Are Individual Differences Germane to the Acceptance of New Information Technologies?

Decision Sciences Volume 30 Number 2 Spring 1999 Printed in the U.S.A.

Are Individual Differences Germane to the Acceptance of New Information Technologies? Ritu Agarwal Floriah State University, Informution and Management Sciences Department, Tallahassee, FL 32306-111 0, [email protected]

Jayesh Prasad University of Dayton, School of Business Administration, Dayton, OH 45469-21 30, [email protected]

ABSTRACT Persuading users to adopt new information technologies persists as an important problem confronting those responsible for implementing new information systems. In order to better understand and manage the process of new technology adoption, several theoretical models have been proposed, of which the technology acceptance model (TAM) has gained considerable support. Beliefs and attitudes represent significant constructs in TAM. A parallel research stream suggests that individual difference factors are important in information technology acceptance but does not explicate the process by which acceptance is influenced. The objective of this paper is to clarify this process by proposing a theoretical model wherein the relationship between individual differences and IT acceptance is hypothesized to be mediated by the constructs of the technology acceptance model. In essence then, these factors are viewed as influencing an individual’s beliefs about an information technology innovation; this relationship is further supported by drawing upon extensive research in learning. The theoretical model was tested in an empirical study of 230 users of an information technology innovation. Results con- f i the basic structure of the model, including the mediating role of beliefs. Results also identify several individual difference variables that have significant effects on TAM’S beliefs. Theoretical contributions and practical implications that follow are discussed.

Subject Areas: Beliefs about New Technologies, Individual Differences, Znfor- mation Technology Innovation, Structural Equation Modeling, Technology Adoption Models, and Technology Implementation.

INTRODUCTION What causes individuals to adopt new information technologies? Is it something in the inherent personality and background of the individual or is information technology acceptance driven by factors under the direct influence of managers? These

361

362 Acceptance of New Information Technologies

questions have persisted as an important concern among information systems managers and researchers alike; a concern arising from the aphorism that systems that are not utilized will not result in expected efficiency and effectiveness gains. As a consequence, there is a growing body of academic research focused on examining the determinants of computer technology acceptance and utilization among users (e.g., Moore & Benbasat, 1991; Mathieson, 1991; Davis, 1989; Davis, Bagozzi, & Warshaw, 1989; Taylor & Todd, 1995). Some of this research draws its theoretical underpinnings from the adoption and diffusion of innovations literature, where individuals’ perceptions about using an innovation are posited to influence adoption behavior (Rogers, 1995; Moore & Benbasat). Other significant theoretical models that attempt to explain the relationship between user attitudes, perceptions, beliefs, and eventual system use include the theory of reasoned action (TRA, Ajzen & Fishbein, 1980), the theory of planned behavior (TPB, Ajzen & Madden, 1986), and the technology acceptance model (TAM, Davis, 1989). Of these, TAM appears to be the most widely accepted among information systems researchers, perhaps because of its parsimony and the wealth of recent empirical support for it.

A set of constructs not specifically included in TAM are variables related to individual differences. The term “individual differences” could be used most generally to be suggestive of any dissimilarities across people, including differences in perceptions and behavior (such as TAM’S beliefs about a technology and acceptance or usage behavior). However, for the purpose of this study, consistent with practice in the information systems research literature (e.g., Alavi & Joachimsthaler, 1992; Harrison & Rainer, 1992). individual differences refer to user factors that include traits such as personality and demographic variables, as well as situational variables that account for differences attributable to circumstances such as experience and training. In spite of Huber’s (1983) criticism of the value of research on cognitive style, which is only one type of individual difference variable, the lack of attention to most other individual differences in models such as TAM is particu- larly surprising in light of the fact that a parallel stream of research in new information technology implementation has posited and verified significant relationships between individual difference variables and various outcomes associated with new technology acceptance (see Nelson, 1990, and Alavi & Joachimsthaler, 1992, for recent reviews). Indeed, Zmud’s (1979) review and synthesis of prior work related to individual differences and management information systems success reveals a rich literature that has paid close attention to individual differences. However, prior work in individual differences, though stressing the importance of these variables to the acceptance of new IT, has not specifically drawn upon recently proposed theories of new technology acceptance, such as TAM, which have accumulated substantial empirical support. As Baron and Kenny (1986) noted, it is important to understand what processes link traits to behavior; but this research has, for the most part, not examined this question.

The work reported here represents a synthesis and extension of the two research streams identified above. We extend the technology acceptance model by specifying the role of individual differences in the model and present an example of how the process through which such variables influence IT acceptance may be explicated. In particular, we theorize that beliefs or perceptions as represented in TAM “intervene” between individual difference variables and IT acceptance. This

Aganval and Prasad 363

expectation stems from extensive research in the theory of learning, which we uti- lize as the conceptual basis for establishing the existence and nature of these effects. Although the issue of mediation by beliefs of various external variables has been studied in prior work (e.g., Venkatesh & Davis, 1996; Thompson, Higgins, & Howell, 1994), empirical results have been mixed, suggesting that there is a need to examine additional theoretical bases for the relationships posited. Thus, a specific contribution of this paper is to shed further light on the issue of mediation by testing a model strongly grounded in an alternative theory.

In addition to extending our understanding of individual differences and new IT acceptance from a theoretical stance, the research presented here also has practical implications for managers responsible for making decisions about the implementation of new IT. Information technology is increasingly permeating all aspects of organizational life, and individuals with a wide variety of backgrounds, prior experiences, and personalities need to use these technologies for organizational work. Establishing the mediating role of beliefs in the relationship between individual differences and IT acceptance leads to two implications. One implication is that as these variables influence beliefs about the new technology, managers can either provide appropriate training and other situational experiences, or they can specifically target individuals for new technology implementation through recruitment and selection (i.e., select individuals with profiles corresponding to those associated with more positive beliefs). Indeed, anecdotal evidence from recent college graduates from a variety of major fields of study suggests that cor- porate interviewers are increasingly prone to perform such an assessment. A second implication that follows directly from the assumption of mediation and the centrality of beliefs is that managers may choose to focus attention away from individual differences, and instead, proactively influence beliefs directly through other strategic managerial action such as broad-based information dissemination and the use of opinion leaders (Rogers, 1995). Thus, there may be no reason to expect new information technologies to be more readily accepted by one type of individual versus another, assuming that the belief development process has been managed appropriately.

The remainder of the paper is organized as follows. The theoretical base underlying the study, which draws on TAM for the basic structure of the model and upon learning theories for both the hypothesized relationship between individual differences and beliefs as well as the choice of these variables, is developed in the following section. The research model driving the empirical study, as well as specific research hypotheses, are then presented. Next, a field study of 230 new users of a graphical user interface (GUI) conducted to test the research hypotheses is described along with the results of these tests. This is followed by a discussion of the results, while the final section examines the implications of the results for research and practice, and discusses directions for future research.

CONCEPTUAL BACKGROUND

The conceptual findings for this research draw upon theories from several areas, including the diffusion of innovations, social psychology, and learning. Each of these streams is briefly discussed below.


Individual Differences and the Acceptance of New IT The notion that individual differences play a crucial role in the implementation of any technological innovation has been a recurrent research theme in a wide variety of disciplines including information systems, production, and marketing (e.g., Harrison & Rainer, 1992; Majchnak & Cotton, 1988; Zinkhan, Joachimsthaler, & Kinnear, 1987). In the information systems domain specifically, a relationship between individual differences and a variety of MIS success outcome variables has been theoretically posited and empirically demonstrated in a large body of prior research (e.g., Zmud, 1979; Harrison & Rainer, 1992). Numerous individual difference variables have been studied, including demographic and situational variables, cognitive variables, and personality-related variables (Zmud, 1979). For example, in spite of Huber (1983), the effects of cognitive style have been extensively researched (e.g., Benbasat & Taylor, 1978; Lusk & Kersnick, 1979; Blaylock & Rees, 1984). Harrison and Rainer investigated the relationship between several individual differences including gender, age, experience, and personality, and the outcome variable of computer skill in the context of end-user computing. A synthesis of prior research also suggests that individual differences with respect to user motivation and capabilities are important determinants of success (Kwon & Zmud, 1987). For instance, DeSanctis (1982) found success to be positively associated with user motivation. Nelson (1990) provided a recent review of work related to examining the effects of a variety of individual difference variables in the context of the implementation of new technologies.

