yang et yoo_2004_all about attitude

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Its all about attitude: revisiting the technology

acceptance model

Hee-dong Yanga,*, Youngjin Yoob,1

aCollege of Management, Ewha Womans University, 11-1 Daehyun-Dong, Sodaemun-Ku, Seoul 120-750, South KoreabInformation Systems Department, Weatherhead School of Management, Case Western Reserve University,

10900 Euclid Avenue, Cleveland, OH 44106, USA

Received 1 April 2002; accepted 1 February 2003

Available online 21 November 2003

Abstract

We expanded Davis et al.s technology acceptance model (TAM) by considering both the affective and the cognitive

dimensions of attitude and the hypothesized internal hierarchy among beliefs, cognitive attitude, affective attitude and

information systems (IS) use. While many of the earlier findings in TAM research were confirmed, the mediating role of

affective attitude between cognitive attitude and IS use was not supported. Our results cast doubts on the use of the affective

attitude construct in explaining IS use. Meanwhile, we found that cognitive attitude is an important variable to consider in

explaining IS usage behaviors. Our results suggest that attitude deserves more attention in IS research for its considerableinfluence on the individual and organizational usage of IS.

D 2003 Elsevier B.V. All rights reserved.

Keywords: Technology acceptance; Attitude; Structural equation model

1. Introduction

Davis [15] and Davis et al. [17] developed the

technology acceptance model (TAM) to explain theacceptance of information technology in performing

tasks and identified two important beliefs that influ-

ence the usage of information systems (IS): perceived

usefulness (PU) and ease of use (PEU). Perceived

usefulness is defined as the degree to which a person

believes that using a particular system would enhance

his or her job performance. It relates to job effec-

tiveness, productivity (time saving) and the relativeimportance of the system to ones job. On the other

hand, perceived ease of use refers to the degree to

which a person believes that using a particular system

would be free of effort, in terms of physical and

mental effort as well as ease of learning. It is these two

beliefs, according to TAM, that determine ones

intention to use technology. Thus, TAM has emerged

as a salient and powerful model that can be used to

predict potential IS usage by measuring users beliefs

after they are exposed to the system even for a short

0167-9236/$ - see front matterD 2003 Elsevier B.V. All rights reserved.doi:10.1016/S0167-9236(03)00062-9

* Corresponding author. Tel.: +82-2-3277-3582; fax: +82-2-

3277-2582.

E-mail addresses: [email protected] (H. Yang),

[email protected] (Y. Yoo).1 Tel.: +1-216-368-0790.

www.elsevier.com/locate/dsw

Decision Support Systems 38 (2004) 1931


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period of time through training, prototype or mock-up

models [53].

Focusing primarily o n t h e affective aspect of

attitude, Davis et al. [17, p. 984] further found thatthe influence of attitude on IS use was at best modest

in predicting future IS use. They found that the

influence of attitude on IS use disappeared when PU

was considered to predict IS use. This led them to

conclude that attitude offers little value in predicting

IS use, leaving two users beliefsperceived useful-

ness and perceived ease of useas powerful and

parsimonious predictors. The validity and reliability

of constructs of the model have been well supported

by various studies [1,2,11,15,19,28,44].

The social psychology literature, however, clearly

suggests thatattitude has both affective and cognitive

components [1,4,5,13,51,56]. The affective compo-

nent of attitude refers to how much the person likes

the object of thought [34], while the cognitive com-

ponent refers to an individuals specific beliefs related

to the object[4,5]. In light of this, a close examination

of the measurement of attitude performed/provided by

Davis et al. raises the following issues. First, although

the underlying theory they used assumed no cognitive

component of attitude, the indicators for the attitude

construct they used included both the cognitive and

affective aspects. Second, provided attitude has bothcognitive and affective aspects, it should be examined

whether both aspects of attitudes mediate the impact

of PU and PEU on IS use. According to a dyadic view

of attitude [13,39,51,56], the cognitive and affective

components of attitude operate through different psy-

chological mechanisms. Therefore, one can argue that

one of the reasons that Davis et al. did not find a

significant influence of attitude in their study was

because the potentially significant influence of cogni-

tion was offset by the insignificant influence of affect.

