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: Approved by Professor Loren V. Geistfeld, Adviser Professor Jonathan J. Fox Adviser Professor Catherine P. Montalto College of Human Ecology Department of Consumer

and Textile Sciences

Copyright by Jihyun Lee

2003

ii

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 1998-99 MacroMonitor Survey. The study sample

consists of 3,780 households completing a mail survey between May and August

of 1998. 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.

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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.

iv

Dedicated to my parents and my husband

v

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

vi

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.

vii

VITA

November 13, 1968 Born Busan, Korea 1989 1993 B.S., Economics,

Busan National University, Busan, Korea 1993 1995 Research Assistant, Department of Economics,

Busan National University, Busan, Korea 1995 1997 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

viii

TABLE OF CONTENTS

Page Abstract...ii Dedication..iv Acknowledgements...v Vita.vii List of Tables.xi List of Figures..xiii Chapters: 1. Introduction....1 1.1 Background of the Study.1 1.2 Importance of the Study..5 1.3 Objectives of the Study6 1.4 Outline of the Study..6 2. Theoretical Background & Literature Review...7 2.1 Technology Acceptance Model (TAM)..7 2.1.1 Overview7 2.1.2 Key Elements of the Technology Acceptance Model...10 2.2 Task-Technology Fit Model (TTF)17

2.2.1 Overview..17 2.2.2 Task-technology fit.18 2.2.3 Performance...19 2.2.4 Task Characteristics..20 2.2.5 Individual Characteristics..22 2.2.6 Technology Characteristics..23

ix

2.3 The Theory of Planned Behavior (TPB)..24 2.3.1 Overview..24 2.3.2 Key Elements of the Theory of Planned Behavior25

2.4 Discussion of Theories..28 2.5 A Conceptual Model of Intention to Use Online Financial

Services...31 2.5.1 Determinants of the Conceptual Model..31 2.5.2 Hypotheses.31

2.5.2.1 Attitude Toward a Behavior...31 2.5.2.2 Subjective Norm..34 2.5.2.3 Perceived Behavioral Control...35

2.5.3 Control Variables..38 2.6 Summary of Hypotheses...42 3. Methods43 3.1 Data Source.43

3.2 Sample.44 3.3 Description of Dependent Variables45

3.4 Description of Independent Variables.47 3.4.1 Attitude.47 3.4.2 Subjective Norm.50 3.4.3 Perceived Behavioral Control..53 3.4.4 Demographic Control Variables......57 3.5 Variable Reduction Procedures: Factor Analysis..64 3.6 Missing Data66

3.7 Descriptive Analyses..70 3.7.1. Comparing Mean Values..71 3.7.2. Comparing Distributions72 3.8 Multivariate Analysis..72 3.8.1 Logistic Regression...72 3.8.2 Interpretation of Logistic Regression..77

3.8.3 General Model Testing and Identification of Independent Variables..78

4. Results..80 4.1 Factor Analysis80 4.1.1 The Procedure80 4.1.2 The Results.82

4.1.3 Linking Factor Analysis Concept Groups to TPB...100 4.2 Descriptive Analysis.102 4.2.1 Comparing Intended Users to Intended Non-Users...103 4.3 Results of Multivariate Analyses112

x

4.3.1 Multicollinearity.112 4.3.2 Missing Values.115 4.3.3 Variables115

4.3.4 Results of Logistic Analyses..119 4.3.4.1 Role of TPB Blocks of Variables119 4.3.4.2 Factors Affecting Intention..125 4.4 Discussion of Findings.131 4.4.1 Attitude Toward Behavior...132 4.4.2 Subjective Norm...134 4.4.3 Perceived Behavioral Control136 5. Summary, Limitations and Implication..140 5.1 Summary140 5.2 Implications142

