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

46 through previous purchasing behavior, direct experience with the product, wordof-mouth information acquired from others, exposure to mass media advertising, the Internet, and so on. In addition, attitudes are relatively consistent with the associated consumer behavior. However, attitudes are not permanent; they do change. Attitude toward Behavior Subjective Norm Perceived Behavioral Control Intention to Adopt Online Financial Services Demographic Control Variables Actual Usage of Online Financial services Figure 2.5: Conceptual model of Technology Adoption based on The Theory of Planned Behavior (TPB). 32

47 Many studies (Au & Enderwick, 2000; Howcroft et al., 2002; Karahanna, et al., 1999; Liao & Cheung, 2002; Moutinho & Smith, 2000) reported that a favorable attitude toward a new technology is an important factor affecting the adoption of online financial services. Herbig and Day (1992) and Gilly and Zeithaml (1985) reported that when consumers make decision to adopt a technology, desirability is an important factor that affects attitude toward a technology. For example, people do not adopt a technology since they don t need it rather than they don t like it. Oliver and Shapiro (1993) and Graphic, Visualization and Usability Center (GVU) (1999) reported that risk aversion is negatively related to the adoption of technology. Individuals with a high level of risk-averse attitude toward technology adoption are more likely not to engage in technology adoption. Moreover, Ho and Victor (1994) stated that attitude toward risk is powerful at explaining consumers behavior since consumers tend to avoid mistakes to maximize utility in performing a behavior. Cunningham (1967, p.37) explained the concept of risk in terms of two components, the amount that would be lost (i.e., that which is at stake) if the consequences of an act were not favorable, and the individual s subjective feeling of certainty that the consequences will be unfavorable. Thus, consumers behavior may be influenced by attitude toward subjective risk and objective risk. 33

48 H1-1: Positive attitude toward a technology positively affects the intention to adopt online financial services. H1-2: Risk seeking positively affects the intention to adopt online financial services Subjective Norm Subjective norm refers to the person s perception that most people who are important to him think he should or should not perform the behavior in question (Fishbein & Ajzen, 1975, p. 302). It is related to intention to do the behavior because people often behave based on their perception of what others think they should do. Hartwick and Barki (1994), and Taylor and Todd (1995a) found that subjective norm is more important prior to, or in the early stages of technology adoption when adopters have limited direct experience from which to develop attitudes. The groups of people around an individual may influence the individual s intention to adopt a technology. Chua (1980) suggests that the adopter s friends, family, and colleagues/peers are groups that have the potential to influence the adoption of technology. Gottlieb (1986) and Wellman and Hall (1985) defined social network as a set of links between two or more persons or groups of people. Through the social network, social interaction occurs in the forms of verbal and nonverbal information, advice, tangible aid (e.g., transportation), 34

49 emotional encouragement, and cognitive and behavioral feedback. Research (Newman & Staelin, 1972; Westbrook & Fornell, 1979; Mazis et al., 1981; Bayus et al., 1985) suggested that individuals use social networks to get more information about technological innovations. Rogers (1995) reported that individuals are exposed to the information of a technology through the groups of people they know, and this exposure has a cumulatively increasing influence on the adoption of the technology. H2-1: Active social interaction through social network increases the intention to adopt online financial services. H2-2: Information acquired through social networks increases the intention to adopt online financial services Perceived Behavioral Control In the TPB model (Figure 2.5), perceived behavioral control reflects having resources needed to perform a behavior. Ajzen (1991) reported that resources affect perceived behavioral control and may be formed by time, money, skills, other specialized resources, and previous experience required to perform a behavior. These forms of resources play key roles in affecting behavioral intention and actual technology use. 35

50 Numerous studies (Rogers & Stanfield, 1968; Plummer, 1971; Rogers & Shoemaker, 1971; Feldman & Armstrong, 1975; Adcock et al., 1977; Labay & Kinnear, 1981; Hambrick & Mason, 1984; Amel, 1986; Taube, 1988; Igbaria et al., 1989; Anderson et al., 1995; Tabak & Barr, 1999; Hoffman et al., 2000) reported that people with higher levels of education are more likely to adopt a new technology than less educated people. Hoffman et al. (2000) reported that the adopters of IT products (e.g., computers and electronic banking) are more likely to have higher education levels than non-adopters. Several researchers (Hambrick & Mason, 1984; Anderson & Melchior, 1995; Tabak & Barr, 1999) concluded insufficient education can be an important barrier to new technology adoption. Previous research (Rogers & Stanfield, 1968; Plummer, 1971; Rogers & Shoemaker, 1971; Feldman & Armstrong, 1975; Adcock et al., 1977; Labay & Kinnear, 1981; Amel. 1986; Taube, 1988; Kennickell & Kwast; 1997; Katz & Aspden, 1997; Hoffman & Novak, 1998; Benton Foundation, 1999; NTIA, 1999; Hoffman et al., 2000) revealed that income is a key determinant of technology adoption. Hoffman et al. (2000) found that the impact of household income on home computer ownership explains differences between adopters and nonadopters in the adoption of the Internet. Moreover, Hoffman et al. (2000) reported that respondents with greater than median income (e.g., $40,000) were more likely to own and use a home computer than people with below the median 36

51 household income. Kennickell and Kwast (1997), and Taube (1988) found that individuals who have computers are from middle- to upper-income households. Several researchers (Hirshman, 1980; Lee, 1986; Davis, 1989; Igbaria et al., 1989; Goodhue, 1995; Igbaria et al., 1995; Taylor & Todd, 1995; Venkatesh & Davis, 1996; Tabak & Barr, 1999; Eastin & LaRose, 2000; Reed et al., 2000) reported that prior experience is an important determinant of the adoption of technology. This research suggests that adopters with greater experience are more likely to use IT products (e.g., computer and electronic banking). Hirshman (1980) reported that people with prior experience are advantaged when adopting a modified technology since they can refer to past experience with a similar technology. Davis (1989) also suggested that prior experience can be used to facilitate understanding and maintaining a new technology. Goodhue (1995) identified experience as a key factor affecting the adoption of technology and reported that prior experience provides a good understanding of the capabilities of a new technology in actual performance. Taylor and Todd (1995) concluded that the more experience consumers have, the more likely they will adopt a new technology. Eastin and LaRose (2000) also found that experience is important when deciding to adopt a technology since experience makes people feel more comfortable when using a new technology for the first time. H3-1: Education level positively affects the intention to adopt online financial services. 37

52 H3-2: Income is positively associated with the intention to adopt online financial services. H3-3: Previous experience positively affects the intention to adopt online financial services Control Variables Age Several studies (Harris & Mill, 1971; Adcock et al., 1977; McEwen, 1978; Pommer et al., 1980; LaBay & Kinnear, 1981; Hoffman et al., 2000) found that younger individuals are more likely to accept new technologies than older people. LaBay and Kinnear (1981) reported that at the first contact with new technologies, younger individuals spent less time and less effort learning how to use new technologies. Harris and Mill (1971) and Pommer et al. (1980) showed that scanner technology adopters tend to be recent graduates who have a knowledge base that is current and are receptive to new ideas. Since adopters tend to be younger, they have a greater span of time over which to use a new technology than do older consumers. Hoffman et al. (2000) reported that the young to middle-aged have an advantage with respect to technology adoption. Younger people tend to have a positive attitude toward accepting technologies through learning-by-doing and past experience. 38

53 Many studies demonstrated that the elderly tend to resist adoption of new technologies (Kasteler et al., 1968; Uhl et al., 1970; Robertson, 1971; Botwinick, 1973; Pollman & Johnson, 1974; Kerschner & Chelsvig, 1981: Lee, 1986; Igbaria et al., 1989; Rousseau & Rogers, 1998). Before the adoption of technologies, the elderly are more likely to be careful and seek greater motivation than do younger individuals (Kasteler et al., 1968; Pollman & Johnson, 1974). Other studies (Lee, 1986; Igbaria et al., 1989; Rousseau & Rogers, 1998) reported that younger adopters spent more time using new technologies after adoption Gender Much research has been conducted on gender differences regarding attitude toward technological products (e.g., computers or Internet) and computer use. Chen (1986) suggested that males generally have more positive attitudes and greater confidence with computers than females. Other researchers found that women appear more afraid of computers than men and are more likely to express concerns about how computers would affect the quality of their work life (Gattiker, 1988). Teo and Lim (1996) found that gender differences exist with respect to how individuals perceive computers to be easy to use. Allen (1995) found that females perceived communication using computers to be easier, more efficient, and more effective than males. Venkatesh et al. (2000) also found that perceived ease of use was important to women, while men were strongly 39

54 influenced by perceived usefulness. Furthermore, Venkatesh and Morris (2000) studied differences between women and men with respect to decision making processes related to new technology adoption and use. They reported that perceived ease of use was more important to women than men throughout the adoption process, while perceived usefulness was more important to men after the initial stage of the adoption process. Kaplan (1994) reported that females are more likely than males to think computers are fun. His findings contradict the results of Qureshi and Hoppel (1995) who reported that males were more likely than females to perceive computer usage as fun. A number of studies using college students found gender differences in using technology and in attitude toward technology (Gilroy & Desai, 1986; Gefen & Straub, 1997). Gilroy and Desai (1986) reported that college men feel more comfortable and competent using computers and the Internet then women. Men use new technological products (e.g., computers or Internet) more frequently than women (Hoffman et al., 2000; Gilroy & Desai, 1986; Gefen & Straub, 1997). Even though a number of studies indicated that the gender gap in computers or Internet use has narrowed over the past several years, men still use computers or the Internet more frequently than women. Women spend less total time using computers or the Internet in a given period, use them less frequently, spend less time per session, and use them for fewer purposes. 40

55 Marital Status Many studies (Dickerson & Gentry, 1983; Gottlieb & Dede, 1984; Tinnell, 1985; Vitalari et al., 1985; Bird et al., 1990; Duxbury et al., 1996) examined the impact of marital status on IT adoption. There is little agreement among these studies concerning the relationship between technology adoption and marital status. Dickerson and Gentry (1983), and Leider (1988) found that married people were more likely to adopt home computers. Other researchers (Gottlieb & Dede, 1984; Tinnell, 1985; Vitalari et al., 1985; Bird et al., 1990; Duxbury et al., 1996) reported that individuals who were married were less likely to accept new technologies Dependent Children Several researchers (Vitalari et al., 1985; Venkatesh & Vitalari, 1987; Katz & Aspen, 1996) examined what differences exist between households with children and without children regarding how the technology is utilized at home. Katz and Aspen (1996) reported that people with dependent children were less likely to adopt the Internet at home. Vitalari et al. (1985) suggested that individuals with children have barriers to using home computers since these people have greater child care responsibilities (e.g., child care and home chores). 41

56 2.6 Summary of hypotheses In summary, the following hypotheses are examined in this study: H1-1: Positive attitude toward a technology positively affects the intention to adopt online financial services. H1-2: Risk seeking positively affects the intention to adopt online financial services. H2-1: Active social interaction through social network positively increases the intention to adopt online financial services. H2-2: Information acquired through social networks increases the intention to adopt online financial services. H3-1: Education level positively affects the intention to adopt online financial services. H3-2: Income is positively associated with the intention to adopt online financial services. H3-3: Previous experience positively affects the intention to adopt online financial services. 42

57 CHAPTER 3 METHODS This chapter begins with a description of the data source. Details are provided on the use of factor analysis to reduce the number of independent variables. The treatment of missing values for each measure and case is discussed. Finally, the measurement for all variables is identified and described, and the methods used for descriptive and multivariate analyses are described. 3.1 Data Source MacroMonitor is a biannual survey first conducted in 1978 by the Consumer Financial Decisions group of SRI Consulting Business Intelligence (SRIC-BI). The survey includes information about consumer attitudes, behaviors and motivations regarding financial products, services, delivery methods, and institutional use. 43

58 The MacroMonitor survey process involves several steps. The first step is disproportionate random sampling. To provide a large sample of affluent households, MacroMonitor oversampled households whose annual income exceeded $100,000 a year or whose total assets exceed $500,000, excluding the primary residence. Following this oversampling, weights were calculated to obtain representativeness of the population. The second step is a simple random sampling. Participants of the MacroMonitor survey were recruited using an RDD (random-digit-dialing) sample frame. Those agreeing to participate were sent a questionnaire by express mail. As a result of this mixed-mode methodology (gaining cooperation by telephone and mail-and-return questionnaire), the response rate of the MacroMonitor was 49%. For the MacroMonitor Survey, a sample of 3,780 households completed the mail survey from May through August of Sample For this study, households responding to questions on the use of online financial services were selected. All 3,780 households responded to the question, Check any online financial services you or anyone in your household would like to use with a personal computer in your home. The 21 types of online financial services are listed in Appendix B. Of the 3,780 respondents, 637 were current users of online financial services, while 3,143 were non-users of online 44

59 financial services. The 637 users indicated use of at least one of the 21 types of online financial services. Of the non-users, 1,689 households were intended users of online financial services, and 1,454 had no intention of using online financial services. The 1689 intended user households indicated intention to use at least one of the 21 types of online financial services. The study sample is unweighted for both the descriptive and the multivariate analyses. 3.3 Description of Dependent Variables Five dependent variables are used in this study to examine the factors that affect household adoption of online financial services. These variables are related to the use of online computer financial services in the home. The dependent variables are summarized in Table Intended Use of Online Financial Services The dependent variables in this study reflect the intended use of at least one of 21 online financial services. The complete list of online financial services is given in Appendix B. These variables are coded as binary variables that reflect intended use of various online financial services. The MacroMonitor data includes variables reflecting intended use of specific online financial service in the areas of: (1) account management, (2) 45

60 loans, (3) investment, and (4) insurance. Account management focuses on paying bills, stopping/canceling checks/payments, opening/closing accounts, making transfers between accounts, and inquiring about account balances. The loan category considers applying for various kinds of loans (i.e., home mortgage, vehicle loans/leases) and obtaining information about loans. The investment category addresses the buying/selling/managing of investment accounts (i.e., mutual funds, stocks or bonds) and obtaining information about investments. The insurance category includes intended uses related to buying insurance (i.e., life, health, and vehicle insurance) and obtaining information about insurance. Each variable is treated as a binary variable (yes/no) that reflects the intended use of at least one online financial service in a particular category. Dependent Variables Description Intended use of online financial services =1 if yes to Would like to use at least one of 21 online financial services, 0 otherwise Account management =1 if yes to Would like to use at least one of 21 online financial services for account management, 0 otherwise Loans =1 if yes to Would like to use at least one of 21 online financial services for loans, 0 otherwise Investment =1 if yes to Would like to use at least one of 21 online financial services for investing, 0 otherwise Insurance =1 if yes to Would like to use at least one of 21 online financial services for insurance, 0 otherwise Table 3.1: A summary of dependent variables. 46

61 3.4 Description of Independent Variables As mentioned in the previous chapter, the Theory of Planned Behavior suggests that behavioral intentions are influenced by attitude, subjective norm, and perceived behavioral control. In the following paragraphs, the variables used to measure attitude toward a behavior, subjective norm, and perceived behavioral control are described. The independent variables are a combination of continuous, interval, and categorical variables. The independent variables are listed in Table Attitude Attitude toward a behavior plays an important role in the adoption of a technology. Attitude is based on the beliefs that people have about a technology and the importance of those beliefs. If people believe that a behavior results in good consequences, they will have positive attitudes toward the behavior. Therefore, beliefs relating to positive or negative aspects of a new technology should lead to positive or negative attitudes, respectively, toward the technology. Twenty-three questions in the MacroMonitor survey related to attitude toward risk and online financial services. Responses to the questions reflect the extent to which the respondents agreed or disagreed with the following 47

62 statements on a 4-point scale ranging from Mostly agree(1), Agree(2), Disagree(3), to Mostly disagree(4): I am satisfied with my household s current financial situation. I am afraid my household is not saving enough for its future needs. My household should make some important changes in our savings and investments. I do not need advice on investment options. I feel qualified to make my own investment decisions. I feel uncomfortable making judgments about the riskiness of investment. I enjoy learning about different investment opportunities. Over the past several years, I have become much more knowledgeable about savings and investments. I consider myself a sophisticated investor. I resent any profits financial institutions make from my doing business with them. Dealing with financial institutions is about as much fun as being stuck in a traffic jam. I worry about the safety of my deposits in banks or savings institutions. I am willing to take high risks to realize substantial financial gains from investments. It is wise to put some portion of savings in uninsured investments to get a high yield. 48

63 I am willing to accept some risk of losing money if an investment is likely to come out ahead of inflation in the long run. Over the long run, say 10 or 20 years, stocks will be a very good investment. The stock market is too risky for me. It is very important to me to have both a guaranteed interest rate and federal insurance on my savings. I am concerned that our household has more debt than it should. In the past, I sometimes spent more than I really wanted to because credit cards made it easy. I am concerned about problems my debts would cause should I die or become disabled. I would never get a personal or auto loan that had an interest rate that could change. I would never get a mortgage that had an interest rate that could change. Another attitude measure is a household s financial strategy based on four responses: Specific financial strategy (1), General financial strategy (2), Partial but incomplete financial strategy (3), and No financial strategy (4). The last attitude question related to a household s degree of risk tolerance concerning savings and investment. Responses to this question were on a 5- point scale: Very low risk/very low return (1), Below average risk/below average 49

64 return (2), Average risk/average return (3), Above average risk/above average return (4), and Very high risk/very high return (5) Subjective Norm Subjective norm is defined as people s perceptions of social pressure from significant others to perform the behavior (Fishbein & Ajzen, 1975). Social interactions through social networks (e.g., friends and family members) influence an individual s decision of technology adoption (Gottlieb, 1986; Wellman & Hall, 1985). Moreover, information from social networks affect individuals adoption of technological innovations (Newman & Staelin, 1972; Westbrook & Fornell, 1979; Mazis et al., 1981; Bayus et al., 1985; Rogers, 1995). Household size reflects the potential number of close personal contacts around an individual. It is a continuous variable that indicates the total number of household members. The MacroMonitor Survey respondents were asked how often they receive advice before making major household investment decisions: Always (1), Sometimes (2), Rarely (3), Never (4), Don t know (5), and Unspecified (6). Households seeking more advice are expected to be more likely to adopt online financial services. 50

