Internet ResearchPersonal values’ influence on e‐shopping attitude and behaviourChanaka Jayawardhena

Article information:To cite this document:Chanaka Jayawardhena, (2004),"Personal values’ influence on e#shopping attitude and behaviour", Internet Research, Vol. 14Iss 2 pp. 127 - 138Permanent link to this document:http://dx.doi.org/10.1108/10662240410530844

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Personal values’influence on e-shoppingattitude and behaviour

Chanaka Jayawardhena

The author

Chanaka Jayawardhena is a Barclays Research Fellowat the Loughborough University Business School,Loughborough University, Loughborough, Leicestershire, UK.

Keywords

Social values, Attitudes, Consumer behaviour, Shopping

Abstract

A value-attitude-behaviour model was applied to investigate theroles of personal values in e-shopping consumer behaviour.Structural equation modelling identified that personal values(self-direction values, enjoyment values and self-achievementvalues) were significantly related to positive attitudes towarde-shopping. Individual attitudes toward e-shopping were a directpredicator of e-shopping behaviour and mediated therelationship between personal values and behaviour. Thishierarchical relationship among personal values, attitudes andbehaviour may be exploited by e-tailers to position e-shops andprovide a persuasive means for e-shoppers to satisfy their needs.

Electronic access

The Emerald Research Register for this journal isavailable atwww.emeraldinsight.com/researchregister

The current issue and full text archive of this journal isavailable atwww.emeraldinsight.com/1066-2243.htm

Introduction

An increasingly significant number of people

around the world are accessing the Internet. The

number of Internet users has grown significantly

over the last few years, from virtually nothing to an

estimated 605 million world wide users (NUA,

2003). This rapid growth in the number of

Internet users had promoted a belief in many

business circles that the Web represents a huge

marketing opportunity (Hoffman, 2000).

However, there is much evidence to suggest that

initial forecasts of the value in business-to-

consumer sales were overly optimistic (Biswas and

Krishnan, 2002; Ranganathan and Ganapathy,

2002). Smith and Sivakumar (2002) contend that

while e-tailers have gone to great lengths to

establish brand recognition, they still have many

questions regarding how to successfully induce

purchase behaviour and build customer loyalty.

Many researchers assert that this apparent lack of

translation from predictions to reality may be as a

consequence of our (i.e. both academic

researchers and practitioners) limited

understanding of e-consumer purchase behaviour

(Shim et al., 2001; Hoffman, 2000).

In an attempt to enhance understanding of

e-consumer purchase behaviour, researchers have

begun to examine various strands of literature

(Szymanski and Hise, 2000). Arguably, the

influence of personal values on consumer attitude

and behaviour, in an e-shopping environment, has

not received adequate attention. Such an approach

may be warranted in light of increasing

competition among e-tailers. The increased

competition has seen e-tailers changing Web site

design and merchandising mix in an attempt to

attract e-consumers by providing a strong theme

appeal (Jayawardhena et al., 2003). The design

strategies employed by e-tailers have tended to

favour to underlying consumer motives and values

(Jayawardhena et al., 2003; Jayawardhena, 2004).

In this context, the creation of discussion forums,

inclusion of enjoyable downloadable content,

other interactive content, etc. are good examples.

The effectiveness of these strategies has yet to be

investigated. To date, to the best of the author’s

knowledge, no studies have examined the

relationship between personal values and

shoppers’ attitude toward e-shopping. Therefore,

an objective of this study was to apply the

hierarchical value-attitude-behaviour model to

assess e-shopper behaviour. The work undertaken,

involved empirical examination of whether

e-shoppers’ underlying personal values influenced

their e-shopping attitude and behaviour. For the

purpose of this study, the author defined

e-shopping attitude as including attributes of

Internet Research

Volume 14 · Number 2 · 2004 · pp. 127-138

q Emerald Group Publishing Limited · ISSN 1066-2243

DOI 10.1108/10662240410530844

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shoppers’ attitudes toward transactions,

merchandising, pricing and e-tailers service

attributes. These attributes were assessed using

e-shoppers’ cognitive belief toward, and affective

evaluation of, these attributes. E-shopping

behaviour was defined as the extent to which

consumers shopped at e-tailers; and was quantified

from desire to browse, repatronage intentions and

switching intentions.

The study contributes to the body of knowledge

in e-consumer literature at the theoretical and

practitioner level in the following ways. First, while

academic literature on traditional consumer

shopping shows that consumer behaviour is

influenced by personal values (Carman, 1977;

Homer and Kahle, 1988; Shim and Eastlick,

1998), e-consumer personal values and their

shopping behaviour has not been subjected to

conceptual or empirical scrutiny. Second,

understanding the underlying dynamics

influencing consumer attitudes and patronage

behaviour in the e-shopping environment can

empower e-tailers to present e-shops in ways that

serve to enhance patronage by meeting basic

consumer needs and desires. It is envisaged that

this study will provide e-tailers with insights into

how gaining understanding of e-consumers’ value-

attitude-behaviour relationship can be integrated

into marketing programmes.

