FACTORS AFFECTING INTENTION TO USE ONLINE FINANCIAL SERVICES DISSERTATION

Transcription

1 FACTORS AFFECTING INTENTION TO USE ONLINE FINANCIAL SERVICES DISSERTATION Presented in Partial Fulfillment of the Requirement for The Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Jihyun Lee, M.S. * * * * * The Ohio State University 2003 Dissertation Committee: Professor Loren V. Geistfeld, Adviser Professor Jonathan J. Fox Professor Catherine P. Montalto Approved by Adviser College of Human Ecology Department of Consumer and Textile Sciences

2 Copyright by Jihyun Lee 2003

3 ABSTRACT The primary purpose of this study was to identify determinants affecting consumers intention to use online financial services. The effects of attitude toward a behavior, subjective norm, and perceived behavioral control variables on the intention to use online financial services were examined. Demographic control variables were included as control variables. The conceptual framework underlying the study was based on the Theory of Planned Behavior. This theory suggests that attitude toward a behavior, subjective norm, and perceived behavioral control affect behavioral intention to engage in a behavior. Behavioral intention, then, leads to engaging in a behavior. Data came from the MacroMonitor Survey. The study sample consists of 3,780 households completing a mail survey between May and August of This data set includes information about consumer attitudes, behaviors and motivations regarding financial products, services, delivery methods, and institutional use. Factor analysis was used to reduce the number of independent variables. Logistic regression analysis was used to examine the effect of the independent variables on the probability of the intention to use online financial services. ii

4 The findings based on five different dependent measures of online financial service uses revealed that the seven variables consistently affect intention to use online financial services: satisfaction with finances, positive attitude toward credit market, professional advice unneeded, personal contact desired, one-on-one interaction unneeded, education, and prefer less complex financial strategies. Individuals dissatisfied with their financial situations were more likely to intend to use online financial services. Consumers who had positive attitudes toward credit markets had a greater probability of intention to use online financial services. Individuals with preferences for professional advice were more likely to use online financial services. Consumers having lower preferences for personal contact had a higher likelihood of intention to use online financial services. Individuals lacking a need for one-on-one interaction were more likely to intend to use online financial services. Consumers preferring complex financial strategies were more likely to intend to adopt online financial services. An important implication of this study is that individuals intending to use online financial services seek professional information using a non-personal medium to improve their financial situation. However, this raises an equally important issue in that the quality of information received through online financial services needs to be considered since inaccurate and incomplete information may lead to undesired outcomes. iii

5 Dedicated to my parents and my husband iv

6 ACKNOWLEDGMENTS I would like to express my deepest gratitude to my advisor, Dr. Loren V. Geistfeld, for his encouragement, support and patience through my entire graduate school in the U.S.A. His guidance and valuable advice enabled me to finish this dissertation. My gratitude also goes to my committee members, Dr. Jonathan Fox and Dr. Catherine P. Montalto, for their intuitive suggestions and invaluable comments through all stages of this dissertation. I would like to thank the Department of Consumer & Textile Sciences for providing financial support during my Ph.D. study at The Ohio State University. I extend my appreciation to Dr. Sherman D. Hanna, Dr. Kathryn Stafford, and fellow graduate students in my department for their help and support. Sincere appreciation is extended to my parents, two sisters, and a brother who shared my joys and sorrows in graduate school life with me. Special thanks go to my parents who have provided continuous love and encouragement for me. My appreciation also goes to my parent-in-laws for their support and understanding. I would like to express appreciation to my grandmother for her daily early morning prayers for me. I also thank my sister, Jung-Eun Lee, for v

7 taking care of my family for a long time. My special thanks go to dear Susie and Michael. I am proud to be your mother. To my husband, Tae-Hoon Kim, I would like to express my heartfelt gratitude for his love, endless support, and willingness to endure with me. vi

8 VITA November 13, 1968 Born Busan, Korea B.S., Economics, Busan National University, Busan, Korea Research Assistant, Department of Economics, Busan National University, Busan, Korea M.S. Student, Department of Economics, The Ohio State University, Columbus, Ohio 1999 M.S., Family Resource Management, The Ohio State University, Columbus, Ohio 1997 present Graduate Teaching and Research Associate, Consumer and Textile Sciences, The Ohio State University, Columbus, Ohio FIELD OF STUDY Major Field: Human Ecology, Consumer Science Support Field: Economics vii

9 TABLE OF CONTENTS Page Abstract...ii Dedication..iv Acknowledgements...v Vita.vii List of Tables.xi List of Figures..xiii Chapters: 1. Introduction Background of the Study Importance of the Study Objectives of the Study Outline of the Study Theoretical Background & Literature Review Technology Acceptance Model (TAM) Overview Key Elements of the Technology Acceptance Model Task-Technology Fit Model (TTF) Overview Task-technology fit Performance Task Characteristics Individual Characteristics Technology Characteristics..23 viii

