This is a preprint (pre peer-review) version of a paper accepted in its definitive form by the European Journal of Marketing, © Emerald Group
Publishing Ltd, http://www.emeraldinsight.com and has been posted by permission of Emerald Group Publishing Ltd for personal use, not for
redistribution. The article will be published in the European Journal of Marketing, Volume 43, Issue 9/10: 1121-1139 (2009).
The definitive version of the paper can be accessed from:
Charles Dennis1, Bill Merrilees2, Chanaka Jayawardhena3 and Len Tiu
1 Brunel University,
Uxbridge UB8 3PH
Tel: +44 (0) 185 265242
e-mail: charles.dennis@brunel.ac.uk
2 Professor of Marketing,
Deputy Head of Department of Marketing
Griffith Business School
Griffith University, Queensland 4222
Tel: +61 (0) 7 55527176
Fax: +61 (0) 7 55529039
e-mail: bill.merrilees@griffith.edu.au
3 Loughborough University Business School
Loughborough University
Leicestershire LE11 3TU
Tel: +44 (0) 1509 228831
Fax: +44 (0) 1509 223960
e-mail: c.jayawardhena@lboro.ac.uk
4 Leicester Business School
De Montfort University Business School
Bede Island
Tel: +44 (0)116 250 6096
Email: lwright@dmu.ac.uk

Brief professional biographies
Charles Dennis is a Senior Lecturer at Brunel University, London, UK. His teaching and research area is (e-)retail and consumer behaviour – the vital final link of the Marketing process – satisfying the end consumer. Charles is a Chartered Marketer and has been elected as a Fellow of the Chartered Institute of Marketing for work helping to modernise the teaching of the discipline.
2 Charles was awarded the Vice Chancellor’s Award for Teaching Excellence for improving the interactive student learning experience. Charles’s publications include Marketing the e-Business, (1st & 2nd editions) (joint-authored with Dr Lisa Harris), the research-based e-Retailing (jointauthored with Professor Bill Merrilees and Dr Tino Fenech) and research monograph Objects of Desire: Consumer Behaviour in Shopping Centre Choice. His research into shopping styles has received extensive coverage in the popular media.

Purpose – The primary purpose of this article is to bring together apparently disparate and yet
interconnected strands of research and present an integrated model of e-consumer behaviour. It
has a secondary objective of stimulating more research in areas identified as still being underexplored.
Design/methodology/approach – The paper is discursive, based on analysis and synthesis of econsumer
Findings – Despite a broad spectrum of disciplines that investigate e-consumer behaviour and
despite this special issue in the area of marketing, there are still areas open for research into econsumer
behaviour in marketing, for example the role of image, trust and e-interactivity. The
paper develops a model to explain e-consumer behaviour.
Research limitations/implications – As a conceptual paper, this study is limited to literature and
prior empirical research. It offers the benefit of new research directions for e-retailers in
understanding and satisfying e-consumers. The paper provides researchers with a proposed
integrated model of e-consumer behaviour.
Originality/value – The value of the paper lies in linking a significant body of literature within a
unifying theoretical framework and the identification of under-researched areas of e-consumer
behaviour in a marketing context.
Keywords: e-consumer behaviour, E-consumer behaviour, e-marketing, e-shopping, online
shopping, e-retailing.
Paper type: Conceptual paper.
Early e-shopping consumer research (e.g. Brown et al., 2003) indicated that e-shoppers tended to be concerned mainly with functional and utilitarian considerations. As typical ‘innovators’ (Donthu and Garcia, 1999; Siu and Cheng, 2001), they tended to be more educated (Li et al, 1999), higher socio-economic status (SES) (Tan, 1999), younger than average and more likely to be male (Korgaonkar and Wolin, 1999). This suggested that the e-consumer tended to differ from the typical traditional shopper. More recent research, on the other hand, casts doubt on this notion. Jayawardhena et al., (2007) found that consumer purchase orientations in both the traditional world and on the Internet are largely similar and there is evidence for the importance of social interaction (e.g. Parsons, 2002; Rohm and Swaminathan, 2004) and recreational motives (Rohm and Swaminathan, 2004), as demonstrated by virtual ethnography (webnography) of
‘Web 2.0’ blogs, social networking sites and e-word of mouth (eWOM) (Wright, 2008).
