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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature review method
  5. The concept of OCE
  6. Antecedents of OCE
  7. Consequences of OCE
  8. Conclusions
  9. References
  10. Appendices

Customer interactions with an organization's website create opportunities for positive experiences that can lead to long-term relationship building. The range of potential interactions is now quite diverse, including product information search, purchase transaction and/or service delivery. The domain of customer experience (CE) is well developed in the face-to-face context, but little attention has been paid to exploring the concept in the online context. The purpose of this paper is to provide a review of the online consumer literature in order to inform understanding of the antecedents and consequences of online customer experience (OCE) in the purchase context. The paper offers four important contributions for both academics and practitioners. First, it adds to understanding of OCE in the purchase context and, second, specifically recognizes and discusses the antecedents of OCE by drawing on existing literature relating to online consumer purchase. Third, it proposes the potential consequences of OCE and provides a framework for future testing. Finally, the paper addresses a problem of relevance to both academics and practitioners, and proposes future research and managerial implications.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature review method
  5. The concept of OCE
  6. Antecedents of OCE
  7. Consequences of OCE
  8. Conclusions
  9. References
  10. Appendices

The emergence of the Internet as both a distribution and a communication channel has created the opportunity for a range of online organization–customer interactions. These interactions occur during customer activities such as information search for company and/or product details, using online services such as banking, online purchase or engaging in social networking or participating in online communities or leisure pursuits. Online purchase in particular continues to rise, as adoption and penetration levels of Internet technology continuously increase. By 2007, European Internet penetration stood at 43% of the population with a 231% usage growth year on year. In North America, penetration was at 71% of the population with 120% growth (Internet World Stats 2007). This is also evidenced by increasing levels of online sales, which in the US reached US$128.1bn in 2007 and were projected to reach US$165.9bn by 2009 (source: US Census Bureau 2009).

At the same time, advances in technology have created new opportunities for online purchase in terms of when and where customers are able to interact online with an organization. The advent of mobile commerce has seen an increase in consumer use of the Internet via mobile devices, which have evolved from one-to-one communication pieces to highly versatile forms of technology facilitating a wide range of tasks supporting customer interaction (Coursaris and Hassanein 2001). M-commerce has been defined as ‘where commerce occurs on an anywhere, anytime basis’ (Balasubramanian et al. 2002). The technological advances in mobile devices now enable customers to access information instantly, communicate with each other, receive up-to-the-minute services, download relevant information, make online purchases and engage in a range of educational and entertainment services. A number of studies have explored the social and behavioural impacts of such mobile technology (Matsuda 2006; Palen et al. 2001), while others have looked at the factors influencing the adoption of such new customer interfaces (Lee and Benbasat 2003; Sarker and Wells 2003; Varshney and Vetter 2002). The work of Lee and Benbasat (2003) is useful here, as it suggests ways in which consumers can enrich their shopping experience by taking advantage of the instant access to the Internet created by such technology. They propose seven key elements which enhance consumers' experience of M-commerce. These include the degree of customization, communication, connectivity and content, aspects that can also be seen as relevant within computer-based online access.

A significant number of online studies exist which can help one identify the likely drivers of online customer experience (OCE). These are focused predominantly in three areas. First, there is a body of literature that looks at website quality, including the development of measurement instruments (Kaynama and Black 2000; Loiacono et al. 2002; Shchiglik and Barnes 2004). This body of work identifies a range of factors or dimensions which result in effective website performance. These studies develop measurement metrics and operational scales which support the e-marketer. A summary of this literature and the dimensions identified can be found in Appendix 1.

Second, a significant body of work focuses upon online customer behaviour, particularly in relation to the linked activities of online search and online purchase (Cases 2002; Cheung et al. 2005; Childers et al. 2001; Grant et al. 2007; Johnson et al. 2004; Klein and Ford 2002; Kumar et al. 2005). Early work such as that by Bellman et al. (1999) sought to identify whether specific personal factors such as shortage of time or adoption of ‘wired technology’ are predictors of online buying behaviour. Investigations that seek to identify the antecedents of consumer intentions to use the Internet to purchase have identified the importance of factors such as Internet experience (in the sense of frequency) and the link to trust of the environment (George 2002). Linkage between a range of influencing factors such as risk, perceived customer service and shopping experience to attitudes and intentions to shop online has been found (Vijayasarathy and Jones 2000).

Third, online service experience has been the subject of enquiry, providing a significant domain of literature (Kaynama and Black 2000; Khalifa and Liu 2003; Lee and Lin 2005). The Internet enables the delivery of a range of online services, and these have been subject to investigation. Such services include the delivery of online banking, news and weather, travel bookings, education programmes and knowledge communities.

These three areas of literature demonstrate that consumers interact with the Internet across a diverse range of activities, leading to a number of different behaviours and, ultimately, experiences. It has been suggested that the benefits afforded to the online customer change the balance of power within the organization–customer relationship, creating a more powerful and proactive customer (Moynagh and Worsley 2002). For this reason, a review of the literature from the perspective of how it informs our understanding of OCE is called for, and is provided in this paper for academics and practitioners.

The aim of this paper is to provide a comprehensive review and critique of a diverse range of contemporary literature that informs our understanding of the antecedents and consequences of an effective OCE within a purchase context. The review is undertaken in order to highlight the importance of this emerging area of interest. Using frameworks for contribution within academic papers provided by Brown and Dant (2008) and Summers (2001), this paper makes four important contributions. The first contribution is in terms of adding to our knowledge of the subject of OCE in the purchase context. The paper extends understanding of the factors that potentially impact OCE and provides a discussion not found so far in the literature. The second contribution of the paper is in terms of the recognition of antecedents of OCE, identified by drawing on existing literature relating to online purchase behaviour, intentions and motivations. Thirdly, along with antecedents, the OCE framework proposes potential consequences of the OCE construct, and the inclusion of both antecedents and consequences enables us to develop a framework of OCE for future testing. Finally, this paper enables us to address a problem of relevance and interest to academics and practitioners alike and to propose future research.

The structure of the paper is as follows. The first section presents the research method used for the literature review. A discussion of the concept of OCE is then provided, and the proposed conceptual framework is presented. The following section provides the substantive literature review, structured according to the framework. Finally, the paper ends with a summary of the conclusions that can be drawn from the review and makes proposals for further research.

Literature review method

  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature review method
  5. The concept of OCE
  6. Antecedents of OCE
  7. Consequences of OCE
  8. Conclusions
  9. References
  10. Appendices

A systematic review of the literature was undertaken using the following method. The research team comprised three academics and one practitioner, a mix favoured by systematic review methodologists (Tranfield et al. 2003). A review question was identified: ‘What is the nature of OCE in business-to-consumer (B2C) environments?’ and search terms were drawn up by the team, which included: customer experience; OCE; perfect online experience; ideal online experience; online behaviour; Internet behaviour; online purchase; and online shopping. The focus of the search was exclusively within peer-reviewed journal articles from a range of international sources, using databases including: Abi Proquest; EBSCO; Web of Science – Social Citations Index; and Business Source Premier.

The articles were analysed according to inclusion criteria involving exploring the theme of the paper as expressed in both the title and the abstract of the paper. The research team met to agree the final selection of papers. The content analysis was done manually, and a data extraction form was used (see Appendix 2, for example) to summarize critical data such as key findings and methodological features. This enabled the researchers to identify quickly the overall nature of existing research, its epistemological assumptions and methodological features. An independent practitioner evaluated the extraction form and created a list of key factors by identifying major and sub constructs explored in the article. This list was then collated into a system of classification that created a framework through which to analyse the literature.

