MANAGEMENT SCIENCE
Vo l. 35. No. 8. August 1989
Pnn/ ιirnUSA
USER ACCEPTANCE OF COMPUTER TECHNOLOGY:
A COMPARISON OF TWO THEORETICAL MODELS*
FRED D. DA VIS ‘ RICHARD P. BAGOZZI A;\i D PAUL R. WARSHA \九/
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Computer systems cannot improve organizational performance íf they aren 、 t used. Unfortunately , resístance to end-user systems by managers and professionals is a widespread problem.
T0 better predict ‘ explωn ‘ and increase user acceptance 司 we need to better understand why people
accept or reject computers. This research addresses the ability to pædict peoples' computer acceptance from a measure of their intentions, and the ability to explain their intentions in terms
oftheir attitudes , subjective norms、 perceived usefulness , perceived ease ofuse , and related variables.
In a longitudinal study of 107 users‘ intentions to use a specific system , measured after a onehour introduction to the svstem ‘ were corτelated 0.35 with svstem use 14 weeks late r. The íntentionusage correlatíon was 0.63 at the end of thís time períod. Perceíved usefulness strongly ínfluenced
peoples' intentions‘ explaining more than half ofthe variance in intentions at the end of 14 weeks.
Perceived ease of use had a small but sigoificant e 仔ect on intentions as wel l. although thís e仔ect
subsided over time. Attitudes only partially mediated the effects of these beliefs on íntentions.
Subjective norms had 00 effect on intentions. These results su且在est the possibility of simple but
powerful models of the determinants of user acceptance可 with practical 、.alue for evaluating systems
and guiding managerial interventions aimed at reducing the problem of underutilized computer
technology
(l NFORMATION TECHNOLOGY: USER ACCEPTANCE; INTENTJON MODELS)
1. Introduction
Organizational investments in computer-based tools to support planning、 decision­
making , and communication processes are inherently risky. Unlike clerical paperworkprocessing systems, these "enιuser computing" tools often require managers and professionals to interact direc t1 y with hardware and software. However , end-users are often
unwilling to use available computer systems that , if used , would generate significant
performance gains (e.g. , Alavi and Henderson 1981; Nickerson 1981 , Swanson 1988).
The raw power of computer technology continues to improve tenfold each decade (Peled
1987) , making sophisticated applications economically feasible. As technical barriers
disappear , a pivotal factor in harnessing this expanding power becomes our ability to
create applications that people are wil1ing to use. Identifying the appropriate functional
and interface characteristics to be included in end-user systems has proven more challenging and subtle than expected (March 1987; Mitroff and Mason 1983). Recognizing
the di ffi.culty of specifying the right system requirements based on their own logic and
intuition , designers are seeking methods for evaluating the acceptability of systems as
early as possible in the design and implementation process (e.g叮 Alavi 1984; Bewley et
a l. 1983; Branscomb and Thomas 1984; Gould and Lewis 1985). Practitioners and 間，
searchers require a better understanding of why people resist using computers in order
to devise practical methods for evaluating systems, predicting how users wil1 respond to
them ‘ and improving user acceptance by altering the nature of systems and the processes
by which they are implemented.
U nderstanding why people accept or rejèct computers has proven to be one of the
most challenging issues in information systems (I S) research (Swanson 1988). Investigators have studied the impact of users' internal beliefs and attitudes on their usage
* Accepted by Richard M. Burton; received November 10 , 1987. This paper has been with the authors 4
months for 2 revisions.
982
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USER
ACCEPτANCE
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behavior (DeSanctis 1983; Fuerst and Cheney 1982; Ginzberg 1981; 卸的， Olson and
Baroudi 1983; Lucas 1975; Robey 1979: Schu1tz and Slevin 1975: Srinivasan 1985;
Swanson 1974 , 1987) 、 and how these interna1 beliefs and attitudes are , in turn司 influenced
by various external factors、 includi 月: the system 's technical design characteristics ( Benbasat and Dexter 1986; Benbas泣， Dexter and Todd 1986; Dickson , DeSanctis and
McBride 1986; Gôuld、 Conti an吐 Hovanyecz 1983; Malone 1981); user involvement in
sy前em development (Baroudi , Olson and Ives 1986; Franz and Robey 1986): the type
of system development process used (e.g. 、 Alavi 1984; King and Rodriguez 1981); the
nature of the implementation process (Ginzberg 1978; Vertinsky , Barth and Mitchell
1975: Zand and Sorensen 1975); and cognitive style (Huber 1983). In genera l. however 、
these research findings have been mixed and inconclusive. In part , this may be due to
the wide array of di 旺erent belief, attitude , and satisfaction measures which have been
employed , often without adequate theoretical or psychometric justification. Research
progress may be stimulate吐 by the establishment of an integrating paradigm to guide
theory development and to provide a common frame ofreference within which to integrate
various research streams.
Information systems (IS) investigators have suggested intention models from social
psychology as a potentia1 theoretical foundation for research on the determinants of user
behavior (Swanson 1982: Christie 1981 ). Fishbein and Ajzen 's ( 1975) (Ajzen and Fishbein 1980) theory of reasoned action (TRA) is an especially well-researched intention
mode1 that has proven successful in predicting and explaining behavior across a wide
variety of domains. TRA is very general , "designed to explain virtually any human behavior" (Ajzen and Fishbein 1980, p. 4 ), and should therefore be appropriate for studying
the determinants of computer usage behavior as a special case.
Davis ( 1986) introduced an adaptation of TRA , the technology acceptance model
(T AM) , which is specifically meant to exp1ain computer usage behavio r. T A 孔1 uses TRA
as a theoretical basis for specifying the causal1inkages between two key beliefs: perceived
usefulness and perceived ease ofuse , and users' attitudes ‘ intentions and actual computer
adoption behavio r. T AM is considerab1y less general than TRA 、 designed to apply only
to compu
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TRA is a widely studied model from social psychology which is concerned with the
dcterminants of consciously intended behaviors (Ajzen and Fishbein 1980; Fishbein and
\Ilcn 1975). According to TR人、 a person ‘ s performance of a specified behavior is del<.'r mined by his or her behavioral intention (BI) to perform the behavior‘ and BI isjointly
d t.'lcrmined by the person ‘ s attitude (A) and subjective norm (SN) concerning the behavior
In qucstion (Figure 1 )、 with relative weights typically estimated by regression:
RT - Ä
Q\T
f
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2. Theory of Reasoned Action (TRA)
984
FRED D. DAVIS. RICHARD P. BAGOZZI AND PAUL R. WARSHAW
Be liefs and
Evaluations
(1: b , ei)
Normative Be liefs
and Motivation to
comply (1: nb imc ,)
FIGURE 1.
Theory of Reasoned Action (TRA)
BI is a measure ofthe strength of one 's intention to perform a specified behavior (e.g. ,
Fishbein and Ajzen 1975 、 p. 288). A is defined as an individual 's positive or negative
feelings (e\叫 uative affect) about performing the target behavior (e.g. , Fishbein and Ajzen
1975 , p. 216). Subjective norm refers to "the person's perception that most people who
are important to him think he should or should not perform the behavior in question"
(Fishbein and Ajzen 1975 ‘ p. 302).