In addition, attitude deserves more careful atten-tion in IS, not only in terms of refining the measure-

ments in TAM constructs, but also its potentially

powerful influence on the implementation of technol-

ogy and the diffusion of IT-enabled innovation in

organizations. According to attitude literature, attitude

is has a social function [22,36]. It is contagious,

malleable and fragile in that people influence each

others attitudes by affirming or contradicting them

through interactions and mutual experiences. By

better understanding which aspect of attitude is more

influential in the technology acceptance process, we

can support organizations efforts to implement infor-

mation technology.

To address these issues, we attempt to answer thefollowing two questions. First, can we empirically

distinguish the affective and cognitive aspects of

attitude in the context of explaining IS use? Second,

if the answer to the first question is presumably yes,

what is the causal relationship between two belief

constructs in TAM (PU and PEU) and two attitude

constructs (affective and cognitive attitude) leading to

IS use?

Our research makes important contributions to the

growing body of technology acceptance literature by

showing that a better understanding of the role of

attitude can enhance the models predictability about

users acceptance of information technology. Our

study shows that cognition and affect operate through

different psychological mechanisms in order to influ-

ence the use of IS. In particular, the cognitive dimen-

sion of attitude directly influences individuals IS use,

while the affective dimension needs to be treated as an

outcome variable of its own. Given the social function

of attitude [37], and the positive impact of its cogni-

tive dimension established in our study, organizations

can focus on improving the cognitive dimensions of

attitude in order to improve an individuals adoptionof information systems as well as an eventual, orga-

nization-wide implementation.

In addition, our research contributes to the attitude

literature by empirically demonstrating that two dimen-

sions of attitude operate through different psycholog-

ical mechanisms with respect to individual technology

use behaviors. In particular, we attempt to make

theoretical distinctions between similar constructs

the affective component of attitude, the cognitive

component of attitude and beliefsand empirically

demonstrate their differences.This paper is organized as follows. After the

introduction, we explain the backgrounds of our

research. We review the attitude construct and explain

how the affective and cognitive aspects of attitude are

different from each other. We also point out the

different mediating role of two attitudes between

PU, PEU and IS use. We introduce our research

models and followed by a description of research

methodology. Finally, we analyze the results and

discuss their implications.

H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 193120


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2. Research rationale and motivation

Ajzen and Fishbeins [3] theory of action (TRA) is

a widely used general theory on the determinants ofconsciously intended behaviors. Building on TRA,

TAM posits that individuals form their intention to

perform certain behaviors based in part on their

affective feelings about the systemsa condition

labeled as attitudeand in part by their beliefs, which

also influence their attitude. In doing so, TAM con-

ceptualizes attitude as an affective uni-dimensional

construct.

Contrary to TAM, however, Petty et al. [39] have

argued that the most common classification for the

basis of attitude is affect and cognition (p. 613).2 The

affective dimension of attitude focuses on how much

the person likes the object of thought [34] and

measures the degree of emotional attraction toward

the object [4]. On the other hand, the cognitive

dimension of attitude refers to an individuals specific

beliefs related to the object[4,5] and consists of the

evaluation, judgment, reception, or perception of the

object of thought based on values [9].

The dyadic view presumes the affective and cog-

nitive dimensions to be independent variables that

affect behavioral intention. Weiss and Cropanzano

[56] introduced four empirical studies that identifiedthe independent influences of the affective and cog-

nitive components of attitude. Similarly, Triandis [51]

argued that a better understanding of the relationship

between attitude and behavior can be gained through

the separation of the affective and cognitive compo-

nents of attitude. Within the IS literature, Goodhue

[25] and Swanson [45] have both recognized that the

distinction between affective and cognitive dimen-

sions has frequently been overlooked in IS attitude

research. An evaluative disposition toward behavior

might be different depending on whether it is inferredfrom an affective or a cognitive response [35].

Crites et al. [13] listed the semantic pairs to measure

each aspect of an attitude construct. They defined the

affective scales as the position that best describes

respondents feelings toward the object, while the

cognitive scales indicate the position that best

describes the traits or characteristics of the object.