Although there are pints of similarity in prior research in terms of specific individual difference variables considered to be germane influences on the implementation of a new IT, it is evident from the mixed empirical results obtained in prior work that the “processes” through which individual differences influence system success are not well understood. To be specific, this research has rarely attempted to explicate the variables that intervene between individual differences and success. In this context, a couple of notable exceptions are the work by Zmud (1979) and, over a decade later, by Nelson (1990). Zmud (1979) presented a model that includes such intervening variables as cognitive behavior and user attitude, along with system design characteristics and user involvement. However, this model is inferred from a review and synthesis of extant literature and, although useful as a framework for organizing past research, is not theoretically derived. Nelson described a model of individual adjustment to new information technologies derived from interactional psychology and concluded that most prior work has focused on job characteristics and situational factors. Nelson also noted that individual characteristics have primarily been correlated with computer attitudes, without an examination of more complex multivariate relationships that might include moderating and mediating variables. Motivated by the insights from prior work about the potential existence of mediating variables, and based on theoretical developments since that time specifically in the domain of new information technology implementation, it is now possible to propose a richer explanation of how individual differences influence system success via intervening variables.

It is also clear from the wide variety of criteria used in prior empirical research in individual differences to assess the success of a system that success in IS implementation is a multidimensional construct. Depending on the specific

Agarwal and Prasad 365

research questions posed, researchers have chosen to focus on different aspects of IS success. A recent conceptualization of success is offered by DeLone and McLean (1 992). They described six interdependent success variables, one of which is system usage, which is essentially a behavior that precedes any individual or organizational performance gains from IT. Consistent with our emphasis on user acceptance of new technologies, we choose to focus on systems utilization as an indicator of systems acceptance, and draw upon the technology acceptance model (Davis, 1989; Davis et al., 1989) to identify suitable intervening variables.

Individual Differences and the Technology Acceptance Model The technology acceptance model (TAM) attempts to explain and predict the determinants of individual behavior toward a system, manifest through system utilization. In essence then, TAM equates system success to actual utilization of the system. As noted earlier, although several other outcomes could represent system success, utilization represents a crucial prerequisite to actualizing the other benefits. According to TAM, beliefs about using the target system influence usage intentions and behavior via their effect on a potential adopter’s attitude. TAM further suggests that two beliefs-perceived usefulness and perceived ease of use- are instrumental in explaining the variance in attitude.

Perceived usefilness captures the extent to which a potential adopter views the innovation as offering value over alternative ways of performing the same task. Ease ofuse encapsulates the degree to which a potential adopter views usage of the target technology to be relatively free of effort (Davis et al., 1989). As noted by Davis et al., innovations that are perceived to be easier to use and less complex have a higher likelihood of being accepted and used by potential users. Indeed, both usefulness and ease of use are perceptual concepts and not innate attributes of the innovation. In TAM, attitude is defined as the mediating affective response between beliefs and usage intentions; that is, as Fishbein (1967) noted, attitude is a learned implicit response that refers to an individual’s evaluation of a concept. Among the beliefs, perceived ease of use is hypothesized to be a predictor of perceived usefulness. Further, perceived usefulness, but not perceived ease of use, is postulated to have a direct effect on behavioral intentions to use the innovation over and above its influence via attitude (Davis et al.). In addition to possessing the desirable theoretical property of parsimony, TAM has received considerable empirical support through a number of studies examining different information technologies (e.g., Adams, Nelson, & Todd, 1992; Davis, 1993).

Davis et al. (1989) suggested that the internal psychological variables (i.e., the beliefs) that are central to TAM fully mediate the effects that all other variables in the external environment may have on an individual’s use of an innovation. They noted, for instance, that “External variables . . . provide the bridge between the internal beliefs, attitudes, and intentions represented in TAM and the various individual differences, situational constraints, and managerially controllable interventions impinging on behavior” (Davis et al., p. 988). Full mediation by beliefs and attitudes implies that all external variables would not exhibit any direct influence on usage intentions or usage behavior. Rather, such effects would only be exhibited indirectly through their relationship with beliefs. The class of variables representing individual differences is therefore exogenous to the conceptualization


of TAM. Indeed, in the theory of reasoned action (Fishbein & Ajzen, 1975) upon which TAM is based, personality was identified explicitly as a type of exogenous or external variable. Thus, although the prior work discussed in the previous section provides strong evidence that individual differences play a central, albeit not conclusively specified role in IT acceptance, this research theorizes that individual differences exhibit an influence on individual behavior toward a new information technology via their effects on an individual’s beliefs about the new IT. The theoretical basis for this relationship derives from two different research streams: social psychology and learning theories, and is discussed below.

Individual Differences and Beliefs The social psychology literature, which provides a discussion of belief formation, is an appropriate theoretical base for understanding what variables might affect the development of beliefs about new information technologies. The distinction between beliefs and attitude is relatively recent; most early work focused solely on attitudes. One such characterization of attitude proposed that attitudes are learned predispositions to respond in a particular way towards a concept or object (Doob, 1947). In this context it has been noted by Staats and Staats (1958) that Doob emphasized the significance of learning; in fact, they quote him as stating that “The learning process, therefore, is crucial to an understanding of the behavior of attitudes” (p. 37). Later, Fishbein (1967) suggested that it is important to separate the cognitive component of attitude from the affective. The cognitive component is what came to be known as beliefs, while the affective component alone com- prises attitude; this conceptualization of attitude underlies the theory of reasoned action (Fishbein & Ajzen, 1975), and subsequently, TAM. Indeed, Fishbein and Ajzen, in their exposition of the theory of reasoned action, stated that “according to the behavior-theory approach, belief formation should follow the laws of leam- ing” (p. 29, italics added). Other work in attitude formation (e.g., Staats & Staats) also suggests that since attitudes are to be considered responses, they must be affected by learning processes. In addition to the social psychology literature con- cerned with the development of attitudes, work specifically in the diffusion of innovations (Rogers, 1995) has echoed similar sentiments by proposing that belief formation is essentially a learning process. For example, according to Rogers, information about an innovation flows through the social system of a potential adopter. This information is gathered and synthesized by the individual; such information processing and learning crystallizes into personal beliefs about the innovation. Given the parallels drawn in the research literature between the learning process and belief formation, factors that might influence the formation of beliefs are posited to be the same as those that influence learning processes.

The study of learning processes has received much attention in the last ten decades. Although an exhaustive review of learning theories is beyond the scope and tangential to the objectives of this paper, we point to certain aspects of the learning process that are directly relevant to our research. The interested reader may consult Bower and Hilgard (1981) for a more in-depth discussion.

Several theories have been proposed that attempt to understand and predict how learning takes place, including Behavioral-Associational theories and Cognitive- Organizational theories (Bower & Hilgard, 1981). Included in the first set of theories


are Pavlov’s classical conditioning theory (Pavlov, 194 l), associative learning (e.g., McGeoch & Irion, 1952), and Skinner’s operant conditioning (Skinner, 1969). Other explanations of learning such as Bandura’s social learning theory (Bandura, 1977) are classified as cognitive-organizational theories. Despite differences in philosophical orientation however, as noted by Bower and Hilgard, there is consistency among alternative theories with regard to what constitutes learning. Furthermore, in spite of differences in the conceptualizations of the mechanisms through which learning takes place, there is agreement among the various researchers that the “influences” on the learning process are largely similar. For instance, in the theory of operant conditioning, Skinner suggested that an understanding of behavior must take into consideration the past history of the learner. In the recent aptitude-treatment interaction view of learning, the role of individual differences on the learning process is acknowledged (Cronbach & Snow, 1977). Indeed, Cronbach and Snow assert that individual difference variables such as knowledge, skills, and previous experiences, determine what an individual learns in a given situation. In a similar vein, in social learning theory (Bandura), it has been proposed that individual variables influence learning through observation. To the extent that beliefs are a learned response, then, individual differences are expected to influence belief formation.

In summary, the research described in this paper seeks to explicate the relationship between individual differences variables and the usage of a new infonna- tion technology innovation by examining potential intervening influences. The constructs and relationships underlying TAM are hypothesized as representing these mediating influences. A relationship between individual difference variables and beliefs is theorized based on extensive work in learning, which suggests that beliefs are learned responses, and that individual differences play a pivotal role in learning.