A total of 120 papers were identified as relevant to the study's review question. This number reflects the emergent nature of the subject. Of these, 69 were from North America, 16 from Asian/Australasia, 28 from Europe, and 7 from other locations. The dates of the studies range across a 20-year period from 1985 to 2009 (with a peak of work between 2000 and 2005), covering a variety of online consumer purchase situations and service sectors including: e-banking, apparel retailing, travel/holiday reservations, the airline industry, text-book purchases and e-government sites.

The concept of OCE

  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature review method
  5. The concept of OCE
  6. Antecedents of OCE
  7. Consequences of OCE
  8. Conclusions
  9. References
  10. Appendices

The CE concept has been explored across a range of business situations, including consumer marketing, service delivery, tourism and retailing (Arnold et al. 2005; Bonnin 2006; Jones 1999; Quan and Wang 2004; Tsai 2005), but now also the Internet context (Novak et al. 2000). Online customer activities now encompass online shopping across a wide range of product categories, online services such as banking, travel and theatre bookings, access to news and information, as well as social networking for both business and leisure purposes. Online customer experience therefore becomes an important concept for e-marketers responsible for the online B2C environment, and particularly in the context of online purchase, given the increasing performance in online sales.

Given the existence today of two retail contexts, i.e. face to face (termed offline) and Internet based (termed online), a comparison of the two is called for. Clarity regarding the differences between them aids understanding of the differences between offline CE and OCE. The strategic importance of CE in relation to online retailing is acknowledged today (Grewal et al. 2009). However, the CE concept has been defined in both academic and practitioner terms in numerous ways (Frow and Payne 2007) making comparison of theoretical explanations difficult. The next section provides a comparison of CE across the two contexts and presents a conceptual framework for OCE.

Comparison between online and offline customer experience

The first key difference between the two contexts is the degree of personal contact, which can range from very intensive in a face-to-face context to non-existent online. Personal interaction provides a very rich source of contact from which subjective responses will result. Secondly, differences exist in the two contexts in relation to the manner in which information is provided. The online context enables very rich provision of information, whereas face to face this may be more limited or may occur over a range of formats (e.g. brochures, posters, customer sales representatives). A third distinction is the time period. Customers can purchase online at a time and in a place suited to themselves, particularly now with Web access via mobile devices. Within the face-to-face context, customer interactions are defined and restricted by the opening hours of the organization (although this is increasingly extended today). Finally, differences may exist in terms of how the brand is presented. Online, the brand is presented in a predominantly audio-visual way, whereas offline opportunities exist for the brand to be experienced via a range of artefacts such as staff and their presentation, buildings and facilities, vehicles, livery and other tangible elements. Table 1 presents a comparison of these key contextual differences.

Table 1. Comparison of the traditional face-to-face versus online customer contexts
 Offline contextOnline context
Personal contactHigh to mediumLow
Information provisionVaries in intensity over different mediaIntensive
Time Period for interactionsDictated by the organizationDictated by the consumer. Anytime, anywhere
Brand presentationRange of tangible devices used to present the brandAudio-visual

A conceptual framework of OCE

The importance of CE is driven by the organization's need to augment basic product and/or service offerings in order to create a differential offering, particularly within saturated, highly competitive markets. The distinction between the tangible product offer and the value of a distinctive experience is made clear by Meyer and Schwager (2007, p. 2), who define CE as ‘the internal and subjective response customers have to any direct or indirect contact with a company’. The creation of the subjective response is via the customer's interaction with the various components of the organization's offer, which includes the performance of the product itself, packaging, pricing, advertising, retail environment and customer service handling. Viewing CE as a subjective response (i.e. something that resides internally within the customer) is useful, as this distinguishes CE from other customer management concepts such as customer data or customer relations management systems.

There is further support in the literature for this view of CE. Carbone and Haeckel (1994) similarly suggest that a customer experience takes place whenever a customer interacts with an organization and its activities. They define CE as ‘the take-away impression formed by people's encounter with products, services and businesses’ (Carbone and Haeckel 1994, p. 9). This ‘take-away’ is viewed as a perception that is left behind in the customer's mind, formed by the amalgamation of the many pieces of sensory information taken in during the encounter. This definition similarly relates CE to an internal subjective state of the customer. However, CE is viewed here as something left within the customer's mind rather than as a response made by the customer.

Finally, it is important to differentiate CE from other customer management concepts such as customer satisfaction (CS). Meyer and Schwager (2007, p. 2) view CS as a measure of ‘the culmination of a series of customer experiences or, one could say, the net result of the good ones minus the bad ones’. This provides researchers with a useful distinction between the concepts of CE and CS, as well an opportunity to explore the link between the two. This link has been explored in the online literature, where Kim (2004, p. 53) has developed the concept of e-customer satisfaction which is distinct from experience and defined as: ‘the customer's psychological evaluation of accumulated purchase process experience and product usage experience’. Such literature establishes both a distinction and also an empirical test of the causal relationship between e-customer satisfaction and the drivers of online experiences.

Such definitions and explanations identify CE as an internal psychological state, and therefore experience can be assumed to be multidimensional and individual to each customer (Gentile et al. 2007). A useful conceptual model of OCE must identify the elements or states that make up an experience. Authors such as Frow and Payne (2007) propose that both rational, cognitive processing and emotional, affective processing form part of the experience formation. In terms of the cognitive element, Frow and Payne (2007) identify the role of internal processing of incoming stimulation to the individual. They propose that the customer is involved in reviewing incoming information in relation to past, present and potentially future experiences. This approach is consistent with the cognitive information processing (IP) model, which is well developed as an approach to the explanation of consumer purchase behaviour (Bettman et al. 1998). This suggests that part of the assessment of the customer encounter will be relatively goal-directed and involve rational processing of information regarding the encounter which is ultimately stored in memory.

However, the idea of the customer as a solely rational, cognitive being can be viewed as incomplete (Shiv and Fedorikhin 1999). Hansen (2005) suggests that recognition of both the cognitive and the emotional, and the interplay between the two, is a more appropriate approach. The role of affective responses in consumer behaviour is now well established (Bagozzi et al. 1999; Holbrook and Hirschman 1982). Hansen (2005) defines emotion as a response to a stimulus and, in the online context, the stimulus would be the components of the website to which the customer is exposed. Further, Hansen (2005) proposes that the outcome of cognitive and affective processing is the development of attitudes and beliefs within the individual. In CE terms, it has been suggested that affective, emotional processing leads to longer-term associations in the memory (Edvardsson 2005). The emphasis upon the emotional component of CE is very evident in both academic and practitioner discussions of the subject of CE (Holbrook and Hirschman 1982; Gentile et al. 2007; Pine and Gilmore 1999; Schmitt 2003; Shaw 2002).

The OCE literature is limited at present and is inconsistent in terms of definitions and explanations of the concept. In developing the concept of OCE, we similarly assume it to be made up of cognitive and affective states. The rationale for this is that cognitive and affective factors have similarly been identified as internal states within online purchase behaviour models (Eroglu et al. 2001).

A variety of close but slightly different expressions of OCE exist in the literature, including ‘Internet experience’ (Nysveen and Pedersen 2004), ‘online experience’ (Christodoulides et al. 2006; Novak et al. 2000), ‘website brand experience’ (Ha and Perks 2005), ‘online purchase experience’ (Jin and Park 2006) and ‘online shopping experience’ (Khalifa and Liu 2007). The difference in such concepts is predominantly in relation to the nature of the experience being explained. For example, Nysveen and Pedersen (2004) define ‘Internet experience’ as being composed of both the generalized experience of using the Internet medium as well as the specific interactions of the customer on the website. Novak et al. (2000) similarly investigate online experience from the specific customer–organization perspective. They provide a useful structural model which is centred upon the cognitive view of experience and which tests the relationships between various internal customer components (such as computer skill and control, level of arousal or degree of attention) that impact the user's experience of the website. Alternative views such as Christodoulides et al. (2006) explain online experience in terms of the performance of the website functionality (such as ease of use, navigation or speed).