Accordi~g to TRA , a person's attitude toward a behavior is determined by his or her
salient belièfs (b i ) about consequences of performing the behavior multiplied by the
evaluation (ei) of those consequences:
A
= 2:
biei'
(2)
Beliefs (b i ) are defined as the individual ‘ s subjective probability that performing the
target behavior will result in consequence i. The evaluation term (ei) refers to "an implicit
evaluative response" to the consequence (Fishbein and Ajzen 1975 ‘ p. 29). Equation
(2) represents an information-processing view of attitude formation and change which
posits that external stimuli influence attitudes only indirectly through changes in the
person 、 s belief structure (Ajzen and Fishbein 1980 , pp. 82-86) ‘
TRA theorizes that an individual ‘ s subjective norm (SN) is determined by a multiplicative function of his or her normative beliefs 忱的‘l. e. 、 perceived expectations of
specific referent individuals or groups、 and his or her motivation to comply (mc ,) with
these expectations (Fishbein and Ajzen 1975 ‘ p. 302):
SN =
2:
nbimci .
(3)
TRA is a general model ‘ and ‘ as such , it does not specify the beliefs that are operative
for a particular behavio r. Researchers using TRA must first identify the be !i efs that are
salient for subjects regarding the behavior under investigation. Fishbein and Ajzen ( 1975 ,
p. 218) and Ajzen and Fishbein ( 1980 ‘ p. 68) suggest eliciting five to nine salient beliefs
using free response interviews with representative members of the subject population.
They recommend using '.modal" salient beliefs for the population 、 obtained by taking
the beliefs most frequently elicited from a representative sample of the population.
A particularly helpful aspect of TRA from an IS perspective is its assertion that any
other factors that influence behavior do so on1y indirectly by influencing A , SN ‘ or their
relative weights. Thus, variables such as system design characteristics、 user characteristics
(including cognitive style and other personality variables) 、 task characteristics ‘ nature of
the development or implementation process司 political influences、 organizational structure
and so on would fall into this category 、 which Fishbein and Ajzen (Ajzen and Fishbein
1975) refer to as "external variables." This implies that TRA mediates the impact of
uncontro l1 able environmental variables and controllable interventions on user behavio r.
If so ‘ then TRA captures the inlernal psychological variables through which numerous
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USER ACCEPTA :--J CE OF COMPUTER TECHNOLOGY
985
m 的. provide a common frame of reference within which to integrate various disparate
lines of inquiry.
A substantial body of empirical data in support of TRA has accumulated (Ajzen and
Fishbein 1980: Fishbein and Ajzen 1975: Ryan and Bonfield 1975: Sheppard 司 Hartwick
and Warshaw in press). TRA has been widely used in applied research settings spanning
a variety of subjeét areas ‘ while at the same time stimulating a great deal of theoretical
research aimed at understanding the thcory ‘ s limitations ‘ testing key assumptions and
analyzing various re t1 nemcnts and extcnsions (Bagozzi 1981. 1982 ‘ 1984: Saltzer 1981:
Warshaw 1980a‘ b: Warshaw and Davis 1984 ‘ 1985. 1986: Warshaw ‘ Sheppard and
Hartwick in press).
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3. Technology
Acceptance 入10del (TA 九1)
τ 。久~.1.
introduced by Davis ( 1986). is an adaptation of TRA specifically tailored for
modcling user acceptance of information systems. The goal of T AM is to provide an
explanation of the determinants of computer acceptance that is general ‘ capable of 侃"
plaining user behavior across a broad range of end-user computing technologies and user
populatio 川、 while at the same time being both parsimonious and theoretically justified.
Ideally one would like a model that is hclpful not only for prediction but also for explanation ‘ so that researchers and practitioncrs can identify why a particular system may
bc unacceptable ‘ and pursue appropriate corrective steps. A key purpose of T AM 角 there­
fore. is to provide a basis for tracing the impact of external factors on internal beliefs ‘
attitudes ‘ and intentions. TAM was formulated in an attempt to achieve these goals by
idcntifyinεa small number of fundamental 、 ariables suggested by previous research
dealing with the cognitive and atfecti 、 e determinants ofcomputer acceptance ‘ and using
TRA as a theoretical backdrop for modeling the theoretical relationships among these
variables. Sever叫 adaptations to the basic TRA approach were made ‘ supported by available theoηand evidence ‘ based on these goals for TAM.
TAM posits that two particular beliefs ‘ [J ercei\'ed IIsejì t!ness and perceived ease o{ llse‘
are of primary relevance for computer acceptance behaviors (Figure 2). Perceived usefulness (U) is defined as the prospective user ‘ s subjective probability that using a specific
application system will increase his or her job performance within an organizational
contex t. Perceived ease of use (EOU) refers to the degree to which the prospective user
expects the target system to be free of etfort. As discussed further below‘ several studies
have found variables similar to the 目的 be linked to attitudes and usage. In addition ‘
factor analyses suggest that U and EOU are statistically distinct dimensions (Hauser and
Shugan 1980: Larcker and Lessig 1980: Swanson 1987).
Similar to TRA 、 T AM postulates that computer usage is determined by BI. but ditfers
in that BI is vie\ved as bcing jointly determined by the person ‘ s attitude toward using
the system (A ) and percei 丸 ed usefulness (ü) 、 with relative weights estimated by regression:
BI
=
A
+
(4)
U.
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FRED D. DA VIS. RICHARD P. BAGOZZI AND PAUL R. WARSHA W
A = U
+ EOU.
(5)
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The A-BI relationship represented in TAM implies that ‘ all else being equal 、 people
form inten tÏ ons to perform behaviors toward which they have positive affec t. The A-BI
relationship is fundamental to TRA and to related models presented by Triandis ( 1977)
and Bagozzi ( 1981 ). Although the direct effect of a be !i ef (such as U) on BI runs counter
to TRA 、 alterpative intention models provide theoretical justification and empirical evidence of direct belief-intention links (Bagozzi 1982: Triandis 1977: Brinberg 1979).
The U-BI relationship in equation (4) is based on the idea that , within organizational
settings、 peop 1e form intentions toward behaviors they believe will increase their job
performance ‘ over and above whatever positive or negative feelings may be evoked toward
the behavior per se. This is because enhanced performance is instrumental to achieving
various rewards that are extrinsic to the content of the work itself, such as pay increases
and promotions (e.g. , V room 1964). 1ntentions toward such means-end behaviors are
theorized to be based largely on cognitive decision rules to improve performance , without
each tìme requiring a reappraisal of how improved performance contributes to purposes
and goals higher in one ‘ s goal hierarchy , and therefore without necessarily activating the
positive affect associated with performance-contingent rewards (Bagozzi 1982~ Vallacher
and Wegner 1985). If affect is not fully activated when deciding whether to use a particular
system ‘ one 's attitude would not be expected to completely capture the impact of performance considerations on one 、 s intention. Hence、 the U-BI relationship in T AM represents the resulting direct effect ‘ hypothesizing that people form intentions toward using
computer systems based largely on a cognitive appraisal of how it wi lI improve their
performance.
T AM does not include TRA ‘ s subjective norm (SN) as a determinant ofB I. As Fishbein
and Ajzen acknowledge ( 1975 , p. 304) ‘ this is one of least understood aspects of TRA.