Theoretical context

To gain a greater understanding of e-tailing,

researchers have approached e-shopping

patronage from varied perspectives. Consumer

attitudes remain an important predicator of

consumer behaviour and, unsurprisingly,

e-consumer attitudes to e-shopping are beginning

to attract significant attention from researchers.

Childers et al. (2001) argue that e-consumer

shopping motives include both utilitarian and

hedonic dimensions. They conclude that hedonic

aspects of the new media are particularly

immersive and play an equal (to utilitarian) role in

motivating e-shoppers. Work carried out by

Garbarino and Strahilevitz (2002), and Doherty

and Ellis-Chadwick (2003), extend these strands

of research and examine how men and women

differ in their perceptions to the risks associated

with e-shopping and the effect on purchase

intentions. Keen et al. (2002) investigate the

structure for consumer preferences to make

product purchases, and how they arrive at

determining the importance of attributes in the

decision making process. Other researchers have

also attempted to understand e-shopping

behaviour by drawing on the technology

acceptance model (TAM) from the information

systems (IS) literature (Davis, 1989; Leder et al.,

2000;Moon andKim, 2001; Venkatesh and Davis,

2000; Shih, 2004). These researchers conclude

that individual attitudes toward e-shopping are

strongly and positively correlated with user

acceptance. Consumer satisfaction has also

received attention, with Szymanski and Hise

(2000) and Jayawardhena and Foley (2000)

identifying that convenience, site design, and

financial security are the dominant factors in

consumer assessment of e-satisfaction.

Research has focused on a number of

predicators of e-shopping behaviour, however no

studies have investigated the role of personal values

as an influence on e-shopping behaviour. Since

personal values are acknowledged as an underlying

determinant of consumers’ attitudes and

behaviour (Homer and Kahle, 1988; Shim and

Eastlick, 1998), it is argued that a personal value-

based approach may provide a valuable insight into

e-shopper patronage. Accordingly, in this study, a

model of e-shopper personal values and attitudes is

developed. Next, the conceptual foundations for

the development of hypotheses are presented.

Values, attitudes and behaviours

A significant number of researchers suggest that

values affect various aspects of consumption

behaviours and attitudes (Becker and Connor,

1981; Donthu and Cherian, 1994; Prakash and

Munson, 1985; Valencia, 1989; Vinson et al.,

1977). Rokeach (1973) referred to value as an

enduring belief that a specific mode of conduct or

end-state is personally preferable to its opposite or

converse mode of conduct or end-state of

existence. A value system is an enduring

organisation of beliefs concerning preferred modes

of conduct or end-states along an importance

continuum. In other words, Rokeach (1973)

conceived personality as a system of values.

According to social adaptation theory, values are a

type of social cognition that function to facilitate

adaptation to one’s environment (Kahle, 1983).

Values account for the selection and

maintenance of goals toward which individuals

strive, while simultaneously regulating the manner

in which this striving takes place (Vinson et al.,

1977). Kahle (1983) argues that values are similar

to attitudes in that both are adaptation

abstractions that emerge continuously from the

assimilation, accommodation, organisation, and

integration of environmental information in order

to promote interchanges with the environment

favourable to the preservation of an optimum

function. Other researchers, for example, Becker

and Connor (1981); Donthu and Cherian (1994);

Shim and Eastlick (1998) suggest that values affect

Personal values’ influence on e-shopping attitude and behaviour

Chanaka Jayawardhena

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various aspects of consumption behaviours and

attitudes.

There are two schools of thought on how values

affect consumer behaviour. On one hand, some

researchers project personal values in a means-end

chain model, i.e. values function as grounds for

behavioural decisions in general and consumption

behaviours (e.g. Carman, 1977; Williams, 1979). It

has been suggested that consumption behaviours

such as product selection and retail shopping

patterns, are means to achieving desired end states

or values (Reynolds and Gutman, 1988). While

Kahle (1980) argued that values have an indirect

effect on consumer behaviour through less abstract

mediating factors such as domain-specific attitudes,

whereby the influence of values should theoretically

flow from abstract values to mid-range attitudes to

specific behaviours. This sequence is called the

value attitude behaviour hierarchy.

That values have a causal influence on

subsequent behaviours is a theoretical argument

proposed by a number of researchers. For

example, Williams (1979) contends that explicit

and fully conceptualised values become criteria for

judgement, preferences, and choice. Similarly, a

causal relationship between terminal and

instrumental values and consumption behaviours

was put forward by Carman (1977). Several

researchers have attempted to empirically test

these theoretical models. In researching this

hierarchical relationship, Pitts and Woodside

(1983) report that there is a strong relationship

between values and attitude, but a weak

relationship between values and behaviour. Homer

and Kahle (1988) found support for this

hierarchical relationship in the context of natural

food shopping. More recently, Shim and Eastlick

(1998) examined the relative importance of

personal values on the attitudes and behaviour in

the context of mall shopping and found that there

is some evidence of a hierarchical relationship.