10 2.3 The Theory of Planned Behavior (TPB) Overview Key Elements of the Theory of Planned Behavior Discussion of Theories A Conceptual Model of Intention to Use Online Financial Services Determinants of the Conceptual Model Hypotheses Attitude Toward a Behavior Subjective Norm Perceived Behavioral Control Control Variables Summary of Hypotheses Methods Data Source Sample Description of Dependent Variables Description of Independent Variables Attitude Subjective Norm Perceived Behavioral Control Demographic Control Variables Variable Reduction Procedures: Factor Analysis Missing Data Descriptive Analyses Comparing Mean Values Comparing Distributions Multivariate Analysis Logistic Regression Interpretation of Logistic Regression General Model Testing and Identification of Independent Variables Results Factor Analysis The Procedure The Results Linking Factor Analysis Concept Groups to TPB Descriptive Analysis Comparing Intended Users to Intended Non-Users Results of Multivariate Analyses 112 ix

11 4.3.1 Multicollinearity Missing Values Variables Results of Logistic Analyses Role of TPB Blocks of Variables Factors Affecting Intention Discussion of Findings Attitude Toward Behavior Subjective Norm Perceived Behavioral Control Summary, Limitations and Implication Summary Implications Marketing Consumers Financial Planner Conclusion Limitations Suggestions for Future Research..148 Bibliography Appendices 163 A. SPSS Syntax 163 B. Lists of Possible Responses..170 C. Descriptive Statistics for Current Users and Non-Users D. Logistic Regression Before Missing Data Imputation & VIF.179 E. Logistic Regression Results for Four Uses of Online Financial Services..184 F. Peason s Correlation Matrix x

12 LIST OF TABLES Table Page 3.1 A summary of dependent variables A summary of independent variables Summary of number of missing value and imputation Attitude and knowledge questions: Factor analysis Personal interaction questions: Factor analysis Financial planning questions: Factor analysis Frequency of current users and non-users for specific use of online financial services Demographic control variables of intended users and intended non-users of online financial services Attitude variables (intended users compared to intended non-users) Subjective norm variables (intended users compared to intended nonusers) Perceived behavioral control variables (intended users compared to intended non-users) A summary description of the study variables (sample = 3143) Independent variable groups and intention for general use of online financial services..123 xi

13 4.11 Significance of variable blocks for the four types of online financial services Odds ratios for five uses of online financial services Variables significantly affecting the likelihood of intended use of online financial services..139 C.1 Demographic control variables (current users compared to nonusers)..174 C.2 Attitude variables (current users compared to non-users).175 C.3 Subjective norm variables (current users compared to non-users).176 C.4 Perceived behavioral control variables (current users compared to nonusers)..178 D.1 Logistic regression: Intended users of online financial services (1 = intended users, 0 = Intended non-users)..180 D.2 The results of collinearity statistics in linear regression: Tolerance, VIF, Eigenvalue, condition indice (1 = intended users, 0 = intended nonusers)..182 E.1 Independent variable groups and intention for account management uses 185 E.2 Independent variable groups and intention for loan uses..187 E.3 Independent variable groups and intention for investment uses..189 E.4 Independent variable groups and intention for insurance uses 191 xii

14 LIST OF FIGURES Figure Page 2.1 Original Technology Acceptance Model Task-Technology Fit Model Theory of Reasoned Action Theory of Planned Behavior Conceptual model of technology adoption based on the Theory of Planned Behavior...32 F.1 Pearson s Correlation Coefficient xiii

15 CHAPTER 1 INTRODUCTION 1.1 Background of the Study Use of information technology (IT) products 1 has grown rapidly throughout the world. The Internet facilitates linking and accessing many IT products. However, resistance to IT innovations exists even though people realize that not using IT innovations can place them at a disadvantage in both their working and personal lives. This suggests a need to identify factors associated with the reluctance to adopt IT innovations. Once these factors are known it may be possible to help people overcome their reluctance to use new information technologies. 1 Personal computers, cellular phones, fax machines, pagers, modem, etc. 1

16 Electronic banking as an IT is not new. Wire transfers are almost as old as the telegraph (Garbade & Silber 2, 1978). The first commercial use of the telephone was by two bankers to check balances in the 19 th century (Brooks, 1975). FedWire funds transfer 3 began shortly after the establishment of the Federal Reserve system and the Clearing House Interbank Payment System (CHIPS) 4 was started in In addition, bank credit cards have been in existence for about 40 years, and automated teller machines (ATMs) have been in place for over 30 years. Even though the concept of electronic banking is not new, the emerging electronic banking technologies in the 1990s are different from previous innovations. New technologies in banking involve banks retail transactions and contacts with customers so that these innovations have the potential to increase efficiency and generate cost-saving for banks and consumers. Contemporary banking and online financial services have emerged by combining the Internet with financial management (Bank Marketing, 2000). The use of electronic banking (or online financial services) has rapidly grown in the U.S. In 1999, 85 percent of households had at least one Electronic Fund Transfer (EFT) on their accounts; the number of Automated Teller Machine (ATM) 2 They described that an early use of the telegraph was to transmit financial price information and thus to facilitate arbitrage. 3 The Fedwire funds transfer is a real-time gross settlement system that the Federal Reserve Bank uses to send payments to, or receive payments from, other account holders. Now the Fedwire funds transfer uses either a mainframe or PC connection and telephone from 12:30 am to 6:30 pm eastern time, Monday through Friday. 4 CHIPS is a bank-owned, privately operated real-time, final settlement electronic payments system for business-to-business and inter-bank transactions in U.S. dollars. 2