A weakness of such approaches is that they are more cognitively focused and ignore the emotive, subjective aspect of OCE. If a definition of OCE is to build on the existing conceptualization of CE in the offline context, it should encompass elements of emotional processing as well (Hair et al. 2009). Figure 1 presents a conceptual framework of OCE which becomes the focus for our discussion of the literature reviewed. Within the framework, the OCE construct is assumed to be composed of both cognitive and affective states, as proposed by Carbone and Haeckel (1994). The complete framework incorporates the antecedents, consequences and outcome of OCE which are identified from our review.

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Figure 1. A conceptual framework of OCE

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The following sections of the paper provide a discussion of the literature supporting the antecedents and consequences of the proposed framework. A full summary of the literature supporting the antecedents can be found in Appendix 3.

Antecedents of OCE

  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature review method
  5. The concept of OCE
  6. Antecedents of OCE
  7. Consequences of OCE
  8. Conclusions
  9. References
  10. Appendices

The review of the literature highlights a significant number of concepts which motivate and influence the online consumer. First, the role of IP, used widely in the crucial stages of search and evaluation during customer purchase decisions (Bettman 1979), is recognized as a vital part of the online process (Grant et al. 2007). Information processing is concerned with how individuals use their internal senses and mental processes to make sense of their world (Eysenck 1993). The twin concepts of prior knowledge and prior experience are of relevance here. Prior knowledge of a product/service is a key factor in effective search, as it provides the basis for the customer's evaluation of new, incoming information (Alba and Hutchinson 1987; Brucks 1985; Lee et al. 1999; Punj and Staelin 1983). Similarly, in the online context Zeng and Reinartz (2003) recognize the demands placed upon existing internal knowledge and expertise when sourcing and evaluating online information via a website.

The literature provides useful explanations of the link between prior knowledge and prior experience which are at work during information search and navigation and how these affect future intentions towards online activity. Chih-Chung and Chang (2005) propose that a circular feedback loop takes place during online navigation. As online experience builds, so too does prior knowledge which influences future behavioural intentions. The literature provides consistent evidence of the influence of prior experience upon future intentions. Perea y Monsuwéet al. (2004) (citing Mathwick et al. 2001; Parasuraman and Zinkhan 2002) propose that customers continuously evaluate their online experience using perceptions of a range of website features, including product information, form of payment, delivery terms, service provided, risk involved, privacy, security, navigation and so on. Past experiences affect feelings of risk and likelihood to continue to use a particular online retailer. Heightened levels of prior product knowledge and memory of previous experiences provide the customer with a benchmark from which expectations are set and assessments are made regarding current purchase experiences (Shim et al. 2001). For this reason, we include IP within the OCE framework.

Secondly, the twin factors of perceived ease-of-use (PEOU) and perceived usefulness (PU) appear consistently in the online consumer literature and therefore need consideration in the context of OCE. These two concepts are identified in models of technology adoption (TAM) (Davis 1989; Davis et al. 1992) and have similarly been found to influence the adoption of online shopping (Geffen 2003; Geffen et al. 2003; Perea y Monsuwéet al. 2004). The perception of how easy a site is to use is extensively linked to a positive online experience (Chen and Dubinsky 2003; Cheung et al. 2005; Cho and Park 2001). Usefulness is well supported in the literature (Cao et al. 2005; Geffen, 2003; Geffen et al. 2003) and is the idea that the website will fit with and support the customer's daily life (i.e. when shopping, banking, etc.). These two concepts are well developed in the literature (see Appendix 3) and provide evidence of a number of features of a site that lead to perceived ease-of-use, which include: uncluttered screens; clear organization; logical flow; and easy navigation (Elliott and Speck 2005). Cao et al. (2005) develop a framework for evaluating website quality which proposes that the determinants perceived ease-of-use and perceived usefulness are a series of features such as search facility, responsiveness of the site, the multimedia capability, and the accuracy and relevance of the information. Such frameworks provide practitioners with clear direction in the development of effective websites that provide efficient and effective customer experience, thereby leading to a positive emotional state.

The third set of proposed antecedents of OCE relates to skill (SK) and perceived control (PC). The acquisition of skill is the customer's ability to use the Internet with proficiency (Klein and Ford 2002). This has been specifically identified as the ability to navigate and interact with a website and links strongly to the cognitive state of the customer. Assuming that learning by doing is relevant to the development of Internet skills, it is proposed that ability builds with experience over time (Lehto et al. 2006). High levels of use and therefore experience will lead to higher levels of ability and build prior knowledge and experience. Therefore, we suggest a strong interactive effect between antecedent factors and their impact upon OCE. The literature so far does not suggest this proposition and, to this extent, is weak in its linkage between proposed antecedents.

Cognitive models of OCE incorporate both of these concepts as drivers of OCE (Novak et al. 2000). Perceived control relates to consumers' feelings about the degree to which they have control over their own access, search and evaluation of the content of an organization's website. One of the benefits of the Internet is the depth of information that it can provide to customers, and this factor has become the driver for control by online customers. The desire for control is driven by the interactive effect of: (a) the significant increase in information now available online; (b) the limitations within the consumer's cognitive processing capabilities to deal with it; and (c) the reduction in time available to consumers today (Koufaris et al. 2002).

The literature varies in its discussion of the concept of control and user attitudes towards it. An alternative approach views control in relation to the security and privacy of the website. In this stream of work, customer behaviour is explored in relation to responses to levels of security within a website. Authentication, confidentiality, privacy protection and payment protection are all examples of methods identified as supporting high levels of feelings of security and control among online customers (Suh and Han 2003).

An emergent area of literature relates to the rewards received by the customer from the use of a website in terms of perceived benefits (BN) and enjoyment (EN). Positive online shopping experience is closely associated with attitudes towards the Internet as a shopping medium and its recognized benefits, with a strong association between perceived benefits and likelihood of shopping online (Corner et al. 2005). The value of this area of research is that it provides evidence to link perceived benefits to brand commitment, mediated through brand trust and involvement (Ha 2004). Such findings are of value to our exploration of OCE, as they suggest that feelings of reward and positive benefit (i.e. positive emotion) generate support for the online brand.

While the purpose of being online for the customer may be mostly functional when purchasing, at the same time, we find evidence for the role of enjoyment (Koufaris et al. 2002). This concept is included in drivers of quality across a diverse range of websites, including US e-banking portals (Bauer et al. 2005) and online shopping (Brown et al. 2003; Childers et al. 2001). Cao et al. (2005) incorporate the associated construct of ‘playfulness’ in a measurement model of B2C website quality, including examples of online games, software downloads or Q&A opportunities. Citing Watson et al. (1998), Cao et al. (2005) propose that online customers seek gratification in escapism, entertainment and interaction. An inter-related effect of enjoyment and control is that they are found to determine the customer's likelihood of returning to a website (Koufaris et al. 2002). Evidence suggests that a positive OCE results from the creation of an experience that is both fun and enjoyable, as well as generating feelings of control, and therefore freedom, for the customer (Wolfinbarger and Gilly 2001). This area of literature is not so strongly developed, and opportunities exist to explore more fully the relationship between hedonic, rewarding states of OCE with the need for feelings of remaining in control.

The final area relevant to OCE identified within the literature relates to the twin concepts of risk (RK) and trust (TS) (Corbitt et al. 2003; Jarvenpaa et al. 2000; McKnight and Chervany 2001; Tan and Sutherland 2004; van der Heijden et al. 2003). The literature is not consistent in its discussion of these concepts, and Lim (2003) provides an effective summary of the various ways in which the relationship between the two variables have been hypothesized (i.e. which is the antecedent and which is the outcome variable) (see Cheung and Lee 2000; Mitchell 1999; Stewart 1999). It is because these two concepts have been so closely linked in the literature that we discuss them together here.