In addition to empirical evidence cited above

(Pitts and Woodside, 1983; Homer and Kahle,

1988; Shim and Eastlick, 1998) it also possible to

draw other related studies carried out in traditional

retailing for further insights on this relationship.

For example, Roy (1994) asserts that the needs for

affiliation, power, or stimulation have positive

relationships with shopping. Taken together, it can

be inferred from these studies that varied

dimensions of personal values – recreational, or

personal power – may be positively related to a

favourable attitude toward shopping.

From the above, with respect to e-shopping,

it can be argued that a consumer shopping at an

e-tailer is doing so for they believe that e-shopping

is personally preferable to shopping at a traditional

retailer. It therefore follows that the attributes that

contribute to intrinsic characteristics of

e-shopping may be related to certain dimensions of

personal values. Figure 1 shows the hypothesised

relationships in a value-attitude-behaviour model

employed in this study to investigate the role of

personal values in e-shopping.

H1 relates to the influence of personal values on

shoppers” attitudes toward the e-shopping

attributes. Consider the following attributes of

e-shopping. Compared to other types of shopping,

e-shopping is more private, less formal (for

example, there is no need to go “out”, there is no

need to “dress” up, etc.). e-shopping is an activity

with no opportunity for socialising and meeting

people, as may be the case when shopping at a

shopping mall. To take another attribute of

e-shopping, Bakos (1997) contends that the cost of

acquiring product information in e-shopping is

considerably less and therefore allows a far greater

degree of comparison opportunities. In fact, there

are a significant number of e-tailers that specialise

in providing this type of information as part of the

purchase process. On the other hand, an e-shopper

is denied the opportunity afforded to a traditional

shopper of physically observing (and touching)

when purchasing a tangible product. Therefore,

the attributes that contribute to the intrinsic

characteristics e-shopping should be related to

dimensions of personal values.

In light of the absence of e-tailing studies

examining personal values, two studies in particular

provide the strongest argument for the hypothesised

relationship. First, Homer and Kahle (1988) found

that, of three value dimensions measured by the list

of value (LOV) scale, two were positively related to

favourable attitudes toward natural food shopping.

They found that consumers with stronger self-

actualising value and a stronger social affiliation

value had more favourable attitudes toward natural

food shopping than those with weaker values.

Second, in a study by Shim and Eastlick (1998) it

was found that self actualising and social affiliation

values were positively related to a favourable attitude

toward regional shopping malls. The LOV scale was

adopted in this study and, therefore, it was expected

that value dimensions similar to those found in these

two studies would emerge. Therefore:

H1. Personal value dimensions are directly related

to attitude toward e-shopping. More

specifically, shoppers who place stronger

emphasis on self-direction values, self

enjoyment values and self-achievement

values are more likely to have a favourable

attitude towards e-shopping attributes as

compared to those who place weaker

emphasis on those personal values.

In an earlier section it was stated that the

theoretical model for value-attitude-behaviour

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hierarchy was suggested by a number of

researchers and later empirically tested by others.

That values have external and internal dimensions,

which influence attitudes and these in turn

influence behaviours was established by Homer

and Kahle (1988). A study by Kahle (1980) in a

shopping mall context found that personal values

only had indirect effect on mall shopping

behaviour through mall attitudes. Similarly, Shim

and Eastlick (1998) demonstrated that shoppers

with a favourable attitude toward a specific mode

of shopping will be more apt to seek that specific

shopping mode for their needs. From the above

theoretical foundations and the empirical work

carried out it can be asserted that a particular

attitude toward a particular shopping location (or

medium) would be a primary determinant of

actual shopping behaviour relative to other

variables of interest. Therefore, it can be predicted

that consumers with favourable attitude toward

e-shopping will be more willing to shop and spend

time browsing e-tailers for their needs. Similarly,

such e-shoppers are expected to re-patronise

favoured e-tailers more frequently and may spend

more money at favoured e-tailers than e-shoppers

with less positive attitudes.

Furthermore, it can also be argued that,

consumers with an unfavourable attitude toward

e-shopping are likely shop elsewhere. That is, there is

likely to be a negative relationship between

favourable attitudes and switching behaviour.Hence:

H2. Attitude toward attributes of e-shopping

directly influences e-shopping behaviour.

More specifically, consumers’ favourable

attitude toward e-tailers will positively

influence repatronage, will positively influence

desire to browse e-tailer Web sites and

negatively influence their desire to switch.

Method

Questionnaire development

An online questionnaire was developed to test the

hypotheses. It was developed in two phases. First,

a focus group was conducted to facilitate the

process of instrument development. The group

consisted of tenMBA students from an established

UK university with e-shopping experience. Having

explained the objective behind the exercise, the

group was presented with a number of examples of

e-shopping situations to generate discussion. The

composition of group members is consistent with

Churchill (1979, p. 67) recommendation when

developing measures, namely it “. . . is not a

probability sample but a judgement sample of

persons who can offer some ideas and insights”.

