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Int. J. Human-Computer Studies 59 (2003) 383395

Perceived usefulness, ease of use and electronic

supermarket use

Ron Hendersona,*, Megan J. Divettb

a

Cuetel Pty Ltd., P.O. Box 458, Belconnen ACT 2616, AustraliabDepartment 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 TechnologyAcceptance 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 thesystem 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

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*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 contin-

uous market innovations and organizational improvements in order to remaincompetitive (Reichheld, 1993; Howard, 1995). As such, Information Technology

has become an essential tool for a large number of organizations, with work-

places 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 (Par!eand 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 TechnologyAcceptance 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 attitudeinfluences 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 jobperformance (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).

WithDavis (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 theTechnology 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 als., 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. Unexpect-edly, 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) andthree 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 ques-

tionnaires).

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 usefulnessPerceived 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 groceryshopping 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, r245 0:35; po0:001;

it is necessary to rule out the possibility that the perceived usefulness measure issimply 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 byComrey and Lee

(1992)for factor analysis, the current sample size (N247) is considered as fair. The

two factors were extracted using Principal Component Analysis, and rotated using

Varimax with Kaiser Normalization. A significant Bartletts tests of Sphericity,

w215 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

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Table 1

Rotated factor matrix for attachment loyalty and satisfaction

Scale Key elements within item Component

1.00 2.00

Perceived usefulness Gets shopping done efficiently 0.902a 0.103

Useful in getting shopping done 0.844a 0.167

Provides more time for other things 0.789a 0.145

Perceived ease of use Easy to get what you want from system 0.119 0.818a

Easy to locate items 0.006 0.884

a

Easy to track items 0.163 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, adiskette 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 4:25; s.d.=0.50) and useful (X 3:96;

s.d.=0.66). In contrast, the behavioural indicator measures demonstrated large

standard deviations (seeTable 2). Perceived ease of use and usefulness demonstrated

a significant association, r245 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 andusefulness 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 useand usefulness,r245 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 (seeTable 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 (log-

on 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 behaviouralindicators. 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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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 didnot 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 thet-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 researchersoutline 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 2

Descriptive statistics for the average deliveries, log-ons and purchase value within each month

Month Indicator N Mean s.d.

Questionnaire month Deliveries 209 1.19 1.42

Log-on 209 2.85 4.45

Purchase 209 193.78 256.87

Month 1 Deliveries 188 0.95 1.30

Log-on 188 2.08 3.33

Purchase 188 165.66 252.73


Log-on 178 1.87 2.95

Purchase 178 155.11 243.22


Log-on 175 1.32 2.48

Purchase 175 118.03 220.49


Log-on 172 1.05 1.97

Purchase 172 106.17 201.72


Log-on 168 1.25 2.61

Purchase 168 109.20 208.65


Log-on 155 1.17 2.31

Purchase 155 106.64 203.04

Note: Changes in N are due to maturation.



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of use) (path a). Second, the predictor (perceived ease of use) is regressed onto themediator (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 (seeTable 5).

Examination of the multivariate results indicates that the direct relationship

between perceived ease of use and the behavioural indicators changed when in thepresence 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 variablesof 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 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** 0.12 0.12 0.13 0.16* 0.17* 0.14

(208) (188) (177) (174) (171) (167) (154)

Usefulness 0.29*** 0.21** 0.24*** 0.16* 0.19* 0.16* 0.15

(208) (188) (177) (174) (171) (167) (154)

Total deliveries

Ease of use 0.25*** 0.14 0.18* 0.17* 0.18* 0.16* 0.21**

(208) (187) (177) (174) (171) (167) (154)

Usefulness 0.36*** 0.26*** 0.28*** 0.19* 0.22** 0.21** 0.21**

(208) (187) (177) (174) (171) (167) (154)

Total purchase value

Ease of use 0.24** 0.09 0.17* 0.15 0.18* 0.15 0.19*

(208) (187) (177) (174) (171) (167) (154)

Usefulness 0.34*** 0.22** 0.29*** 0.17* 0.19* 0.19* 0.19*

(208) (187) (177) (174) (171) (167) (154)

Note:*po0.05, **po0.01, ***po0.001. Nindicated within brackets.



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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 0.10 (2, 205) 11.13*** 3.61* 1.60

Deliveries 0.15 (2, 205) 17.35*** 4.46* 2.07*

Purchase 0.13 (2, 205) 15.53*** 4.21* 1.20*

Month 1 Log-on 0.05 (2, 185) 4.49* 2.52* 0.65

Deliveries 0.07 (2, 184) 6.81** 3.11* 0.80

Purchase 0.05 (2, 184) 4.72** 2.78* 0.29

Month 2 Log-on 0.06 (2, 174) 5.62** 2.97* 0.43

Deliveries 0.09 (2, 174) 8.26*** 3.24* 1.19

Purchase 0.09 (2, 174) 8.63*** 3.38* 1.10

Month 3 Log-on 0.03 (2, 171) 2.88 1.70 1.01

Deliveries 0.05 (2, 171) 4.41* 1.93 1.45

Purchase 0.04 (2, 171) 3.48* 1.75 1.25

Month 4 Log-on 0.04 (2, 168) 4.11* 1.86 1.40

Deliveries 0.06 (2, 168) 5.47** 2.18* 1.15

Purchase 0.05 (2, 168) 4.44* 1.81 1.60

Month 5 Log-on 0.04 (2, 164) 3.5* 1.45 1.57

Deliveries 0.05 (2, 164) 4.57* 2.16* 1.21

Purchase 0.04 (2, 163) 3.61* 1.88 1.16

Month 6 Log-on 0.03 (2, 152) 2.47 1.35 1.20

Deliveries 0.06 (2, 151) 5.18** 1.86 1.89

Purchase 0.05 (2, 151) 4.16* 1.61 1.70

Note: *po0.05, **po0.01, ***po0.001.

Predictor Criterion

Moderator

b

a

d

c

Fig. 1. Moderating relationship. The dotted line represents association between predictor and criterion,

controlling for paths b and c.



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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, withboth 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 therather 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 propositionis supported withinDavis (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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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 F Usefulness (t) Ease of use (t)

Log-on A 0.03 (1, 206) 6.51* 2.55*

B 0.13 (1, 243) 37.38*** 6.11***

C 0.08 (1, 206) 18.32*** 4.28***

D 0.09 (2, 205) 9.70*** 3.54*** 1.04

Deliveries A 0.03 (1, 206) 5.51* 2.35*

B 0.13 (1, 243) 37.38*** 6.11***

C 0.12 (1, 206) 27.07*** 5.20***

D 0.12 (2, 205) 13.60*** 4.60*** 0.47

Purchase A 0.02 (1, 206) 3.94* 1.99*

B 0.13 (1, 243) 37.38*** 6.11***

C 0.11 (1, 206) 24.77*** 4.98***

D 0.11 (2, 205) 12.34*** 4.51*** 0.15

Note: *po0.05, **po0.01, ***po0.001.



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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 home-

shopping 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 becomemore important. That is, ease of use may be a characteristic associated with

competitive edge. This proposition is supported within the work ofOliva et al. (1992)

as well asGarbarino 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, onemust consider the nature of the activity under question (use of an electronic

supermarket), and the nature of the constructs measured (systemusefulness 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 theTechnology 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. Itis also recommended that future research explore the factors associated with the

possible mediation effect of usefulness upon ease of use.

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