It is di 品 cult to disentangle direct effects of SN on BI from indirect effects via A. SN may
in f1 uence BI indirectly via A , due to internalization and identification processes , or inf1 uence BI directly via compliance (Kelman 1958: Warshaw 1980b). Although it is generally thought that computer use by managers and professionals is mostly voluntary
(DeSanctis 1983: Robey 1979: Swanson 1987) , in some c
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USER ACCEPTANCE OF COMPUTER TECHNOLOGY
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This equation is inspired by TRA ‘ s view that attitudes toward a behavior are determined
bv relevant beliefs. As discussed above ‘ TAM posits that U has a direct e叮ect on BI over
and above A. Equation (5) indicates that U influences A as well. Although we contend
that one 、 s affect toward a behavior need not fu I1 y incorporate affect toward any rewards
due to performance outcomes contingent on that behavior、 we acknowledge that司 through
learning and a能ctÎve-cognitive consistency mechanisms (Bagozzi 1982) ‘ positively valued
outcomes often increase one 、 s affect toward the means to achieving those outcomes
(Peak 1955: Rosenberg 1956: Vroom 1964). Hence ‘ U is hypothesized to have a positive
influence on A (as shown in equation (5 )‘ above). Previous IS research contains empirical
evidence consistent with a U-A link (Barrett、 Thornton and Cabe 1968: Schultz and
Slevin 1975).
EOU is also hypothesized to have a signifìcant effect on A. TAM distinguishes two
basic mechanisms bv which EOU influences attitudes and behavior: self-e 晶 cacv and
instrumentality. The easier a system is to interact with ‘ the greater should be the user's
sense of e 品cacy (Bandura 1982) and personal control (Lepper 1985) regarding his or
her ability to carry out the sequences of behavior needed to operate the system. E 品 cacy
is thought to operate autonomously from instrumental determinants ofbehavior (Bandura
1982) 、 and influences affect ‘ effort persistence 可 and motivation due to inborn drives for
competence and selιdetermination (Bandura 1982: Deci 1975). E 品 cacy is one of the
major factors theorized to underly intrinsic motivation (Bandura 1982: Lepper 1985).
The direct EOU-A relationship is meant to capture this intrinsically motivating aspect
of EOU (Carroll and Thomas 1988: Davis 1986; Malone 1981).
Improvements in EOU may also be instrument剖， contributing to increased performance. Effort saved due to improved EOU may be redeployed 、 enab Ii ng a person to
accomplish more work for the same effort. To the extent that increased EOU contributes
to improved performance、 as would be expected ‘ EOU would have a direct effect on U:
U 立 EOU
+ External
Variables.
(6)
Hence , we view U and EOU as distinct but related constructs. As indicated earlier司
empirical evidence from factor analyses suggests these are dis tÏ nct dimensions. At the
same time ‘ empirical associations between variables similar to U and EOU have been
observed in prior research (Barrett ‘ Thornton and Cabe 1968: Swanson 1987).
As equation (6) implies, perceived use 九Jl ness (U ) can be affected by various external
variables over and above EOU. For example、 consider two forecasting systems which are
equa I1 y easy to operate. If one of them produces an objectively more accurate forecast 呵
it would Ii kely be seen as the more useful (U) system 、 despite the EOU parity. Likewise,
if one graphics program produces higher quality graphs than its equally easy-to- 的e counterparts、 it should be considered more usefu l. Hence 、 the objecti ve design characteristics
of a system can have a direct effect on U in addition to indirect effects via EOU. Several
investigators have found a signifìcant relationship between system characteristics and
measures similar to perceived usefulness (e.g叫 Benbasat and Dexter 1986: Benb的訓，
Dexter and Todd 1986: Miller 1977). Similarly ‘ educational programs designed to pursuade potential users of the power offered by a given system and the degree to which it
may improve users' producti 丸哎y could we I1 influence U. Learning based on feedback is
another type of external variable apt to influence usefulness beliefs.
Perceived ease of use (E) is also theorized to be determined by external variables:
EOU = External Variables.
(7)
Many system features such as menus ‘ icons , mice , and touch screens are specifìcally
mtended to enhance usability (Bewley et a l. 1983). The impact of system features on
EOU has been documented (e.g. 、 Benbas剖， Dexter and Todd 1986; Bewley et a l. 1983:
988
FRED D.
D 久 VIS ‘ RIC、 H 代 RD
P
BMìOZZI
久 '<D
PAUL R. WARSHAW
Dickson ‘ DeSanctis and McBride 1986: Miller 1<)77). Training司 documentation ‘ and
user support consultants are other external f~lCtors which may also in f1 uence EOU.
Despite their similarity ‘ TAM and TRA differ in several theoretical aspects. some of
which warrant explanation. Both TAM and TRA posit that 人的 determined bv one ‘ s
relevant beliefs. Two ke丸 difkrences between how TAM and TRA model the determinants
of A should 已e pointed ou t. First ‘ using TRA. salient beliefs are e1 icited anew for each
new contex t. The resulting bcliefs are considered idiosyncratic to the specitìc contexL
not to be generalize止 for example. to other systems and uscrs (人jzen and Fishbein 1980).
In contrast ‘ TAM ‘ s U and EOU are postulated a priori ‘ and are meant to be fairly general
determinants of user acceptance.τhis approach was chosen in an attcmpt to arrive at a
beliefset that more readily generalizes to diftèrent computer systems and user populations.
Second. whereas TRA sums together all beliefs (h/) multiplied by corresponding evaluation
weights (e/) into a single construct (equation (2) above L TAM treats U and EOU as two
fundamental and distinct constructs. Modeling beliefs in this disaggregated manner enables
one to compare the relative in f1 uence of eλch beliefin determining A ‘ providing important
diagnostic information. Furthel\representing beliefs separately allows the researcher to
better trace the in f1 uence of external variables ‘ such as s、 stem fe泣 ures. user characteristics
and the like ‘ on ultimate behavio r. From a practical standpoin t. this enables an investigator
to better formulate strategies for in f1 uencing user acceptance 、 ia controllable external
interventions that have measurable in f1 uences on particular beliefs. For example ‘ some
strategies may focus on increasing EOU\such as providing an improved user interface
or better training. Other strategies may target U\ 的 increasing the accuracy or amount
of information accessible through a system.
Following the view that U and EOU are distinct constructs ‘ their relative in f1 uences
on A are statistically estÍ mated using linear regression (or related methods such as conjoint
measurement or structural equations). Within TAM ‘ U and EOU are not multiplied by
self-stated evaluation weights. Given that neither beliefs nor evaluations are ratio-scaled ‘
the estimated relationship (corr
llSER ACCEPTANCE OF COMPUTER TECHNOLOGY
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989
ticular study describcd below is to examine our ability to predict and explain user behavior
with TAM. working from U and EOU forward to user acceptance司 we explicitly in cI ude
extcrnal variables in our description 0 1' the model to underscore the fact that one of its
purposcs is to provide a foundation for studying the impact ofexternal 、 ariables on user
behavio r. Our gc泌 1 in thc study reportcd bclow is to examine the relationships among
EOl人 U ‘ A ‘ Bl a f1 d system usage in ordcr to scc how well we can predict and explain
user acccptance with TAM. ln so doing ‘ \Vc hope to gain insight about T AM 's strengths
and \vcaknesses by comparing it to the wcll-established TRA.