65 Another measure relating to subjective norm is the use of professional financial advisors 5 for the last two years and their anticipated use for the next 12 months. These were measured by the number of 12 types of professional financial advisors/planners that the respondents used over the last two years or planned to use within the next 12 months. The respondents were also asked how their households obtain information used to make financial decisions. They responded to two questions (not only for the present, but also for the future) indicating: Mostly on their own (1), Mostly from a financial professional (2), and Some on their own and some from a financial professional (3). Two other survey questions that dealt with subjective norm reflect how households currently make financial decisions and how they plan to make them in the future. Responses to these questions were: Mostly on their own (1), Mostly from a financial professional (2), and Some on their own and some from a financial professional (3). Another question related to subjective norm reflects a household s preference for information sources used to make financial decisions over the last 12 months. This variable is the number of 21 types of information sources used. The complete list of information of sources is listed in Appendix B. 5 Professional financial advisors can be defined as individuals or representatives of institutions with whom the respondent has an established relationship while acquiring assistance or advice concerning the household s finances or investments. 51

66 The following 16 statements reflect preference for social interaction and for information on a 4-point scale ranging between Mostly agree (1) and Mostly disagree (4): It is important that a financial services representative makes recommendations I should consider. It is important that a financial services representative keeps me informed of where I stand financially. I like to discuss my financial options before making a decision about them. I would be willing to pay for professional financial advice. I prefer to consult a specialist when making financial decisions. Using my financial institution as a sounding board for ideas about my finances is important to me. Building long-term relationships with financial institutions is more important than always getting the best prices or newest products. I am more concerned with the quality of service than with cost when I deal with financial institutions. It is important to me that the people I deal with for financial matters recognize me and know me by name. Chatting with the people I know at financial institutions is an important part of doing financial business for me. I would rather use automated teller machines, personal computers, the telephone, or mail than face representatives of financial institutions. 52

67 The less I talk to financial institution personnel the better. I would like to go to just one person who can help me with my savings, investments, and credit needs. I am unlikely to try a new financial service until someone I know recommends it. I prefer to do most of my financial business in person Perceived Behavioral Control Perceived behavioral control reflects perception of access to the resources needed to successfully engage in a behavior, such as time, money, other specialized resources, and an individual s experience Education In the MacroMonitor survey, educational attainment level was measured as a categorical variable for each male and female household head. The nine categories reflecting educational attainment are: 8 th grade or less (1), Some high school (2), High school degree (3), Some college or technology school (4), College degree (5), Some postgraduate work (6), Master s degree (7), Professional doctorate (education, law, medicine, etc.)(8), and Ph.D. (9). 53

68 Previous research (Brines, 1994; Bianchi et al., 2000) suggested that the more educated person between a husband and a wife is an appropriate indicator of a household s education level, the assumption being that the less educated spouse tends to rely on the more educated spouse s opinions and decisions. Other studies (Brines, 1994; Bianchi et al., 2000) indicated that a limited number of education categories are needed to explain the effect of educational attainment on technology adoption. Therefore, education is coded into 3 categories that reflect the highest education attainment level of husband or wife. The categories included: High school graduation or less than high school (1), Some college (2), and College degree or more (3) Income Income in the survey was measured by 14 categories reflecting households total gross income in 1997 before taxes or any other deductions: Less than $10,000 (1), $10,000-$19,999 (2), $20,000-$29,999 (3), $30,000- $39,999 (4), $40,000-$49,999 (5), $50,000-$59,999 (6), $60,000-$74,999 (7), $75,000-$99,999 (8), $100,000-$124,999 (9), $125,000-$149,999 (10), $150,000-$199,999 (11), $200,000-$299,999 (12), $300,000-$499,999 (13), More than $500,000 (14). Previous researchers (Brines, 1994; Bianchi et al., 2000; DeNew et al., 2000) divided household income into quartiles. In this study, income will also be 54

69 divided into quartiles reflecting households total gross income in 1997 before taxes or any other deductions: $29,999 or less (1), $30,000-$59,999 (2), $60,000-$99,999 (3), and $100,000 or more (4) Previous Experience and Confidence Households confidence concerning their ability to achieve their most important financial goals was rated on a 5-point scale: Extremely confident (1), Very confident (2), Somewhat confident (3), Not very confident (4), and Not at all confident (5). Five questions were used to assess past experience with computers. The respondents were asked how many kinds of financial software programs they used on their home computer. The list of financial software programs is listed in Appendix B. The respondents were also asked to report the number of hours they spent using a home computer. Two other experience questions were related to ATM and debit card use. These were binary variables (Yes or No). The respondents were also asked how many times in the last three months they used each of 10 ways to make financial transactions. The complete list of financial transactions is presented in Appendix B. In addition to the above, respondents answered 15 questions related to financial experience on a 4-point scale from Mostly agree (1) to Mostly disagree (4): 55

70 I do a very good job of keeping my financial affairs in order. I am very organized in my approach to financial matters. My household knows how to choose financial products and services that are best for us. Managing my financial affairs is something like a hobby. I enjoy taking care of them. My household is successful in sticking to its budget. I am very disciplined in savings and spending decisions. Often I am not sure whether the financial decisions I have made are the right ones. Finding tax-exempt or tax-deferred investment is important to me. I would pay a one-time 5% fee for an investment guaranteed to grow 3% faster than inflation. I think the best way to save is to have savings or investments made automatically from my income. I prefer investments where the return is in the form of long-term capital gains to defer taxes. I would never borrow from my retirement plan. I am careful not to use credit more than I should. I am always looking for the lowest cost financial services. I shop around for financial products/services. 56

71 3.4.4 Demographic Control Variables Age Age is a continuous variable reflecting the actual age of each male and female household head. In this study, where there were two household heads, the average of their ages was used to give the household age ; where there was only one household head, that person s age was used Gender Gender is a categorical variable that reflects who completed the questionnaire: Male head only (1), Female head only (2), Both male and female household head (3), and Another person (4). This variable reflects the gender of the person (s) completing the questionnaire. While responses to questions related to the dependent variables reflect the household, many questions related to independent variables reflect respondents agreement or disagreement. For example, how much you agree or disagree with the following statements by circling the number that comes closest to describing how you feel. Given the gender differences noted in Chapter 2, it is important to control for response differences due to the gender of the respondent. 57

72 Marital Status Marital status is a categorical variable: Single (1), Divorced (2), Separated (3), Widowed (4), Married (5), and Living together but not married (6) Number of Dependent Children This is a continuous variable reflecting the number of dependent children within a respondent s household. 58

73 Independent Variables Demographic Control Variables Age Gender of respondents who completed the questionnaires Marital status Description Actual age of male and female household head 1. Male household head, 2. Female household head, 3. Both male and female household head, 4. Another person. 1. Single, 2. Divorced, 3. Separated, 4. Widowed, 5. Married, 6. Living together but not married. # of dependent children Total number of dependent children within a respondent s household. Attitude Variables Household's financial strategy Attitude toward risk of a household s savings and investments 1. Specific financial strategy, 2. General financial strategy, 3. Partial but incomplete financial strategy, 4. No financial strategy. 1. Very low risk/very low return, 2. Below average risk/below average return, 3. Average risk/average return, 4. Above average risk/above average return, 5. Very high risk/very high return. Continued Table 3.2: A summary of independent variables. 59

74 Table 3.2 continued Evaluation of 23 financial services or products 1. I am satisfied with my household s current financial situation. 2. I am afraid my household is not saving enough for its future needs. 3. My household should make some important changes in our savings and investments. 4. I do not need advice on investment options. 5. I feel qualified to make my own investment decisions. 6. I feel uncomfortable making judgments about the riskiness of investment. 7. I enjoy learning about different investment opportunities. 8. Over the past several years, I have become much more knowledgeable about savings and investments. 9. I consider myself a sophisticated investor. 10. I resent any profits financial institutions make from my doing business with them. 11. Dealing with financial institutions is about as much fun as being stuck in a traffic jam. 12. I worry about the safety of my deposits in banks or savings institutions. 13. I am willing to take high risks to realize substantial financial gains from investments. 14. It is wise to put some portion of savings in uninsured investments to get a high yield. 15. I am willing to accept some risk of losing money if an investment is likely to come out ahead of inflation in the long run. 16. Over the long run, say 10 or 20 years, stocks will be a very good investment 17. The stock market is too risky for me. 18. It is very important to me to have both a guaranteed interest rate and federal insurance on my savings. 19. I am concerned that our household has more debt than it should. 20. In the past, I sometimes spent more than I really wanted to because credit cards made it easy. 21. I am concerned about problems my debts would cause should I die or become disabled. 22. I would never get a personal or auto loan that had an interest rate that could change. 23. I would never get a mortgage that had an interest rate that could change. 1. Mostly agree, 2. Agree, 3. Disagree, 4. Mostly disagree. Continued 60

75 Table 3.2 continued Subjective Norm Variables Total number of household members Frequency of receiving advice for major household financial decisions The number of past and expected use of professional financial advisors Preference of information sources a household used about financial products/services or financial decisions in the last 12 months How to obtain information for making financial decisions for the present and for the future How they make financial decisions both for the present and for the future Total number of household members 1. Always, 2. Sometimes, 3. Rarely, 4. Never, 5. Don t know, 6. Unspecified. Total number of professional financial advisor use Total number of information sources a household used 1. Mostly on their own, 2. Mostly from a financial professional, 3. Some on their own and some from a financial professional. 1. Mostly on their own, 2. Mostly from a financial professional, 3. Some on their own and some from a financial professional. Continued 61

76 Table 3.2 continued Evaluation of 15 preferences for social interaction 1. It is important that a financial services representative makes recommendations I should consider. 2. It is important that a financial services representative keeps me informed of where I stand financially. 3. I like to discuss my financial options before making a decision about them. 4. I would be willing to pay for professional financial advice. 5. I prefer to consult a specialist when making financial decisions. 6. Using my financial institution as a sounding board for ideas about my finances is important to me. 7. Building long-term relationships with financial institutions is more important than always getting the best prices or newest products. 8. I am more concerned with the quality of service than with cost when I deal with financial institutions. 9. It is important to me that the people I deal with for financial matters recognize me and know me by name. 10. Chatting with the people I know at financial institutions is an important part of doing financial business for me 11. I would rather use automated teller machines, personal computers, the telephone, or mail than face representatives of financial institutions. 12. The less I talk to financial institution personnel the better, 13. I would like to go to just one person who could help me with my savings, investments, and credit needs. 14. I am unlikely to try a new financial service until someone I know recommends it. 15. I prefer to do most of my financial business in person. Perceived Behavioral Control Variables Education Income Household s Confidence of achievement of the most important financial goals 1. Mostly agree, 2. Agree, 3. Disagree, 4. Mostly disagree. 1. High school or less, 2. Some college, 3. College degree or more. 1. $29,999 or less, 2. $30,000-$59,999, 3. $60,000-$99,999, 4. $100,000 or more. 1. Extremely confident, 2. Very confident, 3. Somewhat confident, 4. Not very confident, 5. Not at all confident. Continued 62

77 Table 3.2 continued Number of financial software programs on home PC Hours spent using a home PC Total number of financial software programs used Total hours Having ATM cards/ debit cards =1 if have ATM cards/debit cards, 0 otherwise Frequency of using 10 financial transactions in the last Total number of use of financial three months transactions 15 questions about financial planning 1. I do a very good job of keeping my financial affairs in order, 2. I am very organized in my approach to financial matters, 3. My household knows how to choose financial products and services that are best for us, 4. Managing my financial affairs is something like a hobby. I enjoy taking care of them, 5. My household is successful in sticking to its budget, 6. I am very disciplined in savings and spending decisions, 7. Often I am not sure whether the financial decisions I have made are the right ones, 8. Finding tax-exempt or tax-deferred investments is important to me, 9. I would pay a one-time 5% fee for an investment guaranteed to grow 3% faster than inflation, 10. I think the best way to save is to have savings or investments made automatically from my income, 11. I prefer investments where the return is in the form of long-term capital gains to defer taxes, 12. I would never borrow from my retirement plan, 13. I am careful not to use credit more than I should, 14. I am always looking for lowest cost financial services, and 15. I shop around for financial products/services 1. Mostly agree, 2. Agree, 3. Disagree, 4. Mostly disagree. 63

78 3.5 Variable Reduction Procedures: Factor Analysis Factor analysis is often used to reduce the number of independent variables by identifying a small number of factors that explain most of the variance observed in a larger number of variables. It is well known that multicollinearity can be a serious problem in estimating how groups of variables influence criterion variables (Green, 1978; Knapp, 1998; Stewart, 1981). Multicollinearity often occurs when dealing with large numbers of related questions measured with the same scale. In the MacroMonitor Survey, many questions related to attitudes, subjective norm and perceived behavioral control were measured on a 4-point scale from Mostly agree to Mostly disagree. The consequences of multicollinearity among the variables could lead to biased estimates and inflated standard errors (Green, 1978; Knapp, 1998; Stewart, 1981). Principal components factor analyses with varimax orthogonal rotation was used in this study. Kaiser-Eigenvalue Criterion was used to reduce the number of the independent variables. Variables with a factor loading of.50 or more were included in the factors. The principal components method of extraction begins by finding a linear combination of variables (a component) that accounts for as much variation in the original variables as possible. It then finds another component that accounts for as much of the remaining variation as possible and is uncorrelated with the 64

79 previous component, continuing in this way until there are as many components as original variables (Kim & Mueller, 1978a, 1978b). Varimax, which is most commonly used for orthogonal rotation, was used to rotate the factors. The purpose of varimax rotation is to maximize the variance of factor loadings by making high loadings higher and low loadings lower for each factor. Varimax orthogonal rotation yields factors that are uncorrelated, so as to minimize the multicollinearity problem (Tucker & MacCallum, 1993). There are several procedures for estimating factor scores. One of them is to sum the values of variables that load highly on each factor. The problem with this approach is that variables with larger standard deviations contribute more heavily to the factor scores (Comrey & Lee, 1992). This problem can be alleviated if variable scores are standardized and/or if the variables have roughly equal standard deviations to begin with. In order to address this problem, the Anderson-Rubin approach, which is a component of the SPSS 11.5 factor analysis option, is used in this study. This approach yields standardized factor scores and estimates them with a mean of zero and a standard deviation of 1. A further advantage of using the Anderson-Rubin approach is that factor scores are uncorrelated with each other (Gorsuch, 1983). An Eigenvalue is the sum of the squared loadings of the indicators on the factor with which the Eigenvalue is associated (Loehkin, 1992). An examination of Eigenvalues indicates whether there is significant conceptual overlap among various subgroups of the indicators. The factors with the largest Eigenvalue 65

80 contain the most common variance among the observed indicators; those with small or negative Eigenvalues are then dropped as factors. To determine the optimal number of factors used in this study, the Kaiser-Eigenvalue Criterion is used (Tabachnick & Fidell, 1996), which involves retaining factors whose Eigenvalues of the correlation matrix are greater than one. For each resulting factor, an internal reliability analysis was conducted. The reliability analysis for the factor scale was tested with Cronbach s Alpha. This reliability analysis procedure provided information about the relationships among individual items in the scale and their internal consistency and examined the properties of a measurement scale and the questions that make it (SPSS, 2001). All statistical procedures were performed using the SPSS 11.5 for the Windows Statistical Package. 3.6 Missing Data Missing data is common in financial surveys due to refusal or omission (Little & Schenker, 1995). The treatment of missing data is an important consideration in this study. There are 3 major problems that could result from missing data: (1) biased estimates resulting from systematic differences between respondents who have complete and incomplete information; (2) estimates that are less efficient due to missing data; and (3) more complicated analysis due to 66

81 missing information (Little & Schenker, 1995). There are several methods available for dealing with missing data. Three common methods include: (1) casewise deletion, which omits any cases with missing data, also known as listwise deletion; (2) pairwise deletion, which utilizes the greatest number of cases available; and (3) mean imputation, which replaces the missing data with the mean value for the variable (Bollen, 1989; Little & Schenker, 1995). Casewise deletion results in regression coefficient estimates that can be biased and imprecise. In general, casewise deletion will bias the estimate of the model s intercept parameter, increase standard errors, and widen confidence intervals. Pairwise deletion can be problematic because this method throws away data and tends to yield biased and inefficient estimates and inference for the parameters of interest. Mean imputation is a preferred way to estimate missing values. Means are calculated from available data which are then used to replace missing values. One of the benefits of this procedure is that the mean for the distribution as a whole does not change and the researcher is not required to guess at missing values. On the other hand, the variance of a variable and standard deviation are reduced, because the mean is closer to itself than to the missing value it replaces, and the correlation the variance has with other variables is reduced because of the reduction in variance. Tabachnick and Fidell (1996) reported that if only a few data points (5% or less) are imputed, the problems are less serious, and almost any procedure for handling missing values yields similar results. 67

82 In this study, mean imputation was used most often to replace missing data, while casewise deletion was used in a few situations where a mean could not be computed (e.g., gender of respondents). For most cases where variables have a missing value, the mean of the available cases was substituted unless otherwise noted (Table 3.3). After the missing data imputation, the data is treated as if it were a complete data set. As noted earlier, the household age for a couple household was determined by averaging the age of the male head and the age of the female head. In this situation for missing values for household age were replaced by the mean household age. For the gender of respondents, missing values were dropped in the sample because this variable was categorical; in addition, the percentage of missing values was small, about 1%. Similarly, the missing values for marital status and educational attainment of male and female householders were deleted in this study. The question of hours per month spent on PC use at home had many missing values; however, a prior question asked whether a respondent was using a computer at home. If the answer was no, the hours spent on PC use was set to zero. The missing values in questions used in the factor analysis were also replaced with mean values. To ensure that bias was not introduced through mean imputation, all multivariate models were estimated using complete-case analyses (no missing values imputed) and imputed value analysis. These results are reported in Table D.1. 68