5.3.1 Marketing..143 5.3.2 Consumers144 5.3.3 Financial Planner.145 5.4.4 Conclusion146

5.3 Limitations..146 5.4 Suggestions for Future Research..148 Bibliography...149 Appendices163 A. SPSS Syntax163 B. Lists of Possible Responses..170 C. Descriptive Statistics for Current Users and Non-Users...173 D. Logistic Regression Before Missing Data Imputation & VIF.179 E. Logistic Regression Results for Four Uses of Online Financial Services..184 F. Peasons Correlation Matrix...193

xi

LIST OF TABLES

Table Page

3.1 A summary of dependent variables.46 3.2 A summary of independent variables..59 3.3 Summary of number of missing value and imputation.69 4.1 Attitude and knowledge questions: Factor analysis..87 4.2 Personal interaction questions: Factor analysis92 4.3 Financial planning questions: Factor analysis...98 4.4 Frequency of current users and non-users for specific use of online

financial services..103 4.5 Demographic control variables of intended users and intended non-users

of online financial services.....105 4.6 Attitude variables (intended users compared to intended non-users).107 4.7 Subjective norm variables (intended users compared to intended non-

users)..109 4.8 Perceived behavioral control variables (intended users compared to

intended non-users).112 4.9 A summary description of the study variables (sample = 3143)...118 4.10 Independent variable groups and intention for general use of online

financial services..123

xii

4.11 Significance of variable blocks for the four types of online financial services..125

4.12 Odds ratios for five uses of online financial services..130 4.13 Variables significantly affecting the likelihood of intended use of online

financial services..139 C.1 Demographic control variables (current users compared to non-

users)..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 non-

users)..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 non-users)..182

E.1 Independent variable groups and intention for account management

uses185 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 uses191

xiii

LIST OF FIGURES

Figure Page

2.1 Original Technology Acceptance Model.10 2.2 Task-Technology Fit Model...20 2.3 Theory of Reasoned Action..27 2.4 Theory of Planned Behavior.27 2.5 Conceptual model of technology adoption based on the Theory of Planned

Behavior...32 F.1 Pearsons Correlation Coefficient..194

1

CHAPTER 1

INTRODUCTION

1.1 Background of the Study

Use of information technology (IT) products1 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.

2

Electronic banking as an IT is not new. Wire transfers are almost as old

as the telegraph (Garbade & Silber2, 1978). The first commercial use of the

telephone was by two bankers to check balances in the 19th century (Brooks,

1975). FedWire funds transfer3 began shortly after the establishment of the

Federal Reserve system and the Clearing House Interbank Payment System

(CHIPS)4 was started in 1970. 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.

3

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 Ones eMoneyMail and ePay, and

Well Fargos Billpoint and PayPal (Janik, 2000; Business Week, 2002). Twenty-

two 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.

4

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

5

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.

6

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.

7

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)

2.1.1 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).

8

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 individuals attitude toward using IT and

perceived usefulness. An individuals 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

9

(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.

10

Figure 2.1: Original Technology Acceptance Model (Davis et al., 1989).

2.1.2 Key Elements of the Technology Acceptance Model (TAM)

2.1.2.1 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

Perceived Usefulness

Perceived Ease of Use

Attitude toward Use

Behavioral Intention to Use

Actual Use External

Variables

11

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).

2.1.2.2 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

12

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

13

the user. Previous experience and training increase an individuals 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.

14

2.1.2.3 Attitude toward Using

According to Schiffman and Kanuk (1997, p.235-236), 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

individuals 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 individuals attitude toward

using is a key determinant of intention to actual use.

2.1.2.4 Behavioral Intention to Use

According to Davis (1986, p.28), behavioral intention reflects the strength

of the prospective users intention to make or to support the usage decision in

15

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

individuals 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 voice-mail and e-mail. 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

16

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 pre-

versus 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 e-mail.

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

17

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)

2.2.1 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 individuals 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.

18

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.

2.2.2 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.

19

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.

2.2.3 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).

20

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.

Figure 2.2: Task-Technology Fit Model (TTF): Goodhue, D.L. (1988).