17 transactions was 907 million per month; the number of point of sale transactions was 202 million a month; and 7 million U.S. households used online financial services (Business Week, 2000). In addition, transferring funds between accounts has increased with the use of online financial services. The largest account-to-account transfer services are Bank One s emoneymail and epay, and Well Fargo s Billpoint and PayPal (Janik, 2000; Business Week, 2002). Twentytwo percent of American households have given up paper checking for online financial services (Bank Marketing, 2002). Some banks reported a 20% increase in online banking enrollment between September and November 2001 (Bank Marketing, 2002). Factors encouraging increased use of online financial services are the greater convenience and reduced cost of online financial services. Individuals benefit from 24 hours/7days access to their accounts and customer services from home or anywhere with computers. Banks or financial service providers realize reduced costs associated with account maintenance and customer service. The following innovations are three examples of recent IT based changes in electronic banking and online financial services. Electronic bill-paying is a system involving a personal computer (PC) and a modem, or a smart telephone and a screen, or an interactive TV system, used by individuals to pay bills electronically. Electronic bill-paying substitutes electronic transfers for check writing and mailing. 3

18 Home banking is a system that involves direct online connections as well as connection through the Internet between an individual and a bank. It encompasses a wide range of transactions including bill-paying, balance inquiries, transfers among bank accounts, the purchase and sale of financial instruments, and applications for a loan or mortgage. Stored-value cards and smart cards are cards with information encoded on a magnetic strip or a microchip. This information can be read by specially designed readers. An institution creates liabilities on itself by issuing cards with encoded values that can then be used as payments via a card reader in subsequent transactions. This includes disposable cards that may be used for limited purposes (e.g., phone calls) as well as reusable forms of stored-value cards. About half of all households have used electronic bill payment as an online financial service (Snel, 2000), and this proportion is not expected to rise much (Morris, 2000). For other online financial services, demand has not been large either. A 1998 Forrest Research survey found that only 10% of the 120,000 respondents said they were likely or extremely likely to use online financial services (Snel, 2000). A possible cause of consumer reluctance is concern with the safety and security of online banking (Giglio, 2001). The slow adoption of online financial services results from technophobia, fear of the unfamiliar, persistence of the paper check and significant costs associated with establishing 4

19 an electronic bank system or network (Katz & Shapiro, 1994; Besen & Farrell, 1994; Liebowitz & Margolis, 1994; White, 1999). 1.2 Importance of the Study Many people hesitate to use online financial services for a variety of reasons. This reluctance results in inconvenience associated with writing and mailing checks, spending time to stop at a branch and consulting to get financial information with bankers. On the other hand, by using online financial services, people can conduct fast and convenient financial transaction activities and obtain their account information without the limitation of office hours and a need to visit an office. It is important to understand what factors affect the adoption of online financial services in order to facilitate household use of information technological products (online financial services) through computers or the Internet. This study will identify variables (demographic control, attitudes, subjective norm, and perceived behavioral control variables) influencing the adoption of online financial services by households. It will be meaningful for financial institutions to understand households acceptance and preferences regarding online financial services. Moreover, it will help policy makers develop policies to improve consumers decision-making abilities as they adopt online financial services. 5

20 1.3 Objectives of the Study The purpose of the study is to examine household adoption of online financial services. Online financial services refer to all financial activities using computers such as making transfers between accounts; inquiring about account balances; opening/closing checking/saving accounts; buying or selling mutual funds, stocks, and bonds; managing investment accounts and so on. The primary objective is to identify those factors influencing households intention to adopt online financial services: demographic control variables, attitudes variables, subjective norm variables, and perceived behavioral control variables. 1.4 Outline of the Study Chapter 2 presents theoretical background related to technology adoption, factors affecting technology adoption, and the research hypotheses. Chapter 3 examines the data source, the dependent and independent variables, and the statistical methods used in this study. Chapter 4 focuses on the findings and a discussion of the findings. Chapter 5 concludes the dissertation with a summary, a discussion of implications, and limitations of this study. 6

21 CHAPTER 2 CONCEPTUAL MODEL, RELATED RESEARCH AND HYPOTHESES This chapter presents an overview of the Technology Acceptance Model, the Task-Technology Fit Model, and the Theory of Planned Behavior. A conceptual model is presented that provides a framework for this study. Hypotheses are also presented. 2.1 Technology Acceptance Model (TAM) Overview The Technology Acceptance Model (TAM), introduced by Davis (1986), is an adaptation of the Theory of Reasoned Action (TRA) specifically modified for modeling user acceptance of information technology (IT) (Davis, 1986; Davis, 1989; Davis et al., 1989). 7