、 neral
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4.
Research Questions
Our analysis of TRA and TAM raises se 、 eral research questions which the study 、
dcscribcd bclow ‘ was dcsigned to addrcss:
( 1) How well do inten‘ tions prcdict usage? Both models predict behavior from behavioral intcntion (BI). Of particular interest is thc ability to predict future usage based on
a brief (e.g. ‘ onc-hour) hands-on intrùduction to a system. This would mirror the applied
situations in \vhich these models may have particular value. I 仁 after brie f1 y exposing
potential users to a candidate systcm that is being considered for purchase and organ 卜
zational implementation ‘ managemcnt is able to take mcasurements that prcdict the
future level ofadoption. a go/no名 o decision on the speci 品 c system could be made from
a more informed standpoin t. Similarl y‘ as new systems are being developed 、 early prototypes can be tested ‘ and intention ratings used 10 assess the prospects 0 1' the design
before a 品 nal svstem is buil t.
( 2) How well do TRA and 于AM explain intentions to use a system? We hypothesize
that TRA and TAM will both explain a significant proportion ofthe variance in people ‘ s
behavioral intention to use a specific system. Although prediction 、 in and of itselC is 0 1'
、 alue to system designers and implementors. explaining why people choose to use or not
use a system is also ofεreat value. Thercfore ‘ wc are also interested in the relative impact
on BI 0 1' TRA 's A. SN and 三 b ， ei constructs and τAM 、 s U and EOU.
( 3) Do attitudes mediate the effect of beliefs on intentions? 人 key principle 0 1' TRA
is that attitudes fullv mediate the effects of beliefs on intentions. Yet ‘ as discussed above ‘
direct belief-intention relationships have been observed before. One of the theoretical
、 irtues 0 1' the attitude construct is that it purports to capture the intluence of be Ii efs.
Much ofits 丸'alue is foregone if it only partially mediates the impact of beliefs.
(4) Is there some alternative theoretical formulation that better accounts for observed
data? We recognize that any model is an abstraction of reality and is likely to have its
own particular strengths and weaknesses. Our goal is less that of proving or disproving
TRA orTAM ‘ than in using them to investigate user behavio r. We are therefore interested
in cxploring alternative specifications. perhaps bringing togcther the bcst ofboth models、
in our pursuit of a theoretical account of user acceptance.
5.
Empirical Study
In order to asscss TRA and TAM ‘ we gathered data from 107 full-time MBA students
during their first of four semesters in the MBA program at the Uni 忱的 ity of Michigan.
久 word processing program 、 WriteOne. was a 丸 ailable for use bv these students in t\\O
public computer laboratories located at the Michigan Business Schoo l. Word processing
\\JS selccted as a test application because: ( 1 ) it is a 丸!oluntarily used package ‘ unlike
中 rcadsheets and statistical programs that students are required to use for one or more
courses、 (2) students would facc opportunities to use a word processor throughout the
:\1 BA program for memos‘ letters‘ reports‘ resumes司 and the like. and ( 3) word processors
990
FRED D. DA VIS ‘ RICHARD P. BAGOZZI AND PAUL R. WARSHAW
are among the most frequently used categories of software among practicing managers
(Benson 1983: Honan 1986; Lee 1986).
At the beginning ofthe semester , MBA students are given a one-hour introduction to
the WriteOne software as part of a computer orientation. At the end ofthis introduction ,
we administered the first wave of a questionnaire containing measures of the TRA and
TAM varia凹的. A second questionnaire , administered at the end of the semester 14
weeks later , contained measures of the T AM and TRA variables as well as a 2-item
measure of self-reported usage.
Sali t> nl
Beliξr Elicitation
To determine the modal salient beliefs for usage of the WriteOne software , telephone
interviews were conducted with 40 心1BA students who were about to enter their second
year of the MBA program. We chose to elicit beliefs from second-year students since
they are very similar to the entering first-year students in terms ofbackground and abilities ,
and had just completed a year of study during which their introduction and access to
the WriteOne system was identical to that which entering 如哎-year students would face.
Since we wanted to have the questionnaire prepared in advance ofthe first l-hour exposure
the first-year students would have with WriteOne , so we could track changes in their
beliefs over time , it would not have been practical 10 ask first-year students their beliefs
prior to this initial indoctrination. Although they are likely to have had similar basic
concerns as the second-year students , first-year students were not expected to be in a
position to articulate those concerns as well with regard to the WriteOne system specifical 作， since they would be unlikely to even know that such a system existed. We would
have faced greater risk of omitting beliefs which would have become salient by the time
first-year students completed their initial usage and learning and usage of WriteOne. On
the other hand , using second year students increased the risk of in c1 uding some beliefs
that are nonsalient for first year students after their initial one-hour introduction. However‘
the consequences of omitting a salient belief are considered more severe than those of
including a nonsalient one. To omit a salient belief, i.e. , one that does significantly influence attitude , degrades the validity of the TRA belief summation term (by omitting
a source of systematic variance) ‘ whereas in c1 uding a nonsalient belief, i.e. , one that does
not influence attitude , degrades the reliability of the belief summation term (by ad吐出g
a source of random variance). Moreover, beliefs lower in the salience hierarchy contribute
less to one's total attitude than do more salient ones (Fishbein and Ajzen 1975 , p. 223).
In view of the tradeoffs involved , we elected to pursue a more in c1 usive be !i ef set by
eliciting it from second-year students.
Interviewees were asked to list separately the advantages‘ disadvantages , and anything
else they associate with becoming a user ofWriteOne. (This procedure is recomme
USER ACCEPTANCE OF COMPUTER TECHNOLOGY
lagers
lon to
c tI on ‘
\ and
er 14
'-ltem
Jhone
econd
smce
、ilities ，
to
face.
Josure
1 theìr
belìefs
basic
、 ess
j
e m a
,pecìf,
would
e llme
1e.On
beliefs
wever ,
。se of
tly in.1 1ttmg
1t does
ldding
tribute
223 ).
set bv
991
。 llc:'Nionnaire
Both TRA and TAM are being used to explain a speci 品 c behavior (usage) toward a
specitìc largel (WriteOne) within a specitìc conlc:({ (the MBA program). The time period
of usage , although not explicitly indicated ‘ is implicitly bounded by the context of the
MBA program. Tþe detìnition and measuremcnt of model constructs correspond in
speCl品 city to these characteristics of the behavioral criterion ‘ so that the measures of
intentions‘ attitudes、 and beliefs are worded in reference to the spccific target ‘ action and
context elements ‘ but are relatively nonspecitìc with respect to time frame (for further
discussion of the correspondence issue ‘ see Ajzen and Fishbein 1980). BI. A 、 SN ‘ b ， and
e, were all operationalized according to Ajzen and Fishbein 、 s ( 1980 , Appendix A) recommended guidelines.
T AM 's U and EOU are each operationalized with 4-item instruments resulting from
an extensive meas:'re dev e1 0pment and validation procedure. As described in Davis
( 1986 )、 the measure development process consisted of: generating 14 candidate items
for each construct based on their detìnitions: p 呵-testing the items to refine their wording
and to pare the item sets down to 10 items per construct , and assessing the reliability
(using Cronbach alpha) and validity (using the mu 1t itrait-multimethod approach) ofthe
lO -item scales. High levels of convergent and discriminant validity of the lO -item scales
were observed. and Cronbach alpha reliabilities were 0.97 for U and 0.91 for EOU. Item
analyses were used to streamline the scales to 6 items per construcL and new data again
revealed high validity and reliability (alpha of 0.97 for U and 0.93 for EOU). Further
item analyses were performed to arrive at the 4-item scales used in the present research.
