1. technology and computing

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Int. J. Human-Computer Studies 59 (2003) 383–395
Perceived usefulness, ease of use and electronic
supermarket use
Ron Hendersona,*, Megan J. Divettb
b
a
Cuetel Pty Ltd., P.O. Box 458, Belconnen ACT 2616, Australia
Department of Employment and Workplace Relations, Garema Court, ACT 2600, Australia
Received 11 December 2002; received in revised form 7 March 2003; accepted 16 March 2003
Abstract
Information Technology has permeated many facets of work life in industrialized nations.
With the expansion of Internet access we are now witnessing an expansion of the use of
information technology in the form of electronic commerce. This current study tests the
applicability of one prominent information technology uptake model, the Technology
Acceptance Model (Int. J. Man Mach. Stud. 38 (1993) 475), within an electronic commerce
setting. Specifically, the relationship between the perceived ease of use, usefulness and three
electronically recorded indicators of use were assessed within the context of an electronic
supermarket. A total of 247 participants completed the attitudinal measures. Electronically
recorded indicators of use in the form of deliveries, purchase value and number of log-ons to
the system were also recorded for the month the participants completed the questionnaire and
6 further months. Results indicated that the Technology Acceptance Model could be
successfully applied to an electronic supermarket setting, providing empirical support for the
ability of the Technology Acceptance Model to predict actual behaviour. The Technology
Acceptance Model explained up to 15% of the variance in the behavioural indicators through
perceived ease of use and usefulness of the system. However, the perceived ease of use of the
system did not uniquely contribute to the prediction of behaviour when usefulness was
considered, indicating a mediation effect. Future research should now focus on product and
service attributes to more fully explain the use of electronic commerce services.
r 2003 Elsevier Science Ltd. All rights reserved.
Keywords: Technology acceptance model; Online supermarket; Technology use
*Corresponding author.
E-mail addresses: [email protected] (R. Henderson), megan [email protected]
(M.J. Divett).
1071-5819/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved.
doi:10.1016/S1071-5819(03)00079-X
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1. Introduction
The current organizational climate demands that organizations provide continuous market innovations and organizational improvements in order to remain
competitive (Reichheld, 1993; Howard, 1995). As such, Information Technology
has become an essential tool for a large number of organizations, with workplaces regularly affected by the implementation of new or upgraded technology
(Korunka et al., 1997; Doherty and King, 1998b). Extrapolating from the present
organizational climate, the continuous implementation of new Information
Technology is likely to occur at a global level (Medcof, 1989; Shani and Sena,
1994; Doherty and King, 1998b). This is especially the case in light of the
increased importance placed on Information Technology by users (Morieux and
Sutherland, 1988).
Despite the time and monetary resources allocated to the implementation of new
Information Technology systems, the performance outcomes associated with the new
systems often fail to meet original performance expectations (e.g. Shani and Sena,
1994; Clegg et al., 1997). It seems that, without doubt, the success with which
Information Systems are implemented needs to be markedly improved (Hornby et al.,
1992). Research indicates that the poor performance of new Information Systems,
post implementation, is typically due to managerial or behavioural factors, rather
than technical factors (Long, 1987; Hornby et al., 1992; Shani and Sena, 1994).
A finding that is supported by research conducted over 20 years ago indicating that
failure of Information Systems was not solely attributable to technical reasons
(Swanson, 1974; Lucas, 1975). User acceptance of information systems impacts post
implementation performance (Swanson, 1974).
Consequently, the acceptance of Information Systems, or microcomputer based
technology has become a fundamental part of Management Information System
(MIS) planning within most organizations (Igbaria et al., 1994). However,
understanding why individuals choose to accept or reject new information
technology is proving to be one of the most challenging research questions in the
Information Systems field (Pare! and Elam, 1995).
