MASTER'S THESIS
Investigating factors influencing customers intension for choosing electronic banking
services
Sina Elli
Master program Electronic Commerce
Luleå University of Technology
Department of Business Administration, Technology and Social Sciences
INVESTIGATING FACTORS INFLUENCING CUSTOMER‘S INTENSION FOR CHOOSING
ELECTRONIC BANKING SERVICES
(Proposing the hybrid model ‗UTILISM‘, an ANP approach)
Supervisors:
Dr. E. Salehi Sangari Dr. A. Keramati
Prepared By:
Sina Elli
Luleå University of Technology
Department of Business Administration and Social Sciences Division of Industrial marketing and e-commerce
International University of Chabahar Joint MSc program in Electronic Commerce
2011
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Table of Contents
CHAPTER 1: INTRODUCTION ... 6
1. Introduction ... 6
1.1 Background ... 6
1.1.1 e-banking... 7
1.1.1.1 ATMs ... 7
1.1.1.2 Internet Banking... 9
1.1.1.3 Mobile Banking ... 11
1.2 Research Problem Statement ... 14
1.2.1 Research Questions ... 15
1.3 Disposition of the Thesis ... 16
CHAPTER 2: LITERATURE REVIEW ... 18
2. Introduction ... 18
2.1 Theory of Reasoned Action (TRA) ... 19
2.2 Theory of Planned Behavior (TPB) ... 21
2.3 Technology Acceptance Model (TAM) ... 23
2.4 Unified Theory of Acceptance and Use of Technology ... 24
2.5 Task-Technology Fit (TTF) ... 26
2.6 Diffusion of Innovations Theory ... 28
2.7 Cognitive Fit Theory (CFT) ... 30
2.8 Lazy user Model (LUM) ... 32
2.9 Comparison of Technology Acceptance Theories ... 34
CHAPTER 3: FRAME OF REFERENCE ... 40
3. Introduction ... 40
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3.1 UTAUT and LUM ... 40
3.1.1 UTAUT structure ... 41
3.1.2 LUM structure ... 42
3.1.2.1 Entailment of switching cost for LUM ... 43
3.2 Philosophical discussion on the need for joining LUM and UTAUT. ... 44
3.2.1 The Path of Least Resistance in Physics and Mathematics ... 44
3.2.2 The Path of Least Resistance in Behavioral Sciences and Human Brain ... 45
3.2.3 Evidence against the Path of Least Resistance in human life ... 48
3.3 Existence of Choice ... 49
3.4 Proposing a hybrid model – UTILISM ... 51
3.4.1 UTILISM Structure ... 52
3.5 Research Questions ... 56
CHAPTER 4: RESEARCH METHODOLOGY ... 58
4. Introduction ... 58
4.1 Research Philosophy ... 59
4.2 Research Approach ... 61
4.3 Research Purpose ... 62
4.4 Research Strategy ... 63
4.5 Choice of research ... 64
4.6 Time Horizon ... 65
4.7 Techniques and Procedures ... 66
4.7.1 Data Collection ... 66
4.7.1.1 Sources of Data ... 67
4.7.1.2 Sampling Plan ... 67
4.7.1.3 Data Quality ... 69
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4.7.1.3.1 Validity ... 69
4.7.1.3.2 Reliability ... 70
4.7.2 Data Analysis ... 71
4.7.2.1 Analytical Network Process ... 72
CHAPTER 5: DATA ANALYSIS ... 74
5. Introduction ... 74
5.1 ANP approach ... 74
5.2 ANP model of UTILISM ... 76
5.3 ANP Analysis ... 81
5.3.1 Development of Supermatrices ... 83
CHAPTER 6: FINDINGS AND CONCLUSION ... 94
6. Introduction ... 94
6.1 Achievement of Objectives ... 94
6.1.1. Ranking of E-banking Services and prioritization of influencing factors ... 96
6.1.1.1 Intention to choose Sub network... 97
6.1.1.2 Facilitating Conditions Sub network ... 99
6.1.1.3 Top Layer UTILISM network ... 100
6.1.2 Sensitivity Analysis ... 101
6.2 Research Constraints ... 106
6.3 Further research implications ... 107
References ... 108
APPENDIX A – Questionnaire ... 115
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Table of Figures
Figure 1 Mobile Banking vs. Online Banking Forecast: 1995 through 2016 ... 13
Figure 2 Outline of the Study... 17
Figure 3 The TRA Model ... 20
Figure 4 The TPB Model ... 22
Figure 5 The TAM model ... 23
Figure 6 The UTAUT model ... 25
Figure 7 The TTF model ... 27
Figure 8 The DOI in IS model ... 29
Figure 9 The Cognitive Fit Model ... 30
Figure 10 The lazy user model ... 33
Figure 11 Mountain Pass Theorem - Path of Least Resistance ... 45
Figure 12 Basal Ganglia in Human Brain ... 46
Figure 13 Action Selection by Basal Ganglia ... 47
Figure 14 Free Will against Determinism ... 50
Figure 15 UTILISM ... 53
Figure 16 Research Onion by Saunders et.al. ... 59
Figure 17 AHP structure ... 76
Figure 18 ANP structure ... 76
Figure 19 SOlution Selection layer of UTILISM ... 77
Figure 20 UTILISM top ANP structure ... 78
Figure 21 UTILISM intention to choose layer ... 78
Figure 22 Intention to choose ANP structure ... 79
Figure 23 Facilitating conditions layer of UTILISM ... 80
Figure 24 Facilitating Conditions ANP structure ... 80
Figure 25 Supermatrix of a network and details of matrix in it ... 84
Figure 26 Supermatrix of a hierarchy ... 85
Figure 27 Supermatrix of a network (loop) ... 85
Figure 28 Synthesized priorities for Intention to Choose sub network ... 97
Figure 29 Prioritization of Intention to Choose influencing factors ... 98
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Figure 30 Synthesized priorities for Facilitating Conditions sub network ... 99
Figure 31 Prioritization of Facilitating Conditions influencing factors ... 100
Figure 32 Synthesized priorities for UTILISM network ... 101
Figure 33 Sensitivity graph for Intention to Choose - current value ... 102
Figure 34 Sensitivity graph for Intention to Choose - increased value ... 103
Figure 35 Sensitivity graph for Facilitating Conditions - current value ... 104
Figure 36 Sensitivity graph for Facilitating Conditions - decreased value ... 105
Table of Tables Table 1 Technology Acceptance Models Comparison ... 36
Table 2- Sub Determinants of UTILISM Core Determinants ... 55
Table 3 Deductive and Inductive Research (Saunders, Lewis and Thornhill 2007) ... 62
