretical debate discussed. Hereafter on the basis of these conclusions
are passed to the main question,
What are the main determinants of the attitude towards mobile apps and how does this attitude
impact the use of mobile apps?
5.1
Answer the Research question
To draw conclusions we need to answer our main research questions based on the results of our
research.
What are the main determinants of the attitude towards mobile apps and how does this attitude
impact the use of mobile apps?
The results show that the factors perceived enjoyment, compatibility and perceived usefulness
has a positive effect on Attitude towards using mobile application. Respondents who see more
useful, very nice and relaxed and who claim that mobile application well fit with existing values
and previous experience of mobile application have a more positive attitude towards mobile
application. While perceived enjoyment, image and perceived usefulness has a positive direct
relationship with the intention to use mobile application. Overall the attitude has not a positive
impact on intention towards using mobile application. [Rössler & Hoflich 2001] declaring that
include enjoying an important factor that affects the attitude toward mobile messaging. The main
idea behind this linkage is that when the mobile service leads to an intrinsic reward will
encourage fun, and so there will be more likely that the person will use this application. Rogers
44
[1983], he considers compatibility to be one of the determinants which influence the adoption of
innovations, such as for example new mobile application.
We summary and we can conclude that intention to use mobile application is directly influenced
by three independent variables, perceived usefulness, Image and perceived enjoyment. All these
three independent variables these have a positive influence on intention use mobile appliaction.
Compatibility, perceived ease of use, did not have a direct influence on intention to use mobile
application, but compatibility have a positive direct influence through Attitude.
Outside of our conceptual framework it was found that age not significant to influence on
intention to use mobile application. We also found that gender is significant to influence on
intention to use mobile application
Of the six hypotheses that have been put together three finally were accepted. A causal
relationship between Perceived ease-to-use and attitudes to-use mobile application is acceptable,
but very small 9.5%. It can be assumed that respondents who expect more difficulty in using
mobile application hence the negative attitude towards the use of mobile application.
The image and attitude are causally linked. So whether much or little choice for a particular
phone, but for a particular service on that phone, can help create a certain image. Imago has a
negative impact so for - 6% caused the attitude to the use of mobile application
The other assumed relationships in the research model (and assumptions) are assumed to be
partial cause - effect relationships. The attitude of people towards mobile application and the
intention to buy it seems to be a cause - effect relationship. A positive attitude towards mobile
application contributes to the degree to which one is inclined to purchase mobile application.
What one finds on the use of mobile application is partly responsible for how far you want to use
the application. More positive about the use of the mobile application is more positive for the
purchase of the application.
The other assumed relationships in the research model and the Perceived Usefulness is partly
responsible for the intention to accept. What significant others think about mobile application is
partly responsible for what they ultimately decide on the intention to accept. The opinion of
45
family and friends is important when accepting new applications for mobile phones.
To what extent they will match the mobile application with all known application is partly
responsible for the extent to which they consider the product as usefulness (the benefits of the
application) and how easy to use, the product estimate.
There is also a plausible cause - effect relationship to establish between the variables Usefulness
and attitude. The easier one to estimate the mobile application using the more benefits we see in
the application. There is a plausible causal relationship between perceived Enjoyment and
attitude, compatibility
and attitude as well as between perceived Enjoyment and attitude.
Positive attitude towards the product increases with the advantages of the application recognizes
the usefulness. Or something easy to use will be partly the cause of a positive or negative attitude
towards the new mobile application. There is also a mediating effect on all independent variable
to find the relationship between Attitude and Intention to use. Perceived usefulness does have
direct influence on intention but on the Attitude. Compatibility is partly responsible for trust in
mobile applications. The cause of the confidence people have in the application is due to the
enjoyment assessment that one has of the product.
5.2
Theoretical implication
Remarkable is the difference between the model that has emerged from this study and adoption
models as discussed in the theoretical framework. In almost all models the concept 'attitude'
accures. Roger already underlined that the perceived usefulness was a major factor in mobile
services, it appears that this factor is inextricably linked to the attitude towards the application.
The same goes for compatibility. When a consumer indicates that mobile application fits in his
daily activities more can be said about his attitude than expected. The effect of mobile
entertainment application, hosted by comparative advantage, can have the same measurement as
the attitude. People who find mobile application unentertaining, also indicate that they do not
like the application. Although there are certainly nuances between the various statements, it is
not clear from the results.
46
The factors in the model Nysveen which has focused on mobile services, are therefore only
partially reflected in the TAM for mobile service. The importance of comparative advantage was
very large indeed. The expected ease of use has no significant impact and the averages revealed
that this in that case has not the expected negative impact. Note also that both Rogers and Nysveen
in their models failed to take into account the perception of image. This study has shown that
image is directly related to the use intention and thus a fairly large impact on the success of the
adoption process. Already in the theoretical framework has been said that the model of Rogers is
very broad and covers innovations in the broadest sense. Nevertheless there are some things in
his model that must be observed. The diffusion model of Rogers, which focuses on social groups
as opposed to the TAM, which focuses on individuals, begins with 'knowledge'. Also in the
TAM for mobile service the knowhow for using the product seems to be a favorable condition
for the adoption process to complete, particularly since the adoption this largely depends on the
knowledge of the product itself. For a proper assessment of the product, the consumer must be
well aware of mobile application.
Knowing that the adoption process largely depends on the perceived usefulness, entertainment
and the joining daily needs, must also be referred back to the domestication theory, mainly
focusing on these factors. There Has been said that domestication of a products involves the
gradual integration into everyday practices, the symbolic meanings may be more important than
the functional applicability. That this symbolic image are important for the functional
applicability of this research is not proven. The emphasis is precisely to the applicability, both on
the functional application, but even more on the applicability in daily practice and the benefit to
be derived.
5.3
Consequences for the practice
Finally we come to how the situation with the intention use and how it could enhance. The
survey of 132 respondents showed that the probability within 18 months for using mobile
application are small to very small. For shorter periods, the chance is even smaller. This is
mainly due to the cost, which they expected to be high, and neutral attitudes towards using
mobile application. People are not convinced of the benefits the product brings and despite the
importance of the mobile phone in the lives of many, respondents are not convinced that mobile
47
application would constitute a part of their daily lives because the product would fit well into
their lifestyle. It should be mentioned that men are generally more positive than women, but that
they are not overflowing with enthusiasm.