In an attempt to better understand user acceptance, Davis and his colleagues
(e.g. Davis, 1989, 1993; Davis et al., 1989a, b, 1992) developed the Technology
Acceptance Model. The Technology Acceptance Model and its derivative (Igbaria
et al., 1994) has become the most comprehensive attempt to articulate the core
psychological aspects associated with technology use. Based on the generic model of
attitude and behaviour (the Theory of Reasoned Action, Ajzen and Fishbein, 1980;
Fishbein and Ajzen, 1975), the Technology Acceptance Model has proved a robust
and valuable model when considering information technology acceptance, or uptake
(Mathieson, 1991; Taylor and Todd, 1995).
In short, Davis and his collegaues (1989a, b) and Davis (1993) postulated that
users’ attitudes toward using a computer system consisted of a cognitive appraisal of
the design features, and an affective response to the system. In turn, this attitude
influences actual use, or acceptance of the computer system. The two major
design features outlined by these researchers included, the perceived usefulness
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of the system (operating as an extrinsic motivator), and perceived ease of use of
the system (operating as an intrinsic motivator) (Davis, 1989, 1993; Davis et al.,
1989a, b, 1992). Perceived usefulness was defined as the ‘‘degree to which an
individual believes that using a particular system would enhance his or her job
performance’’ (Davis, 1993, p. 477). Perceived ease of use was defined as the ‘‘degree
to which an individual believes that using a particular system would be free of
physical and mental effort’’ (Davis, 1993, p. 477). It was argued that these two
features formed the users attitude toward using the computer system, which in turn
impacted upon actual system use. Thus, the more positive the perceived ease of use
and perceived usefulness of the system, the higher the probability of actually using
the system. Furthermore, Davis et al. (1989a, b) and Davis (1993) also postulated
that perceived ease of use had a direct impact upon perceived usefulness, but not vice
versa.
Although the Technology Acceptance Model has been widely adopted, there is a
paucity of Technology Acceptance Model research incorporating actual behavioural
data. Instead, researchers have relied upon surrogate measures of behaviour,
typically involving self-reported estimates of use captured within questionnaires (i.e.
intensity and frequency) (Davis, 1989, 1993; Igbaria et al., 1994, 1996; Mathieson,
1991; Pare and Elam, 1995; Roberts and Henderson, 2000; Thompson et al., 1994).
With Davis (1993, p. 480) reporting that ‘‘such self-report time estimates, although
not necessarily precise in an absolute sense, are accurate as relative indicants of the
amount of time spent on job activities’’. While preliminary research suggest this is
probably a reasonable assertion (e.g. Deane et al., 1998), it is important to test the
Technology Acceptance Model using actual log data to reflect user behaviour until
additional research is able to confirm Davis’ (1993) assertion regarding self-report
estimates.
To date, only one study has assessed the Technology Acceptance Model in light of
actual computer recorded measures of behaviour (Deane, Podd and Henderson,
1998). Deane et al’s., study examined the frequency and duration of electronic log-on
data for 54 health care workers, over a 6-month period. The study demonstrated
criterion-related validity for the Technology Acceptance Model and actual log data.
As expected, Perceived Usefulness significantly correlated with both behavioural
measures (frequency and duration) within five of the 6-month periods. Unexpectedly, Perceived Ease of Use did not significantly correlate with either behavioural
measure (frequency or duration) for any month in which the study was conducted. A
finding that may have been due to the research sample examined. This sample was
comprised of a small specialist user group using a system in a largely non-volitional
setting. Moreover, one dependent measure used in the study (log-on duration) may
have introduced criterion contamination (Dipboye et al., 1994), as the system
operator determined log out times themselves. In light of the unexpected result and
potential methodological weaknesses associated with that study, this current
research aims to test the relationship between the two key constructs of the
Technology Acceptance Model (perceived ease of use and perceived usefulness) and
three behavioural indicators (log-on frequency, deliveries and purchase value) within
a larger sample of volitional users.