Table 4 Research Strategies (R. K. YIN 1994, 6) ... 64
Table 5 Unweighted Supermatrix of Intention to choose sub network ... 87
Table 6 Weighted Supermatrix of Intention to choose sub network ... 88
Table 7 Limit matrix of Intention to choose sub network ... 89
Table 8 Unweighted supermatrix of Facilitating Conditions sub network. ... 90
Table 9 Weighted Supermatrix of Facilitating Conditions sub network ... 91
Table 10 Limit Matrix of Facilitating Conditions sub network ... 92
Table 11 Unweighted supermatrix of UTILISM network ... 93
Table 12 Weighted supermatrix of UTILISM network ... 93
Table 13 Limit Matrix of UTILISM network ... 93
Table 14 Alternative Values and Rankings fed forward in UTILISM network ... 100
6 CHAPTER 1: INTRODUCTION
1. Introduction
In the first chapter of this thesis a background in the research area will be presented that will have a brief look at E-banking and emergence of its different channels and their current status in Iran. Subsequently the main research problem will be discussed from which preliminary research questions are derived. Finally, the outline of this research will be presented.
1.1 Background
In this section, a background in the e-banking and its several different channels will be presented. Issues that banks are facing in order to invest on channels that will be more accepted among users will be discussed. past current and future Internet usage trend will be overviewed and electronic banking and its status in Iran will also be addressed.
“Et ipsa scientia potestas est.” (Latin)
“And knowledge itself, is power"
(Francis Bacon, Meditationes sacrae)
7 1.1.1 e-banking
E-Banking is more widely used in the last few years, e-commerce has emerged and has developed a high-level business environment that created millions of new job opportunities and lowered costs for SMEs and increased performance while saving time (Seyed Javadiyan and Sagatchi 2006). E-banking is considered the foundation of e-commerce and number of industries and businesses that are moving toward e-banking is increasing rapidly.
E-banking refers to financial activities that involve use of electronic technology ranging from the now ubiquitous automatic teller machines (ATMs), to other services such as direct deposit, electronic bill payment, electronic funds transfer (EFT), telephone banking, and on-line banking (Lee 2000).
These financial electronic technologies are in differing stages of development. ATMs, a mature e-banking product, have existed for approximately 30 years and have been widely accepted among consumers. Conversely, telephone banking, electronic bill payment, online banking and mobile banking represent more recent additions to e-banking services. E-Banking in the scope of this study will generally refer to the combination of Online Banking, Mobile Banking and ATMs. These rather new channels of banking will be briefly overviewed below.
1.1.1.1 ATMs
According to Data Monitor, the number of e-banking system users in eight European countries of France, Germany, Italy, Poland, Spain, Sweden and Switzerland, had increased from 5.4 million users in 1999 to 22 million users in 2004. Based on data monitor report on e-banking technology in Europe 75 percent of business owners were using at least one of the e-banking services in 2005 (Data Monitor 2001).
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In the late 1980s, Iranian banks felt the need to automate banking processes and started to computerize their banking tasks (Reporter 2007). At first there was only 7 – 10 ATMs in Tehran and Sepah bank was the premier to issue electronic cards that could be used for very limited number of banking tasks in ATMs. Melli bank was the second to come up with electronic cards with more functionality and soon all banks were installing ATMs in their branches and issuing cards (Kazemi Dinan 2007). The major problem was that none of these banks were integrated with another one and each bank was an isolated island incapable of performing transactions with another one. Each bank had created an e-banking system based on a network that was not connected to other bank networks. Saderat bank had the Sepehr system, Keshavarzi bank created the Mehr system. Melli bank came up with the Siba system and Melat bank introduced the Jam network. Banks were advertising their e-banking systems and were encouraging users to use their system where none of them had a significant advantage over the other (BBC 2007).
In 2002, a network was designed to connect Melli bank with Shahrvand superstore POSs, later that year the idea was generalized and a superhighway of banking transactions that banks could use to integrate their banking services was designed. The network was called Shetab and was tested with integration of 2 major specialized banks of Toseé saderat and Keshavarzi and one commercial bank Saderat. In 2003 Saman bank joined the network to be the first private bank integrated with governmental banks and in 2004 Melli bank as the Iran‘s biggest bank with the largest number of branches joined the network (Seyed Javadiyan and Sagatchi 2006). Today many governmental and private banks have joined the network and Shetab has become the infrastructure network for electronic banking (Kazemi Dinan 2007).
According to the Iran ministry of ICT 14,000 branches out of 15,600 are now connected to national banking network in Iran. In 2007 there were 8,440 ATMs and 244,000 POS with a total number of 27 million electronic cards were present in the whole country and it is expected to have 30,000 ATMs and 900,000 POSs installed and 75 million electronic cards provided for the customers until 2010 (Reporter 2007).