The concept of trust has been extensively explored prior to the development of Internet-based technologies (Moorman et al. 1992; Morgan and Hunt 1994; Rousseau et al. 1998) and similarly so in the online context (Bart et al. 2005; Lee and Turban 2001; Suh and Han 2003; Tan and Sutherland 2004). McKnight and Chervany (2001) point to the importance of an agreed definition in order to facilitate comparisons across research studies. It is a concept that has been widely recognized within models that seek to explain online consumer attitudes, behaviours and intentions, and it has been suggested that it is the ‘most significant long-term barrier for realizing the potential of Internet marketing’ (Corbitt et al. 2003, p. 203).

There are differing views within the literature on the role of trust and its position as an antecedent or consequence of experience. Jin and Park (2006) found evidence to support a model of online purchase experience which demonstrated trust to be an outcome of a number of attributes of the purchase environment. This model proposes trust and satisfaction to be outcome variables that both independently and together have a direct influence upon customer loyalty. An alternative approach views trust as a contributory factor. For example, Tan and Sutherland (2004, p. 45) viewing trust as a multidisciplinary concept, identify three dimensions of trust, one of which is dispositional. This is a psychological approach to trust, where it is viewed as a ‘deep-rooted feeling or belief’. From this perspective, trust can be assumed to be an antecedent of OCE, proposing that it influences the emotional state that results from the online experience.

Trust is repeatedly included in models of online service and/or customer behaviour (Bart et al. 2005; Ha 2004; Ha and Perks 2005; Kim and Stoel 2004; Lee and Lin 2005; Lee and Turban 2001; Suh and Han 2003). Given the relative newness of Internet technology, coupled with the remote nature of the customer–organization relationship online, investigation of the impact of risk and the role of risk relievers features particularly in early papers within the literature surveyed (Cases 2002; Dillon and Reif 2004; Ha 2004; Teo 2002). Vulnerability and fear of the unknown is often cited as a situational or contextual component of trust (Tan and Sutherland 2004) and in the context of the online environment, which can be very low on personal contact, this is accentuated. Such de-personalization and distancing can emphasize feelings of vulnerability and lead to greater need for trust. A number of sources point to the fact that the online context requires higher levels of trust in comparison with face-to-face retailing, given the number of unknowns (Corbitt et al. 2003; van der Heijden et al. 2003). van der Heijden et al. (2003) suggest that high feelings of trust reduce concerns about unknown factors such as product performance or organizational policy.

Lee and Turban (2001) provide support for the view of trust as an antecedent and one that links to the dispositional approach. They suggest that an important factor is the ‘trust propensity’ which is a personality trait of the online customer. This trait affects the customer's view of the trustworthiness of both the ‘Internet Merchant’ as well as the ‘Internet Shopping Medium’. Individual trust propensity on the part of the online customer was found to have a moderating effect upon trust. Consumer perceptions of the integrity and honesty of online retailers were found to be strong influences upon consumer trust in Internet shopping. Similarly, McKnight and Chervany (2001), in modelling a typology of trust, identify differing levels of trust within the online customer. These include trust in the ‘e-vendor’ (interpersonal), trust in the Web itself (institutional) and trust more generally in others (dispositional). McKnight and Chervany (2001) proposed that only by deconstructing the concept of trust into these ever deeper psychological levels can we understand the effect of trust upon customer relationships in the e-commerce context. For this reason, we include trust propensity (TP) within our OCE framework.

The importance of understanding perceptions of risk and its impact upon consumers' willingness to undertake online interactions (particularly shopping) is well documented (Bhatnagar et al. 2000; Lim 2003). Perceived risk has been viewed in terms of two key components: ‘uncertainty and the seriousness of the consequences of the purchase’ (Cases 2002, p. 377). Ha (2004), citing Cox (1967a, 1967b), refers to perceived risk as the ‘unresolved tension’ between the customer's buying goals, the various product offerings that match these goals and possible adverse outcomes of the purchase being made or not made. Within the online context, risk is associated with both the decision regarding the goods or services being purchased or used and the exchange process itself (i.e. use of the website). A range of risk factors are identified in the literature, the key ones being economic/financial risk, performance risk, personal/psychological risk, social risk, physical risk and time loss risk (Bart et al. 2005; Cases 2002; Chen and Dubinsky 2003; Ha 2004; Huang et al. 2004).

Chen and Dubinsky (2003) suggest that understanding risk is important to online marketers because of its link to customer value. Citing Wood and Scheer (1996), they propose that customer value is a function of perceived benefits, costs and risk. Therefore the removal or reduction of concerns about risk for the customer will increase perceptions of customer value in relation to the exchange experience. Being able to link lower levels of perceived risk, benefits and costs to customer value via the mechanism of the exchange experience is a powerful approach for the e-marketer. This, again, calls for an investigation of the interactive effect of the various proposed antecedents of OCE.

Consequences of OCE

  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature review method
  5. The concept of OCE
  6. Antecedents of OCE
  7. Consequences of OCE
  8. Conclusions
  9. References
  10. Appendices

The framework shown in Figure 1 identifies two consequences of OCE: CS and re-purchase intention (RI). As stated earlier, a distinction has been identified between the concept of CE and CS, with CS as the outcome of the cumulative effect of CE (Kim, 2004; Meyer and Schwager, 2007). The framework proposes that CS is a consequence of positive emotional and cognitive states of OCE. This relationship between online experience and satisfaction is evidenced by Janda and Ybarra (2005), who found a relationship between superior online experience and CS. This study provides particular insight into the complexity of online purchase as it was conducted among online customers, taking into consideration their perceived differences between online competing brands. The study demonstrates that experiential elements of the website are more important to customers who are more aware of brand differences. This suggests that awareness and sensitivity to brand differences alerts the customer to differences in online experience.

The final outcome variable within the framework is re-purchase intention (RI) which has been defined as ‘the re-usage of the online channel to buy from a particular retailer’ (Khalifa and Liu 2007). This construct has been used as the outcome variable in models of online experience and satisfaction (Kim 2004; Khalifa and Liu 2007). In testing a model of online consumer retention, Khalifa and Liu (2007) found a positive relationship between satisfaction with the online shopping experience and online re-purchase intention. We similarly propose that the final outcome of OCE should be the customer's re-purchase intention towards the website. In line with the Khalifa and Liu (2007) model we propose that OCE has both a direct effect on re-purchase intention as well as an indirect effect via CS.

Conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature review method
  5. The concept of OCE
  6. Antecedents of OCE
  7. Consequences of OCE
  8. Conclusions
  9. References
  10. Appendices

The paper discusses current definitions and understanding of the concept of customer experience and explores the newly extended concept of OCE. A review of key literature is presented that gives insight and direction to an understanding of the antecedents of OCE which helps to develop a conceptual framework for future testing. A number of conclusions can be drawn.

First, the proposed framework suggests that the outcomes of a positive OCE in online purchase context are CS and, ultimately, intention to re-purchase from a website. The literature is varied in its explanations of a number of key concepts including trust, satisfaction and their links to intended behaviour such as re-purchase/re-use of a website. This framework is only conceptual at present and calls for further development and empirical testing in order to build academic understanding of this topic and to identify the relationships between these outcome variables.