Having designed the questionnaire, it was then

pre-tested using a convenience sample of 30

third-year undergraduates from the same

university. All students in the sample had Internet

experience for at least two years. Of the individuals

in the convenience sample 19 declared that they

had made an online purchase. The pre-testing,

which focused on issues of instrument clarity,

question wording and construct validity, did not

highlight any significant problems.

Measures

Values were adopted from the LOV scale,

consisting of nine items, namely, a sense of

belonging, excitement, fun and enjoyment of life,

warm relationships with others, self-fulfilment,

being well-respected, sense of accomplishment,

security, and self-respect (Kahle, 1983). The items

were measured with reference to a seven-point

Likert-type scale (7 ¼ extremely important,

1 ¼ not important at all). The LOV scale is

particularly suitable for an analysis of this nature

and has been commonly used in research on values

due to its simplicity of administration and high

reliability (Shim and Eastlick, 1998).

Attitude toward the attributes of e-shopping

was assessed using the multivariate attribute

model (Fishbein and Ajzen, 1975). Additionally,

attributes were also derived from the literature

on electronic non-store retailing (Eastlick and

Lotz, 1999; Szymanski and Hise, 2000). This

adaptation is consistent with previous

researchers examining attitudes towards

attributes of shopping (e.g. Shim et al., 2001).

Accordingly, the respondents assessed the

importance (ei) of 13 attributes of e-shopping on

a seven-point Likert type scale (7 ¼ extremely

important, 1 ¼ not important at all). For the

purposes of this study, e-shopping were defined

as business to consumers e-commerce

conducted via the mechanism of Web shopping

Figure 1 Hypothesised model

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(Childers et al., 2001; Szymanski and Hise,

2000). To evaluate the extent of their beliefs, in

another section of the questionnaire, subjects

were asked to indicate on a similar seven point

scale (7 ¼ extremely likely, 1 ¼ extremely

unlikely) how likely they felt (bi) it was that

Internet shopping would provide each attribute.

Each of the attributes was presented in a

different order in each section to avoid order

effects. The mean importance scores of the

attributes varied from 5.13 to 6.41. The

expectancy performance measure of each

attribute was calculated using the formulate eibi.

In this formulate ei represents the importance

assigned to each attribute, and bi represents the

respective belief that e-shopping provides that

attribute. The Cronbach alpha coefficient for

attitude was 0.82. this is a significantly high

degree of realiablity.

e-shopping behaviour was assessed by

examining a range of behaviours, including

browsing activity, repatronage intentions and

switching intentions. These e-shopping

responses were selected for they are commonly

assessed in the traditional shopping context

(Wakefield and Baker, 1998). Bloch et al. (1989)

extended the concept of shopping by examining

the browsing activity, and Wakefield and

Blodgett (1994) examined the consumer’s desire

to stay within a shopping environment. Based on

these two studies, browsing activity was

measured using two measures (7 ¼ very much

agree, 1 ¼ very much disagree). Items measuring

repatronage intentions were adopted from the

Oliver and Swan (1989) study. The items

measuring switching intentions are from

Keaveney and Parthasarathy (2001). These two

behavioural intentions (i.e. repatronage and

switching) were evaluated using four item

measures on seven point semantic differential

scales. The respondents were advised to evaluate

these behavioural intentions with respect to the

e-tailer that respondents shopped most

frequently. In instances where the respondents

shopped in similar frequencies with a number of

e-tailers, they were asked to base their responses

with respect to their favoured e-tailer. Item

measure descriptions for attitude towards

attributes of e-shopping and e-shopping

behaviour can be found in Table I.

Sampling and data collection

Researchers working in e-consumer research

favour an online methodology in place of mail

surveys or random digit surveys for a number of

reasons (Szymanski and Hise, 2000;

Jayawardhena, 2004). First, an online survey is

consistent with the context of this investigation,

as respondents will be in a relevant setting when

completing the questionnaire. Second, an online

methodology can be more effective in identifying

online shoppers. Accordingly, the sample

utilised for the study consisted of 1,500

individuals who were randomly selected from a

consumer panel of 10,000 e-shoppers, whose

contact details were owned by a research firm.

An e-mail was sent to individuals in the sampling

frame, inviting them to participate in the study.

Either the respondents could send the completed

questionnaire that was attached to the invitation

e-mail or they could visit a Web page containing

the questionnaire. Third, some researchers

report that respondents view online surveys to be

more important compared to traditional surveys

(Szymanski and Hise, 2000), increasing the

likelihood that more respondents are likely to

respond to this survey method. It is equally

important to recognise that there are significant

limitations of online surveys and these may have

important consequences in the interpretation of

survey findings. Szymanski and Hise (2000)

assert that online surveys should not be long;

consequently, constructs under investigation

must be captured parsimoniously.