9 1.1.1.2 Internet Banking
Technological advances have changed the world radically. Emergence of IT has altered the manner in which individuals conduct their business affairs. The World Wide Web (WWW) had a significant impact, since it started facilitating the Internet to both individuals and businesses (E.AbuShanab 2007).
World Wide Web users have been increasing exponentially since its introduction in the early 1990s. According to the numbers published by World Internet Stats, there are approximately 1,596 million users worldwide which in year 2000 was only 360 million users.
The average penetration rate is 23.8 percent and the users growth rate is approximately 342.2 percent. (Miniwatts Marketing Group 2009).
The projected users growth rate in the middle east is much higher, in year 2000, there were only 3 million users in the middle east which is approximately 46million users in 2009 therefore middle east has growth rate of 1,296.2 percent (Miniwatts Marketing Group 2009).
Iran in the Middle East second to Syria has the highest growth rate. Based on the numbers provided by World Internet Stats Iran in year 200 had only 250,000 internet users where in the year 2009 with a growth rate of 9,100 percent Iran has approximately 23 million internet users. (Miniwatts Marketing Group 2009).
Internet in Iran was first introduced in 1993 (Iran Ministry of ICT 2005) and it was for academic use only, since then It has grown rapidly and now with the penetration rate of 34.9 percent Iran has more than 50%share in middle east internet users (Miniwatts Marketing Group 2008). Such a tremendous and rapid growth in the number of internet users, have created new opportunities for many businesses and has introduced new horizons for many industries.
Like many other technologies internet was first used to have an edge and to gain competitive advantage but soon being online became a competitive necessity and many industries and businesses are feeling the pressure and moving toward online presence.
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Banking was one of the few industries that have witnessed a significant influence of WWW technology. Over the past few years the banking industry has invested substantial resources in bringing Information Technology and online services in particular to consumers.
The banking industry has implemented new and technology-based services on the internet called
―online banking‖.
From the consumers‘ perspective, online banking provides many benefits to individuals, such as immediate access to accounts and balances, ability to conduct remote banking transactions and investments, and completion of electronic applications (Donner and Dudley 1997).
With online banking, time and location become irrelevant given that these services can be accessed at any time; regardless of where the individual is located. ―The prospect of around-the- clock access to bank services and the convenience of transacting business from anywhere in the world should be especially appealing to consumers, given that the flexibility that e-banking allows seems to fit our increasingly mobile lifestyle‖ (Jayawardhena and Foley 2000).
In the organizational scope, online banking makes simultaneous service to numerous customers with various needs possible. Anking industry intends to make banking easier and more convenient for users when compared to traditional systems and services. (Meuter, et al. 2000).
―Online banking assists banks in their transition from multiple locations to a lucrative and global marketplace‖ (Giannakoudi 1999). What is appealing for banks to move toward onine banking is the benefit that they gain by decreasing the personnel costs and also technology driven costs that makes a huge difference in comparison to the traditional bricks-and-mortar banking (Sarel and Marmorstein 2002).
According to Seyed Javadiyan and Sagatchi, there are three levels of online banking services available for customers: (Seyed Javadiyan and Sagatchi 2006).
Information level: This is the most basic level in online banking. Website id used only to share information about the services offered by the bank that also includes news regarding new events and general news regarding the bank.
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Connection level: slightly more advances than the basic level, full duplex connection between the bank and the customer is made possible. This interactive system is not free of risks and therefore security systems are considered to ensure privacy is not violated and data intergrity remains intact.
Transaction level: This is the most sophisticated online banking level in which transactions are also possible and almost all the traditional banking services are available through the internet which also comes with greater risks that requires advanced security systems.
In the Beginning online banking systems in Iran were providing mostly information and connection level services in which it was possible to view account status but money transfer and opening and managing accounts were not possible. Saman Bank was the pioneer and among the very few private banks to implement online banking technology (Seyed Javadiyan and Sagatchi 2006). Today most of the banking services are accessible through online banking systems but one still needs to open a primary account in a physical branch and intra-bank services are still not mature (Reporter 2007).
1.1.1.3 Mobile Banking
Over the last few years, along with the internet, the mobile and wireless technologies has been evolved and mobile banking was born. Mobile banking referes to all the services offered by a bank that are made available on a mobile device such as mobile phones, hand helds and pocket pcs, some of these services are performing balance checks, account transactions, payments etc.
According to Tiwari and Buse (Tiwari Rajnish 2007) , ―Mobile Banking refers to provision and availment of banking- and financial services with the help of mobile
telecommunication devices. The scope of offered services may include facilities to conduct bank and stock market transactions, to administer accounts and to access customized information.‖
There are a large number of different mobile phone devices and it is a big challenge for banks to offer mobile banking solution on any type of device. There are several paltforms,
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HTML, XML, SOAP, WAP and there is no technological standard to be used in mobile banking.
Selection of the platform to base mobile banking services on, is a critical decision. Some of these devices support J2ME (Java 2 platform Micro Edition) and others support SIM Application Toolkit, a WEB or WAP browser, or only SMS.
Security of financial transactions, being executed from some remote location and transmission of financial information over the air, are the most complicated challenges that need to be addressed jointly by mobile application developers, wireless network service providers and the banks' IT departments.
According to a new report from Celent, US Mobile Banking: Beyond the Buzz, ― Mobile banking is here to stay and will grow significantly faster than online banking‖ (Celent, LLC 2007) approximately 46 million households in United states currently bank online. By the end of 2010, a total of 35% of online banking households has gone mobile.