The four ease of use items were: “ Learning to operate WriteOne would be easy for me ,"
"1 would 位 nd it easy to get WriteOne to do what 1 want it to do ," "It would be easy for
me to become skillful at using WriteOne ," and "1 would find WriteOne easy to use. 刊
The four usefulness items were: "Using WriteOne would improve my performance in
the MBA program ," "Usìng WriteOne in the MBA program would increase my productivity ," "Using WriteOne would enhance my e叮叮tiveness in the MBA program ,"
and “ 1 would find WriteOne useful in the MBA program." The usefulness and ease of
use items were measured with 7-point scales having likely-unlikely endpoints and the
anchor points extremely, quìte, slightl
ything
ended
) uSlng
19 was
ief set
口lO re
d. The
尺 eSllfls
edness.
Scafc Re /i abifities. The two-item BI scale obtained a Cronbach alpha reliability of
0.84 at time 1 (beginning of the semester) and 0.90 at time 2 (end of the semester). The
992
FRED D. DA VIS ‘
RIC Il久 RD
P.
B 人 GOZZI 代 :--';D P 屯 LiL
R.
W 屯 RS !l，\ \\
four-itcm A sca 1c ohtaincd reliahilitics of 0.85 and 0.82 at times 1 and 2 rcspcctivcly.
The four句 itcm U scale achicvcd a rcliabili 竹 of 0.95 and 0.92 for thc two points in timc ‘
and the four-itcm EOU scalc ohtaincd rcliabilitv cocftìcicnts 0 1' 0.91 and 0.90 for timc 1
and timc 2. SN ‘ the /1, s and thc (', s‘ wcrc each opcrationalizcd with single-itcm scalcs ‘
per TRA 、 and hence no internal consistcncy asscssmcnts 0 1' rcliahility arc possible. Thc
two-itcm usñge scale administercd in thc sccond questionnaire achicved an alpha 0 1' 0.79.
These scalc reliahilities are all at lcvels considercd adcquatc for behavioral rescarch.
EXf7 laining ι 'sage. As cxpectcd. 81 was signitìcantly corrclatcd 九九 ith usage. Intcntions
measured right after thc WritcOne introduction \\ere corrclatcd 0.35 with usagc frcqucncy
14 wecks later (Tahlc 1). Intentions and usagc measured contemporaneously at thc end
ofthe semester correlatcd 0.63. Also consistent with thc theorics ‘ nonc 0 1' the othcr TRA
or TAM variables (人、 SN ‘三 h ，('，. U ‘ or E) had a signitìcant effect on usage over and
TABLE 1
P/'cdi Cl ing and !三\plμ inillg Csag(七 I I1I Cll lÌ ons and .J lI lllides \\ilh Ihc Thc刊 IT 刊 fRcas()ned .-lcli刊 II(TR. 日
and Ihe Tcch 月 01刊艾l' . JcCCfllWlι e .\fodel (7: L \I)
Tímè 1
Timc 2
Later
Immèdiatèl 、屯 fter
1 Hr Intro
Equatíon
(1) Explaining Usage at Timc 2
From 81 Measured at Times 1
and 2 (Common to both Models)
Usage (Time 2) = BI
81
只-
14 叭 ecks
Bèta
。 1" 抖*
R2
Bcta
0 .4 0 抖*
0.35***
。 63 抖*
(2)τRA
81
=
A + S:--J
0.32 抖*
A
S:-.i
A= 三 b，e，
TAM
81 = A
十 U
O. 這 8* 叫
0.07
0.10
0.07 抖
工 bjl'，
(3)
0.26 抖*
0.5 多料*
0.30***
0.27**
0 .4 7***
0.55 抖*
0.51 ***
0.27 抖
A
A = U + EOU
0.37* 抖
EOU
人'0[ 1'.
* P < 0.05.
**p<O.OI
*** p < 0.00 1.
81 = 8eha 、:ioral Intention
A = Attitude
S:--J = Subjective ;'\I orm
U = Perceived Usefulness
三 h/'， = Sum of Beliefs Times Evaluations
EOU = Perceived Ease of Use
0.36 件*
0.61 ***
0.02
U
U = EOU
EOU
0.16
0.61 ***
0 .4 8 料*
U
0 多。抖*
0.24**
0.05 抖
0.01
0.10
0.23 抖
USER
li\clv.
t 1n1C.
i l11 c 1
、calcs ‘
\Thc
、 0.79.
h.
n tI ons
Illcncy
'1C cnd
:'TRA
~>r and
尺1)
OF COMPUTER TECHNOLOGY
ACCEPτANCE
993
above intentions at eithcr time 1 or timc 2‘ which suggests that intentions fu lI y mediated
the e 仟ccts of these other variables on llsage.
Exp/aining Bchaviora/ lntcnlio f1 (B1). As theorized ‘ TRA and TAM both explained
a signifìcant propO r1 ion of the variance in BI (Table 1 ). TRA accounted for 32GJo of the
variance at time 1 and 26% of the variance at time 2. TAM explained 47% and 519毛 of
BI ‘ s variance at Ümes 1 and 2 rcspectively. Looking at the individual determinants of
BI. within TRA 、 A had a strong significant influence on BI (ß 立 0.55 、 time 1: ß = 0 .4 8 、
time 2) 、 whereas SN had no significant e 仟ect in either time period (b = 0.07 and 0.10 ‘
rcspectively). Within TAM , U has a very strong e 仔ect in both time periods (ß = 0 .4 8
and 0.6 1, respectively) , while A had a smaller effect in time 1 (ß = 0.27) and a nonsignificant e 仟ect in time 2 (ß = 0.16). The increased influence ofU from time 1 to time 2
is notewo r1 hy. Equation ( 1b). Table 2, shows that U adds signi 品 cant explanatory power
beyond A and SN ‘ at both time 1 and time 2 、 underscoring the influential role of U.
In both models ‘ unexpected direct belief-intention relationships were observed. Counter
toTRA ‘ the belief summation term , L biei , had a significant direct effect on BI over and
above A and SN in time period 2 (ß = 0.21 ) but not in time period 1 (β= 0.08) (Table
2). Counter to T AM ‘ EOU had a significant direct effect on BI over and above A and
U in time period 1 (ß = 0.20) but not time period 2 (ß = 0.11) (Table 2). Hence、
attitude appears to mediate the effects of beliefs on intentions even less than postulated
bv TRA and TAM.
itcr
Bcta
TABLE
Hicrarchical Rcgrcssion Tcs!sjár
.2
Rc的 !iO f1 ships
Erpcc!cd 1 υ
hc 人
Time 1
0.63***
Equation
0.-+8***
0.10
( 1) Behavioral Intention (8 1)
(a) BI = A + SN +三 h1c1
A
SN
R"
。 16
U.61 ***
().5()***
0.2-+**
0.23 抖
Beta
(b) BI = A + U + SN
A
U
SN
0 .4 7 材*
(c) BI = A + U + E
A
U
E
0.51 抖*
R2
Beta
0.30 抖*
0.33 科*
'L hι
0.55***
Timε2
0.53 抖*
0.37***
0.06
0.08"
。 .08
0.21 *b
0.51 ***
0.27**
0.4 8***
0.02"
0.16
。 .63 抖*
0.04"
0.52 抖*
0.19*
0.26 抖
0.4 7***
0.62 料*
0.20 料b
0.11 a
(2) Attitude (A)
A=U+E+ 三 b1c1
0.4 4***
0.38***
U
E
'L b1c1
* [l < 0.05.