A total of 644 completed questionnaires were

received in total, of which 626 were usable. This is

a response rate of 42.93 percent, which is

favourable by comparison with previous surveys

examining similar topics (Shim and Eastlick,

1998; Shim et al., 2001). To test for non-response

bias, the characteristics of early and late

respondents were compared (Armstrong and

Overton, 1977). Armstrong and Overton (1977)

recommend comparing the responses of two

groups, early and late respondents, across

independent and dependant variables. No

observable significant differences exist at the 0.05

level, thus suggesting the impact of non-response

bias is negligible.

Respondent characteristics

Characteristics of respondents were similar to

those reported in other studies examining the

e-consumer behaviour (Doherty and Ellis-

Chadwick, 2003). A large proportion of

respondents (87 percent) fell between the 25-54

age group and 57 percent were male. The majority

of respondents were occupying positions in

predominantly white collar and professional

occupations.

Preliminary data analysis

Adopting the approach taken by Homer and Kahle

(1988) and Shim and Eastlick (1998), a principal

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components analysis with varimax rotation was

initially conducted to identify underlying

dimensions of personal values. A number of

authors (e.g. Beatty et al., 1991; Homer and

Kahle, 1988; Shim and Eastlick, 1998)

recommend that it is prudent to reduce the LOV

items to a smaller number of underlying

dimensions for predicting consumer attitudes.

Additionally, Kahle and Kennedy (1989)

recommend that the items from the LOV be factor

analysed so that the resultant factors can be used in

a causal modelling technique. Furthermore, this

approach can also overcome concerns regarding

single-itemmeasurement that are frequently raised

in value surveys (Braithwaite and Scott, 1991;

Shim and Eastlick, 1998).

As shown in Table II, a three-factor solution for

the LOV scale was identified. Items with factor

loadings of 0.50 and greater on one factor were

retained. Two items (a “sense of belonging” and

“warm relationships with others”) were excluded,

since factor loading on these items was below the

specified value. The resulting three factors were

labelled “self-direction value”, “enjoyment value”,

and “self-achievement value” respectively. These

three value dimensions correspond to Schwartz

and Bilsky’s (1987) framework of individual

motivational domains.

Table I Measurement model results

Construct/indicator

Standardised

factor loading SE tConstruct

reliability

Proportion of

extracted

variance (%)

z1 (Self direction value)

sd1 [self-respect] 0.83 – – 0.81 50.8

sd2 [self-fulfilment] 0.79 0.048 20.75*

z2 (Enjoyment value)

e1 [excitement] 0.81 – – 0.86 62.4

e2 [fun and enjoyment of life] 0.76 0.077 12.59*

z3 (Self achievement value)

sa1 [sense of accomplishment] 0.90 – – 0.71 48.0

sa2 [being well-respected] 0.80 0.072 13.54*

sa3 [security] 0.71 0.075 15.55*

h1 (Attitude towards e-shopping)

a1 [ease of finding products] 0.83 – – 0.75 56.1

a2 [seeing/experiencing new things] 0.76 0.069 19.23*

a3 [comparison shopping] 0.80 0.073 22.12*

a4 [price of goods] 0.83 0.063 18.95*

a5 [variety of product/brand choice] 0.80 0.071 21.32*

a6 [latest product information] 0.82 0.047 18.62*

a7 [payment security] 0.69 0.051 18.66*

a8 [overall speed of process] 0.67 0.045 19.65*

a9 [product guarantees] 0.74 0.046 16.69*

a10 [fun place] 0.81 0.068 22.11*

a11 [returns policy] 0.73 0.064 20.01*

a12 [time savings] 0.72 0.059 17.85*

a13 [privacy] 0.76 0.059 17.85*

h2 (Desire to browse)

b1 [browse for as long as possible] 0.77 – – 0.79 56.80

b2 [enjoy spending time browsing] 0.72 0.060 14.96*

h3 (Repatronage intentions)

r1 [not at all - very frequent] 0.74 – – 0.82 47.20

r2 [unlikely-likely] 0.77 0.070 17.02*

r3 [not probable-very probable] 0.81 0.064 15.92*

r4 [impossible-very possible] 0.75 0.059 13.56*

h4 (Switching intentions)

s1 [not at all-very frequent] 0.90 – – 0.84 57.00

s2 [unlikely-likely] 0.72 0.057 17.56*

s3 [not probable-very probable] 0.77 0.062 16.89*

s4 [impossible-very possible] 0.82 0.049 14.92*

Notes: * p ,0.001; first (l) path was set to 1, therefore, no SEs or t values are given

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Results

LISREL 8.3 was employed to conduct structural

equation modelling using a two-stage analysis

(Joreskog and Sorbom, 1993). The measurement

model is first confirmed and the structural model is

built thereafter. The measurement model allows

the assessment of discriminant and convergent

validity. If the measurement model provides an

acceptable fit to the data, the structural model then

provides an assessment of nomological validity

(Anderson and Gerbing, 1988).

The measurement model was first developed by

conducting a confirmatory factor analysis on multi

item scales (i.e. self-direction value, enjoyment

value, self-achievement value, attitude towards

e-shopping attributes, desire to browse,

repatronage intentions and switching intentions).