Addition of new features and possibilities will bring certain advantages for mobile banking to further differentiate the technologies and be more appealing for internet banking users. (Celent, LLC 2007)
By observing this growth trend, it is predicted to see growth in mobile financial information systems, fund transfer, bill-payment, administration of accounts and customer service solutions.
Prediction of new technology adoption rates has always been challenging, in this case it is possible to use online banking adoption rate as a measuring stick. U.S. consumers currently prefer PCs rather than mobile devices for banking tasks but Figure 1 shows that this gap is narrowing. Based on the Figure 1, it is reasonable to consider that mobile banking will have a similar adoption rate to online banking.
The following chart has been inferred from an Online Banking Report (Online Banking Report 2007) that compares the ramp-up period for online banking to the predicted ramp-up for mobile banking. Online banking has reached 40 million users in a period of 10 years (1996 – 2006). According to the online banking report on online banking it will also take 10 years for mobile banking to reach the same penetration rate.
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Figure 1 Mobile Banking vs. Online Banking Forecast: 1995 through 2016 (Online Banking Report 2007)
Many Governmental banks such as Saderat, Melat, Refah, Melli, Sepah, Maskan and some private banks, Parsian, Pasargad, Sarmaye, Saman, are provising Mobile banking services.
Some of these banks like Melat and saderat offer mobile baning services only to their own sustomers and some like Parsiyan also offer services for every Shetab account holder which includes majority of the Iranian banks. The greatest challenge here, like any other bank in the world is the lack of technological standard for mobile banking. The best choice is to make software to support multiple platforms and many Iranian banks are using Java that is supported by many mobile phones today. Internet on mobile phones using GPRS, EDGE is also new in Iran before which banks had to base their mobile banking services to short message service with very limited functionality. Iranian Mobile banking is almost 5 years old now and it offers banking services like Balance checking and recent transaction history, Fund Transfer, Bill Payment, mobile phone credit charging, etc.
14 1.2 Research Problem Statement
There are many benefits in using Electronic banking for the customers. Internet banking gives them option to perform banking transactions and other related activities on the go or from home, and the convenience of performing most banking transactions twenty-four hours a day, 365 days a year.
Despite the arrival of sophisticated electronic banking systems that are developed to make banking tasks easier and facilitate daily banking activities, unfortunately these technologies are not completely accepted by the customers as it was expected in the beginning and the number of internet banking users are not increased with the rate that was predicted. (Flavian, Torres and Guinaliu 2004). ―Millions of Americans are not using the e-banking technologies, nor are they expected to do so in the near future‖ (Kolodinsky, Hogarth and Hilgert 2004).
This is also the case in Iran, in a recent program in Iran TV channel 1 (Iran National Television 2009), people were interviewed and asked about their banking method preferences, although Electronic banking Channels are there now, many people found it more convenient to use traditional brick and mortar banking. Some thought it was easier and to some it was more reliable, and many worried about the security issues and internet connection quality inadequacies
As it is the case with many new technologies and innovations, electronic banking faces many difficulties regarding its adoption. It is obvious that a technology is considered successful once it is well adopted and therefore there is a need to determine which factors influence the adoption of electronic banking in order to be able to predict or enhance its adoption rate. Factors that are affecting electronic banking adoption has been the subject of several research projects. In United states (Lassar, Manolis and Lassar 2005) & (Kolodinsky, Hogarth and Hilgert 2004), in Europe (Littler and Melanthiou 2006) & (Pikkarainen, et al. 2004), in Australasia (Lichtenstein and Williamson 2006) & (Sathye 1999), in Asia (Yiu, Grant and Edgar 2007) & (Chan and Lu 2004). However, there is limited published work exploring the factors that affect the acceptance of Electronic banking channels from Customer perspective of and in the context of developing countries in the Middle East such as Iran.
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Electronic banking is still in its infancy stage in Iran and its adoption by Iranian customers needs to be researched. Banks are investing huge amount of money to develop several electronic banking channels such as ATMs, Mobile banking and Internet banking and this makes it vital for them to foresee the reasons why customers choose one channel over the other so that they could customize their services for each group of users and offer better services in each channel. Due to the fact that Iranian customers as with any other nation are unique in terms of their cultural and social characteristics, there is a need for a vast investigation of all factors that are effective in customers‘ intention for choosing one channel over the others. Therefore the main research problem of this study is:
How can Influencing factors of Customer’s intention for choosing an Electronic banking channel be characterized?
1.2.1 Research Questions
In this study, we bring together the possible factors that could affect a customer's adoption intention, and then investigate the cause-and-effect relationships among them, so that banks can investigate in these new channels more wisely by utilizing that knowledge. Therefore our research questions are:
What factors will affect user’s intention for acceptance and use of an Electronic Banking channel?
What factors will affect user’s intention for choosing an Electronic Banking channel over the alternatives?
What are the cause-and-effect relationships among factors influencing acceptance and choosing of an Electronic Banking channel?
This study will use hypothesis testing to answer these questions, The proper model for identifying the factors influencing user‘s intention for choosing an electronic banking channel will be selected through the literature review phase.
16 1.3 Disposition of the Thesis
This Thesis consists of six chapters (Figure 2). Chapter One that is already presented, provided brief information on the emergence of e-banking while discussing the existing problem with e- banking adoption, this chapter also stated research problem and identified the preliminary research questions.
Chapter Two of this thesis will exhaustively review the existing literature on the existing models of user‘s adoption of technology in the context of Electronic banking.
Chapter Three will justify the selection of a proper model and identify the frame of reference for this study as well as presenting the main research questions and hypothesis to be tested.
Chapter Four will discuss the research approach, research strategy, data gathering methods and reliability and validity checking and over all the methodology of the research will be presented.
Chapter Five presents portray of the data gathered in the research and its analysis.