2.2.4 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

Task Characteristics

Individual Characteristics

Technology Characteristics

Task-Technology Fit

Performance Impacts

21

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 hands-

on. 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

organizations tasks and an individuals tasks are, the more likely the individual

will be frustrated by these incompatibilities. Thus, incompatibilities individuals feel

may negatively affect individual performance.

22

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 dont deal directly

with the IT.

2.2.5 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.

23

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.

24

2.3 The Theory of Planned Behavior (TPB)

2.3.1 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

25

perform or not to perform the behavior. The third relates to the perceived ease or

difficulty of performing the behavior.

2.3.2 Key Elements of the Theory of Planned Behavior (TPB)

2.3.2.1 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.

2.3.2.2 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,

26

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.

2.3.2.3 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 individuals 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.

27

Figure 2.3: Theory of Reasoned Action (TRA) -- Ajzen, I. and M. Fishbein (1980).

Figure 2.4: Theory of Planned Behavior (TPB) Taylor and Todd (1995).

Beliefs and Evaluations

Normative Beliefs and Motivation to comply

Attitude toward Behavior

Subjective Norm

Behavioral Intention

Actual Behavior

Beliefs and Evaluations

Normative Beliefs and Motivation to comply

Attitude toward Behavior

Subjective Norm

Behavioral Intention

Usage Behavior

Control Beliefs and Perceived facilitation

Perceived Behavioral Control

28

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 E-mail 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

29

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. Goodhues development of the TTF model addresses user-

evaluation of IT in the individuals satisfaction construct. The concept of

satisfaction in the TTF model reflects individuals 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 individuals belief or affection

30

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.

31

2.5 A Conceptual Model of Technology Adoption of Online Financial

Services Usage

2.5.1 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.

2.5.2 Hypotheses

2.5.2.1 Attitude Toward a Behavior

Attitude is defined as an individuals 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

32

through previous purchasing behavior, direct experience with the product, word-

of-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.

Figure 2.5: Conceptual model of Technology Adoption based on The Theory of Planned Behavior (TPB).

Attitude toward Behavior

Subjective Norm

Intention to Adopt Online Financial Services

Perceived Behavioral Control

Actual Usage of Online Financial services

Demographic Control Variables

33

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 dont

need it rather than they dont 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 individuals

subjective feeling of certainty that the consequences will be unfavorable. Thus,

consumers behavior may be influenced by attitude toward subjective risk and

objective risk.

34

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.

2.5.2.2 Subjective Norm

Subjective norm refers to the persons 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 individuals

intention to adopt a technology. Chua (1980) suggests that the adopters 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),

35

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.

2.5.2.3 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.

36

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 non-

adopters 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

37

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.

38

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.

2.5.3 Control Variables

2.5.3.1 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.

39

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.

2.5.3.2 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

40

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.

41

2.5.3.3. 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.

2.5.3.4 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).

42

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.

43

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.

44

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 1998-99 was 49%. For the 1998-99

MacroMonitor Survey, a sample of 3,780 households completed the mail survey

from May through August of 1998.

3.2 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

45

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 3.1.

3.3.1 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)

46

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.

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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 3.2.

3.4.1 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

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statements on a 4-point scale ranging from Mostly agree(1), Agree(2),

Disagree(3), to Mostly disagree(4):

I am satisfied with my households 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.

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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 households 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 households 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

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return (2), Average risk/Average return (3), Above average risk/Above average

return (4), and Very high risk/Very high return (5).

3.4.2 Subjective Norm

Subjective norm is defined as peoples 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 individuals 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), Dont know (5), and Unspecified (6).

Households seeking more advice are expected to be more likely to adopt online

financial services.

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Another measure relating to subjective norm is the use of professional

financial advisors5 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 households

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 households finances or investments.

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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.

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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.

3.4.3 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 individuals experience.

3.4.3.1 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: 8th 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), Masters degree (7),

Professional doctorate (education, law, medicine, etc.)(8), and Ph.D. (9).

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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 households education level, the assumption being that the less educated

spouse tends to rely on the more educated spouses 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).

3.4.3.2 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.,

2