83 Variable Name Number of Missing Value (Total number = 3143) Imputation of Missing Value Age of male head of household 639 = mean Age of female head of household 379 = mean Household age 1015 = mean Gender of respondents who completed 34 Drop cases questionnaires Marital status 12 Drop cases Education among male/female householders 25 Drop cases Household income 0 Hours per month PC use at home 1516 = 0 Frequency of 10 types of financial transactions in the last 3 months 50 = mean Have ATM cards 0 Have debit cards 0 Lack of financial discipline 239 = mean Prefer less complex financial strategies 388 = mean Credit use 140 = mean Minimal search for new financial product 103 = mean Attitude toward risk of HH savings/investment 109 = 3 (Ave. risk/ave. return) Satisfaction with finances 261 = mean Financial decision confidence 285 = mean Poor financial knowledge 276 = mean Positive attitude toward financial institutions 209 = mean Degree of risk aversion 354 = mean Concern with debt 145 = mean Positive attitude toward credit market risk 114 = mean Total number of HH members 0 Personal relationship unimportant 269 = mean Personal relationship unimportant 139 = mean Personal contact desired 127 = mean One-on-one interaction unneeded 166 = mean Table 3.3: Summary of number of missing value and imputation. 69

84 3.7 Descriptive Analyses Means, frequencies, and related statistical tests are used to describe the difference between intended users and intended non-users of online financial services. Pearson s correlation coefficient was used to detect multicollinearity between the independent variables (see Appendix F). Since weighting a sample results in some issues, the sample will not be weighted for both descriptive and multivariate analyses in this study. The first concern is that the weights themselves are endogenous to variables such as income commonly used in multivariate analyses. If the weights are endogenous, the results from the weighted multivariate analysis may be biased. In addition, weighting a sample leads to inflating the degrees of freedom and affects the significance of statistical tests, even if the weight can make a correction of oversampling. Thus, the sample will be unweighted in the multivariate analyses of this study. Moreover, the descriptive analysis also will be unweighted to be consistent with the multivariate analyses. To compare subgroups (intended users and intended non-users of online financial services), T-test and χ 2 test will be used to compare the means or distributions in two groups depending on the characteristics of the variables. 70

85 3.7.1 Comparing Mean Values Two-sample T-tests were used to test for significant differences between two subgroups (intended users and intended non-users of online financial services). The null hypothesis is: H 0 : µ i - µ ni = 0 H a : µ i - µ ni 0 where µ i is the mean for the sample of intended users and µ ni is the mean for the sample of intended non-users. The T-test statistic for comparing mean values is: t = x i - x s ni 2 i n i - ( µ + s n i ni ni 2 µ ni ) If the calculated value of the test statistic exceeds the critical value, the null hypothesis H 0 is rejected. 71

86 3.7.2 Comparing Distributions A χ 2 tests was used to test for differences in the distribution between two subgroups (intended users and intended non-users of online financial services). The general formula for calculating χ 2 is: ( ) 2 i i χ = O E E i 2 Where O i is the observed frequency in the i th category and E i is the expected number in the i th category. If the calculated χ 2 value exceeds the critical value at α =.05, the distributions of the two groups are considered statistically different. 3.8 Multivariate Analysis Logistic Regression Logistic regression is used to predict the presence or absence of a characteristic or outcome based on the values of a set of variables (Knapp, 1998). This is similar to a linear regression model, but is fitted to models where the dependent variable is dichotomous (intended users/ intended non-users). In this study, logistic regression will be used to establish associations between the dichotomous dependent variables (intended/ non-intended use of online financial services) and independent variables identified by the Theory of Planned Behavior 72

87 (TPB). According to Kinsey (1984), logistic regression is designed to estimate the probability of a choice given the characteristics of the decision maker. Maddala (1992) suggested that logistic regression assumes the existence of an underlying latent variable for which we observe a dichotomous realization. In any regression analysis, the key quantity is the mean value of the dependent variable, given the values of the independent variable: E (Y x) = β 0 + β 1 x (1) where Y denotes the dependent variable, x denotes a value of the independent variables, and the β 1 values denote the model parameters. The estimated quantity is called the conditional mean or the expected value of Y given the value of x. Many distribution functions have been proposed for use in the analysis of a dichotomous dependent variable (Hosmer & Lemeshow, 1989). The distribution function used in the logistic regression model is: e = (2) e β0+ β1x π ( x) β + β x where, to simplify the notation, π(x) = E (Y x). The transformation of the π(x) logistic function is known as the logit transformation: 73

88 π( x) ( x) = ln = β0 + β x (3) 1 π( x) g 1 Hosmer and Lemeshow (1989) have summarized the main features of a regression analysis when the dependent variable is dichotomous: (1) The conditional mean of the regression equation must be formulated to be bounded between zero and 1 (Eq. (2) satisfies this constraint), (2) The binomial, not the normal, distribution describes the distribution of the errors and will be the statistical distribution upon which the analysis is based, and (3) The principles that guide an analysis using linear regression will also apply for logistic regression. If Y is coded as zero or 1 (binary variable), the expression π(x) given in Eq. (2) provides the conditional probability that Y is equal to 1, given x, denoted as P(Y= 1 x). It follows that the quantity 1- π(x) gives the conditional probability that Y is equal to zero, given x, P(Y= 0 x). For those pairs (x i, y i ) where y i = 1, the contribution to the likelihood function is π(x i ), and for those pairs (x i, y i ) where y i = 0, the contribution to the likelihood function is 1 - π(x i ), where the quantity π(x i ) denotes the values of π(x) computed at x i. A convenient way to express the contribution to the likelihood function for the pair (x i, y i ) is: ζ ( x i ) = π( x i ) y i [1 - π( x i )] 1- y i (4) 74

89 Since x i values are assumed to be independent, the product for the terms given in Eq. (4) yields the likelihood function: l(β) = Π ζ ( x i ) (5) The log of Eq. (5) gives the following log likelihood expression: L(β) = ln [l(β)] = Σ {y i ln[π( x i )] + (1- y i )ln[1 - π( x i )]} (6) Maximizing Eq. (6) with respect to β and setting the resulting expressions equal to zero will produce the following values of β: Σ [y i - π( x i )] = 0 (7) Σ x i [y i - π( x i )] = 0 (8) These expressions are called likelihood equations. An interesting consequence of Eq. (7) is: y = π x ) i ( i That is, the sum of the observed values of y is equal to the sum of the expected values. This property is especially useful in assessing the fit of the model (Hosmer & Lemeshow, 1989). 75

90 After estimating the coefficients, the significance of the variables in the model is assessed. That is, the observed values of the dependent variable should be compared with the predicted values obtained from models with and without the variable in the equation. In logistic regression the comparison is based on the log likelihood function defined in Eq. (6). The likelihood ratio is: D = 2 y i π i ln + (1 y yi i 1 π i ) ln 1 yi (9) where π = π x ). i ( i The dependent variable in this study, intended use of online financial services, is dichotomous. Therefore, the logistic model used is: g( x) e P(non-intended use) = π ( x) = g( x) (10) 1+ e and thus 1 P(intended use) = 1 - π ( x) = (11) g( x) 1+ e 76

91 where g(x) represents the function of the independent variables: g(x) = β 0 + β 1 x 1 + β 1 x β n x n (12) Interpretation of Logistic Regression There are two widely used methods of interpreting the results of logistic regression: the estimated coefficients of logistic regression and odds ratios. Logistic regression coefficients estimate the effects of the independent variables on the predicted log odds of an outcome. A logistic coefficient estimates the additive change in the predicted log odds for a one-unit increase in the independent variable, controlling for all other independent variables in the model. In interpreting the logistic coefficient in terms of the effect on the log odds, the threshold between negative and positive effects is 0. Logistic regression coefficients can be used to estimate the odds ratios for each of the independent variables in the model (SPSS, 2001). The exponential coefficient (expβ i ) is called the odds ratio. The coefficients, B, are the natural logs of the odds ratios when the odds ratio is e B. The odds ratio is the change in odds of being in one outcome category when the value of the variable changes by one unit. An odds ratio < 1 reflects a negative relationship while a ratio > 1 reflects a positive relationship. Odds ratios can be interpreted as the percentage change in the dependent variable with a one-unit change in an independent 77

92 variable. If the odds ratio is greater than 1, the percentage change can be calculated by the odds ratio minus 1 and then multiplied by 100. If the odds ratio is less than 1, the percentage change can be obtained by one minus the odds ratio and then multiplied by General Model Testing and Identification of Independent Variables In general, logistic regression determines the coefficients that make the observed outcome most likely, using the maximum-likelihood technique. Sequential logistic regression is commonly used in cases where researchers would like to examine collective and relative contributions of variables that belong to each block by the order of the entry of variables into the full model. As described in Chapter 2 of this study, the conceptual model (Figure 2.5) is based on the Theory of Planned Behavior which suggests that attitude toward behavior, subjective norm, and perceived behavioral control affect behavioral intention to use online financial services. In an attempt to follow the development of TPB, the independent variables in this study were divided into three blocks (attitude, subjective norm, and perceived behavioral control). Additionally, another block, demographic control variables, was tested in the logistic regression. All four blocks (demographic control, attitude, subjective norm, and perceived behavioral control) were sequentially entered block-by-block into the logistic regression. Demographic 78

93 control variables were entered first in the logistic regression, because they are the control variables. Next were entered attitude and subjective norm, as they are listed in the Theory of Reasoned Action (TRA), which is based on attitude toward a behavior and subjective norms effect on behavioral intentions. Finally, perceived behavioral control was entered last in the logistic regression, because it was added to the TRA model to create the TPB model. To compare the models as blocks are entered, the value of the Goodness-of-fit χ 2 was used to identify the impact of each block on the overall explanatory power of the model. The greater the increase in χ 2, the more significant the block added to the model controlling for all other variables previously entered. 79

94 CHAPTER 4 RESULTS This chapter provides the procedure and results of factor analysis used to reduce the number of independent variables. A descriptive analysis comparing intended users of online financial services with intended non-users of online financial services is also presented. Following this descriptive analysis, the results of the multivariate analyses are presented. The chapter concludes with a discussion of the findings. 4.1 Factor Analysis The Procedure Factor analysis can be used to summarize patterns of correlations among observed variables, and to reduce a large number of observed variables to a smaller number of factors. Fifty-three statements were listed in Chapter 3 that 80

95 were measured on a 4-point scale (Mostly agree (1), Somewhat agree (2), Somewhat disagree (3), and Mostly disagree (4)). Comrey and Lee (1992) suggested that expected factors believed to underlie the domain of interest be identified with statements related to each concept as being identified. The domains of interest identified in chapter 3 were attitude toward a behavior, subjective norm, and perceived behavioral control. Specific statements were associated with each domain. Using the domains noted above, principal component factor analysis with varimax rotation was performed on each domain to get the value of factor loadings and communalities, percents of variance, and scree plots of Eigenvalues. In addition, a correlation matrix was obtained for the 53 items to check for collinearity. From the results of factor analysis, each group was arranged in order of factor loadings, highest to lowest. Through the comparison of factor loadings, communality, total variance, and scree plot of Eigenvalue between each domain and all items, and correlation matrix of all 53 statements, it was determined that all factors in the second and the third domains were internally consistent and well defined by the statements. However, 11 statements in the first domain (attitude toward a behavior) were not well-defined by the factor solution and 11 items did not load on any factor. Failure of some items to load on a factor reflects heterogeneity of items. 81

96 Principal component factor analysis with varimax rotation was conducted again on each of the three domains with and without these 11 items. As a result, the 11 items were well-fitted on 3 factors in the third domain (perceived behavioral control). At this point, the 3 domains were labeled to give concept groups reflecting the fact that statements did not load as expected: attitude and knowledge, personal interaction, financial planning. The use of the 3 concept groups leads to grouping different from that proposed in Chapter 3 (attitude, subjective norm, and perceived behavioral control). The primary difference related to the financial planning concept group which is broader than the attitude domain or the perceived behavioral control domain. In section the distribution of factors among the TPB elements is discussed The Results As described in Chapter 3, principal component factor analysis with varimax rotation was used to reduce the number of variables related to attitude toward a behavior, subjective norm and perceived behavioral control. The variables were measured on a 4-point scale (Mostly agree (1), Somewhat agree (2), Somewhat disagree (3), and Mostly disagree (4)) in the MacroMonitor Survey. The questions were divided into three concept groups, depending on question similarity: attitude and knowledge, personal interaction, and financial planning. Through principal component factor analysis 15 factors were identified 82

97 explaining most of the variance observed in the 53 underlying variables. Factor scores using the Anderson-Rubin approach in SPSS 11.5 were used in the subsequent analyses. For the concept group attitude and knowledge, four factors were identified: satisfaction with finances, poor financial knowledge, financial decision confidence, and positive attitude toward financial institutions (Table 4.1). For the concept group, financial planning, seven factors were identified: lack of financial discipline, degree of risk aversion, concern with debt, positive attitude toward credit market risk, prefer less complex financial strategies, credit use, and minimal search for new financial products (Table 4.2). For the concept, personal interaction, four factors were identified: professional advice unneeded, personal relationship unimportant, personal contact desired, and one-on-one interaction unneeded (Table 4.3). In the following discussion each factor is described. The coding of variables entering into the factor analysis was 1 = mostly agree to 4 = mostly disagree. In those instances where coding was reversed the effect was 1 = mostly disagree to 4 = mostly agree. In sections that follow, the statement actually evaluated by the respondents is first listed. Then, if the responses were not reverse coded, the statement is restated as to its meaning when 4 = mostly disagree. The four attitude and knowledge factors explained more than half of the variance observed in the variables (56.9%). The factor, satisfaction with finances, included the following variables: 83

98 I am satisfied with my household s current financial situation (no change, scale reversed). I am afraid my household is not saving enough for its future needs (I am not afraid my household is not saving enough for its future needs) 6. My household should make some important changes in our savings and investments (My household should not make some important changes in our savings and investments). Higher factor scores indicated more satisfaction with a household s current financial situation. Satisfaction with finances explained 27.5% of the variance observed in the factor analysis and the internal reliability was high as Cronbach s alpha was If Cronbach s alpha is greater than 0.7, the factor is highly reliable, if between 0.5 and 0.7 reliability is moderately reliable, and if below 0.5 reliability is a concern (SPSS, 2000; Tabachnick & Fidell, 2001). The second factor, financial decision confidence, included the following variables: I do not need advice on investment options (no change, scale reversed). I feel qualified to make my own investment decisions (no change, scale reversed). 6 While awkward, double negatives are used when restating the question. The reason is that not enjoying something is not the same as disliking something. 84

99 I feel uncomfortable making judgments about the riskiness of investment (I do not feel uncomfortable making judgments about the riskiness of investment). Higher factor scores indicated greater confidence with financial decisions-making ability. The factor, financial decision confidence explained 11% of the variance with three items and the value of Cronbach s alpha (.55) was acceptable. The third factor, poor financial knowledge, included the following variables: I enjoy learning about different investment opportunities (I do not enjoy learning about different investment opportunities). Over the past several years, I have become much more knowledgeable about savings and investments (Over the past several years, I have not become much more knowledgeable about savings and investments). I consider myself a sophisticated investor (I do not consider myself a sophisticated investor). Higher factor scores indicated less financial knowledge. Poor financial knowledge explained 10% of the variance with three items. Internal reliability was acceptable with the value of Cronbach s alpha being The fourth factor was positive attitude toward financial institutions. It included the following variables: I resent any profits financial institutions make from my doing business with them (I do not resent any profits financial institutions make from my doing business with them). 85

100 Dealing with financial institutions is about as much fun as being stuck in a traffic jam (Dealing with financial institutions is not about as much fun as being stuck in a traffic jam). I worry about the safety of my deposits in banks or savings institutions (I do not worry about the safety of my deposits in banks or savings institutions). Higher factor scores indicated a more positive attitude toward financial institutions. The factor, positive attitude toward financial institutions explained 9% of the variance with three items and the value of Cronbach s alpha was

101 Factor Satisfaction with Finances (Eigenvalue = 3.58, variance explained = 27.51%, Cronbach s alpha =.78) Financial decision confidence (Eigenvalue = 1.43, variance explained = 10.97%, Cronbach s alpha =.55) Question (Variable) I am satisfied with my household s current financial situation (Scale Reversed). I am afraid my household is not saving enough for its future needs. My household should make some important changes in our savings and investments. I do not need advice on investment options (Scale Reversed). I feel qualified to make my own investment decisions (Scale Reversed). I feel uncomfortable making judgments about the riskiness of investment. Factor Loading Poor financial knowledge (Eigenvalue = 1.29, variance explained = 9.88%, Cronbach s alpha =.67) Positive attitude toward financial institutions (Eigenvalue = 1.11, variance explained = 8.57%, Cronbach s alpha =.51). I enjoy learning about different investment opportunities..800 Over the past several years, I have become much more knowledgeable about savings and investments..759 I consider myself a sophisticated investor..578 I resent any profits financial institutions make from my doing business with them. Dealing with financial institutions is about as much fun as being stuck in a traffic jam. I worry about the safety of my deposits in banks or savings institutions Table 4.1: Attitude and knowledge questions: Factor analysis. 87

102 For the personal interaction concept group, four factors were identified: professional advice unneeded, personal relationship unimportant, personal contact desired, and one-on-one interaction unneeded (Table 4.2). The four personal interaction factors explained more than half of the variance observed in the variables (53.5%). The factor accounting for the greatest variance, professional advice unneeded, included the following variables: It is important that a financial services representative makes recommendations I should consider (It is not important that a financial services representative makes recommendations I should consider). It is important that a financial services representative keeps me informed of where I stand financially (It is not important that a financial services representative keeps me informed of where I stand financially). I like to discuss my financial options before making a decision about them (I do not like to discuss my financial options before making a decision about them). I prefer to consult a specialist when making financial decisions (I do not prefer to consult a specialist when making financial decisions). I would be willing to pay for professional financial advice (I would not be willing to pay for professional financial advice). 88