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2. Method
2.1. Participants
The research sample consisted of 920 potential customers of an electronic home
shopping service located in Auckland New Zealand, who had indicated an interest in
purchasing from the service provider, and an interest in participating in a survey.
From this sample, 800 individuals were selected, alphabetically, to receive the
questionnaire. The response rate for this study was 31% (247 useable questionnaires).
Fifty-four per cent of the respondents were female (133 respondents) and 46%
were male (114 respondents). The average age of respondents was 39.75 years
(s.d.=9.78 years). Sixty-three per cent (156 respondents) indicated that they were in
full time employment, and overall, respondents indicated that they used a computer
approximately 4.53 h (s.d.=3.09 h) per day.
2.2. Measures
2.2.1. Perceived ease of use
Perceived Ease of Use refers to ‘‘ythe degree to which a person believes that using
a particular system would be free of effort’’ (Davis, 1989, p. 82). Given that effort is a
finite resource, an application perceived to be easier to use than another is more
likely to be accepted by users (Davis, 1989).
Perceived Ease of Use was measured using a three-item scale, modified from
previous Technology Acceptance Model research (Deane, Podd and Henderson,
1998). Respondents were asked to indicate the extent of their agreement with each
item on a five point numerical scale, ranging from 1-strongly disagree to 5-strongly
agree. An example of a perceived ease of use item is ‘‘It is easy for me to get the
groceries I want from the system’’. The Cronbach alpha obtained for this scale was
0.62, and is considered acceptable for research purposes (e.g. Nunnally, 1968).
2.2.2. Perceived usefulness
Perceived Usefulness was defined as ‘‘the degree to which a person believes that
using a particular system would enhance his/her job performance’’ (Davis, 1989,
p. 82). Davis (1989) describes a system high in Perceived Usefulness as one for which
a user believes in the existence of a positive user-performance relationship. The user
perceives the system to be an effective way of performing the task(s).
Three items were used to tap the Perceived Usefulness construct, adapted from
previous Technology Acceptance Model research (Deane et al., in press).
Respondents were asked to indicate the extent of their agreement with each item
on a five point numerical scale, ranging from 1-strongly disagree to 5-strongly agree.
An example of a perceived usefulness item is ‘‘The system helps to get my grocery
shopping done efficiently’’. A Cronbach alpha internal consistency coefficient of 0.82
was obtained, within the current study.
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2.2.3. Scale development
Based on the positive, statistically significant bivariate correlation demonstrated
between the perceived usefulness and perceived ease of use, rð245Þ ¼ 0:35; po0:001;
it is necessary to rule out the possibility that the perceived usefulness measure is
simply tapping into the same construct as the perceived ease of use measure.
Therefore, a confirmatory factor analysis was conducted upon the sample to
determine whether the perceived usefulness and perceived ease of use measures were
independent from each other. Based on the guidelines developed by Comrey and Lee
(1992) for factor analysis, the current sample size (N ¼ 247) is considered as fair. The
two factors were extracted using Principal Component Analysis, and rotated using
Varimax with Kaiser Normalization. A significant Bartlett’s tests of Sphericity,
w2 ð15Þ ¼ 376:82; po0:001; as well as the Determinant (0.159) and the Keiser Meyer
Olkin coefficient (0.666) indicated that the analysis was relatively stable.
Furthermore, examination of the scree-plot revealed two distinct factors. Table 1
presents the coefficients within the rotated component matrix. Each of the items
consistently loaded upon the appropriate factor (factor loadings X0.45) (Comrey
and Lee, 1992). Therefore, the two scales (perceived usefulness and perceived ease of
use) were considered independent from each other.
2.2.4. Usage/behaviour
Actual usage was measured using three indicators of behaviour: the number of
log-ons (log-ons), the number of grocery deliveries (deliveries) and dollars spent
shopping with the electronic supermarket (purchase value). These indicators,
collected in monthly periods, were obtained from the registered provider of the
software for the month in which the questionnaire was completed, as well as 6
subsequent months of behaviour.
