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INTRODUCTION

In offline retailing, the salesperson plays an important role in forming buyer-seller relationships (Beatty et al. 1996). Beyond their functional roles of providing information and assistance, salespeople are influential in building relationships and influencing sales (e.g. Crosby et al. 1990). This is especially important where

JOURNAL OF MARKETING MANAGEMENT, 2008, Vol. 24, No. 3-4, pp. 433-461ISSN0267-257X print /ISSN1472-1376 online © Westburn Publishers Ltd. doi: 10.1362/026725708X306176

A typology of roles for avatars in online retailing

Peter J. McGoldrick, MBS, University of Manchester, UK*Kathleen A. Keeling, MBS, University of Manchester, UKSusan F. Beatty, University of Manchester, UK

Abstract Avatars are now appearing as online assistants on transactional websites, yet their scope is still limited. This paper explores their potential roles in providing assistance, a friendlier interface and purchase recommendations. As avatars are at early stages of implementation, the conceptual framework draws upon human-computer interaction research, plus cognate literature on salesperson roles and the use of synthetic characters in other contexts. The empirical study involved two longitudinal panels of online buyers and an international, online survey of 2114 internet users. Following split-sample principal components analysis and k-means clustering, four categories of role preference are identified. The results inform decisions on the appropriateness of avatars, their adaptation to customer needs and buying contexts, and their possible roles. Hypothesised relationships with age, gender and online buying experience are tested, suggesting scope for avatar role segmentation. Suggestions are offered for marketers and website designers, considering deploying avatars, and for future research directions.

Keywords Avatars, E-commerce, Internet shopper typology, Selling roles, Embodied conversational agents, Human-computer interaction.

JOURNAL OF

MARKETINGMANAGEMENT

*Correspondence details and biographies for the authors are located at the end of the article

Journal of Marketing Management, Volume 24

customers may feel high performance ambiguity and perceived risk, frequently the case with online shopping (Belanger et al. 2002). Intuitively, customer relationship building is difficult to achieve in online situations, where interaction is limited to keyboard and mouse clicks. Thus, for online retailers, avatars acting as on-screen assistants, with which the prospective customer can interact, could substitute for face to face interaction and promote user engagement (Fogg 1998).

The literature supports this supposition: one stream of research (e.g. Reeves and Nass 1996) maintains that humans react to computers as ‘social actors’. In offline brand building, synthetic characters are extensively used to present a personality with which customers can connect (Callcott and Lee 1995). Some theorists argue relationships and influence strategies to be aspects of the same cognitive schema of social influence (Poppe et al. 1999). The value for online retailing is clear; building relationships with customers is associated with increased loyalty and purchasing (e.g. Reynolds and Beatty 1999a). Customers are thought to engage in relational market behaviour to obtain benefits, such as task simplification, ‘special treatment’, and reduction in perceived risks (Yen and Gwinner 2003). For online retailing, the consequences of creating relationships with customers through avatars could include:

• using interaction dynamics of the relationship as direct persuasion to purchase;

• building credibility and loyalty as indirect persuasion;

• extending segmentation to include interaction or relationship quality bases, to refine the content and targeting of sales communications.

The HCI literature already includes a body of work on design, implementation and the impact of social responses to computers and avatars, mostly in non-transactional contexts (e.g. Cassell et al. 2000; Lester et al. 1997; Reeves and Nass 1996). Our work extends previous research (Abbattista et al. 2002; McBreen and Jack 2001; Witkowski et al. 2003) by focusing on avatar users as potential purchasers, thereby addressing an important and developing area for avatar use.

As with many innovations, a plethora of terms can be encountered that refer to avatars or similar representations. These include “embodied conversational agents” (ECAs) (Cassell et al. 2000), “virtual agents” (Abbattista et al. 2002), “synthetic personae” (McBreen and Jack 2001), “interactive characters” (Isbister and Nass 2000), “animated pedagogical agents” (Lester et al. 1997), “artificial shopping agents” (Redmond 2002) and “animated interface agents” (Dehn and van Mulken 2000). While there remains is no single widely accepted expression, the term “avatars” is gaining some recognition within the marketing literature (e.g. Wood et al. 2005; Holzwarth et al. 2006) as well as in the HCI literature (e.g. Salem and Earle 2000; Qui and Benbasat 2005). Within the Official Internet Dictionary, avatars are defined as “a pictorial representation of a human in a chat environment” (Bahorsky et al. 1998). However, as Salem and Earle (2000) observe, avatars can be realistic (human-like), naturalistic or abstract, which reflects the range of avatar exhibits used in our study.

The background theory and literature informing this research is necessarily broad, including the rich tradition of researching salespeople effectiveness (e.g. Churchill et al. 1985; Brown et al. 1993) and the use of synthetic characters in brand building (e.g. Stafford et al. 2002; Till and Busler 2001). The Human-Computer Interaction (HCI) literature (e.g. Witkowski et al. 2003; McBreen and Jack 2001) and psychological theories of relational interaction theory, including parasocial interaction (e.g. Auter

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McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing 435

and Moore 1993; Gummerson 2002) complemented these. This paper has four specific objectives.

