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2008:068
M A S T E R ' S T H E S I S
Intention to Use Internet ReservationSystems by Iranian Airline Passengers
Mohsen Manzari
Luleå University of Technology
Master Thesis, Continuation Courses Marketing and e-commerce
Department of Business Administration and Social SciencesDivision of Industrial marketing and e-commerce
2008:068 - ISSN: 1653-0187 - ISRN: LTU-PB-EX--08/068--SE
MASTER’S THESIS
Intention to Use Internet Reservation Systems by Iranian Airline Passengers
Supervisors: DR. Peter Naude
DR. Amir Albadvi
Prepared by: Mohsen Manzari
Tarbiat Modares University Faculty of Engineering Department of Industrial Engineering
Lulea University of Technology
Division of Industrial Marketing and E-Commerce
MSc PROGRAM IN MARKETING AND ELECTRONIC COMMERCE Joint
2008
ABSTRACT:
Purpose – The purpose of this study is to identify factors affecting intention to
use internet reservation systems by Iranian airline passenger.
Design/Methodology/Approach - This study uses an adoption model to
assess Iranian airline passengers’ intention to use an online reservation system. This
study integrates constructs from the United Theory of Acceptance and Use of
Technology model, Transaction Cost Saving, Perceived Risk and Perceived
Enjoyment. A survey is administered to 186 Iranian airline passengers in Mehrabad
airport which were in-experienced with such systems. The data is analyzed using
Structural Equation Model.
Findings – Results indicate that performance expectancy, effort expectancy,
social influences, perceived enjoyment, perceived support and transaction cost saving
have a significant affect on Iranian airline passengers’ intention to use online
reservation systems, where perceived risk did not have and significant affect on
intention. The model explains 77 percent of the variance in Iranian airline passengers’
intention to use an online reservation system.
Research limitations/implications - The study only explores in-experienced
users, whereas future research can be conducted on experienced users of online
reservation systems. Iranian airlines and tour operators can implement more
successful Internet based reservation systems by considering findings of this research.
Keywords – Reservation Systems, UTAT, Airlines, SEM, Intention,
Perceived Risk, Transaction Cost
2
ACKNOWLEDGMENT:
First of all I would like to express my sincere gratitude to my supervisors, Dr.
Peter Naude at Luleå University of Technology, Sweden, for his intelligent guidance
and helpful advice during the whole process, and Dr. Amir Albadvi at Tarbiat
Modares University, Iran, for his very helpful supports.
I would like to show my sincere appreciation to Professor Moez Limayem, for
his inspirations during the first stages of this research.
Also I would like to thank my father, which has been very supportive of me
during the recent years.
3
TABLE OF CONTENTS CHAPTER I:...........................................................................................................................................8 INTRODUCTION ..................................................................................................................................8 1. INTRODUCTION.........................................................................................................................8
1.1 THE IMPACT OF ICTS ON BUSINESS PROCESSES AND PRACTICES ............................................8 1.2 BACKGROUND........................................................................................................................9
1.2.1 Internet and the Travel and Tourism Industry................................................................10 1.2.2 Internet and the airline Industry.....................................................................................11 1.2.3 Internet and disintermediation in the travel industry .....................................................13
1.3 PROBLEM DISCUSSION AND JUSTIFICATION..........................................................................15 1.4 PROBLEM STATEMENT .........................................................................................................17 1.5 RESEARCH QUESTION ..........................................................................................................17 1.6 DEPOSITION OF THE THESIS..................................................................................................17
CHAPTER II: .......................................................................................................................................18 LITERATURE REVIEW ....................................................................................................................18 2 LITERATURE REVIEW...........................................................................................................18
2.1 BEHAVIORAL INTENTION .....................................................................................................19 2.1.1 Theory of Reasoned Action (TRA) ..................................................................................20 2.1.2 Theory of Planned Behavior (TPB) ................................................................................22 2.1.3 Technology acceptance model (TAM) ............................................................................24 2.1.4 United Theory of Acceptance and Use of Technology (UTAUT)....................................25
2.2 TRANSACTION COST ANALYSIS (TCA) ................................................................................30 2.3 PERCEIVED ENJOYMENT.......................................................................................................33 2.4 PERCEIVED RISK ..................................................................................................................33 2.5 THEORETICAL FRAMEWORK.................................................................................................34
CHAPTER III.......................................................................................................................................37 RESEARCH METHODOLOGY........................................................................................................37 3 RESEARCH METHODOLOGY ..............................................................................................37
3.1 RESEARCH PURPOSE.............................................................................................................37 3.2 RESEARCH APPROACH .........................................................................................................38
3.2.1 Quantitative VS Qualitative............................................................................................39 3.2.1.1 Qualitative........................................................................................................................... 39 3.2.1.2 Quantitative......................................................................................................................... 39
3.2.2 Inductive VS Deductive...................................................................................................40 3.3 RESEARCH STRATEGY..........................................................................................................41 3.4 DATA COLLECTION ..............................................................................................................42
3.4.1 Defining the target Population .......................................................................................43 3.4.2 Sample Selection.............................................................................................................45
3.5 QUESTIONER DEVELOPMENT ...............................................................................................45 3.6 VALIDITY & RELIABILITY ....................................................................................................46
3.6.1 Validity ...........................................................................................................................47 3.6.2 Reliability .......................................................................................................................47
3.7 SUMMERY OF THE RESEARCH METHODOLOGY ....................................................................49 CHAPTER IV.......................................................................................................................................50 DATA ANALYSIS ...............................................................................................................................50 4 DATA ANALYSIS......................................................................................................................50
4.1 OVERVIEW OF THE SAMPLE..................................................................................................50
4
4.2 DEMOGRAPHICS & DESCRIPTIVE STATISTICS.......................................................................51 4.3 FACTOR ANALYSIS...............................................................................................................53 4.4 DATA ANALYSIS ..................................................................................................................61
4.4.1 Structural Equation Model (SEM)..................................................................................61 4.4.2 Results.............................................................................................................................62
4.4.2.1 Significant results................................................................................................................ 64 4.4.2.2 Non-significant results ........................................................................................................ 66
CHAPTER V.........................................................................................................................................67 CONCLUSIONS AND IMPLICATIONS ..........................................................................................67 5 CONCLUSIONS AND IMPLICATIONS.................................................................................67
5.1 RESEARCH QUESTION ...........................................................................................................67 5.2 IMPLICATION FOR THEORY ...................................................................................................68 5.3 IMPLICATION FOR PRACTICE.................................................................................................68 5.4 LIMITATIONS AND SUGGESTIONS FOR FUTURE RESEARCH ....................................................69
REFERENCE .......................................................................................................................................71 APPENDIX A: ABBREVIATIONS AND ACRONYMS..................................................................78 APPENDIX B: QUESTIONER...........................................................................................................79 APPENDIX C: SPSS AND LISREL OUTPUTS ...............................................................................85
5
List of Tables Table 2-1 Models of Individual Acceptance of Technology ......................................28 Table 3-1 Relevant Situations for different Research strategies..................................41 Table 3-2 Research Variables and Measurements .......................................................46 Table 3-3 Cronbach’s Alpha reliability test for the model ..........................................48 Table 3-4 Cronbach’s Alpha reliability test results .....................................................49 Table 4-1 Respondents percent frequency by city of residence ..................................53 Table 4-2 Hours Spend on the internet during the week .............................................53 Table 4-3 KMO and Bartlett’s Test .............................................................................55 Table 0-4 Total Variance Explained (I).......................................................................56 Table 0-5 Rotated Component Matrix (I)...…………………………………………..58 Table 0-6 Total Variance Explained (II)...….………………………………………..59 Table 0-7 Rotated Component Matrix (II)...…………………….…………………...60 Table 0-8 Research Hypothesis............................................................................…....61 Table 0-9 Goodness of Fit Indices for the Model........................................................62 Table 0-10 Results of the SEM analysis..............................................................…....64
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List of Figures Figure 1-1 Traditional distribution channel VS revised model ...................................15 Figure 2-1 - Theory of Reason Action (TRA) .............................................................20 Figure 2-2 - Theory of Planned Behavior (TPB) .........................................................22 Figure 2-3 - Technology acceptance model (TAM) ....................................................24 Figure 2-4 - United Theory of Acceptance and Use of Technology (UTAUT) ..........26 Figure 2-5 - Proposed model for Intention to use Internet Reservation Systems ........35 Figure 4-1 - Pie chart, respondents gender ..................................................................51 Figure 4-2 - Bar chart, respondents education level ....................................................52 Figure 4-3 - Bar chart, respondents age .......................................................................52 Figure 4-4 - Results of the SEM analysis of the Model...............................................64
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Chapter I:
Introduction
1. Introduction In the first chapter, an introduction and background of this research will be
presented; followed up by the problem discussion and statement, research question
and deposition of the thesis.
1.1 The impact of ICTs on business processes and practices Information technology generates fundamental changes in the nature and
application of technology in business. Information Communication Technologies
(ICTs) can provide powerful strategic and tactical tools for organizations, which, if
properly applied and used, could bring great advantages in promoting and
strengthening their competitiveness (Porter 2001).
In recent years ICT developments have had enormous implications for the
operation, structure and strategy of organizations. The competitiveness of future
8
economies will, to a great extent, depend both on the development and application of
these technologies. The proliferation of the World Wide Web forced most
organizations to reengineer the way they do business and how they can reengineer
their business processes. As businesses can interact more efficiently, competent
businesses became digital and networked, facing a whole range of fresh opportunities
and challenges (Tapscott 2000). The eCommerce revolution is evident on a global
basis, although the level of success often depends on a wide range of local factors
(Protogeros 2002). Porter illustrates that ultimately technology can totally transform
the way an entire business is done (Porter 2001).
ICTs contribute towards efficiency, productivity and competitiveness
improvements of both inter-organizational and intra-organizational systems. The
relationship between ICTs and competitive advantage and performance is still unclear
(L. Davis, Dehning, B. Stratopoulos ,T 2003). Although there is an indirect and
complex casual relationship between ICTs and profitability, it is difficult to be
quantified and generalized. There is evidence, however, that well managed ICTs can
generate tremendous value for organizations (Lee 2001). Technology has already
revolutionized a wide range of functions including business functions, external
environment monitoring, communicating with partners and with consumers at large
(Spanos 2002).Clear strategic goals and commitment are prerequisites for the
development of an appropriate eCommerce strategy and the development of web sites
and other technological solutions (Kowtha 2000)
1.2 Background The internet is one of the more recent developments in communications and
information transfer. It is considered a technology asset because of its ability to
disseminate large volume of information quickly and efficiently to all types of
stakeholders, including employees, customers, shareholders and suppliers (violino
1996). To date, the internet is more accessible and less expensive than it was, and the
number of internet users is growing rapidly. According to the statistics of the internet
data center (IDC), one of the worlds leading providers of technology intelligence and
industry analysis, it shows that the number of internet users around the world was
9
approximately 943 million by the end of 2005, and the daily traffic constituted almost
2.3 million terabits every day, representing 93 times the volume of traffic in 2000 and
147 percent annual growth in traffic (Nua Internet Surveys, 2006a). As today's
consumers are more focused on time saving and are more likely to access a greater
proliferation of product information, the internet appears to have several advantages
over other media as an information gathering tool (Schonland and Williams 1996).
Apart from information search, Internet users can also make bookings or purchase
products and services through this new channel. According to IDC the numbers of
home internet shoppers have increased from 119 million in 2001 to 317 million in
2005. As more internet users choose to use the web for buying goods and services, the
potential for business to conduct electronic commerce likewise increases. Nowadays,
many business corporations use the internet not only as a valuable marketing tool in
providing a low-cost medium for advertising and promotion, but also as a channel of
communication to generate additional sales.
1.2.1 Internet and the Travel and Tourism Industry The rapid growth of the travel industry requires sophisticated information
technologies (ITs) for managing the increasing volume and quality of tourism traffic.
Prior studies have indicated that modern travelers demand more high quality travel
services, products, information, and value for their money (Christian 2001; Lubetkin
1999; Samenfink 1999). The emergence of new tourism services and products,
coupled with a rapid increase in tourism demand, has driven the wide-scale adoption
of ITs in general, and in particular, the Internet as an electronic intermediary. In other
words, the Internet serves as a new communication and distribution channel for e-
travelers and suppliers of travel services and products. This new channel also enables
tourism businesses to improve their competitiveness and performance.
Tourism researchers have emphasized the importance of the Internet on travel
and tourism. For tourism suppliers, the Internet provides a way for them to sell their
products globally to potential travelers at any time. These suppliers can remotely
control their servers to display information on services/products at an electronic speed
(Law 2000). The successful factors for a travel Web site, from a supplier’s
10
perspective, are lower distribution costs, higher revenues, and a larger market share.
For travelers, the Internet allows them to communicate directly with tourism suppliers
to request information, and to purchase products/services at any time and any place
(Olmeda and Sheldon 2001).