Second, while the literature provides a strong theoretical foundation for understanding key concepts that underpin the responses of the online user, the next stage of development of the literature should move away from its well-defined understanding of website quality and behavioural focus. There is a need to move towards a deeper level of understanding of the component states of OCE, namely the cognitive and affective states. The component states that make up OCE need to be explored, and a useful framework for this may be that of Gentile et al. (2007) who, in addition to the emotional and cognitive, also incorporate other states, including the sensory, pragmatic, lifestyle and relational aspects of the customer. This calls for research into the construct of OCE and the development of measurement scales which enable the component states to be verified and measured.

Third, a number of antecedent factors are identified within the literature that appear to have both a direct and interactive effect upon OCE. There is evidence to suggest that interactive effects exist between individual antecedents and in their effect upon outcomes (e.g. enjoyment and control (Koufaris et al. 2002) or risk, benefits and customer value (Chen and Dubinsky 2003). These relatively complex and inter-related effects require further investigation. The conceptual framework presented here should be developed into a research model that can be empirically tested using a structural equation modelling approach.

Finally, the development of OCE literature must be consistent with technological developments. OCE will increasingly be influenced by the social interactions made possible by Web 2.0 technology. Literature is beginning to emerge in the field of social networking and its impact upon consumer attitudes, behaviours and acceptance of marketing communications messages (Algesheimer and Dholakia 2006; Dholakia et al. 2004; Flavián and Guinalíu 2005; Hennig-Thurau et al. 2004; Poynter, 2008). Our future understanding of OCE must incorporate factors such as the role of virtual communities, the ability to incorporate user-generated content and the ease of one-to-many communication interfaces. At present, the literature is limited in terms of linking OCE to these forms of customer interface, and research into the effect of social networking upon OCE is called for.