103 Using my financial institution as a sounding board for ideas about my finances is important to me (Using my financial institution as a sounding board for ideas about my finances is not important to me). Higher factor scores indicated less desire to get professional advice. The factor explained 23.6% of the variance with six items. Its internal reliability was high in terms of Cronbach s alpha (α =.81). The second factor, personal relationship unimportant, included the following variables: Building long-term relationships with financial institutions is more important than always getting the best prices or newest products (Building long-term relationships with financial institutions is not more important than always getting the best prices or newest products). I am more concerned with the quality of service than with cost when I deal with financial institutions (I am not more concerned with the quality of service than with cost when I deal with financial institutions). It is important to me that the people I deal with for financial matters recognize me and know me by name (It is not important to me that the people I deal with for financial matters recognize me and know me by name). Chatting with the people I know at financial institutions is an important part of doing financial business for me (Chatting with the people I know at 89

104 financial institutions is not an important part of doing financial business for me). Higher factor scores indicated that relationships with people at financial institutions are not important. The factor, personal relationship unimportant, explained 15.1% of the variance observed with four items and reliability was satisfactory in terms of Cronbach s alpha (α =.75). The third factor was personal contact desired. It included the following variables: I would rather use automated teller machines, personal computers, the telephone, or mail than face representatives of financial institutions (I would not rather use automated teller machines, personal computers, the telephone, or mail than face representatives of financial institutions). The less I talk to financial institution personnel the better (The less I talk to financial institution personnel is not better). Higher factor scores indicated greater desire for contact with people at financial institutions. The factor explained 7.6% of the variance with three items and Cronbach s alpha was acceptable (α =.56). The fourth factor, one-on-one interaction unneeded, included the following variables: I would like to go to just one person who can help me with my savings, investments, and credit needs (I would not like to go to just one person who can help me with my savings, investments, and credit needs). 90

105 I am unlikely to try a new financial service until someone I know recommends it (I am likely to try a new financial service until someone I know recommends it). I prefer to do most of my financial business in person (I do not prefer to do most of my financial business in person). Higher factor scores indicated less preference for one-on-one interaction. Oneon-one interaction unneeded explained 7.3% of the variance observed with three items and reliability measure was low (α =.41). 91

106 Factor Professional advice unneeded (Eigenvalue = 3.78, variance explained = 23.63%, Cronbach s alpha =.81) Question (Variable) It is important that a financial services representative makes recommendations I should consider. It is important that a financial services representative keeps me informed of where I stand financially. I like to discuss my financial options before making a decision about them. Factor Loading I would be willing to pay for professional financial advice..671 Personal relationship unimportant (Eigenvalue = 2.41, variance explained = 15.06%, Cronbach s alpha =.75) Personal contact desired (Eigenvalue = 1.21, variance explained = 7.57%, Cronbach s alpha =.56) One-on-one Interaction unneeded I prefer to consult a specialist when making financial decisions. Using my financial institutions as a sounding board for ideas about my finances is important to me. Building long-term relationships with financial institutions is more important than always getting the best prices or newest products. I am more concerned with the quality of service than with cost when I deal with financial institutions. It is important to me that the people I deal with for financial matters recognize me and know me by name. Chatting with the people I know at financial institutions is an important than always getting the best prices or newest products. I would rather use automated teller machines, personal computers, the telephone, or mail than face representatives of financial institutions. The less I talk to financial institutions personnel the better. I would like to go to just one person who could help me with my savings, investments, and credit needs (Eigenvalue = 1.17, variance explained = 7.28%, Cronbach s alpha =.41) I am unlikely to try a new financial service until someone I know recommends it..600 I prefer to do most of my financial business in person..579 Table 4.2: Personal interaction questions: Factor analysis. 92

107 For the financial planning concept group, seven factors were identified: lack of financial discipline, degree of risk aversion, concern with debt, positive attitude toward credit market risk, prefer less complex financial strategies, credit use, and minimal search for new financial products (Table 4.3). The seven financial planning factors explained more than half the variance observed in the variables (50.6%) The factor accounting for the greatest variance was lack of financial discipline. This factor included the following variables: I do a very good job of keeping my financial affairs in order (I do not do a very good job of keeping my financial affairs in order). I am very organized in my approach to financial matters (I am not very organized in my approach to financial matters). My household knows how to choose financial products and services that are best for us (My household does not know how to choose financial products and services that are best for us). Managing my financial affairs is something like a hobby. I enjoy taking care of them (Managing my financial affairs is not something like a hobby. I do not enjoy taking care of them). My household is successful in sticking to its budget (My household is not successful in sticking to its budget). I am very disciplined in savings and spending decisions (I am not very disciplined in savings and spending decisions). 93

108 Often I am not sure whether the financial decisions I have made are the right ones (no change, scale reversed). Higher factor scores indicated less financial discipline with respect to household financial management. The factor, lack of financial discipline, explained 17.3% of the variance with seven items, and a Cronbach alpha of 0.82 indicated the internal reliability was acceptable. The second factor was degree of risk aversion. It included the following variables: I am willing to take high risks to realize substantial financial gains from investments (I am not willing to take high risks to realize substantial financial gains from investments). It is wise to put some portion of savings in uninsured investments to get a high yield (It is not wise to put some portion of savings in uninsured investments to get a high yield). I am willing to accept some risk of losing money if an investment is likely to come out ahead of inflation in the long run (I am not willing to accept some risk of losing money if an investment is likely to come out ahead of inflation in the long run). Over the long run, say 10 or 20 years, stocks will be a very good investment (Over the long run, say 10 or 20 years, stocks will not be a very good investment). The stock market is too risky for me (no change, scale reversed). 94

109 It is very important to me to have both a guaranteed interest rate and federal insurance on my savings (no change, scale reversed). Higher factor scores indicated a consumer is risk-averse in stock market. The factor, degree of risk aversion, explained 10.2% of the variance with six items and internal reliability was acceptable with a Cronbach s alpha of Concern with debt was the third factor. It included the following variables: I am concerned that our household has more debt than it should (no change, scale reversed). In the past, I sometimes spent more than I really wanted to because credit cards made it easy (no change, scale reversed). I am concerned about problems my debts would cause should I die or become disabled (no change, scale reversed). Higher factor scores indicated more worry about household debt. The factor, concern with debt, explained 6.9% of the variance in the factor analysis. Reliability was high as reflected through Cronbach s alpha (α=.66). The fourth factor was labeled, positive attitude toward credit market risk. It included the following variables: I would never get a personal or auto loan that had an interest rate that could change (I would get a personal or auto loan that had an interest rate that could change). I would never get a mortgage that had an interest rate that could change (I would get a mortgage that had an interest rate that could change). 95

110 Higher factor scores indicated risk seeking in the credit market. The factor, positive attitude toward credit market risk, explained 5.3% of the variance with two items and the factor also exhibited strong internal reliability as Cronbach s alpha was Prefer less complex financial strategies was the fifth factor that was extracted. It included the following variables: Finding tax-exempt or tax-deferred investment is important to me (Finding tax-exempt or tax-deferred investment is not important to me). I would pay a one-time 5% fee for an investment guaranteed to grow 3% faster than inflation (I would not pay a one-time 5% fee for an investment guaranteed to grow 3% faster than inflation). I think the best way to save is to have savings or investments made automatically from my income (I do not think the best way to save is to have savings or investments made automatically from my income). I prefer investments where the return is in the form of long-term capital gains to defer taxes (I do not prefer investments where the return is in the form of long-term capital gains to defer taxes). Higher factor scores indicated less preference for complex financial strategies. Prefer less complex financial strategies explained 4.3% of the variance with four items. Reliability was a concern (α = 0.41). The sixth factor, credit use, included the following variables, 96

111 I would never borrow from my retirement plan (I would borrow from my retirement plan). I am careful not to use credit more than I should (I am not careful not to use credit more than I should). Higher factor scores indicated less caution when using credit. This factor explained 3.5% of the variance observed in two items and its internal reliability was low (α = 0.38). Finally, the last factor, minimal search for new financial products, consisted of the following variables: I am always looking for the lowest cost financial services (I am not looking for the lowest cost financial services). I shop around for financial products/services (I do not shop around for financial products/services). Higher factor scores indicated less preference for searching for new financial products. This factor explained a total of 3.2% of the variance among this set of factors and the value of Cronbach s alpha was 0.66; its internal reliability was acceptable. 97

112 Factor Lack of financial discipline (Eigenvalue = 5.53, variance explained = 17.29%, Cronbach s alpha =.82) Question (Variable) I do a very good job of keeping my financial affairs in order. Factor Loading.790 I am very organized in my approach to financial matters..763 My household knows how to choose financial products and services that are best for us..711 Managing my financial affairs is something like a hobby. I enjoy taking care of them..681 My household is successful in sticking to its budget..574 I am very disciplined in savings and spending decisions..566 Degree of risk aversion (Eigenvalue = 3.26, variance explained = 10.19%, Cronbach s alpha =.76) Often I am not sure whether the financial decisions I have made are the right ones (Scale Reversed). I am willing to take high risks to realize substantial financial gains from investments. It is wise to put some portion of savings in uninsured investments to get a high yield. I am willing to accept some risk of losing money if an investment is likely to come out ahead of inflation in the long run. Over the long run, say 10 or 20 years, stocks will be a very good investment The stock market is too risky for me (Scale Reversed)..616 Concern with debt (Eigenvalue = 2.2, variance explained = 6.87%, Cronbach s alpha =.66) It is very important to me to have both a guaranteed interest rate and federal insurance on my savings (Scale Reversed). I am concerned that our household has more debt than it should (Scale Reversed). In the past, I sometimes spent more than I really wanted to because credit cards made it so easy (Scale Reversed) Continued Table 4.3: Financial planning questions: Factor analysis. 98

113 Table 4.3 Continued Positive attitude toward credit market risk (Eigenvalue = 1.68, variance explained = 5.26%, Cronbach s alpha =.82) Prefer less complex financial strategies (Eigenvalue = 1.38, variance explained = 4.32%, Cronbach s alpha =.41) I am concerned about problems my debts would cause should I die or become disabled (Scale Reversed). I would never get a personal or auto loan that had an interest rate that could change. I would never get a mortgage that had an interest rate that could change (J2_4). Finding tax-exempt or tax-deferred investments is important to me. I would pay a one-time 5% fee for an investment guaranteed to grow 3% faster than inflation. I think the best way to save is to have savings or investments made automatically from my income. I prefer investments where the return is in the form of long-term capital gains to defer taxes Credit use (Eigenvalue = 1.11, variance explained = 3.47%, Cronbach s alpha =.38) Minimal search for new financial products (Eigenvalue = 1.02, variance explained = 3.19%, Cronbach s alpha =.66) I would never borrow from my retirement plan..626 I am careful not to use credit more than I should..548 I am always looking for lowest cost financial services..830 I shop around for financial products/services

114 4.1.3 Linking factor analysis concept groups to TPB The factor analysis led to three concept groups (attitude and knowledge, personal interaction, financial planning) of related factors. The three concept groups were not a direct match with the three elements (attitude, subjective norm, and perceived behavioral control) in TPB. For the descriptive and the multivariate analyses, the 15 factors identified through factor analysis were assigned to the TPB elements based on the definition of attitude, subjective norm, and perceived behavioral control. Attitude is defined as an individual s positive or negative feelings (evaluative affect) about performing a behavior (Fishbein & Ajzen, 1975). Seven factors, satisfaction with finances, financial decision confidence, poor financial knowledge, positive attitude toward financial institutions, degree of risk aversion, concern with debt, and positive attitude toward credit market risk, were identified as factors relating to attitude toward a behavior when intending to adopt online financial services. Individuals dissatisfied with the state of their finances may tend to have negative attitude toward their financial situation leading them to seek new ways to learn of financial products and services. People who are confident or knowledgeable of financial matters may have positive feelings toward financial decisions seeking ways to improve financial decisions. Individuals who have positive attitude toward financial institutions may be willing to try new financial 100

115 products/services. Individuals who are risk seeking in stock and credit market may also have positive feeling toward stock and credit market so that they are more likely to use new financial products/sevices. Concern with debt may make people want to reduce their debt. Thus, all factors listed above are related to attitude toward a behavior for the intention to use online financial services. Subjective norm refers to the person s perception that most people who are important to him think he should or should not perform the behavior in question (Fishbein & Ajzen, 1975, p. 302). Four factors (professional advice unneeded, personal relationship unimportant, personal contact desired, one-onone interaction unneeded) were consistent with the definition of subjective norm. Perceived need for professional advice relates to an individual need to seek information and the approval of others. Social interaction such as personal relationships, personal contact, and one-on-one interaction with people also reflects a need to involve others in a decision making process. Thus, all four factors are fitted with the definition of subjective norm. Perceived behavioral control reflect beliefs regarding access to the resources needed to perform a behavior (Ajzen, 1991). Resources affect perceived behavioral control and may be formed by time, money, skills, other specialized resources, and previous experience required to perform a behavior (Ajzen, 1991). Four factors (lack of financial discipline, prefer less complex financial strategies, credit use, and minimal search for new financial products) fit with the definition of perceived behavioral control. 101

116 Perceived financial discipline reflects how well an individual manages financial resources and whether they have the skills needed for financial management. People who were not disciplined in financial matters may seek new ways of making their financial decisions. The factor, prefer less complex financial strategies, reflects individual s willingness to engage in activities requiring greater levels of sophistication. Individuals with preferences for complex financial strategies are more likely to have the knowledge and understanding needed for successful financial management. The factor, credit use, also relates to willingness to engage in activities requiring greater levels of knowledge and understanding. People who search for new financial products are likely to have more experience and knowledge. Thus, all four factors reflect the nature of perceived behavioral control. 4.2 Descriptive Analysis The sample for this study consists of 3,780 households who responded to the question about current or future use of online financial services. Of these 3,780 respondents, 637 (17%) households were classified as current users of online financial services, while 3,143 (83%) households were classified as nonusers of online financial services. Table 4.4 shows the frequency of current users and non-users for specific online financial services. Two aspects of the table stand out. First within the group of users, account management and investment 102

117 online financial service uses clearly dominate other uses. Second, while relatively more non-users indicate account management as a possible use, the distribution across the groups is relatively even. Appendix C provides descriptive statistics comparing current users and non-users. Types of Online Current Users Non-Users All (N = 3780) Financial Services (N = 637) (N = 3143) Account Management 362 (9.6%) 362 (56.8%) 1482 (47.2%) Loans 133 (3.5%) 133 (20.9%) 1121 (35.7%) Investment 395 (10.4%) 395 (62.0%) 1240 (39.5%) Insurance 89 (2.4%) 89 (14.0%) 1065 (33.9%) Table 4.4: Frequency of current users and non-users for specific use of online financial services Comparing Intended Users to Intended Non-Users Two statistical tests (T-test and χ 2 test) were conducted to identify significant differences between intended users and intended non-users of online financial services. The results, summarized in Tables 4.5 through 4.8, show 103

118 significant statistical differences for most of the variables between intended users and intended non-users of online financial services. Table 4.5 presents the descriptive statistics and test statistics for the control variables. With significance set at the p 0.05, the results show statistical differences between intended users and intended non-users with respect to age, gender, marital status, and the number of dependent children. Intended users tend to be older (males, 52 years; females, 54 years) than intended non-users (males, 45 years; female, 46 years). Seventeen percent of the intended users had a female head only complete the questionnaire, while 21% of the intended non-users had a female head only complete the questionnaire. A higher percent of both male and female intended user heads (73%) completed the survey than non-user heads (68%). Seventy-three percent of intended users were married or living together, compared to 67% of the intended non-users. Intended users had more dependent children (.99) on average compared to intended non-users (.70). 104

119 Variables Intended Users (N = 1689) Intended nonusers (N = 1454) P- values for T- test or χ 2 test Mean age of Male Head of Household (1395) a (1151) a.000 Mean age of Female Head of Household (1530) (1288).000 Gender of respondents who completed questionnaires n = 1689 n = Male head only Female head only Both M and F heads 9.4% 17.4% 73.2% 11.4% 20.8% 67.7% Marital Status n = 1685 n = Single Divorced Separated Widowed Married Living together but not married 12.7% 9.7% 1.7% 2.7% 68.1% 5.0% 9.7% 11.1% 1.6% 9.7% 63.4% 4.0% Number of dependent children 0.99 (1672) 0.70 (1415).000 T-test for means and χ 2 test for distribution a mean value (number of cases) Table 4.5: Demographic control variables of intended users and intended nonusers of online financial services. Table 4.6 shows the results of the T-test or χ 2 test related to financial behaviors and attitudes. At p.05, the T-test or χ 2 test identify significant differences between intended users and intended non-users for household 105

120 financial strategy, attitude toward risk, satisfaction with finances, poor financial knowledge, positive attitude toward financial institutions, degree of risk aversion in the stock market, positive attitude toward credit market risk, and concern with debt. Eighty-one percent of intended users of online financial services had some level of a financial strategy, compared to 74% of intended non-users. Intended users were also more likely to be risk-seekers. Thirty-four percent of intended users tended to prefer above average risk/return in the households savings and investments, compared to 16% of intended non-users. Intended users of online financial services were more dissatisfied with their financial situation as measured by the Anderson-Rubin factor score than intended non-users. Moreover, intended users of online financial services had a more positive attitude toward financial institutions and credit market risk than intended non-users, while intended users were more risk averse than intended non-users. In addition, intended users had more financial knowledge than intended non-users. Intended users were also more concerned about debt than intended non-users. 106