Accordingly, this paper aims to examine concepts of e-consumer behaviour, including those derived from traditional consumer behaviour. The study of e-consumer behaviour is gaining in importance due to the proliferation of online shopping (Dennis et al., 2004; Harris and Dennis, 2008; Jarvenpaa and Todd 1997). Consumeroriented research has examined psychological characteristics (Hoffman and Novak 1996; Lynch and Beck 2001; Novak et al., 2000; Wolfinbarger and Gilly 2002; Xia 2002), demographics
(Brown et al., 2003; Korgaonkar and Wolin, 1999), perceptions of risks and benefits (Bhatnagar and Ghose 2004; Huang et al., 2004; Kolsaker et al., 2004;), shopping motivation (Childers et al.2001; Johnson et al. 2007; Wolfinbarger and Gilly 2002), and shopping orientation (Jayawardhena et al., 2007; Swaminathan et al., 1999). The technology approach has examined technical specifications of an online store (Zhou et al., 2007), including interface, design and navigation (Zhang and Von Dran, 2002); payment (Torksadeth and Dhillon, 2002; Liao and Cheung, 2002); information (Palmer, 2002; McKinney et al., 2002); intention to use (Chen and Hitt, 2002); and ease of use (Devaraj et al., 2002; Stern and Stafford, 2006). The two perspectives do not contradict each other but there remains a scarcity of published research that combines both. Accordingly, the objective of this paper is to develop and argue in support of an integrated model of e-consumer behaviour, drawing from both the consumer and technology viewpoints. The paper also has a secondary objective of stimulating more research in areas identified as still being under-explored. The research area is potentially fruitful since, even in recession, shopping volumes in the UK, for example, are continuing with double-digit growth (Deloitte, 2007; IMRG/Capgemini, 2008), whereas traditional shopping is languishing in zero growth or less (BRC, 2008).
The remainder of this article is organised as follows. We develop our model in two stages. First,we draw from existing literature to present well-known factors that influence consumer behavior and form the core of our model. Second, we present a framework that can be adopted to examine both the influences and interrelationships between the factors in predicting e-consumer behaviour. Finally we present our concluding remarks.

Factors influencing e-consumer behaviour
The basic model argues that functional considerations influence attitudes to an e-retailer which in
turn influence intentions to shop with the e-retailer and then finally actual e-retail activity, including shopping and continued loyalty behaviour. Our model is underpinned by the theory of
reasoned action (TRA). The choice of this theoretical lens lies in its acceptance as a useful theory
in the study of consumer behaviour, which ‘provides a relatively simple basis for identifying
where and how to target consumers’ behavioural change attempts’ (Sheppard et al., 1988: 325).
The conceptual foundations are illustrated in Figure 1. Researchers attempting to answer why people (e-)shop have looked to various components of the ‘image’ of (e-)retailing (Wolfinbarger and Gilly, 2002). This may be a valid approach for two reasons. First, ‘image’ is a concept used to signify our overall evaluation or rating of somethingin such a way as to guide our actions (Boulding, 1956). For example, we are more likely to buy from a store that we consider has a positive image on considerations that we may consider important, such as price or customer service. Second, this is an approach that has been demonstrated for traditional stores and shopping centres over many years (e.g. Berry, 1969; Dennis et al., 2002a; Lindquist, 1974). This is particularly relevant because it is the traditional retailers with strong images that have long been making the running in e-retail (IMRG/Capgemini, 2008; Kimber, 2001). According to Kimber (2001), shopper loyalty instore and online are linked. For example, according to http://www.tesco.com (accessed 26 October, 2001), the supermarket Tesco’s customers using both on and offline shopping channels spend 20 percent more on average than customers who only use the traditional store. Tesco is well known as having a positive image