Chapter Six will derive conclusion and results. Suggestions for further research in the future will also be presented in this chapter.
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Figure 2 Outline of the Study
Introduction
Literature Review
Frame of reference
Research Methodology
Data Analysis
Findings and Conclusion
18 CHAPTER 2: LITERATURE REVIEW
2. Introduction
In the second chapter of this thesis, a literature background in the information systems science research history will be presented. Since emerging of information systems science, technology acceptance has become a field of interest for many researchers and because of the large number of researches in this area, it is now a mature section of information systems research. (Hu, et al. 1999). In the first section, we will have a look at the theories and models that some are initially developed in different disciplines but then are used in predicting, explaining, and understanding individuals‘ acceptance and adoption of new Information system products or technologies.
Many researchers have tried to determine the behavioral factors that influence the individual to adopt a certain technology. Each using a framework to study the adoption, has identified elements to measure intention to use and behavior. ―Perceived usefulness, enjoyment, perceived risk, security and privacy issues, perceived ease of use, Image, subjective norms, knowledge and information on the related subject‖, are some of the factors mentioned in previous research (Venkatesh, et al. 2003) that are going to be discussed in more detail in this chapter.
“ nanos gigantium humeris insidente ” (Latin)
“Dwarfs standing on the shoulders of giants”
Bernard of Chartres
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The vast research in the information system field has resulted in a number of theoretical models that are attempts to explain user behavior in the context of arrival of a new technology.
Eight IT acceptance theories will be discussed in this thesis , seven of which are recognized by Schneberger and Wade (Schneberger and Mike 2008), Halawi and McCarthy (Halawi and McCarthy 2006) as ―seven major IT theories involving technology acceptance research‖. The last one is a rather ―Technology choosing‖ theory than adoption. These theories in order of appearance in this section are:
Theory of Reasoned Action (TRA) (Fishbein and Ajzen 1975)
Theory of Planned Behavior (I. Ajzen 1991)
Technology Acceptance Model (TAM; adaption of the TRA-model) (F. D. Davis 1986)
Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, et al. 2003)
Task-Technology Fit (TTF) (Goodhue and Thompson 1995)
Diffusion of Innovation Theory in IS (DOI) (Moore and Benbasat 1991)
Cognitive Fit Theory (CFT) (Vessey 1991)
Lazy User Model (LUM) (Collan and Tetard, Lazy User Theory: A Dynamic Model to Understand User Selection of Products and Services. 2009).
Theories that are mentioned above will be discussed in more detail in the next section.
Each of these models has a different approach toward investigating the factors affecting the intention and behavior; however there are some common characteristics among them which will be outlined toward the end of this chapter. A comparison table will also be presented with emphasis on the points of difference between these models.
2.1 Theory of Reasoned Action (TRA)
Fishbein and Ajzen presented the theory of reasoned action in 1975 (Fishbein and Ajzen 1975). TRA is developed in the context of social psychology and is an attempt to explain the effect of attitude on the behavior. Theory aims not only to explain but also to predict behavior considering beliefs, attitude and intention, Three factors that are recognized by the theory as
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main effectors of behavior. According to Fishbein and Ajzen behavioral intention drives behavior and behavioral intention itself is affected by individual‘s attitude toward the behavior and also the subjective norms. The construct of Theory of Reasoned Action is depicted in Figure 3.
Figure 3 The TRA Model
Theory of Reasoned Action considers behavior as direct result of intention to behave, that is driven by the attitude toward the behavior and also subjective norm (Fishbein and Ajzen 1975)
Attitudes toward behavior are the result of several kinds of experiences beliefs that one accumulates through his/her course of life. The beliefs can be of descriptive type that are created by firsthand experience, they can be informational created by information that is collected from an outside source or they can be inferential, that are not gained by direct observations of the individual and they have their roots in descriptive type of beliefs.
People are constantly evaluating and creating beliefs regarding objects in their lives and at the same time, they acquire negative, positive or neutral attitude toward that object. ―In other words, people like objects that are associated with positive things and acquire negative feelings toward object associated with bad things‖ (Fishbein and Ajzen 1975). Individuals develop an inner evaluation system for beliefs regarding the consequences caused by a certain behavior and
The Person‘s belief that the behavior leads to certain outcomes and his/her evaluations of these outcomes
The Person‘s beliefs that specific individuals or groups think he/she should or should not perform the behavior and his/her motivation to comply
Attitude toward the behavior
Relative importance of attitudinal and normative considerations
Subjective Norm
Intention Behavior
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also an assessment of how desirable these consequences are. This is also the case for acquiring positive or negative feelings toward performing a behavior. (Fishbein and Ajzen 1975)
Subjective norms are the result of two distinct factors, normative belief and motivation to comply. Normative beliefs are the assumption of the individual regarding the opinions of other significant individuals on a certain behavior that determines if behavior should be carried out or not. Motivation to comply is the second factor affecting the Subjective norm that defines to what degree an individual is willing to weight the motivation to behavior by expectations and opinions of other significant individuals. In other words, subjective norm is a function of ―the perceived expectations of specific referent individuals or groups, and by the person‘s motivation to comply with those expectations‖ (Fishbein and Ajzen 1975).
Both factors of attitudes and subjective norms affect the individual‘s behavioral intention which determines the strength of intention or the likelihood of behavior in individual‘s point of view. Fishbein and Ajzen have found that behavioral intention predicts actual behavior.
(Fishbein and Ajzen 1975).