• Through examination of relevant literature and insights from a panel of internet shoppers, identify roles potentially fulfilled by avatars.

• From a large-scale web survey, involving over 2,000 respondents internationally, measure preferences for specific avatar roles and assess the underlying dimensions of these role preferences.

• Using cluster analysis, identify distinct segments of online shoppers in terms of the type of roles they prefer to see fulfilled by avatars.

• Test the extent to which cluster membership relates to gender, age and online buying experience.

CONCEPTUAL FRAMEWORK AND HYPOTHESES

Help systems and interactivity

Virtually all websites include some form of help system but the HCI literature contains some scathing accounts of their acceptability and usability for the majority of users. Grayling (1998) refers to their “fear and loathing” of the help menu, most using it reluctantly, hastily and often without success. Spool (1997) concurs that users mostly prefer trial and error, use help systems only when stuck, and often bail out without obtaining the information. Grayling (2002) prescribes characteristics of a well designed help system, including being obvious to invoke, easily available and non-intrusive. At first sight, avatars would appear to fulfil the first two criteria admirably, assuming that they are available by default or by a single click. As for non-intrusive, that clearly depends on specific characteristics, but their abilities to simulate social interaction may well compensate for a degree of intrusion.

Online retailing websites in particular need to provide more than merely information through their help systems. Many users are deterred from online shopping by the lack of social interaction and pleasurable experiences (Barlow et al. 2004; Holzwarth et al. 2006). It is unsurprising that some shoppers are seeking hedonic as well as utilitarian benefits from their online experiences (Mathwick et al. 2001), as in other forms of shopping. Burgoon et al. (2000) illustrate that, by increasing the anthropomorphic features of an interface, people feel more understood, as well as deriving more utility from the website. If artificial representations possess properties such as language and personality, automatic social responses tend to ensue in users (Lee and Nass 2003). Avatars therefore offer the potential for greater information and entertainment value, thus greater satisfaction with online shopping experiences (Redmond 2002). They have also been found to contribute to consumers’ feelings of telepresence, i.e., the feeling of being present in a remote environment, potentially an interpersonal buying situation in a retail store (Qui and Benbasat 2005). Consequently, the interactive and social credentials of avatars appear promising; the literature on salespersons offline suggests strong associations between these characteristics, customer satisfaction and sales.


Salesperson and avatar roles

Practitioners and researchers agree that the salesperson role should go far beyond just clinching transactions. Bettancourt and Brown (1997) highlight the importance of creating an image for the company, as well as selling goods and services. Many retailers train their staff to greet or at least acknowledge customers as they enter a department (Sparks 1992), even in stores using primarily self-service. They are well aware that warmth, during what may be regarded by some as ‘non-productive’ retail encounters, can be essential in producing a favourable service encounter (Lemmink and Mattsson 1998). Mittal and Lassar (1996) concur that staff who “send out warmth” offer customers a more personally rewarding shopping experience. Baron et al. (1996) further note that social interchange is a key element in the “remembered role” of a retail store. Gremler and Gwinner (2000) identify the importance of customer-employee rapport in creating customer satisfaction. The social benefits of the customer-salesperson interaction are also widely recognised in the context of relationship marketing (e.g. Gwinner et al. 1998; Hennig-Thurau et al. 2002).

Repeat customer-employee interactions are not however based solely on trust and friendship; salesperson work and help in realising customer goals are also significant (Beatty et al. 1996). Based on content analysis of salesperson job descriptions, Kim and Stoel (2005) identify twelve customer service roles that need to be reproduced in online retailing. Semeraro et al. (2003) propose that an avatar could reflect these roles by instructing customers in the use of a website, pointing out offers, helping to find products, guiding customers through the purchase process and suggesting products based on customer requirements or past purchase history. Thus, it is constructive to gauge customer views on the usefulness of possible avatar roles. Providing those functions most wanted by customers will enhance user experiences, focus development efforts and minimise costs for online retailers. Segmentation by service

Bateson (1985) found very different levels of preference for personal service and self-service in offline service environments. Many customers prefer the non-personal service options, even if they do not save time, money or effort, in part to maintain a greater degree of control. Similarly, customers vary in their desire to maintain salesperson-customer relationships, creating opportunities to segment on the basis of these social needs (Reynolds and Beatty 1999b). Whether such relational benefits remain relevant online is questioned by Yen and Gwinner (2003), yet a lack of rapport and continuing interactions with service employees may, for some, undermine attachment and loyalty (Gutek et al. 2000). The evidence from offline suggests likely opportunities for online retailers to gain advantage through segmentation in the use of avatars, fulfilling various functional and (para)social roles.