Managerial implications stem from this literature review. The proposed framework suggests that e-marketers need to be aware that, while functional performance of a website is important (as embedded in the proposed antecedents, e.g. easy navigation, usefulness, information provision), it is also important to understand the experiential state of customers and the responses they are likely to generate. Managerial effort tends to focus on the former in terms of the development of website performance. Consumer research should focus upon understanding the emotional and cognitive state of customers both during and following the online purchase process, and understanding how to adjust the features of a website to improve these. At the same time, there may be differences in the relevance of different experiential states, depending on the nature of the product or service type being delivered via the website. The output of the research proposed in this paper would lead to effective measurement scales for the e-marketer to use in the identification of relevant inputs and outputs of an effective OCE for retail websites.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature review method
  5. The concept of OCE
  6. Antecedents of OCE
  7. Consequences of OCE
  8. Conclusions
  9. References
  10. Appendices
  • Alba, J.W. and Hutchinson, J.W. (1987). Dimensions of consumer expertise. Journal of Consumer Research, 13, pp. 411454.
  • Algesheimer, R. and Dholakia, P.M. (2006). Do customer communities pay off? Harvard Business Review, 84, pp. 2630.
  • Arnold, M.J., Reynolds, K.E., Ponder, N. and Lueg, J.E. (2005). Customer delight in a retail context: investigating delightful and terrible shopping experiences. Journal of Business Research, 58, pp. 11321145.
  • Bagozzi, R.P., Gopinath, M. and Prashanth, U.N. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27, pp. 184206.
  • Balasubramanian, S., Peterson, R.A. and Jarvenpaa, S.L. (2002). Exploring the implications of M-commerce for markets and marketing. Journal of the Academy of Marketing Science, 30, 348361.
  • Barnes, S.J. and Vidgen, R.T. (2006). Data triangulation and web quality metrics: a case study in e-government. Information and Management, 43, pp. 767777.
  • Bart, Y., Shankar, V., Sultan, F. and Urban, G.L. (2005). Are the drivers and role of online trust the same for all web sites and consumers? A large-scale exploratory empirical study. Journal of Marketing, 69, pp. 133152.
  • Bauer, H.H., Hammerschmidt, M. and Falk, T. (2005). Measuring the quality of e-banking portals. International Journal of Bank Marketing, 23, pp. 153175.
  • Bellman, S., Lohse, G.L. and Johnson, E.J. (1999). Predictors of online buying behavior. Communication of the ACM, 42, pp. 3238.
  • Bettman, J.R. (1979). An Information Processing Theory of Consumer Choice. Reading, MA: Addison-Wesley.
  • Bettman, J.R., Luce, M.F. and Payne, J.W. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25, pp. 187217.
  • Bhatnagar, A., Misra, S. and Rao, H.R. (2000). On risk, convenience and Internet shopping behaviour. Communications of the ACM, 43, pp. 98105.
  • Bonnin, G. (2006). Physical environment and service experience: an appropriation-based model. Journal of Services Research, 6, pp. 4565.
  • Brown, J.R. and Dant, R.P. (2008). On what makes a significant contribution to the retailing literature. Journal of Retailing, 84, pp. 131135.
  • Brown, M., Pope, N. and Voges, N. (2003). Buying or browsing? An exploration of shopping orientations and online purchase intention. European Journal of Marketing, 37, pp. 16661684.
  • Brucks, M. (1985). The effects of product class knowledge on information search behaviour. Journal of Consumer Research, 12, pp. 116.
  • Cai, S. and Jun, M. (2003). Internet users' perceptions of online service quality: a comparison of online buyers and information searchers. Managing Service Quality, 13, pp. 504519.
  • Cao, M., Zhang, Q. and Seydel, J. (2005). B2C e-commerce web site quality: an empirical examination. Industrial Management & Data Systems, 105, pp. 645661.
  • Carbone, L. and Haeckel, S. (1994). Engineering customer experiences. Marketing Management, 3, pp. 911.
  • Cases, A.-S. (2002). Perceived risk and risk-reduction strategies in Internet shopping. International Review of Retail, Distribution and Consumer Research, 12, pp. 375394.
  • Chen, S.-J. and Chang, T.-Z. (2003). A descriptive model of online shopping process: some empirical results. International Journal of Service Industry Management, 14, pp. 556569.
  • Chen, Z. and Dubinsky, A.J. (2003). A conceptual model of perceived customer value in e-commerce: a preliminary investigation. Psychology and Marketing, 20, pp. 323347.
  • Cheung, C. and Lee, M.K.O. (2000). Trust in Internet shopping: a proposed model and measurement instrument. Paper presented at the 6th Americas Conference on Information Systems, Long Beach, USA.
  • Cheung, C.M.K., Chan, G.W.W. and Limayem, M. (2005). A critical review of online consumer behaviour: empirical research. Journal of Electronic Commerce in Organizations, 3, pp. 119.
  • Chih-Chung, C. and Chang, S.-C. (2005). Discussion on the behavior intention model of consumer online shopping. Journal of Business and Management, 11, pp. 4157.
  • Childers, T.L., Carr, C.L., Peck, J. and Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behaviour. Journal of Retailing, 77, pp. 511535.
  • Cho, N. and Park, S. (2001). Development of electronic commerce user–consumer satisfaction index (ECUSI) for Internet shopping. Industrial Management & Data Systems, 101, pp. 400405.
  • Christodoulides, G., de Chernatony, L., Furrer, O., Shiu, E. and Abimbola, E. (2006). Conceptualising and measuring the equity of online brands. Journal of Marketing Management, 22, pp. 799825.
  • Corbitt, B.J., Thanasankit, T. and Yi, H. (2003). Trust and e-commerce: a study of consumer perceptions. Electronic Commerce Research and Applications, 2, pp. 203215.
  • Corner, J.L., Thompson, F., Dillon, S. and Doolin, B. (2005). Perceived risk, the Internet shopping experience and online purchasing behaviour: a New Zealand perspective. Journal of Global Information Management, 13, pp. 6688.
  • Coursaris, C. and Hassanein, K. (2001). Understanding M-commerce: a consumer centric model. Quarterly Journal of Electronic Commerce, 3, pp. 247271.
  • Cox, D.F. (1967a). Risk handling in consumer behaviour – an intensive study of two cases. In Cox, D.F. (ed.), Risk Taking and Information Handling in Consumer Behaviour. Boston, MA: Graduate School of Business Administration, Harvard University, pp. 3481.
  • Cox, D.F. (1967b). The sorting rule model of consumer product evaluation process. In Cox, D.F. (ed.), Risk Taking and Information Handling in Consumer Behaviour. Boston, MA: Graduate School of Business Administration, Harvard University, pp. 317323.
  • Davis, F.D. (1989). Perceived usefulness, perceived ease-of-use and user acceptance of information technology. MIS Quarterly, 13, pp. 319340.
  • Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology., 22, pp. 11091130.
  • Dholakia, R.R. and Uusitalo, O. (2002). Switching to electronic stores: consumer characteristics and the perception of shopping benefits. International Journal of Retail & Distribution Management, 30, pp. 459469.
  • Dholakia, U.M., Bagozzi, R.P. and Pearo, L.K. (2004). A social influence model of consumer participation in network and small group-based virtual communities. International Journal of Research in Marketing, 21, pp. 241263.
  • Dillon, T.W. and Reif, H.L. (2004). Factors influencing consumers' e-commerce commodity purchases. Information Technology, Learning and Performance Journal, 22, pp. 112.
  • Dowling, G.R. and Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21, pp. 119134.
  • Edvardsson, B. (2005). Service quality: beyond cognitive assessment. Managing Service Quality, 15, pp. 127131.
  • Elliott, M.T. and Speck, P.S. (2005). Factors that affect attitude toward a retail web site. Journal of Marketing Theory and Practice, 13, pp. 4050.
  • Eroglu, S.A., Machleit, K.A. and Davis, L.M. (2001). Atmospheric qualities of online retailing. A conceptual model and implications. Journal of Business Research, 54, pp. 177184.
  • Eysenck, M.W. (1993). Principles of Cognitive Psychology. Hove: Lawrence Erlbaum Associates Ltd.
  • Flavián, C. and Guinalíu, M. (2005). The influence of virtual communities on distribution strategies in the internet. International Journal of Retail & Distribution Management, 33, pp. 405425.
  • Frow, P. and Payne, A. (2007). Towards the ‘perfect’ customer experience. Journal of Brand Management, 15, pp. 89101.
  • Geffen, D. (2003). TAM or just plain habit: a look at experienced online shoppers. Journal of End User Computing, 15, pp. 113.
  • Geffen, D., Karahanna, E. and Straub, D.W. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27, pp. 5190.
  • Gentile, C., Spiller, N. and Noci, G. (2007). How to sustain the customer experience: an overview of experience components that co-create value with the customer. European Management Journal, 25, pp. 395410.
  • George, J.F. (2002). Influences on the intent to make Internet purchases. Internet Research, 12, pp. 165180.
  • Grant, R., Clarke, R.J. and Kyriazis, E. (2007). A review of factors affecting online consumer search behaviour from an information value perspective. Journal of Marketing Management, 23, pp. 519533.
  • Grewal, D., Levy, M. and Kumar, V. (2009). Customer experience management in retailing: an organizing framework. Journal of Retailing, 85, pp. 114.
  • Ha, H.-Y. (2004). Factors affecting online relationships and impacts. Marketing Review, 4, pp. 189209.
  • Ha, H.-Y. and Perks, H. (2005). Effects of consumer perceptions of brand experience on the web: brand familiarity, satisfaction and brand trust. Journal of Consumer Behaviour, 4, pp. 438452.
  • Hair, N., Rose, S. and Clark, M. (2009). Using qualitative repertory grid techniques to explore perceptions of business-to-business online customer experience. Journal of Customer Behaviour, 8, pp. 5165.
  • Hansen, T. (2005). Perspectives on consumer decision-making: an integrated approach. Journal of Consumer Behaviour, 4, pp. 420437.
  • Hennig-Thurau, T., Gwinner, K.P., Walsh, G. and Gremler, D.D. (2004). Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18, p. 38.
  • Hodkinson, C. and Kiel, G. (2003). Understanding web information search behaviour: an exploratory model. Journal of End User Computing, 15, pp. 2748.
  • Holbrook, M.B. and Hirschman, E.C. (1982). The experiential aspects of consumption: consumer fantasies, feelings and fun. Journal of Consumer Research, 9, pp. 132140.
  • Holloway, B.B., Wang, S. and Parish, J.T. (2005). The role of cumulative online purchasing experience in service recovery management. Journal of Interactive Marketing, 19, pp. 5466.
  • Huang, W.-Y., Schrank, H. and Dubinsky, A.J. (2004). Effect of brand name on consumers' risk perceptions of online shopping. Journal of Consumer Behaviour, 4, pp. 4050.
  • Internet World Stats (2007). Retrieved from http://www.Internetworldstats.com (accessed 14 March 2008).
  • Jaillet, H.F. (2002). Web metrics: measuring patterns in online shopping. Journal of Consumer Behaviour, 2, pp. 369381.