Therefore, 12 affective word pairs (love/hateful, de-lighted/sad, happy/annoyed, calm/tense, excited/

bored, relaxed/angry, acceptance/disgusted, joy/sor-

row, positive/negative, like/dislike, good/bad, and

desirable/undesirable) and 7 cognitive word pairs

(useful/useless, wise/foolish, safe/unsafe, beneficial/

harmful, valuable/worthless, perfect/imperfect, and

wholesome/unhealthy), respectively, constitute the

affective and cognitive versions of attitude [13].

Davis et al. [17] measured attitude according to the

following five items on 7-point semantic differential

rating scales: All things considered, my using Write-

One in my job is good/bad, wise/foolish, favorable/

unfavorable, beneficial/harmful, and positive/nega-

tive. According to the definition of Crites et al.

[13], Davis et al.s measures of attitude contain both

affective and cognitive aspects in a single attitude

construct.

Meanwhile, the affective dimension of attitude is

influenced by beliefs [49] and the beliefs can be

either evaluative or non-evaluative (true or false)

[37]. An evaluative belief can be considered as a

cognitive attitude, thus developed from non-evalu-

ative beliefs and values [50]. Values mean preferredend states and preferred ways of doing things

[39,49]. For example, building technology that is

useful for task completion and easy to use is gener-

ally accepted as having value in the IS community.

Upon the use of the tool, users can form non-

evaluative beliefs about the usefulness and ease of

use of the tool. Based on such non-evaluative beliefs,

users will form their evaluative beliefs about the

system. Such evaluative beliefs (i.e., the cognitive

attitude) in turn develop into users affective attitudes

(like or hate). Thus, there is a hierarchical relation-ship among these four constructs: affective attitude is

influenced by cognitive attitude, which is affected by

non-evaluative beliefs, which is in turn developed by

values [50].

3. Research models

Fig. 1 shows the two models we tested in this

study. Model I depicts the original TAM with mixed

2 While the tripartite model argues that attitude has affective,

cognitive and conative components [3,8,34,37], the affective and

conative scales loaded on the same factor in the study of Stephen et

al. (1994) [41]. This empirical result suggests that the conative

component is associated strongly with the affective aspects.

H. Yang, Y. Yoo / Decision Support Systems 38 (2004) 1931 21


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attitude measure. Model II is the revised TAM that

we developed in the previous section, i.e., cognitive

and affective attitude constructs are distinguished by

their different mediating roles. Furthermore, in Mod-el II, we hypothesize the internal hierarchy of

attitude between its cognitive and affective dimen-

sions. Comparisons of these two models allow us to

see the efficacy of the attitude construct in explain-

ing individuals use of information technology. In

particular, we can examine whether the weak role of

attitude in TAM is due to the constructs intrinsic

characteristics as explained by Davis et al. [17], or

due to the way the construct was measured in their

study.

There are four things in common in bothmodels. First, we choose IS use, not the behavior

intention (to use IS), as the dependent variable.

Ajzen and Fishbein [3] indicated intentions should

be measured close to the behavioral observation to

ensure an accurate prediction. Thus, behavioral

intention may lack practical value in predicting

long-term future IS use (Ref. [7, p. 135]). Fur-

thermore, stable use of IS, not early adoption after

a brief exposure to the technology, is a more

appropriate measure of technology innovation dif-

fusion [20]. Our interest here is to predict usage

behaviors rather than intentions to use. Thus, con-

sistent with a number of TAM studies in the past

that excluded intension and instead included IS use[1,43], we chose to use IS use as our dependent

variable.

Second, following Davis [15, pp. 477478], we

hypothesize that PEU would influence PU but not

vice versa. This hypothesized relationship has been

supported by much empirical evidence [2,10,15

17,31,33,4648,53].

Third, following the original TAM, we assume PU

directly influences IS use, whereas PEU does not.

Many empirical studies [10,1517,42,44,4648]

have consistently identified PU as a primary factorthat influences IS use, while PEU plays a much less

important role, particularly later in the adoption

process.

Fourth, attitude is hypothesized to mediate the

influences of PU and PEU on IS use. There are

many empirical studies supporting this hypothesis

[16,17,30,33]. The significance of these mediating

paths, along with affective or cognitive attitude

measures, is one of our main interests in this

study.

Fig. 1. Research models. Model I. Original TAM with mixed attitude measure. Model II. Revised TAM with separate affective and cognitive

attitudes.