The literature does suggest potential problems in the development and use of avatars, which could contribute to the rejection of some or all of their potential roles by a segment of internet shoppers:

• increased time and cognitive demands of the customer (Cassell et al. 2000; Dehn and van Mulken 2000);

• heightened and inappropriate expectancies for the system (Cassell et al. 2000; Isbister and Nass 2000);

• choosing an appropriate image for the context (Wood et al. 2005; Luo et al. 2006);

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• providing appropriate interaction content and interaction style for the context that also addresses cultural and personality differences (Isbister and Nass 2000);

• failure to deliver user benefits beyond novelty value (Witkowski et al. 2003).

We suggest that there will be some internet customers for whom the advantages of an avatar will outweigh the disadvantages and vice versa. The challenge is to identify which groups of customers derive most benefit and what roles are sought from avatars. The available evidence also suggests the likelihood that customers will differ in the type of help that they will require from avatars. Hence:

H1: Distinct clusters of online buyers exist, significantly different in the extent and types of help required from avatars.

One of the opportunities afforded by having an online assistant on an internet shopping site is that of giving the customer choices, e.g., whether or not to use the assistant, and what sort of assistant to select, in terms of appearance and nature of the interaction. It is important to understand more about the preferences of different customer segments for online assistants, including gender, age groups and levels of online shopping experience.

Gender Previous research suggests that females differ from males on social needs in elec-tronic interaction and the perceived risk associated with e-commerce (Fallows 2005). Females use more features associated with the maintenance of rapport and intimacy than males during electronic interactions (Colley and Todd 2002). A meta-analysis of 50 studies of computer-mediated interactions found that females’ communica-tion tends to be more collaboratively oriented (Li 2006). Rodgers and Harris (2003) argue that women want a more expressive orientation than men from e-commerce websites and that a “lack of perceived emotional benefits hinders e-commerce uptake among women”. Women perceive a higher level of risk in online purchasing than men (Sheehan 1999), even when controlling for differences in internet usage (Gar-barino and Strahilevitz 2004). We propose that their preferences for more emotional benefits and risk reduction indicate that females will show a greater preference for the presence of an avatar. Hence:

H2: Females are more likely to want the help of an avatar.

H2a : Females are more likely to appreciate the friendly roles of an avatar.

Age

Studies by Pew Internet find that age is less important as a differentiating factor in internet use, following a major increase in usage amongst the over 65s (Fox 2004). Many internet applications, including sending and receiving e-mail, making travel plans, playing games, shopping, and finding medical advice, are used extensively by those with internet access in the different age groups. While age-related differences in the proportions of usage are being identified (Fox and Madden 2006), these do not necessarily detract from the potential roles for avatars. Researchers have also begun to question whether age is still an important factor in the evaluation and use of technology based self-service, due to the maturing of technologies and growing

customer familiarity with the concepts (Dabholkar et al. 2003). Hence:

H3: Age is not related to preference for avatar help.

Online shopping experience

More experienced and practised shoppers will find the usual e-retail interface more predictable, quicker and easier to use than less experienced internet shoppers. Consequently, for experienced users, the increased effort in interacting with an avatar may outweigh any perceived advantages. The drive for efficiency in consumer decision-making (Ross and Creyer 1992) predicts that, in these circumstances, an avatar will be more negatively evaluated. Several researchers report problems with user perceptions of increased effort when using onscreen characters (Dehn and van Mulken 2000; Witkowski et al. 2003). In particular, Spiekermann and Paraschiv (2002) report that customers with more experience and knowledge of a product are less willing to interact with an avatar.

On the other hand, those with less experience at online shopping, or complete novices, may attach greater value to interaction and help from some form of assistant. It is therefore probable that an avatar, acting as a friendly guide through the internet shopping process and lending some aspects of familiarity with the offline task, will appeal to inexperienced online shoppers more than to experienced online shoppers. While the number of months/years that someone has shopped online is the best measure of experience in this context, experience may also be defined in terms of the frequency of online buying. Hence:

H4a : People who have bought online for longer will have less need for avatar help.

H4b : People who buy online less frequently will have more need for avatar help.

Figure 1 summarises the conceptual influences upon this study, which informed the initial shortlist of potential roles for avatars. These were further tested and refined through the early stages of the methodology, as outlined below. The emboldened sections of Figure 1 depict the analytical framework, in which key roles are identified and a typology, based upon these roles, is developed. Given the hypothesised associations with age, gender and on-line purchasing experience, role preferences are tested by these criteria. Also depicted are some of the practitioner implications of this study and decision areas that will be informed by these results.

METHODS

The results reported in this paper are derived from a two-year investigation into the potential for avatars in e-commerce contexts. Consequently, extensive preliminary work was undertaken, prior to this survey through which users’ preferences for avatar roles were measured. A panel of 30 internet shoppers was recruited to undertake a series of tasks involving interaction with existing avatars. In order to avoid “panel fatigue”, a second panel of 27 e-shoppers was recruited after the first nine months. These panel members participated in focus groups, telephone interviews and diary studies. Additionally, they undertook the initial screening of web-based questionnaires, before these were tested on larger groups of respondents not previously involved with the study.