To the extent that the Internet enables e-travelers to easily arrange and purchase
their own services/products, the future of travel agencies – the traditional intermediary
– becomes uncertain. In the travel and tourism context, the topic of disintermediation,
i.e. the elimination of the middleman by using the Internet in the traveler agent
destination/supplier network in the travel industry, has been debated by different
tourism researchers. To some researchers, the accessibility of online travel Web sites
reduces the importance of travel agencies, and might ultimately result in travelers
bypassing travel agencies altogether (Barnett and Standing 2001; Buhalis 1998).
However, Palmer and McCole (1999) argue that a key strength of travel agencies is
their ability to provide personal information and advice to travelers continuously. The
role of travel agencies would consequently remain secure if their advice-offering
capability were strengthened by the presence of the Internet, rather than if they
functioned according to the more negative image of being simply a “booking agency”.
While some tourism researchers have investigated the views of suppliers and travel
agencies (Fong 2001; Law et al. 2001) and academics and consultants (Buhalis and
Licata 2002) on the issue of disintermediation, the views of travelers have largely not
been investigated. In other words, it is generally unclear whether traveler's judge
travel agencies are less valuable with the presence of online travel Web sites.
1.2.2 Internet and the airline Industry
The emergence of the Internet in the mid-1990s as well as the development of
Intranets and Extranets forced airlines to refocus their strategy on technological
innovations in order to enhance their competitiveness. Airlines identified the Internet
as a major opportunity to tackle distribution costs and to reengineer the structure of
the industry. British Airways CEO, Rod Edenton admitted that BA spent £ 1.1 billion
on distribution in 2001 and that was their third most significant expense after labor
and fuel (Noakes and Coulter 2002). In the Internet era, GDSs (global distribution
11
systems) as independent business from airlines developed their offerings to provide
the backbone for the entire industry to establish the infrastructure for the transactions
undertaken by a number of Internet travel portals. In addition, they gradually
reinvented themselves to main technology suppliers for a wide range of tourism
organizations including airlines, travel agencies and Internet travel portals. At the
same time, a number of no-frills airlines emerged in both Europe and the US. These
airlines concentrated on lower input cost in as many areas of their operations (Barkin
et al. 1995). They also developed simple distribution strategies and took full
advantage of the Internet for communicating with their clients (Mintel 2001). Internet
early adopters, including both well-established and newly-founded airlines identified
a clear opportunity. They invested heavily in order to develop their online brand name
and to capture a significant market share. Several carriers even painted their aircraft
with their Internet address while they arranged special promotions with newspapers to
drive traffic to their web sites. They provided incentives for consumers to book online
and ensured that they were not distributed through the GDSs, in a way forcing their
clients online (Chu 2001). EasyJet and Ryannair for example were taking the vast
majority of their bookings through the Internet by 2002 and passed on their cost
savings to consumers by giving a £ 5 discount on a return fare. No frills airlines,
empowered by the Internet and other ICT tools, made the industry reengineer itself as
it introduced a number of ICT-enabled innovations including:
• Electronic/paperless tickets,
• Transparent and clear pricing led by proactive and reactive yield management,
• Commission capping and publication of net fares,
• Financial incentives for self-booking online,
• Auctions and online promotions,
• Powerful Customer Relationship Management Systems,
• Online and context-relevant advertising.
As consumers enjoyed interacting directly with airlines and benefited from
lower rates, traffic for traditional scheduled airlines and flag carriers declined. They
therefore had to follow the lead of no-frills carriers and to develop their online
presence in order to maintain their competitiveness. In the 2001 Airlines IT Trends
12
survey, it was revealed that airlines moved fast to Internet Protocol (IP) based
systems, having either to modernize legacy systems or to invest in new technological
solutions. Getting closer to customer and cutting costs were the main key drivers. It is
estimated that by 2007, online sales and e-ticketing will become the major distribution
mechanisms worldwide (O’Toole 2002).
Research showed that in 2000, 21 million US residents purchased travel online
(Tierney 2000). This figure practically doubled from the preceding year. As recently
as 2001, travel represented the largest portion of online sales, when travel sales were
30 percent of all Internet purchases. It would appear that with the proliferation of
technology and the increased use of the Internet, online travel marketplaces will
continue to grow, and will be able to provide discount airfares that cannot be found
elsewhere(Smith 2004; Tierney 2000). Considering the success cases in other
countries in this research we try to investigate the intention to use online booking
systems by Iranian passengers.
1.2.3 Internet and disintermediation in the travel industry
Various studies have shown the direct fit of the Internet and travel and tourism
products (Buhalis and Licata 2002; Christian 2001; Poon 2001). With the emergence
of the Internet, the process of fast information transmission can be addressed
effectively at a low cost. In other words, tourists can now receive comprehensive,
timely and relevant information in a virtual environment to assist their decision-
making process. This, in turn, necessitates the balancing of perishable tourism
products and changeable tourist demand. Furthermore, the tourism industry is
diversified, with a plethora of different suppliers that operate independently, even as
tourists expect traveling to be a complete experience. To resolve this mismatch, the
Internet offers an effective means for developing a single and sustainable electronic
infrastructure for information gathering and business transactions for both travelers
and suppliers. A natural outcome of this is that the suppliers can carry out one-to-one
marketing and mass customization. In other words, travel suppliers can now
understand each customer’s needs, and therefore target each customer individually
and deliver tailor-made products. More importantly, travel suppliers can understand
13
how to deliver information and sell their products and services to customers directly
through their Web sites.
As a consequence of the online travel developments, business competition for
traditional travel agencies has increased. Poon (2001) argues that relying more heavily
on the Internet gives suppliers a new independence that will gradually decrease their
dependence on, and their commission payable to, travel agencies. Similarly, travelers
may buy more directly from suppliers, thus bypassing travel agencies. Inevitably, the
travel agencies’ traditional intermediary role as a distribution channel has changed
(Buhalis, 1998), leading to the possible ultimate disintermediation of travel agencies.
The airline industry is well recognized for its use of Information Technology
and has always been a pioneer in taking advantage of new technology and
innovations. Computer reservation systems (CRS) and Extranets have been used for
reservation and inventory management since early 1970. As it was mentioned, in this
study we are focusing on the new distribution channel that internet has introduced to
the airline industry and costumers intention to use such channels.
In the past, whenever someone wanted to plan a trip or vacation, they would
probably contact a traditional travel agent. As shown in the Figure, the travel agent
was the one who held the necessary knowledge and could provide a complete
packaged service for the consuming public through personalized, one-on-one
interaction with the customer. This process usually took place over the phone, or in
person. Airlines, knowing that customers wanted tailored service, would contact the
necessary brokers to obtain the desired products. If an airline ticket were desired, the
agent would generally use a global distribution system (GDS) to search for flights
with the various airlines. Although travel agents could also contact the airlines
directly to procure tickets, typically agents are especially helpful with complex travel
needs (Lewis et al. 1998; Patrick et al. 2001).
However, with the advent of e-commerce and the rampant growth of
information-intensive technologies and strategies, many traditional travel agents are
forced to either change their methods of business or simply close down their
businesses. With the recent trend towards Internet-related travel sites, many travel
14
agencies are consolidating to adapt to the changing landscape of e-travel realities.
Unfortunately, one of the main reasons that travel agencies are struggling is that
nearly all airlines are slashing their fees and reducing the operating costs that they will
pay travel agencies for booking passengers on their planes, in order to remain
competitive. The airlines are feeling the same recent economic crunch as the
manufacturing and service sectors, and are looking at ways to strategically leverage
knowledge, while at the same time becoming more operationally efficient.
Figure 1-1 Traditional distribution channel VS revised model
Hopefully in this research we will try to develop a model to identify the main
factors influencing intention to use online booking systems by Iranian passengers to
help Airlines and e-travel agencies to attract more customers and offer better services
with reduced costs.
1.3 Problem Discussion and justification
Selling in cyberspace, however, is very different from selling in physical
markets and requires a critical understanding of online consumer behavior and how
new technologies challenge the traditional assumptions underlying conventional
theories and models (Limayem et al. 2000).
15
Online consumer behavior is defined as activities directly involved in obtaining,
consuming, and disposing of products and services online, including the decision
processes that precede and follow these actions (Engel et al. 1995). Butler and
Peppard (1998), for example, explain the failure of IBM’s sponsored Web shopping
malls by the naive comprehension of the true nature of consumer behavior on the net.
Online consumer behavior is an emerging research area with an increasing number of
publications per year. The research articles appear in a variety of journals and
conference proceedings in the fields of Information Systems, Marketing, Management
and Psychology.
Though researchers have made noticeable progress with respect to the scope,
quality and quantity of research, there are still significant Disagreements about the
findings in this area, and the research results appear to be rather Fragmented (Khalifa
and Limayem 2003). This indicates the lack of good understanding of the factors
affecting consumers’ decision to buy from the Web.
Butler and Peppard (1998) eloquently express the need for such Understanding: “Whether in the cyber-world or the physical world, the heart of marketing
management understands consumers and their behavior patterns.”
This lack of understanding caused a wide confusion regarding what is really
happening, how much potential there is, and what companies should be doing to take
advantage of online shopping. As a result, commerce on the Net has turned out to be
baffling, even to experienced managers and marketers (Aldridge et al. 1997).
Critical understanding of consumer behavior in cyberspace towards airline ticket
purchasing, as in the physical world, cannot be achieved without a good appreciation
of the factors affecting the purchase decision. If cyber marketers know how
consumers make these decisions, they can adjust their marketing strategies to fit this
new way of selling in order to convert their potential customers to real ones and then
to retain them. Similarly, Web site designers, who are faced with the difficult question
of how to design pages to make them not only popular but also effective in increasing
sales, can benefit from such an understanding (Limayem et al., 2000).
16
1.4 Problem Statement
The above discussion leads us to identify the following research statement:
To gain a better understanding of the online consumer behavior in Iran, that will
result in gaining knowledge regarding the factors that affect the Iranian consumers to
purchase goods and services through internet in general and specifically purchasing
airline tickets through internet reservation systems.
1.5 Research Question The emerged research question is:
What are the main factors that influence Iranian airline passenger’s intention
to purchase tickets through online reservation systems?
We propose hypothesis testing in trying to find answers to our research question.
Through literature review we will try to make a proper model to identify factors
affecting the intention to purchase tickets through internet reservation systems.
Identification of such factors will shed light to the online consumer behavior in our
country, Iran.
1.6 Deposition of the Thesis
This thesis is divided into five chapters. In the first chapter the background of
the selected research area is presented followed by a problem area discussion that
ends with the research problem and the research question. In chapter two theories and
previous studies related to the topic will be presented. Methodology is fully brought in
chapter three. Chapter four presents the data which is gathered through the survey and
of course Data Analysis and hypothesis testing. And last but certainly not least,
chapter five contains conclusion, limitations and further studies.
17
Chapter II:
Literature Review
2 Literature Review
With the growth of E-commerce many studies regarding costumer intentions to
buy online have been conducted in recent years (Devaraj et al. 2003; Limayem et al.
2000; McCloskey 2004; Monsuwe et al. 2004) etc.
Every research has tried to determine the behavioral factors that influence the
individual to purchase in a cyber marketplace. Each using a framework to study the
matter has identified elements to measure intention to use. Perceived usefulness,
enjoyment, perceived risk, security and privacy issues, perceived ease of use, Image,
subjective norms, knowledge and information on the related subject (like how well
informed the user is with internet banking or such) are examples of some of the
factors mentioned in previous research. Studies have undertaken a theoretical
framework and have tailored the model to their case considering the environmental
and cultural matters of each case. In the case of online reservation systems I have not
found any validated model to assess passenger’s intentions, therefore a literature
18
review on previous studies regarding intention to use information technology and e-
services has been carried out.
In this chapter first we will have a look at different constructs used in previous
literature that has been proven to affect intention to shop online and then we will
introduce a model based on literature suitable for measuring intention to use online
reservation systems by Iranian passengers.
2.1 Behavioral Intention
One stream of information technology researchers focuses on adoption of
technology by using intention or usage as a dependant variable (Fred Davis 1989). In
fact researchers require a better understanding of why people resist using information
technologies in order to devise practical methods for evaluating technologies,
predicting how users will respond to them and improving user acceptance by altering
the nature of technologies and processes by which they are implemented (V
Venkatesh and Davis 2000). In the last several years, some theoretical models for
research in the adoption of information technology and information systems (IT/IS)
have been provided. Explaining user acceptance of new technology is often described
as one of the mature research areas in the contemporary information systems literature
(V Venkatesh and Davis 2000). In the recent years many researchers have focused on
the adoption of eCommerce and internet purchasing of goods and services. They have
used models for technology adoption and added new construct that are relevant for
intention to use the internet for purchasing. Research in this area has resulted in
several theoretical models with roots in information systems, psychology and
sociology that routinely explain over 40% of the variance in individual intention to
adopt (F. Davis et al. 1989).
In this section the modification of Technology adoption models for describing
usage behaviors in a result of behavioral intention will be presented.
19
2.1.1 Theory of Reasoned Action (TRA)
This theory has been used widely in technology adoption research. According to
this theory an individual’s intention to adopt an innovation is influenced by attitude
toward the behavior and subjective norm and subsequently person’s behavior is
determined by his intention to perform the behavior. Figure below shows the
relationships among constructs in TRA.