What could be done to this neutral Attitude for the intention to use to increase? Of all the
"bottlenecks" image are probably the easiest to influence. 95 percent of respondents are not
familiar with mobile application and when the image is low in the eyes of the consumer this
percentage will not increase. In addition, the score on the perception of the product will be
raised. This means that consumers have come to see mobile application as something fun,
something useful and something that will would fit their daily activities. These perceptions are
difficult to control, but it is quite possible that the neutral perceptions are measured due to a lack
of knowledge. Respondents have difficulty form an idea of what mobile application in the future
will bring and how the operating environment will look like. Furthermore, these perceptions are
based on mobile application as it looks now, same content on mobile phone. And although the
more practical questions showed that respondents preferred this content, they do not seem very
enthusiastic about it.
48
Chapter 6
6.1
Discussion & Recommendation
Chapter overview
In this chapter we will discusses the results presented in Chapter 4 and identifies differences and
similarities with prior studies and literature. Some of the findings support previous results, while
some contradict prior findings. The findings of the study are reported in this chapter.
6.2
Research progress and issues
Results from the demographic profile of the respondents summarize the characteristics of the
participants in this particular sample. Both males and females have used mobile application.
However, more females responded to this survey than male (57.6.4% females and 42.4% male),
which means that the proportion of female users is slightly higher than that of male users in this
sample. This differs from previous findings that more males use mobile application than females
(Singh, 2004), however the difference between males and females in this sample is not high so
the issue may need be further investigated.
People in the younger age group are more likely to use mobile application with 40.9% in the 1925 year age group as indicated in the data described in Chapter 4. This suggests that young
Surinamese could easily handle and accept new technologies and applications; they are one of
the first consumer groups to attempt to use these technologies. In addition, they are normally
well educated and possess the knowledge to learn and master new technology quickly and easily.
The more experience the user has with SMS and the use of mobile phones or applications, the
more likely the user is to adopt a application on mobile phone.
Not only would current mobile application users like to continue to use application on mobile
phone, non users also express their interest and intention to adopt this new technology
application - mobile banking. One possible reason is that the sample population are mostly
students and young people; they are more inclined to accept new technology regardless of how
well the technology actually works. A lot of rich feedback was received from them. This is
49
valuable to researchers as it could be used to explore social issues in regard to how new
technologies are accepted in society.
A number of hypotheses for the use of mobile application were formulated based on a literature
review. The results obtained after testing the hypotheses by using regression analysis are
displayed in Table 6.1 along with results from previous studies. The reliability values base on
cronbach’s alfa are all greater than the recommended minimum of 0.7[Hair et al. 2005]
Variables
Hypothesis
Dependent
This study
Reference
Attitude
Supported
Nysveen et al
[2005a]
Peceived Ease of use
(PEU)
Compatibility
(COMP)
Image (IM)
Attitude
Rejected
Attitude
Supported
Attitude
Rejected
Perceived Usefulness
(PU)
Attitude (ATT)
Attitude
Supported
Intension
Rejected
variable
H1
Perceived Enjoyment
(PENJ)
H2
H3
H4
H5
H6
Davis [1989]
Slyke et al [2004
Moore and Benbasat
et al [1991]
Davis [1989]
Taylor and Todd et
al [1995]
Table 6.1: Result of hypothesis testing with previous researches
Another issue is the value and use of regression analysis. Overall, the explanation of the method
is stated that this analysis almost always knows how to find a connection, but the task of the
researcher is to theoretically explain this link. There are several ways by regression analysis
experiments, as seen in chapter 4. Nevertheless, choices could be explained by mergers and
substantiated based on the above point, the overlap of the constructs within the relevant
variables.
Then there is the question whether the substantive results yet to be explained by other factors.
The questionnaire and the study of the perceptions of respondents based mobile application, as it
50
currently looks like. This happened for two reasons. First, because this situation is closest to
reality, but also because this type of mobile application is easiest to imagine for the respondent.
When the representations of what is mobile application by the respondents would walk widely
differ, the results say little about the product. The result, however, that if the product properties
change in the future, these results are outdated. On the other hand, information obtained in this
way that can be integrated into the development of future products, because the positive and
negative points of the current form came into light.
Analysis one figure 3 includes five independent variables identified as (Perceived Enjoyment,
Perceived Ease to use, Compatibility, Image, Perceived Usefulness), and Attitude. The results of
the linear regression analysis in Chapter 4 shows that only Perceived Enjoyment, Image and
Perceived Usefulness are found to have a significant positive effect on Intention, which in turn
has a positive influence on intention to use mobile application.
Perceived Enjoyment, Image and Perceived Usefulness have an indirect influence on intention
to use mobile application through their effect on attitude.
This result supports the summary from the consumer discussion the more use of mobile
application are posted in the public, the more knowledge and information is provided to the
consumers, using mobile application can perform the simple task easily and quickly.
The variable Perceived Enjoyment, Image and Perceived Usefulness implies that users seek a
simple, easier, faster performance process and environment for mobile application. They prefer
to use mobile application, because it could provide a useful, pleasant service. Users are more
likely to complete a simple task using more complex technology than more complex tasks,
regardless of their level of education. When new technologies emerge, people may not feel like
utilizing them because they only have limited time to learn how to use them.
The results in Analysis two figure 4 show that Perceived Enjoyment, compatibility and Perceived
Usefulness have a significant positive effect on Attitude, which in turn has a positive influence
on intention to use mobile application. Some supporting studies find that computer Perceived
Enjoyment, compatibility and Perceived Usefulness would influence Attitude (Agarwal et al.,
2000; Hong et al., 2001; Igbaria&Iivari, 1995; Venkatesh, 2000, Roger at al). As shown by
51
Goldsmith and Bridges (2000), life compatibility might affect the consumer’s opinion of other ecommerce purchases. In addition, Perceived Enjoyment, compatibility and Perceived Usefulness
are also significant factors which indirectly influence the attitude to use mobile application.
6.3
Theoretical Recommendations
There's so much research on user adoption. Several models were built and a lot of the
determinants mentioned which shall have use intention or attitude towards the application would
affect. This study has shown that generic models are only partially applicable to specific
application. This study showed that the intention to use depends on the perception of the
application. It is expected that this characteristic could be gadget-like products. This is not
necessarily a good or a necessity of life, but an additional option of a device that a large
proportion of the population already owns. Application perception in this study is formed, based
on variables that also took place in the original model. More research is needed to determine
whether there are more items where the product of perception could exist. Another question that
remains unanswered is whether the role of application perception can indeed be explained by the
nature of the innovation and whether this new variable plays an important role in the adoption of
similar "application" where the consumer makes a less deliberate decisions.