THE RESEARCH MODEL AND RESEARCH HYPOTHESES

The research model tested in this study is shown in Figure 1. It is based directly on the discussion above, except that actual usage of the innovation is not measured. Instead, usage intentions are assessed, which, as hypothesized in TAM and empirically confirmed in studies of TAM (Davis et al., 1989), are predictors of actual usage. Actual usage is not measured because, as will be explained subsequently, data were gathered at a single point in time and not longitudinally, and usage in a current time period would be based on beliefs and attitude in a preceding time period. For such a research design, intentions are more appropriate since they are measured contemporaneously with beliefs. Jackson, Chow, and Leitch (1997) suggested that intentions are reasonable indicators of future system usage, based on their review of the evidence in this regard. Even Szajna’s (1996) work, which dis- tinguished between self-reported and actual system usage, showed that in situa- tions in which the technology being studied has already been installed, as is the case in the present research, intentions predict both self-reported and actual usage. The research model further identifies individual variables of interest, which are discussed in greater detail below. It should be noted that these variables are relatively stable, descriptive traits, which can be reliably measured simultaneously with the other variables in the model.

Figu

re 1

: The

rese

arch

mod

el.

W

o\

m

Indi

vidu

al D

iffer

ence

s I

I I R

ole

with

Reg

ard

to T

echn

olog

d I R

ole

with

Reg

ard

to T

echn

olog

d ~~

~

~

I Ten

ure

in W

orkf

orce

I

~~

~ ~

I Ten

ure

in W

orkf

orce

I

~~

~ ~

Leve

l of E

duca

tion

1 ~~

~

~

Leve

l of E

duca

tion

1 Pr

ior,

Sim

ilar E

xper

ienc

es

I Pr

ior,

Sim

ilar E

xper

ienc

es

I Pa

rtici

patio

n in

Trai

ning

I

Parti

cipa

tion i

n Tr

aini

ng

I

I

. B

elie

fs A

bout

,-+ B

ehav

iora

l In

tent

ions

A

ttitu

de

Bel

iefs

Abo

ut

,-+ B

ehav

iora

l In

tent

ions

A

ttitu

de

Ease

of

Use

I E

aseo

f U

se


As observed already, a central tenet of TAM is that beliefs mediate the influence of all other factors in the environment that may exhibit effects on individual acceptance of a new IT. Whereas the beliefs-attitude-intentions relationships in TAM have been subject to extensive empirical scrutiny, considerably less work has focused on the hypothesized mediating role of beliefs. A few studies (e.g., Davis, 1993, and Venkatesh & Davis, 1996) have shown full mediation of the effects of systems variables. Other research has examined individual difference variables such as user involvement (e.g., Jackson et al., 1997), training (e.g., Compeau & Higgins, 1995, and Venkatesh & Davis, 1996), and prior experience (e.g., Thompson et al., 1994), utilizing as mediators beliefs from TAM or other variables such as computer self-efficacy, but has been unable to demonstrate full mediation unequivocally. Indeed, only the results of Venkatesh and Davis (1996) and one of the two studies reported in Compeau and Higgins (1995) supported full mediation. Much of this work is theoretically motivated by social cognitive theory (Bandura, 1977) or the work of Triandis (1980) on attitudes. The current research attempts to address the inconclusive results on mediation of individual differences by examining this issue from a different theoretical perspective, utilizing learning theory and a variety of individual difference variables. As a consequence, the first hypothesis tested here is:

H1: Ease of use and usefulness beliefs fully mediate the influence of selected individual difference variables on attitude and usage intentions.

Individual Differences and Beliefs We argued that the belief formation process is essentially comparable to a learning process; thus, learning theory provides a basis for understanding how individual difference variables might influence the development of beliefs. Learning theories contain several axioms and laws describing causal relationships between a learner’s environment and what is learned (Bower & Hilgard, 1981). As the research reported here seeks to establish that learning theory does indeed provide an appropriate theoretical base for positing a relationship between individual variables and beliefs, one law was selected for illustrative purposes to empirically examine the existence of the postulated relationships.

In the human associative view of learning, it has been proposed that the law of proactive inhibition or interference (McGeoch & Irion, 1952) describes and helps predict the effects that individual difference variables have on the learning process. This law suggests that individuals’ prior knowledge and experiences interfere with their ability to learn to exhibit specific behaviors. The fundamental notion underlying this law is the extent of similarity or dissimilarity between an individual’s prior experiences and knowledge, and the new behavior being learned. Primarily through cognitive associative processes (Gick & Holyoak, 1987), similar prior experiences result in greater learning, and therefore, might be expected to lead to more positive beliefs, whereas the opposite effect is expected for dissimilar prior experiences. For instance, although knowledge of the BASIC programming language might facilitate the learning of PASCAL, knowledge of COBOL might, in fact, interfere with the learning of a nonprocedural programming language such as SmallTalk (e.g., Scholtz & Wiedenbeck, 1990).


The research literature provides a few typologies of individual difference variables. Zmud (1979) derived a taxonomy of individual difference variables, whch included the categories of demographics, personality, and cognitive style. In a recent study, Bostrom, Olfman, and Sein (1990) reviewed prior research in individual differences and software learning and classified individual difference variables into four categories: states, which include constructs such as attitude toward computers and attitudes toward the job; structures-strategies, which include mem- ory, reasoning, and visual abilities; cognitive traits such as intelligence and locus of control; and descriptive traits such as educational background, work experience, and experience with specific software. In an alternate classification, Sein, Bostrom, and Olfman (1987) posited that each individual brings a certain set of traits and characteristics to a learning experience, including cognitive traits, motivational traits, task domain knowledge, or previous experiences with different tasks or work that an individual performs; and referent experiences, or experiences with other information systems.

Guided by the typologies described above and consistent with the law of proactive inhibition, individual difference variables were selected such that they described the prior knowledge base individuals would possess at the time of inter- acting with the new IT. In essence then, these variables represent traits that can potentially interfere (positively or negatively) with their acceptance of the new technology. The specific individual difference variables included in this study are an individual’s primary h l e with respect to information technology (technology provider versus technology user); workforce tenure; the level of education of the individual; an individual’s prior experience with technologies that are similar to the technology being examined; and whether the individual has received any formal training on the target system. Indeed, it has been argued that user capabilities are usually a result of user training, age, educational background, experience with computer-based IS, work experience, etc. (Fuerst & Cheney, 1986; Sanders & Courtney, 1985). In terms of the Bostrom et al. (1990) classification, the variables examined here fall into the descriptive category, whereas in Zmud’s (1979) classification, they represent demographic/situational variables. It is also worth noting that the variables included in this study can be classified as either user-situational or demographic variables in Alavi et al.’s (1992) scheme. That meta-analysis found the former to have the greatest impact on system success, while the effect of the latter could not be determined due to the paucity of studies involving demographic variables.

Individual’s organizational role with regard to technology, that is, whether their primary responsibility is to be a provider or a user of technology, has implications for their general level of experience with computing technology. Although with the growth of end-user computing it is possible that end-users might also have extensive experience with different information technologies, it is reasonable to suggest that IT professionals, by virtue of their formal training and indoctrination in the discipline of computing have developed mental models and conceptual knowledge that allows them to transcend the idiosyncratic behavior of any specific new technology (Soloway, Adelson, & Ehrlich, 1988). Such conceptual knowledge or schema then provides the positive interference and allows such individuals to learn and assimilate a new technology more readily (Gick & Holyoak, 1987). Following from these arguments we test the hypothesis:

Aganval and Prasad 37 1

H2: Ease of use and usefulness beliefs about an information technology innovation are more positive for technology providers than for technology users.

Prior work suggests that older workers and those with greater company tenure are more likely to resist new technologies (Kerr & Hiltz, 1988; Majchrzak & Cotton, 1988). In a study of new production technologies, Majchrzak and Cotton found that workers with less work experience were more committed to the changes wrought by the new technology. Insofar as tenure in the workforce represents a surrogate for the age of the individual, there is extensive evidence pointing to a negative relationship between age and acceptance of technological change (e.g., Harrison & Rainer, 1992; Nickel & Pinto, 1986). Additional support for the inclusion of tenure in the workforce can be found in another recent review of research focused on the acquisition of computer skills (Gattiker, 1992), in which the author notes that significant effects were found for age in skill acquisition and retention. When the technology is radically different from other technologies in existence, possibly for reasons of habit, greater need for stability, or affective processes, individuals’ age and tenure in the workforce may be negatively associated with beliefs. This is supported by the findings of early research in learning among adults: Results suggest that those who are older are more susceptible to negative interference, especially under changing conditions of learning (McGeoch & Irion, 1952). Thus, the following hypothesis is tested:

H3: The length of tenure in the workforce is negatively associated with ease of use and usefulness beliefs about an information technology innovation.