The theory of reasoned action (TRA), developed by Martin Fishbein and Icek
Ajzen (1975, 1980), derived from previous research that started out as the theory of
attitude, which lead to the study of attitude and behavior. The theory was, “born
largely out of frustration with traditional attitude-behavior research, much of which
found weak correlations between attitude measures and performance of volitional
behaviors” (Hale et al. 2003).
The theory “proposes that behavioral intention is a function of both attitudes
toward a behavior and subjective norms toward that behavior” (Miller 2005). And a
person’s behavioral intention is a predictor of actual behavior.
To put the definition into simple terms: a person's volitional (voluntary)
behavior is predicted by his/her attitude toward that behavior and how he/she thinks
that other people would view them if they performed the behavior. A person’s
attitude, combined with subjective norms, forms his/her behavioral intention.
Figure 2-1 - Theory of Reason Action (TRA)
Source : (Ajzen and Fishbein , 1980)
20
Martin and Ajzen say, though, that attitudes and norms are not weighted equally
in predicting behavior. “Indeed, depending on the individual and the situation, these
factors might be very different effects on behavioral intention; thus a weight is
associated with each of these factors in the predictive formula of the theory. For
example, you might be the kind of person who cares little for what others think. If this
is the case, than subjective norms would carry little weight in predicting your
behavior” (Miller, 2005, p. 127).
Miller (2005) defines each of the three components of the theory as follows and
uses the example of embarking on a new exercise program to illustrate the theory:
• Attitudes: the sum of beliefs about a particular behavior weighted by
evaluations of these beliefs. You might have the beliefs that exercise is good for your
health, that exercise makes you look good, that exercise takes too much time, and that
exercise is uncomfortable. Each of these beliefs can be weighted (e.g., health issues
might be more important to you than issues of time and comfort).
• Subjective norms: looks at the influence of people in one’s social environment
on his/her behavioral intentions, the beliefs of people, weighted by the importance one
attributes to each of their opinions, will influence one’s behavioral intention. You
might have some friends who are avid exercisers and constantly encourage you to join
them. However, your spouse might prefer a more sedentary lifestyle and scoff at those
who work out. The beliefs of these people, weighted by the importance you attribute
to each of their opinions, will influence your behavioral intention to exercise, which
will lead to your behavior to exercise or not exercise.
• Behavioral intention: a function of both attitudes toward a behavior and
subjective norms toward that behavior, which has been found to predict actual
behavior. Your attitudes about exercise combined with the subjective norms about
exercise, each with their own weight, will lead you to your intention to exercise (or
not), which will then lead to your actual behavior.
21
2.1.2 Theory of Planned Behavior (TPB) The theory of planned behavior has been proposed as an extension of the theory
of reasoned action to account for conditions where individuals do not have complete
control over their behavior (1991). To deal with these problems, (I. Ajzen 1991)
extended the theory of reasoned action by including another construct called
behavioral control to predict behaviors in which individuals have in complete
volitional control. The extended model is called the theory of planned behavior.
Figure 2-2 - Theory of Planned Behavior (TPB)
It consists of 5 concepts. As in the TRA model, it includes behavioral attitudes,
subjective norm, intention to use and actual use. The components of behavioral
attitude and subjective norm are the same in TPB as in TRA. In addition, the model
includes behavioral control as a perceived construct. Intention is an indication of a
person's readiness to perform a given behavior, and it is considered to be the
immediate antecedent of behavior. The intention is based on attitude toward the
behavior, subjective norm, and perceived behavioral control, with each predictor
weighted for its importance in relation to the behavior and population of interest.
Behavior is the manifest, observable response in a given situation with respect to a
given target.
22
Actual behavioral control refers to the extent to which a person has the skills,
resources, and other prerequisites needed to perform a given behavior. Successful
performance of the behavior depends not only on a favorable intention but also on a
sufficient level of behavioral control. To the extent that perceived behavioral control
is accurate, it can serve as a proxy of actual control and can be used for the prediction
of behavior.
Perceived behavioral control refers to people's perceptions of their ability to
perform a given behavior or in the other words the degree to which an individual feels
that the decision to perform or not perform is within his control. It encompasses two
components. The first component is "facilitating conditions" representing the
resources required to use a specific system. Examples of such resources are time,
financial resources or other ICT-related resources. The second component is self-
efficacy; that is "an individual's self-confidence in his/her ability to perform a
behavior" (Taylor and Todd 1995).
TPB and TRA have both been criticized for not suggesting operational
components or determinants of behavioral attitudes, subjective norm and, to some
extent, behavioral control. To meet some of this criticism, many researchers have
suggested specific components or determinants of the attitudinal concepts of the TPB-
model. For example, (Bhattacherjee 2001) suggests incorporating the TAM model
(which will be described in the next section) in TPB with perceived usefulness and
user friendliness as the determinants of attitudes towards use. He also suggests
subjective norm may be determined by external and interpersonal influence, and that
the two components of perceived behavioral control may also be treated as the
determinants of behavioral control.
Taylor and Todd (1995) suggest what they term a decomposed TPB which also
includes the TAM model in the attitudinal part of TBP. However, they also include
compatibility as a third determinant of attitude towards use, mainly inspired by the
diffusion theory of (Rogers 2003). Finally, the decomposed TPB suggests self
efficacy, and resource facilitating conditions and technology facilitating conditions
are the most relevant determinants of behavioral control.
23
2.1.3 Technology acceptance model (TAM) Another frequently model that is used in adoption of information system
research is Davis’s 1986 Technology Acceptance Model(F Davis 1986). It has been
extensively applied and utilized in the studies of technology adoption and diffusion at
individual levels (Aganwal and Prasad 1997; Fred Davis 1989; V Venkatesh and
Davis 2000). This model is an adaptation of TRA specifically tailored for modeling
user acceptance of information systems. It provides a basis for tracing the impact of
external factors on internal beliefs, attitude and intention (Davis et al., 1989).
According to TAM an individual’s behavioral intention to use a technology is
determined by two beliefs: perceived usefulness and perceived ease of use. The
perceived usefulness means a person’s perception of using an information system that
benefits him or her in an organizational context. The other construct, Perceived Ease
of Use, was defined by Davis et al. (1989) as the degree to which the prospective user
expects the target system will be free of effort .Even though both PU and EOU were
significantly correlated with usage, Davis' findings suggest that PU mediates the
effect of EOU on usage.
Figure 2-3 - Technology acceptance model (TAM)
TAM claims that actual use is determined by behavioral intention and
subsequently behavioral intention is determined by attitude or in the other words
behavioral intentions are influenced indirectly by external variables through perceived
ease of use and perceived usefulness. Usage is determined by behavior intention to
use a system, which is jointly determined by a person’s attitude towards using the
24
system and its perceived usefulness. This attitude is also jointly determined by both
perceived ease of use and perceived usefulness. In addition, both perceived usefulness
and perceived ease of use were influenced by external variables. Consequently, the
technology acceptance model is considered relevant in studying the acceptance and
adoption and use of a wide range of ICT-based services, including electronic
commerce services. TAM is one of the most influential research models in studies of
determinants of information system /information technology adoption (Chau 1996).
2.1.4 United Theory of Acceptance and Use of Technology (UTAUT)
The UTAUT aims to explain user intentions to use an IS and subsequent usage
behavior. The theory holds that four key constructs (performance expectancy, effort
expectancy, social influence, and facilitating conditions) are direct determinants of
usage intention and behavior (V. Venkatesh et al. 2003). Gender, age, experience,
and voluntaries of use are posited to mediate the impact of the four key constructs on
usage intention and behavior (Venkatesh et. al., 2003). The theory was developed
through a review and consolidation of the constructs of eight models that earlier
research had employed to explain IS usage behavior (theory of reasoned action,
technology acceptance model, and motivational model, theory of planned behavior, a
combined theory of planned behavior/technology acceptance model, model of PC
utilization, innovation diffusion theory, and social cognitive theory). Subsequent
validation of UTAUT in a longitudinal study found it to account for 70% of the
variance in usage intention (Venkatesh et. al., 2003). In his study, Venkatesh
followed four objectives:
1. To review the extant of user acceptance models: The primary purpose of this
review is to assess the current state of knowledge with respect to understanding
individual acceptance of new information technologies. This review identifies eight
prominent models and discusses their similarities and differences. Some authors have
previously observed some of the similarities across models. However, our review is
the first to assess similarities and differences across all eight models, a necessary first
25
2. To empirically compare the eight models: We conduct a within-subjects,
longitudinal validation and comparison of the eight models using data from four
organizations. This provides a baseline assessment of the relative explanatory power
of the individual models against which the unified model can be compared. The
empirical model comparison is presented in the third section.
3. To formulate the Unified Theory of Acceptance and Use of Technology
(UTAUT): Based upon conceptual and empirical similarities across models, we
formulate a unified model. The formulation of UTAUT is presented in the fourth
section.
4. To empirically validate UTAUT: An empirical test of UTAUT on the original
data provides preliminary support for our contention that UTAUT outperforms each
of the eight original models. UTAUT is then cross-validated using data from two new
organizations. The empirical validation of UTAUT is presented in the fifth section.
Figure 2-4 - United Theory of Acceptance and Use of Technology (UTAUT)
Source: Venkatesh et al. (2003)
26
Venkatesh first summarized the models of individual acceptance of
technology, and then had an empirical comparison of the eight models. For that some
longitudinal field studies were conducted at four organizations among individuals
being introduced to a new technology in the workplace. To help ensure the results
would be robust across contexts, he sampled for heterogeneity across technologies,
organizations, industries, business functions, and nature of use (voluntary vs.
mandatory). In addition, he captured perceptions as the users' experience with the
technology increased. At each firm, he was able to time his data collection in
conjunction with a training program associated with the new technology introduction.
This approach is consistent with prior training and individual acceptance research
where individual reactions to a new technology were studied e.g. (F. Davis et al.
1989; Olfman and Mandviwalla. 1994; V Venkatesh and Davis 2000). A pre-tested
questionnaire containing items measuring constructs from all eight models was
administered at three different points in time: post-training (Tl), one month after
implementation (T2), and three months after implementation (T3). Actual usage
behavior was measured over the six month post-training period.
Seven constructs appeared to be significant direct determinants of intention or
usage in one or more of the individual models. Of these, he theorizes that four
constructs will play a significant role as direct determinants of user acceptance and
usage behavior: performance expectancy, effort expectancy, social influence, and
facilitating conditions. Venkatesh explains why attitude toward using technology, self
efficacy, and anxiety are theorized not to be direct determinants of intention. In the
remainder of this section, we define each of the determinants, and provide the
theoretical justification for the hypotheses.
• Performance Expectancy: Performance expectancy is defined as the degree to
which an individual believes that using the system will help him or her to attain gains
in job performance. The five constructs from the different models that pertain to
performance expectancy are perceived usefulness {TAM/TAM2 and C-TAM-TPB),
extrinsic motivation {MM), job-fit (MPCU), relative advantage (IDT), and outcome
expectations (SCT). Even as these constructs evolved in the literature, some authors
acknowledged their similarities: usefulness and extrinsic motivation (Davis et al.
27
1989, 1992), usefulness and job-fit (Thompson et al. 1991), usefulness and relative
advantage (F. Davis et al. 1989; Moore and Benbasat 1991; Plouffe et al. 2001),
usefulness and outcome expectations (Compeau and Higgins 1995; F. Davis et al.
1989), and job-fit and outcome expectations (Compeau and Higgins 1995).
• Effort expectancy is defined as the degree of ease associated with the use of
the system. Three constructs from the existing models capture the concept of effort
expectancy: perceived ease of use {TAM/TAM2), complexity (MPCU), and ease of
use (IDT). As can be seen in Table 2-1, there is substantial similarity among the
construct definitions and measurement scales. The similarities among these constructs
have been noted in prior research (Davis et al. 1989; Moore and Benbasat 1991;
Plouffe et al. 2001; Thompson et al. 1991). This construct is the integration of three
constructs from the previous developed models. These constructs represent the
following areas: perceived ease of use (TAM/TAM2), complexity (MPCU), and ease
of use (IDT). The constructs are similar in design and definition and all proved to be
significant predictors of attitude and intention.
Table 2-1 Models of Individual Acceptance of Technology
Model Core Constructs
Attitude Toward BehaviorTheory of Reasoned Action (TRA)
TRA suggests that a person’s behavior is determined by
his/her intention to perform the behavior and that this intention is,
in turn, a function of his/her attitude toward the behavior and
his/her subjective norm. Subjective Norm
Perceived Usefulness
Perceived Ease of Use
Technology Acceptance Model (TAM/TAM2)
TAM/TAM2 models how users come to accept and use a
technology. The models suggest that when users are presented with
new technology, a number of factors influence their decision about
how and when they will use it. Subjective Norm
Extrinsic MotivationMotivational Model (MM)
MM is the result of research in psychology that has
supported general motivation theory as an explanation for
behavior. The motivational model was adapted to technology user
acceptance by Davis et al. (1992). Intrinsic Motivation
28
Attitude Toward Behavior
Subjective Norm
Theory of Planned Behavior (TPB/DTPB)
TPB extended TRA by adding the construct of perceived
behavioral control as an additional determinant of intention and
behavior. A related model is the Decomposed Theory of Planned
Behavior (DTPB), which “decomposes” attitude, subjective norm,
and perceived behavioral control into the underlying belief
structure within technology adoption contexts. Perceived Behavioral Control
Attitude Toward Behavior
Subjective NormPerceived Behavioral Control
Combined TAM and TPB (C-TAM-TPB)
This model combines the predictors of TPB with perceived
usefulness from TAM to provide a hybrid model (Taylor and Todd
1995). Perceived Usefulness
Job-fit
ComplexityLong-term Consequences
Affect Towards UseSocial Factors
Model of PC Utilization (MPCU)
MPCU represents a model to predict IT utilization behavior
adapted for personal computing. In addition, the characteristics of
the model make it appropriate to predict individual acceptance and
use of a range of information technologies.