22 Davis (1986) stated that the main goal of TAM is to explain the determinants of IT acceptance across a broad range of information technologies and user populations. Moreover, Davis suggested that acceptance of IT can be determined by two primary constructs: perceived usefulness and perceived ease of use of the technology. TAM (Davis et al., 1989) is summarized in Figure 2.1. As can be seen, TAM posits that IT use is determined by the behavioral intention to use IT. The behavioral intention is affected by an individual s attitude toward using IT and perceived usefulness. An individual s attitudes are a joint function of perceived usefulness and perceived ease of use. Finally, perceived usefulness is determined by perceived ease of use as well as external variables, while perceived ease of use is influenced only by external variables. When predicting the acceptance of information technologies, TAM suggests the following factors are important: external variables; beliefs about information technology (perceived usefulness and perceived ease of use); attitudes; behavioral intention; and finally, actual IT use. Since the original work of Davis (1986), numerous studies have validated TAM in a variety of field settings and across a broad range of IT applications: e- mail or voice mail (Adams et al., 1992; Davis, 1989; Gefen & Straub, 1997; Keil et al., 1995; Rose & Straub, 1998; Straub et al., 1995; Venkatesh & Davis, 1994), spreadsheets (Adams et al., 1992; Hendrickson et al., 1993; Mathieson, 1991), word processing (Adams et al., 1992; Davis et al., 1989), databases 8

23 (Hendrickson et al., 1993; Szajna, 1994), microcomputer usage (Igbaria et al., 1996; Igbaria et al., 1997), FAX (Straub, 1994), and expert systems (Keil et al., 1995). TAM has also been examined across cultures (Straub, 1994; Gefen & Straub, 1997; Rose & Straub, 1998). Some studies also focused on TAM related measurement scales. Adams et al. (1992) examined the psychometric properties of the perceived usefulness and perceived ease of use scales to insure valid measurement of these scales. Hendrickson et al. (1993) assessed the reliability of perceived usefulness and perceived ease of use by investigating user acceptance of two software packages. The reliability and validity of the measurement scales for TAM were also examined by Segars & Grover (1993). Throughout the body of TAM research, perceived usefulness and ease of use were found to be strong determinants and predictors of behavioral intention with behavioral intention being linked to IT use. TAM has successfully explained about 35% of the variance in behavioral intention to use IT. 9

24 Perceived Usefulness External Variables Attitude toward Use Behavioral Intention to Use Actual Use Perceived Ease of Use Figure 2.1: Original Technology Acceptance Model (Davis et al., 1989) Key Elements of the Technology Acceptance Model (TAM) External Variables External variables directly influence perceived usefulness and perceived ease of use. Perceived ease of use is affected by external variable relating to system features that enhance IT usability such as menus, icons, mouse, and touch screen. In addition, training and user support consultants also affect perceived ease of use. The more training users receive, the higher the level of perceived ease of use. Perceived usefulness is also affected by external variables. For example, consider two information technologies that are equally easy to use. If one of them 10

25 causes fewer errors, it would likely be seen as the more useful information technology. Objective IT system design characteristics have a direct effect on perceived usefulness in addition to indirect effects via perceived ease of use. According to Davis et al. (1989), even though external variables do not have a direct influence on attitudes and behavioral intention to use, TAM underlies the bridge role of beliefs and attitudes between external variables and behavioral intention. This occurs through individual differences (e.g., individual preference or personality) and situational constraints (e.g., physical disability). Davis et al. (1989) also indicated that such effects would only be exhibited indirectly through their relationship with the two beliefs (perceived usefulness and perceived ease of use) (Davis et al., 1989) Perceived Usefulness and Perceived Ease of Use According to Davis (1986, p.82), perceived usefulness can be defined as the degree to which an individual believes subjectively that using a particular IT would enhance his or her job performance. In other words, the individual believes that the use of the IT would yield positive benefits for task performance associated with his/her job. Perceived ease of use reflects the degree to which an individual believes that using a particular IT would be free of effort, both physical and mental (Davis, 1986, p.82). Davis argued that all others things 11

26 being equal, an IT perceived to be easier to use than another is more likely to be accepted by the individual. The constructs, perceived usefulness and perceived ease of use, have been extensively investigated by researchers. These studies generally confirmed that perceived usefulness and perceived ease of use are important factors in affecting IT use (Adams et al., 1992; Davis, 1989; Davis et al., 1989; Hendrickson et al., 1993; Keil et al., 1995; Mathieson, 1991; Straub et al., 1995; Szajna, 1994; Venkatesh & Davis, 1994). Perceived usefulness suggests a user believes that using a particular IT will be beneficial. For the user to hold such a belief several conditions must be met. First, the user must have prior experience with the particular problem suggesting at least some understanding of the nature of the problem, even if the problem is not yet understood sufficiently to derive a solution. Generally, the user must also have experience with information technologies. This experience gives the user a basis for evaluating the capabilities of information technologies and how and in what circumstances they may be useful. In the formation of initial opinions, the user will not have much hands-on experience, but may know of the capabilities of information technologies through the media (e.g., television, newspaper) or other communication channels (e.g., friends). Perceived ease of use has both a direct effect and an indirect effect on attitude toward using. Perceived ease of use is determined, at least in part, by prior experience in the use of IT as well as by the amount of training received by 12