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439McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing

FIGURE 1 Conceptual and operational framework

The number of avatars actually deployed on retail websites was very limited at the early stages of the study and some of those available were deemed unsuitable for academic study. Therefore, the characters used within the longitudinal studies were Office Logo (one of Microsoft’s Office Assistants, chosen for its neutral image), BonziBUDDY (a downloadable animated gorilla) and Lucy (an online avatar on Cross Country TravCorps). Recently, Anna has become one of the better-known retail avatars and appears at a ‘virtual helpdesk’ on the IKEA websites in many countries.

AssistingPersuadingRelationship

buildingReducing riskImageService vs

self-service

SALESPERSONROLES

Usage in advertising

Brand buildingRelational

interactionPara-social

relationshipsECA design

SYNTHETICCHARACTERS

Appropriateness of avatar to contextOffer option of avatar or notChoice of avatar: gender, formatInteraction style: friendly, functionalAdapt to demonstrated preferencesAdapt to browsing/buying contextPoints in process at which to assistTypes of assistance to offer

GUIDELINES FOR USE/DESIGN OF AVATARS

TYPE OF

AVATAR

ROLES OF

AVATAR

PERSONALSHOPPER

HELPERROLE

FRIENDROLE

No help wanted

Functional help

Friendly help

All help

PREFERENCESTYPOLOGY

AgeGenderOnline buying

how long?how often?

Regional

INTERNET USERSEGMENTS

The representation of this character differs between national sites, indicating some adaptation to local expectations and preferences. For example, the USA shares the representation of Anna that is used in Sweden, whereas Germany and the UK share a different representation.

Over the course of this investigation, two previous online surveys had been conducted prior to this study, using a specially constructed website Screenresearch.co.uk. Consequently, the construction of this online questionnaire drew both from the qualitative studies and from the experience of these two previous online surveys. Additional to the benefit of lower costs per respondent (after the initial site construction costs), this form of survey also minimises missing data by prompting for questions not answered. Potential errors in data transcription are effectively eliminated through the direct transfer of data into SPSS files.

It was accepted that many respondents would have little or no previous experience of interacting with avatars on transactional websites, so they were shown potential avatars on two sites constructed for the purpose. Respondents were asked to choose between five possible avatars that had been voted most appropriate in one of the previous studies. Based on the search, experience and credence typology (Alba et al. 1997), a bookshop was used to represent primarily a search task, while travel insurance represented the credence end of this spectrum. Respondents were given the choice to opt out of avatar assistance completely, an option taken by 11.6 percent in the bookstore context, 8.3 percent for the travel insurance site. The term “avatar” was unfamiliar to most respondents, so the expression “online assistant” was used within the survey. The five example avatars, from which they selected one or none, ensured that the meaning was unambiguous.

After choosing an avatar (or choosing none), all respondents were asked to rate their preferences for 14 roles that one could fulfil, including those who opted out of an avatar in the initial part of the survey. Consequently, the views of those with a negative predisposition towards avatars were included in the analyses. Appendix 1 lists the 14 roles that were chosen to represent those identified from the existing literature and from the longitudinal studies. These ranged from essentially social courtesies, such as welcoming people to the site, to a number of more functional roles. Our choice of potential roles was influenced by previous studies within the HCI literature, including Abbattista et al. (2002), Cassell et al. (2000), Lester et al. (1997), McBreen and Jack (2001) and Witkowski et al. (2003), as well as relevant work on sales management and relationship marketing (e.g. Churchill et al. 1985; Beatty et al. 1996). However, as these studies had not focussed on the issue of potential roles for avatars, ready-made batteries of relevant items were not available. Consequently, our items and scale were developed and tested within the longitudinal panels and through questionnaire testing. Each of the 14 items was rated on a five-point scale extending from definitely not (1), through neutral (3) to definitely (5). Although the objective of this study phase was quantification, the survey instrument also permitted the collection of open-ended comments, such as reasons for rejecting the avatar and other suggestions for avatar roles. The survey concluded with questions on internet usage and demographic variables.


Respondent sample

A sample of 2114 internet users was achieved in this survey. These were recruited by three means. Firstly, a large and international database has been developed of people willing to be contacted regarding web-based surveys; emails were sent inviting them to visit the site. Secondly, a sample of 920 internet users was provided from a commercial agency. Thirdly, it was known from the earlier studies that word-of-mouth (or word-of-web) contact spreads information about online surveys, increasing further the number of respondents. A prize draw no doubt increased participation, with a range of cash prizes totalling £2,000, or the equivalent in US dollars or Euros.

Table 1 summarises the sample characteristics. Female respondents outnumber the males but, given the overall sample size, men are well represented within the sample. The household income categories most common amongst internet users are well represented: based upon category midpoints, the mean household income level is £31,779. The minimum age of survey participants was set at 18 years of age and most respondents are between 18 and 64, with a mean age of 38.7. Most have shopped online, on average for 3.3 years, but 17.2 percent are relative newcomers, having first shopped online in the last 12 months.