In addition, it might be interesting to follow-up studies to go deeper into the role of
domestication theory in the adoption of mobile application to see which factors play a role in the
mainstreaming of mobile application in everyday practice. Perhaps this perspective can also
bring opportunities to the scores of the new variable, application perception and improve them
6.4
Managerial Recommendations
Much of the practical recommendations has already emerged in the discussion of the results of
the practical part, the mean scores on the variables. This research has focused on the perception
of users. To change scores on some points this perception must be affected.
The importance of this knowledge is very large. It is important to inform consumers about
product content. What can one expect from the use of application on mobile phone? What
information or content do the consumer have? What is the benefit of these opportunities? The
52
transfer of this information can be used for the knowledge gained in the practical part of the
study. It can thus be emphasized that mobile application can be use properly in public transport
and a good way to get the latest news, weather and traffic monitor. By indicating the potential for
use in different user environments and a picture of the contents of the application, the consumer's
attention to opportunities that he could not envisioned. Another possibility to influence the
consumers opinion is possibility for him to become familiar with the application through
economical test packages. This study has shown that negative image are a great barrier to the
intention to use. Due to the moderate attitude towards the use of mobile appliaction at the
moment and very low use intention it is not expected that consumers will soon e.g. opt for a paid
subscription. Perhaps the offer of very cheap or even free trial periods will attract users. This
gives suppliers the opportunity to acquaint users with mobile application with the rationale that if
the product appeals to them, they could engage in the purchase packages.
53
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56
Appendix A. Pre-test questionnaire- Dutch language
Beste Collega,
Ik ben Banda Lloyd, student aan het FHR Lim A Po Instituut voor sociale studies in Paramaribo.
Ter afronding mijn studie Master of Business Administration (MBA) ben ik bezig met een
onderzoek naar het gebruik van applicaties op mobiele telefoons.
Enkele van bedoelde applicaties zijn: applicatie voor Facebook, gaming, smsen, chatten met
“whatsapp” mobiele applicatie en de Blackberry Messenger applicatie voor het pingen met
elkaar.
Met dit onderzoek wil ik nagaan wat de voornaamste factoren zijn die het gebruik van een
applicatie op een mobiele telefoon beïnvloeden, en de vragen uit deze enquête hebben betrekking
op dit onderwerp. Voor dit onderzoek zou ik graag uw mening willen meenemen middels deze
vragenlijst.
Het invullen van de vragenlijst duurt maximaal 15 minuten. Uw antwoorden zullen strikt
vertrouwelijk worden behandeld en zullen uitsluitend worden gebruikt voor het doel van dit
onderzoek.
Ik wil u vragen om de enquête volledig in te vullen. Er zijn geen goede of foute antwoorden, het
gaat om uw persoonlijke mening. Uw medewerking hierin wordt bijzonder gewaardeerd.
Een voorbeeld van een vraag is: De game applicatie op de mobiele telefoon is nuttig voor
mijGeef uw beoordeling op de schaal van 1 tot en met 5 aan met een X, onder het cijfer, dat u
kiest:
1.
2.
3.
4.
5.
Helemaal oneens
Oneens
Oneens noch eens (Neutraal)
Eens
Helemaal eens
Deze vragenlijst bestaat uit 32 vragen. Vult u a.u.b. alle vragen in.
Ik waardeer uw tijd en medewerking bij dit onderzoek. Ik heb graag dat u de vragenlijst voor 10
oktober 2011 ingevuld terugstuurt. Voor eventuele vragen t.a.v de vragenlijst kunt u contact
met mij opnemen op het nummer 8500018 of e-mailen naar lloyd.banda@telesur.sr
Met vriendelijke groet,
Banda lloyd
57
Persoonlijke informatie
(Vul uw antwoord met een (X) in het desbetreffende vak)
1. Hoe oud bent u?
0-18 y
19-25 y
26-34 y
35-49y
> 50 y
2. Wat is uw geslacht?
man
vrouw
3. Wat is uw opleidingsniveau?
LTO
Mulo
Havo
VWO
MBO
HBO
Univer
4. Wat voor type mobiele applicatie gebruik U het meest?
Gelieve één type te kiezen: slechts één antwoord is mogelijk.
Email, Web
browsing
sms
game
Facebook
Andere type
5. Hoeveel uren per dag maakt u gebruikt van uw mobiele telefoon?
< 1 uur
1-2 uren
2 -3 uren
3-4 uren
> 5 uren
6. Hoeveel uren per dag maakt u gebruikt van een mobiele applicatie ?
< 1 uur
1-2 uren
2 -3 uren
3-4 uren
> 5 uren
7. Aan wie heeft u een mobiele applicatie voor gebruik aanbevolen ?
Meerdere antwoorden zijn mogelijk .
Ouders
Collega’s
Vrienden
School
Kinderen
Niemand
58
Gebruikspatronen
Ik wil u vragen onderstaande vragen in te vullen voor de mobiele applicatie die u het meest gebruikt. Als
u bijvoorbeeld bij vraag 4 heeft aangegeven dat u facebook het meest gebruikt, dan vult u onderstaande
vragen in voor de facebook applicatie. Voor de zekerheid vraag ik u om nogmaals aan te geven voor
welke applicatie u de vragenlijst invult.
Type Mobiele applicatie_____________________________
8
9
10
11
12
13
14
15
16
17
18
19
20
21
In hoeverre ben u het eens met de volgende
stellingen?