121 Variables Household's financial strategy 1 Have specific financial strategy 2 Have general financial strategy 3 Have a partial, but incomplete financial strategy 4 Have no financial strategy Intended Users (N = 1689) n = % 45.8% 24.6% 19.4% Intended nonusers (N = 1454) n = % 42.4% 17.2% 26.4% P- values for T- test or χ 2 test.000 Attitude toward risk of households savings and investments 1 Very low risk/very low return 2 Below average risk/below average return 3 Average risk/average return 4 Above average risk/above average return 5 Very high risk/very high return n = % 7.9% 50.3% 33.6% 4.1% n = % 8.3% 63.6% 14.8% 3.0%.000 Satisfaction with finances (1689) a.1598 (1454) a.000 Financial decision confidence (1689) (1454).213 Poor financial knowledge (1689).2249 (1454).000 Positive attitude toward financial institutions.0843 (1689) (1454).000 Degree of risk aversion (1689).3958 (1454).000 Concern with debt.0756 (1689) (1454).001 Positive attitude toward credit market risk.0372 (1689) (1454).000 T-test for means and χ 2 test for distribution a mean value (number of cases) Table 4.6: Attitude variables (intended users compared to intended non-users). Table 4.7 presents the results of the T-test or χ 2 test of variables associated with subjective norms. With p.05, significant statistical differences between intended users and intended non-users were found for total number of household members, frequency of receiving advice for financial decisions, use of 107

122 professional financial advisors in the last 2 years and next 12 months, number of information sources for financial service or decisions made in the last 12 months, professional advice unneeded, personal relationship unimportant, personal contact desired, one-on-one interaction unneeded, how to obtain financial information both now and in the future, and how to make financial decisions in the future. Household size of intended users of online financial services was greater (2.8) than non-users (2.49). Intended users received advice more frequently, used more professional financial services, and used a larger number of information sources for their financial decisions than intended non-users. Intended users of online financial services perceived a greater need for professional advice than intended non-users. Moreover, intended non-users had a greater preference for personal contact and a greater need for one-on-one interaction than did intended users. Intended users were more likely to obtain financial information from professionals both now and in the near future than intended non-users. In addition, more intended non-users (66%) than intended users (56%) would like to make financial decisions by themselves. 108

123 P- Variables Intended Intended nonusers for T- values Users (N = 1689) (N = 1454) test or χ 2 test Total number of household members 2.80 (1689) a 2.49 (1454) a.000 Frequency of receiving advice for major household financial decisions 1 Always 2 Sometimes 3 Rarely 4 Never n = % 35.4% 29.0% 22.6% n = % 26.4% 31.5% 29.6%.000 Use of professional financial advisors last (1689) 0.79 (1454).000 years Use of professional financial advisors next (1689) 1.00 (1454).000 months Number of information sources for financial 4.05 (1689) 2.44 (1454).000 service or decisions used in the last 12 months Professional advice unneeded (1689).1554 (1454).000 Personal relationship unimportant.0455 (1689) (1454).012 Personal contact desired (1689).2662 (1454).000 One-on-one interaction unneeded.0669 (1689) (1454).000 How household now obtains financial information 1 Mostly own 2 Mostly professional 3 Joint n = % 8.7% 32.9% n = % 8.7% 27.4%.003 How household would like to obtain financial information 1 Mostly own 2 Mostly professional 3 Joint n = % 13.7% 54.0% n = % 11.4% 42.9%.000 How household now makes financial decisions 1 Mostly own 2 Mostly professional 3 Joint n = % 1.6% 17.5% n = % 2.2% 17.7%.487 Continued Table 4.7: Subjective norm variables (intended users compared to intended nonusers). 109

124 Table 4.7 continued How household would like to make financial decision 1 Mostly own 2 Mostly professional 3 Joint T-test for means and χ 2 test for distribution a mean value (number of cases) n = % 3.5% n = % 3.5% 31.0%.000 Table 4.8 presents the results of the T-test and χ 2 test for the perceived behavioral control variables. Based on the T-test and χ 2 test at p 0.05 levels, significant statistical differences between intended users and intended non-users were found with respect to education level, household gross income, use of financial computer software programs, frequency of past financial transactions, having ATM/Debit card, lack of financial discipline, prefer less complex financial strategies, and credit use. Intended users of online financial services were more likely to be college graduates (60%) than intended non-users (38%). Household gross income for intended users of online financial services was higher than intended non-users. Fifty percent of intended users had incomes of $60,000 or more, compared to 35% of intended non-users. Intended users of online financial services used financial computer software more frequently (2.10) than intended non-users (1.07). In addition, intended users more frequently used financial services in the last 3 months (32) than intended non-users (23). More intended users (61%) had 110

125 ATM Cards than intended non-users (47%). Forty-four percent of intended users had Debit cards while 32% of intended non-users had them. Intended users of online financial services lacked financial discipline based on the mean of the Anderson-Rubin factor score compared to intended non-users. In addition, intended users of online financial services preferred complex financial strategies more than intended non-users, while intended nonusers were more cautious of using credit than intended users. 111

126 Variables Highest level of Education Attainment among Male/Female Householder 1 High school or less 2 Some college 3 College degree or more Intended Users (N = 1689) n = % 29.1% 59.9% Intended nonusers (N = 1454) n = % 36.5% 38.1% P- values for T- test or χ 2 test.000 Household s 1997 gross income 1 $29,999 or less 2 $30,000-$59,999 3 $60,000-$99,999 4 $100,000 or more n = % 28.8% 27.9% 22.3% n = % 32.9% 21.4% 13.1%.000 Confidence level in reaching financial goals 1 Extremely confident 2 Very confident 3 Somewhat confident 4 Not very confident 5 Not at all confident n = % 30.6% 42.0% 12.7% 5.3% n = % 30.2% 39.3% 12.7% 6.5%.174 Number of financial computer software 2.10 (1689) a 1.07 (1454) a.000 programs used with home PC Number of hours per month of PC use at home (1063) (580).198 Frequency of financial transactions in the last (1679) (1418).000 months Have ATM cards 0.61 (1689) 0.47 (1454).000 Have Debit cards 0.44 (1689) 0.32 (1454).000 Lack of financial discipline.1468 (1689) (1454).000 Prefer less complex financial strategies (1689).0518 (1454).000 Credit use.0614 (1689) (1454).000 Minimal search for new financial products.0420 (1689) (1454).113 T-test for means and χ 2 test for distribution a mean value (number of cases) Table 4.8: Perceived behavioral control variables (intended users compared to intended non-users). 112

127 4.3 Result of Multivariate Analyses Multicollinearity Logistic regression was used in this study to examine the determinants of a household s intention to adopt online financial services. In estimating logistic regressions, many independent variables, which may be correlated, were regressed on the household s intended use of online financial services. Multicollinearity among the independent variables can lead to biased estimates and inflated standard errors of the regression coefficient estimates (Knapp, 1998). In order to limit the problem related to multicollinearity in this study, a correlation test that could detect high intercorrelations between independent variables was conducted (Appendix F). The test did not find independent variables intercorrelated to a degree that would produce a multicollinearity concern. In factor analysis, the varimax method was used to rotate the factors, which usually loads variables highly on factors, while maintaining orthogonal (uncorrelated) factors (SPSS, 1998). Moreover, factor scores were obtained in this study by the Anderson-Rubin approach that provides scores that are uncorrelated with each other. Collinearity diagnostics were also examined to check for multicollinearity (Table D.2). In Appendix D, statistics such as Eigenvalues, variance inflation 113

128 factors (VIF), condition indices, and tolerances for individual variables are reported. Tolerance refers to the percentage of the variance in a given variable that cannot be explained by the other variables. When the tolerance values are close to 0, there is high multicollinearity and the standard error of the regression coefficients will be inflated. In Table D.2, the tolerance values ranged from 0.40 to One way to quantify collinearity is with variance inflation factors (VIF). A variance inflation factor (VIF) greater than 3 is considered to indicate a serious problem of multicollinearity. There were no VIF values over 3 in the model. Eigenvalues close to 0 indicate that the variable was highly intercorrelated and that small changes in the data values may lead to large changes in the estimates of the coefficients. Only one eigenvalue (minimal search for new financial products: 0.061) was close to 0. Moreover, this variable was not significant in the subsequent multivariate analyses. The condition index is a measure of the tightness or dependency of one variable upon the others. The condition indices are computed as the square roots of the ratios of the largest eigenvalue to each successive eigenvalue. A high condition index is associated with variance inflation in the standard error of the parameter estimates. Values greater than 15 indicate a possible problem with collinearity. In this study, there was nothing over 15, and the greatest value was (Table D.2). 114

129 4.3.2 Missing Values Missing data is one of the most pervasive problems in data analysis based on financial characteristics. As noted earlier, mean substitution was used for most cases of missing data. The disadvantage of mean imputation is reduced variance, because the estimate is close to the mean. To check for any bias introduced through imputation, two logistic regressions to compare results before and after missing data imputation were run (Table D.1). Independent variables had the same direction and magnitude of effect on the intention to use online financial services in both models. The logistic regression model after mean imputation, as expected, yielded more significant coefficients, given the larger sample size Variables In this study, independent variables used in the descriptive analysis were slightly different from those used in the multivariate analysis. Independent variables used in the multivariate analysis were continuous, interval, and categorical (Table 4.9). Age, gender of respondents, and marital status were included as control variables in the multivariate analysis. Household age was used for male / female headed households and individual ages for single head 115

130 households. The number of dependent children was not included in the multivariate analysis as household size already captured most of this element. The attitude variables included attitude toward risk, satisfaction with finances, financial decision confidence, poor financial knowledge, positive attitude toward financial institutions, degree of risk aversion in the stock market, positive attitude toward credit market risk, and concern with debt. Since a household s financial strategy was highly correlated with other attitude variables, it was excluded from the multivariate model. The subjective norm variables included household size, professional advice unneeded, personal relationship unimportant, personal contact desired, and one-on-one interaction unneeded as its elements. The questions, frequency of receiving advice, use of professional financial advisors in the last two years, number of information sources used in the last 12 months, how household now/would like to obtain financial information, and how household now/would like to make financial decisions were highly correlated with other subjective norm variables and excluded from the multivariate analysis. The perceived behavioral control block of variables included education, income, hours per month of PC use at home, frequency of financial transactions in the past, having ATM/Debit cards, lack of financial discipline, prefer less complex financial strategies, credit use, and minimal search for new financial products. The confidence level in reaching financial goals was highly correlated with perceived behavioral control variables and was excluded from the 116

131 multivariate analysis. Use of financial computer software with a home PC was correlated with hours per month of PC use at home, and excluded from the multivariate analysis. 117

132 Variables Demographic Control Variables Household age Gender Male Female (Reference) Male/Female Marital status Single Married (Reference) Others Attitude Variables Attitude toward risk Low risk/return Average risk/return (Reference) High risk/return Description Actual mean age of male and female household heads or actual age of single head Male household head = 1, else = 0 Female household head = 1, else = 0 Both Male and Female household head = 1, else =0 single = 1, else = 0 married or living together but not married =1, else =0 divorced, separated, and widowed =1, else =0 prefers very low or below average risk/return =1, else=0 prefers average risk/return =1, else=0 prefers above average or very high risk/return =1, else=0 Satisfaction with finances Factor scores Financial decision confidence Factor scores Poor financial knowledge Factor scores Positive attitude toward financial Factor scores institutions Degree of risk aversion in the stock Factor scores market Concern with debt Factor scores Positive attitude toward credit market Factor scores risk Subjective Norm Variables Household size Number of household members Professional advice unneeded Factor scores Personal relationship unimportant Factor scores Personal contact desired Factor scores One-on-one interaction unneeded Factor scores Perceived Behavioral Control Variables Education High school or less Some college(reference) College more less than high school or high school graduate =1, else=0 some college =1, else=0 college degree or more =1, else=0 Continued Table 4.9: A summary description of the study variables (sample = 3143). 118

133 Table 4.9 continued Income $29,999 or below (Reference) $30,000-$59,999 $60,000-$99,999 $100,000 or more Hours per month of PC use Frequency of 10 types of financial transactions in the last 3 months Have ATM cards Have Debit cards Lack of financial discipline Prefer less complex financial strategies Credit use Minimal search for new financial products =1, else=0 =1, else=0 =1, else=0 =1, else=0 Number of hours Number of transactions =1, else=0 =1, else=0 Factor scores Factor scores Factor scores Factor scores Results of Logistic Analyses Logistic regression was used in this study to predict the dichotomous dependent variable (intended user or intended non-user of online financial services). Logistic regression coefficients were also used to estimate odd ratios for the independent variables Role of TPB Blocks of Variables According to the Theory of Planned Behavior (TPB) (Figure 2.3), a behavior is determined by behavioral intention, and behavioral intention is determined by attitude toward the behavior, subjective norm, and perceived 119

134 behavioral control. In the TPB model perceived behavioral control is added to the two factors in the Theory of Reasoned Action (TRA) (attitude toward the behavior and the subjective norm). Based on the development of these theoretical approaches, the independent variables were divided into three blocks (attitude toward the behavior, subjective norm, and perceived behavioral control) with demographic control variables making up a fourth block used as controls. The demographic control variable block was entered first. The remaining three blocks were entered in the following order attitude toward the behavior, subjective norm, and perceived behavioral control (Table 4.10). The value of the Goodness-of-Fit χ 2 was used to compare the relative contribution of each added block. In the demographic control model, the results reported in Table 4.10 show the fitted probabilities as a function of the intended general uses of online financial services. The Pearson Goodness-of-Fit Chi Square was , (df = 5, p <.0001). Therefore, the model was statistically significant and intended general uses of online financial services could be predicted correctly with this model 10% of the time over a random assignment (Nagelkerke R 2 =.099). When adding attitude variables to the model and controlling for demographic control variables, the impact of attitude variables was significant (the value of block model χ 2 is ), the Pearson Goodness-of-Fit Chi Square was (df = 14, p <.0001). Thus, the attitude model was statistically significant and the intended use of online financial services could be 120

135 predicted correctly 21% of the time over a random assignment with the combined demographic control and attitude model (Nagelkerke R 2 =.210). When subjective norm variables were added to the model, the impact of subjective norm variables was significant (χ 2 increase of ), the Pearson Goodness-of-Fit Chi Square was (df = 19, p <.0001). The intended general use of online financial services could be predicted correctly 24% of the time over a random assignment with the combined demographic control, attitudes, and subjective norm model (Nagelkerke R 2 =.242). Finally, when perceived behavioral control variables were added to the model, the estimated model was significant (χ 2 increase of ), the Pearson Goodness-of-Fit Chi Square was (df = 32, p <.0001). The intended general use of online financial services could be predicted correctly 28% of the time over a random assignment when combined with the demographic control, attitudes, subjective norm, and perceived behavioral control model (Nagelkerke R 2 =.280). In Table 4.10, the model with all four blocks (demographic control, attitude, subjective norm, and perceived behavioral control) produced a log likelihood ratio of and the value of χ 2 of The likelihood ratio statistic was highly significant when compared to the χ 2 distribution, indicating a good fit of the model. Adding each block into the model, the impact of attitude variables (the value of block χ 2 was ) was the most significant among 121

136 the other blocks. Thus, we can say that attitude variables had greater impact on intention to use online financial services than the other groups. 122

137 Variables Demographic Control Attitude toward Behavior Subjective Norm Perceived Behavioral Control Constant *** *** *** *** Demographic Control Variables Household age.043***.036***.030***.029*** Gender of respondents:.262** male head Gender of respondents: both M/F heads or another person Marital status: single Marital status: divorced, -.352** separated, widowed Attitude Variables Attitude toward risk of HH savings/investments: low risk/low return Attitude toward risk of HH.422***.387***.298** savings/investments: high risk/high return Satisfaction with finances -.298*** -.269*** -.202** Financial decision -.129** -.098* confidence Poor financial knowledge Attitude toward financial.178***.170***.149** institutions Degree of risk aversion -.466*** -.360*** -.262*** Concern with debt -.110* Positive attitude toward.165***.132**.103* credit market risk Subjective Norm Variables Total number of HH members Professional advice -.155** -.113* unneeded Personal relationship unimportant Personal contact desired -.329*** -.251*** One-on-one interaction.232***.180*** unneeded Perceived Behavioral Control Variables Education among male/female householder: HS or less Continued Table 4.10: Independent variable groups and intention for general use of online financial services. 123

138 Table 4.10 continued Education among.490*** male/female householder: college degree or more Household income $30,000-$59,999 Household income.079 $60,000-$99,999 Household income.247 $100,000 or more Hours per month of PC.003* use at home Frequency of 10 types of.003* financial transactions in the last 3 months Have ATM cards.169* Have Debit cards.246** Lack of financial discipline.137* Prefer less complex -.106* financial strategies Credit use.113** Minimal search for new.027 financial products χ 2 Increase for added *** *** *** variables Pearson χ *** *** *** *** Nagelkerke R ***p<.001, **p<.01, *p<.05 The results for each of the four specific types of online financial services were similar to the results for general intended uses. The complete tables are provided in Appendix E with the findings summarized in Table The model for each group of variables was significant with the percent of intended users being predicted correctly over a random assignment model increasing with the 124

139 addition of another block. In addition, the χ 2 for the fully specified model was significant in each of the cases. Group Variable Demographic Control Variables Attitude Variables Subjective Norm Variables Perceived Behavioral Control variables χ 2 increase Nagelkerke R 2 Pearson χ 2 χ 2 increase Nagelkerke R 2 Pearson χ 2 χ 2 increase Nagelkerke R 2 Pearson χ 2 χ 2 increase Nagelkerke R 2 Pearson χ 2 ***p<.001, **p<.01, *p<.05 Account Management *** *** *** *** *** *** *** Loans Investment Insurance *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ** *** Table 4.11: Significance of variable blocks for the four types of online financial services Factors Affecting Intention Table 4.12 shows the odds ratios for all of the types of intended uses of online financial services. The exponential coefficient of logistic regression (expβ i ) is odds ratio. The odds ratio is the increase (or decrease if the ratio is less than one) in the odds of being in one outcome category when the value of the 125