Prior work suggests that education is negatively related to computer anxiety (e.g., Igbaria & Parsuraman, 1989). In a study of training techniques and personal characteristics in the context of end users, Davis and Davis (1990) found a relationship between level of education and performance in a training environment. The level of education then, is indicative of a potential adopter’s ability to learn and, therefore, should be positively associated with beliefs. As noted in the cognitive approaches to learning (Bower & Hilgard, 1981), more sophisticated cognitive structures, perhaps acquired through higher education, lead to a greater ability to learn in a novel situation. These structures, or acquired general learning strategies, facilitate positive interference or transfer (McGeoch & Irion, 1952; Gick & Holyoak, 1987). These results suggest the following hypothesis:

H4: Level of education is positively associated with ease of use and usefulness beliefs about an information technology innovation.

In general, prior research has established a positive relationship between experience with computing technology and a variety of outcomes such as affect towards computers and computing skill (Levin & Gordon, 1989; Harrison & Rainer, 1992). However, not all experience is necessarily efficacious in the acceptance of new information technologies. Indeed, the switching costs associated with moving to a very dissimilar technology may offset any positive gains due to experience. In this context, Scholtz and Wiedenbeck (1990) observed deleterious effects when programmers moved to a radically new programming environment.


Furthermore, studies focused on the transfer of skills between text editors (e.g., Polson, Bovair, & Kieras, 1987) emphasize that positive transfer effects will be noted only when the two editors share elements in common. In general terms, positive transfer or interference occurs when stimulus generalization exists (McGeoch & Irion, 1952; Gick & Holyoak, 1987). Thus, we assert a positive relationship between an individual’s prior compatible experiences and the new information technology. Following from research results obtained by others as well as the law of proactive inhibition, we test the following hypothesis:

H5: The extent of prior experience with similar technologies is positively associated with ease of use and usefulness beliefs about an information technology innovation.

The final individual difference variable examined here, participation in training, is indicative of the mental model of the target system that a potential adopter has developed. Training serves to reduce uncertainty about an innovation by providing information about the features of the innovation. Assuming that the innovation possesses some “objective” value for potential adopters, greater learning about the innovation should amplify perceptions in a positive direction. Further, training directly demonstrates elements identical to the target system and serves to provide stimulus generalization and, hence, positive transfer effects (McGeoch & Irion, 1952; Gick & Holyoak, 1987). Empirical results obtained in other work related to information technology acceptance also support the positive influence of training on usefulness and ease of use beliefs (Igbaria, Garners, & Davis, 1995). The final hypothesis tested here is:

H6: Participation in training on an information technology innovation is positively associated with ease of use and usefulness beliefs about that innovation.

METHODOLOGY

The overall strategy employed to empirically test the research hypotheses was a field survey. The study context, operationalization of research constructs, data analysis, and results are described below.

The Study Context and Sample The study was conducted at a Fortune lo0 corporation in the Midwest. The f m is an information technology vendor, ranks in the top five U.S. providers of information technology and related services, and operates in over 120 nations. Two specific divisions within the corporation were targeted for the survey. The selection of divisions was guided by two considerations: first, the sponsoring manager believed that these two divisions were adequately representative of the employee profile in the corporation as a whole, and second, both divisions included information systems as well as user departments. A total of 468 surveys were distributed, with 230 usable responses being returned for a response rate of 49%.

By virtue of its high technology business, the corporation has a better than average technology awareness among its personnel. Thus, the user population is


fairly technology literate; this organizational milieu must be taken into account when interpreting the results. However, the bulk of technology-related experiences of the users have been with large systems including minis and mainframes because these were the major products of the corporation; the personal computer was still fairly new to the organization. The two divisions surveyed had personal computers available to everybody for a period ranging from two to five years, and prior to that, used terminal-mainframe access extensively. During the first few years of personal computer use, the operating system utilized a command-line interface; then, both divisions were provided with a standard GUI environment on all their personal computers. Users had the option of using either the command line or graphical user interface (with appropriate software packages) on their worksta- tions; both environments were equally easily accessible and adequately supported. The basic functionality offered in the two environments, for example, word processing, spreadsheets, graphics, etc., was quite similar. At the time of data collection users had not completely switched over to the new system. Training in the new environment was offered to all users in the form of a class taught by an external training provider; users voluntarily chose to use it. This particular training oppor- tunity was the only one that management made available.

Operationalization of Research Variables TAM’S variables were operationalized (see the Appendix) according to the recommendations made by Davis (1 993) and Ajzen and Fishbein (1980). Davis (1 993) reported the use of 10-item scales for the measurement of perceived usefulness and perceived ease of use. To keep the length of the instrument reasonable, eight items were selected from this set for the measurement of perceived usefulness, and seven were selected for perceived ease of use. The items that were excluded satisfied one of two criteria: either they were not relevant to the specific innovation being examined here or they were very similar to another item that had been included. For instance, the item “[The target technology] supports critical aspects of my job” was excluded because the technology is primarily an interface and, per se, does not provide direct functionality associated with a job as a spreadsheet package might. Thus, the items selected ensured complete coverage of the meaning of the construct. Attitude was measured using a four-item scale constructed according to the guidelines provided by Ajzen and Fishbein, and future use intentions were assessed using two items constructed following the recommendation of Davis et al. (1989). Respondents scored all items on a 7-point Likert scale, with Strongly disagree and Strongly agree as the two endpoints.

In contrast to the scales for measuring attitudes and intentions which were standard, the scales for perceived usefulness and perceived ease of use utilized here were derived from those proposed by Davis (1993). To verify construct validity, a confirmatory factor analysis (CFA) using LISREL 8 (Joreskog & Sorbom, 1993) was performed for these latter scales. Items that had squared multiple correlations with the latent variables of less than .40 were dropped from the analysis (Bollen, 1989). The final model included all eight perceived usefulness items and four perceived ease-of-use items. Although the overall x 2 statistic for model fit was significant ( x 2 = 172.59, p = .OO), all other indicators of fit were within appropriate values. Pedhazur (1982) noted that the x2 statistic is very sensitive to


sample size and frequently indicates poor model fit even though the model fits the data well. Hence, as suggested by Bollen, four other indicators of fit for CFA were utilized: Bollen’s indicators were all at or above the acceptable .9 level ( A1 = .92, A2 = .94, p1 = .9, pz = .93). The Adjusted Goodness of Fit Index (AGFI) proposed by Joreskog & Sorbom was at .82, where .8 is generally considered to be a reasonable cut-off for model fit (Taylor & Todd, 1995). The Root Mean Square Error of Approximation (RMSEA), another fit indicator that has gained acceptance for structural equation models (Browne & Cudeck, 1993), was at the acceptable limit (RMSEA = .lo).

In addition to the fit statistics, squared multiple correlations between the items and their associated constructs were all at or above .49, indicating that the items explained substantial variability in the latent constructs. The relationships between the items and the constructs were all in the expected direction. These analyses confirmed the validity of the perceived usefulness and perceived ease-of-use constructs. Finally, Cronbach’s alpha (Cronbach, 1970) was computed for each scale to ascertain internal consistency among the items; scale reliabilities are reported in Table 1. All reliabilities except the one for intentions were above the .7 level, generally considered acceptable for field research (Nunnally, 1978). The reliability for intentions was .6; the implications of this slightly less than ideal reliability are discussed subsequently. See the Appendix for items comprising all scales.

For individual difference variables, prior familiarity with similar technologies was assessed using three items: level of familiarity with personal computers, prior usage of GUIs, and prior usage of input devices such as mice and joysticks. Respondents scored each item on a 7-point scale, with Never used one and Used one u lot as the two endpoints, and Used one occasionally as the midpoint (the reliability for this scale was .6). These prior experiences were included because of their immediate relevance and similarity to the innovation being examined. The other individual difference variables were less subjective and measured accord- ingly. Thus, respondents were classified as technology providers (i.e., information systems professionals) or technology users based on the job classification they indicated on the survey. Respondents selected the highest degree they had obtained from a list provided that ranged from High School to Master’s and indicated whether or not they had benefited from training on the target technology. They also indicated how many years they had spent in the workforce.