2.3. Procedure
During registration with the supermarket, users were requested to participate in a
research project examining their attitudes to the service (software). Participants who
Table 1
Rotated factor matrix for attachment loyalty and satisfaction
Scale
Key elements within item
Component
1.00
2.00
a
Perceived usefulness
Gets shopping done efficiently
Useful in getting shopping done
Provides more time for other things
0.902
0.844a
0.789a
0.103
0.167
0.145
Perceived ease of use
Easy to get what you want from system
Easy to locate items
Easy to track items
0.119
0.006
0.163
0.818a
0.884a
0.523
a
Indicates the largest factor loading for the item.
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signaled their willingness to participate in the study were identified and a mailing list
was supplied to the researchers via the electronic supermarket provider. Identified
participants were then sent a computerized self-report questionnaire package for
their completion. The package contained a questionnaire, a reply paid envelope, a
diskette containing the computerized questionnaire, a covering letter with regards to
the aim of the study, and an informed consent form that also requested permission to
access the users personal shopping data from the supermarket. Within this consent
form, it was highlighted that the data would be used for research purposes only, and
would not be made available to external agents. The behavioural indicators were
collected automatically via the registered provider of the software.
3. Results
Examination of the obtained mean scores indicated that, overall, the user group
perceived the system as easy to use (X ¼ 4:25; s.d.=0.50) and useful (X ¼ 3:96;
s.d.=0.66). In contrast, the behavioural indicator measures demonstrated large
standard deviations (see Table 2). Perceived ease of use and usefulness demonstrated
a significant association, rð245Þ ¼ 0:35; po0:001: Table 2 presents the bivariate
correlation coefficients between perceived ease of use, usefulness with each of the
three behavioural indicators by month.
In the month the questionnaire was completed, both perceived ease of use and
usefulness statistically correlated with each of the three behavioural indicators in a
positive direction. Log-ons, Deliveries and Purchase Value increased as perceived
ease of use and usefulness increased. A similar pattern exists for the relationship
between perceived usefulness and each of the three behavioural indicators for the
next 6 months, where significant relations were observed in all but one instance.
When considering the perceived ease of use measure, the results demonstrated that
significant relations were generally observed with all three behavioural indicators,
but unlike the perceived usefulness measure, non-significant relations were observed
on a number of occasions.
Based on the significant bivariate correlation between the perceived ease of use
and usefulness, rð245Þ ¼ 0:35; po0:001; multiple regression analyses were conducted
to ascertain the combined impact of the two predictor variables against each of the
three behavioural indicators (see Table 3). As can be seen within Table 3, together
perceived ease of use and usefulness were predictive of each of the three behavioural
indicators for all of the months considered, with the exception of two occasions (logon frequency for month 3 and 6). The explained variance ranged from 0.03 through
to 0.15, over the 7-month period. Consistent with the original boundary conditions
of the model the Technology Acceptance Model was derived from, the Theory of
Reasoned Action, the attitudinal measures had the most predictive power when
considering the proximal behavioural indicators as opposed to the distal behavioural
indicators. That is, attitude (perceived ease of use and usefulness) was a better
predictor of behaviour (number of log-ons, deliveries, and purchase value) for the
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Table 2
Descriptive statistics for the average deliveries, log-ons and purchase value within each month
Month
Indicator
N
Mean
s.d.
Questionnaire month
Deliveries
Log-on
Purchase
209
209
209
1.19
2.85
193.78
1.42
4.45
256.87
Month 1
Deliveries
Log-on
Purchase
188
188
188
0.95
2.08
165.66
1.30
3.33
252.73
Month 2
Deliveries
Log-on
Purchase
178
178
178
0.85
1.87
155.11
1.26
2.95
243.22
Month 3
Deliveries
Log-on
Purchase
175
175
175
0.66
1.32
118.03
1.12
2.48
220.49
Month 4
Deliveries
Log-on
Purchase
172
172
172
0.54
1.05
106.17
0.95
1.97
201.72
Month 5
Deliveries
Log-on
Purchase
168
168
168
0.62
1.25
109.20
1.15
2.61
208.65
Month 6
Deliveries
Log-on
Purchase
155
155
155
0.57
1.17
106.64
1.10
2.31
203.04
Note: Changes in N are due to maturation.
month in which the questionnaire was completed, compared to subsequent months
(Table 4).