140 independent variable increases by one unit. Odds ratios can be interpreted as the percentage change in the dependent variable with a one-unit change in an independent variable. Overall, there were no large differences in odds ratios among the five types of intended uses of online financial services (general use, account management, loans, investment, and insurance). Among the demographic control variables, age was significant across all five equations (Table 4.12). The probability of intended uses across the various online financial services was increased by about 3% for each year increase in age. The only other control variable found to be significant was male and female headed households where both heads responded to the questionnaire. This was a significant determinant for only the investment model. In this model, households with both a male and female head completing the questionnaire were about 35% more likely to be willing to use online financial services for investment than households with only a female head completing the questionnaire (the reference group). Among the attitude variables, a preference for high risk/return savings and investments increased the probability of using online financial services between 26% and 36% for general intended uses, loan uses and investment uses, relative to the reference group (average risk/return). Satisfaction with finances was a significant factor in all five types of uses. The probability of intention decreased between 18% and 21% across the five models for each unit increase in satisfaction with finances. Poor financial knowledge was found significant for 126

141 investment and insurance. As poor financial knowledge increased by one unit, the probability of intention to use online financial services decreased by 19% for investment use and by 9% for loan uses of online financial services. Positive attitude toward financial institutions was a significant variable for general uses, account management uses, and investment uses. The probability of intention to use increased by 16% for general uses and account management uses, and by 17% for investment uses for each unit increase in positive attitude toward financial institutions. Risk aversion was also found to be a significant determinant of the likelihood of intention to use for general uses, account management uses, and investment uses of online financial services. For each unit increase in degree of risk aversion, the probability of intention to use declined by 23% for general uses, 21% for account management uses and 20% for investment uses of online financial services. Concern with debt increased the likelihood of intention to use by 17% for loan uses of online financial services for each unit increase in concern with debt. Positive attitude toward credit market risk significantly affected intention to use across all five types of online financial service uses. The likelihood of intention to use increased by 11-12% for each unit increase in positive attitude toward credit market risk across the five equations. Moving to the subjective norm variables, professional advice unneeded was a significant determinant of the probability to use in all five equations. As professional advice unneeded increased by one unit, the likelihood of intended use decreased between 11% to 16%. Personal contact desired was also 127

142 significant across the five models. This variable, for each unit increase, leads to a decline of 15% to 24% in the likelihood of intention to use with the largest effect for account management uses and the smallest for loan uses of online financial services. One-on-one interaction unneeded had a significant positive effect on the intention probability for all five equations. A one unit increases in one-on-one interaction unneeded reduced the likelihood of intended use by 20% for general uses, 12% for account management uses, 14% for loan uses, 16% for investment uses, and 19% for insurance uses of online financial services. With respect to perceived behavioral control variables, having at least a college degree by at least one head relative to those with some college increased the likelihood of intended use by 63% for general uses, 46% for account management uses, 27% for loan uses, 51% for investment uses, and 34% for insurance uses. Households with an annual income of $100,000 or more had a likelihood of intention to use online investment uses that was 43% greater than those in the reference group (income $29,999 or less). Hours per month spent in PC use at home was significantly associated with general uses and investment uses. For each additional hour of PC use per month the likelihood of intention to use increased by 0.3% for general uses and by 0.2% for investment uses. Each additional financial transaction increased the likelihood of intending a general use by 0.3%. Having an ATM card increased the probability of intended use by 18% for general uses and by 20% for account management uses. Possession of a debit card increased intention probabilities 128

143 by 28% for general uses, 30% for account management uses, 18% for loan uses, and 17% for investment uses. Lack of financial discipline was also a significant determinant of intention probability, except for insurance uses. A unit increase in lack of financial discipline led to increased probability of intention to use: 15% for general uses, 18% account management uses, 15% for loan uses, and 15% for investment uses. Preference for less complex financial strategies was negatively associated with the probability of intention for all five types of online financial service uses. For each unit increase in preference for less complex financial strategies, the probability of intending to use decreased between 10% and 16% across the five equations. Credit use was a significant determinant for general uses, account management uses, and loan uses. The respondents were 12% more likely to intend to use online financial services for general uses and account management uses with a one-unit increase in credit use, and 18% more likely to intend to use online financial services for loan uses. 129

144 Odds Ratio Variables General Account Loan Investmt. Insurance uses Mgt. Uses Uses Uses Uses Constant.148***.137***.046***.115***.085*** Demographic Control Variables Household age 1.029*** 1.026*** 1.038*** 1.021*** 1.028*** Gender of respondents: male head Gender of respondents: ** both M/F heads or another person Marital status: single Marital status: divorced, separated, widowed Attitude Variables Attitude toward risk of HH savings/investments: low risk/low return Attitude toward risk of HH 1.347** ** 1.255* savings/investments: high risk/high return Satisfaction with finances.817**.818**.772***.792***.789*** Financial decision confidence Poor financial knowledge ***.906* Positive attitude toward 1.160** 1.159** *** financial institutions Degree of risk aversion.770***.787*** ***.912 Concern with debt ** Positive attitude toward 1.108* 1.105** 1.116** 1.119** 1.106** credit market risk Subjective Norm Variables Total number of HH members Professional advice.893*.865**.837***.887**.860** unneeded Personal relationship unimportant Personal contact desired.778***.757***.786***.807***.850*** One-on-one interaction unneeded 1.197*** 1.124** 1.139** 1.160** 1.186*** Continued Table 4.12: Odds ratios for five uses of online financial services. 130

145 Table 4.12 continued Perceived Behavioral Control Variables Education among male/female householder: HS or less Education among 1.632*** 1.455*** 1.274* 1.512*** 1.339** male/female householder: college degree or more Household income $30, $59,999 Household income $60, $99,999 Household income *.831 $100,000 or more Hours per month of PC use 1.003* * at home Frequency of 10 types of 1.003* financial transactions in the last 3 months Have ATM cards 1.184* 1.196* Have Debit cards 1.279** 1.304*** 1.178* 1.174* Lack of financial discipline 1.147* 1.180** 1.148** 1.147* Prefer less complex.899*.869**.845***.842***.877** financial strategies Credit use 1.119** 1.116** 1.183*** Minimal search for new financial products χ 2 Increase for added *** *** *** *** *** variables Pearson χ *** *** *** *** *** Nagelkerke R ***p<.001, **p<.01, *p< Discussion of Findings In the previous section, the log-odds for the logistic regression for the five uses of online financial services were presented (Table 4.12). The discussion of 131

146 these findings is focused on variables which were significant in all five equations. Since the seven variables were significant for all five uses of online financial services, the findings provide strong evidence that seven variables are significant determinants affecting intention to use online financial services regardless of the specific type of online financial service. The seven significant variables are: satisfaction with finances, positive attitude toward credit market risk, professional advice unneeded, personal contact desired, one-on-one interaction unneeded, education (college degree or more), prefer less for complex financial strategies. These variables are discussed as related to the hypotheses presented in Chapter Attitude toward Behavior H1-1: Positive attitude toward a technology positively affects the adoption of online financial services. H1-2: Risk seeking positively affects the adoption of online financial services. The findings of the multivariate analyses identified two attitude variables, satisfaction with finances and positive attitude toward credit market risk, that were significant determinants of intention to use all types of online financial services. 132

147 Higher factor scores of satisfaction with finances indicate greater satisfaction with the household s current financial situation. This factor was negatively associated with the probability for intention to use online financial services. This suggests that individuals who are satisfied with their financial situation are less likely to use online financial services. The converse is that people who are not satisfied with their current financial situation are more likely to intend to use online financial services. Satisfaction with finances indirectly reflects attitude toward a technology. People who are dissatisfied with their financial situation may want to make changes to improve their financial situation suggesting a positive attitude toward change. This is consistent with the results of previous studies (Mols, 1998; Polatoglu & Ekin, 2001; Sathye,1999) where it was reported that consumers who are dissatisfied with their finances are more likely to switch or seek new ways of managing their financial matters. Using online financial services gives the dissatisfied an opportunity for change that could lead to improving their current financial situation leading to a favorable attitude toward online financial services and a greater inclination to adopt online financial services. This result supports Hypothesis 1-1. Other studies (Robey & Farrow, 1982; Goodhue & Thompson, 1995; Goodhue, 1995; Rogers, 1995) reported that when people perceive a new technology as being important, personally relevant, and a good way to fulfill their needs and desires, they were more likely to adopt the technology. 133

148 Positive attitude toward credit market risk was positively associated with the probability of intention to use online financial services. Higher factor scores reflect risk seeking in the credit market. The statements 7 associated with this factor reflect a consumer s orientation toward credit market risk as reflected in variable interest rates. Accepting the risk associated with variable rates suggests consumers are willing to take risk to get potential advantages associated with lower interest rates for loan uses. This suggests that such risk seekers are willing to find and use new ways of managing their finances despite possible risks; therefore they are more likely to adopt online financial services. Thus, this result supports Hypothesis 1-2. Several studies (Cunningham, 1967; Oliver & Shapiro, 1993; Ho & Victor, 1994; GVU, 1999) reported that attitude toward risk was an important factor affecting technology adoption indicating that risk seeking attitudes were positively related to the adoption of technology. Cunningham (1967), and Ho and Victor (1994) indicated that risk seeking is useful for understanding consumers technology adoption Subjective Norm H2-1: Active social interaction through social network positively affects the adoption of online financial services. 7 Two statements are I would get a personal or auto loan that had an interest rate that could change and I would get a mortgage that had an interest rate that could change. 134

149 H2-2: Information through social network positively affects the adoption of online financial services. From the results of multivariate analyses, three independent variables, professional advice unneeded, personal contact desired, one-on-one interaction unneeded, were significant determinants of intention to use online financial services. Professional advice unneeded was negatively associated with the probability for intention to use online financial services use. Higher factor scores indicate less desire to get professional advice. This suggests that individuals who do not need professional advice are less likely to use online financial services, or that people who seek professional advice related to financial matters tend to have higher likelihood of intention to use online financial services. The statements underlying this factor reflect a need to seek information rather than interpersonal contact, suggesting that people who want to obtain professional advice or information perceive online financial services as a source for financial information leading to the adoption of online financial services. Thus, this result supports Hypothesis 2-2. Rogers (1995) reported that the more exposure to information about a technology, the greater probability of technology adoption. Personal contact desired was negatively associated with the probability for intention to use online financial services. Higher factor scores indicate greater desire for contact with people at financial institutions. This suggests that 135

150 individuals who have preference for interacting with people at financial institutions are less likely to use online financial services. One-on-one interaction unneeded increased the probability for intention to use online financial services use. Higher factor scores indicate less preference for one-on-one interaction. This suggests that individuals who do not perceive one-on-one interaction as important are more likely to use online financial services. These two findings are not consistent with Hypothesis 2-1. A possible explanation for these unexpected findings is that online financial services were in the early stages of adoption when data were collected. This could result in respondents not perceiving reference groups as being knowledgeable about online financial services leading to a situation where social interaction did not affect intention to adopt online financial services. Another possible reason is that people intending to adopt online financial services were loners who did not like to interact with others. These findings are consistent with other research (Gerrard & Cunningham, 2003; Howcroft et al., 2002) reporting that reference groups did not affect the adoption of technology Perceived Behavioral Control services. H3-1: Education level positively affects the adoption of online financial 136

151 H3-2: Income is positively associated with the adoption of online financial services 8. H3-3: Previous experience positively affects the adoption of online financial services. From the results of the multivariate analyses, two factors, education and prefer less complex financial strategies were significant determinants of intention to use online financial services. The respondents who had a college degree or more were more likely to use online financial services than the reference group (some college). This suggests that college graduates are more likely than others to use online financial services. Education provides people with greater resources related to doing a behavior. Education can improve the ability to learn and understand something new. These aspects encourage consumers to use a new technology such as online financial services. Thus, this result supports Hypothesis 3-1. Many studies (Rogers & Stanfield, 1968; Plummer, 1971; Rogers & Shoemaker, 1971; Feldman & Armstrong, 1975; Adcock et al., 1977; Labay & Kinnear, 1981; Hambrick & Mason, 1984; Amel, 1986; Taube, 1988; Igbaria et al., 1989; Anderson et al., 1995; Tabak & Barr, 1999; Hoffman et al., 2000) suggested that higher education levels lead to a greater likelihood of adoption of technology. This agrees with the findings of Hoffman et al. (2000) who reported 8 No variable in the group of 7 significant independent variables related to income. The only time income was significant was for investment uses. 137

152 that computer adopters are more likely to have higher education levels than nonadopters. Prefer less complex financial strategies decreased the probability for intention to use online financial services. Higher factor scores indicate less preference for complex financial strategies. This suggests that individuals who do not perceive complex financial strategies as important are less likely to use online financial services. In other words, people who prefer more complex financial strategies have a higher probability of intention to use online financial services. This factor reflects individuals understanding and knowledge of managing their financial matters. These individuals are more sophisticated with respect to financial management and are more likely to be confident of their financial management abilities. This leads to perceiving online financial services as useful, relevant, and easy to use, and greater intention to using online financial services. This is consistent with the findings of previous studies (Rogers, 1995; Sathye, 1999), which found that people who are confident tend to adopt new innovations. Thus, this result supports Hypothesis

153 Effect of Intended Use of Online Financial Services Uses Independent Variables Account General Mgt Loan Investment Insurance Attitude Variables Attitude toward risk of HH savings/investment: high risk/high return Satisfaction with finances Poor financial knowledge Positive attitude toward financial institutions Degree of risk aversion Concern with debt + Positive attitude toward credit market risk Subjective Norm Variables Professional advice unneeded Personal contact desired One-on-one interaction unneeded Perceived Behavioral Control Variables Education: college degree or more Income over $100,000 + Hours of PC use at home + + Frequency of financial + transactions in the last 3 months Have ATM cards + + Have Debit cards Lack of financial discipline Prefer less complex financial strategies Credit use Table 4.13: Variables significantly affecting the likelihood of intended use of online financial services. 139

154 CHAPTER 5 SUMMARY, IMPLICATIONS AND LIMITATIONS This chapter presents a brief summary of the study and addresses several limitations and implications. 5.1 Summary The purpose of this study was to identify factors affecting the intention to use online financial services. The effect of attitudes, subjective norm, and perceived behavioral control variables on the intended use of online financial services was examined controlling for differences in demographic control variables. The study considered general intention to use online financial services as well as intention with respect to four specific uses (account management uses, loan uses, investment uses, and insurance uses) of online financial services. 140

155 The Theory of Planned Behavior (TPB) provided the conceptual framework for this study. TPB suggests that attitude toward behavior, subjective norm, and perceived behavioral control affect behavioral intention to use a technology, which, in turn, effects actual usage of the technology. The hypotheses tested in this study were based on the TPB. Data came from the MacroMonitor Survey. This data set includes information about consumer attitudes, behaviors and motivations regarding financial products, services, delivery methods, and institutional use. Three thousand seven hundred eighty households completed the mail survey between May and August of Factor analysis was used to reduce the number of independent variables related to attitude toward a behavior, subjective norm, and perceived behavioral control by identifying a small number of factors that explained most of the variance observed in a large number of independent variables. Logistic regression was used to examine the probability of the intention to use online financial services. The independent variables were of 4 types: demographic control variables, attitude variables, subjective norm variables, and perceived behavioral control variables. This study identified seven variables significantly associated with the probability of intention to adopt all types of online financial services (general, account management, loans, investment, and insurance): satisfaction with finances, positive attitude toward credit market risk, professional advice 141

156 unneeded, personal contact desired, one-on-one interaction unneeded, prefer less complex financial strategies and a college degree or more. Consumers who were satisfied with financial situations in savings and investments had a lower probability of intention to use online financial services. People with a positive attitude toward credit market risk were more likely to use online financial services. Consumers who did not need professional advice were less likely to use online financial services. Consumers who desire personal contact were less likely to use online financial services. Consumers who did not need one-on-one interaction had greater likelihood of intention to use online financial services. Consumers who did not prefer complex financial strategies were less likely to use online financial services. Consumers who had a college degree or more were more likely to use online financial services, compared to the reference group (some college). 5.2 Implications and Conclusion The seven significant variables can be stated in a way that they lead to a higher probability of using online financial services, regardless the type of use: dissatisfaction with financial situations, risk seeking in the credit market, greater preference for professional advice, less preference for personal contact and oneon-one interaction, a college degree or more, and greater preferences for complex financial strategies. This suggests the following profile for consumers 142

157 with higher probabilities of intention to use online financial services. Consumers who were not pleased with their household s current financial situation were more likely to have intention to use online financial services. These consumers were ready for a change in their financial situation. People who seek greater risk in the credit market had a greater probability of intention to use online financial services. These individuals were willing to accept risk associated with possible gain. Consumers who seek professional advice were more likely to use online financial services suggesting they were information seekers. People who do not want personal contact and one-on-one interaction were more likely to intend to use online financial services. These people do not seek interpersonal contact as related to their finances. Consumers who preferred complex financial strategies were more likely to use online financial services. These consumers had greater degree of understanding/knowledge of financial issues (i.e., somewhat sophisticated financial managers). Consumers with a college degree or more were more likely to use online financial services Marketing Individuals intending to use online financial services seek professional information related to financial matters, want to change their financial situation and are willing to take risks. This suggests that marketers should focus on providing financial information through online financial services since those 143

158 intending to adopt are willing to take risk associated with change to improve financial situation for which they need information. Marketers should take account of the fact that consumers with high intention to use online financial services preferred a non-personal medium when managing their finances Consumers The findings that people wanting professional advice to change their financial situation and lacking a desire for personal interaction were more likely to use online financial services has implications concerning information quality. These individuals need accurate and complete financial information to make decisions that will lead to the desired changes; however, as Mayer et al. (2003) reported while these [insurance] product comparison web sites may save consumers time and money, consumers still face the challenge of distinguishing among these comparison sites in terms of their credibility the results suggest that many sites fail to provide cues of credibility, and when present, only a few cues distinguish between better and worse sites in sum, there are few shortcuts for consumers who want to quickly choose among life insurance comparison sites. Consumers still have to spend time and relinquish personal information to compare the comparison sites. This suggests caution for consumers who seek professional information to improve their financial situations though the use of 144