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4. Methodology

4.1. Data collection

Data were collected from undergraduate students

who major in management information systems (MIS)

at a college of management in the New England

region of the United States. Students were asked to

fill out spreadsheet usage surveys anonymously and

submit them to the class instructors on a voluntary

basis. One point (out of 100 total possible points) was

accredited to their voluntary participation to this

survey. It took 9 weeks to finish collecting surveys.

Harris and Schaubroeck [26] recommended a mini-

mum sample size of 200 to guarantee robust structural

equation modeling. In total, 211 completed question-

naires were returned for spreadsheet usage out of 420

handouts, satisfying this recommendation. The return

ratio was as 50.2%.

4.2. Measurements

Appendix A shows all the measurement items that

were used in the study. We used Davis et al.s [17]

original items for perceived usefulness (four items),

perceived ease of use (four items), and system use.

Through several empirical studies, these items valid-ity and reliability have been established [1,11,17,

46,53].

Out of their original 12 pairs of affective and 7

pairs of cognitive measurement of Crites et al. [13],

three items were chosen for each group. Three se-

mantic pairs for affective measures included good/

bad, happy/annoyed, and positive/negative. Three

semantic pairs for cognitive measures included wise/

foolish, beneficial/harmful, and valuable/worthless.

Two affective pairs (good/bad, positive/negative)

and two cognitive pairs (wise/foolish, beneficial/harmful) were chosen because they were used by

Davis et al. [17]. Two additional pairs, one for

affective and one for cognitive attitude, were chosen

from the list of Crites et al. [13].

5. Data analysis

Data analysis was conducted using a structural

equation modeling tool, Amos, to investigate the

influence of attitude operationalization and the rela-

tionship between two attitudes and the other variables.

Structural equation modeling has many advantages

over path analysis or regression analysis, especiallywhen the observed variables contain measurement

errors and the interesting relationship is among the

latent (unobservable) variables [24].

5.1. Test of the measurement model

Table 1 presents the results of the reliability testing

using Cronbach alpha coefficients, which ranged from

0.8341 to 0.9427. Construct validity was assessed

using confirmatory factor analysis. In our dataset, all

the measures loaded onto their underlying factors.

Generally, to show convergent validity, all item load-

ing scores need to be greater than 0.707 [23,29]. As

shown in Table 2, all factor loading scores were

higher than the suggested 0.707.

Given the conceptual proximity among four con-

structs (affective attitude, cognitive attitude, PEU,

and PU), we examine the discriminant validity of

measures using three different measurement models

and Amos. The first measurement model assumes

that there is only one latent variable, having all

indicators loaded on a single factor. The second

model assumes that there are three latent variables(attitude, PEU, and PU). Finally, the last model

assumes that there are four latent variables (cognitive

attitude, affective attitude, PEU, and PU). The dif-

ference in Chi-square statistics was used to test the

superiority of one measurement model over another

in these comparisons [23]. Table 3 shows the results

of the hierarchical comparisons that we conducted on

our data set. The first comparison demonstrated the

superiority of the three-factor model over the one-

factor model. The second comparison demonstrated

the superiority of the four-factor model over thethree-factor model.

Table 1

Reliability estimates

Construct Items Cronbachs

alpha

PU 4 0.9427

PEU 4 0.8991

ATTITUDE 6 0.9177

IS USE 2 0.8341



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We further examined the discriminant validity

using the square root of the average variance extracted

[14,21,40]. As shown in Table 4, all square roots of

the average variance extracted displayed on a diagonal

of a correlation matrix are greater than the off-diag-

onal construct correlations in the corresponding rows

and columns. Combined with the results from confir-

matory factor analysis, this indicates that each con-

struct shared more variance with its items than it

shared with other constructs, thereby confirming the

discriminant validity.

5.2. Test of the model

Structural equation modeling was conducted using

Amos to test the fit between the research models (Fig.

1) and the data set. In the literature, a variety of

measures are suggested to test the fit between the

model and data [6,21,27]. In general, the goodness-of-

fit is satisfactory when the Goodness of Fit Index

(GFI) is greater than 0.9, the Adjusted Goodness of

Fit Index (AGFI) is greater than 0.8, the Root MeanSquare Residual (RMSR) is lower than 0.1, and the

chi-square divided by degree of freedom (v2/df) is less

than 5 [27].3 Fig. 2 shows the fit indices of the

original TAM and the revised model.