The online shopping frequency mean is 1.86 times per month but the range includes 15.7 percent who shop online at least weekly, through to 21.5 percent shopping online infrequently (five times a year or less). While the majority is from the UK, 43.1 percent are from other regions, including viable sub-samples from North America, Oceania and Asia. With 2114 respondents, this sample is relatively large and therefore well able to support the analyses required to meet the study objectives. Furthermore, the international dimension of the sample has yielded detailed qualitative insights from five continents. However, it cannot be regarded


TABLE 1 Characteristics of the sample

Gender % Shop how long?* % Age group %Male 37.8 Less than a month 2.7 18 – 24 12.6Female 62.2 1 – 6 months 6.6 25 – 34 29.7Household income % 7 – 12 months 7.9 35 – 44 27.5 Less than £5,000 5.3 1 – 2 years 25.5 45 – 54 20.4 £5,000 - £9,999 6.3 3 – 5 years 38.2 55 – 64 8.8£10,000 - £19,999 20.0 Over 5 years 19.1 65 or over 1.0£20,000 - £29,999 23.0 Shop how often?* Location£30,000 - £39,999 17.2 Several times a week 4.2 U.K. 56.9£40,000 - £49,999 12.6 About weekly 11.5 Rest of Europe 2.4£50,000 - £59,999 7.1 2 – 3 times a month 24.0 North America 20.8£60,000 or over 8.5 About monthly 21.7 South America 0.9On-Line Shopper? % 6 – 10 times a year 17.1 Africa 1.4Yes 91.7 1 – 5 times a year 18.7 Asia 4.5No 8.3 Less than yearly 2.8 Oceania 13.2

Total sample 2,114 *Percentages of online shoppers


as representative of the populations of these diverse regions, and only tentative geographical comparisons can be attempted.

RESULTS

Potential roles for an avatar

The role that respondents most want an avatar to fulfil is to draw attention to possible errors in their selection, with a mean score of 4.09 on the five-point scale. In spite of the fact that some respondents opted out of an avatar at the start of the survey, the only role that received a mean score below the ‘neutral’ point on the scale was receiving recommendations based on the purchases of other people. Table 2 summarises the means and standard deviations of the 14 role items, as well as summarising the results of a series of principal components analyses. The suitability of the data for PCA was established through the Bartlett’s test (21261.7, df = 91, p = .000) and the KMO Measure of Sampling Adequacy, which at .950 was well above the recommended minimum of .50. Orthogonal rotation was applied to identify items that could be utilised to construct summary scales. As the overall sample is large, it was randomly split and PCA was run on each half individually.

Based on the indications of the scree plots and the avoidance of cross-loadings,

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TABLE 2 Preferred roles of an avatar

Types of Role Specific potential roles Mean SD Loadings

Helper, solving problems and labour saving 3.87 0.88 Split 1 Split 2

Drawing attention to possible errors in selection. 4.09 0.99 .858 .800

Pointing out special offers. 4.03 1.04 .586 .677

Guiding me through the payment process. 3.92 1.07 .800 .851

Asking if I need help finding what I want. 3.90 1.06 .542 .663

Pointing out sources of further information. 3.72 1.05 .764 .814

Searching the Internet for other items I ask for. 3.72 1.10 .728 .814

Re-assuring about security aspects of the site. 3.69 1.14 .730 .825

Friendly, sociable, welcoming host 3.56 1.01

Concluding visit to site by saying “goodbye”. 3.66 1.17 .657 .625

Welcoming me to the Internet shopping site. 3.64 1.13 .822 .835

Offering to give me a guided tour of the site. 3.61 1.13 .553 .564

Greeting me by name. 3.35 1.28 .865 .944

Personal shopper, recommending agent 3.26 0.95

Recommendations based on previous purchases. 3.60 1.09 .640 .623

Suggesting items to match things already chosen. 3.42 1.11 .681 .635

Recommendations based on others’ purchases. 2.74 1.10 .904 950

Notes: The 14 potential role descriptions are presented in full in Appendix 1. Factor loadings are based on orthogonal rotations and split sample analyses.


a three component solution was suggested in each case, accounting for 72.1 and 72.9 percent of variance. Table 2 indicates a high degree of similarity between the two split-sample solutions, indicating that scales comprising these items could be constructed with confidence. Three summated scales were developed, rather than deploying component z-scores, in order to retain equivalence to the individual item scale values. Within each of the three scales, inter-item correlations all exceed .50, and item-to-total correlations are all above .80, well within the reliability guidelines of Hair et al. (2006). Similarly, the three Cronbach’s Alphas are .9224 (Helper role scale), .8793 (Friendly role scale) and .8202 (Personal shopper role scale).