  • Janda, S. and Ybarra, A. (2005). Do product and consumer characteristics affect the relationship between online experience and customer satisfaction? Journal of Internet Commerce, 4, pp. 133151.
  • Jarvenpaa, S.L., Tractinsky, N. and Vitale, M. (2000). Consumer trust in an Internet store. Information Technology and Management, 1, pp. 4571.
  • Jayawardhena, C. (2004). Personal values' influence on e-shopping attitude and behaviour. Internet Research, 14, pp. 127138.
  • Jin, B. and Park, J.Y. (2006). The moderating effect of online purchase experience on the evaluation of online store attributes and the subsequent impact on market response outcomes. Advances in Consumer Research, 33, pp. 203211.
  • Johnson, E.J., Moe, W.M., Fader, P.S., Bellman, S. and Lohse, G.L. (2004). On the depth and dynamics of online search behaviour. Management Science, 50, pp. 299308.
  • Jones, M.A. (1999). Entertaining shopping experiences: an exploratory investigation. Journal of Retailing and Consumer Services, 6, pp. 129139.
  • Kaynama, S.A. and Black, C.I. (2000). A proposal to assess the service quality of online travel agencies: an exploratory study. Journal of Professional Services Marketing, 21, pp. 6388.
  • Khalifa, M. and Liu, V. (2003). Satisfaction with Internet-based services: the role of expectations and desires. International Journal of Electronic Commerce, 7, pp. 3149.
  • Khalifa, M. and Liu, V. (2007). Online consumer retention: contingent effects of online shopping habit and online shopping experience. European Journal of Information Systems, 16, 780792.
  • Kim, Y. (2002). Consumer value: an application to mall and Internet shopping. International Journal of Retail & Distribution Management, 30, pp. 595602.
  • Kim, H.-R. (2004). Developing an index of online customer satisfaction. Journal of Financial Services Marketing, 10, pp. 4964.
  • Kim, S. and Stoel, L. (2004). Apparel retailers: website quality, dimensions and satisfaction. Journal of Retailing and Consumer Services, 11, pp. 109117.
  • Klein, L.R. and Ford, G.T. (2002). Consumer search for information in the digital age: an empirical study of pre-purchase search for automobiles. Advances in Consumer Research, 29, pp. 100101.
  • Koufaris, M., Kambil, A. and LaBarbera, P.A. (2002). Consumer behaviour in web-based commerce: an empirical study. International Journal of Electronic Commerce, 6, pp. 115138.
  • Kumar, N., Lang, K.R. and Peng, Q. (2005). Consumer search behaviour in online shopping environments. E-Service Journal, pp. 87102.
  • Lee, G.-G. and Lin, H.-F. (2005). Customer perceptions of e-service quality in online shopping. International Journal of Retail & Distribution Management, 33, pp. 161176.
  • Lee, H., Herr, P.M., Kardes, F.R. and Kim, C. (1999). Motivated search: effects of choice accountability, issue involvement and prior knowledge on information acquisition and use. Journal of Business Research, 45, pp. 7588.
  • Lee, M.K.O. and Turban, E. (2001). A trust model for consumer Internet shopping. International Journal of Electronic Commerce, 6, pp. 7591.
  • Lee, Y.E. and Benbasat, I. (2003). Interface design for mobile commerce. Communications of the ACM, 46, pp. 4852.
  • Lehto, X.Y., Kim, D.-Y. and Morrison, A.M. (2006). The effect of prior destination experience on online information search behaviour. Tourism and Hospitality Research, 6, pp. 160178.
  • Lim, N. (2003). Consumers' perceived risk: sources versus consequences. Electronic Commerce Research and Applications, 2, pp. 216228.
  • Loiacono, E.T., Watson, R.T. and Goodhue, D.L. (2002). WEBQUAL: a measure of website quality. American Marketing Association. Conference Proceedings, 13, pp. 432438.
  • Mathwick, C., Malhotra, N.K. and Rigdon, E. (2001). Experiential value: conceptualization, measurement and application in the catalog and Internet shopping environment. Journal of Retailing, 77, pp. 3956.
  • Matsuda, M. (2006). Mobile communication and selective sociality. In Ito, M., Okabe, D. and Matsuda, M. (eds), Personal, Portable, Pedestrian: Mobile Phones in Japanese Life. Cambridge, MA: MIT Press, pp. 123142.
  • McKnight, D.H. and Chervany, N.L. (2001). What trust means in e-commerce customer relationships: an interdisciplinary conceptual typology. International Journal of Electronic Commerce, 6, pp. 3559.
  • Meyer, C. and Schwager, A. (2007). Understanding customer experience. Harvard Business Review, 85, pp. 116126.
  • Mitchell, V. (1999). Consumer perceived risk: conceptualizations and models. European Journal of Marketing, 33, pp. 163195.
  • Moorman, C., Zaltman, G. and Deshpandé, R. (1992). Relationships between providers and users of market research: the dynamics of trust within and between organisations. Journal of Marketing Research, 29, pp. 314329.
  • Morgan, R.M. and Hunt, S.D. (1994). The commitment–trust theory of relationship marketing. Journal of Marketing, 58, pp. 2038.
  • Moynagh, M. and Worsley, R. (2002). Tomorrow's consumer – the shifting balance of power. Journal of Consumer Behaviour, 1, pp. 293301.
  • Murray, K.B. and Haubl, G. (2002). The fiction of no friction: a user skills approach to cognitive lock-in. Advances in Consumer Research, 29, pp. 1118.
  • Novak, T.P., Hoffman, D.L. and Yung, Y.-F. (2000). Measuring the customer experience in online environments: a structural modelling approach. Marketing Science, 19, pp. 2242.
  • Nysveen, H. and Pedersen, P.E. (2004). An exploratory study of customers' perception of company websites offering various interactive applications: moderating effects of customers' Internet experience. Decision Support Systems, 37, pp. 137150.
  • Palen, L., Salzman, M. and Youngs, E. (2001). Discovery and integration of mobile communications in everyday life. Pesonal and Ubiquitous Computing, 5, pp. 109122.
  • Parasuraman, A. and Zinkhan, G.M. (2002). Marketing to and serving customers through the Internet: an overview and research agenda. Journal of the Academy of Marketing Science, 30, pp. 286295.
  • Perea y Monsuwé, T., Dellaert, B.G.C. and de Ruyter, K. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15, pp. 102121.
  • Pine, II, B.J. and Gilmore, J.H. (1999). The Experience Economy: Work Is Theatre and Every Business A Stage. Boston, MA: Harvard Business School Press.
  • Poynter, R. (2008). Facebook: the future of networking with customers. International Journal of Market Research, 50, pp. 1112.
  • Punj, G.N. and Staelin, R. (1983). A model of consumer information search behavior for new automobiles. Journal of Consumer Research, 9, pp. 366380.
  • Quan, S. and Wang, N. (2004). Towards a structural model of the tourist experience: an illustration from food experiences in tourism. Tourism Management, 25, pp. 297305.
  • Rodgers, W., Negash, S. and Suk, K. (2005). The moderating effect of online experience on the antecedents and consequences of online satisfaction. Psychology and Marketing, 22, pp. 313331.
  • Rosa, J.A. and Malter, A.J. (2003). E-(embodied) knowledge and E-commerce: How physiological factors affect online sales of experiential products. Journal of Consumer Psychology, 13, pp. 6373.
  • Rousseau, D.M., Bitkin, S.B., Burt, R.S. and Camerer, C. (1998). Not so different after all: a cross discipline view of trust. Academy of Management Review, 23, pp. 393404.
  • Sarker, S. and Wells, J.D. (2003). Understanding mobile handheld device use and adoption. Communications of the ACM, 46, pp. 3540.
  • Schmitt, B.H. (2003). Customer Experience Management: A Revolutionary Approach to Connecting with Your Customer. Hoboken NJ: John Wiley.
  • Shaw, C. (2002). The DNA of Customer Experience – How Emotions Drive Value. New York: Palgrave.
  • Shchiglik, C. and Barnes, S.J. (2004). Evaluating website quality in the airline industry. Journal of Computer Information Systems, 44, pp. 1725.
  • Shim, S., Eastlick, M.A., Lotz, S.L. and Warrington, P. (2001). An online pre-purchase intentions model: the role of intention to search. Journal of Retailing, 77, pp. 397416.
  • Shiv, B. and Fedorikhin, A. (1999). Heart and mind in conflict. The interplay of affect and cognition in consumer decision-making. Journal of Consumer Research, 26, pp. 278292.
  • So, W.C.M., Wong, T.N.D. and Sculli, D. (2005). Factors affecting intentions to purchase via the Internet. Industrial Management & Data Systems, 105, pp. 12251244.
  • Stewart, K.J. (1999). Transference as a means of building trust in world wide web sites. Proceedings of the 20th International Conference on Information Systems, 1999 pp. 459464.
  • Suh, B. and Han, I. (2003). The impact of customer trust and perception of security control on the acceptance of electronic commerce. International Journal of Electronic Commerce, 7, pp. 135161.
  • Summers, J.O. (2001). Guidelines for conducting research and publishing in Marketing: from conceptualization through the review process. Journal of the Academy of Marketing Science, 29, pp. 405401.
  • Tan, F.B. and Sutherland, P. (2004). Online consumer trust: a multi-dimensional model. Journal of Electronic Commerce in Organizations, 2, pp. 4159.
  • Teo, T.S.H. (2002). Attitudes toward online shopping and the Internet. Behaviour & Information Technology, 21, pp. 259271.
  • Trabold, L.M., Heim, G.R. and Field, J.M. (2006). Comparing e-service performance across industry sectors. International Journal of Retail & Distribution Management, 34, pp. 240257.
  • Tranfield, D., Denyer, D. and Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14, pp. 207222.
  • Trevino, L.K. and Webster, J. (1992). Flow in computer-mediated computer communication: electronic mail and voice mail evaluation and impacts. Communication Research, 19, pp. 539573.
  • Tsai, S.-P. (2005). Integrated marketing as management of holistic consumer experience. Business Horizons, 8, pp. 431441.
  • US Census Bureau (2009). Wholesale and retail trading: online retail sales. Available at http://www.census.gov/compendia/statab/cats/wholesale_retail_trade/online_retail_sales.html (accessed 13 November 2009).
  • van der Heijden, H., Verhagen, T. and Creemers, M. (2003). Understanding online purchase intentions: contributions from technology and trust perspectives. European Journal of Information Systems, 12, pp. 4148.
  • Varshney, U. and Vetter, R. (2002). Mobile commerce: framework, applications and networking support. Mobile Networks and Applications, 7, pp. 185198.
  • Vijayasarathy, L.R. and Jones, J.M. (2000). Print and Internet catalog shopping: assessing attitudes and intentions. Internet Research, 10, pp. 191200.
  • Watson, R.T., Akselsen, S. and Pitt, L.F. (1998). Attractors: building mountains in the flat landscape of the world wide web. California Management Review, 40, pp. 3656.
  • Wolfinbarger, M. and Gilly, M.C. (2001). Shopping online for freedom, control and fun. California Management Review, 43, pp. 3455.
  • Wood, C.M. and Scheer, L.K. (1996). Incorporating perceived risk into models of consumer deal assessment and purchased intention. Advances in Consumer Research, 23, pp. 399404.
  • Zeng, M. and Reinartz, W. (2003). Beyond online search: the road to profitability. California Management Review, 45, pp. 107130.