Data Analysis and Results Descriptive statistics for all research variables are shown in Tables 2a and 2b. Respondents had spent an average of 12.5 years in the workforce. A mean value for the three items capturing a respondent’s prior experiences with similar systems was used in the analysis. The data show that, on the average, respondents had relatively low prior knowledge of systems similar to the new technology. Because our emphasis was on examining the effects of level of education on beliefs and not those of a specific degree, degree was recoded for the analysis into two categories: degrees less than a Baccalaureate *gh school or associate) and degrees that were Bacca- laureate or higher. Degree was recoded because substantively, its treatment as an interval variable is not appropriate. On the other hand, its inclusion as a nominal


Table 1: Scale reliabilities.

Scale Number of Items Reliability* Usefulness 8 .95 Ease of Use 4 .87 Attitude 4 .83 Behavioral Intentions** 2 .60 *Cronbach's alpha is reported

** Interitem correlation for Behavioral Intentions was .43, significant at p < .Ole

'lbble 2: Descriptive statistics.

a. Interval ScaleUContinuous Variables Variable Mean Standard Deviation Missing (n) Usefulness 5.48 1.01 0 Ease of Use 5.23 0.99 0 Attitude 5.78 0.86 0 Behavioral Intentions 5.53 1.02 3 Tenure in Workforce 12.50 9.99 7 Prior, Similar Experiences 2.34 1.57 1

Notes: Usefulness, Ease of Use, Attitude, Behavioral Intentions, and Prior, Similar Expe- rience are measured on 7-point scales. Tenure in Workforce is number of years.

b. Categorical Variables Variable Category n Missing (n)

regard to Technology Users 191 Organizational Role with Providers 35 4

Level of Education Less than Baccalaureate 89 8

Baccalaureate or Higher 133

Participation in Training Yes 182 0

No 48

variable with five categories would have added considerable complexity to the analysis and reduced degrees of freedom.

To test H1, we utilized LISREL 8 (Joreskog & Sorbom, 1993) with maxi- mum likelihood estimation (MLE) to compare the model, including direct effects of individual differences on attitude and intentions (Model l), with the model without such direct effects (Model 2). It is clear that Model 2, which is obtainable from Model 1 by constraining the direct effects to be zero, is nested within Model 1. The use of structural equation modeling and the comparison of alternate, nested


models as an analysis technique is appropriate here in light of the fact that we are testing models strongly grounded in theory (Joreskog & Sorbom; Hoyle & Panter, 1995). Following Taylor and Todd (1995), because of sample size limitations, each multi-item construct was measured using a summated scale derived as the average value of all items pertaining to that construct. Although the data were not multivariate normal, MLE was performed because the sample size did not permit the application of distribution free estimation such as weighted least squares (Joreskog & Sorbom).

To account for the lack of multivariate normality, following Stine (1989), additional simulation tests were performed (as explained below). Overall fit was confirmed for both models with the x 2 test ( x 2 = 0.014, p = .91 for Model 1; x 2 = 11.35, p = .41 for Model 2). However, the change in x2 when moving from Model 1 to Model 2 was not significant ( Ax2 = 11.34. p > . l ) , thus providing statistical support for Model 2 (Joreskog & Sorbom, 1993). From the perspective of parsimony as well (Mulaik, James, Van Alstine, Bennet, Lind, 8z Stilwell, 1989), Model 2 is preferable; all the effects of individual difference variables on either attitude or usage intentions were mediated fully by beliefs about usefulness and ease of use. The results of testing Models 1 and 2, including path coefficients and standard errors are shown in Figures 2 and 3, respectively, while the covariance matrix used in the analysis is shown in Table 3. Additional fit indicators confirm the fit of Model 2 (AGFI = .96; RMSEA = .Ol).

To further validate model fit by testing the robustness of the model to distri- butional assumptions and sample size, a bootstrap simulation (Stine, 1989) was performed. In a bootstrapping procedure, repeated samples are taken from the original sample with replacement. For each sample, the parameter estimates of interest are computed, resulting in an empirical, as opposed to a theoretical, sampling distribution (West, Finch, & Curran, 1995). Two thousand covariance matrices were randomly generated by selecting observations from the original data set with replacement. The sample size for each replication was set equal to the sample size of the original data set (i.e., 230). Model 2 was then fit to each of the 2,000 covariance matrices. The solution converged in 1,998 cases. Although the overall x2 , which is sensitive to sample size, was significant, other fit indicators proposed in the literature were within acceptable ranges and all indicative of good fit. The 90% confidence interval for RMSEA was (.018, .049), where the recommended cut-off for good fit is .08 for the upper confidence limit (Browne & Cudek, 1993). Joreskog & Sorbom’s (1993) GFI and AGFI were at .95 and .86, respectively. As proposed by Bentler (1 990), a comparative fit index (CFI) was also utilized to account for the sample size. Bentler posited that CFI is preferred to the normed and non-normed fit indices because it has small sampling variability. The CFI for the bootstrap simulation was .95. These indicators collectively point to the appropri- ateness of Model 2 as a good approximation of the underlying population.

As expected, attitude and perceived usefulness were significant predictors of intentions and together accounted for 26% of the variance in intentions. Attitude was determined jointly by perceived usefulness and perceived ease of use; these two variables explained 63% of the variance in attitude. Finally, as posited in TAM, perceived ease of use was a significant predictor of perceived usefulness. The direction of all relationships was precisely as specified in TAM. Path analyses


.......... ......-.... " " ....--.... 1"" ....-..-....- "I.. * * ............ " ............ ".." -...... "1" .... "......I

...................... " .............................................. " -..

........................................................................ " Fb ................................................ "

1; - e

............ " " .... - .... - --.- "-..""-""..".."...II-..". ...............................................


Tab

le 3

: Cov

aria

nce matrix f

or te

st o

f ful

l mod

el.

Prio

r, Pa

rtic

ipat

ion

4

Eas

e Te

chno

logy

W

orkf

orce

Si

mila

r in

U

sefu

lnes

s of

Use

A

ttitu

de

Inte

ntio

ns

Rol

e Te

nure

Ed

ucat

ion

Exp

erie

nces

Tr

aini

ng

Use

fuln

ess

0.99

1 Ea

se o

f Use

0.

737

0.98

7 A

ttitu

de

0.65

9 0.

603

0.73

8 In

tent

ions

0.

467

0.36

2 0.

432

1.03

2 T

echn

olog

y ro

le

0.06

0 0.

083

0.04

3 0.

010

0.13

2 W

orkf

orce

tenu

re

-1.5

91

-2.3

76

-0.9

18

0.33

8 -0

.820

10

0.78

7 Ed

ucat

ion

0.08

2 0.

131

0.05

5 -0

.034

0.044

-2.4

83

0.24

2 Pr

ior,

simila

r exp

erie

nces

0.

300

0.37

8 0.

210

0.08

1 0.

077

-3.5

44

0.07

1 1.

461

Part

icip

atio

n in

trai

ning

0.

038

0.00

2 0.

028

0.006

-0.0

3 1

0.68

9 -0

.090

-0

.009

0.

159


indicated that perceived ease of use had a total effect of .61 on attitude (sum of the direct effect and the indirect effect through perceived usefulness). Path analysis also revealed that perceived usefulness and perceived ease of use had an approxi- mately equivalent total effect on behavioral intentions (.39 and .40, respectively).

An examination of the path coefficients for individual differences shown in Figure 3 reveals that perceived ease of use as well as participation in training had a significant positive effect on perceived usefulness, and together, explained 57% of the variance in the dependent variable. For perceived ease of use, an individual’s organizational role with regard to technology, that is, whether they were primarily technology users or technology providers, their prior experiences with similar systems, and their level of education were all significant determinants, collectively accounting for 18% of the variance in perceived ease of use.

Based on the above analysis, H1 was supported, as the model that did not include any direct effects of individual differences on either attitude or behavioral intentions (Model 2) was preferred to the model with direct effects (Model 1). In addition, none of the direct effects in Model 1 were individually statistically significant. The differential effects on beliefs for technology providers versus technology users (H2) was supported for perceived ease of use; technology providers had more positive ease-of-use beliefs than technology users. Workforce tenure did not have an effect on either belief; thus H3 was not supported. Level of education was positively associated with ease-of-use beliefs but not with perceived usefulness; thus H4 received partial support. A similar result was obtained for extent of prior experiences with similar technologies, which had a positive association with ease-of-use beliefs only (H5). Finally, participation in training (H6) was positively associated with perceived usefulness but not with perceived ease of use. These results are discussed in the following section.