Het gebruik van de mobiele applicatie op de mobiele
telefoon is voor mij tijd-besparend
Het gebruik van de mobiele applicatie op de mobiele
telefoon is voor mij efficiënt
De mobiele applicatie op de mobiele telefoon is nuttig
voor mij
Het gebruik van de mobiele applicatie op de mobiele
telefoon is gemakkelijk te leren
Het is gemakkelijk de mobiele applicatie op de
mobiele telefoon in te stellen naar mij behoeftes
Mijn interactie met de mobiele applicatie op de
mobiele telefoon is duidelijk en begrijpelijk
Het is gemakkelijk om een mobiele applicatie op de
mobiele telefoon te gebruiken
Ik vind dat de mobiele applicatie op de mobiele
telefoon mij amusement biedt
Ik vind de mobiele applicatie op de mobiele telefoon
aangenaam
Ik vind de mobiele applicatie op de mobiele telefoon
spannend
Ik vind de mobiele applicatie op de mobiele telefoon
leuk
Ik denk dat de manier waarop ik de mobiele applicatie
op de mobiele telefoon gebruik, goed past bij de
manier waarop ik dat graag wil
Gebruik van de mobiele applicatie op de mobiele
telefoon past bij mijn stijl
Gebruik van de mobiele aplicatie op de telefoon past
bij de manier waarop ik graag dingen wil doen
Helemaal
oneens
1
Oneens Neutraal Eens Helemaal
eens
2
3
4
5
59
Gebruikspatronen (vervolg)
22
23
24
25
26
27
28
29
30
31
32
Helemaal
In hoeverre ben u het eens met de volgende
oneens
stellingen?
1
Alleen jonge mensen gebruiken mobiele applicaties
op de mobiele telefoon
Met het gebruik van een mobiele applicatie op mijn
mobiele telefoon onderscheid ik mij van anderen
Mensen met een mobiele applicatie op hun mobiele
telefoon zijn trendy
Het gebruik van een mobiele applicatie op mijn
mobiele telefoon is goed voor mijn imago
Ik acht de kans groot dat ik binnen 6 maanden gebruik
zal maken van een andere type mobiele applicatie op
mijn mobiele telefoon
Ik acht de kans groot dat ik binnen 12 maanden
gebruik zal maken van een andere type mobiele
applicatie op mijn mobiele telefoon
Ik acht de kans groot dat ik binnen 18 maanden
gebruik zal maken van een andere type mobiele
applicatie op mijn mobiele telefoon
Het idee voor het gebruik van een mobiele applicatie
op een mobiele telefoon is verstandig
Het idee van een mobiele applicatie op een mobiele
telefoon spreekt mij aan.
Het is prettig om een mobiele applicatie op een
mobiele telefoon te gebruiken.
Het gebruik van een mobiele applicatie op een
mobiele telefoon heeft zijn voordelen
Oneens Neutraal Eens Helemaal
eens
2
3
4
5
Hartelijk dank voor uw medewerking!
60
Appendix B. Main questionnaire- English language
Dear Colleague/Students,
I'm Lloyd Banda, a student at the FHR Lim A Po Institute for Social Studies in Paramaribo. To complete
a Masters of Business Administration (MBA) I am doing a research on the use of applications on mobile
phones.
Some of such applications include applications for Facebook, gaming, texting, chatting with "WhatsApp"
mobile application and the BlackBerry Messenger application to ping each other.
With this research I consider the main reasons what the use of an application on a mobile phone
influence, and the questions from this survey relate to this subject. For this study I would like to include
your views via the questionnaire.
Completing the questionnaire takes up to 15 minutes. Your answers will be kept confidential and will
only be used for the purpose of this study.
I would ask you to complete the survey form. There are no right or wrong answers, it's about your
personal opinion. Your cooperation in this is greatly appreciated.
An example of a question: The game application on the mobile phone is useful for me
Give your rating on a scale of 1 to 5 with an X below the number that you choose:
1. totally disagree
2. disagree
3. Agree nor disagree (neutral)
4. once
5. totally agree
This questionnaire contains 32 questions. Can you please fill in all the questions.
I appreciate your time and cooperation in this investigation. I like the questionnaire to be completed
returned on October 10 2011. For any questions regarding the questionnaire, please contact me by phone
8500018 or e-mail to lloyd.banda @ telesur.sr
Sincerely,
Banda lloyd
61
Personal information
(Enter your answer with an (X) in the appropriate box)
1. How old are you?
0-18 y
19-25 y
26-34 y
35-49y
> 50 y
2. What is your gender?
man
vrouw
3. What is your education level?
Lower
more
higher
pursued
secondary
technical
education
expanded
lower education
general
Formative education
scientific
education
vocational
education
LTO
MULO
HAVO
VWO
MBO
University
High
school
HBO
4. What type of mobile application you use most? Please select a type: only one answer is
possible.
Internet, Web
browsing
sms
game
Facebook
Andere type
5. How many hours a day you use your mobile phone?
< 1 uur
1-2 uren
2 -3 uren
3-4 uren
> 5 uren
6. How many hours a day, you use a mobile application?
< 1 uur
1-2 uren
2 -3 uren
3-4 uren
> 5 uren
7. To whom have you recommended the mobile services, you use ?
Parents Colleagues
Friends
School
Children
No one
62
Gebruikspatronen
Ik wil u vragen onderstaande vragen in te vullen voor de mobiele applicatie die u het meest gebruikt. Als
u bijvoorbeeld bij vraag 4 heeft aangegeven dat u facebook het meest gebruikt, dan vult u onderstaande
vragen in voor de facebook applicatie. Voor de zekerheid vraag ik u om nogmaals aan te geven voor
welke applicatie u de vragenlijst invult.
Type of Mobile application_____________________________
In hoeverre ben u het eens met de volgende
stellingen?
8
9
10
11
12
13
14
15
16
17
18
totally
disagree
1
disagree Neutral
2
3
Agree
Totally
agree
5
4
Using the mobile application on the mobile phone is for
me time-saving
Using the mobile application on the mobile phone is an
efficient for me
The mobile application on mobile phone is useful to me
Learning to use the mobile application on the mobile
phone is easy to me
it is easy to make the mobile application on the mobile do
what I want it do
My interaction with the mobile application on the mobile
phone is clear and understandable
It is easy to use the mobile application on the mobile
phone
I find the mobile application on the mobile phone
entertaining
I find the mobile application on the mobile phone pleasant
I find the mobile application on the mobile phone exciting
I find the mobile application on the mobile phone fun
19 I think that using the mobile application on the
mobile phone, fits the way I would like
20 Using the mobile application on the mobile phone fits my
style
21 Using the mobile application on the phone fits the way I
like to do things
63
Gebruikspatronen (vervolg)
In hoeverre ben u het eens met de volgende
stellingen?