27 the user. Previous experience and training increase an individual s ability to use IT. For example, if an individual feels self-confident from prior experience with a particular IT, the individual will have a positive attitude toward the IT. This is the direct effect of perceived ease of use on attitudes. Davis (1986) also suggests a relationship between perceived ease of use and perceived usefulness. An increase in perceived ease of use may contribute to improved performance. Effort saved due to increased perceived ease of use may allow an individual to accomplish more work for the same effort (Davis et al., 1989). Research shows that the two beliefs (perceived usefulness and perceived ease of use) are highly correlated but distinct. Perceived usefulness is related to IT use, while perceived ease of use is less important in predicting IT use (Adams et al., 1992; Davis, 1989; Davis et al, 1989; Keil et al., 1995; Mathieson, 1991; Straub et al., 1995; Szajna, 1994). Adams et al. (1992) suggests that perceived ease of use may be an antecedent to perceived usefulness, rather than a parallel, direct determinant of behavioral intention to use. Davis et al. (1989) suggests that perceived usefulness is a major determinant, and perceived ease of use is a secondary determinant, of behavioral intention to use. 13

28 Attitude toward Using According to Schiffman and Kanuk (1997, p ), attitude is a learned predisposition to behave in a consistently favorable or unfavorable way with respect to a given object. For example, in the case of attitude toward computers, the given object is a computer. Moreover, attitudes can be learned through purchasing behavior, direct experience with the product, information acquired from others, and exposure to mass media advertising. In addition, attitudes are relatively consistent with the associated consumer behavior. However, attitudes are not permanent; they do change. In the context of TAM, Davis (1986, p.25) defined attitude as an individual s degree of evaluative affect toward the usage behavior. As mentioned before, attitude toward using is jointly determined by the two beliefs (perceived usefulness and ease of use) (Adams et al., 1992; Davis, 1986; 1989; Davis et al., 1989; Hendrickson et al., 1993; Keil et al., 1995; Mathieson, 1991; Straub et al., 1995; Szajna, 1994; Venkatesh & Davis, 1994). An individual s attitude toward using is a key determinant of intention to actual use Behavioral Intention to Use According to Davis (1986, p.28), behavioral intention reflects the strength of the prospective user s intention to make or to support the usage decision in 14

29 their mind. Behavioral intention is jointly determined by attitudes and perceived usefulness. The relationship between attitudes and behavioral intention implies that, all else being equal, individuals with positive attitudes will intend to perform the behavior (Adams et al., 1992; Davis, 1986; 1989; Davis et al., 1989; Davis & Venkatesh, 1996; Mathieson, 1991; Szajna, 1994; Taylor & Todd, 1995). In addition, perceived usefulness directly influences behavioral intention. For example, even though an individual may dislike a particular IT, the individual may still use the IT if it has high level of perceived usefulness, regardless of the individual s overall attitude toward the IT. Behavioral intention to use determines IT use (Adams et al., 1992; Davis, 1986; 1989; Davis et al., 1989; Davis & Venkatesh, 1996; Mathieson, 1991; Szajna, 1994; Taylor & Todd, 1995). Adams et al. (1992) described two studies that replicate work by Davis. The first study investigates the relationship between perceived usefulness, perceived ease of use, and system use for both voic and . Usage was measured by asking respondents about the number of messages sent and received the previous working day and the number sent and received on a typical day. These two measures were highly correlated. Findings of this study indicate that perceived usefulness is related to usage, perceived ease of use is less important in predicting use. In the second study, they investigated usage patterns for WordPerfect, Lotus 1-2-3, and Harvard Graphics. Usage was assessed by two self-reported measures. These measures of system use were statistically correlated for the three packages. Adams et al. (1992) found that both perceived 15

30 usefulness and perceived ease of use are important determinants of system usage. User acceptance of computer systems is driven to a large extent by perceived usefulness (Adams et al.,1992; Davis et al.,1989; Straub et al.,1995; Szajna, 1996). Other studies have also reported that perceived usefulness is positively associated with system usage (Igbaria et al., 1997). Mathieson (1991) and Szajna(1996) each reported that perceived ease of use explains a significant amount of the variance in perceived usefulness. Straub et al. (1995) used TAM to compare self-reported and computer monitored voice mail use in a field setting; their focus was on finding appropriate measures of usage rather than a test of TAM. Szajna (1996) found that a revised TAM, dropping attitudes from the model and making a slight change for preversus post-implementation, predicted use, but that adding a variable to account for experience with the technology would be a worthwhile extension of the model. He suggested that measures of actual use may work better than self-reported measures, at least when studying the use of . Venkatesh & Davis (1996) extended TAM to include external variables that might predict perceived usefulness and perceived ease of use. They found that an objective measure of system usability had an impact on perceptions only after direct experience with the system. Jackson et al. (1997) noted that behavioral intention depends on the nature of the organization to which a user belongs, extending the model to include constructs such as user involvement. Their results 16