Theory of Reasoned Action while being successful to some extent in predicting one‘s behavior also has some limitations such as the possible error in subjective reporting due to the fact that observation cannot be applied to the model. The model is also limited to attitudes and norms only. TRA also assumes that a certain behavior is always consciously thought out before the action is performed. Furthermore, it ignores the fact that some behaviors are not willingly done and are out of an individual‘s control. (Lorig 2001)
2.2 Theory of Planned Behavior (TPB)
The Theory of Planned Behavior (TPB) developed by Ajzen in 1985 (I. Ajzen 1985) is an extension of TRA. In addition to the factors of attitude and subjective norm, Theory of Planned Behavior (figure 4) incorporates an additional construct of perceived behavioral control. It is to address the inability of TRA to account for conditions where individuals do not have total volitional control over their behavior. Perceived behavioral control refers to user‘s internal
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perceptions of the degree to which skills, resources and opportunities are available that may either be inhibiting or facilitating behavior. It addresses both internal control (e.g. a person‘s skills and abilities or self-efficacy) and external constraints (e.g. opportunities and facilities) need to perform behavior. According to TPB, an actual behavior is a function of behavioral intention and perceived behavioral control. Behavioral intention is determined by attitude, subjective norm and perceived behavioral control. It argues that perceived behavior control (the individual's perception of his/her ability to perform the behavior) influences both intentions and behavior. This theory have received substantial empirical support in information systems and many other domains, and its constructs are easier to operationalize as well. (Limayem, Khalifa and Frini 2000).
Figure 4 The TPB Model
Theory of Planned Behavior was augmented by Limayem (Limayem, Khalifa and Frini 2000) with two new constructs; personal innovativeness and perceived consequences. Hence his research model included all the hypothesized links of TPB as well as the new links explored in that research. Limayem hypothesized that personal innovativeness had both direct and indirect effects mediated by attitude, on intentions of innovation adoption. The indirect effect implying that innovative individuals are more likely to be favorable toward online shopping which in turn affects positively their intentions to shop on the internet. The direct link between innovativeness and intentions, on the other hand, meant to capture possible effects that are not completely mediated by attitude. The other new links added to TPB by Limayem (Figure 4) were the ones
Attitude toward Act or Behavior
Subjective Norm
Perceived Behavioral Control
Behavioral
Intention Behavior
Behavioral Beliefs
Normative Beliefs
Control Beliefs
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representing the potential effects of "perceived consequences." This construct was borrowed from Triandis‘ model (Triandis 1980). Lymayem suggested another change to Ajzen‘s TPB model that consolidated all beliefs in to one construct, named perceived consequences.
2.3 Technology Acceptance Model (TAM)
The technology acceptance model (TAM) is developed based on the theory of reasoned action, and it was developed to fit the field of information systems. It was originally specified by Davis in 1986 (F. D. Davis 1986) and later refined by Davis, Bagozzi and Warshaw in 1989 (Davis, Bagozzi and Warshaw 1989). TAM replaces behavioral attitude and subjective norm factors of the TRA with two technology acceptance measures; the perceived ease of use and the perceived usefulness. These two measures have clearly differentiated the TAM from the TRA, although the TAM remains strongly influenced by behavioral elements due to its origin.
Figure 5 The TAM model
TAM models how an individual accepts and uses the technology. As figure 5 shows, actual system use is believed to be determined by behavioral intention of use, which is affected by the attitude toward use and the perceived usefulness of using the new system. An individual‘s attitude toward use of technology is jointly determined by perceived ease of use and perceived usefulness. These two factors are affected by external variables (Davis, Bagozzi and Warshaw 1989). Perceived ease of use is defined by "the degree to which an individual believes that using a particular system would be free of physical and mental effort" (F. D. Davis 1986, 26).
External Variables
Perceived Usefulness (U)
Perceived Ease of Use (E)
Attitude toward Using (A)
Behavioral Intention to Use (BI)
Actual System Use
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Perceived ease of use has a causal and significant effect on the perceived usefulness, which is defined by "the degree to which an individual believes that using a particular system would enhance his or her job performance" (F. D. Davis 1986, 26). TAM assumes that when an individual has formed the intention to act, she will be free to act. However, several factors, such as social or environmental limitations, may affect whether or not the individual will act (Bagozzi 2007)
Several attempts have been made to extend TAM, and the most widely used extended version is known as TAM2. According to (Halawi and McCarthy 2006) TAM2 is used to study end-user acceptance for adoption of information technology systems in a number of different disciplines. TAM2 ―clearly investigates and tackles the role of the end-user when new technology is initiated‖ (Halawi and McCarthy 2006, 254)
TAM theory made an important distinction in identifying the factors ―perceived usefulness‖ and ―perceived ease of use‖. The theory gives attention to the fact that the user has an individual image or estimation of the new technology, which affects the behavior of the user.
TAM focuses on a user‘s attitude toward one specific technology (Davis, Bagozzi and Warshaw 1989)
2.4 Unified Theory of Acceptance and Use of Technology
The unified theory of acceptance and use of technology (UTAUT) in proposed by Venkatesh in 2003 (Venkatesh, et al. 2003). The theory seeks to explain the user intention to use an information system, as well as the subsequent behavior of users. The theory has its background in a number of other theories, which have been combined in an attempt to produce a more complete model of user behavior (User acceptance of information technology: Toward a unified view 2003).
The UTAUT theory holds that there are four main factors determining user behavior and eventually the user acceptance. These four factors are performance expectancy, effort expectancy, social influence and facilitating conditions. The first three constructs create a
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behavioral intention to act and, thus, jointly affect use behavior. The fourth construct, facilitating conditions, does not affect user intentions, but directly influences use behavior. In addition to the four constructs that directly impact use behavior, there are four moderators that indirectly impact behavioral intention and use behavior. The four moderators are gender, age, experience and voluntariness of use. Each moderator impacts one or more of the four constructs (Venkatesh, et al. 2003). The construct of the UTAUT theory is depicted in figure 6.