both in-store and online, being the UK grocery market leader in both channels and the world’s largest e-grocer (Eurofood, 2000). More recently, the same approach has been applied for e-image components (Babakus and Boller, 1992; Dennis et al., 2002b; Kooli et al., 2007; Parasuraman et al., 1988; Teas, 1993). Examples of e-service instruments include: Loiacono’s et al.’s, (2002) WebQual; Parasuraman’s et al.’s, (2005) E-S-QUAL; Wolfinbarger’s and Gilly’s (2003) eTailQ; and Yoo’s and Donthu’s (2001) SITEQUAL. The most common image components in the e-retail context include product selection, customer service and delivery or fulfilment. We therefore propose that: P1 e-Consumer attitude towards an e-retailer will be positively influenced by customer perceptions of e-retailer image. TRA (Ajzen and Fishbein, 1980) suggests that intentions are the direct outcome of attitudes (plus social aspects or ‘subjective norms’, as discussed below) such that there are no intervening mechanisms between the attitude and the intention. Therefore: P2 e-Consumer intentions to purchase from an e-retailer will be positively influenced by positive attitudes towards the e-retailer. Most studies have gone only as far as modelling ‘intention’, with few addressing actual adoption (Cheung et al., 2005) and still fewer, continuance behaviour or loyalty. Nevertheless, as mentioned in this section below, as consumers achieve more satisfactory e-shopping experiences, they are more likely to trust and re-patronise, extending our framework to behavioural responses. 6 This is in line with the stimulus-organism-response (S-O-R) paradigm (Mehrabian and Russell, 1974) and adoption/continuance (Cheung et al., 2005). Thus: P3 Actual purchases from an e-retailer will be positively influenced by intentions to
purchase from an e-retailer. The consumer purchase process is a series of interlinked multiple stages including information collection, evaluation of alternatives, the purchase itself and post purchase evaluation (Engel et al., 1991; Gabbot and Hogg, 1998). To evaluate the information demands of services, Zeithaml (1981) suggested a framework based on the inherent search, experience, and credence qualities of products. Since online shopping is a comparatively new activity, online purchases are still perceived as riskier than terrestrial ones (Laroche et al., 2005) and an online shopping consumer therefore relies heavily on experience qualities, which can be acquired only through prior purchase (Lee and Tan, 2003). This leads to: P4 Intention to shop with a particular e-retailer will be positively influenced by past experience; and P5 Actual purchases from an e-retailer will positively influence experience. Trust, ‘a willingness to rely on an exchange partner in whom one has confidence’ (Moorman et al., 1992) is central to e shopping intentions (Fortin et al., 2002; Goode and Harris, 2007; Lee and Turban, 2001). Security (safety of the computer and financial information) (Bart et al., 2005; Jones and Vijayasarathy, 1998), and privacy (individually identifiable information on the Internet) (Bart et al., 2005; Swaminathan et al., 1999) are closely related to trust. Notwithstanding that these constructs differ, in the interests of simplicity we consider them here to be related aspects of the same concept, which we name ‘trust’: P6 e-Consumer trust in an e-retailer will positively influence intention to e-shop. As e-shoppers become more experienced, trust grows and they tend to shop more and become less concerned about security (Chen and Barnes, 2007; OxIS, 2005) Thus: P7 Past experience and cues that reassure the consumer will positively influence trust in an e-retailer. Drawing on early work on another construct of consumer behaviour, learning, (Bettman 1979; Kuehn 1962), an e-retail site becomes more attractive and efficient with increased use as learning leads to a greater intention to purchase (Bhatnagar and Ghose, 2004; Johnson et al., 2007).
P8 e-Consumers’ learning about an e-retailer web site will positively influence their intention to purchase. We now extend our model to include social and experiential aspects of e-consumer behavior along with consumer traits. The extended model is illustrated in Figure 2.