DISCUSSION A specific research objective guiding the study presented here was to shed further light on the relationship between individual differences and the acceptance of a new IT to address both theoretical and pragmatic concerns. To this end, we proposed and tested a model theorizing the mediating effects of constructs of TAM on this relationship.

The Mediating Role of Beliefs Our results point to the validity of the relationship between individual differences and beliefs as framed in our research model and supported by learning theories. They also further validate the remainder of TAM’S relationships between beliefs, attitude, and behavioral intentions. The confirmation of these relationships has several important implications. Because beliefs do mediate the effects of individual differences on attitude toward, and behavioral intentions to use, an information technology innovation, implementors of new information technologies need concern themselves less with directly influencing attitude and behavioral intentions. As suggested by the theory, these internal psychological processes should result if belief formation is appropriately managed. Thus, management attention might more fruitfully focus on the “development” of beliefs.


Although recent research has proposed a variety of other beliefs that may be relevant to the acceptance of innovations (e.g., Rogers, 1995; Moore & Benbasat, 1991), it is interesting that perceived usefulness and perceived ease of use by themselves contributed nontrivially in mediating the effects of the individual differences that were examined in this research. Further, the strength of the relationship between these beliefs and attitude is indicative of the significant role they play in the determination of attitude. Thus, although there is some residual unexplained variance, the model appears to have the desirable characteristics of both predictive power as well as parsimony. Our results, therefore, incidentally add to the growing body of evidence in support of TAM (e.g, Mathieson, 1991; Taylor & Todd, 1995). There is, however, a substantial amount of variance in usage intentions that is not accounted for by perceived usefulness and attitude. Hence, there may be a need to search for additional variables that improve our ability to predict usage intentions more accurately. For example, variables related to social factors similar to subjective norm, and facilitating conditions similar to perceived behavioral control that are present in other behavioral models (e.g., Ajzen & Madden, 1986) of technology acceptance might be added to TAM.

As TAM proposes, both perceived usefulness and perceived ease of use are important in technology acceptance. However, their relative importance in the acceptance process has been shown to be different in prior work. For instance, Davis (1993) found that usefulness dominated ease of use, whereas Adams, Nelson, and Todd (1992) found ease of use to be more influential than usefulness. Our results are more balanced in that usefulness and ease of use exhibit roughly equivalent influence on behavioral intentions (see Table 4). These results are entirely consistent with the nature of the specific technology examined here. Although we believed at the start of the study that the technology is primarily an interface, where ease of use would be an important concern for users, the results suggest that perhaps it does provide additional functionality through the ability to move objects between applications, where perceived usefulness becomes a relevant concern for users.

Having provided evidence for the centrality of beliefs in technology acceptance, recall that exogenous variables such as individual differences represent an important channel through which management can influence the formation of these beliefs. In fact, as the remainder of the technology acceptance process is hypothesized to be an internal psychological process that is not amenable to direct manipulation, individual differences and other external variables may represent the only channels for influencing behavior.

Individual Differences and Perceived Ease of Use Of the five individual difference variables examined for their influence on ease-ofuse beliefs, three had significant effects. Variables that did not affect ease-of-use beliefs were an individual’s tenure in the workforce and participation in training on the information technology innovation. Insofar as greater tenure in the workplace is a surrogate for age, we expected number of years in the workforce to be negatively associated with ease-of-use beliefs. The fact that this expectation was not confirmed is perhaps indicative of the intuitive nature of the interface. Indeed, the graphical user interface incorporated in the innovation examined here has been characterized as one of the “most revolutionary changes to occur in the evolution


Table 4: Direct and indirect effects on behavioral intentions.

Path Effect Usefulness to Behavioral Intentions

Direct effect .20 Indirect effect through Attitude .19 Total effect .39

Ease of Use to Behavioral Intentions Indirect effect through Usefulness .15 Indirect effect through Usefulness and Attitude Indirect effect through Attitude . l l

.14

Total effect .40

of modem computing systems” (Mandelkern, 1993, p. 37), and one that is signif- icantly easier to use than its command line-based predecessors. Alternative explanations might be that tenure in the workplace is not sufficiently robust to capture age considerations, or perhaps that age is not a significant influence on ease-of-use beliefs.

The lack of significance of the specific training examined here as an influence on ease of use has two possible explanations. One, that GUIs are so easy to use that training has no effects at all. The second explanation derives from recent work examining training on GUIs, in which Olfman and Mandviwalla (1994) found that effective GUI training is inherently difficult to deliver.

The positive effects of an individual’s role with respect to technology, an individual’s level of education, and prior, similar experiences, on the development of ease-of-use beliefs confirm the relationships hypothesized in learning theories. The implications of these results are discussed subsequently.

Individual Differences and Perceived Usefulness As hypothesized in TAM, ease of use predicts perceived usefulness; in fact, it is the most influential predictor, suggesting that a reduction in effort is a significant component of the utility an individual derives from a system. The reduction in effort expended can, in turn, free up time to perform other tasks, thereby increasing overall productivity.

An interesting finding in examining the influences on perceived usefulness is that beyond the utility derived from ease of use, only participation in training had a direct effect. Other variables, that is, individuals’ roles with respect to technology (provider versus user), their level of education, and their prior experiences with similar technologies, demonstrated effects only indirectly through perceived ease of use. The only external variable that did not have any effect on perceived usefulness was tenure in the workforce, possibly for the same reasons as discussed earlier.

These results for perceived usefulness are somewhat surprising because the GUI does provide more capabilities than just a user-friendly interface, such as the ability to dynamically link applications together and to exchange information between applications seamlessly. One plausible explanation for these findings might be that for a large number of the users, the added functionality of the interface had simply not been “discovered” yet, and hence, they did not perceive its


value differently. In this context, although training did not have any effects on perceived ease of use, perhaps because of the intuitive nature of the technology, it did have an effect on usefulness beliefs, suggesting that the training might have been instrumental in exposing individuals to the additional functionality offered by the interface as compared with command-line based systems.

The results presented above must be interpreted within the context of certain limitations of the study. The threats to the external validity of the study arise from the fact that a single research site was utilized for data collection, which may inhibit the generalizability of the results. The sample exhibited some unique characteristics that may not be completely representative of broad-based user samples in business; because of the nature of the organization, the sample probably had a higher technology awareness than the business population at large. However, a desirable consequence of the single-site data collection is that, because our primary unit of analysis is the individual, it allows for the control of other factors such as organizational context. In terms of internal validity, the fact that this study was conducted in the field is both a strength and a weakness. The strength lies in the realism of the sample and the study context; the weakness is the lack of controls inherent in a field study. In our measurement procedures, we made some assumptions about what is an appropriate indicator of conceptual constructs under study. For instance, tenure in the workforce was used as a surrogate for age, and others may wish to use a more direct, if less politically correct, measure. Finally, the reliability of one of the outcome scales (behavioral intentions) and one individual difference scale (prior, similar experiences) was slightly less than ideal. This however, should not diminish the relationships that were found to be significant.

IMPLICATIONS AND CONCLUSIONS The persistence of problems related to the acceptance of new information technologies constituted the broad motivation for this work. Several theoretical and practical implications follow.

Implications for Research From the perspective of theory development, we have posited and found empirical support for a theory of how individual differences drive the acceptance of new information technologies through their influence on beliefs about the new IT. We also find, as others have, support for the technology acceptance model as an ade- quate and parsimonious conceptualization of acceptance behavior and the salience of usefulness and ease-of-use beliefs. In addition, we have demonstrated the feasi- bility of viewing the process of belief formation as essentially one of learning; consequently, we show that learning theories provide a rich theoretical foundation for identifying potential influences on beliefs.

Evidence for the assumption of mediation raises some intriguing implications for the construction of research models related to examining information technology adoption phenomena. A radical suggestion then, if these results are further confirmed in subsequent work, might be that researchers construct simpler models that exclude individual differences altogether. In fact, that is precisely what theories such as the technology acceptance model and the theory of reasoned action argue. As an


alternative, researchers could seek to find those individual differences that are instrumental in explaining a large proportion of the variance in beliefs. Another implication follows when we juxtapose our results with those obtained by others with regard to the relative importance of perceived usefulness and perceived ease of use. It appears that the relative importance of these two beliefs is not invariant across systems, suggesting that for different types of information technologies, one belief might be more salient than the other. Future research could be focused on identifying classes of systems in which one belief dominates the other. Furthermore, there may be reasons to believe that the relative strength of the two beliefs is a consequence of the stage of the IT implementation effort at which data is collected. For instance, Davis et al. (1989) found that perceived ease of use was not a significant determinant of intentions immediately after subjects were exposed to a new technology, but was significant for the same subjects and technology 14 weeks later. Thus, the relationships expected to be significant in the study of such phenomena might need to explicitly acknowledge the timing issue with regard to IT implementation.