An examination of the t-statistics for the two predictor variables revealed that in
the presence of the perceived usefulness variable, perceived ease of use generally did
not impact upon the behavioural indicator dependent variables. The only exception
to this finding was in the month the questionnaire was completed, where perceived
ease of use contributed unique variance to the prediction of deliveries and purchase
value.
Based on the results of the t-statistics, which highlighted that perceived ease of use
typically failed to contribute to the unique variance associated with the behavioural
indicators in the presence of perceived usefulness, ad hoc analysis into a potential
mediation effect through perceived usefulness was conducted. Baron and Kenny
(1986) state that a mediating relationship affects the strength of the predictor–
criterion association. In order to identify a change in strength, these researchers
outline the steps necessary to evaluate a mediating relationship (Fig. 1). First, the
criterion (e.g. behavioural indicators) is regressed onto the predictor (perceived ease
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Table 3
Bivariate correlation coefficients and sample size for perceived ease of use, usefulness and the behavioural
indicators
Predictor
Survey month
Month 1
Month 2
Month 3
Month 4
Month 5
Month 6
Total number of log-ons
Ease of use
0.20**
(208)
Usefulness
0.29***
(208)
0.12
(188)
0.21**
(188)
0.12
(177)
0.24***
(177)
0.13
(174)
0.16*
(174)
0.16*
(171)
0.19*
(171)
0.17*
(167)
0.16*
(167)
0.14
(154)
0.15
(154)
Total deliveries
Ease of use
0.25***
(208)
Usefulness
0.36***
(208)
0.14
(187)
0.26***
(187)
0.18*
(177)
0.28***
(177)
0.17*
(174)
0.19*
(174)
0.18*
(171)
0.22**
(171)
0.16*
(167)
0.21**
(167)
0.21**
(154)
0.21**
(154)
Total purchase value
Ease of use
0.24**
(208)
Usefulness
0.34***
(208)
0.09
(187)
0.22**
(187)
0.17*
(177)
0.29***
(177)
0.15
(174)
0.17*
(174)
0.18*
(171)
0.19*
(171)
0.15
(167)
0.19*
(167)
0.19*
(154)
0.19*
(154)
Note: *po0.05, **po0.01, ***po0.001. N indicated within brackets.
of use) (path a). Second, the predictor (perceived ease of use) is regressed onto the
mediator (perceived usefulness) (path b). Third, the criterion (behavioural
indicators) is regressed onto the mediator (perceived usefulness) (path c). Finally,
the criterion (behavioural indicators) is regressed onto the predictor (perceived ease
of use), controlling for paths b and c (path d).
As outlined by the analysis model of Baron and Kenny (1986), this study tested the
mediation effect of perceived usefulness upon the relationship between perceived
ease of use and the indicators of behaviour for the month in which the questionnaire
was completed (see Table 5).
Examination of the multivariate results indicates that the direct relationship
between perceived ease of use and the behavioural indicators changed when in the
presence of perceived usefulness. Based on the model of analysis outlined by
Baron and Kenny (1986), the direct relationship between perceived ease of use and
total log-ons, deliveries, and purchase within the month that the questionnaire
was completed became non-significant, in the presence of perceived usefulness (see
Table 5).