Appendices

  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature review method
  5. The concept of OCE
  6. Antecedents of OCE
  7. Consequences of OCE
  8. Conclusions
  9. References
  10. Appendices

Appendix 1

Table 2. Website quality dimensions identified in measurement models
Measurement modelLiterature sourceWebsite quality dimensions
E-QUAL assessment toolKaynama and Black (2000)Content and purpose Accessibility Navigation Design and presentation Responsiveness Background Personalization and customization
WEBQUAL™Loiacono et al. (2002)Ease of use Usefulness Entertainment Complementary relationship (with other elements of the organization) Customer service
PAWQI (Perceived Airline Website Quality Instrument)Shchiglik and Barnes (2004)Web information quality Web interaction quality Site design quality Airline specific quality
eQual (previously WEBQUAL)Barnes and Vidgen (2006)Usability Information quality Service interaction

Appendix 2

Table 3. Data extraction form with examples
Author dateFocus of paperConstructsApproachMethodologyKey findingsNotes
Chen and Dubinsky (2003)Investigation into user perceptions of online customer value.Valence of experience Perceived product quality Product/Price Perceived customer value E-retailer reputation Purchase intention Perceived riskEmpirical/ DeductiveEmpirical, deductive. Survey questionnaire of 99 usable responses of a student sample using a five-point Likert scale. Multiple regression analysis used.Provides testing of the model of the antecedents of customer value. Identifies a number of precursors of perceived value; Valence of experience; Perceived product quality; Perceived risk; Product Price which are linked to Purchase Intention. Provides evidence to the antecedents of ease of use, linked to online shopper experience and purchase intention.Develops and tests a model of customer value in the online context. The model has purchase intention as the final dependent variable of the model. Provides discussion of online shopping experience concept. Provides discussion in the context of both cognitive and emotional elements of experience. Inclusion of valence links to the emotional aspect of the online shopping experience. Use of student sample.
Geffen (2003)Technology adoption model applied to online shopping.Perceived Ease-of-Use Perceived Usefulness Habit Intended Usage TAMEmpirical/ DeductiveEmpirical, deductive. Survey questionnaire of 179 students using a seven-point Likert scale. LISREL used in the analysis.Identifies via SEM that habit as well as perceived ease of use and perceived usefulness in terns if why experienced users' continue to use a website. Finds that habit explains the greatest variance of continued use of a website and evidence that it acts as a predictor of perceived ease of use and perceived usefulness. The findings identify two major antecedents to OCE, perceived ease of use and perceived usefulness.Provides evidence of the application of TAM in relation to online shopping. Links antecedents of PEOU and PU to intention to re-use a website. Use of student sample.

Appendix 3

Table 4. Summary of all studies identified within the review supporting the antecedent factors of OCE
AntecedentDefinitionIdentified online studies
IP: Prior knowledge‘. . . the contents of memory, which provides part of the knowledge that is involved when consumers actively think about products and services.’Rosa and Malter (2003)Jaillet (2002); Rosa and Malter (2003); Zeng and Reinartz (2003); Cheung et al. (2005); Lehto et al. (2006)
IP: Prior experience‘. . . the number of product related experiences that have been accumulated by the consumer’Alba and Hutchinson (1987)Jaillet (2002); Perea y Monsuwéet al. (2004); Chih-Chung and Chang 2005; So et al. (2005); Lehto et al. (2006); Holloway et al. (2005)
PC‘. . . “control” relates to an individual's perception of the availability of knowledge, resources, and opportunities required to perform a specific behaviour, in our case online shopping’Perea y Monsuwéet al. (2004)Trevino and Webster (1992); Novak et al. (2000); Wolfinbarger and Gilly (2001); Lee and Turban (2001); Koufaris et al. (2002); Suh and Han (2003); Perea y Monsuwéet al. (2004)
PEOU‘the extent to which a person believes that using the new technology will be free of effort’Perea y Monsuwéet al. (2004)Davis (1989); Cho and Park (2001); Childers et al. (2001); Chen and Dubinsky (2003); Geffen (2003); Geffen et al. (2003); Perea y Monsuwéet al. (2004); Cheung et al. (2005); Elliott and Speck (2005); Cao et al. (2005)
PU‘the degree to which a person believes using the new technology will improve his/her performance or productivity’Perea y Monsuwéet al. (2004)Childers et al. (2001); Geffen (2003); Geffen et al. (2003); Perea y Monsuwéet al. (2004); Cao et al. (2005); Cheung et al. (2005)
BN‘. . . seeking benefits in the marketplace and the benefits of using interactive shopping as compared to traditional channels . . .’Childers et al. (2001)Childers et al. (2001); Teo (2002); Hodkinson and Kiel (2003); Chen and Chang (2003); Ha (2004); Perea y Monsuwéet al. (2004); Corner et al. (2005)
SK‘User Ability is an acquired skill to use the Internet with proficiency’Klein and Ford (2002)Klein and Ford (2002); Murray and Haubl (2002); Hodkinson and Kiel (2003); Jayawardhena (2004); Kumar et al. (2005); Cheung et al. (2005; Lehto et al. 2006)
TP‘. . . a personality trait of the concerned consumers’Lee and Turban (2001)Jarvenpaa et al. (2000); McKnight and Chervany (2001); Cheung and Lee (2000); Lee and Turban (2001); Corbitt et al. (2003); Cai and Jun (2003); Suh and Han (2003); Tan and Sutherland (2004); Kim and Stoel (2004); Perea y Monsuwéet al. (2004); Ha (2004); Ha and Perks (2005); Lee and Lin (2005); Elliott and Speck (2005); Cheung et al. (2005); Cao et al. (2005); Bart et al. (2005); Trabold et al. (2006)
RK‘Perceived risk is the consumer's perception of the uncertainty and concomitant adverse consequences of buying a product or service’Dowling and Staelin (1994)Vijayasarathy and Jones (2000); Bhatnagar et al. (2000); Cases (2002); Teo (2002); Chen and Dubinsky (2003); Lim (2003); van der Heijden et al. (2003); Dillon and Reif (2004); Ha (2004); Huang et al. (2004); Corner et al. 2005
EN‘the extent to which the activity of using the new technology is perceived to provide reinforcement in its own right, apart from any performance consequences that may be anticipated’. Davis et al. (1992)Eroglu et al. (2001); Wolfinbarger and Gilly (2001); Childers et al. (2001); Dholakia and Uusitalo (2002); Koufaris et al. (2002); Kim (2002); McKnight and Chervany (2001); Brown et al. (2003); Chen and Chang (2003); Dillon and Reif (2004); Perea y Monsuwéet al. (2004); Jayawardhena (2004); Bauer et al. (2005); Cao et al. (2005); Elliott and Speck (2005); Rodgers et al. (2005)