159 online financial services. While this may be a convenient information source, the quality of the information needs to be taken into consideration. From the above discussion, the importance of unbiased and complete information related to financial matters for consumers is apparent. This suggests a possible need for indicators reflecting the quality of information provided by online financial services. A quality scale could be used to indicate the level of accuracy and completeness of information provided by specific sites. This could be done by either a private organization or a governmental agency. In addition, the quality of information issue suggests the importance of consumer education. Through education, consumers can acquire the tools needed to analyze the accuracy and completeness of information obtained from online financial services Financial Planners The finding that individuals having intention to use online financial services had no desire for personal interaction reduces chances to work with financial planners. However, financial planners should notice that people with greater probability of intention to use online financial services also wanted to obtain professional information to change their financial situation. The findings of the study hold important practical implications for financial planners. Since intended users of online financial services were found to prefer a 145

160 non-personal medium and have a desire for professional information related to their financial situation, financial planners should consider the use of Internet websites as a source of professional information for consumers. Fee-for-service financial planners could stress the professional, unbiased nature of their information relative to other sources Conclusion Information on the Internet is important since the findings of this study revealed that consumers who had higher intention to use online financial services rely on non-personal medium and seek information to change their financial situation and to get chances of potential gain with risk. Moreover, the quality of information is also crucial for consumers who want to obtain accurate and complete information when managing their finances. 5.3 Limitations A limitation of this study relates to the nature of the sample. Data used for this study reflected an oversample of high-income households. Therefore, the findings of the study should not be generalized to the population as a whole. A future study investigating the determinants of the adoption of online financial services should use a nationally representative sample. 146

161 The creation of the dependent variables could also be a limitation. The questions used to create the dependent variables are Check any online financial services among 21 kinds you or anyone in your household would like to use with a personal computer in your home. The created dependent variables were binary variables (yes/no) to reflect intended use of at least one of 21 online financial services. The reason for using binary dependent variables was to identify factors affecting the intention to use online financial services comparing intended users with intended non-users, rather than a study of the intensity of intended use of online financial services among intended users. Future research may examine the determinants that affect the intensity of intended use of online financial services. Rogers (1995) noted the diffusion research should consider data from multiple points in the diffusion process. The cross-section data used in this study does not indicate change and cannot examine what actually happens based on intentions. Data from more than one points in time allows examination of the determinants of actual use of online financial services and not just intention to adopt online financial services. Also, data from multiple time periods would help identify differences between earlier adopters and later adopters of online financial services. The major focus of this study was to examine the effects of attitude, subjective norm, and perceived behavioral control on consumers intention to adopt online financial services. However, other factors related to the Internet 147

162 infrastructure 9 were not considered in this study. If environmental factors such as computer ownership were included, there could be some differences in the effect of attitude and perceived behavioral control on intention to use online financial services. 5.4 Suggestions for Future Research This study was conducted to explore the factors influencing intention to use online financial services. Following are some suggestions for future studies investigating adoption of online financial services. First, future studies could be examined the intensity of intended use of online financial services among intended users. Second, since online financial services were relatively new when the data for this study were collected, this study examined the intended use of online financial services. Future studies could expand this study to actual usage of online financial services over time. Finally, future studies could examine factors related to the Internet infrastructure which affect on the use of online financial services. 9 For example, Internet infrastructure includes computer ownership, higher-speed access services (cable modems, DSL, and dial-up phone service), and assess to a computer. 148

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177 APPENDIX A SPSS Syntax 163

178 FACTOR /VARIABLES b7_4f b7_6 c4_1 c4_2 n3_4 n3_2f n3_11f e4_3 e4_6 e4_10 e4_11 e4_13 e4_20 /MISSING MEANSUB /ANALYSIS b7_4f b7_6 c4_1 c4_2 n3_4 n3_2f n3_11f e4_3 e4_6 e4_10 e4_11 e4_13 e4_20 /PRINT EXTRACTION ROTATION FSCORE /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /SAVE AR(ALL) /METHOD=CORRELATION. FACTOR /VARIABLES b7_2 b7_3 b7_5 b7_11 b7_13 b7_14 b7_16 b7_17 b7_10f n3_9 j2_2 j2_3 j2_4 j2_5 j2_6 j2_8 j2_10 e4_1f e4_2 e4_4 e4_5 e4_7 e4_8 e4_19 e4_12f e4_14 e4_15 e4_16 b7_15f j2_1f j2_7f j2_9f /MISSING MEANSUB /ANALYSIS b7_2 b7_3 b7_5 b7_11 b7_13 b7_14 b7_16 b7_17 b7_10f n3_9 j2_2 j2_3 j2_4 j2_5 j2_6 j2_8 j2_10 e4_1f e4_2 e4_4 e4_5 e4_7 e4_8 e4_19 e4_12f e4_14 e4_15 e4_16 b7_15f j2_1f j2_7f j2_9f /PRINT EXTRACTION ROTATION FSCORE /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /SAVE AR(ALL) /METHOD=CORRELATION. FACTOR /VARIABLES B7_1 b7_9 C4_3 C4_4 C4_5 C4_6 C4_7 C4_8 C4_9 N3_1 n3_3 n3_6 n3_7 n3_10 n3_13 n3_14 /MISSING MEANSUB /ANALYSIS B7_1 b7_9 C4_3 C4_4 C4_5 C4_6 C4_7 C4_8 C4_9 N3_1 n3_3 n3_6 n3_7 n3_10 n3_13 n3_14 /PRINT EXTRACTION ROTATION FSCORE /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /SAVE AR(ALL) /METHOD=CORRELATION. RELIABILITY /VARIABLES=b7_4f e4_13 e4_10 /FORMAT=NOLABELS 164

179 /SCALE(ALPHA)=ALL/MODEL=ALPHA. CORRELATIONS /VARIABLES=o4_m o4_f o18 a4b a4a n1 e3 fac1_1 fac2_1 fac3_1 fac4_1 fac2_2 fac3_2 fac4_2 fac2_3 fac3_3 fac4_3 fac1_2 fac5_2 fac6_2 fac7_2 a1a n4 n5 n9 n10 n7a n7b n7c n7d o5_edu o14_rr n2 b8a_a_r b10 b11 c3 d4a_a d4a_b /PRINT=TWOTAIL NOSIG /MISSING=PAIRWISE. RECODE o18 (1=1) (ELSE=0) INTO Sex_m. VARIABLE LABELS sex_m 'Respondents Male head'. RECODE o18 (2=1) (ELSE=0) INTO Sex_f. VARIABLE LABELS sex_f 'Respondents female head'. RECODE o18 (3 thru 4=1) (ELSE=0) INTO sex_mf. EXECUTE. RECODE a4b (1=1) (ELSE=0) INTO single. VARIABLE LABELS single 'Marital status single'. RECODE a4b (2 thru 4=1) (ELSE=0) INTO others. VARIABLE LABELS others 'Divorced/Separated/Widowed'. RECODE a4b (5 thru 6=1) (ELSE=0) INTO marrliv. VARIABLE LABELS marrliv 'married or living together'. RECODE e3 (1 thru 2=1) (ELSE=0) INTO lowrisk. VARIABLE LABELS lowrisk 'Low risk/return'. 165

180 RECODE e3 (3=1) (ELSE=0) INTO averisk. VARIABLE LABELS averisk 'average risk/return'. RECODE e3 (4 thru 5=1) (ELSE=0) INTO highrisk. VARIABLE LABELS highrisk 'high risk/return'. RECODE o5_edu (3=1) (ELSE=0) INTO college. VARIABLE LABELS college 'some college Educated'. RECODE o5_edu (4=1) (ELSE=0) INTO morecol. VARIABLE LABELS morecol 'college degree or more'. RECODE o5_edu (1 thru 2=1) (ELSE=0) INTO hsorless. EXECUTE. RECODE o14_rr (1=1) (ELSE=0) INTO incom_1. VARIABLE LABELS incom_1 'Less than $29,999'. RECODE o14_rr (2=1) (ELSE=0) INTO incom_2. VARIABLE LABELS incom_2 '$30,000-$59,999'. RECODE o14_rr (3=1) (ELSE=0) INTO incom_3. VARIABLE LABELS incom_3 '$60,000-$99,999'. RECODE o14_rr (4=1) (ELSE=0) INTO incom_4. VARIABLE LABELS incom_4 'over $100,000'. T-TEST 166

181 GROUPS=b17users(0 1) /MISSING=ANALYSIS /VARIABLES=o4_m o4_f a2a fac1_1 fac2_1 fac3_1 fac4_1 fac2_2 fac3_2 fac4_2 a1a n5 n9 n10 fac2_3 fac3_3 fac4_3 b10 b11 c3 fac1_2 fac5_2 fac6_2 fac7_2 /CRITERIA=CIN(.95). CROSSTABS /TABLES=b17users BY o18 a4b n1 e3 n4 n7a n7b n7c n7d o5_edu o14_rr n2 d4a_a d4a_b o5_edu o14_rr n2 /FORMAT= AVALUE TABLES /STATISTIC=CHISQ /CELLS= COUNT. T-TEST GROUPS=b17inten(0 1) /MISSING=ANALYSIS /VARIABLES=o4_m o4_f a2a fac1_1 fac2_1 fac3_1 fac4_1 fac2_2 fac3_2 fac4_2 a1a n5 n9 n10 fac2_3 fac3_3 fac4_3 b10 b11 c3 fac1_2 fac5_2 fac6_2 fac7_2 /CRITERIA=CIN(.95). CROSSTABS /TABLES=b17inten BY o18 a4b n1 e3 n4 n7a n7b n7c n7d o5_edu o14_rr n2 d4a_a d4a_b o5_edu o14_rr n2 /FORMAT= AVALUE TABLES /STATISTIC=CHISQ /CELLS= COUNT. LOGISTIC REGRESSION VAR=b17inten /METHOD=ENTER mage sex_m sex_mf single others /METHOD=ENTER hrisk lrisk att1 att2 att3 att4 fp2 fp3 fp4 /METHOD=ENTER a1a p1 p2 p3 p4 /METHOD=ENTER hsorless morecol incom_2 incom_3 incom_4 b11 c3 d4a_a d4a_b fp1 fp5 fp6 fp7 /PRINT=GOODFIT CORR CI(95) /CRITERIA PIN(.05) POUT(.10) ITERATE(20) CUT(.5). LOGISTIC REGRESSION VAR=b17inten /METHOD=ENTER age sex_m sex_mf single others /METHOD=ENTER lowrisk highrisk fac1_1 fac2_1 fac3_1 fac4_1 fac2_2 fac3_2 fac4_2 /METHOD=ENTER a1a fac1_3 fac2_3 fac3_3 fac4_3 /METHOD=ENTER hsorless morecol incom_2 incom_3 incom_4 b11_r 167

182 c3_r d4a_a d4a_b fac1_2 fac5_2 fac6_2 fac7_2 /PRINT=GOODFIT CORR CI(95) /CRITERIA PIN(.05) POUT(.10) ITERATE(20) CUT(.5). REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS BCOV R ANOVA COLLIN TOL ZPP /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT b17inten /METHOD=ENTER age sex_m sex_mf single others lowrisk highrisk fac1_1 fac2_1 fac3_1 fac4_1 fac2_2 fac3_2 fac4_2 a1a fac1_3 fac2_3 fac3_3 fac4_3 hsorless morecol incom_2 incom_3 incom_4 b11_r c3_r d4a_a d4a_b fac1_2 fac5_2 fac6_2 fac7_2 /SAVE COOK LEVER. LOGISTIC REGRESSION VAR=b17b_ac2 /METHOD=ENTER age sex_m sex_mf single others /METHOD=ENTER lowrisk highrisk fac1_1 fac2_1 fac3_1 fac4_1 fac2_2 fac3_2 fac4_2 /METHOD=ENTER a1a fac1_3 fac2_3 fac3_3 fac4_3 /METHOD=ENTER hsorless morecol incom_2 incom_3 incom_4 b11_r c3_r d4a_a d4a_b fac1_2 fac5_2 fac6_2 fac7_2 /PRINT=GOODFIT CORR CI(95) /CRITERIA PIN(.05) POUT(.10) ITERATE(20) CUT(.5). LOGISTIC REGRESSION VAR=b17b_lo2 /METHOD=ENTER age sex_m sex_mf single others /METHOD=ENTER lowrisk highrisk fac1_1 fac2_1 fac3_1 fac4_1 fac2_2 fac3_2 fac4_2 /METHOD=ENTER a1a fac1_3 fac2_3 fac3_3 fac4_3 /METHOD=ENTER hsorless morecol incom_2 incom_3 incom_4 b11_r c3_r d4a_a d4a_b fac1_2 fac5_2 fac6_2 fac7_2 /PRINT=GOODFIT CORR CI(95) /CRITERIA PIN(.05) POUT(.10) ITERATE(20) CUT(.5). LOGISTIC REGRESSION VAR=b17b_in2 /METHOD=ENTER age sex_m sex_mf single others /METHOD=ENTER lowrisk highrisk fac1_1 fac2_1 fac3_1 fac4_1 fac2_2 fac3_2 fac4_2 /METHOD=ENTER a1a fac1_3 fac2_3 fac3_3 fac4_3 /METHOD=ENTER hsorless morecol incom_2 incom_3 incom_4 b11_r c3_r d4a_a d4a_b fac1_2 fac5_2 fac6_2 fac7_2 /PRINT=GOODFIT CORR CI(95) 168

183 /CRITERIA PIN(.05) POUT(.10) ITERATE(20) CUT(.5). LOGISTIC REGRESSION VAR=b17b_is2 /METHOD=ENTER age sex_m sex_mf single others /METHOD=ENTER lowrisk highrisk fac1_1 fac2_1 fac3_1 fac4_1 fac2_2 fac3_2 fac4_2 /METHOD=ENTER a1a fac1_3 fac2_3 fac3_3 fac4_3 /METHOD=ENTER hsorless morecol incom_2 incom_3 incom_4 b11_r c3_r d4a_a d4a_b fac1_2 fac5_2 fac6_2 fac7_2 /PRINT=GOODFIT CORR CI(95) /CRITERIA PIN(.05) POUT(.10) ITERATE(20) CUT(.5). 169

184 APPENDIX B Lists of Possible Responses 170

185 1. Kinds of online financial services In the MacroMonitor survey, the twenty-one online financial services are paying bill, stopping/canceling checks/payments, making transfers between accounts, inquiring about account balances, opening/closing checking/saving accounts, applying for vehicle loans/leases, applying for a first mortgage, applying for home equity credit lines or second mortgage, applying for other loans/credit lines, obtaining information about loans/credit lines, buying or rolling over CDs, buying or selling mutual funds, buying or selling stocks or bonds, managing investment accounts, obtaining information/research reports about investments, buying life insurance, buying health insurance, buying homeowner s/renter s insurance, buying vehicle insurance, obtaining information about insurance. 2. Financial products The 15 financial products are provided in the MacroMonitor survey are motor vehicle insurance, homeowner s renter s insurance, life insurance, stock or bond mutual fund, annuity, certificate of deposit, disability income insurance, retirement savings plan, loan, credit card, mortgage, home equity loan/line of credit, Christmas/vacation club, asset management/investment management account, and packaged/relationship banking account. 3. Past and expected use of professional financial advisors Professional financial advisors can be defined as individuals or representatives of institutions with whom you have established a relationship while acquiring assistance or advice concerning household s finances or investments. 171

186 4. Kinds of information sources In the MacroMonitor survey, the 21 information sources include books, consumer magazines, other magazines, newspaper articles, financial newsletters, financial institution brochures/written materials, radio programs, broadcast TV programs, educational TV programs, cable TV programs, radio advertisements, television advertisements, daily newspaper or magazine advertisements, financial newspaper or magazine advertisements, friends/relatives/associates, persons at workplace, financial institution personnel, toll-free numbers, seminars, internet/online service, and other. 5. Software used on home computers. Kinds of computer software included Intuit Quicken, Microsoft Money, Word Processing, Electronic mail, Income tax preparation, Spreadsheets, Database management, other financial management. 6. Ways financial transactions were conducted in the last three months The 10 ways include talk with a representative inside a financial institution, talk with a teller for a routine transaction, use a drive-though facility, use a walkup window, make a phone call using a touch-tone menu, make a phone call to financial institution representative, use an ATM belonging to your financial institution, use an ATM belonging to another financial institution, use a personal computer connection to financial institution, and other. 172

187 APPENDIX C Descriptive Statistics For Current Users and Non-Users 173

188 Current P-values Non-Users Variables Users for T-test or (N = 3143) (N = 637) χ 2 test Mean age of male head of household 50.91(595) a (2546) a.000 Mean age of female head of household (586) (2818).000 Gender of respondents who completed questionnaires 1 Male head only 2 Female head only 3 Both M and F heads 4 Another person n = % 27.3% 17.7% 0.9% n = % 44.7% 15.5% 1.0%.000 Marital Status 1 Single 2 Divorced 3 Separated 4 Widowed 5 Married 6 Living together but not married n = % 3.6% 0.6% 2.0% 81.9% 3.3% n = % 10.3% 1.6% 5.9% 65.9% 4.5%.000 # of dependent children 1.02 (629) 0.86 (3087).001 T-test for means and χ 2 test for distribution a mean value (number of cases) Table C.1: Demographic control variables (current users compared to nonusers). 174