22
23
24
25
26
totally
disagree
disagree Neutral
1
2
3
Agree
Totally
agree
5
4
Only young people use mobile application on the mobile
phone
Using a mobile application on my mobile phone i
distinguish from others
People who use mobile application on the mobile phone
are trendy
Using mobile application on the mobile phone improves
my image
I think the chances are that within 6 months i will use
another type of mobile application on my mobile phone
27 I think the chances are that within 12 months I will use
another type of mobile application on my mobile phone
28 I think the chances are that within 18 months I will use
another type of mobile application on my mobile phone
29 The idea for using a mobile application on a mobile
phone makes sense
30 The idea of a mobile application on a mobile phone
appeals to me.
31 I like the idea of using a mobile application on the
mobile phone
32 Using a mobile application on a mobile phone has its
advantages
Hartelijk dank voor uw medewerking!
64
APPENDIX C: Substantive analysis of the determinants
Mean of Intention to use mobile application
GBI * Age
GBI
Age
Mean
N
Std. Deviation
Maximum
Minimum
0-18 years
2.8205
26
1.01206
5.00
1.00
19-25 years
2.8765
54
1.06063
5.00
1.00
26-34 years
2.9815
18
1.14602
5.00
1.00
35-49 years
3.1111
24
.67148
4.67
1.67
> 50 years
3.3000
10
1.35583
5.00
1.00
Total
2.9545
132
1.02287
5.00
1.00
GBI * Gender
GBI
Gender
Mean
N
Std. Deviation
Maximum
Minimum
male
3.2143
56
1.06445
5.00
1.00
female
2.7632
76
.95346
5.00
1.00
Total
2.9545
132
1.02287
5.00
1.00
GBI * Education
GBI
Education
Mean
N
Std. Deviation
Maximum
Minimum
LTO
2.6333
10
1.04763
4.00
1.33
MULO
2.9123
19
1.01131
5.00
1.00
HAVO
2.7692
13
.78628
4.00
1.67
VWO
3.2083
8
.77536
4.00
2.00
MBO
2.9259
9
1.35173
5.00
1.00
HBO
3.4386
19
1.16562
5.00
1.00
University
2.8704
54
.98291
5.00
1.00
Total
2.9545
132
1.02287
5.00
1.00
65
GBI * Most used mobile application
GBI
Most used mobile
application
Mean
N
Std. Deviation
Maximum
Minimum
email, web, browsing
3.4444
30
1.06614
5.00
1.00
SMS
2.7795
65
.92848
5.00
1.00
Game
2.9000
10
1.15523
5.00
1.33
Facebook
3.3571
14
.76755
5.00
2.00
other
2.3077
13
1.01344
3.67
1.00
Total
2.9545
132
1.02287
5.00
1.00
GBI * Usage of mobile phone
GBI
Usage of mobile phone
Mean
N
Std. Deviation
Maximum
Minimum
< 1 hour
2.9556
15
1.09012
5.00
1.00
1-2 hours
3.0444
30
1.04215
5.00
1.00
2-3 hours
2.9583
8
1.25277
5.00
1.33
3-4 hours
3.2778
18
1.09813
5.00
1.00
> 5 hours
2.8142
61
.94971
5.00
1.00
Total
2.9545
132
1.02287
5.00
1.00
GBI * Usage of mobile application
GBI
Usage of mobile
application
Mean
N
Std. Deviation
Maximum
Minimum
< 1 hour
2.9915
39
1.17101
5.00
1.00
1-2 hours
2.9080
29
.95492
5.00
1.00
2-3 hours
2.9667
20
1.23260
5.00
1.00
3-4 hours
2.9091
11
.39696
3.33
2.00
> 5 hours
2.9596
33
.94926
5.00
1.00
Total
2.9545
132
1.02287
5.00
1.00
66
GBI * Mobile services are recommended to
GBI
Mobile services are
recommended to
Mean
N
Std. Deviation
Maximum
Minimum
parents
2.7619
28
1.22006
5.00
1.00
colleague's
3.0238
14
1.08182
4.67
1.00
friends
3.2222
36
.94281
5.00
1.33
school
3.1667
8
.77664
4.33
2.00
children
3.3889
6
1.02017
5.00
2.00
no one
2.7167
40
.92926
5.00
1.00
Total
2.9545
132
1.02287
5.00
1.00
Mean of Attitude
Report
GATT
Mean
N
3.8390
Std. Deviation
132
Maximum
.68318
Minimum
5.00
1.00
GATT * Age
GATT
Age
Mean
N
Std. Deviation
Maximum
Minimum
0-18 years
3.5192
26
.89979
5.00
1.25
19-25 years
3.8056
54
.65637
5.00
1.00
26-34 years
4.0000
18
.45374
5.00
3.25
35-49 years
4.0625
24
.53796
5.00
3.00
> 50 years
4.0250
10
.59454
5.00
3.25
Total
3.8390
132
.68318
5.00
1.00
GATT * Gender
GATT
Gender
Mean
N
Std. Deviation
Maximum
Minimum
male
3.8214
56
.66694
5.00
2.25
female
3.8520
76
.69902
5.00
1.00
67
GATT * Gender
GATT
Gender
Mean
N
Std. Deviation
Maximum
Minimum
male
3.8214
56
.66694
5.00
2.25
female
3.8520
76
.69902
5.00
1.00
Total
3.8390
132
.68318
5.00
1.00
GATT * Education
GATT
Education
Mean
N
Std. Deviation
Maximum
Minimum
LTO
3.8000
10
.68516
5.00
2.50
MULO
3.5789
19
.96124
5.00
1.25
HAVO
3.7500
13
.52042
4.50
2.50
VWO
3.9375
8
.72887
5.00
3.00
MBO
3.6944
9
1.20402
5.00
1.00
HBO
4.0395
19
.50182
5.00
3.00
University
3.8981
54
.52422
5.00
2.75
Total
3.8390
132
.68318
5.00
1.00
GATT * Most used mobile application
GATT
Most used mobile
application
Mean
N
Std. Deviation
Maximum
Minimum
email, web, browsing
3.9417
30
.54410
5.00
3.00
SMS
3.7385
65
.77740
5.00
1.00
Game
4.1250
10
.41248
5.00
3.75
Facebook
3.7321
14
.49482
4.25
2.25
other
4.0000