31 suggest that involvement needs to be broken into psychological and participative components to understand its impact on systems development. Igbaria et al. (1997) used an extended version of TAM to study personal computer use in small businesses in New Zealand. They added external factors related to support and training from within and outside the organization. Their results supported TAM and the extensions. 2.2 Task-Technology Fit Model (TTF) Overview The Task-Technology Fit Model (TTF) is a theoretical foundation for studying the fit between task and technology, and individual performance (Goodhue, 1988, 1995, 1997; Goodhue & Thompson, 1995). The TTF is summarized in Figure 2.2. Individual performance reflects an individual s ability to perform tasks using information technologies (ITs). An underlying assumption of TTF is that an IT to be applied to a problem is mandated by an organization to which a person belongs. Individuals will use the IT and then evaluate it. The strongest link between IT and performance comes from the relationship between task needs and task-technology fit. As task needs change, the appropriate IT will also change. The goal of TTF is to explain how well a technology fits the task, and how well a technology fits the abilities of the individuals engaged in the task. These combine to give task- technology fit. 17

32 The TTF model suggests that task characteristics, individual characteristics, and technology characteristics combine to lead to the adoption of a technology (Goodhue, 1988). Task characteristics and individual characteristics will moderate the strength of the link between specific IT characteristics and individuals evaluations of an IT (Goodhue, 1995, p.1830). All other things being equal, changes to the technology characteristics along the lines needed by the user for the tasks at hand should improve task-technology fit. Likewise, changes in tasks that result in the user making greater demands on the technology characteristics should decrease task-technology fit. Task- technology fit could be increased by improving the technology characteristics to better meet the task needs. Finally, the fit between a task and a technology affects individual performance Task-technology fit The TTF is helpful when trying to understand the impact of technology on performance (Goodhue, 1988, 1995, 1997; Goodhue & Thompson, 1995). Task- Technology Fit is the degree to which an information technology or a technology system environment assists an individual in performing his or her portfolio of tasks (Goodhue, 1988, p.48). More specifically, it is the fit among task requirements, individual abilities (or needs), and the functionality and interface of the technology. 18

33 Goodhue (1995) identified experience as an important moderating element in task-technology fit. Experience can affect performance through technology characteristics and task characteristics. Experience with technology characteristics provides an understanding of the capabilities of an IT in actual performance. The greater the level of experience the more likely an IT will be used for an appropriate task. Experience is actually a proxy for knowledge of IT capabilities. The assumption (Goodhue, 1995) is made that knowledge is obtained by prior use in actual performance. Prior experience with task characteristics reflects experience with the IT. This type of experience is understood to moderate the relationship between task demands and fit. The higher the amount of experience with a particular IT, the lower the expected performance. If an individual has a lot of experience with an IT, the individual will have lower need to maintain the condition of the IT Performance Performance results from the combination of the three elements (task, individuals, and technology characteristics) into task-technology fit (Goodhue, 1988, 1995, 1997; Goodhue & Thompson, 1995). Performance in Figure 2.2 is the accomplishment of a task, or a portfolio of tasks, by an individual. To achieve higher levels of performance, individuals need to save time or effort or both (efficiency and effectiveness). 19

34 Task-technology fit affects individual performance. High task-technology fit increases the likelihood of improved individual performance due to the IT. This is because greater task-technology fit means the technology more closely meets the task needs of the individual. Task Characteristics Individual Characteristics Technology Characteristics Task- Technology Fit Performance Impacts Figure 2.2: Task-Technology Fit Model (TTF): Goodhue, D.L. (1988) Task Characteristics A task, in the task-technology fit literature, is defined as an activity to be accomplished by a knowledge worker (Goodhue, 1988, p.44). A task can relate 20

35 to problem-solving such as auditing or software maintenance (Dishaw & Strong, 1998) or can be associated with decision-making (Goodhue, 1995). Relevant task characteristics include those that might move a user to rely more heavily on certain aspects of an information technology. Goodhue (1988, 1995) characterized tasks using a three dimensional construct of task characteristics: variety or difficulty, interdependence, and handson. Variety and difficulty is divided into routine and non-routine (Goodhue, 1995). Individuals who deal with routine will, over time, develop ways to work around weaknesses in the way an IT supports those tasks. On the other hand, individuals dealing with many non-routine situations may need to evaluate how a particular IT fits a task. These individuals may be frustrated by difficulties encountered by identifying unfamiliar tasks and determining how to apply IT to it. The concept of interdependence relates to the relationship between an individual and an organizational unit to which an individual belongs. Individuals belonging to an organizational unit and having some assigned tasks, need to identify, access, and integrate tasks for fulfilling their tasks from a variety of ITs (Goodhue, 1995). Such individuals are more likely to use an IT for their tasks. As a result, individuals will be frustrated by incompatibilities in some tasks and access routines for these different ITs. The more interdependent the organization s tasks and an individual s tasks are, the more likely the individual will be frustrated by these incompatibilities. Thus, incompatibilities individuals feel may negatively affect individual performance. 21