An integrative framework
Social factors
The TRA family theories, which are central to our model (Cheung et al., 2005; Sheppard et al., 1988), include the Theory of Planned Behaviour (TPB) (Ajzen, 1991), the Technology Acceptance Model (TAM) (Davis, 1989) and the Unified Theory of Acceptance and Use of
Technology (UTAUT) (Venkatesh et al., 2003). As introduced in ‘The role of functional attributes’ section above, intention is influenced by two factors, ‘attitude toward the behaviour’
and ‘subjective norms’ (Fishbein and Ajzen, 1975; Ajzen and Fishbein, 1980). ‘Subjective norm’
refers on one hand to beliefs that specific referents dictate whether or not one should perform the
behaviour or not, and on the other the motivation to comply with specific referents (Ajzen and
Fishbein, 1980). Simply put, these are ‘social factors’, by which we mean the influences of others on purchase intentions. For example, TRA argues that whether our best friends think that we should make a particular purchase influences our intention. Numerous studies of traditional
shopping have drawn attention to these aspects (e.g. Dennis 2005; Dholakia, 1999). Social
influences are also important for e-shopping, but e-retailers have difficulty in satisfying these
needs (Kolesar and Galbraith 2000; Shim et al., 2000). Rohm and Swaminathan (2004) found
that social interaction was a significant motivator for e-shopping (along with variety seeking and
convenience, which we consider with situational factors, below). Similarly, Parsons (2002) found that social motives such as: social experiences outside home; communication with others with similar interests; membership of peer groups; and status and authority were valid for e-shopping. Social benefits of e-shopping, such as communications with like-minded people, can be important motivators that influence intention. Web 2.0 social networking sites can link social
interactions concerning personal interests with relevant e-shopping. For example, people with a
specific, specialist fascination for athletic footwear may be members of http://www.sneakerplay.com.
Consumers with a more general interest in social e-shopping are catered for by http://www.osoyou.com. Thus: P9 e-Consumer attitude towards an e-retailer will be positively influenced by social factors.
Since attitude and subjective norm cannot be the exclusive determinants of behaviour where an
individual’s control over the behaviour is incomplete, the TPB purports to improve on the TRA
by adding ‘perceived behavioural control’ (PBC), defined as the ease or difficulty that the person
perceives of performing the behaviour. Empirical studies demonstrate that the addition of PBC
significantly improves the modelling of behaviour (Ajzen 1991). In the information systems
literature, the concept of PBC has an equivalent in ‘self-efficacy’, defined as the judgment of
one’s ability to use a computer (Compeau and Higgins, 1995). Researchers have shown that there
is a positive relationship between experience with computing technology, perceived outcome and
usage (Agarwal and Prasad, 1999). There is considerable empirical evidence on the effect of
computer self-efficacy (e.g. Agarwal et al, 2000; Venkatesh, 2000). These studies confirm the
essential effect of computer self-efficacy in understanding individual responses to information
technology in general and e-shopping in particular. There is conceptual and empirical overlap of
the constructs of PBC and self-efficacy with past experience (Alsajjan and Dennis, forthcoming),
which we therefore concentrate into our ‘Past experience’ variable (see ‘The role of functional
attributes’ section above).
8 TAM was originally conceived to model the adoption of information systems in the workplace
(Davis, 1989) but two specific dimensions relevant to e-shopping have been identified: usefulness and ease of use. Usefulness refers to consumers’ perceptions that using the Internet will enhance the outcome of their shopping and information seeking (Chen et al., 2002). In our model, usefulness is incorporated into the image components of product selection, customer service and delivery or fulfilment, in the ‘Role of functional attributes’ section, above. Ease of use concerns the degree to which e-shopping is perceived as involving a minimum of effort, e.g. in navigability and clarity (Chen et al., 2002). Ease of use is central to the e-interactivity dimension of our model, considered in the ‘Experiential aspects of e-shopping’ section, below.
Davis et al., (1992) have added a new dimension of attitude into TAM: enjoyment. Enjoyment
reflects the hedonic aspects discussed in the ‘Experiential aspects of e-shopping’ section, below.
In a further development of TAM, the UTAUT, Venkatesh and colleagues (2003) recognised the
moderating effects of consumer traits, considered in the ‘Consumer traits’ section, below. The
TRA family theories including TPB, TAM and UTAUT thus constitute the ‘glue’ of the integrative theoretical framework for our propositions P1-P7 above, as illustrated in Figure 2.
TAM has been criticised for ignoring a number of influences on e-consumer behaviour. These
include social ones (included in the TRA aspect of our model, above) (Chen et al., 2002) and
others such as situational factors (Moon and Kim, 2001); and consumer traits (Venkatesh et al.,
2003). Perea et al., (2004) add four factors: consumer traits; situational factors; product
characteristics; and trust (trust is considered in ‘The role of functional attributes’ section, above).