Figure 6 The UTAUT model
Factors affecting behavioural intention and use behavior (Venkatesh et al 2003, p. 447).
According to Venkatesh (Venkatesh, et al. 2003, 447) the first of the four constructs, performance expectancy, is ―the degree to which an individual believes that using the system will help him or her to attain gains in job performance‖. Performance expectancy is the strongest predictor of user intention. The construct is moderated by gender and age and it depicts that men and especially younger men have more intense effect.
Effort expectancy regards that the ease of use is actually a determinant of the use of a system or service (Venkatesh, et al. 2003) Earlier models capture this concept in perceived ease of use, complexity, and ease of use. The effect of this construct will be most clearly moderated by gender, age and experience, where especially young women at early stages of experience are expected to be affected (Venkatesh, et al. 2003).
Performance Expectancy
Effort Expectancy
Social Influence
Facilitating Conditions
Behavioral Intention
Use Behavior
Gender Age Experience Voluntariness
of Use
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The third construct, social influence, signifies ―the degree to which an individual perceives that important others believe he or she could use the new system‖ (Venkatesh, et al.
2003, 451). The construct holds that an individual is influenced by the way she thinks others will view her having used the particular technology. Social influence is represented (as subjective norm) in six of the theories contributing to the UTAUT. This construct is affected by all four indirect moderators; gender, age, voluntariness and experience, and the most influenced parties will be older women in early stages of learning (Venkatesh, et al. 2003)
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‖ (Venkatesh, et al.
2003, 453). Researchers have found that older workers attach more importance to receiving support than younger workers do, and more experienced workers find support through several channels within an organization. Accordingly, facilitating conditions are moderated by experience and age in particular, which have a significant impact on usage (Venkatesh, et al.
2003, 454).
UTAUT considers aspects of the user‘s characteristics, as well as some conditions at the time of the possibility to use a certain system or service. Furthermore, it considers the degree of voluntariness of the user, which is unmentioned by several other theories. The focus of UTAUT is on using one technology (Venkatesh, et al. 2003).
2.5 Task-Technology Fit (TTF)
The task technology fit (TTF) theory was designed based on two research areas complementing each other when joined; user attitudes as determinant of usage and task- technology fit as performance indicator. The theory states that once the technology fits the tasks that the user must perform can affect individual performance in positive way. (Goodhue and Thompson 1995).
According to (Goodhue and Thompson 1995, 216) TTF is ―the degree to which a technology assists an individual in performing his or her portfolio of tasks‖ and, more
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specifically, ―the correspondence between task requirements, individual abilities and the functionality of the technology‖. In order to measure the task technology fit, four components affecting performance were identified: Task characteristics (non routineness, interdependence and job title), Technology characteristics (measured focusing on the information system used, as well as the department in which they are used), Utilization (the proportion of times users choose to utilize systems, or the perceived dependence on a system), and performance impact (the perceived impact on effectiveness, productivity and performance). The model and its components are depicted in figure 7 (Goodhue and Thompson 1995, 221-223)
Figure 7 The TTF model
According to TTF, a fit between task and technology characteristics leads to improved performance. (Goodhue and Thompson 1995)
The fit between the four performance components can be tested by measuring eight significant factors identified by Goodhue and Thompson. The eight significant factors for measuring task technology fit are ―data quality, locatability of data, authorization to access data, data compatibility between systems, training and ease of use, production timeliness, systems reliability, and relationship with users, all of which are measured using two to ten questions‖
(Goodhue and Thompson 1995, 221). By measuring the eight TTF factors, the compatibility of the components can be established, showing possible weaknesses in the fit. A better fit is expected to create improved performance impacts, and a worse fit leads to poorer performance.
In similarity with several other technology acceptance models, TTF places focus on the fit between performance and task and technology characteristics (Goodhue and Thompson 1995)
Task Characteristics
Technology Characteristics
Task -Technology Fit
Performance Impacts
Utilization
28 2.6 Diffusion of Innovations Theory
The theory of the Diffusion of Innovations was presented by Rogers in 1962 (Rogers 1962), and was designed to apply to most innovations, from food to technology. According to (Rogers 1962, 12), there are ―four crucial elements in the analysis of the diffusion of innovations; the innovation (something that is new to the individual), its communication from one individual to another in a social system (a population of individuals and engaged in collective behavior) over time. In this context, communication is synonymous with diffusion, the process by which an innovation spreads from its source to the ultimate users. Time of the adoption process includes the user stages awareness, interest, evaluation, trial and adoption.
The diffusion of an innovation is impacted by the type of individual faced with the innovation, as well as five factors affecting the user‘s perception of the innovation. An individual‘s willingness to accept an innovation is steered by her characteristics, placing her in one of five categories of individual innovativeness - innovators, early adopters, early majority, late majority or laggards - depending on their willingness to adopt innovations.
The rate of adoption is, in turn, impacted by how the innovation is perceived in terms of
relative advantage – superiority of an innovation to ideas it supersedes
compatibility – consistency of the innovation with existing values and learning of the previous experiences
complexity – relative difficulty of the innovation to understand and use
divisibility – limited trialability of the innovation
communicability – observability of the results of an innovation beinge diffused or communicated to others (Rogers 1962, 124-132).
additionally, the rate of adoption is affected by the initial innovation growth as well as the rate of later growth.