13
.75691
5.00
2.25
Total
3.8390
132
.68318
5.00
1.00
GATT * Usage of mobile phone
GATT
Usage of mobile phone
Mean
N
Std. Deviation
Maximum
Minimum
< 1 hour
3.5500
15
1.00089
5.00
1.25
1-2 hours
3.9500
30
.66760
5.00
2.25
2-3 hours
3.9375
8
.81009
5.00
2.50
3-4 hours
3.8611
18
.45554
4.50
2.75
68
> 5 hours
3.8361
61
.63719
5.00
1.00
Total
3.8390
132
.68318
5.00
1.00
GATT * Usage of mobile application
GATT
Usage of mobile
application
Mean
N
Std. Deviation
Maximum
Minimum
< 1 hour
3.6218
39
.89198
5.00
1.00
1-2 hours
3.9483
29
.65595
5.00
2.50
2-3 hours
4.0500
20
.61022
5.00
2.75
3-4 hours
4.0227
11
.36150
4.75
3.50
> 5 hours
3.8106
33
.46783
5.00
3.00
Total
3.8390
132
.68318
5.00
1.00
GATT * Mobile services are recommended to
GATT
Mobile services are
recommended to
Mean
N
Std. Deviation
Maximum
Minimum
parents
3.9107
28
.78237
5.00
1.00
colleague's
3.8393
14
.55128
5.00
2.75
friends
3.9028
36
.71783
5.00
1.25
school
3.9687
8
.28150
4.25
3.50
children
4.1667
6
.70119
5.00
3.25
no one
3.6562
40
.66431
5.00
2.25
Total
3.8390
132
.68318
5.00
1.00
69
Mean of Perceived enjoyment
GPENJ * Age
GPENJ
Age
Mean
N
Std. Deviation
0-18 years
3.3365
26
.82445
19-25 years
3.6204
54
.89250
26-34 years
3.4444
18
.61570
35-49 years
3.7292
24
.69905
> 50 years
3.5000
10
.50000
Total
3.5511
132
.78920
GPENJ * Gender
GPENJ
Gender
Mean
N
Std. Deviation
male
3.6205
56
.69575
female
3.5000
76
.85245
Total
3.5511
132
.78920
GPENJ * Education
GPENJ
Education
Mean
N
Std. Deviation
LTO
3.4250
10
.86643
MULO
3.5658
19
.79862
HAVO
3.4423
13
.69338
VWO
3.7500
8
.62678
MBO
3.4722
9
1.25277
HBO
3.4342
19
.74462
University
3.6204
54
.76593
Total
3.5511
132
.78920
GPENJ * Most used mobile application
GPENJ
70
Most used mobile
application
Mean
N
Std. Deviation
email, web, browsing
3.6917
30
.64555
SMS
3.4423
65
.80619
Game
3.8000
10
.93393
Facebook
3.5714
14
.85726
other
3.5577
13
.84258
Total
3.5511
132
.78920
GPENJ * Usage of mobile application
GPENJ
Usage of mobile
application
Mean
N
Std. Deviation
< 1 hour
3.3910
39
.92087
1-2 hours
3.5259
29
.71447
2-3 hours
3.7250
20
.66836
3-4 hours
3.7045
11
.65017
> 5 hours
3.6061
33
.79802
Total
3.5511
132
.78920
GPENJ * Mobile services are recommended to
GPENJ
Mobile services are
recommended to
Mean
N
Std. Deviation
parents
3.5893
28
.96995
colleague's
3.6250
14
.45731
friends
3.6528
36
.82001
school
3.6250
8
1.20268
children
3.7083
6
1.00519
no one
3.3688
40
.57174
Total
3.5511
132
.78920
Mean of Perceived Usefulness
71
GPU * Age
GPU
Age
Mean
N
Std. Deviation
0-18 years
3.2115
26
.87376
19-25 years
3.6944
54
.86010
26-34 years
4.1389
18
.65989
35-49 years
3.7500
24
1.03209
> 50 years
4.2500
10
.58926
Total
3.7121
132
.89979
GPU * Gender
GPU
Gender
Mean
N
Std. Deviation
male
3.7589
56
.94864
female
3.6776
76
.86681
Total
3.7121
132
.89979
GPU * Education
GPU
Education
Mean
N
Std. Deviation
LTO
3.6000
10
.90676
MULO
3.4737
19
.92005
HAVO
3.5769
13
.75955
VWO
3.8125
8
.65124
MBO
3.1667
9
1.71391
HBO
4.1053
19
.67862
University
3.7870
54
.80448
Total
3.7121
132
.89979
GPU * Most used mobile application
GPU
Most used mobile
application
Mean
N
Std. Deviation
email, web, browsing
4.0500
30
.76957
SMS
3.6385
65
.92495
Game
3.4500
10
1.01242
72
Facebook
3.4286
14
.70321
other
3.8077
13
1.03155
Total
3.7121
132
.89979
GPU * Usage of mobile phone
GPU
Usage of mobile phone
Mean
N
Std. Deviation
< 1 hour
3.1000
15
1.16803
1-2 hours
3.5500
30
.90354
2-3 hours
4.0000
8
.70711
3-4 hours
3.9444
18
.61570
> 5 hours
3.8361
61
.85985
Total
3.7121
132
.89979
GPU * Usage of mobile application
GPU
Usage of mobile
application
Mean
N
Std. Deviation
< 1 hour
3.3462
39
1.10116
1-2 hours
3.7931
29
.82934
2-3 hours
4.1250
20
.79265
3-4 hours
3.4091
11
.66401
> 5 hours
3.9242
33
.63886
Total
3.7121
132
.89979
GPU * Mobile services are recommended to
GPU
Mobile services are
recommended to
Mean
N
Std. Deviation
parents
3.6786
28
1.05597
colleague's
3.8571
14
.79490
friends
3.7361
36
.75106
school
4.5000
8
.53452
children
4.5833
6
.66458
no one
3.3750
40
.86787
73
GPU * Mobile services are recommended to
GPU
Mobile services are
recommended to
Mean
N
Std. Deviation
parents
3.6786
28
1.05597
colleague's
3.8571
14
.79490
friends
3.7361
36
.75106
school
4.5000
8
.53452
children
4.5833
6
.66458
no one
3.3750
40
.86787
Total
3.7121
132
.89979
Mean of Image
GIM * Age
GIM
Age
Mean
N
Std. Deviation
0-18 years
2.7692
26
.70683
19-25 years
2.2546
54
.70626
26-34 years
2.3333
18
.87447
35-49 years
2.2500
24
.72607
> 50 years
2.5250
10