Facilitating Conditions
Relative AdvantageEase of Use
ImageVisibility
CompatibilityResults Demonstrability
Innovation Diffusion Theory (IDT)
IDT is concerned with the manner in which a new
technology migrates from creation to use. Moore and Benbasat
(1991) adapted the characteristics of innovations presented in IDT
and refined a set of constructs that could be used to study
individual technology acceptance.
Voluntaries of Use
Outcome Expectations
Personal Self-efficacy
Affect
Social Cognitive Theory (SCT)
According to SCT, an individual’s behavior is uniquely
determined by personal factors, behavior, and the environment.
Compeau and Higgins (1995) applied and extended SCT to the
context of computer utilization with an extension to acceptance
and use of information technology in general. Anxiety
The effort expectancy construct within each model is significant in both
voluntary and mandatory usage contexts; however, each one is significant only during
the first time period (post-training, T1), becoming non significant over periods of
extended and sustained usage. Consistent with previous research for e.g (Aganwal and
Prasad 1997; Davis et al. 1989; Thompson et al. 1991, 1994), effort-oriented
constructs are expected to be more salient in the early stages of a new behavior, when
process issues represent hurdles to be overcome, and later become overshadowed by
29
instrumentality concerns (F. Davis et al. 1989; Szajna 1996; V Venkatesh and Davis
2000).
• Social influence is defined as the degree to which an individual perceives that
important others believe he or she should use the new system. Social influence as a
direct determinant of behavioral intention is represented as subjective norm in TRA,
TAM2, TPB/DTPB and C-TAM-TPB, social factors in MPCU, and image in IDT.
Thompson et al. (1991) used the term social norms in defining their construct, and
acknowledge its similarity to subjective norm within TRA. While they have different
labels, each of these constructs contains the explicit or implicit notion that the
individual's behavior is influenced by the way in which they believe others will view
them as a result of having used the technology. The three constructs related to social
influence are subjective norm (TRA, TAM2, TPB/ DTPB, and C-TAM-TPB), social
factors (MPCU), and image (IDT).
• Facilitating conditions are defined as the degree to which an Individual
believes that an organizational and technical infrastructure exists to support use of the
system. This definition captures concepts embodied by three different constructs:
perceived behavioral control (TPB/ DTPB. C-TAM-TPB), facilitating conditions
(MPCU), and compatibility (IDT). Each of these constructs is operationalized to
include aspects of the technological and/or organizational environment that are
designed to remove barriers to use. Taylor and Todd (1995) acknowledged the
theoretical overlap by modeling facilitating conditions as a core component of
perceived behavioral control in TPB/DTPB. The compatibility construct from IDT
incorporates items that tap the fit between the individual s work style and the use of
the system in the organization.
2.2 Transaction Cost Analysis (TCA) Transaction cost theory was developed primarily to understand the organization
and governance structures of its economic activities (Oliver E. Williamson 1975,
1987). TCA has experienced significant development in the last two decades. Part of
the development is termed the "New Institutional Economics" (NIE) (Furubotn et al.
30
1997), in which TCA is used to study a variety of economic and social phenomena,
ranging from vertical integration, corporate finance, and financial markets, to
marketing, contracting, franchising, regulation, business models, and political systems
(Shelanski and Klein. 1995). The central message of TCA and NIE is that when it
comes to economic performance, institutional structure matters and certain
institutional structures affect governance better than others (Shelanski and Klein
1995). The governance structure in TCA is described as a spectrum, ranging from free
markets to hierarchies with a variety of hybrid models in the middle. Traditionally,
firms have received more attention than markets in TCA studies. With the rapid
growth of the Internet and EC, markets as an institution play a more important role in
economic activities as more firms are involved in B2B commerce and individuals are
participating in various types of electronic markets. The framework of TCA builds on
the interplay between two main assumptions of human behavior-bounded rationality
and opportunism, and three dimensions of transactions-uncertainty, asset specificity,
and frequency. The "uncertainty" reflects the inability to predict relevant
contingencies from two sources-unpredictable changes and information asymmetry
resulting from strategic nondisclosure or distortion of information by the sellers (O. E.
Williamson and Masten 1999). Asset specificity arises when certain business
investments are made to support a particular transaction. This makes it difficult for the
buyer as well as the supplier to switch. Frequency refers to the recurring nature of the
transactions. However, frequency has received only limited attention in empirical
TCA work (Rindfleisch and Heide. 1995). In this study we focus upon the analysis
based on uncertainty and asset specificity.
As a key assumption of TCA, opportunism claims that given the circumstance,
participants in a transaction relationship may seek their self-interest (Oliver E.
Williamson 1987). Opportunism increases transaction costs in the presence of
uncertainty and asset specificity. Decision makers may behave opportunistically and,
therefore, create behavior uncertainties (Rindfleisch and Heide 1995). One effect of
behavior uncertainty is that decision makers may choose not to disclose complete or
accurate information. Another effect of behavior uncertainty is a performance
evaluation problem. For example, a buyer may have difficulty determining whether an
online travel agent is making full effort to find the best deal. Opportunism poses
additional problems when specific assets support a transaction relationship. Asset
31
specificity will then create a safeguarding problem because market competition no
longer serves as a restraint for opportunism (Rindfleisch and Heide 1995). Despite its
importance in TCA, opportunism is generally operationalized through the transaction
dimensions—uncertainty and asset specificity (Rindfleisch and Heide 1995, Shelanski
and Klein 1995). By providing extensive information on products and shopping
processes, and offering a wide range of choices including vendors and products, the
online channel can potentially become a "truly" competitive market and allow
consumers to make informed decisions. This will reduce the vendors' incentive to
behave opportunistically and reduce customer surprises such as hidden charges,
delivery of wrong products, and shipping delays.
Williamson notes that a transaction occurs when goods or services are
transferred across a technologically separable interface. Transaction costs are added to
production costs and should include the market transaction costs and the costs of
intra-firm managerial transactions (Furubotn et al. 1997). Transaction costs for retail
market organizations such as online stores consist of (i) market transaction costs for
searching, bargaining, and after-sale activities and (ii) managerial transaction costs to
run a store. The market transaction costs measure the efficiency level of the
interactions of buyers and sellers during a particular market setting, while the
managerial transaction costs measure the process efficiency in market organizations.
In the context of market transaction costs, as a potential consumer attempts to make
an online purchase, the site may provide the product image, description, price, and
feedback from other customers, in an easy-to-read format. Essentially, transaction
costs are captured with two constructs to measure the benefits to the market:
perceived ease of use (PEOU) and time efficiency. PEOU, also a TAM construct,
measures the effort in shopping including searching, bargaining, and after-sale
monitoring. Time efficiency is a measure of the transaction time costs. The pioneering
work of (Becker 1965) in consumer behavior suggests that the consumer maximizes
his or her utility subject to not only income constraints but also time constraints
(Dellaert et al. 1998). By reducing information asymmetry and surprises, such as
delivering wrong products and missing delivery dates, customers find online shopping
easy to use and less time consuming.
32
Price savings can be considered as a measure of store efficiency because as
managerial costs decrease, savings could be passed on to consumers. In the finance
literature, the transaction costs of financial markets generally include commission
fees, bid-ask spread, and price impact costs (Berkowitz et al. 1988; Hasbrouck 1993).
These costs are the compensation to market makers or dealers and are considered as a
measure of market efficiency. As market institutions become more efficient, the cost
of trading is lowered and consumers get better prices.
Overall, the three dimensions of transaction costs are PEOU, time efficiency,
and price saving measure different aspects of the efficiency of retail transactions.
While PEOU and time efficiency are measures of the costs between buyer and seller
interactions, relative price saving is a measure of online or conventional store
transaction efficiency. Thus TCA extends TAM constructs to the cost dimension of
online transactions.
2.3 Perceived Enjoyment Enjoyment refers to the extent to which the activity of using the computer is
perceived to be enjoyable in its own right, apart from any performance consequences
that may be anticipated (Teo 2001). The importance of enjoyment in online shopping
has been challenged in the past. (Koufaris 2002) did not find any difference between
non online buyers, occasional online buyers, and frequent online buyers. However,
(Goldsmith 2002) found enjoyment to be an important factor determining consumer
online shopping behavior.
2.4 Perceived Risk Researchers in psychology and other disciplines have widely studied the risk
theory. Raymond A. Bauer introduced the notion of ‘perceived risk’ to consumer
behavior research. He suggested, “Consumer behavior involves risk in the sense that
any action of a consumer will produce consequences that he cannot anticipate with
anything approximating certainty, and some of which are likely to be unpleasant”.
33
(Stone and Grønhaug 1993), in their studies on perceived risk, showed the
existence of an important difference between how the risk concept is introduced and
adopted in consumer behavior research and how risk concept is conceived and used in
other disciplines such as economics, psychology, statistical decision theory and game
theory. They pointed out that, in other disciplines, “The concept of risk is related to
choice situations involving both potentially positive and potentially negative
outcome” while in studying perceived risk in consumer behavior, however, “the focus
has primarily been on potentially negative outcomes only”. In the context of
consumers’ E-commerce adoption behavior, when studying perceived risk, the focus
is primarily on potentially negative outcome or potential losses/harm. Thus, in this
study, perceived risk is defined as a person’s perception of the possibility of having
negative outcome or suffering harm or losses associated with E-commerce.
The implicit uncertainty of using Internet technology in the online shopping
environment has been acknowledged by researchers. Perceived risk, as a conceptual
construct of negative utility, has been explored by researchers in studying consumers’
E-commerce adoption behavior. In the online environment, if consumers perceive
huge potential losses/harm, i.e., if they perceive a high level of risk, it is likely that
they will not intend to make purchases over the Internet. So, it is expected that
perceived risk would negatively influence consumers’ intention to adopt E-commerce.
2.5 Theoretical Framework For recent years the migration from legacy systems to IP based booking
applications have been a concerned issue in Iran’s airline industry. Iran air has been
the first to take action and has launched its online booking system in October 2005. At
the beginning only Tehran-Mashhad Tickets were available to passengers on the
reservation system which currently covers almost all major domestic destinations.
Also in the past few years a trend towards E-intermediaries in the tourism
industry has also been observed among Iranian tour operators and IT companies.
Various web sites with the aim to deliver hotel and airline reservation services have
been launched but are yet to be successful in fulfilling their purpose.
34
In this chapter different constructs used to explain behavioral intention to use
service based technologies on the internet in earlier research have been addressed. By
that the following framework to explain intention to use internet reservation systems
by Iranian passengers is proposed. The constructs that have been used in this
framework are all validated and tested in the literature. Further on in chapter four the
following hypotheses are tested.
H1: Performance Expectancy has a significant and positive affect on Iranian airline
passengers’ intention to use internet reservation systems.
H2: Effort Expectancy has a significant and positive affect on airline passengers’ intention to
use internet reservation systems.
Figure 2-5 - Proposed model for Intention to use Internet Reservation Systems
H3: Social Influence has a significant and positive affect on Iranian airline passengers’
intention to use internet reservation systems.
H4: Facilitating Conditions has a significant and positive affect on Iranian airline
passengers’ intention to use internet reservation systems.
35
H5: Time saving has a significant and positive affect on Iranian airline passengers’ intention
to use internet reservation systems.
H6: Price saving has a significant and positive affect on Iranian airline passengers’ intention
to use internet reservation systems.
H7: Perceived enjoyment has a significant and positive affect on Iranian airline passengers’
intention to use internet reservation systems.
H8: Perceived risk has a significant and negative affect on Iranian airline passengers’
intention to use internet reservation systems.
36
37
Chapter III
Research Methodology
3 Research Methodology
This chapter will present a detailed idea about the conducted research. This
includes the purpose of the research, research approach, research strategy, sample
selection and data collection methods and questioner development. At the end of this
chapter validity and reliability issues will be discussed to follow the quality standards
of the research.
3.1 Research purpose According to (Yin 1994), studies can be classified in terms of their purpose and
it is possible to pursue three different kinds of research depending on the nature of
study and based on the type of information needed. These are exploratory,
descriptive and explanatory (causal) research. Each of these types is going to be
explained briefly.
Exploratory studies are used to clarify and define the nature of a problem. It is
useful when the problem is difficult to limit and when the perception of which model
to use is difficult because it is unclear what characteristics and relations are important.