** p < 0.0 l.
*** p < 0.001
a: Expected and found nonsignifican t.
b Expected nonsignificant but found significan t.
0.58 抖*
0.35 料*
0.01
0.10'
0.32***b
0.18*
繡檢驗蟬昕一…一…一一一
994
FRED D. DA VIS、 RICHARD P. BAGOZ Zl AND PAUL R. WARSHAW
E'(fJ laininf? .~tlil1lde. As expected ‘ both TAM and TRA explain a signi f1 cant percentage
of variance in attitude (Table 1). TRA explained 7% of A ‘ s variance at time 1 and 30%
at time 2. TAM explained 37% and 36% at times 1 and 2‘ respectively. U has a strong
signi f1 cant e 叮ect on A in both time periods (ß = 0.61 and 0.50 ‘ respectively). althouεh
EOU is significant at time 2 only (ß = 0.24).
In both rnodels ‘ there were some interesting developmental changes over time in the
relationship among beliefs‘ A and BI. Within TAM 、民 time 1 EOU appears to have a
direct effect on BI (ß = 0.20). with no indirect effect through A or U 、的 time 2 EOU's
e汀ect is entirely indirect via U ‘ and the A-BI link becomes nonsigni f1 can t. TRA 、 s belief
summation term 、之 bie l 、 has a significant effect on A above and beyond U and EOU in
time period 2 (ß = 0.32) but not in time period 1 (ß = 0.10) (Table 2). Our analysis
below investigates the nature ofthese patterns further by analyzing the internal structure
ofTRA 冶 beliefs and analyzing their τelationship to U and EOU , A and BI.
Further A 月alysis 0/ Be!i e/ SlruClllre. In order to gain greater insight into the nature
of TRA 's belie品， as well as their relationship to U and EOU 、 a factor analysis was conducted. Table 3 shows a varimax rotated principal components factor analysis of TRA 冶
7 belief items and T AM's 4 U items and 4 EOU items ‘ using a 1.0 eigenvalue cutoff
criterion. For time period 1, a five-factor solution was obtained , with the 7 TRA beliefs
factoring into three distinct dimensions , the other two factors corresponding to T AM's
U and EOU.τRA beliefs 1, 2 and 3 load on a common factor which taps speci f1 c aspects
of"expected performance gains. 弓， Whereas T AM's U is a comparatively general assessment
of expected performance gains (e.g. ‘ "increase my productivity") ‘ TRA 's f1 rst three items
are more specific aspects (i.e. , "saving time in creating and editing documents\" f1 nding
it easier to create and edit documents司令， and "making higher quality documents"). We
will refer to this specific usefulness construct comprised of TRA 、 s f1 rst three belief items
as U 5' Consistent with this interpretation 、 U 5 correlates signi f1 cantly with U (r = 0 .4 6 、 p
< 0.001 for time 1 and r = 0.65 , p < 0.001 for time 2). At time period 2, a four-factor
solution was obtained、 with U5 converging to T AM's U to form a single facto r.叭'e will
denote this combined 7-item usefulness index U[. for total usefulness. Cronbach alpha
reliabilities for U[ were O.
一
USER ACCEPT ANCE OF COMPUTER TECHNOLOGY
ccntage
nd 30 o/c
l strong
Ithough
~
in the
have a
EOU 冶
s belief
EOU in
tnalvsis
ructur e
‘
nature
as con、 TRA ‘ S
:: cu10ff
, beliefs
TAM 、 s
TABLE 3
Factor
'e Items
fìnding
"). We
~f
items
0 .4 6 , p
r-factor
Ne will
1 alpha
has 10
1
.‘ would
'd on a
了 in the
)te this
ldency
JJH 卜 dv
kmd
prl{MfA
ce
、/-v
'。 VTJe
}ILcd
吋叫
。
、1
,nsights
ing the
on the
'nerally
m IS to
above 司
lted the
dACC
AnalJ悅
Time 1 Factors
Belief
It em
Time 2 Factors
2
3
4
0.05
0.13
0.03
-0.11
0.09
0.89
0.88
0. 80
0.10
0.11
0.13
一 0.01
0.09
0.06
0.17
-0.12
0.81
一 0.04
0.24
0.20
0.83
一 0.02
0.79
0.07
0.32
一 0.34
0.66
一 0 .4 5
0.18
0.26
0.16
0.24
0.06
0.14
0.06
0.17
一 0.02
0.79
0.82
0.84
0.85
5
2
3
4
0.08
0.13
0.12
0.01
0.05
0.06
0.32
0.08
0.05
0.14
0.34
0.34
0.4 2
(a) TRA It ems
TRAI
TRA2
TRA3
TRA4
TRA5
TRA6
TRA7
(b)
0.28
0.27
0.18
0.17
0.02
0.08
-0.26
一 0.09
0.00
UI
U2
U3
U4
0.90
一 0.03
0. .90.
0. .91
0. .85
一 0.03
0.01
0.03
(c) TAM Ease ofUse (EOU) Items
EOUI
一 0.08
0. .84
EOU2
0.01
0. .90
EOU3
一 0.05
0.91
EOU4
0.10
0.91
Eigen.
Var
Cum%
戶已
一 0.01
一 0.02
0.82
0.84
0.74
。.88
一 0.02
0.10
0.82
0. 69
0.30
-0.06
一 0.07
TAM Usefulness (U) Items
aspects
~ssment
995
4.83
32 .3
32.3
3.35
22.3
54.6
一 0.04
0.05
一 0.13
0.08
0.12
0.03
0.03
0.07
一 0.05
一 0.09
0.03
0.00
1. 51
10.1
64.7
1.1 4
7.6
72. 3
0.08
0.01
0.11
0.07
0.08
0.17
0.31
0.22
0.12
0.18
一 0.01
0.09
0.15
0.05
0.30
1. 06
7.0
79.3
5.87
39.2
39.2
2.99
19.9
59.1
一 0.15
一 0.16
0.24
0.07
0.88
一 0.06
0.07
0. .85
0.12
0.05
0.89
一 0.04
0.07
0. .84
一 0.02
0.03
1. 27
8.5
67.6
1. 01
6.7
74.3
(see Table 4). Togeth er , these variables explai ned 51 % of BI' s varian
ce in time 1 and
61 % in time 2. U , U 5 and EOU were significant for time 1, but EOU becam
e nonsignificant
in time 2. In additi on ‘ U 5 increased in impor tance from time 1
(b == 0.20) to time 2
(注== 0.39). Next , we combi ned the two usefulness
subdim ension s to form the U t index ,
and ran anothe r regression. U{ was highly signifìcant in both time period
s (ß == 0.59 and
0.71 、 respectively) ， and EOU was signi 品 cant for time period 1 only
(ß == 0.20).