Several other areas for fruitful future research remain. Perhaps the most compelling question that needs to be addressed in this regard is how one might identify other more controllable influences on beliefs. We used learning theories and one specific law of learning to identify a set of potentially salient individual differences. Others could focus on examining additional laws and individual difference variables that these laws might yield. It might also be desirable to extend our understanding of the influences on beliefs by seeking alternative conceptual classes of variables derived from theoretical paradigms distinct from learning. For example, the notion of “fit” has been used recently to explain general problem-solv- ing behavior (Vessey, 1991), as well as the performance impacts of information technologies (Goodhue & Thompson, 1995). This idea of “fit” could be utilized to guide the selection of external variables.

We focused on the technology acceptance model to illustrate the process by which individual differences influence technology acceptance. Most empirical studies of TAM have examined relatively simple end-user technologies. It is not clear whether the constructs and relationships embodied in TAM would be equally applicable to more complex technologies. For example, for such technologies, it might be the case that a richer set of beliefs, such as those found in the work of Moore and Benbasat (1991), need to be examined as predictors of acceptance. Or it is also possible that constructs such as perceived behavioral control from TPB (Ajzen & Madden, 1986) might be important for the study of complex technologies. It is evident that other models and beliefs such as computer self-efficacy could be investigated.

Implications for Practice Perhaps the most significant implication of our findings, closely related to the research objectives, is that we can now identify certain management actions that can be instrumental in facilitating technology acceptance through their positive influence on usefulness and ease-of-use beliefs.

Recruitment and careful selection of individuals to be targeted for new technologies represent important managerial actions that can promote technology acceptance. Our results point to a certain profile as being receptive to information


technology innovations: Individuals who have greater familiarity with technology in general, those with higher educational levels, and those who have greater prior experiences with similar technologies are likely to have more positive beliefs about new technologies. These are the kinds of individuals organizations might wish to recruit or target as they introduce new technologies. However, this class of individuals generally tends to learn information technologies well anyway, due perhaps to their education and general socialization with technology. The more pressing question facing organizations is how to diffise the technology to the rest of its workforce that does not match this profile.

Training may be utilized as a mechanism to diffise new information technologies by virtue of its influence on beliefs. Our results showed that training had a significant effect on perceived usefulness, and other work has also underscored the value of training in technology acceptance (e.g., Davis & Bostrom, 1993). We examined only one type of training: formal classroom training delivered in an instructor-led format. However, many other training options are now available and could be explored for their role in facilitating acceptance.

For managers implementing new information technologies in work groups where individuals’ profiles are not quite consistent with the type of profile indicative of easier acceptance of new IT, our findings are encouraging. It appears that there may be nothing inherent in individual differences that strongly determines acceptance and, because of the mediating role played by beliefs, it is possible to find alternative means of facilitating technology acceptance and increasing individual productivity. Although the alternative means will require the design of mechanisms that influence beliefs and are independent of individual differences, the fact that it is possible to be unconstrained by such differences is promising. Managers often cannot pick and choose individuals to become users of IT. Indeed, as noted earlier, the pervasiveness of IT in organizational work renders such a strategy untenable, and often the true benefits of a new IT may be realized only when all intended users accept it. The use of appropriate interventions focused on influencing beliefs can be instrumental then in facilitating such acceptance, not- withstanding the profile of the work group.

A broader implication that emerges from our results is the importance of incorporating a learning culture in the organization. We argued for the similarity between learning processes and belief formation; consequently, learning is critical to technology acceptance. What can managers do to create such a culture? Perhaps the provision of relevant work and a support system such as a helpline can facilitate individual learning and experimentation without the presence of an instructor. Given that learning by trial and error requires time that an individual might rather expend on more pressing work-related matters, the most crucial issue to be addressed here by management is the provision of appropriate incentives to engage in self-learning. Experimentation requires the creation of an “organic” environment (Bums & Stalker, 1961) or a climate in which a user finds many opportunities and incentives for technology exploration (Zmud, 1982). Self-training might need to be encouraged by explicitly incorporating it into the performance expectations of the employee.

The contributions of this research include theory advancement and testing, as well as insights to guide practice. We have explicated the process through which


individual differences play a role in IT acceptance through mediation by constructs of the technology acceptance model. We have illustrated that learning theory may be effectively utilized to understand what specific individual differences affect an adopter’s beliefs about an information technology innovation. We have tested our hypotheses with data collected in the field and analyzed the full model utilizing structural equation modeling techniques in contrast to a lot of the research on TAM, which was conducted in educational settings and used regression analysis. Finally. we make recommendations for how these insights may be used by implementors of new information technologies to influence usage behavior. [Received: September 22, 1997. Accepted: June 24, 1998.1

REFERENCES

Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly,

Ajzen, I . , & Fishbein, M. (1980). Understanding attitudes and predicting social behavio,: Englewood Cliffs, NJ: Prentice-Hall.

Ajzen, I., & Madden, T. J. (1986). Prediction of goal directed behaviors: Attitudes, intentions. and perceived behavioral control. Journal of Experimental Social

Alavi, M., & Joachimsthaler, E. A. (1992). Revisiting DSS implementation research: A meta-analysis of the literature and suggestions for researchers. MIS Quarterly, 16( l), 95-1 16.

Bandura. A . ( 1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable in social

psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1 182.

Benbasat. I.. & Taylor, R. N. (1978). The impact of cognitive styles on information systems design. MIS Quarterly, 2(2), 43-54.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.

Blaylock, B. K., & Rees, L. P. (1984). Cognitive style and the usefulness of information. Decision Sciences, 15( l ) , 74-91.

Bollen, K . A. (1989). Structural equations with latent mriables. New York: John Wiley.

Bostrom, R. P., Olfman, L., & Sein, M. K. (1990). The importance of learning style in end-user training. MIS Quarterly, 14(1), 101-119.

Bower, G. H., & Hilgard, E. R. (1981). Theories of learning. Englewood Cliffs, N J : Prentice Hall.

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S . Long (Eds.), Testing structural equation models. New- bury Park, CA: Sage, 136-162.

Burns, T., & Stalker, G. (1961). The management of innovation. London: Tavistock.

16(2), 227-247.

Psychology. 22,453-474.


Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118-143.

Cronbach, L. J. (1970). Essentials of psychological testing (3rd ed.). New York: Harper and Row.

Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: A handbook for research on interactions. New York: Irvington.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 3 18-339.

Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38,475-487.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

Davis, L. D., & Davis, D. F. (1990). The effect of training techniques and personal characteristics on training end-users of information systems. Journal of Management Information Systems, 7(2), 93-1 10.

Davis, S. A., & Bostrom, R. P. (1993). Training end users: An experimental inves- tigation of the roles of the computer interface and training methods. MIS Quarterly, 17(1), 61-85.

DeLone, W., & McLean, F. (1990). Information systems success: The quest for the dependent wiable. Information Systems Research, 3( l), 60-95.

DeSanctis, G. (1982). An examination of an expectancy theory model of decision support system use. Proceedings of the Third International Conference on Information Systems, Ann Arbor, MI, 121-135.

Doob, L. W. (1947). The behavior of attitudes. Psychological Review, 54,135-156. Fishbein, M. (1967). A behavior theory approach to the relations between beliefs

about an object and the attitude toward the object. In M. Fishbein (Ed.), Readings in attitude theory and measurement. New York John Wiley, 389- 400.

Fishbein, M., & Ajzen, I. (1975). Beliej attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

Fuerst, W. L., & Cheney, P. H. (1986). Factors affecting the perceived utilization of computer-based decision support systems. Decision Sciences, I7(3), 329- 356.

Gattiker, U. (1 992). Computer skills acquisition: A review and future directions for research. Journal of Management, 18(3), 547-574.

Gick, M. L., & Holyoak, K. J. (1987). The cognitive basis of knowledge transfer. In S. M. Corrnier & J. D. Hagman (Eds.), Transfer of learning: Contempo- rary research and applications. San Diego, CA: Academic Press, 9-46.