.39878
Total
2.3864
132
.73578
GIM * Gender
GIM
Gender
Mean
N
Std. Deviation
male
2.4598
56
.76985
female
2.3322
76
.70992
Total
2.3864
132
.73578
GIM * Education
GIM
Education
Mean
N
Std. Deviation
74
LTO
2.7750
10
.72121
MULO
2.8553
19
.68878
HAVO
2.5769
13
.58971
VWO
2.0000
8
.42258
MBO
1.9444
9
.82706
HBO
2.2105
19
.82606
University
2.2963
54
.67829
Total
2.3864
132
.73578
GIM * Most used mobile application
GIM
Most used mobile
application
Mean
N
Std. Deviation
email, web, browsing
2.4583
30
.54172
SMS
2.3231
65
.80492
Game
2.8500
10
.63683
Facebook
2.6250
14
.64859
other
1.9231
13
.68757
Total
2.3864
132
.73578
GIM * Usage of mobile phone
GIM
Usage of mobile phone
Mean
N
Std. Deviation
< 1 hour
2.2333
15
.88372
1-2 hours
2.3417
30
.61407
2-3 hours
2.6563
8
.53348
3-4 hours
2.5694
18
.85666
> 5 hours
2.3566
61
.74086
Total
2.3864
132
.73578
GIM * Usage of mobile application
GIM
Usage of mobile
application
< 1 hour
Mean
2.2885
N
Std. Deviation
39
.67992
75
1-2 hours
2.3017
29
.85664
2-3 hours
2.5125
20
.73213
3-4 hours
2.7727
11
.34378
> 5 hours
2.3712
33
.76832
Total
2.3864
132
.73578
GIM * Mobile services are recommended to
GIM
Mobile services are
recommended to
Mean
N
Std. Deviation
parents
2.2589
28
.89877
colleague's
2.4821
14
.64647
friends
2.4653
36
.62436
school
2.4688
8
.97685
children
2.1667
6
.60553
no one
2.3875
40
.72269
Total
2.3864
132
.73578
Mean of Perceived Ease of Use
GPEU * Age
GPEU
Age
Mean
N
Std. Deviation
0-18 years
3.5962
26
.79397
19-25 years
3.9398
54
.75068
26-34 years
4.0694
18
.78500
35-49 years
3.7292
24
.82065
> 50 years
3.4250
10
.67752
Total
3.8125
132
.78474
GPEU * Gender
GPEU
Gender
Mean
N
Std. Deviation
male
3.7723
56
.71554
female
3.8421
76
.83551
76
GPEU * Gender
GPEU
Gender
Mean
N
Std. Deviation
male
3.7723
56
.71554
female
3.8421
76
.83551
Total
3.8125
132
.78474
GPEU * Education
GPEU
Education
Mean
N
Std. Deviation
LTO
4.0500
10
.63246
MULO
3.5263
19
.85348
HAVO
3.7500
13
.58630
VWO
4.0938
8
.89580
MBO
2.8056
9
1.14413
HBO
3.8684
19
.83070
University
3.9907
54
.57659
Total
3.8125
132
.78474
GPEU * Most used mobile application
GPEU
Most used mobile
application
Mean
N
Std. Deviation
email, web, browsing
3.7833
30
.62537
SMS
3.8192
65
.77876
Game
4.0000
10
.75462
Facebook
3.6250
14
.96451
other
3.9038
13
1.01313
Total
3.8125
132
.78474
GPEU * Usage of mobile phone
GPEU
Usage of mobile phone
Mean
N
Std. Deviation
< 1 hour
3.8500
15
1.00800
1-2 hours
3.6583
30
.74995
77
2-3 hours
3.7813
8
.87052
3-4 hours
3.9583
18
.43933
> 5 hours
3.8402
61
.82036
Total
3.8125
132
.78474
GPEU * Usage of mobile application
GPEU
Usage of mobile
application
Mean
N
Std. Deviation
< 1 hour
3.6154
39
.98816
1-2 hours
3.7586
29
.51963
2-3 hours
4.0625
20
.64825
3-4 hours
3.7045
11
.74009
> 5 hours
3.9773
33
.76384
Total
3.8125
132
.78474
GPEU * Mobile services are recommended to
GPEU
Mobile services are
recommended to
Mean
N
Std. Deviation
parents
4.0000
28
.79640
colleague's
3.7321
14
.84617
friends
3.7083
36
.78717
school
3.8125
8
.97055
children
3.5000
6
.65192
no one
3.8500
40
.74636
Total
3.8125
132
.78474
78
APPENDIX D: Regression analysis
1. Linear regressions for relationships between all independent
and mediator (attitude) as dependent variable
Variables Entered/Removed
Variables
Variables
Entered
Removed
Model
1
GIM, GPEU,
b
Method
. Enter
GPU, GPENJ,
GCOMP
a. All requested variables entered.
b. Dependent Variable: GATT
Model Summary
Model
R
1
.702
R Square
a
.493
Adjusted R
Std. Error of the
Square
Estimate
.473
.49605
a. Predictors: (Constant), GIM, GPEU, GPU, GPENJ, GCOMP
Coefficients
a
Standardized
Unstandardized Coefficients
Model
1
B
(Constant)
Std. Error
1.337
.276
GPU
.155
.058
GPEU
.095
GPENJ
GCOMP
GIM
Coefficients
Beta
t
Sig.
4.841
.000
.204
2.662
.009
.070
.110
1.364
.175
.250
.075
.289
3.344
.001
.234
.079
.275
2.973
.004
-.060
.062
-.065
-.970
.334
a. Dependent Variable: GATT
79
2. Linear regressions for relationships between all independent
and (intention) as dependent variable
Variables Entered/Removed
Variables
Variables
Entered
Removed
Model
1
GIM, GPEU,
b
Method
. Enter
GPU, GPENJ,
GCOMP
a. All requested variables entered.
b. Dependent Variable: GBI
Model Summary
Model
R
1
.480
R Square
a
.230
Adjusted R
Std. Error of the
Square
Estimate
.200
.91513
a. Predictors: (Constant), GIM, GPEU, GPU, GPENJ, GCOMP
Coefficients
a
Standardized
Unstandardized Coefficients
Model
1
B
Std. Error
(Constant)
.593
.510
GPU
.206
.107
GPEU
-.205
GPENJ
Coefficients
Beta
t
Sig.