The purpose of an exploratory research is to gather as much information as possible
about a specific subject. It is further common to use many different sources to gather
information (Patton 2002). An exploratory study should be designed by starting a
purpose and stating the criteria to judge the exploration successful (Yin 1994).
The objective of the descriptive research is to portray an accurate profile of
persons, events or situations. In this kind of research it is necessary to have a clear
picture of the phenomenon on which the researcher wishes to collect data, prior to the
collection of the data (Saunders et al. 2006).
This type of study can involve the description of the extent of association
between variables. For example, the researcher may observe that there is an
association between the gender of consumers and their tendency to consume white
meat. Note that the researcher is able to describe the relationship rather than explain
it.
An explanatory research is when the focus is on cause-effect relationships,
explaining what causes produces what effects (Yin 1994). Explanatory (or causal)
research seeks to find cause and affect relationships between variables. It
accomplishes this goal through laboratory and field experiments.
The model which is going to be evaluated in this thesis is based on sound
theories mentioned in chapter two; enough literature is available for this purpose and
we rely on the variables and pattern drawn from the theory. As explained all
constructs used to measure Intention to use online reservation systems have already
been justified in previous research; therefore, based on the above mentioned
explanations this research has a descriptive purpose.
3.2 Research Approach This section focuses on the way that the main issue of the research is going to be
addressed. The selection of which research approach is appropriate in a given study
38
should be based upon the problem of interest, resources available, the skills and
training of the researcher, and the audience for the research. The research approach
can be Quantitative or Qualitative, Deductive or Inductive.
3.2.1 Quantitative VS Qualitative
According to (Guba and Lincoln 2005), two approaches or methods are
available to researchers: qualitative and quantitative .The qualitative approach implies
an emphasis on processes and meanings that are not measured in terms of quantity,
amount, intensity or frequency. The qualitative approach provides a deeper
understanding of the phenomenon within its context. Moreover, qualitative
researchers stress the socially constructed nature of reality that states the relationship
between the researcher and the phenomenon under investigation. On the other hand,
quantitative researchers emphasize the measurement and analysis of causal
relationships between variables, not processes.
3.2.1.1 Qualitative (Denzin and Lincoln 2003) define research as a situated activity locates the
observer in the world. Qualitative research is exceptionally helpful for identifying the
scope of the research and should be used to fully understand the views, opinions and
attitudes that researcher might come across. It is quite common that a hypothesis is
produced in the earl stage of a qualitative research, not at the very beginning of it
(Silverman 2001). According to Silverman (2001) the strength of a qualitative
research is that it focuses on actual practice and looks at how social interactions are
routinely performed. According to (Travers 2001) there are five main methods to be
used for qualitative research: observation, interviewing ethnographic fieldwork,
discourse analysis and textual analysis.
3.2.1.2 Quantitative Quantitative approach is one in which the investigator primarily uses post
positivist claims for developing knowledge (i.e. cause and effect thinking, reduction
39
to specific variables and hypotheses and questions, use of instrument and observation,
and the test of theories), employs strategies of inquiry such as experiments and
surveys and collects data on predetermined instruments that yield statistical data
(Creswell 2003).
Quantitative research is frequently referred to as hypothesis-testing research.
Characteristically, studies begin with statements of theory from which research
hypotheses are derived. Then an experimental design is established in which the
variables in question (the dependent variables) are measured while controlling for the
effects of selected independent variables. Subject included in the study are selected at
random is desirable to reduce error and to cancel bias. The sample of subjects is
drawn to reflect the population (Newman and Benz 1998).
The procedures are deductive in nature, contributing to the scientific knowledge
base by theory testing. This is the nature of quantitative methodology. Because true
experimental designs require tightly controlled conditions, the richness and depth of
measuring for participant may be sacrificed. As a validity concern, this may be a
limitation of quantitative designs (Newman & Benz 1998).
In this research, based on the research questions and above discussions,
considering that the purpose of the research is to measure the constructs affecting
intention to use online reservation systems by Iranian Airline Passengers; a
quantitative approach will be used for this study.
3.2.2 Inductive VS Deductive
According to Saunders (2006), the research should use the inductive approach,
where the author would collect data and develop theory as a result of the data
analysis. While the deductive approach where the authors develop a theory and
hypothesis (or hypotheses) and design a research strategy to test the hypotheses.
Deductive reasoning works from the more general to the more specific. Sometimes
this is informally called a “top-down” approach; inductive reasoning works the other
way, moving from specific observations to border generalizations and theories.
Informally, we sometimes call this approach a “bottom-up” approach (Trochim 2001).
40
In this study a deductive approach was chosen, since the research starts with a
literature review which later on is compared with the empirical findings. In addition,
the purpose with this study is not to produce any new theories based on the
observations made, which is the major purpose of an inductive approach.
3.3 Research Strategy
Research strategy will be a general plan of how researcher will go about
answering the research questions that has been set by the researcher. It will contain
clear objectives, derived from research questions specify the sources from which
researcher intend to collect data and consider the constraints that researcher will
inevitably have such as access to data, time, location and money, ethical issues
(Saunders et al. 2006).
Yin (1994) specifies five different research strategies to be used when colleting
and analyzing empirical evidence, and provides three conditions to determine which
strategy to use. According to Yin (1994), each strategy can be used for exploratory,
descriptive, or explanatory (causal) research purposes.
Table 3-1 Relevant Situations for different Research strategies
Research Strategy Form of Research Question
Requires Control over Behavior events?
Focuses on contemporary Events?
Experiment How, Where Yes Yes
Survey Who, What, Where, How many, How much No Yes
Archival Analysis Who, What, Where, How many, How much No Yes/No
History How, Why No No Case Study How, Why No Yes
Source: (Yin 1994)
Experiment is a technique in which individuals who are knowledgeable about a
particular research problem are surveyed. The purpose of the experiment study is to
help formulate the problem and clarify concepts, rather than develop conclusive
evidence (Zikmund 1999). Yin (1994) describes experiments as possible when an
investigator is able to focus on one or two isolated variable and can manipulate and
control behavior directly, precisely and systematically.
41
The Survey strategy is a popular and common one in business and marketing
research that is usually associated with the deductive approach. It allows the
collection of large amount of data from a sizeable population in a highly economic
way. Questionnaire, structured observation and structured interviews often fall into
this strategy (Saunders et al. 2006).
Moreover, in the Archival Analysis there is no control over behavioral events,
which implies that this strategy is preferable when the goal is to describe the
occurrence of a phenomenon and when the aim is to predict certain outcomes.
Yin (1994) explains Historical Research as dealing with the past when no
relevant person is alive to report, and where an investigator must rely on documents
and cultural and physical artifacts as the main sources of evidence.
Case Study involves when the researcher whishes to gain a rich understanding
of the context of the research. The data collection in the case study may include
questionnaires, interviews, observation and documentary analysis (Saunders et. al.,
2006). More specifically, (Yin 2003) defines a case study as an empirical inquiry that
investigates a contemporary phenomenon within its real-life context, especially when
boundaries between phenomenon and context are not clearly evident.
Since the research question in this study is based on “what” question and the
investigator has no control over the actual behavioral events, Survey is found to be a
more appropriate approach in order to gain a better understanding of the research area.
Also survey is found to be more appropriate for quantitative studies.
3.4 Data Collection Data collection is a fundamental step in a research. In data collection, sampled
data are collected through various means that provide a basis for analyzing the market
behavior of a general population from which the data are sampled.
42
Saunders & Thornhill (2006) explain that when gathering data and information
to meet the objectives for the research questions and hypotheses there are two options
to face, Primary and Secondary Data.
Lekvall & Wahlbin (1993) together declare that secondary data is information
collected from former existing studies and literatures, gathered for the purposes other
than the problem at hand. They continue to explain that the main advantage of
secondary- compared to primary data is that it is fairly inexpensive and can be
gathered more quickly.
Primary research is conducted from scratch. It is original and collected to solve
the problem in hand. It is gathered and assembled specifically for the current research
project (Zikmund, 1999). According to (Kotler and Armstrong 1996) Primary data is
information collected for the specific purpose at hand. Primary data can be collected
through questionnaires, telephone/personal interviews, observations and experiment.
When collecting data and information for investigating research questions and
hypotheses in this study, no secondary data was available; as mentioned before
Quantitative Survey was used as the primary data source.
3.4.1 Defining the target Population
Sampling design begins by specifying the target population. This is the
collection of elements or objects that possess the information sought by the researcher
and about which inferences are to be made (Malhotra and Birks 2003). Considering
that Iran Air has launched its online reservation system for only domestic flights
almost a year now, and a trivial number of passengers have used the online
reservation system to buy tickets, it was decided to target only those passengers who
had never used the system (inexperienced users of the system).
Since we were interested in the concept of intention, the fact that the
respondents are inexperienced users of the online reservation systems does not affect
the result of this study. Testing the acceptance models based on the data gathered
43
from inexperienced users is not something unusual and has been studied in previous
literatures. Taylor and Todd in 1995, Conducted a study to assess the role of prior
experience in assessing IT usage. They tested the predictive ability of the Augmented
TAM model based upon the data gathered from two distinct groups of experienced
and inexperienced users of the computer resource center separately and compared the
results to assess the role of experience. Taylor and Todd (1995) encouraged the
researchers to test:
1. Whether models such as TAM are predictive of behavior for inexperienced
users of the information technology.
2. Whether the determinants of IT usage are the same for experienced and
inexperienced users of a system.
Furthermore, (Yu et al. 2005), who conducted a study to verify TAM for t-
commerce, used two distinct groups of samples of inexperienced and experienced
users of the t-commerce and compared the results.
Based on the above explanations we continue to define the target population of
this study. The target population should be defined in terms of elements, sampling
units, extent and time (Malhotra and Briks, 2003). An element is the object about
which or from which the information is desired. In survey research, the element is
usually the respondent. A sampling unit is an element, or a unit containing the
element, that is available for selection at some stage of the sampling process. Extent
refers to the geographical boundaries of the research and the time refers to the period
under consideration (Malhotra and Briks 2003).
According to the explanations mentioned above, the target population of this
study is defined as:
Elements: inexperienced users of the online reservation systems.
Sampling units: Iranian Airline passengers waiting in the transit area to get on
board the aircraft.
Extent: Mehrabad International Airport, Terminal four, Domestic departures.
Time: Fall 2007
44
3.4.2 Sample Selection
According to Saunders et al., (2006), sampling techniques can be divided into
two types:
Probability or representative sampling
Non-probability or judgmental sampling
In probability sampling, sampling units are selected by chance. Probability
sampling is most commonly associated with survey-based research. This method of
sampling permits the researcher to make inferences or projections about the target
population from which the sample was drawn (Saunders et. al., 2006).
Non probability sampling relies on the personal judgment of the researcher
rather than on chance to select sample elements. Non probability samples may yield
good estimates of the population characteristics, but they do not allow for objective
evaluation of the precision of the sample results (Malhotra and Briks 2003).
In this study Probability or representative sampling has been used to allow us to
make inferences or projections about the target population. The subjects were chosen
randomly from the transit area of domestic departures terminal of Mehrabad Airport
traveling to different cities in Iran.
3.5 Questioner Development
To ensure that a comprehensive list of items was selected in the model, an
extensive review of previous work was conducted. To ensure the validity and
reliability of the research constructs, I have selected items that had been validated in
previous research. Table 3-2 shows the source of measures used to develop questions
for each construct. The questionnaire consists of questions that relate to possible
factors affecting Intention to use online reservations systems.
45
Likert five point scales ranging from “strongly agree” to “strongly disagree”
were used as a basis of questions. This scale has been used in previous e-commerce
adoption research.
Table 3-2 Research Variables and Measurements
Construct Source Performance Expectancy Venkatesh, 2003 Effort Expectancy Venkatesh, 2003 Social Influence Venkatesh, 2003 Facilitating Conditions Venkatesh, 2003 Time Saving Devaraj, 2002 Price Saving Devaraj, 2002 Perceived enjoyment Goldsmith, 2002 & Teo 2001 Perceived Risk Liu, 2003
To further validate the questioner eight interviews with experts of air transport
industry was conducted to make sure the questions were compatible with the Iranian
context. Minor changes were suggested by interviewers during this stage.
After the interviews I translated the questioner to Persian. A Pilot test on 15
airline passengers was conducted to assure questions were fully understood by the
subjects after translation. With the help of the pilot test the questioner was finalized
which is presented in Appendix B.
3.6 Validity & Reliability It is important that a research project has high quality, and this cannot be
achieved only through collecting data. The criterion for testing whether a thesis has
high quality or not is whether the research instruments are neutral and if the same
conclusions should be drawn by other researchers (Denzin and Lincoln 2003). To
increase the possibility of getting the right meaning of the answers, the researcher has
to pay extra attention to reliability and validity (Saunders 2006).