The open-ended comments volunteered by some people provided further elaboration upon these potential roles. Nearly 600 additional remarks or suggestions about roles were collected in this non-obligatory part of the online questionnaire, completed by 15.7 percent of respondents. Those from the UK proved to be the least forthcoming with additional comments, the Asian sub-sample by far the most forthcoming, with 0.17 and 0.72 comments per respondent respectively. As these differences in optional question response rates are of methodological interest, a breakdown of regional responses is provided in Appendix 2. As the main survey was developed in part through qualitative work with user panels in the UK, qualitative responses from other regions provide an especially valuable additional perspective. It is not feasible to present all of these optional comments but a sample is provided in Appendix 3, classified broadly into the three dimensions identified above. General, negative remarks about avatars are also listed, along with those advocating a choice in whether or not to have an avatar. This mixed-method approach (Tashakkori and Teddlie, 2003) can assist in the identification and interpretation of consumer typologies (e.g. Rohm et al. 2006).

Table 2 indicates that the items comprising the helper role are the highest scored, resulting in a scale mean of 3.87 and standard deviation of 0.88. This dimension includes most of the functional roles, although both the principal components solutions suggested that the more proactive, recommending roles be grouped as a separate dimension. Additional comments from the main sample confirmed the importance of the helper roles initially identified, as well as providing some very specific suggestions. For example, the idea that numbers could be spoken back by the avatar at the payment stage, to check accuracy. Another suggestion was that the avatar could offer help if excessive decision time was noted. Reflecting the international sample, there were also suggestions that the avatar could help with currency conversions or by offering language options.

The scale combining four items representing the friendly, sociable roles produces a mean of 3.56 (SD =1.01), significantly lower than the mean of the helper role scale (t = 21.590, df = 2113, p = .000). Notably, the standard deviation on the friend role scale is somewhat higher, indicating a wider dispersion of views on the desirability of this feature. The friendly roles of welcoming and saying goodbye, as might be expected in offline retailing, received higher ratings but views were more mixed on the prospect of being greeted by name. Again, the additional comments supported and expanded upon the rated items. Far from being put off by a personal greeting, some suggested they would actually welcome a birthday greeting. A number of comments signalled a wish to be thanked at some stage in the process, as in a personal transaction. Some also suggested that avatars could contribute an element of humour to the online experience.

The scale representing the recommending and suggesting functions we have called the personal shopper role. Overall this receives the lowest mean score at 3.26, which


again is significantly lower than the friendly role scale (t = 16.617, df = 2113, p = .000). However, the relatively low ratings given to the idea of giving recommendations based on the purchases of other people accounts for most of this difference. In practice, salespeople, real or virtual, will inevitably base their recommendations in part upon a broad view of purchase patterns but it is maybe better that this is not too explicit. The open-ended comments supported the need for fairly proactive roles for avatars. Several indicated they would welcome suggestions of suitable alternatives if the first choice were out of stock, while others would like the avatar to translate their needs into product or service solutions. Some were clearly happy that the avatar should “know” individual colour preferences, even the ages of family members, and recommend accordingly.

Role preference clusters

An objective of the study was to explore whether a clear typology of role preferences could be identified; cluster analysis on the three role scales was therefore undertaken. There are no entirely adequate methods for choosing the number of population clusters (Hair et al. 2006) but inspection of dendrograms, based on hierarchical cluster analysis using a series of sub-samples, suggested a three or four-cluster solution. K-means clustering, an iterative partitioning algorithm, was then applied: split-samples were again used but not based on the same random split as the principal components analyses.

The final cluster centres for a four cluster solution are given below in table 3. These three analyses demonstrate sufficient similarity, in terms of cluster sizes and cluster centre scores, to accept these as a reliable and valid typology of avatar role preferences. The differences between the role centres are proven by Anova tests, the F ratios being highly significant in each case. As an additional measure, post hoc Bonferroni tests show each inter-cluster contrast to be significant at the p = .000

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TABLE 3 Cluster centres – whole and split sample

ClustersFriendRole

Personal ShopperRole

HelperRole

ClusterSize

Split Sample 1 Mean Mean Mean NCluster 1 1.44 1.31 1.77 79

2 2.74 3.10 3.47 2773 3.89 2.83 3.95 2844 4.29 4.06 4.50 429

Split Sample 2Cluster 1 1.48 1.43 1.95 107

2 2.98 3.25 3.62 3123 3.98 2.75 3.99 2554 4.35 4.06 4.50 371

Whole SampleCluster 1 1.49 1.40 1.89 191

2 2.88 3.16 3.53 5923 3.96 2.77 4.00 5144 4.29 4.06 4.48 817

F Ratio (p = ) 2101.71 (.000) 1593.15 (.000) 1578.39 (.000)


level on each of the three scales. On the basis of these more stringent tests, hypothesis H1 is confirmed: distinct clusters of online buyers exist, significantly different in the extent and types help required from avatars.

Cluster 1 is labelled “No help wanted”, in that all of its centres are below two on the role scales, which extend from one to five. It is the smallest of the clusters, comprising just nine percent of the whole sample, although it could of course be significantly larger in some other internet contexts. Amongst those who had earlier opted out of using an avatar at the bookshop site, 47.2 percent are within this cluster, compared with 63.1 percent of those opting out at the travel insurance site. On the one hand, this illustrates some correspondence with the pre-dispositions of respondents towards avatars. On the other, it suggests that many gave more positive role preference ratings, when various specific roles were suggested.