1.164
.247
.181
1.923
.057
.129
-.157
-1.587
.115
.359
.138
.277
2.603
.010
GCOMP
.119
.145
.094
.821
.413
GIM
.287
.114
.206
2.514
.013
a. Dependent Variable: GBI
80
3. Linear regressions for relationships between all independent,
mediator variable (attitude) and dependent variable
(intention)
Variables Entered/Removed
Variables
Variables
Entered
Removed
Model
1
GATT, GIM,
b
Method
. Enter
GPEU, GPU,
GPENJ,
GCOMP
a. All requested variables entered.
b. Dependent Variable: GBI
Model Summary
Model
R
1
.480
R Square
a
.230
Adjusted R
Std. Error of the
Square
Estimate
.193
.91865
a. Predictors: (Constant), GATT, GIM, GPEU, GPU, GPENJ, GCOMP
Coefficients
a
Standardized
Unstandardized Coefficients
Model
1
B
Std. Error
(Constant)
.552
.557
GPU
.201
.111
GPEU
-.208
GPENJ
Coefficients
Beta
t
Sig.
.990
.324
.177
1.820
.071
.130
-.159
-1.592
.114
.351
.144
.271
2.432
.016
GCOMP
.112
.151
.088
.743
.459
GIM
.288
.115
.207
2.511
.013
GATT
.031
.165
.021
.188
.851
81
Coefficients
a
Standardized
Unstandardized Coefficients
Model
1
B
Std. Error
(Constant)
.552
.557
GPU
.201
.111
GPEU
-.208
GPENJ
Coefficients
Beta
t
Sig.
.990
.324
.177
1.820
.071
.130
-.159
-1.592
.114
.351
.144
.271
2.432
.016
GCOMP
.112
.151
.088
.743
.459
GIM
.288
.115
.207
2.511
.013
GATT
.031
.165
.021
.188
.851
a. Dependent Variable: GBI
82
APPENDIX E.
Reliability of all variables
Scale: ALL VARIABLES
Case Processing Summary
N
Cases
%
Valid
132
100.0
0
.0
132
100.0
a
Excluded
Total
a. Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's Alpha
N of Items
.786
7
Item Statistics
Mean
Std. Deviation
N
GPU
3.7121
.89979
132
GPEU
3.8125
.78474
132
GPENJ
3.5511
.78920
132
GCOMP
3.5000
.80341
132
GIM
2.3864
.73578
132
GBI
2.9545
1.02287
132
GATT
3.8390
.68318
132
Item-Total Statistics
Corrected Item-
Cronbach's
Scale Mean if
Scale Variance
Total
Alpha if Item
Item Deleted
if Item Deleted
Correlation
Deleted
GPU
20.0436
10.595
.535
.754
GPEU
19.9432
11.268
.503
.760
GPENJ
20.2045
10.358
.700
.722
GCOMP
20.2557
10.235
.711
.719
GIM
21.3693
12.945
.198
.810
GBI
20.8011
10.847
.392
.790
GATT
19.9167
11.179
.633
.740
83
APPENDIX F: Respondent frequency profile
Breakdown by Age
Age
Cumulative
Frequency
Valid
Percent
Valid Percent
Percent
0-18 years
26
19.7
19.7
19.7
19-25 years
54
40.9
40.9
60.6
26-34 years
18
13.6
13.6
74.2
35-49 years
24
18.2
18.2
92.4
> 50 years
10
7.6
7.6
100.0
132
100.0
100.0
Total
Breakdown by Gender
Gender
Cumulative
Frequency
Valid
Percent
Valid Percent
Percent
male
56
42.4
42.4
42.4
female
76
57.6
57.6
100.0
132
100.0
100.0
Total
Breakdown by Education
Education
Cumulative
Frequency
Valid
Percent
Valid Percent
Percent
LTO
10
7.6
7.6
7.6
MULO
19
14.4
14.4
22.0
HAVO
13
9.8
9.8
31.8
VWO
8
6.1
6.1
37.9
MBO
9
6.8
6.8
44.7
HBO
19
14.4
14.4
59.1
University
54
40.9
40.9
100.0
132
100.0
100.0
Total
Breakdown by Most used mobile application
84
Most used mobile application
Cumulative
Frequency
Valid
Percent
Valid Percent
Percent
email, web, browsing
30
22.7
22.7
22.7
SMS
65
49.2
49.2
72.0
Game
10
7.6
7.6
79.5
Facebook
14
10.6
10.6
90.2
other
13
9.8
9.8
100.0
Total
132
100.0
100.0
Breakdown by Usage of mobile phone
Usage of mobile phone
Cumulative
Frequency
Valid
Percent
Valid Percent
Percent
< 1 hour
15
11.4
11.4
11.4
1-2 hours
30
22.7
22.7
34.1
2-3 hours
8
6.1
6.1
40.2
3-4 hours
18
13.6
13.6
53.8
> 5 hours
61
46.2
46.2
100.0
132
100.0
100.0
Total
Breakdown by Usage of mobile application
Usage of mobile application
Cumulative
Frequency
Valid
Percent
Valid Percent
Percent
< 1 hour
39
29.5
29.5
29.5
1-2 hours
29
22.0
22.0
51.5
2-3 hours
20
15.2
15.2
66.7
3-4 hours
11
8.3
8.3
75.0
> 5 hours
33
25.0
25.0
100.0
132
100.0
100.0
Total
85
APPENDIX G: Regression analysis all variables on intention to use,
including demographics
Coefficients
a
Standardized
Unstandardized Coefficients
Model
1
B
(Constant)
Std. Error
.494
.622
.168
-.173
-2.127
.035
.060
.041
.128
1.472
.144
-.001
.000
-.179
-2.227
.028
-.087
.072
-.129
-1.200
.233
.009
.071
.014
.133
.895
-.020
.044
-.037
-.449
.654
.216
.114
.190
1.901
.060
GPEU
-.153
.133
-.117
-1.151
.252
GPENJ
.336
.141
.259
2.378
.019
GCOMP
.013
.151
.010
.086
.932
GIM
.297
.121
.214
2.457
.015
GATT
.053
.165
.036
.323
.747
Most used mobile
.073
-.357
Sig.
.043
Education
.036
t
.097
Gender
.703
Beta
1.672
Age
1.176
Coefficients
application
Usage of mobile phone
Usage of mobile application
Mobile services are
recommended to
GPU
a. Dependent Variable: GBI
86