Situational factors may include variety seeking and convenience (identified by Rohm and
Swaminathan, 2004, as a significant motivator for e-shopping). We therefore extend our
framework to include relevant experiential and situational factors; and consumer traits in the
three sections below.

Experiential aspects of e-shopping
For decades, retailers and researchers have been aware that shopping is not just a matter of
obtaining tangible products but also about experience, enjoyment and entertainment (Martineau,
1958; Tauber, 1972). In the e-shopping context, experience and enjoyment derive from econsumers’ interactions with an e-retail site, which we refer to as ‘e-interactivity’. E Interactivity encompasses the equivalent of salesperson-customer interaction as well as visual merchandising and indeed the impact of all senses on consumer behaviour. Empirically, interactivity has been found to be a major determinant of consumer attitudes (Fiore et al., 2005; Richard and Chandra, 2005). Studies include, e.g., personalising greeting cards (Wu, 1999), and creating visual images of clothing combinations (Fiore et al., 2005; Kim and Forsythe, 2009 in this issue). More generally, Merrilees and Fry (2002) found that overall interactivity was the most important determinant of consumer attitudes to a particular e-retailer and interactivity could influence both trust and attitudes to the e-retailer. Therefore: P10 e-Consumer attitudes towards an e-retailer will be positively influenced by einteractivity; and P11 Trust in an e-retailer will be positively influenced by e-interactivity. A favourable perception of e-interactivity is likely to be influenced by ease of use of a web-site (Merrilees and Fry, 2002). Navigability is a key aspect, i.e. the ability of the user to find their way around a site and keep track of where they are (Richard and Chandra, 2005). Thus:9
P12 e-Consumers’ perceptions of e-interactivity will be positively influenced by ease of navigation. Many studies in the bricks-and-mortar world have used an environmental psychology framework to demonstrate that cues in the retail ‘atmosphere’ or environment can affect consumers’ emotions, which in turn can influence behaviour. The importance of this S-O-R model (Mehrabian and Russell, 1974) is that the stimulus cues such as colour, music or aroma can be manipulated by marketers to increase shoppers’ pleasure and arousal, which in turn should lead to more ‘approach’ behaviour, e.g. spending (rather than ‘avoidance’). Dailey (1999); and Eroglu et al., (2003) demonstrated that the same type of ‘web atmospherics’ model can be applied to econsumer behaviour. Graphics, visuals, audio, colour, product presentation at different levels of resolution, video and 3D displays are among the most common stimuli. Richard (2005) divided atmospheric cues into central, high task relevant ones (including structure, organization, informativeness, effectiveness and navigational); and a single peripheral, low-task relevant one (entertainment). Consistent with the Elaboration Likelihood Model (Petty and Cacioppo, 1986), the high task-relevant cues impacted attitude. Both high and low task-relevant cues had a secondary impact on exploratory purchase intention. Elements that replicate the offline experience lead to loyal, satisfied customers (Goode and Harris, 2007). Manganari and colleagues (2009) summarise the current state of knowledge on web atmospherics in e-retailing in this issue, illustrated schematically in their Figures 2 and 3 (Manganari et al., 2009). In theory, atmospherics can also include: touch (which can be simulated using a vibrating touch pad) and aroma (which might be incorporated by offering to send samples although odour simulation systems have yet to achieve widespread adoption) (Chicksand and Knowles, 2002).
P13 e-Consumer perceptions of e-interactivity will be positively influenced by web atmospherics. Environmental psychology suggests that people’s initial response to any environment is affective, and this emotional impact generally guides the subsequent relations within the environment (Machleit and Eroglu, 2000; Wakefield and Baker, 1998). Many studies suggest that web atmospherics are akin to the physical retail environment (e.g. Alba et al., 1997; Childers et al., 2001). In this issue, Jayawardhena and Wright found that emotional considerations are one of the main influences on attitudes towards e-shopping (Jayawardhena and Wright, 2009). Therefore:
P14 e-Consumer emotional states will be positively influenced by web atmospherics
And P15 e-Consumer attitude towards an e-retailer will be positively influenced by
emotional states.