Simple statistics of model constructs are reported

in Table III.

Table I presents the results of the measurement

model, including the standardised factor loadings,

standard errors, construct reliabilities, and

proportion of variance extracted for each

construct. Factor loadings of the indicators for

each construct were statistically significant and

sufficiently high to demonstrate that the indicators

and their underlying constructs fit the model. The

reliabilities and variance extracted for each latent

variable revealed that the measurement model was

reliable and valid. The extracted reliabilities and

variance ranged from 0.71 to 0.86 and from 48.0

percent to 62.4 percent respectively; these were

computed using indicator standardised factor

loadings and measurement errors (Hair et al.,

1998).

Having confirmed the measurement model the

structural model was then examined. The model

estimates are consistent with the hypothesized

model. Figure 2 presents the model and structural

path coefficients for each relationship.

The overall fit indices for the structural model

indicate a chi-square (x2) of 370.88 with 136

degrees of freedom ( p # 0.0001). The x2 test

becomes more sensitive as the number of

indicators increases (Hair et al., 1998). Hair et al.

(1998) further assert that large sample sizes

(.200) also have a propensity to produce

significant x2 statistics even when the data set may

be well fitted to the hypothesized model structure.

In such cases, the Tucker-Lewis Index

(TLI ¼ 0:954) and Normed Fit Index

(NFI ¼ 0:963) provide relatively unbiased

estimations of incremental fit of the proposed

structural model. Furthermore, a goodness of fit

index (GFI) of 0.919 is a good fit for a relatively

large sample size (Marsh et al., 1988).

As shown in Table IV, a number of additional

factors were taken into consideration in assessing

the model fit (Hair et al., 1998; Kelly et al., 1996;

Tabachnick and Fidell, 2000). Normed chi-

square, the ratio of chi-square to degrees of

freedom (x2/df) of 2.72 indicates a good model fit

(Hair et al., 1998). Two additional fit indices were

also calculated. On one hand the comparative fit

index (CFI) with 0.921 is comfortably above the

0.9 standard for model fit (Kelly et al., 1996).

Similarly, the adjusted goodness of fit index

(AGFI) shows a value of 0.911. Additionally, a

root mean square error of approximation

(RMSEA) of 0.056 indicates a close fit. According

to Brown and Cudeck (1993) RMSEA values up to

0.08 represent a reasonable fit. Finally, this model

yields a superior fit than the value-attitude-

behaviour models reported by Homer and Kahle

(1988) and Shim and Eastlick (1998).

Table II Factor analysis of list of values (LOV)

Eigen value Proportion of variance explained (%) Reliability coefficients Rotated factor score

Self-direction value 3.02 33.2 0.91

Self-respect 0.85

Self-fulfilment 0.82

Enjoyment value 2.49 27.4 0.87

Excitement 0.89

Fun and enjoyment of life 0.84

Self-achievement value 1.33 14.6 0.79

Sense of accomplishment 0.82

Being well-respected 0.76

Security 0.69

Total variance explained 75.2

Table III Model variables descriptive statistics

Model variables Mean Std. Dev.

Self direction value 4.8 1.5

Enjoyment value 4.2 1.6

Self achievement value 3.7 1.4

Attitude toward e-shopping 19.6 9.5

Desire to browse 4.2 0.9

Repatronage intentions 3.4 1.1

Switching intentions 3.1 1.5

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One may wonder whether the causal attribution

suggested by this model is better than potential

alternative models, since only one model has been

presented. A model that excludes the intermediary

of attitudinal construct was analysed. When

appropriate causal paths are fixed, a difference of

chi-square tests along with other indicators reveal

that the initial model is a significantly better fit.

This analysis demonstrates additional support for

the mediating role of attitudes between specific

values and specific behaviours.

One reviewer suggested that it could be argued

that the direction of causal paths could potentially

be reversed. That is, individuals who shop online

could answer in ways that make them appear to be

more internally oriented and to have more positive

attitudes toward e-shopping. It is erroneous to

apply the difference of chi-square test in this

instance, since the resulting model and the model

presented here are not nested. Additionally, such a

conclusion necessitates a strong theoretical base

and this paper has provided adequate prior

research and persuasive theoretical foundations to

justify the value-attitude-behaviour hierarchy.