As for the original TAM, the various goodness-of-

fit statistics indicate that the model shows a poor fit

with the data. In our dataset, the value of GFI is 0.73,

the AGFI is 0.64, the RMSR is 0.12, and the v2/dfis

5.7. On the other hand, for the revised model, the

value of GFI is 0.90, the AGFI is 0.85, the RMSR is

0.09, and v2/dfis 2.2. Thus, the revised model shows

the improved goodness-of-fit statistics in all four

fitness indices, compared to the original TAM. Fur-

thermore, all fitness indices of the revised model

passed the criterion-value. Overall, it is clear that

the revised model shows a better fit with the data,

demonstrating a superior explanatory power of the

technology usage by individuals.

Table 3

Competing measurement modeling

v2 df

One-factor Model (M1) 1379.40 77

Three-factor Model (M2) 513.46 74

Four-factor Model (M3) 161.62 71

Model comparisons Dv2 Ddf P

M1 M2 865.94 3 < 0.001

M2 M3 290.46 3 < 0.001

Table 4

Discriminant validity

PU PEU Affective

attitude

Cognitive

attitude

IS use

PU 0.847a

PEU 0.445b 0.834

Affective attitude 0.528 0.398 0.780

Cognitive attitude 0.480 0.348 0.614 0.788

IS use 0.311 0.364 0.255 0.372 0.898

a Diagonal: (average variance extracted from the observed

variables by the latent variables)1/2=(Sk2/q)1/2.b Off-diagonals: correlation between latent variables=(shared

variance)1/2.

Table 2

Confirmatory factor analysis model

Items PU PEU Affective

attitude

Cognitive

attitude

IS use

1 0.89

(na)

2 0.91

(19.91)

3 0.92

(20.55)

4 0.87

(18.23)

5 0.87 (na)

6 0.75 (12.80)

7 0.84 (15.25)

8 0.87 (16.34)

9 0.84 (na)

10 0.91 (17.60)11 0.95 (18.73)

12 0.92 (na)

13 0.90 (20.73)

14 0.91 (21.05)

15 0.78

(na)

16 0.91

(5.57)

na is set to metric.

The numbers in the parentheses are t-values. Loadings greater than

0.7 or t-values greater than 2.0 (which is significant at a = 0.05)

indicate the convergent validity.

3 More restrictive criteria are sometimes cited: e.g., 0.90 for

AGFI, 0.05 for RMSR, and 3:1 forv2/df[23].



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Fig. 2 also shows the path coefficients in the

models. Because the revised model has a better

fit with our data set than the original TAM, we

would focus on the path coefficients of Model II.

In this model, all the paths were significant, except

for the one from the affective attitude to IS use

and the path from PEU to affective attitude. Most

findings of traditional TAM are repeated here:

PEU influences PU; PU has more influences on

attitude than does PEU; PU has a direct influence

on IS use.One significant difference was the important me-

diating role of the cognitive attitude between users

beliefs (PU and PEU) and IS use. Contrary to Davis et

al. [17], our results showed that the cognitive dimen-

sion of attitude played an important role in explaining

IS use. The beta coefficient from cognitive attitude to

IS use (0.51) is more than twice the value of the beta

coefficient from PU to IS use (0.25). On the other

hand, the path between affective attitude and IS use is

not significant, suggesting that affective attitude does

not mediate the relationship between cognitive atti-

tude and IS use.

6. Discussions and conclusions

The purposes of our study were (a) to empirically

examine whether two aspects of attitude (cognitive vs.

affective) can be separated with high degrees of

reliability and validity in the context of IS technology

acceptance and (b) to examine whether IS use isinfluenced by affective attitude, which is influenced

by cognitive attitude, which is in turn influenced by

PU and PEU. The answer to the first questions was

yes and the answer to the second question is in part

positive.