Cluster 4 is the most positively disposed towards all the types of roles suggested for avatars: it is labelled “All help appreciated”, in that all of the centres fall within the uppermost part of the role scales. This is the largest of the four clusters, accounting for 38.6 percent of respondents. Both the other two clusters also have centres in the upper half of the helper role scale. Cluster 2 (28.0 percent) has the second highest score on the personal shopper role scale but only the third highest on the friend role. Combined with the score on the helper role scale, this cluster is labelled “Functional help”. While cluster 3 (24.3 percent) also has a fairly high score on the helper scale, the situation is reversed on the friend and personal shopper scales; consequently, this is labelled “Friendly help”. Amongst those who do not necessarily require all the roles potentially offered by an avatar, a distinction is therefore being made between those preferring the functional and the friendly roles. Those who opted out of an avatar earlier in the survey, yet are not in the “No help wanted” cluster, are mostly within the “Functional help” cluster.

Gender and age

Previous research had suggested the likelihood that females would be more likely to appreciate avatars and, in particular, their friendly roles. Table 4 confirms this, showing the males much more likely to be in the “No help wanted” cluster and the females more likely to appreciate all the potential role types. The overall difference, tested by Chi-sq., is highly significant. Thus, H2 is also supported: females are more likely to want the help of an avatar.

However, it is equally clear that the majority of males do see roles for avatars, so it would be incorrect to assume that men are usually averse to them. Noticeably, males are more likely to be in the “Functional help” cluster, the reverse being the case in the “Friendly help” cluster. Table 4 also contrasts the three component role scale scores for each gender, the scores for the females being significantly higher in each case. Noticeably, the mean difference on the personal shopper scale is smaller but still significant. These results support H2a: females are more likely to appreciate the friendly roles of an avatar.

The literature is more ambivalent about likely associations with age, yet this is one important indicator of the future role of avatars on transactional websites. Table 4 suggests that the younger (under 35) age groups are less likely than the others to be in the “No help wanted” cluster, and more likely to be in the “Functional help” cluster. The over 65 age group is excluded from this analysis, due to the small number of cases (21). “Friendly help” shows a bias towards the older groups but, within all of the age ranges, the largest single cluster is “All help appreciated”.


The mean ages illustrate differences between the clusters, a result confirmed by the Brown-Forsythe test (11.948, p = .000). This more robust test is used instead of the F ratio here, as these means are estimated from category midpoints, which only approximate to normal distributions. Bonferroni post hoc tests showed cluster 1 to be significantly older than cluster 2 (p = .000) and cluster 4 (p = .030), while cluster 3 is older than cluster 2 (p = .000). Returning to the three component scores, each correlates significantly with age but the directions differ. The friend and helper role scales correlate positively, whereas the personal shopper role correlation is negative, confirming greater acceptance of this role amongst the younger groups. Although this is a somewhat mixed set of results, they are suggestive of some age-related differences in avatar role preferences. Consequently H3, that age is not related to preference for avatar help, is rejected.

Online buying experience

Based on existing evidence, it was expected that more experienced online shoppers

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TABLE 4 Differences by gender and age

% in ClusterRespondent

No HelpWanted

FunctionalHelp

FriendlyHelp

All HelpAppreciated

GenderMaleFemale

13.5 6.3

30.626.4

22.425.5

33.541.8

Chi-sq (p = ) 42.37 3df (.000)Age

18 – 2425 – 3435 – 4445 – 5455 – 64

6.4 7.210.0 9.515.7

34.632.527.023.218.9

18.822.624.726.729.7

40.237.738.340.635.7

Chi-sq (p = ) 40.41 12df (.000)

Mean Age 41.3 36.7 40.2 38.7

Brown-Forsythe (p = ) 11.95 3df (.000)

Avatar Role ScalesFriend Role

MeanPersonal Shopper

Role MeanHelper Role

MeanGender

MaleFemale

3.383.67

3.133.33

3.683.97

T-test (p = ) (.000) (.000) (.000)

Age18 – 2425 – 3435 – 4445 – 5455 – 6465 and over

3.493.553.553.673.473.67

3.383.323.213.223.003.17

3.853.843.833.973.783.92

Spearman Corr (p = ) .061 (.005) -.084 (.000) .063 (.004)


TABLE 5 Differences by online buying experience

% in ClusterExperience Buying Online

No HelpWanted

FunctionalHelp

FriendlyHelp

All HelpAppreciated

How Long Bought Online Less than a month1 – 6 months7 – 12 months1 – 2 years3 – 5 yearsOver 5 years

1.90.84.65.5

12.315.4

19.221.126.125.529.632.4

21.221.920.930.724.318.4

57.756.348.438.433.833.8

Chi-sq (p = ) 94.56 15df (.000)

Mean Years 4.4 3.6 3.1 3.0

Brown-Forsythe (p = ) 24.69 3df (.000)How Often Buy Online

Several times a weekAbout weekly2 – 3 times a monthAbout monthly6 – 10 times a year1 – 5 times a yearLess than yearly