46
3.6.1 Validity Validity is concerned with whether the findings are really about what they
appear to be about (Saunders et. al., 2006). Validity defined as the extent to which
data collection method or methods accurately measure what they were intended to
measure (Saunders et. al., 2006). Cooper & Schindler (2003) believe that validity
refers to the extent to which a test measures what we actually wish to measure. There
are two major forms: external and internal validity. The external validity of research
findings refers to the data’s ability to be generalized across persons, settings, and
times. Internal validity is the ability of a research instrument to measure what is
purposed to measure(Cooper and Schindler 2003). Below measures where taken to
ensure the Validity:
Data was collected from the reliable sources, from respondents who are have
already used the traditional mean (Travel Agency) of purchasing a ticket and are
about to travel.
Survey question were made based on literature review to ensure the validity of
the result.
Questionnaire has been pre-tested by Experts and people involved in the
process of implementing online reservation systems.
3.6.2 Reliability
Reliability demonstrates to which extent the operations of a study, such as the
data collection procedures can be repeated with the same results. A measure is
considered reliable if a person’s score on the same test given twice is similar. It is
important to remember that reliability is not measured it is estimated (Yin, 1994).
One way to think of reliability is that other things being equal, a person should
get the same score on a questionnaire if they complete it at two different points in
time (test-retest reliability. Another way to look at reliability is to say that two people,
who are the same in terms of the construct being measured, should get the same score.
In statistical terms, the usual way to look at reliability is based on the idea that
47
individual items (or sets of items) should produce results consistent with the overall
questionnaire.
The simplest way to do this is in practice is to use split half reliability. This
method randomly splits the data set into two. A score for each participant is then
calculated based on each half of the scale. If a scale is very reliable a person’s score
on one half of the scale should be the same (or similar) to their score on the other half:
therefore, across several participants scores from the two halves of the questionnaire
should correlate perfectly (well, very highly). The correlation between the two halves
is the statistic computed in the split half method, with large correlations being a sign
of reliability. The problem with this method is that there are several ways in which a
set of data can be split into two and so the results could be a product of the way in
which the data were split. To overcome this problem, (Cronbach 1951) came up with
a measure that is loosely equivalent to splitting data in two in every possible way and
computing the correlation coefficient for each split. The average of these values is
equivalent to Cronbach’s alpha, α, which is the most common measure of scale
reliability.
Using SPSS for windows ver15 all the 35 indicators were tested which the result
is displayed in Table 3-3. Often in books and Journal Articles are said that a value
above 0.7 is an acceptable value for Cronbach’s Alpha; values substantially lower
indicate and unreliable scale. A value of 0.820 shows a fair extend of reliability for
the questioner.
Table 3-3 Cronbach’s Alpha reliability test for the model
Cronbach’s Alpha N of Items0.820 35
Cronbach’s Alpha reliability test was conducted for constructs individually as
well which also indicates high reliability as displayed in Table 3-4.
48
Table 3-4 Cronbach’s Alpha reliability test results Construct Cronbach’s Alpha N of Items
Performance Expectancy 0.910 5 Effort Expectancy 0.874 4 Social Influence 0.772 5 Facilitating Conditions 0.742 5 Time Saving 0.727 2 Perceived enjoyment 0.776 3 Perceived Risk 0.834 7 Intention to Use 0.740 3
3.7 Summery of the Research Methodology
In this chapter I have determined that the research follows as descriptive
purpose, approach to the research is quantitative, for the research strategy; survey has
been chosen, primary data was collected using a questioner and which a probability
sample selection was conducted.
49
Chapter IV
Data Analysis
4 Data Analysis
In this chapter demographics and descriptive statistics, constructs reliability
and validity assessment and then results of hypotheses tests are delivered.
4.1 Overview of the Sample Data collection took place August 2007 in Mehrabad International Airport.
230 questioners were distributed among Airline passengers in terminal four which is
used for domestic departures. First the questioners were distributed between Airline
passengers who were in the checking area which resulted in low co-operation of
subjects as they were too busy with checking-in luggage and other general procedures
involved with receiving the boarding pass. Considering this situation I decided to
change my location and distribute the questioner in the transit area which most of the
passengers are usually idle and waiting for the gate to open.
Out of the 230 questioners which were distributed all 230 were completed and
collected. To exclude incomplete and inappropriate questioners a simple cleansing
method was used which reduced the sample size to 186 which then was used for Data
presentation and analysis.
4.2 Demographics & Descriptive Statistics
As was mentioned before our subjects are inexperienced Iranian Airline
passengers which have at leased purchased and traveled once with an Airline.
Demographic statistics are provided within figures 4-1, 4-2 and 4-3 which describes
Gender, educational level and Age of respondents respectively.
As displayed in below pie chart, 61.82% of the respondents are male and
38.17% of the respondents are females.
male, 62.82%
female, 38.17%
female
male
Figure 4-1Pie chart, respondents gender
Below Bar chart displays Education level of the respondents. Majority of the
respondents have above high school education. 47.8% of the respondents have a B.A
degree which was no surprise considering Iranian governments policies towards
increasing capacity for undergraduates in recent years.
51
41
89
35
21
0102030405060708090
100
Diploma Undergraduate M.A P.H.D
Figure 4-2 Bar chart, respondents education level
The majority of the respondents were between 20 to 30 years old which
represents 45.6% of the total sample. Youngsters below 20 years of age represented
only 6.45%. Considering demographical statistics of the population the result had no
surprise.
4049
85
12
0
10
20
30
4050
60
70
80
90
Below 20 20 - 30 30 - 40 Above 40
Figure 4-3 Bar chart, respondents age
As mentioned before the survey was among all Iranian Airline passengers,
since visiting different airports of the country and distributing the questioner among
residence of all provinces was not feasible terminal four of Mehrabad Airport was
chosen to distribute the questioners. Mehrabad Airport is considered as the number
one airport in domestic cities regarding the daily inbound and out bound traffic.
Below table categorizes respondents by their residence. Citizens of 22 cities of Iran
have participated in the survey which justifies the sample as Iranian Airline passenger
regardless of the questioner being distributed in Mehrabad airport.
52
Table 4-1 Respondents percent frequency by city of residence City of Residence Percent frequency Ahvaz 7.5 BandarAbas 4.7 Boushehr 3.7 Chalos 0.9 Esfahan 5.6 Hamedan 0.9 Ilam 0.9 Kerman 0.9 Kermanshah 12.1 Kish 1.9 Kordestan 0.9 Mahshahr 0.9 Mashhad 5.6 Orumiye 1.9 Shiraz 15.9 Tabriz 6.5 Tehran 26.2 Yasouj 0.9 Yazd 0.9 Zahedan 0.9
As it is illustrated in Table 4-2, 93.5% of the respondents spend some time on
the Internet during the week.
Table 4-2 Hours Spend on the internet during the week
Hours Percent frequency Zero 6.45 Below 5 25.8 5 - 10 32.7 Above 10 34.9
4.3 Factor Analysis
Factor Analysis is a statistical approach that can be used to analyze
interrelationships among a large number of variables and to explain these variables in
terms of their common underlying dimensions (factors). The statistical approach
involving finding a way of condensing the information contained in a number of
original variables into a smaller set of dimensions (factors) with a minimum loss of
information (Hair Jr et al. 1995). There are two main types of factor analysis:
53
• Principal component analysis -- this method provides a unique solution,
so that the original data can be reconstructed from the results. It looks at
the total variance among the variables, so the solution generated will
include as many factors as there are variables, although it is unlikely that
they will all meet the criteria for retention.
• Common factor analysis -- This family of techniques uses an estimate of
common variance among the original variables to generate the factor
solution. Because of this, the number of factors will always be less than
the number of original variables.
In this research, principal component analysis with varimax rotation was
conducted using the statistical package SPSS for windows version 15.0. Results are
presented in tables 4-3, 4-4 and 4-5.
The Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's test of
sphericity are presented in table 4-3. The KMO statistic varies between 0 and 1. A
value of 0 indicates that the sum of partial correlations is large relative to the sum
of correlations, indicating diffusion in the pattern of correlations (hence, factor
analysis is likely to be inappropriate). A value close to 1 indicates that patterns of
correlations are relatively compact and so factor analysis should yield distinct and
reliable factors (Field 2005). (Kaiser 1974) recommends accepting values greater
than 0.5 as acceptable (values below this should lead you to either collect more
data or rethink which variables to include). Furthermore, values between 0.5 and
0.7 are mediocre, values between 0.7 and 0.8 are good, values between 0.8 and 0.9
are great and values above 0.9 are superb. For these data the value is 0.847, which
falls into the range of being great: so, we should be confident that factor analysis
is appropriate for these data.
54
Table 4-3 KMO and Bartlett’s Test
KMO and Bartlett's Test
.847
2066.215496.000
Kaiser-Meyer-Olkin Measure of SamplingAdequacy.
Approx. Chi-SquaredfSig.
Bartlett's Test ofSphericity
Bartlett's measure tests the null hypothesis that the original correlation matrix
is an identity matrix. Field (2005) recommends for factor analysis to work we
need some relationships between variables and if the R-matrix were an identity
matrix then all correlation coefficients would be zero. Therefore, we want this test
to be significant (i.e. have a significance value less than 0.05). A significant test
tells us that the R-matrix is not an identity matrix; therefore, there are some
relationships between the variables we hope to include in the analysis. For these
data, Bartlett's test is highly significant (p < 0.001), and therefore factor analysis is
appropriate.
Table 4-4 lists the eigenvalues associated with each linear component (factor)
before extraction, after extraction and after rotation. Before extraction, SPSS has
identified 32 linear components within the data set. It is obvious that there should
be as many eigenvectors as there are variables and so there will be as many factors
as variables. The eigenvalues associated with each factor represent the variance
explained by that particular linear component and SPSS also displays the
eigenvalues in terms of the percentage of variance explained (so, factor 1 explains
33.196% of total variance). In the columns labeled Extraction Sums of Squared
Loadings all factors with eigenvalues greater than 1, which leaves us with seven
factors. The eigenvalues associated with these factors are again displayed. The
values in this part of the table are the same as the values before extraction, except
that the values for the discarded factors are ignored (hence, the table is blank after
the fourth factor). In the final part of the table labeled Rotation Sums of Squared
Loadings, the eigenvalues of the factors after rotation are displayed.
55
Table 4-4 Total Variance Explained (I)
Total Variance Explained
10.62 33.196 33.196 10.62 33.196 33.196 4.345 13.579 13.579
3.067 9.585 42.781 3.067 9.585 42.781 3.685 11.515 25.094
2.318 7.242 50.024 2.318 7.242 50.024 3.403 10.635 35.729
1.944 6.075 56.098 1.944 6.075 56.098 3.288 10.274 46.003
1.527 4.773 60.872 1.527 4.773 60.872 2.609 8.155 54.157
1.311 4.097 64.968 1.311 4.097 64.968 2.351 7.347 61.504
1.050 3.282 68.250 1.050 3.282 68.250 2.159 6.746 68.250
.959 2.997 71.247
.844 2.638 73.885
.804 2.512 76.397
.706 2.206 78.603
.686 2.142 80.746
.663 2.072 82.818
.581 1.816 84.634
.527 1.647 86.281
.474 1.480 87.761
.429 1.342 89.103
.416 1.301 90.404
.365 1.141 91.544
.330 1.032 92.576
.298 .931 93.507
.293 .914 94.422
.281 .878 95.299
.250 .781 96.080
.217 .678 96.758
.211 .659 97.418
.191 .598 98.016
.175 .546 98.561
.136 .425 98.987
.120 .375 99.362
.110 .343 99.705
.094 .295 100.000
Component1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Total% of
VarianceCumulative
% Total% of
VarianceCumulative
% Total% of
Variance Cumulative %
Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Rotation has the effect of optimizing the factor structure and one consequence for
these data is that the relative importance of the four factors is equalized. Before
rotation, factor 1 accounted for considerably more variance than the remaining six
(33.196% compared to 9.585%, 7.242, 6.075%, 4.773%, 4.097%, and 3.282),
however after extraction it accounts for only 13.579% of variance (compared to
11.515%, 10.635%, 10.274%, 8.155%, 7.347% and 6.746% respectively).
Consequently this shows that the 32 Indicators (questions) represent 7 Factor
(constructs) and explains 68.25% of the total variance of Intention to use online
reservation systems.
56
Table 4-5 displays the Rotated Component Matrix which is a matrix of the
factor loadings for each variable onto each factor. As can be seen from below
Table most items loaded properly on construct. However cross loading such as
FC01 and FC02 where dropped from further analysis. Since Time Saving and
Price saving loaded together in the factor analysis, we combined the items from
each construct and tested it as one construct: Transaction Cost. Also FC03, FC04,
SI03 and SI05 loaded under one component. Considering that all four questions
(indicators) were referring to the perceived support of using online reservation
systems new constructs was added to the model for further analysis and
Facilitating Conditions (FC) was dropped out of the model.
57
Since two construct had been combined into one (Transaction Cost Saving)
and Facilitating Conditions replaced with Perceived Support Factor Analysis was
con
and Facilitating Conditions replaced with Perceived Support Factor Analysis was
conducted again to confirm the mentioned.
ducted again to confirm the mentioned.