4. Discussion
The current research explored the relationship between the two predictor variables
of a prominent information technology uptake model (perceived ease of use and
perceived usefulness), and three indicators of behavioural use (log-on, deliveries,
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Table 4
Regression analysis with perceived usefulness and perceived ease of use as independent variables and the
behavioural indicators as dependent variables
Month
Indicator
R2
df
F
Usefulness (t)
Ease of use (t)
Survey month
Log-on
Deliveries
Purchase
0.10
0.15
0.13
(2, 205)
(2, 205)
(2, 205)
11.13***
17.35***
15.53***
3.61*
4.46*
4.21*
1.60
2.07*
1.20*
Month 1
Log-on
Deliveries
Purchase
0.05
0.07
0.05
(2, 185)
(2, 184)
(2, 184)
4.49*
6.81**
4.72**
2.52*
3.11*
2.78*
0.65
0.80
0.29
Month 2
Log-on
Deliveries
Purchase
0.06
0.09
0.09
(2, 174)
(2, 174)
(2, 174)
5.62**
8.26***
8.63***
2.97*
3.24*
3.38*
0.43
1.19
1.10
Month 3
Log-on
Deliveries
Purchase
0.03
0.05
0.04
(2, 171)
(2, 171)
(2, 171)
2.88
4.41*
3.48*
1.70
1.93
1.75
1.01
1.45
1.25
Month 4
Log-on
Deliveries
Purchase
0.04
0.06
0.05
(2, 168)
(2, 168)
(2, 168)
4.11*
5.47**
4.44*
1.86
2.18*
1.81
1.40
1.15
1.60
Month 5
Log-on
Deliveries
Purchase
0.04
0.05
0.04
(2, 164)
(2, 164)
(2, 163)
3.5*
4.57*
3.61*
1.45
2.16*
1.88
1.57
1.21
1.16
Month 6
Log-on
Deliveries
Purchase
0.03
0.06
0.05
(2, 152)
(2, 151)
(2, 151)
2.47
5.18**
4.16*
1.35
1.86
1.61
1.20
1.89
1.70
Note: *po0.05, **po0.01, ***po0.001.
Moderator
b
c
a
Criterion
Predictor
d
Fig. 1. Moderating relationship. The dotted line represents association between predictor and criterion,
controlling for paths b and c.
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Table 5
Series of regression analysis testing the moderation effect of perceived usefulness upon the relationship
between perceived ease of use and total log-ons’ per month, deliveries per month and purchase value per
month as dependent variables for the month the questionnaire was completed
Indicator
Path
R2
df
Log-on
A
B
C
D
0.03
0.13
0.08
0.09
(1,
(1,
(1,
(2,
Deliveries
A
B
C
D
0.03
0.13
0.12
0.12
Purchase
A
B
C
D
0.02
0.13
0.11
0.11
F
Usefulness (t)
Ease of use (t)
206)
243)
206)
205)
6.51*
37.38***
18.32***
9.70***
—
6.11***
4.28***
3.54***
2.55*
—
—
1.04
(1,
(1,
(1,
(2,
206)
243)
206)
205)
5.51*
37.38***
27.07***
13.60***
—
6.11***
5.20***
4.60***
2.35*
—
—
0.47
(1,
(1,
(1,
(2,
206)
243)
206)
205)
3.94*
37.38***
24.77***
12.34***
—
6.11***
4.98***
4.51***
1.99*
—
—
0.15
Note: *po0.05, **po0.01, ***po0.001.
purchase value), within a volitional setting. This is the first of such studies to be
reported.
At the bivariate level, the data confirmed the relations largely as expected, with
both predictor variables relating to all three behavioural indicators in the month the
questionnaire was completed. These relationships also typically held over time. At
the multivariate level, the multiple regression analysis revealed that the model was
predictive of each behavioural indicator, explaining up to 15% of the variance in
deliveries, 13% in purchase value and 10% in log-on behaviour.