36 Hands-on means that individuals using multiple ITs will have more flexibility to meet their other needs, but also face confusing access routines making a task potentially more difficult (Goodhue, 1995). These individuals are not insulated from the complexity and difficulty of the IT, and all other things equal, may be more aware of its shortcomings than those who don t deal directly with the IT Individual Characteristics Individual characteristics are a moderating variable affecting both task and technology characteristics (Goodhue, 1988). Characteristics of the individual (e.g., demographic characteristics, attitude toward IT, prior experience, and IT literacy) affect how easily and well a consumer utilizes the technology. Prior experience or familiarity with a given IT has a positive association with IT use (Goodhue, 1995). Familiarity with similar tasks and the capabilities of the technology are posited to moderate the task-technology fit relationships through task and technology characteristics. The difficulty of a given task depends on the abilities of an individual. Individuals who are more competent, better trained, or more familiar with an IT will be better able to identify, access, and solve tasks. 22

37 2.2.6 Technology Characteristics Technology characteristics are those elements of a technology used by individuals in carrying out tasks. In the task-technology fit literature, technology characteristics reflect a wide range of information technologies, such as hardware, software, and computer programming languages or any combination of these (Goodhue & Thompson, 1995). For example, hardware technology characteristics include floppy drive, hard drive, CD ROM drive, color monitor, mouse control, printer, modem, fax, joystick control, scanner, zip drive/tape backup, and Internet. Software and programming languages technology characteristics include MS-DOS, Unix, etc. Technology characteristics provide the technological environment which influences task-technology fit (Goodhue, 1988, 1995). When an individual accomplishes tasks with an IT, technology characteristics provide the individual with a given technology environment, which affect use of the IT through the degree of task-technology fit. 23

38 2.3 The Theory of Planned Behavior (TPB) Overview The Theory of planned behavior (TPB) is an extension of the Theory of Reasoned Action (TRA)(Fishbein & Ajzen, 1975), which is widely used in social psychology and marketing studies to explain the determinants of intended behaviors (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975). Both the TRA and TPB suggest that behavior is directly influenced by behavioral intention. According to the TRA (Figure 2.3), an actual behavior is determined by behavioral intention to perform the behavior, and the behavioral intention is jointly determined by the attitude toward the behavior and the subjective norm (i.e., perceived social influence of important people to individuals) (Fishbein & Ajzen, 1975). TPB (Ajzen, 1991, 1992; Taylor & Todd, 1995) is shown in Figure 2.4. The TPB also postulates that behavioral intention is influenced by attitude toward the behavior and subjective norm. However, the TPB model adds perceived behavioral control to the Theory of Reasoned Action (TRA). TPB (Ajzen, 1991) suggests that three key elements, attitude toward the behavior, subjective norm, and perceived behavioral control, determine a behavioral intention. The first is the attitude toward the behavior and refers to the degree to which a person has a favorable or unfavorable evaluation of the specified behavior (Ajzen, 1991; Fishbein & Ajzen, 1975). The second relates to the perceived social pressure to 24

39 perform or not to perform the behavior. The third relates to the perceived ease or difficulty of performing the behavior Key Elements of the Theory of Planned Behavior (TPB) Beliefs and Attitudes TPB postulates that attitude toward the behavior refers to the degree to which people have a positive or negative feeling toward the behavior. Fishbein and Ajzen (1975) suggested that attitudes are determined by the beliefs people have about the object of the attitude and beliefs are formed by the characteristics of the attitude object. Ajzen (1991) also stated that individuals positive or negative attitudes depend on desirable or undesirable expected outcomes or results that are associated with an object. For example, people have a positive attitude toward online financial services when they believe that online financial services are a convenient technology for dealing with financial activities Normative Beliefs and Subjective Norm Subjective norms are influenced by the normative beliefs that refer to the perceived social pressure to perform or not to perform the behavior (Ajzen, 1991; Fishbein & Ajzen, 1975). Normative belief might be related to the influence of opinion among social groups such as family and friends. Much research (Ajzen, 25

40 1991; Fishbein & Ajzen, 1975; Lee & Green, 1991; Mathieson, 1991) reported that the opinion or interaction with social groups such as family or friends influences consumer decision making Control Belief and Perceived Behavioral Control According to Ajzen (1991), perceived behavioral control reflects beliefs regarding access to the resources needed to perform a behavior. There are two components affecting perceived behavioral control. The first element is facilitating conditions which reflect the availability of resources needed to perform a behavior. This might include access to the time, money, skills and other specialized resources required to perform a behavior. The second element is self-efficacy. It is an individual s self-confidence in his/her ability to perform a behavior. Taylor and Todd (1995b) suggest that resources (i.e., time, money) and the individuals self-efficacy are important elements affecting behavioral intention and actual technology use. According to Ajzen (1991) and Madden et al. (1992), when individuals believe that they have more resources, they believe they have fewer obstacles and perceive greater control over the behavior, while people lacking requisite resources and confidence perceive little control over the behavior thereby reducing intentions to perform the behavior. 26