Situational factors
One of the most significant attractions of e-shopping is perceptions of convenience (Evanschitzky et al., 2004; Szymanski and Hise, 2000), for example, a reduction of search costs when the consumer is under time pressure (Bakos, 1991; Beatty and Smith, 1987). Kim, Kim and
Kandampully, in this issue, found that convenience was one of the main influences on satisfaction (Kim et al., 2009). Convenience in e-shopping therefore increases search efficiency
by eliminating travel costs and associated frustrations (psychological costs). e-Retailers
differentiate themselves by emphasising convenience (Jayawardhena, 2004). For example,
http://www.amazon.com allows regular customers to complete the purchase process with ‘one click’.
Similarly, Amazon have allowed customers to review products, enhancing the quantity and
quality of product information for potential customers, helping in the customer information
search process to reduce search costs and time. Variety of products is a related aspect of online
shopping that also reduces search costs (Evanschitzky et al., 2004; Grewal et al., 2004).
Retailing literature suggests that shopping frequency may influence purchase intentions. For
example, Evans et al. (2001) found that experienced Internet users were more likely to participate in virtual communities for informational reasons, whereas novice users were more likely to participate for social interaction. e-Shopping becomes more routine as e-shoppers gain
experience of an e-retailer’s site (Liang and Huang, 1998; Overby and Lee, 2006). Hand and
colleagues, in this issue, draw attention to the influence of specific, individual factors such as
having a baby (Hand et al., 2009). In sum: P16 Consumer attitude towards an e-retailer will be influenced by situational factors such as convenience, variety, frequency of purchase and specific individual circumstances.

Consumer traits
In the interests of parsimony, we concentrate on four of the most commonly examined a priori
consumer traits: gender, education, income and age; plus two post hoc ones relevant to eattitudes: need for cognition (NFC) and optimum stimulation level (OSL) (Richard and Chandra,2005). The moderating effect of gender can be explained by drawing on social role theory and evolutionary psychology (Dennis and McCall, 2005; Saad and Gill, 2000). Men tend to be more task-orientated (Minton and Schneider, 1980), systems-orientated (Baron-Cohen, 2004) and more willing to take risks than are women (Powell and Ansic, 1997). This is because, socially, people are expected to behave in these ways (social role theory) and because this adaptive behaviour has given people with particular traits advantages in the process of natural selection (evolutionary psychology). In line with the task-orientation difference, Venkatesh and Morris (2000) found that men’s decisions to use a computer system were more influenced by the perceived usefulness than were women’s. On the other hand, in line with the systems-orientation difference (Felter, 1985), women’s decisions were more influenced by the ease of use of the system (Venkatesh and Morris, 2000). Gender moderates the relationship between various aspects of behavioural outcomes (Cyr and Bonanni, 2005; Yang and Lester, 2005). Psychology research over many years has identified numerous gender differences that are potentially relevant to e-consumer behaviour, e.g. in spatial navigation, perception and styles of communication. Nevertheless, the effects of these differences in e-consumer behaviour have received little research attention to date. In a parallel to Dennis’s and McCall’s (2005) ‘hunter-gatherer’ approach to shopping behaviour, Stenstrom et al. (2008) use an evolutionary perspective to study sex differences in website preferences and navigation. In this interpretation, males tend to use an ‘internal map’ style of navigation because hunting required accurate navigation over long distances. Females, on the other hand, tend to use ‘landmark’ navigation because gathering was carried out over a smaller area close to the home base. e-Navigation is analogous because users must navigate in order to travel through pages, objects and landmarks in a manner similar to physical navigation.
Strenstrom’s and colleagues’ results demonstrate that extended hierarchical levels of an shopping website are more easily navigated by males than by females. Extending gender differences previously reported for ‘bricks’ shopping (Dennis and McCall, 2005) to e-shopping, in this issue, Hansen and Jensen found that men tend to be ‘quick shoppers’ whereas women are11
more ‘shopping for fun’ (Hansen and Jensen, 2009). These results suggest that masculine and
feminine segmented websites might be more successful in satisfying e-consumers.