H1 was specified as path g11, g12 and g13. H2

was specified as path b21, b31 and b41. These

results indicate that all hypothesized paths are

supported. As hypothesized (H1), all three

Figure 2 Value-attitude-behaviour model in e-shopping

Table IV Goodness of fit indices

Values obtained Values recommended

Absolute fit measures

Chi-square 370.880 a

p-level 0.000 Non-significant

Non-centrality parameter (NCP) 234.880 a

Goodness of fit index (GFI) 0.919 Values above 0.90

Root mean square residual (RMSR) 0.060 Values close to 0

Root mean square error of approximation (RMSEA) 0.050 Values between 0.05 and 0.08

Expected cross validation index (ECVI) 0.705 a

Incremental fit measures

Adjusted goodness of fit index (AGFI) 0.911 Values above 0.90

Tucker Lewis index (TLI) 0.954 Values above 0.90

Normed fit index (NFI) 0.963 Values above 0.90

Comparative fit index (CFI) 0.927 Values above 0.90

Parsimonious fit measures

Parsimonious normed fit index (PNFI) 0.808 a

Parsimonious goodness of fit index (PGFI) 0.287 a

Normed chi-square 2.720 Values between 1 and 3.5

Akaike information criterion (AIC) 440.880 a

Note: aNo established thresholds for these indices. Judged in comparison to alternative models

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dimensions of personal values were positively and

directly related to attitude toward e-shopping. The

self-direction value had a path coefficient (g11) of

0.29 (p # 0.01), the social affiliation value had a

path coefficient (g12) of 0.24 ( p # 0.01), and the

self-achievement value had a path coefficient (g13)

of 0.20 ( p# 0.01), indicating a direct and positive

relationship between values and attitude.

Therefore, H1 was accepted.

The model shows that attitudes influence

e-shopping behaviour. Consumers’ attitude

toward e-shopping influenced their desire spend

time browsing (b21 ¼ 0:178, p # 0.01), to

repatronize e-tailers (b31 ¼ 0:396, p # 0.01) and

negatively influence switching behaviour

(b41 ¼ 20:245, p # 0.01). Therefore, H2 was

accepted, indicating that shoppers with a more

favourable attitude toward shopping attributes of

e-shopping were more likely shop and spend time

at e-tailers and less likely to switch.

Discussion and conclusions

Effects of personal values on attitudes

This study tested the hypothesis (H1) that

personal value dimensions are directly related to

attitude toward e-shopping. The findings of this

study confirm that e-shoppers placing stronger

emphasis on self-direction, enjoyment and self-

achievement values are more likely to have a

favourable attitude toward e-shopping than those

with a weaker emphasis on these values. As

hypothesised (H2), such an attitude, in turn, had a

direct influence on e-shopping behaviour, and this

was measured by their desire to browse,

repatronage intentions and switching intentions. It

is significant to observe that personal values had

only indirect effects on e-shopping behaviour via

attitude toward e-shopping. Past studies (Kahle

and Kennedy, 1989; Shim and Eastlick, 1998)

suggest that people often purchase products for the

benefit of value fulfilment, and the findings of this

study are broadly in line with those findings.

The results of this study, taken together with

results from studies by Homer and Kahle (1988)

and Shim and Eastlick (1998), provides evidence

that the value-attitude-behaviour hierarchy model

can be applied to e-consumer shopping behaviour.

This causal flow of personal values-attitudes-

behaviour in an e-shopping setting indicate that

e-tailers can positively influence consumer

behaviour by developing strategies aimed at

appealing to personal values. These values in turn

will have direct influence on the e-consumer

attitudes toward e-shopping attributes as well as an

indirect influence on the behaviour prompted by

these attitudes.

Although all dimensions of personal values were

significant predictors of favourable attitudes

toward attributes of e-shopping, this study

indicates that the power of self-direction values

exceeded that of the other two values. This

indicates that consumers with favourable attitude

towards attributes of e-shopping are self directed

individuals. This research suggests that enjoyment

value is an important predicator of favourable

attitudes towards attributes of e-shopping. In

particular, it indicates that e-consumers with

sound terminal values for excitement and

enjoyment of life may find e-shopping in harmony

with their enjoyment values. Previous research in

the shopping centre context (Wakefield and Baker,

1998) and e-shopping (Szymanski andHise, 2000)

postulate that excitement influences attitudes,

which in turn is a predicator of shopping

behaviour. Attributes of e-shopping such as the

ease of finding desired products, seeing and

experiencing new things, a quick purchase process,

makes the e-tailer Web site a fun place to visit and

will make e-shopping an enjoyable experience

(Shim et al., 2001). In this respect, it may also be

prudent to monitor e-consumer preferences on an

ongoing basis to update attributes of e-shopping to

appeal to e-consumers’ enjoyment values.

Despite the dominant power to self-direction

values and enjoyment values in predicting

favourable attitudes towards attributes of

e-shopping, the importance of self-achievement

values should not be taken lightly. One of the

advantages of e-shopping is the availability of

unique comparison opportunities (Bakos, 1997)

which are not readily imitable in traditional

shopping contexts. This attribute of e-shopping

promotes an atmosphere of intense competition

and consequently it is perceived that there are

significant price advantages of e-shopping (Bakos

and Brynjolfsson, 2000). Additionally, it is well

documented that e-shopping is a very convenient

method of shopping (Degeratu et al., 2000).

Buying something cheaply, with the full knowledge

that you paid the “best” price for the product with

the minimum inconvenience is likely to appeal to

e-shoppers with self-achievement values. This

finding is in line with previous research examining

price comparison opportunities in e-shopping

(Bakos and Brynjolfsson, 2000) and lends support

to the argument that e-tailers should enhance

comparison shopping opportunities rather than

hinder them (Verbeke and Bagozzi, 2000).