41 Beliefs and Evaluations Normative Beliefs and Motivation to comply Attitude toward Behavior Subjective Norm Behavioral Intention Actual Behavior Figure 2.3: Theory of Reasoned Action (TRA) -- Ajzen, I. and M. Fishbein (1980). Beliefs and Evaluations Attitude toward Behavior Normative Beliefs and Motivation to comply Subjective Norm Behavioral Intention Usage Behavior Control Beliefs and Perceived facilitation Perceived Behavioral Control Figure 2.4: Theory of Planned Behavior (TPB) Taylor and Todd (1995). 27

42 2.4 Discussion of Theories There has been a steady flow of research on the acceptance and use of information technology (IT). First of all, the Technology Acceptance Model (TAM) is widely regarded as a good theoretical model for explaining IT use. TAM is useful for predicting whether users will adopt new information technologies. From the results of the many studies based on TAM, perceived usefulness and perceived ease of use have been found to be important determinants of behavioral intention and behavioral intention has been related to IT use. Thus, TAM can be easily applied to different situations across a range of technologies; furthermore, TAM can explain well the determinants of IT acceptance. It is important to recognize, however, that TAM provides the answer of yes or no for the acceptance of IT, but not the extent or degree of IT use (e.g., performance). That is to say that a weakness of TAM is a lack of task or performance for IT utilization. Information technology is a tool by which users accomplish their tasks (e.g., communication using system and writing a paper using word processor). Thus, the lack of task or performance in evaluation of IT and its acceptance lead to mixed results in IT evaluations in many empirical studies based on TAM. Only one element, the concept of perceived usefulness in TAM, implicitly includes the task concept, that is to say usefulness means useful for something. More explicit inclusion of task characteristics may provide a better model of IT utilization. Moreover, little research has actually focused on 28

43 determining whether TAM mediates the effect of experience on attitudes and behavioral intention. A key source of information people use to form the two beliefs (perceived usefulness and perceived ease of use) is their past performance in similar situations. However, observed performance of a similar task by some others may also serve as an anchor point for the two beliefs (perceived usefulness and perceived ease of use). Davis et al. (1989) pointed out that external variables have an indirect effect on attitudes and behavioral intention through two beliefs (perceived usefulness and perceived ease of use) in TAM. However, internal psychological variables (i.e., social norms) cannot be easily explained by only a bridge role between external variables and other variables (i.e., attitudes and behavioral intention) in TAM. The task-technology fit (TTF) model is an important construct for understanding the performance of information technology (IT) when individuals have the freedom to choose a particular IT and determine the extent of performance. Goodhue s development of the TTF model addresses userevaluation of IT in the individual s satisfaction construct. The concept of satisfaction in the TTF model reflects individual s evaluation after using an IT. In TTF satisfaction is the determinant of behavior and other beliefs (i.e., social norms) not based on a rational user assumption are excluded. For example, an individual may not like or have positive feelings about a piece of software but may still use the software as it leads to a favorable job or task outcome. The task-technology fit model construct captures an individual s belief or affection 29

44 regarding the possible outcomes of task-technology fit that result from information technology use. Thus, the focus of the TTF model is on performance rather than IT adoption as in TAM. In addition, the TTF model focuses on users (e.g., individuals) belonging to an organization. The TPB model is useful when examining the factors affecting the adoption of a new information technology. Some researchers (Mathieson, 1991; Taylor & Todd, 1995a, b; Szajna, 1996) argue that the TPB model has more room for considering individual attitudes and subjective norms affecting the decision making process for technology adoption than TAM and TTF. For example, Taylor and Todd (1995b) compared TAM with TPB in a longitudinal study of a resource center. They concluded that the TPB provided more insights than TAM, though TAM received support. They suggested that two factors (attitude toward behavior and perceived behavioral control) in the TPB are similar with two components (perceived usefulness and perceived ease of use) and the external elements in the TAM. Neither TAM nor TTF consider subjective norm as an important factor for technology adoption. In another study (Taylor & Todd, 1995a), found that TAM should be modified to include subjective norms and perceived behavioral control for better prediction of IT use for both experienced and inexperienced users. 30

45 2.5 A Conceptual Model of Technology Adoption of Online Financial Services Usage Determinants of the Conceptual Model The conceptual model (Figure 2.5) based on TPB shows that attitude toward behavior, subjective norm, and perceived behavioral control affect behavioral intention to use a technology, which, in turn, affects actual usage of the technology. Attitude toward behavior can be determined by attitude toward risk and attitude toward technology. Social support and information sources can affect subjective norm, while experience and education can affect perceived behavioral control. These points are developed more fully in the remainder of this chapter Hypotheses Attitude Toward a Behavior Attitude is defined as an individual s positive or negative feelings (evaluative affect) about performing a behavior (Fishbein & Ajzen, 1975). It is related to behavioral intention as people form intentions to perform behaviors toward which they are positively oriented. For example, in the case of attitude toward computers, if people have positive attitude toward computers, they are more likely to have a greater intention to use computers. Attitudes can be formed 31