In order to test wheth er A fully media ted either the EOU-BI or U-BI
relatio nships‘ we
introd uced A into the second equati on. This had little effect on the
coe 品 cients for either
U{ or EOU , suggesting that although A may partially mediate these
relatio nships , it did
not fully media te them. The relationship between EOU and U{ , hypoth
esized by TAM 句
was nonsignifìcant for time 1, but becam e signifìcant for time 2 (ß
== 0.24). Therefo悶，
the causal structu re suggested is that U{ had a direct impac t on BI
in both time periods
and EOU had a direct effect on BI at time 1 and an indirect e仔ect
via U{ at time 2.
In order to obtain more precise estimates ofthes e signifìcant effects 、 regress
ions omitti ng
nonsignifìcant variables were run (see Final Mode站， Table 4). At
time 1 句 U{ and EOU
accounted for 45% of the variance in intent ion , with coefficients
of 0.62 and 0.20 respectively. At time 2 , U{ by itself accou nted for 57% of BI' s varian
ce (ß == 0.76) , and
EOU had a small but significant e仔ect on U, (ß == 0.24).
As mentio ned earlier, to the extent that people are heterogeneous
in their evalua tion
0 1' or motiva tion toward perfor mance , our statisti
cal estima te ofthe useful ness-in tention
Imk may be distort ed. In order to test for wheth er differences in motiv
ation moder ated
996
WARSH A W
FRED D. D 屯 \ïS‘ RICHARD P. BAGOZ ZI AND PAUL R.
TABLE 4
III'hrid
I l1l enll 刊 n
.\lodcfs
τime
Equ3tw n
R2
BI = II +仁、← EO L' + D + ACC
U
。 .51
ACC
0.35 材*
0.39 料*
一 0.04
0.14
0.12
0.61
0.50
0.71***
0.59 料*
ll ,
D
0.15
-0.12
。 .12
ACC
U , = EOU
一 0.06
0.20**
0.09
EOLt
0.02
Beta
0.61
0.21 **
0.09
-0.11
D
D + ACC
R2
。 20*
EO L'
T
Beta
0 .4 8 抖*
II 可
BI = U , + EOL'
Time 2
1
0.15
0.06
0.24*
0.57
0.06
。 .76 抖*
Final ModeJs:
A. Time 1
BI = U , -'- EOU
U,
EO L'
。.4 5
0.62***
0.20 抖
B. Tíme 2
BI = U ,
U , = EOU
0.2吽*
*p
< 0.05
< 0.01
*** p < 0.00 1.
scale (4 items). U s = TRA ‘ s specific
川 JIC.' U = TA\I ‘ 5 general perceive d usefulne ss
ofU and U s : 7 items).
usefulness scale (ítems 1-3). U , = Total usefulne ss index (compri sed
抖 p
the extent to which they
the useful ness-in tention relatio nship ‘ we asked subjec ts to report
a good job. 刊 By
believ ed "perfo rmanc e in the MBA progra m is impor tant to getting
t with U[ in either time
hierarc hical regression ‘ this questi on did not significan t1 y interac
corres pondin g to TRA
period . We a1so used the sum of the three evalua tion terms (ei)
as an outcom e. This
belief itenls 1-3 as an indica nt of subjects 、 evaluation of useful ness
. Thus , in our
period
also did not signi 品 cantly interac t with useful ness in either time
( 1) their percei ved
sample . it appear s that indi 、 iduals did not di 百er enoug h in either
job or (2) their
good
a
impac t of perfor mance in the MBA progra m on their getting
effect of U 1 on B1.
evalua tion of perfor mance to seriou sly distort our estima te of the
in both time period s ,
The picture that emerges is that U is a strong determ inant of BI
at time 2. EOU's direct
and that EOU also has a significant effect on BI at time 1 but not
‘ throug h usefulness,
effect on BI in timεperiod 1 develo ped into a signifi cant indirec t effect
in time period 2.
6. Conclu sions
of manag erial comOur results yield three main insigh ts conce rning the determ inants
puter use:
USER ACCEPT ANCE OF COMPUTER TECHNOLOGY
997
( 1) People ‘ s computer use can be predicted reasonably well from their intentions.
(2) Perceived usefulness is a major determinant ofpeople's intentions to use computers.
( 3) Perceived ease of use is a significant secondary determinant of people 's intentions
10 use computers.
Although our data provided mixed support for the two specific theoretical models that
guided our investigation ， τRA and TAM ‘ their confluence led to the identification of a
more parsimonious causal structure that is powerful for predicting and explaining user
behavior based on only three theoretical constructs: behavioral intention (BI) ‘ perceived
usefulness (U) and perceived ease of use (EOU). Specifically , after the one-hour introduction to the system ‘ people's intentions were jointly determined by perceived usefulness
(ß = 0.62) and perceived ease of use (ß = 0.20). At the end of 14 weeks , intention was
directly affected by usefulness alone (ß = 0.79) 司 with ease of use affecting intention only
indirectly via usefulness (ß = 0.24). This simple model accounted for 45% and 57% of
the variance in intentions at the beginning and end of the 14-week study period , respectively.
80th TRA and TAM postulated that BI is the major determinant of usage behavior;
that behavior should be predictable from measures of BI 、 and that any other factors that
influence user behavior do so indirectly by influencing 8 I. These hypotheses were all
supported by our data. Intentions measured after a one-hour introduction to a word
processing system were correlated 0.35 with behavior 14 weeks late r. This is promising
for those who wish to evaluate systems very early in their developme肘， and cannot
obtain extensive user experience with prototypes in order to assess its potential acceptability. This is also promising for those who would like to assess user reactions to systems
used on a trial basis in advance 0 1' purchase decisions. Intentions and usage measured
contemporaneously correlated 0.63. Given that intentions are subject to change between
the time of intention measurement and behavioral performance , one would expect the
intention-behavior correlation to diminish with increased elapsed time (Ajzen and Fishbein 1975 , p. 370). In addition , at time 1, given the limited experience with the system ,
peoples' intentions would not be expected to be extremely well-formed and stable. Consistent with expectations, hierarchical regressio
,ich they
ob." By
her time
to TRA
l1 e. This
m our
,erceived
2) their
n 8 I.
periods‘
j ‘ s direct
.;efulness,
rial com-
998
FRED D. DA VIS ‘ RICHARD P. BAGOZZI AND PAUL R. WARSHAW
for the two time periods investigated in the present research compare favorably with
these previous IS findings.
Both TRA and TAM hypothesized that expected performance impacts due to using
the specified system 句l. e. ‘ perceived usefulness ‘ would be a major determinant of B1.
Interestingly ‘ the models arrived at this hypothesis by veηdi 何erent lines of reasoning.
Within TAM ‘ perceived llseflllness was specified a priori ‘ based on the observation that
variables having to do with performance gains had surfaced as influential determinants
ofuser acceptance in previolls IS studies. In contrast、 TRA called for eliciting the specific
perceived consequences held by specific sllbjects concerning the specific system llnder
investigation. Using this method ‘ the first three be !i efs elicited were specific performance
gains. These three TRA beliefs ‘ which were much more specific than T AM 's perceived
usefulness measures (e.g. ‘“ save time in creating and editing documents" versus "increase
my productivity") loaded together on a single dimension in a factor analysis. Although
TRA 's specific usefulness dimension (U,) was factorially distinct from T AM 's U at time
1 (just after the one-hour demonstration) 、 they were signi 自 cantly correlated (r = 0 .46).