Perhaps this may also explain the growing

popularity of specialised price comparison sites

and portals which strive to reduce transaction costs

by reducing search costs and time.

The suggestion that it is prudent to present the

product range with comprehensive product

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information and facilitate comparison

opportunities may be perceived by some e-tailers

as a problematic scenario – especially if they

know that they are not the cheapest. The

respondents of this study and many other similar

studies (Doherty and Ellis-Chadwick, 2003)

indicate that e-shoppers are better educated and

have higher incomes than the general population.

Such shoppers’ purchase decisions may not be

entirely guided by price (Lynch and Ariely, 2000;

Verbeke and Bagozzi, 2000), but by a combination

of purchase considerations, including, service

quality, purchase conditions, e-tailer reputation,

etc. Therefore, it can be argued that if e-tailers do

not allow comparison opportunities, consumers

might designate such e-tailers into their inert set

when faced with a purchase.

Values-attitude-behaviour

The findings of this research have shown that all

three dimensions of personal values, namely,

self-direction, enjoyment and self-achievement,

were significantly associated to a favourable

attitude toward e-shopping attributes. The

resulting attitude, in turn, had a direct influence on

e-shopping behaviour, and was characterised by

consumers’ desire to browse, repatronage

intentions and switching intentions.

Somewhat predictably, this research indicates

that a favourable attitude toward e-shopping

attributes strongly influence repatronage

intentions. Similarly, it also finds that those

consumers with a favourable attitude toward

e-shopping attributes are also likely to spend time

browsing e-tailer Web sites. Finally, it is also

important to highlight that this research shows

that consumers with positive attitude toward

e-shopping attributes of e-tailers are unlikely to

switch them in favour of others. Therefore, the

findings of this research also provide an

understanding of the conditions that lead to

switching by e-shoppers, which is of both

theoretical and practical interest.

Significantly, this research shows that personal

values only have an indirect effect on e-shopping

behaviour through attitude. This suggests that

attitude had a mediating role in the values-

attitude-behaviour model. This causal flow of

values-attitude-behaviour in e-shopping adds

weight to the work of a number of authors who

argue that the influence flows from abstract value

to mid-range attitudes and to specific behaviours,

and that values have only an indirect effect on

consumer behaviour through domain-specific

attitudes (Homer and Kahle, 1988; Shim and

Eastlick, 1998).

These findings confirm that a value-attitude-

behaviour hierarchy model can be applied not only

to patronage behaviour in traditional shopping

contexts (e.g. Homer and Kahle, 1988; Shim and

Eastlick, 1998) but also to e-shopping. Moreover,

the predictive power of attitude for behaviour in

this study is superior to Shim and Eastlicks’ (1998)

study. The causal flow of personal values-

attitudes-behaviour in e-shopping signify that

e-tailers can positively influence consumer

behaviour by careful planning of Web site

attributes, merchandising attributes and service

attributes aimed at appealing to self-direction,

enjoyment and self-achievement values of

e-shoppers. These values in turn will have a

direct influence on consumer attitudes toward

e-shopping attributes as well as indirect influence

on the behaviour prompted by these attitudes.

Limitations and future research

This study provides an insight into relationships

among values, attitude and shopping behaviours in

e-shoppers. Several limitations of this study should

be considered when interpreting the study’s results

and developing future research to extend and

expand its scope. The findings of this study do,

however, offer a number of directions for future

research.

This study did not distinguish between the types

of goods that e-shoppers purchased. Future

research should explore the types of media

attributes and consumer characteristics that

influence personal values for experience goods.

Shim et al. (2001) state that a fundamental

question facing e-tailers is whether the antecedents

that predict the Internet purchase of goods are

different from those that predict the purchase of

experience goods. A possible future extension of

this study could be to investigate whether the

results from this study can be applied to experience

goods.

Although the income and educational

characteristics of the sample were similar to those

of previous e-shopper studies, the resulting sample

was more economically upscale and older than the

UK population as a whole. Age, household

composition and life cycle stage, which may

influence values, were not tested in the model.

These variables undoubtedly warrant testing.

Additionally, the robustness of the model could be

criticised on the grounds that data collection did

not extend to both e-shoppers and non e-shoppers,

and therefore variance in data set would have been

limited.

Finally, and perhaps most importantly, future

studies should apply the value-attitude-behaviour

framework in a comparative examination of

different types of e-shopping goods. In addition,

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experimental designs could be used to expand the

behavioural component to include multiple

examples of actual behaviours. Such studies might

test, for example, the relationships among

ethnicity, ethnic identification, values, attitudes

and behaviour in the context of other ethnic

groups and products.

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Personal values’ influence on e-shopping attitude and behaviour

Chanaka Jayawardhena

Internet Research

Volume 14 · Number 2 · 2004 · 127-138

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