First, we found that, in the context of technology

acceptance, affective and cognitive attitudes are two

separate socio-psychological constructs. Since Davis

et al.s [17] finding that attitude adds little value in

explaining IS use, the attitude construct has often been

Fig. 2. Model path estimates: standardized estimates (t-values). Model I. Original TAM with mixed attitude measure. Model II. Revised TAM

with separated affective and cognitive attitude.



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ignored in understanding technology acceptance.

Some studies even treated user beliefs and attitudes

as if they were interchangeable. Our results show that

they are not. This is an important point to note in thecontext of technology acceptance literature, which

often treats attitude as an affective construct, ignoring

its cognitive dimension (e.g., Ref. [17]). Future re-

search in this area should pay closer attention to the

differences between the cognitive and affective

dimensions of attitude in users acceptance of tech-

nology.

Second, consistent with many previous TAM stud-

ies, we found that PU has a direct influence on IS use.

We found, however, that only cognitive attitude medi-

ates the influence of PU and PEU on IS use. Contrary

to our expectation, affective attitude did not mediate

the influence of cognitive attitude on IS use. This

result raises a question about the plausibility of the

proposed hierarchical structure among affective atti-

tude, cognitive attitude, and non-evaluative beliefs as

hypothesized (non-evaluative beliefs! cognitive atti-

tude! affective attitude).

The results also explain why Davis et al. [17]

did not find that the role of attitude was significant

in their study. The weak relationship between at-

titude and IS use in their study might have been

due to the mixed measure of the attitude construct.As our results suggest, when the cognitive dimen-

sion of attitude is considered, attitude explains more

than twice as many variances of IS use as does PU.

This clearly suggests that we can significantly

enhance our understanding and prediction of IS

use by considering the cognitive dimension of at-

titude.

It is also important to note that the affective

dimension of attitude does not explain IS use at

all. Interestingly, however, cognitive attitude influen-

ces the affective attitude in various models of ourstudy. Taken together, we propose that the affective

attitude in technology acceptance needs to be treated

as a dependent variable of its own, not as a mediator.

Or, perhaps it is more directly related to another

important dependent variable in IS research, such as

user satisfaction. Given the significant differences

between the cognitive and affective dimensions of

attitude, we suggest focusing on the cognitive di-

mension of attitude in explaining or predicting IS

use.

Attitude has a social function. Attitude serves both

private and public identity concerns [37]. Even though

attitude has been treated as a vague and fragile

construct in the IS area, its importance on individualbehavior and social influence has been steadily rec-

ognized in psychology [37]. Attitude is contagious

and as people work together, they express their own

and listen to each others attitudes [50]. Therefore,

organizations and managers need to care about the

positive attitude change.

Changes in attitude occur quickly and require

less challenge than the changes in non-evaluative

beliefs or values [50]. Many theories and pro-

grams have been developed for positive attitude

change such as the direct influence of individuals

(e.g., enhancing peoples motivations, abilities, me-

mories, or moods), the improvement of contextual

cues (e.g., classical conditioning), or the consider-

ation of persuasive messages (e.g., message cred-

ibility, message memory, two-sided communication,

etc.).

Even though attitude can be changed quickly,

continuous efforts should also be given to maintain

the attitude because it is temporary, unstable, and

malleable [50]. Motivations, capability, experiences

and education all influence attitude development and

maintenance. Thus, attitude maintenance and changeshould be considered as a complementary tool to

traditional implementation techniques that can be

used to improve the users acceptance of new tech-

nology.

6.1. Limitations

The current study has several limitations. First, the

original TAM includes behavioral intention as a

mediator in the model, whereas we did not include

this factor in our model. For a more rigid investiga-tion of TAM, we could have used behavioral inten-

tion as well as self-reported use behavior. The value

of behavioral intention within the context of technol-

ogy acceptance is found in its early diagnostic func-

tion which enables management to predict the

potential acceptance (or rejection) of the systems by

the intended users after a short exposure to the

system. However, we believe that the research of

technology acceptance (including ours) is ultimately

concerned with explaining and predicting the users



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usage behavior rather their intentions. Therefore, we

feel that our use of self-reported usage without

behavioral intention is consistent with the goal of

our study.Second, we used only perceptual measures of IS

use. Earlier studies have shown that individuals

perceptions of IS usage are sometimes different

from their actual usage pattern [12,18,43,52].