Fourteen weeks later (time 2) ‘ the general and specific items converged to load on single
facto r.
But why was it the case that U had more influence on BI than U s right after the onehour introduction , whereas U s increased in influence , and converged to l入 over time?
One possibility relates to the concreteness幅abstractness distinction from psychology (e.g. ,
Mervis and Rosch , 1981). As Bettman and Sujan ( 1987) point out、 nov1ce consumers
are more apt to process choice alternatives using abstract ‘ general criteria , since they
have not undergone the learning needed to understand and make judgments about more
concrete ‘ specific criteria. This learning process could account for the increased importance
of U 5 over time 、 as well as its convergence to U ‘ as the subjects in our study gained
additional knowledge about the consequences of using of WriteOne over the 14-week
period following the initial introduction. The implication is that、 since people form general
impressions of usefulness quickly after a brief period of using a system , the more general
usefulness construct provides a somewhat better explanation of intentions at such a point
m t1 口1e.
Combini
USER ACCEPTANCE OF COMPUTER TECHNOLOGY
可 ly
with
uSlng
t of BI.
lsomng.
ion that
iTI mants
specific
n under
LO
)r口1ance
erceived
、 mcrease
Jthough
J at time
= 0 .4 6).
m single
the oneer time?
)皂y (e.g. ,
nsu 口lers
nce they
mtmore
portance
y gained
14-week
n general
ç general
h a pomt
999
to more multi-person applications such as elcctronic mail ‘ project management or group
dccision support systems. Further research is needed to address the generalizability of
our SN findings、 to better understand the nature of socia! influences ‘ and to investigate
conditions and mechanisms govcrning the impact ofsocial influences on usagc behavio r.
The absence of a significant e 仟ect of accessibility on intentions or behavior was also
surpnsmg in light ()f the importance of this variable in studies of information source
usage (Culnan 1983: 0、 Reilly 1982). Since our measure of accessibility was nonvalidated 句
having been developed by exploratory factor analysis , psychometric weaknesses may be
partly at faul t. 1n addition ‘ although access was a salient concern frequently mentioned
in the be!ief elicitation , the system under investigation was fai r1 y uniformly accessible to
all respondents. Accessibility may well have played a more predominant role if greater
variations in system accessibility were prcsent in the study. Also surprising was the finding
that attitudes intervened between beliefs and intentions far less than hypothesized by
either TRA or TAM. A 1t hough svme 叭'ork on the direct e 叮ect of belicfs has been done
(e.g. , Bagozzi 1982; Brinberg 1979; Triandis 1977) ‘ more research is needed to identify
the conditions under which attitudes mediate the belief-intention link. In either case , the
attitude construct did little to help elucidate the causal linkages between beliefs and
intentions in the present study since ‘ at best、 it only partially mediated these relationships.
There are several aspects of the present study which circumscribe the extent to which
our findings generalize. MBA students are not completely representative of the entire
population of managers and professionals whose computer usage behavior we would like
to mode l. These students are younger and 、 as a group、 probably more computer literate
than their counterparts in industry. Hence 可 EOU may have been less an issue for this
sample than it would have been for managers and professionals more generally. The
WriteOne system , while typical ofthe types ofsystems available to end users ‘ is still only
one system. With more complex or di 品 cu 1t systems , ease of use may have had a greater
impact on intentions. These subjects were also probably more highly motivated to perform
well than the general population ‘ wh
sefulness
10th time
:ord proheir per)U had a
ffectively
了d ， bein皂
appeared
uld be to
ver tlme,
7. Practical Implications
Issue 、 re-
1 百ect
the
1 stressmg
o reasons
\M , cornl. the SN
, methods
ter acce p、 rocessmg，
:ompared
叭!hat
do our results imply for managerial practice? When planning a new system 司 IS
practitioners would like to be able to predict whether the new system will be acceptable
10 users、 diagnose the reasons why a planned system may not be fu l1 y acceptable to users ‘
:md to take corrective action to increase the acceptability ofthe system in order to enhance
Ihe business impact resulting from the large investments in time and money associated
with introducing new information technologies into organizations. The present research
IS relevant to all of these concerns.
人 s Ginzberg ( 1981 ) pointed out in his discussion of "ea r1 y-warning" techniques for
Jlllicipating potenüal user acceptance problems ‘ at the iniüal design stages of a system
，k 心 lopment effort ‘ a relatively small fraction of a project 司 s resources has been expended ‘
1000
FRED D. DA VIS ‘ RICHARD P. BAGOZZI A:--i D PAUL R. WARSHAW
and yet ‘ many of the design decisions concerning the functional and interface features
ofthe new system are made. Moreover‘ a t th is earl y poi n t i n the process、 there is greatest
flexibility in a Itering the proposed desiεn since little if any actual programming or equipment procurement has occurred. Hence ‘ this would appear to represent an ideal time to
measure user assessments of a proposed 勾引em in order to get an early reading on its
acceptability. Standinεin the way ‘ however ‘ has been the lack of good predictive models.
The present research contributes to the solution of this dilemma by helping to identify
and provide valid measures of key variables Ii nked to user behavio r.
A key challenge facing "user acceptance testing'" early in the development process is
the difficulty of conveying to users in a realistic way what a proposed system will consist
of. The "paper designs 刊 that typify the status of a sy叫em at the initial design stage may
not be an adequate stimulus for users to form accurate assessments. However ‘ several
techniques can be used to overcome this shortcoming. Rapid prototypers ‘ user interface
management systems‘ and videotape mockups are increasingly being used to create realistic
"facades刊 0 1' what a svstem wiU consist of‘ at a fraction ofthe cost ofbuilding the complete
system. This raises the question whether a brief exposure (e.g. 、 less than an hour) to a
prototype system is adequate to permit the potential user to acquire stable ‘ well-formed
beliefs. Especially relevant here is our finding that ‘ after a one恥 hour hands-on introduction ‘
people formed general perceptions of a system ‘ s usefulness that were strongly linked to
usage intentions ‘ and their intentions were significantly correlated \vith their future acceptance ofthe system. Further research into the e旺ectiveness of noninteractive mockups ‘
such as videotapes、 is important in order to establish how far upstream in the development
process we can push user acceptance testing. Throughout such evaluation programs ‘
practitioners and researchers should not lose sight ofthe fact that usage is only a necessary ‘
but not sufficient、 condition for realizing performance improvements due to information
technology; if a system is not really useful (even if users perceive it to be) it should not
be "marketed" to users.
Our findings have implications for improving user acceptance as wel 1. Many designers
beli
AJZE :-.i句1.
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AND
AVAV
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t'c atures
greatest
r eq Ul ptI me to
g on ItS
models.
idcntìfy
rocess IS
1 consíst
may
i皂e
several
nterface
realisti-.:
omplete
'ur) to a
-formed
duct lO n.
inked 10
Iture aclO ckups ‘
lopment
ogra 口lS •
.:cessary ‘
,rmat lO TI
ùuld not
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