However, the multiple regression analysis also highlighted that in the presence of
perceived usefulness, perceived ease of use generally lacked unique impact upon the
behavioural indicators. This result is surprising as the system in question is of a
highly volitional nature, and one would expect perceived ease of use to be important
in such circumstances (e.g. Ajzen, 1988). However, these results seem to reinforce the
rather important feature that if things are not perceived as useful, people will simply
not use them. A finding that may indicate that perceived usefulness is a mediator of
the perceived ease of use associated with a system. In light of this finding, ad hoc
analysis into the potential mediation effect of perceived usefulness upon the
relationship between perceived ease of use and behaviour were conducted. The ad
hoc analysis successfully demonstrated that the direct relationship between perceived
ease of use and the indicators of behaviour (Log-on, Deliveries and Purchase)
became non-significant in the presence of perceived usefulness.
Within the current study it appears that the contribution of perceived ease of use
to the prediction of behaviour is mediated by perceived usefulness. This proposition
is supported within Davis’ (1993) earlier work into the relationship between ease of
use and usefulness. Davis argues that ease of use has an impact upon usefulness, yet
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usefulness does have an impact upon ease of use. Therefore, the current findings lend
further support to Davis’ conclusion, with the unique contribution of perceived ease
of use to the prediction of behaviour seemingly channelled through perceived
usefulness.
The relative importance of the potential mediation effect of perceived usefulness
upon perceived ease of use could be disregarded as an artifact of the measures used.
However, the confirmatory factor analysis demonstrates the independent nature of
the two measures (perceived usefulness and perceived ease of use). Perceived
usefulness and perceived ease of use were indeed tapping into two distinct constructs.
The mediation effect evident within the current study appears to be valid. Therefore,
it is recommended that future research explore further the mediating nature of
perceived usefulness upon perceived ease of use with a system.
As mentioned previously, the inability of ease of use to uniquely contribute to
actual use was surprising in light of the volitional nature of the electronic homeshopping context. However, an alternative explanation for the lack of unique
variance associated with ease of use, within the presence of usefulness may be due to
the effect of the quality of alternatives. The quality of alternatives refers to the extent
to which individuals perceive alternative services to be better (Maute and Forrester,
1993). There may not have been many alternative electronic home-shopping services
available to participants at the time of the research (e.g. low quality of alternatives).
Subsequently, usefulness was the only important characteristic of the system.
However, as perceived competition increases, more alternatives become available,
the ease with which each of these systems can be used (ease of use) may then become
more important. That is, ease of use may be a characteristic associated with
competitive edge. This proposition is supported within the work of Oliva et al. (1992)
as well as Garbarino and Johnson (1999) who demonstrated quality of alternatives
as a moderator within consumer satisfaction research. Therefore, future research
should examine the effect of ease of use in light of usefulness within different
competitive settings.
The Technology Acceptance Model model was able to account for up to 15% of
the explained variance associated with behaviour. This suggests that other key
factors have an impact upon behaviour, and still need to be addressed. When
considering the total variance explained in each of the three dependent variables, one
must consider the nature of the activity under question (use of an electronic
supermarket), and the nature of the constructs measured (system usefulness and ease
of use). The key word here is system. Other attributes associated with the home
shopping service, including the quality of produce, or the price, were not addressed
within the current research. However, it is likely that these other attributes
associated with the service have an impact upon behaviour (Oliver, 1980; Gotlieb
et al., 1994). Therefore, the explained variance reported within the current study
refers simply to the electronic commerce infrastructure offered. Future research
should consider these other elements, such as price and quality of goods.
In conclusion, the current study provides additional empirical support for the
Technology Acceptance Model and actual behaviour. Furthermore, the Technology
Acceptance Model has some validity when applied to the electronic commerce
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setting. The model itself was predictive of all three behavioural indicators, although
the total explained variance was modest. Future research should now focus on the
examining the relative impact of perceived ease of use and usefulness, in light of the
product, service and system elements associated with electronic commerce settings. It
is also recommended that future research explore the factors associated with the
possible mediation effect of usefulness upon ease of use.
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