The role of education in e-shopping has been given little research attention. It is argued that
people with higher levels of education usually engage more in information gathering and
processing; and use more information prior to decision making, whereas less well educated
people rely more on fewer information cues (Capon and Burke, 1980; Claxton et al., 1974). In
contrast to people with lower educational attainments, it is postulated that better educated
consumers feel more comfortable when dealing with, and relying on, new information (Homburg
and Giering, 2001). A body of research suggests that income is related to e-consumer behaviour
(Li et al., 1999; Swinyard and Smith, 2003). This is expected as people with higher income have
usually achieved higher levels of education (Farley, 1964). We expect, therefore, that better
educated and wealthier consumers seek alternative information about a particular e-retailer, apart
from their satisfaction level, whereas less well educated, poorer consumers see satisfaction as an
information cue on which to base their purchase decision.
Older consumers are less likely to seek new information (Moskovitch 1982; Wells and Gubar
1966), relying on fewer decision criteria, whereas younger consumers seek alternative
information. Age moderates the links between satisfaction with the product and loyalty such that
these links will be stronger for older consumers (Homburg and Giering, 2001).
Similarly, individuals with a personality high on NFC engage in more search activities that lead
to greater e-interactivity (Richard and Chandra, 2005), a principle supported by Kim and
Forsythe (2009) in this issue, who found that consumer innovativeness was associated with
greater use of 3D rotational views. In contrast, high OSL people have a higher need for
environmental stimulation and are more likely to browse, motivated more by emotion than
cognition (Richard and Chandra, 2005).
The various consumer traits will not necessarily have the same moderating effects but in line with
space limitations, we summarise the main expectations as:
P17M1 The relationship between social factors and attitude towards an e-retailer
will be moderated by consumer traits,
P17M2 The relationship between emotion and attitude toward e-retailer will be
moderated by consumer traits
P17M3 The relationship between e-interactivity and attitude toward e-retailer will
be moderated by consumer traits.
These moderators complete our integrated model, simplified and illustrated schematically in
Figure 2.

Discussion and conclusion
There is a substantial body of literature examining e-consumer behaviour in both academia and in
practitioner publications. Both strands agree that many factors influence e-shopping.
Nevertheless, there are significant gaps in our understanding of e-consumer behaviour. This
paper attempts to fill this gap by conducting an analysis of the literature and presenting a unified
model that explains e-consumer behaviour that is founded on a sound theoretical underpinning.
We developed a dynamic model to explain e-consumer behaviour in two stages, underpinned by
the Theory of Reasoned Action (Ajzen and Fishbein, 1980; Fishbein and Ajzen, 1975) family of
theories, which postulate that that peoples’ behaviour is governed by their beliefs, attitudes, and
intentions towards performing that behaviour. We argue that attitudes drive e-consumer
behavioural intentions which lead into actual purchases. This is followed by the development of
further propositions for our model. A significant contribution that our model makes is the
appreciation of the image construct and its influence on e-consumer decision making process. We
enhance our model by examining the antecedents of attitude and trust, drawing attention to econsumer
emotional states and e-interactivity along with social factors and consumer traits.
Furthermore, we indicate that situational factors influence behaviour. To explain consumer
emotional states we rely on Mehrabian and Russell’s (1974), S-O-R model and reason that the
stimulus cues such as web atmospherics and navigation are directly related e-consumer emotional
It is acknowledged that building a complex conceptual model ‘from the ground up’ can pose as
many questions as it answers and we identify fruitful directions for future research. First, our
framework forms a basis to explore holistically the factors affecting e-consumer behaviour.
Second, we acknowledge that our proposed model may not incorporate all the variables or links
between them that potentially affect e-consumer behaviour and invite researchers to examine
more influences. Third, research is needed into how various constructs might be in play (or not)
depending upon the prior shopping, site familiarity and/or site purchasing experience of
consumers. Fourth, we observe that a large number of studies appear to concentrate on single
countries, whereas consumer responses have been demonstrated to vary between cultures (Davis
et al., 2008). We believe that our conceptual model is an ideal framework for such purposes for
academic researchers, e-retailers, policy-makers and practitioners.
In conclusion, this paper has explored the conceptual development of an integrated model of econsumer
behaviour. e-Shopping is still growing fast at a time when traditional shopping is
struggling to maintain any growth at all. The time is therefore opportune to further explore the
propositions elicited in this paper towards a better understanding of e-consumer behaviour.