Rotated Component Matrixa
.804 -.098 .234 -.033 .005 -.026 .272
.858 -.085 .245 .122 .097 -.004 .075
.768 -.220 .194 .053 .060 .157 .233
.764 -.195 .086 .013 .313 .150 .061
.727 -.081 .176 .051 .393 .180 .051
.349 -.165 .591 .353 .118 .107 .172
.287 -.207 .833 .036 .072 .094 .063
.229 -.233 .735 .100 .150 .127 .106
.353 -.275 .648 .007 .183 .236 -.030
.310 -.143 .137 -.077 .556 .366 .222
.345 -.164 .261 -.119 .668 .138 .089
.424 -.097 .043 .145 .573 .186 .073
.119 .069 .507 .071 .187 -.205 .545
.011 -.086 .586 .173 .440 -.165 .228-.004 -.187 .021 .742 .035 .085 .282-.003 -.093 .270 .692 .218 -.045 .139.062 -.059 .318 .273 .678 .174 .215.324 -.143 .090 .250 .311 .004 .630.330 -.245 .200 .037 .116 .280 .587.183 -.164 .059 .285 .087 .289 .631.097 -.106 .142 .150 .314 .716 .264.150 -.065 .162 .196 .151 .763 .102.074 .001 .056 .808 -.027 .255 -.013.138 -.129 -.119 .414 .074 .509 -.157.047 -.142 .062 .877 -.015 .104 .082
-.105 .571 -.150 .019 -.323 .334 .021-.028 .587 -.277 .140 -.094 -.259 .038-.220 .694 -.220 -.078 .034 .012 -.240-.270 .718 -.277 -.077 -.069 -.086 -.108-.112 .660 .001 -.217 -.238 .059 -.294-.117 .656 -.027 -.179 .129 -.314 -.063-.013 .753 -.021 -.201 -.105 -.057 .042
PE01PE02PE03PE04PE05EE01EE02EE03EE04PEJ01PEJ02PEJ03FC01FC02FC03FC04FC05TS01TS02PS01SI01SI02SI03SI04SI05PR01PR02PR03PR04PR05PR06PR07
PE0 PRO EEO FC PEJ SIO TSOComponent
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Ra. otation converged in 8 iterations.
Table 4-5 Rotated Component Matrix (I)
58
New results are presented in Table 4-6 and 4-7. After dropping the two
indicators mentioned above as per the Total Variance Table now seven constructs
are explaining 69.401 % of intention to use online reservation sy reservation system compared to
68.25% shown before.
The Rotated Component Matrix confirms the applied changes and has
extracted seven construct; Perceived Expectancy, Effort Expectancy, Perceived
Risk, Perceived Support, Perceived Enjoyment, Social Influence and Transaction
Cost Saving. Details of the changes are illustrated in Table 4-7.
stem compared to
68.25% shown before.
The Rotated Component Matrix confirms the applied changes and has
extracted seven construct; Perceived Expectancy, Effort Expectancy, Perceived
Risk, Perceived Support, Perceived Enjoyment, Social Influence and Transaction
Cost Saving. Details of the changes are illustrated in Table 4-7.
Total Variance Explained
10.17 33.916 33.916 10.17 33.916 33.916 4.210 14.034 14.034
3.052 10.173 44.089 3.052 10.173 44.089 3.579 11.929 25.963
2.307 7.691 51.780 2.307 7.691 51.780 3.276 10.918 36.882
1.558 5.192 56.972 1.558 5.192 56.972 2.951 9.838 46.719
1.442 4.807 61.779 1.442 4.807 61.779 2.643 8.811 55.531
1.276 4.255 66.034 1.276 4.255 66.034 2.150 7.166 62.697
1.010 3.367 69.401 1.010 3.367 69.401 2.011 6.704 69.401
.892 2.972 72.373
.816 2.721 75.095
.715 2.384 77.479
.687 2.291 79.770
.667 2.225 81.995
.613 2.045 84.039
.513 1.709 85.749
.476 1.588 87.336
.432 1.441 88.777
.417 1.390 90.167
.381 1.271 91.438
.328 1.094 92.532
.308 1.026 93.557
.294 .980 94.537
.270 .898 95.435
.249 .832 96.267
.225 .750 97.017
.213 .711 97.728
.176 .588 98.316
.142 .472 98.788
.128 .428 99.216
.121 .403 99.618
.114 .382 100.000
Component1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Total% of
Variance Cumulative % Total% of
Variance Cumulative % Total% of
Variance Cumulative %
Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Table 4-6 Total Variance Explained (II)
59
Factor Analysis using SPSS was also conducted on Intention to use online
reservations indicators which explained 67.579% of the total variance. Detailed
tabl
chapter two have been decreased from eight to
es and statistics can be found in the Appendix C.
Considering results of the confirmatory factor analysis hypothesis of the
research which was presented in
Rotated Component Matrixa
.806 -.094 -.019 .242 .031 -.046 .245
.851 -.083 .125 .255 .118 .004 .055
.775 -.212 .047 .199 .068 .163 .239
.757 -.188 .001 .103 .316 .156 .086
.693 -.066 .046 .212 .419 .148 .095
.326 -.152 .382 .603 .168 .046 .167
.282 -.204 .065 .825 .105 .050 .052
.180 -.199 .132 .787 .205 .008 .178
.315 -.250 .018 .690 .220 .172 .041
.251 -.110 -.072 .197 .612 .251 .314
.315 -.156 -.105 .275 .692 .064 .122
.461 -.124 .138 -.018 .548 .242 .014
.005 -.185 .747 -.004 .032 .098 .260
.008 -.102 .724 .232 .232 -.053 .068
.089 -.077 .298 .252 .677 .148 .156
.327 -.135 .291 .077 .349 -.091 .588
.324 -.209 .053 .229 .144 .174 .650
.160 -.127 .294 .091 .119 .185 .705
.077 -.082 .141 .171 .372 .664 .314
.126 -.045 .169 .193 .203 .757 .159
.056 .026 .787 .075 -.036 .275 .055
.145 -.125 .369 -.117 .063 .598 -.124
.058 -.140 .867 .036 -.038 .158 .078-.132 .601 -.011 -.099 -.304 .306 .111-.017 .578 .147 -.298 -.104 -.260 -.008-.262 .705 -.085 -.181 .059 -.031 -.200-.286 .725 -.074 -.259 -.055 -.127 -.105-.099 .645 -.231 -.012 -.233 .101 -.333-.126 .648 -.146 -.030 .149 -.382 -.113.046 .730 -.206 -.090 -.148 -.022 -.021
PE01PE02PE03PE04PE05EE01EE02EE03EE04PEJ01PEJ02PEJ03FC03FC04FC05TS01TS02PS01SI01SI02SI03SI04SI05PR01PR02PR03PR04PR05PR06PR07
PE PR PS EE PEJ SI TSComponent
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 9 iterations.a.
Table 4-7 Rotated Component Matrix (II)
60
seven. Table 4-8 illustrates the seven hypotheses which further on will be
investigated using SEM analysis. Table 4-8 Research Hypothesis
H1: PE has a positive & significant affect on Iranian airline passengers’ intention to use internet reservation systems.
H2: EE has a positive & significant affect on Iranian airline passengers’ intention to use internet reservation systems.
H3: SI has a positive & significant affect on Iranian airline passengers’ intention to use internet reservation systems.
H4: PS has a positive & significant affect on Iranian airline passengers’ intention to use internet reservation systems.
H5: PEJ has a positive & significant affect on Iranian airline passengers’ intention to use internet reservation systems.
H6: PR has a negative & significant affect
H7: TS has a positive & significant affect o
on Iranian airline passengers’ intention to use internet reservation systems.
n Iranian airline passengers’ intention to use internet reservation systems.
4.4 Data Analysis
eived Support, Social Influences, Perceived Transaction Cost, Perceived
Enjoyment – and one dependent variable, intention to use online reservation
systems.
4.4
ion models such as linear regression, ANOVA, and MANOVA, which can
analyze only one layer of linkages between independent and independent variable at a
tim
Model and hypotheses testing was conducted Using LISREL 8.50 with seven
independent variables – Performance Expectancy, Effort Expectancy, Perceived
Risk, Perc
.1 Structural Equation Model (SEM)
Analysis of the data was done by using the LISREL approach, which is one of the
SEM techniques. Structural Equation Modeling (SEM) techniques such as LISREL
and Partial Least Squares (PLS) are second generation data techniques that can be
used to test the extent to which IS research meets recognized standards for high
quality statistical analysis (Gefen 2000). SEM enables researchers to answer a set of
interrelated research questions in a single, systematic and comprehensive analysis by
modeling the relationships among multiple and dependent constructs simultaneously.
This capability for simultaneous analysis differs greatly from most first generation
regress
e.
61
LISREL is a statistical technique that has been developed since the 1970s as an
approach to structural equation modeling. Essentially, the LISREL approach to
structural equation modeling is the outcome of combining two well-established
approaches to model fitting: the structural approach of multiple regression analysis
and the measurement approach of factor analysis. Thus a LISREL model, in its most
general form, consists of two parts: the measurement model and the structural
equation model. The measurement model specifies how the latent variables or
hypothetical constructs are measured in terms of the observed variables, and it
describes the measurement properties (validities and reliabilities) of the observed
variables. The structural equation model specifies the causal relationships among the
late
ll model simultaneously incorporating the measurement and structural
model is specified to test the fitness between theoretical specifications and the
acceptable fit as displayed in
below table:
T Goodness of Fit Indic the Model
del,
as shown in figure 4-4. The model’s key statistics are good since the GFI is 0.86, the
s of Fit Presen odel Acceptable Le
nt variables and describes the causal effects and the amounts of unexplained
variance.
To strictly test our proposed theoretical relationships, the contemporary second
generation multivariate analytic technique, SEM, is employed (Fornell and Bookstein
1982). The fu
empirical data set. Encouragingly, the model reaches an
able 4-9 es forGoodnes ted M vel
χ ² 47
f
FI 0.84 >.8
MSEA 0.012 <.08
0.77 >.9
0.98 >.9
9.55 --
df 467 --
χ ² /d 1.02 <3
GFI 0.86 >.9
AG
R
NFI
CFI
4.4.2 Results
As mentioned before the hypotheses are tested in a structural equations mo
62
CFI is
l behavior towards
intention. The results also provide strong support for Transaction Cost Saving and
Perceiv
stems. Performance Expectancy had the strongest
effect with a path coefficient of 0.54 emphasizing the important role of an individual’s
performance expectancy in driving his/her intentions toward using online reservation
systems to purchase airline tickets.
0.98 and the RMSEA is 0.012. We can thus safely conclude that the model is
valid and can continue to analyze the outcome of the hypothesized causal effects.
Figure 4-4 provides the results of testing the structural links of the proposed
research model using LISREL. The estimated path coefficients (standardized) are
given along with the associated t-value. All path coefficients except Perceived Risk
are significant at the 99% significance level providing strong support for six out of the
seven hypothesized relationships. These results represent yet another confirmation of
the appropriateness of the UTAT for explaining individua
ed Enjoyment. Perceived Support which was added to the model after Factor
Analysis has also shown a significant affect on Intention to Use.
The effects of the seven antecedents of intention (i.e., perceived support, effort
expectancy, performance expectancy, social influence, perceived enjoyment,
transaction cost saving and perceived risk) accounted for over 77% of the variance in
this variable. This is an indication of the good explanatory power of the model for
intention to use online reservation sy
63
Figure sis of the Model
shown in table 4-10.
10 Results of the SEM analysis othesis Variable Path Coefficient T-statistic Supported
4-4 Results of the SEM analy
Summery of the hypothesis and related path coefficient and t-statistics are
Table 4-
HypH1 PE 0.54 5.32 YES
H2 EE 0.33 3.51 YES
H3 SI 0.42 4.38 YES
H4 PS 0.25 2.75 YES
H5 PEJ 0.33 3.37 YES H6 PR -0.14 -1.49 NO H7 TS 0.51 4.81 YES
4.4.2 results
er’s intentions to use an online reservation system will
.1 Significant
Performance Expectancy
Intention to use online reservation systems is positively affected by performance
expectancy (β= 0.54, P<0.01) thereby supporting hypothesis one. This indicates
that airline passeng
64
increase if they expect the service will help them attain gains in purchasing a
, P<0.01) thereby supporting hypothesis two. This indicates
at airline passenger’s intentions to use an online reservation system will
the service make it easier for them to purchasing a ticket.
ne reservation system will
crease if they perceive that others believe he or she should use the service to
ions to use an online reservation system will
crease if they perceive he or she will receive support for using the service to
purchasing a ticket.
erceived Enjoyment
online reservation systems is positively affected by perceived
ransaction Cost Saving
tention to use online reservation systems is positively affected by transaction
ost saving (β= 0.51, P<0.01) thereby supporting hypothesis seven. This
ticket.
Effort Expectancy
Intention to use online reservation systems is positively affected by effort
expectancy (β= 0.33
th
increase if they expect
Social Influence
Intention to use online reservation systems is positively affected by social
influence (β= 0.42, P<0.01) thereby supporting hypothesis three. This indicates
that airline passenger’s intentions to use an onli
in
purchasing a ticket.