Therefore, our results need to be cautiously inter-

preted.

Third, while the goodness of fit indices of our

model meet the minimum requirements suggested

by Hayduk [27], it does not meet more stringent

requirements (i.e., RMSR = 0.05) recommended by

Gefen et al. [23], which is more respected by IS

researchers. They warned that [i]t is important to

note that large [RMSR] values mean high residual

variance, and that such values reflect a poorly

fitting model (p. 35). Therefore, our results

could have been influenced by correlated residual

variances.

Finally, our sample consisted of college students

learning these tools for course credit. Therefore, no

organizational setting is considered in our data set.

However, past research suggests that social, orga-

nizational, and cultural contexts influence individu-

als decisions about technology acceptance [22,32,38,54,55]. Our study did not consider those so-

cial variables. Future research can study how

social norms and existing social practices influence

the formation of individuals attitude in the con-

text of technology acceptance. In particular, one

can examine the relative influences of the mechan-

ical characteristics of the technology as well as

social norms on individuals attitudes toward tech-

nology.

6.2. Implications for future research

Despite these limitations, our results offer several

insights into the technology acceptance process.

First, given the important role of attitude (particu-

larly cognitive attitude) in the technology accep-

tance process, it is critical to examine the evolution

patterns of both cognitive and affective attitudes

over time and how their relationships change. It is

well known that individuals attitudes and beliefs

toward technology change over time as they become

more experienced. In this light, one can expand the

TAM into a reciprocal model in which attitudeinfluences IS use, which in turn influences the

users attitude in the subsequent phases. It would

require further theorization efforts and more sophis-

ticated empirical techniques to examine such dy-

namic relationships.

Also, one can examine the relationship between

TAM and another well-studied dependent variable in

IS research, user satisfaction [18]. Our results show

that affective attitude should be treated as the

dependent variable rather than as the mediator. We

see a possible conceptual linkage between affective

attitude and user satisfaction, through which TAM

can be expanded to include user satisfaction as

another important dependent variable in addition to

IS use.

6.3. Implications for practice

Two important implications for IS managers can

be drawn from the results. First, by replacing Davis

et al.s five attitude measurement items with our

three-item cognitive attitude measure, managers can

predict IS use more successfully. Second, and moreimportantly, our results suggest that individual users

attitudes do influence technology acceptance. Par-

ticularly, our results suggest that encouraging among

users a positive cognitive approach to the systems

can also substantially improve the users acceptance

of the technology. Also, managers can indirectly

improve the users acceptance of the technology

by affecting individuals cognitive attitude. While

the importance of users beliefs underscores the

value of system design and users training, the im-

portance of users attitudes underscores the signifi-cance of communication with users about the

systems.

Acknowledgements

We thank Sora Kang for her assistance in

conducting statistical analyses for this paper.



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Appendix A. Measurement instrument



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nology acceptance model: four longitudinal field studies, Man-

agement Science 46 (2000) 2.

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Hee-Dong Yang is an Assistant Professor in the College of

Management at Ewha Womans University in Korea. He has a

PhD from Case Western Reserve University in Management of

Information Systems. He was previously an Assistant Professor at

the University of Massachusetts-Boston. His research interests

include electronic commerce, the organizational impact of informa-

tion technology, the technology acceptance model, and strategic use

of information systems. His papers have appeared in the Journal of

Information Technology Management, International Journal of

Electronic Commerce, and have been presented at many leading

international conferences (ICIS, HICSS, Academy of Management,

ASAC).

Youngjin Yoo is an Assistant Professor in the Information Systems

Department at the Weatherhead School of Management at Case

Western Reserve University. He holds a PhD in Information

Systems from the University of Maryland and an MBA and BS in

Business Administration from Seoul National University. His re-search interests include knowledge management in global organ-

izations and technology-enabled organizational transformation. His

papers have been presented at several leading conferences (the

Academy of Management, ICIS, AIS, and HICSS) and have

appeared in leading academic and practitioner journals such as

Information Systems Research, MIS Quarterly, the Academy of

Management Journal, Journal of Strategic Information Systems,

Journal of Management Education, and International Journal of

Organizational Analysis and Information Systems Management. He

serves on the editorial board of the Journal of AIS.


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