189 Variables Household's financial strategy 1 Have specific financial strategy 2 Have general financial strategy 3 Have a partial, but incomplete financial strategy 4 Have no financial strategy Current Users (N = 637) n = % 60.2% 14.5% 8.9% Non-Users (N = 3143) n = % 44.2% 21.2% 22.6% P-values for T-test or χ 2 test.000 Attitude toward risk of households savings and investments 1 Very low risk/very low return 2 Below average risk/below average return 3 Average risk/average return 4 Above average risk/above average return 5 Very high risk/very high return 9 Don t know n = % 6.0% 36.8% 43.9% 10.0% 2.4% n = % 8.4% 42.7% 25.8% 3.8% 12.2%.000 Satisfaction with finances.1532 (637) a (3143) a.000 Financial decision confidence.3558 (637) (3143).000 Poor financial knowledge (637).0858 (3143).000 Attitude toward financial institutions.1370 (637) (3143).000 Degree of risk aversion (637).1254 (3143).000 Concern with debt (637).0192 (3143).009 Attitude toward credit market risk.1860 (637) (3143).000 T-test for means and χ 2 test for distribution a mean value (number of cases) Table C.2: Attitude variables (current users compared to non-users). 175

190 Current P-values Non-users (N Variables Users for T-test = 3143) (N = 637) or χ 2 test Total number of household members 2.96 (637) a 2.66 (3143) a.000 Frequency of receiving advice for major household financial decisions 1 Always 2 Sometimes 3 Rarely 4 Never 5 Don't know 6 Unspecified n = % 39.6% 29.3% 17.0% 2.2% 1.9% n = % 31.7% 19.2% 26.2% 7.4% 2.5%.000 Use of professional financial advisors last 2 years Use of professional financial advisors next 12 months # of info source for financial service or decisions used in the last 12 months 1.24 (637) 0.93 (3143) (637) 1.22 (3143) (637) 3.31 (3143).000 Professional advice unneeded.0534 (637) (3143).000 Personal relationship unimportant (637).0037 (3143).610 Personal contact desired (637).0846 (3143).000 One-on-one interaction unneeded.4523 (637) (3143).000 How household now obtains financial information 1 Mostly own 2 Mostly professional 3 Joint n = % 5.0% 29.9% n = % 8.7% 30.4%.005 How household would like to obtain financial information 1 Mostly own 2 Mostly professional 3 Joint n = % 7.9% 46.7% n = % 12.6% 48.9%.000 Continued Table C.3: Subjective norm variables (current users compared to non-users). 176

191 Table C.3 continued How household now makes financial decisions 1 Mostly own 2 Mostly professional 3 Joint n = % 0.6% 15.9% n = % 1.9% 17.6%.049 How household would like to make financial decision 1 Mostly own 2 Mostly professional 3 Joint T-test for means and χ 2 test for distribution a mean value (number of cases) n = % 1.1% 30.3% n = % 3.5% 36.2%

192 Variables Highest level of Education Attainment among Male/Female Householder 1 High school or less 2 Some college 3 College degree or more Current Users (N = 637) n = % 17.6% 78.0% Non-users (N = 3143) n = % 32.5% 49.9% P-values for T-test or χ 2 test.000 Household s 1997 gross income 1 $29,999 or less 2 $30,000-$59,999 3 $60,000-$99,999 4 $100,000 or more n = % 15.9% 31.4% 44.7% n = % 30.7% 24.9% 18.1%.000 Confidence level in reaching financial goals 1 Extremely confident 2 Very confident 3 Somewhat confident 4 Not very confident 5 Not at all confident n = % 39.4% 35.2% 6.3% 1.9% n = % 30.4% 40.8% 12.7% 5.8%.000 # Use of financial computer software programs 4.57 (637) a 1.62 (3143) a.000 with home PC # Hours per month of PC use at home (621) (1643).000 Frequency of 10 financial transactions in the last (633) (3097) months Have ATM cards 0.67 (637) 0.55 (3143).000 Have Debit cards 0.57 (637) 0.38 (3143).000 Lack of financial discipline (637).0283 (3143).000 Prefer less complex financial strategies.1364 (637) (3143).000 Credit use.1980 (637) (3143).000 Minimal search for new financial products (637).0155 (3143).034 T-test for means and χ 2 test for distribution a mean value (number of cases) Table C.4: Perceived behavioral control variables (current users compared to non-users). 178

193 APPENDIX D Logistic Regression Before Missing Data Imputation & Variance Inflation Factors (VIF) 179

194 Variables Demographic Control Variables Model Attitude toward Behavior Subjective Norm Perceived Behavioral Control Constant -.916** 1.776* 2.807** Demographic Control Variables Household age.028***.019**.013*.018* Gender of respondents: male head Gender of respondents:.404*.419* both M/F heads or another person Marital status: single Marital status: divorced, separated, widowed Attitude Variables Attitude toward risk of HH savings/investments: low risk/low return Attitude toward risk of HH.048*.029* savings/investments: high risk/high return Satisfaction with finances -.100** -.083* Financial decision -.100* -.093* confidence Poor financial knowledge Positive attitude toward financial institutions Degree of risk aversion -.101*** -.079** -.056* Concern with debt Positive attitude toward.125**.106**.086* credit market risk Subjective Norm Variables Total number of HH members Professional advice -.045* unneeded Personal relationship unimportant Personal contact desired -.235*** -.189** One-on-one interaction unneeded.100*.108* Continued Table D.1: Logistic regression: Intended users of online financial services (1 = intended users, 0= intended non-users). 180

195 Table D.1 continued Perceived Behavioral Control Variables Education among.088 male/female householder: HS or less Education among.541** male/female householder: college degree or more Household income $30,000-$59,999 Household income.234 $60,000-$99,999 Household income.153 $100,000 or more Hours per month of PC use.003 at home Frequency of 10 types of.001 financial transactions in the last 3 months Have ATM cards.089 Have Debit cards.199 Lack of financial discipline.015 Prefer less complex financial strategies Credit use Minimal search for new.061 financial products χ 2 Increase for added *** *** ** variables Pearson χ *** *** *** *** Nagelkerke R ***p<.001, **p<.01, *p<

196 Variables Tolerance VIF Eigenvalue Condition Index Demographic Control Variables Household age Gender of respondents: male head Gender of respondents: both M/F heads or another person Marital status: single Marital status: divorced, separated, widowed Attitude Variables Attitudes toward risk of HH savings/investment: low risk/low return Attitudes toward risk of HH savings/investment: high risk/high return Satisfaction with finances Financial decision confidence Poor financial knowledge Positive attitudes toward financial institutions Degree of risk aversion in the stock market Concern with debt Positive attitudes toward credit market Subjective Norm Variables Total number of HH members Professional advice unneeded Personal relationship unimportant Personal contact desired One-to-one interaction unneeded Continued Table D.2: The results of collinearity statistics in linear regression: Tolerance, VIF, Eigenvalue, condition indice (1 = intended users, 0= intended non-users). 182

197 Table D.2 Continued Perceived Behavioral Control Variables Education among male/female householder: HS or less Education among male/female householder: college degree or more Household income $30,000-$59,999 Household income $60,000-$99,999 Household income $100,000 or more Hours per month use PC at home Frequency of 10 types of financial transactions in the last 3 months Have ATM cards Have Debit cards Lack of financial discipline Prefer less complex financial strategies Credit use Minimal search for new financial products

198 APPENDIX E Logistic Regression Results for Four Uses of Online Financial Services 184

199 Variables Demographic Control 185 Attitude toward Behavior Subjective Norm Perceived Behavioral Control Constant *** *** *** *** Demographic Control Variables Household age.042***.033***.028***.026*** Gender of respondents:.347*** male head Gender of respondents: both M/F heads or another person Marital status: single Marital status: divorced, -.294** separated, widowed Attitude Variables Attitude toward risk of HH savings/investments: low risk/low return Attitude toward risk of HH.261**.232*.172 savings/investments: high risk/high return Satisfaction with finances -.316*** -.294*** -.201** Financial decision -.116** confidence Poor financial knowledge Positive attitude toward.135***.153***.147** financial institutions Degree of risk aversion -.448*** -.336*** -.239*** Concern with debt Positive attitude toward.161***.131***.099** credit market risk Subjective Norm Variables Total number of HH members Professional advice -.190*** -.145** unneeded Personal relationship unimportant Personal contact desired -.346*** -.279*** One-on-one interaction.150***.117** unneeded Perceived Behavioral Control Variables Education among male/female householder: HS or less Continued Table E.1: Independent variable groups and intention for account management uses.

200 Table E.1 continued Education among.375*** male/female householder: college degree or more Household income $30, $59,999 Household income $60, $99,999 Household income.103 $100,000 or more Hours per month of PC use.001 at home Frequency of 10 types of.001 financial transactions in the last 3 months Have ATM cards.179* Have Debit cards.265*** Lack of financial discipline.165** Prefer less complex -.141** financial strategies Credit use.110** Minimal search for new.070 financial products χ 2 Increase for added *** *** *** variables Pearson χ *** *** *** *** Nagelkerke R ***p<.001, **p<.01, *p<

201 Variables Demographic Control 187 Attitude toward Behavior Subjective Norm Perceived Behavioral Control Constant *** *** *** *** Demographic Control Variables Household age.054***.042***.037***.037*** Gender of respondents:.267** male head Gender of respondents: both M/F heads or another person Marital status: single Marital status: divorced, separated, widowed Attitude Variables Attitude toward risk of HH savings/investments: low risk/low return Attitude toward risk of HH.389***.363***.305** savings/investments: high risk/high return Satisfaction with finances -.378*** -.360*** -.259*** Financial decision -.123** confidence Poor financial knowledge Positive attitude toward financial institutions Degree of risk aversion -.258*** -.147** Concern with debt ** Positive attitude toward.161***.131**.109** credit market risk Subjective Norm Variables Total number of HH members Professional advice -.207*** -.178*** unneeded Personal relationship unimportant Personal contact desired -.295*** -.240*** One-on-one interaction.151***.130** unneeded Perceived Behavioral Control Variables Education among male/female householder: HS or less Table E.2: Independent variable groups and intention for loan uses. Continued

202 Table E.2 continued Education among.242* male/female householder: college degree or more Household income $30, $59,999 Household income $60, $99,999 Household income.186 $100,000 or more Hours per month of PC use.002 at home Frequency of 10 types of.001 financial transactions in the last 3 months Have ATM cards.108 Have Debit cards.164* Lack of financial discipline.138** Prefer less complex -.168*** financial strategies Credit use.168*** Minimal search for new financial products χ 2 Increase for added *** *** *** variables Pearson χ *** *** *** *** Nagelkerke R ***p<.001, **p<.01, *p<

203 Variables Demographic Control 189 Attitude toward Behavior Subjective Norm Perceived Behavioral Control Constant *** *** *** *** Demographic Control Variables Household age.032***.025***.021***.021*** Gender of respondents:.495***.199* male head Gender of respondents:.282**.289**.283**.304** both M/F heads or another person Marital status: single Marital status: divorced, -.459*** -.253* -.239* separated, widowed Attitude Variables Attitude toward risk of HH savings/investments: low risk/low return Attitude toward risk of HH.319***.290**.227* savings/investments: high risk/high return Satisfaction with finances -.323*** -.311*** -.233*** Financial decision -.090* confidence Poor financial knowledge -.196*** -.192*** -.210*** Positive attitude toward.153***.152***.161*** financial institutions Degree of risk aversion -.438*** -.333*** -.224*** Concern with debt -.169*** -.141** Positive attitude toward.176***.145***.113** credit market risk Subjective Norm Variables Total number of HH members Professional advice -.171*** -.120** unneeded Personal relationship unimportant Personal contact desired -.277*** -.215*** One-on-one interaction.170***.149** unneeded Perceived Behavioral Control Variables Education among male/female householder: HS or less Table E.3: Independent variable groups and intention for investment uses. Continued

204 Table E.3 continued Education among.413*** male/female householder: college degree or more Household income $30, $59,999 Household income $60, $99,999 Household income.357* $100,000 or more Hours per month of PC use.002* at home Frequency of 10 types of.000 financial transactions in the last 3 months Have ATM cards.117 Have Debit cards.161* Lack of financial discipline.137* Prefer less complex -.172*** financial strategies Credit use.044 Minimal search for new financial products χ 2 Increase for added *** *** *** variables Pearson χ *** *** *** *** Nagelkerke R ***p<.001, **p<.01, *p<

205 Variables Demographic Control Attitude toward Behavior Subjective Norm Perceived Behavioral Control Constant *** *** *** *** Demographic Control Variables Household age.044***.034***.029***.028*** Gender of respondents: male head Gender of respondents: both M/F heads or another person Marital status: single.223*.251*.305*.258 Marital status: divorced, separated, widowed Attitude Variables Attitude toward risk of HH savings/investments: low risk/low return Attitude toward risk of HH.214*.187*.165 savings/investments: high risk/high return Satisfaction with finances -.340*** -.320*** -.238*** Financial con -.131** -.097* Poor financial knowledge -.106* -.094* -.099* Positive attitude toward financial institutions Degree of risk aversion -.235*** -.144** Concern with debt Positive attitude toward.144***.120**.101** credit market risk Subjective Norm Variables Total number of HH members Professional advice -.179*** -.151** unneeded Personal relationship unimportant Personal contact desired -.192*** -.163*** One-on-one interaction.173***.170*** unneeded Perceived Behavioral Control Variables Education among male/female householder: HS or less Continued Table E.4: Independent variable groups and intention for insurance uses. 191

206 Table E.4 continued Education among.292** male/female householder: college degree or more Household income $30, $59,999 Household income $60, $99,999 Household income $100,000 or more Hours per month of PC use.001 at home Frequency of 10 types of.001 financial transactions in the last 3 months Have ATM cards.100 Have Debit cards.019 Lack of financial discipline.094 Prefer less complex -.131** financial strategies Credit use.058 Minimal search for new financial products χ 2 Increase for added *** *** variables Pearson χ *** *** *** *** Nagelkerke R ***p<.001, **p<.01, *p<

207 APPENDIX F Pearson s Correlation Coefficient 193

208 Age/ M Age/ F Gender Marital status Fin strat Pos att. toward risk Satisfac with finances Poor fin konwled Age/M Age/F Gender Marital status Fin strategy Pos. att toward risk Satisfac with fin Poor fin knowled Fin confidence Pos att tow fin. instit Degree of risk aversion Concern with debt Pos att toward credit mkt risk HH size Advice bef maj inv decision Use of prof advisor last 2 yrs Use of prof advisor next 12 mon Use of info last mons Prof advice Personal relation unneeded Personal contact desired One-on-one interact unimpor How obtain fncl info now How like to obtn fncl info How make fin decis now How like to mak fin decis Highest edu among M/F Income Confi of fin goals Use of computer software Figure F.1: Pearson s Correlation Coefficient. Continued 194

209 Figure F.1 Continued Age/ M Age/ F Gender Marital status Fin strat Pos att. toward risk Satisfac with finances Poor fin konwled # hrs/mo of PC Freq of fin trans last mons Have ATM cards Have Debit cards Lack of Fin discipline Prefer less complex fin strategy Credit use Min search for new fin prods

210 Figure F.1 Continued Fin confi Pos att fin instit Degree of risk aversion Concern with debt Pos att. credit mkt risk HH size Advice for inv decis Age/M Age/F Gender Marital status Fin strategy Pos. att toward risk Satisfac with fin Poor fin knowled Fin confidence Pos att tow fin. instit Degree of risk aversion Concern with debt Pos att toward credit mkt risk HH size Advice bef maj inv decision Use of prof advisor last 2 yrs Use of prof advisor next 12 mon Use of info last mons Prof advice Personal relation unneeded Personal contact desired One-on-one interact unimpor How obtain fncl info now How like to obtn fncl info How make fin decis now How like to mak fin decis Highest edu among M/F Income Confi of fin goals Use of computer software # hrs/mo of PC Freq of fin trans last 3mons

211 Figure F.1 Continued Fin confi Pos att fin instit Degree of risk aversion Concern with debt Pos att. credit mkt risk HH size Advice for inv decis Have ATM cards Have Debit cards Lack of Fin discipline Prefer less complex fin strategy Credit use Min search for new fin prods

212 Figure F.1 Continued Use of prof fin advisor How make fin decs now How like to mak fin decis Highe st edu M/F Income Confi of fin goals Use of computer software Age/M Age/F Gender Marital Statu Fin strategy Att toward risk Satisfac with fin Poor fin knowled Fin confidence Pos att toward fin instit Degree of risk aversion Concern with debt Pos att toward credit mkt risk HH size Advice bef maj inv decision Use of prof advisor last 2 yrs Use of prof advisor next 12 mon Use of info last mons Prof advice Personal relation unneeded Personal contact desired One-on-one interact unimpor How obtain fncl info now How like to obtn fncl info How make fin decis now How like to mak fin decis Highest edu among M/F Income Confi of fin goals Use of computer software # hrs/mo of PC Freq of fin trans last 3mons

213 Figure F.1 Continued Use of prof fin advisor How make fin decs now How like to mak fin decis Highe st edu M/F Income Confi of fin goals Use of computer software Have ATM cards Have Debit cards Lack of Fin discipline Prefer less complex fin strategy Credit use Min search for new fin prods

214 Figure F.1 Continued hrs/m of PC use Freq fn trans last 3mon Have ATM cards Have Debit cards Lack of fin discip Pref. Less complex fin strat Credit use Min sear new fin prod Age/M Age/F Gender Marital Statu Fin strategy Att toward risk Satisfac fin Poor fin knowled Fin confi Pos att tow fin. instit Degree of risk aversion Concern with debt Pos att toward credit mkt risk HH size Advice bef maj inv decision Use of prof advisor last 2 yrs Use of prof advisor next 12 mon Use of info last mons Prof advice Personal relation unneeded Personal contact desired One-on-one interact unimpor How obtain fncl info now How like to obtn fncl info How make fin decis now How like to mak fin decis Highest edu among M/F Income Confi of fin goals Use of computer software # hrs/mo of PC Freq of fin trans last 3mons

215 Figure F.1 Continued hrs/m of PC use Freq fn trans last 3mon Have ATM cards Have Debit cards Lack of fin discip Pref. Less complex fin strat Credit use Min sear new fin prod Have ATM cards Have Debit cards Lack of Fin discipline Prefer less complex fin strategy Credit use Min search for new fin prods