Perceived Support
Intention to use online reservation systems is positively affected by perceived
support (β= 0.25, P<0.01) thereby supporting hypothesis four. This indicates
that airline passenger’s intent
in
P
Intention to use
enjoyment (β= 0.33, P<0.01) thereby supporting hypothesis five. This indicates
that airline passenger’s intentions to use an online reservation system will
increase if they perceive using the service to purchasing a ticket will cause more
joyful activity than going to physical travel agency.
T
In
c
65
indicates that airline passenger’s intentions to use an online reservation system
will increase if they perceive using the service to purchase tickets will allow
them to save time and money.
4.2.2 Non-significant results
Perceived Risk
Intention to use online reservation systems is found not to be significantly
affected by perceived risk (β= -0.14, P<0.01) thereby rejecting hypothesis six.
This indicates that airline passenger’s intentions to use an online reservation
system will not decrease if they perceive using the service to purchase tickets
will be associated with risk.
4.
66
lications Based upon data analysis and findings, in this final chapter I will first answer
the re
search question
Chapter V
Conclusions and Implications
5 Conclusions and Imp
search question. Next implication for research and implication for practice will
be presented followed up by the research limitations and suggestions for future
research.
5.1 Re The research question mentioned in chapter two was “What are the main
factors that influence Iranian airline passenger’s intention to purchase tickets
through online reservation systems?”
67
As explained in details in chapter four; performance expectancy, effort
expectancy, social influence, perceived support, perceived enjoyment and transaction
cost saving has and positive and significant affect on Iranian airline passengers
intention to use online reservation systems. Perceived risk showed that a negative
affect on intention but was not significant at (P< 0.05).
n systems. Facilitating conditions
was replaced by perceived support which is defined as the degree of which an
indivi
vations.
than one percent of
the local airline tickets are sold using internet reservation systems. In fact only one
out of the eleven active airlines in Iran is using an internet based reservation system to
5.2 Implication for theory This study validates three constructs out of the four constructs proposed by
Venkatesh (2003) model of user acceptance of information technology (UTAT) in a
different context, intention to use online reservatio
dual believes that he or she will be supported while using the online reservation
system. Also perceived enjoyment and transaction cost saving were proven to have a
significant and positive effect on intention to use online reservation systems. The
model explains 77 percent of the variance in intention to use an online reservation
system. It will hopefully spark more research into the factors that influence adoption
of other technical inno
5.3 Implication for practice This study has shed light on some of the main factors which influence airline
passenger’s intention to use online reservation system. Finding of this research can be
considered by both the Iranian Airlines who are directly involved in implementing
online reservation systems and of course benefit from it; and the government as the
policy maker and responsible for the continuous development and growth of the
industry.
According to Iran civil aviation organization, currently less
68
sell th
ebsite instead of travel agencies they will automatically save five
perce the ticket price which is the commission paid to the travel agent.
Consi
hlighted.
chapter one I have also mentioned a trend towards virtual travel agencies in
recen years by Iranian IT companies and tour operators which have also proven to be
unsuccessful in offering travel and tourism services such as airline tickets and hotel
reserv acilities. By considering the findings of this research while launching
similar websites and offering such services, they will have better progress in
achie ng their aim.
5.4 imitations and suggestions for future research
le used in this study consisted of in-experienced users of online
reservation systems from different cities of Iran. Due to the limited people with actual
experience with using online reservation systems at the time which the research was
conducted in-experienced users were chosen to measure intention. It is highly
recommended for future research in this context to use experienced users to determine
factors affecting actual usage of online reservation systems implemented by Iranian
airlines. Further more assessing the e-service quality of online reservation services
provided by airlines and virtual travel agents would be essential.
eir tickets. The importance of applying an online reservation system for airlines
has been stressed in chapter one; further more by investigating the major concerns of
Iranian airlines we will find that the level of cost related to the industry compared to
other countries due to the international sanctions raised on purchasing and employing
modern aircrafts are very high. Thus it is highly essential for Iranian airlines to
decrease cost of operation by any means.
By correctly implementing an online reservation system and diverting sales
through the w
nt of
dering that fuel cost has the highest portion of the total cost associated with the
operation which is approximately eight to 10 percent of the total cost; the five percent
commission saving will even be more hig
In
t
ation f
vi
L The samp
69
Additionally, the applicability of UTAT constructs, Transaction cost analysis,
and Perceived risk in the context of online reservation systems
eeds to be explored in future research. Respondents in this study evaluated their
sage intentions based on their perceptions of an ideological online reservation
may in surveyed after actual usage.
Finally, other factors affecting intention to use online reservation systems to
apture
McCol
Perceived enjoyment
n
u
system and as a result factors that were found to be insignificant like Perceived risk
fact be found to be significant if subjects were
Additional research is warranted along these lines. The testing of our model in a pre-
implemented and post-implemented system and a comparison of the two would be of
great interest.
purchase airline’s tickets may exist. Further testing and expansion of our model may
factors not contemplated herein. c
(I. a. F. Ajzen 1980; Butler and Peppard 1998; Lekvall and Wahlbin 1993; Palmer and e 1999)
70
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Appendix A: Abbreviations and Acronyms Systems
Information and Communication Technologies IATA International Air Transport Association ICAO International Civil Aviation Organization
Electronic C erce IP Internet Protocol TRA Theory of Reasoned Action TAM Technology Acceptance Model TAM2 Extension of echnology Acceptance Model PEOU Perceived Ease of Use PU Perceived Usefulness MM MotivationaUTAT United Theo of Acceptanc and Use of echnology
Theory of Planned Behavior IDT Innovation D fusion ThSCT Social Cogn e Theory
NIE New Institutional Economics CEO Chief Executive Officer CRS Computer Reservation System GDS Global Distributing System
l usiness Machines EOU Ease of Use TCA Transaction Cost AnalysPE Performance Expectancy EE Effort Expectancy
e FC Facilitating Conditions PS Perceived Support
TS Time SavingPEJ Perceived Enjoyment PR Perceived Risk
-Commerce Television Commerce LS Partial Least Square EM Structural Equation Model
IS Information
Information Technology ITICT
EC omm
T
l Model ry e T
TPBif eory
itivMPCU Model of PC Utilization B2B Business to Business
IBM Internationa B
is
SI Social Influenc
TS Transaction Cost saving PS Price Saving
TPS
78
Appendix B: Questioner I - General Information
. City of Residence:
1 2. Gender:
Male F emale
3. Age:
20 to 0 40 & Above
Under 20 to 30 30 4
4. Education
ploma achelors Masters PHD.
Di B 5. How many hours a week do you spend on h int
Zero
Less than 5
5 to 10
ore Than 10
t e ernet?
M
6. What is the Purpose of your travel?
tudent
S Visit Friends
& famil Busin ss l re
y e eisu
7. I am t Domestic International
raveling to a ………… destination.
8. I have attempted to purchase a airlines t o net. Never re
n ticke n the interOnce Twice Mo
79
II - Performance Expectancy
more quickly.
1. Using Online reservation system in my travel planning enables me to
purchase a ticket
Strongly Disagree Disagree Neutral Agree
Strongly agree
2. Using Online reservation system would make it easier to Purchase a ticket
.
trongly
SNeutral Agree
tronglyS Disagree
agree Disagree
3. sing Online reservation system in my t ning wo ld increase the
Strongly
U ravel plan uproductivity of my trip.
Neutral Agree Strongly
Disagree
agree Disagree 4. sing Online reservation system would e h nc my effec eness in
Strongly
U n a e tiv purchasing a ticket.
Neutral Agree Strongly
Disagree
agree Disagree 5. I would find the online reservation system useful in purchasing a ticket. Strongly
Neutral Agree Strongly
Disagree Disagree agree
III - Perceived Enjoy ent
al tr v l ag ncy. trongly
m
1. Using online reservation systems for purchasing tickets will provide more joyful activity than going to a physic a
e
eS
Neutral Agree
tronglyS Disagree
agree Disagree
2. Overall, it’ll be enjoyable to use online reservation systems for ticket
purc
Strongly
hasing.
Neutral Agree Strongly
Disagree Disagree
agree
3. sing an online reservation sy em is posit e. Strongly
U st iv
Neutral Agree Strongly
Disagree Disagree agree
80
IV - Effort Expectancy
1. My interaction with the online reservation system would be clear and understandable.
Strongly Disagree Disagree Neutral Agree
Strongly agree
2. It would be easy for me to become skillful at using the online reservation
stem.
sy
Strongly Disagree Disagree Neutral Agree
Strongly agree
3. the online reservation system easy to use.
I would find
Strongly Disagree Disagree Neutral Agree
Strongly agree
4. to operate the online reservation system is easy for me.
Learning
Strongly Disagree Disagree Neutral Agree
Strongly agree
V - Time Saving
have to spend too much time to compltrongly
1.
I do not
ete the transaction.
Neutral Agree trongly
Disagree Disagree agree 2. Compared to the traditional method I spend less time purchasing a ticket
using an online reservation system
Disagree Disagree Neutral A ree Strongly
g agree
VI - Price Saving
using online reservation sy ems I could buyo
eaper. 1.
By
st
tickets ch Str ngly
Disagree Disagree Neutral A ree tronglyS
g agree
81
VII - Facilitating Co ditions
. co hat I can use to ccess the online reservation system.
n
have a personal computer & table inte n t1 Ia
s r e nnection t
Strongly Disagree Disagree Neutral A ree g
Strongly agree
2. have the knowledge necessary to use online reservation system.
I
Strongly
Disagree Disagree Neutral A ree gStrongly agree
3. A specific person or gr esk) is available for assistance with systeoup (help d m
diffic
Stronglyulties.
Disagree Disagree Neutral Agree
Strongly agree
4. Specialized instructions concerning online reser
me.
trongly
vation systems are available to
SNeutral Agree
tronglyS Disagree Disagree
agree
5. Using the online Reservation system fits into m
trongly
y life style. S
Neutral Agree tronglyS
Disagre
e Disagree agree
VIII - Social Influences
online reservation stem for purchasing tickets.
1. People who influence my behavior think that I should usesy
Strongly Disagree Disagree Neutral Agree
Strongly agree
2. People who are important to me think that I should use online reservation
stem for purchasing tickets.
sy
Strongly D
Strongly isagree Disagree Neutral Agree agree
3. irlines are very supportive of the use o line n systems for
purchasing tickets.
trongly
A f on reservatio
S Disagree Disagree Neutral Agree
Strongly agree
82
4. n systems for ticket purchasing
ave more prestige than those who do n t People in my society who use online reservatio
h o . Strongly
Disagree Disagree Neutral Agree Strongly agree
5. In general, the Airlines have supported the use of online reservation system. Strongly
Strongly
Disagree Disagree Neutral Agree agree
IX - Perceived Risk
ystem I w o be able to finish th steps purchase a ticket.
By using the online reservation s1. ill n t e to
Strongly Disagree Disagree Neutral Agree
Strongly agree
2. People I know would not strongly recommend the usage of online
reservation systems for purchasing a ticket.
Strongly Disagree Disagree Neutral Agree
Strongly agree
3. Usin line reservation systems will waste my time.
Strongly
g on
Neutral Agree Strongly
Disagree Disagree agree 4. Usin line reservation systems will waste my money.
Strongly
g on
Neutral Agree Strongly
Disagree Disagree agree 5. The Internet is not a secure means to conduct online transactions. Strongly
Neutral Agree Strongly
Disagree Disagree agree 6.
By u g the online reservation system, enough information about the flight will not be available to me.
sin
Neutral Agree Strongly Strongly
Disagree Disagree agree
83
7. Seeking flight information and purchasing a ticket using online reservation syste
Strongly
ms involves a significant amount of risk.
Neutral Agree Strongly
Disagree Disagree agree
X - Intention to Use Online Reservation Systems
1. I intend to purchase a ticket using online reservation systems in the near future. (next three months)
StronglyNeutral Agree
Strongly Disagree Disagree agree
2. I thin it would be very good to use the Internet for purchasing a ticket in
addit on to traditional methods.
Strongly
ki
Neutral Agree Strongly
Disagree Disagree agree 3. I expect to purchase a ticket using online reservation systems in the near
future. (next three months)
StronglyNeutral Agree
Strongly Disagree Disagree agree
84
Appendix C: SPSS and LISREL Outputs
– Factor Analysis for Intention to use online reservation systems
I
KMO and Bartlett's Test
.624
94.3063
.000
Kaiser-Meyer-Olkin Measure of SamplingAdequacy.
Approx. Chi-SquaredfSig.
Bartlett's Test ofSphericity
Communalities
1.000 .7941.000 .4891.000 .744
IU01IU02IU03
Initial Extraction
Extraction Method: Principal Component Analysis.
Total Variance Explained
2.027 67.579 67.579 2.027 67.579 67.579.679 22.625 90.204.294 9.796 100.000
Component123
Total % of Variance Cumulative % Total % of Variance Cumulative %Initial Eigenvalues Extraction Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
Component Matrixa
.891
.700
.863
IU01IU02IU03
IU0Component
Extraction Method: Principal Component Analysis.1 components extracted.a.
85
II – Conceptual model in LISREL
86
III – Standard Estimate result in LISREL
87
88
IV – T-Value result in LISREL