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236.


Harrison, A. W., & Rainer, R. K., Jr. (1992). The influence of individual differences on skill in end-user computing. Journal of Management Information Systems, 9( l), 93- 1 1 1.

Hoyle, R., & Panter, A. (1995). Writing about structural equation models. In R. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications. Thousand Oaks, CA: Sage, 158-176.

Huber, G. P. (1983). Cognitive style as a basis for MIS and DSS design: Much ado about nothing? Management Science, 29(5), 567-579.

Igbaria, M., Garners, T., & Davis, G. B. (1995). Testing the determinants of micro- computer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87-1 14.

Igbaria, M., & Parsuraman, S. (1989). A path analytic study of individual characteristics, computer anxiety, and attitudes toward microcomputers. Journal of Management, 15(3), 373-388.

Jackson, C. M., Chow, S., & hitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision Sciences, 28(2),

Joreskog, K. G., & Sorbom, D. (1993). LISREL 8, User’s reference guide. Chi- cago, LL: Scientific Software International.

Kerr, E. B., & Hiltz, S. R. (1988). Computer mediated communication systems: Sratus report. New York: Academic Press.

Kwon, T. H., & Zmud, R. W. (1987). Unifying the fragmented models of information systems implementation. In R. Boland & R. Hirscheim (Eds.), Critical issues in information systems research. Chichester: Wiley, 88-97.

Levin, T., & Gordon, C. (1989). Effect of gender and computer experience on attitudes towards computers. Journal of Educational Computing Research, 5( l),

Lusk, E. J., & Kersnick, M. (1979). The effect of cognitive style and report format on task performance: The MIS design consequence. Management Science,

Majchrzak, A., & Cotton, J. (1988). A longitudinal study of adjustment to technological change: From mass to computer-automated batch production. Jour- nal of Occupational Psychology, 61,43-66.

Mandelkern, D. (1993). GUIs: The next generation. Communications of the ACM,

Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191.

McGeoch, J. A., & Irion, A. L. (1952). The psychology of human learning. New York: Longmans.

Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.

357-389.

69-88.

25(8), 787-798.

36(4), 36-40.


Mulaik, S. A., James, L. R., Van Alstine, J., Bennet, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness of fit indices for structural equation models. Psychological Bulletin, 105(3), 430-455.

Nelson, D. L. (1990). Individual adjustment to information driven technologies: A critical review. MIS Quarterly, 14(1), 79-100.

Nickel, G. S., & Pinto, J. N. (1986). The computer attitude scale. Computers in Human Behaviol; 2,301-306.

Nunnally, J. C. (1978). Psychometric theory. New York McGraw Hill. Olfman, L., & Mandviwalla, M. (1994). Conceptual versus procedural software

training for graphical user interfaces: A longitudinal field experiment. MIS Quarterly, 18(4), 405-426.

Pavlov, I. P. (1941). Conditioned reflexes andpsychiatry. New York International Publishers.

Pedhazur, E. J. ( 1982). Multiple regression in behavioral research: Explanation and prediction. New York Dryden Press.

Polson, P. G., Bovair, S., & Kieras, D. (1987). Transfer between text editors. CHI ‘87 Proceedings: Human Factors in Computing Systems. New York: Asso- ciation for Computing Machinery, 27-32.

Rogers, E. M. (1995). Difision of innovations (4th ed.). New York The Free Press. Sanders, G. L., & Courtney, J. F. (1985). A field study of organizational factors

affecting DSS success. MIS Quarterly, 9( 1), 77-93. Scholtz, J., & Wiedenbeck, S. (1990). Learning second and subsequent program-

ming languages: A problem of transfer. International Jouml of Human- Computer Interaction, 2( l), 51-72.

Sein, M. K., Bostrom, R. P., & Olfman, L. (1987). Training end-users to compute: Cognitive, motivational and social issues. INFOR, 25(3), 236-254.

Skinner, B. F. (1969). Contingencies of reinforcement: A theoretical analysis. Englewood Cliffs, NJ: Prentice-Hall.

Soloway, E., Adelson, B., & Ehrlich, K. (1988). Knowledge and processes in the comprehension of computer programs. In M. T. H. Chi, R. Glaser, & M. J. Farr (Eds.), The nature of expertise. Hillsdale, NJ: Lawrence Erlbaum.

Staats, A. W., & Staats, C. K. (1958). Attitudes established by classical conditioning. Journal of Abnormal and Social Psychology, 57,37-40.

Stine, R. (1989). An introduction to bootstrap methods: Examples and ideas. Sociological Methods and Research, 8,243-291.

Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42( l), 85-92.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.

Thompson, R. L., Higgins, C. A., & Howell, J. M. (1994). Influence of experience on personal computer utilization: Testing a conceptual model. Journal of Management Information Systems, I I ( l), 167- 187.


Triandis, H. C. (1980). Values, attitudes and interpersonal behavior. In H. E. Howe (Ed.), Nebraska Symposium on Motivation, 1979: Beliefs, Attitudes and Val- ues. Lincoln, NE: University of Nebraska Press, 195-259.

Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 45 1-48 1.

Vessey, I. (199 1). Cognitive fit: A theory-based analysis of the graphs versus tables literature. Decision Sciences, 22, 2 19-240.

West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applicarions. Thousand Oaks, CA: Sage, 57-75.

Zinkhan, G. M., Joachimsthaler, E. A., & IOnnear, T. C. (1987). Individual differences and marketing decision support system usage and satisfaction. Journal of Marketing Research, 24(2), 208-214.

Zmud, R. W. (1979). Individual differences and MIS success: A review of the empirical literature. Managemenr Science, 25( lo), 966-979.

Zmud, R. W. (1982). Diffusion of modem software practices: Influence of central- ization and formalization. Management Science, 28( 12), 1421- 143 1.

APPENDIX: Scales and Items

Perceived Usefulness 1. Using [the target technology] enables me to accomplish tasks more quickly. 2. Using [the target technology] improves my job performance. 3. Using [the target technology] gives me greater control over my work. 4. Using [the target technology] improves the quality of the work I do. 5. Using [the target technology] improves my productivity. 6. Using [the target technology] enhances my effectiveness on the job. 7. Using [the target technology] makes it easier to do my job. 8. Overall, I find using [the target technology] useful in my job.

Perceived Ease of Use 1 . It is easy for me to remember how to perform tasks using [the target tech-

2. I believe that it is easy to get [the target technology] to do what I want it to do. 3. My interaction with [the target technology] is clear and understandable. 4. Overall, I believe that [the target technology] is easy to use.

nology].

Attitude

1. I like using [the target technology]. 2. [The target technology] is fun to use. 3. *I dislike using [the target technology]. 4. [The target technology] provides an attractive working environment.

Aganval and Prasad 39 1

Usage Intentions 1. I intend to completely switch over to [the target technology]. 2. I intend to increase my use of [the target technology] in the future.

Notes: The target technology’s specific name as used in the organization was sub- stituted on the questionnaire. All items are measured on a 7-point scale, from Strongly disagree to Strongly agree.

*Reverse scaled item

Ritu Agarwal is an associate professor of MIS in the Department of Information and Management Sciences at Florida State University. She received her PhD in MIS and an MS in computer science from Syracuse University, and an MBA from the Indian Institute of Management, Calcutta. Dr. Agarwal’s publications have appeared or are forthcoming in Information Systems Research, Communications of the ACM, IEEE Transactions, Journal of Management Information Systems, Decision Support Systems, and elsewhere. Her current research focuses on individual learning and organizational diffusion of new information technologies, as well as object-oriented technologies. She serves as an associate editor for MIS Quarterly and International Journal of Human-Computer Studies.

Jayesh Prasad is an associate professor of MIS in the Department of MIS and Decision Sciences at the University of Dayton. He earned his PhD in MIS at the Katz Graduate School of Business at the University of Pittsburgh. He has an MBA degree from the Indian Institute of Management, Calcutta, as well as a bachelor’s degree in engineering from the Indian Institute of Technology, Kharagpur. His current research interests focus on the adoption, implementation, and use of information technologies by individuals and organizations as well as on the management of information systems development projects. His research results have been published in journals such as MIS Quarterly, Znformation Systems Research, Communications of the ACM, Decision Sciences, and IEEE Transactions on Software Engineering.

Documents

Are Individual Differences Germane to the Acceptance of New Information Technologies?