DOI has been adapted to the field of Information Systems (IS) by (Moore and Benbasat 1991) Unlike Rogers, Moore and Benbasat proposed that the rate of adoption was impacted by users‘ perceptions of using an innovation rather than the innovation itself, thus separating
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characteristics of innovations into primary attribute, such as cost price, and secondary attribute, such as the perception of cost (Moore and Benbasat 1991, 194). By focusing on perceived characteristics of using an innovation, Moore and Benbasat found that Rogers‘ original five factors were inadequate, and adjusted them to be more versatile and reliable. Consequently, DOI in IS uses eight factors affecting the adoption of innovations: trialability, relative advantage, compatibility, voluntariness (―the degree to which use of the innovation is perceived as being voluntary‖ (Moore and Benbasat 1991, 194)), image (―the degree to which use of an innovation is perceived to enhance one‘s image or status in one‘s social system‖ (Moore and Benbasat 1991, 194)), ease of use, result demonstrability (―the more the innovation is demonstrated and the more visible the advantages are, the more likely it is to be adopted‖ (Moore and Benbasat 1991, 194)) and visibility (―the actual visibility of the innovation‖ (Moore and Benbasat 1991, 194)) (Moore and Benbasat 1991, 195).
Figure 8 The DOI in IS model
The DOI in IS model. Implementation of IS is mainly dependent on technical compatibility, technical complexity and relative advantage (Schneberger and Mike 2008)
DOI theory has been applied and adopted widely in the field of IS. Three factors in particular, technical compatibility and complexity, as well as relative advantage. These three factors have come to play a significant role in studies regarding acceptance of IS, and their influence on technology acceptance is depicted in figure 8. DOI is one of the oldest theories used
Technical Compatibility
Technical Complexity (Ease of Use)
Relative Advantage (Perceived Need)
IS Implementation
Success (Adoption
Infusion)
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in acceptance research, but is still used today. In DOI, the focus is on one (Rogers 1962) technology and how the use of the specific technology spreads to other users.
2.7 Cognitive Fit Theory (CFT)
Cognitive fit theory (CFT) was developed by Iris Vessey from a general theory of problem solving (Vessey 1991, 220). Cognitive fit proposes that problem solving is ―an outcome of the relationship between problem representation and problem-solving task‖ (Vessey 1991, 220). According to CFT, the solution to a problem is derived from the mental representation, which is formulated from the problem representation and the problem solving task, and the interaction between the two (Vessey 1991, 221)
Vessey proposes that when the same type of information is emphasized by both problem solving processes (task and representation), these processes create a similar mental representation. Consequently, the mental representation also uses similar processes to produce the problem solution. Hence, when the process used to act on the representation and completing the task match, the entire problem solving-process is facilitated. This process is depicted in figure 9. This means that the problem solving performance will be superior to any task where the processes are not facilitated.
Figure 9 The Cognitive Fit Model Problem
Representation
Problem Solving Task
Mental Representation
Problem Solution
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The Cognitive Fit Model. Creating a fit between the problem representation and the problem solving task results in improved performance (Vessey 1991, p. 221).
When the problem representation and the task do not match, the problem solver cannot use similar processes for acting on problem representation and solving the problem. In such cases the problem-solver will not be guided in the choice of problem-solving process, but will be forced to base the mental representation on one of the two problem solving elements. According to (Vessey 1991, 221) the performance in such a case will be worse than in cases where the problem solver is supplied with a representation emphasizing what type of information to use for a particular case.
Galletta and Vessey extended the theory to include problem-solving skill as a factor affecting mental representation of a task. Skill is defined as the procedures which are used to deal with situation as they arise, and skill exists only in the context of a task. Additionally, the terms external (problem) representation internal (mental) representation were introduced, internal representation being ―the way the problem represents the problem in human working memory‖ (Galletta and Vessey 1991, 66-67). The problem maintainer‘s knowledge of software and software development were later also incorporated in the model as factors affecting the mental representation of the software (Shaft and Vessey 2006, 48). Cognitive fit theory focuses mainly on how to create a fit between task and problem representation in order to improve performance. It enables the possibility of testing various technologies to improve the result.
However, the theory does not consider characteristics or the experience of the user, it does not consider the circumstances surrounding the task, nor does the voluntariness of use become clear in the theory.
The overview of theories shows different viewpoints, as well as similarities presented in the theories. In the following chapter, the lazy user model is presented and compared to the six other theories presented.
32 2.8 Lazy user Model (LUM)
Collan in 2007 (M. Collan, Lazy User Behaviour 2007) did come up with the Lazy User model and further developed it later that year with Tetard (Collan and Tetard 2007). The philosophy of the model was to insist on the role of the user in the technology acceptance process where majority of the current popular models such as TRA, DOI, TTF are technology focused.
Lazy User Model takes the needs and characteristics of the user into consideration and even further sees them as the main players in the technology acceptance and perhaps choosing procedure. This model does that by calculating the effort put by the user in the process of choosing a solution out of a pool of all possible alternatives. According to the model user has the tendency to choose the solution that least demand effort. (Collan and Tetard 2007) (Collan and Tetard, Lazy User Theory: A Dynamic Model to Understand User Selection of Products and Services. 2009)
Briefly, the Lazy user model is developed on the basis of one of the most important rules of physics, the path of least resistance. It is also true in the field of informatics and is called the theory of least effort in informatics. This principle can be applied to the problem of solution selection to choose a specific solution out of a pool of solutions that all meet the needs requirements. The outcome of this application can be used to the design of products and services that are most likely to be adopted and chosen.
The Lazy User Model is an answer to the question of how the technology user chooses a solution that fulfills her needs from a pool of possible solutions that they all fulfill the mentioned need. It is obvious that there are two solution sets in the process of choosing, first a set of all possible solutions or the universal set and secondly the set of solutions that are available to the user which are in fact a subset of the universal set. The second set is defined by restrictions (circumstances) of the user. In other words Lazy User Model suggests that from the set of all possible solutions the user automatically applies the path of least resistance and actually selects the solution that is least effort demanding. (Figure 10 The lazy user model)