14.812.29.29.09.48.81.8

32.132.026.729.524.828.421.8

16.019.423.223.527.226.241.8

37.036.540.938.038.736.634.5

Chi-sq (p = ) 27.09 18df (.077)Mean Buying per Month 2.2 1.9 1.6 1.9Brown-Forsythe (p = ) 3.93 3df (.008)TOTAL 9.0 28.0 24.3 38.6

Avatar Role ScalesExperience Buying Online

Friend RoleMean

Personal ShopperRole Mean

Helper RoleMean

How Long Bought OnlineLess than a month1 - 6 months7 - 12 months1 - 2 years3 - 5 yearsOver 5 years

3.993.983.673.713.423.31

3.713.583.443.283.143.13

4.304.214.023.993.743.64

ANOVA F Ratio (p = ) 16.50 (.000) 10.12 (.000) 17.29 (.000)Spearman Corr. (p = ) -.182 (.000) -.117 (.000) -.191 (.000)

Partial Corr. [control for age] -.193 (.000) -.117 (.000) -.200 (.000)How Often Buy Online

Several times a weekAbout weekly2 - 3 times a monthAbout monthly6 - 10 times a year1 - 5 times a yearLess than yearly

3.353.413.593.533.563.583.78

3.243.213.293.263.243.173.27

3.633.743.873.863.883.884.18

ANOVA F Ratio (p = ) 1.86 (.083) 0.67 (n.s.) 2.88 (.008)

Spearman Corr. (p = ) -.045 (.047) .035 (n.s.) -.061 (.007)


would be less likely to need the help of an avatar. Two measures of online shopping experience were taken: the number of months/years the respondent has been buying online and frequency of buying online. Table 5 summarises tests based on both these measures, with regard to cluster membership and means of the component scores. Membership of the “No help wanted” cluster does indeed increase as years of experience online grow. However, membership of the “Functional help” cluster also grows with experience, indicating that these groups become more specific and sophisticated in their help requirements. Accordingly, the “All help appreciated” cluster declines with experience but the pattern for “Friendly help” is more mixed, peaking in the one-two year experience category. Bonferroni tests showed all these inter-cluster differences to be significant at or beyond the p = .002 level, with the exception of clusters 3 and 4, between which there is no significant difference in terms of experience buying online.

Hypothesis 4a therefore proved to be somewhat of an over-simplification of the relationship between how long buying online and need for avatar help. However, it is apparent that a larger minority of the seasoned online buyers would reject avatar help completely. On this basis, H4a, people who have bought online for longer will have less need for avatar help, is partially supported.

The main observation however is that the vast majority in each experience category seems to prefer some form of help. The analyses of the component scale means confirm a general decline with experience but all group means remain above the scale midpoints (i.e., 3.00). Noticeably, the mean of the personal shopper scale decreases the least with experience. Further partial correlation analyses were conducted, in order to test whether the results for experience are being confounded by the effects of age. As Table 5 demonstrates, the partial coefficients and their significance levels are very similar to the bivariate coefficients and no significant 2-way interaction effects

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FIGURE 2 Role scales and customer segments

2.5

3

3.5

4

4.5

Friend Role

Helper Role

PersonalShopper

Gender Age Range How Long Using

Mal

e

Fem

ale

18-2

4

25-3

4

35-4

4

45-5

4

55-6

4

<1

Mon

ths

1-6

Mon

7-12

Mon

1-2

Yrs

3-5

Yrs

>5

Yrs

SCA

LE M

EAN

S


were identified across the three scales. Figure 2 provides a graphic summary of the relationships between these scales and gender, age and online buying experience.

The results are less definitive in the analyses by frequency of online shopping. Membership of the “No help” and “Functional help” clusters appears to be greatest at higher frequency levels, while the less frequent shoppers seem more likely to be within the “Friendly help” cluster. These differences are only marginally significant, based on the Chi-square test, but the Brown-Forsythe robust test shows differences between mean online shopping frequencies by cluster. The analyses of role scale means also produce mixed results. The friendly and helper role means decline with increasing online shopping frequency but the correlations are relatively weak (-.045; -.061). Note that these non-parametric tests are based on category midpoints, which is why the signs may appear counter-intuitive. The means of the personal shopper role scale remain relatively stable across all of the frequency categories. This could suggest that the utility of such roles to more frequent online shoppers may counteract a tendency to feel that avatar help is time consuming and/or not needed. On the basis of these results, H4b, people who buy online less frequently will have more need for avatar help, is partially supported.

Regional differences

It was not part of the original design to test for location-based differences and clearly the sub-samples cannot be regarded as representative of such broad regions. However, observation of geographical differentiation in avatar use by IKEA and the availability of viable sub-samples motivated these further exploratory investigations. The table in appendix 4 firstly shows application of the chi-square test to just the three sub-samples with over 250 cases each. This suggests that the UK sample is significantly more likely to fall within the “No help wanted” or the “Functional help” clusters, less likely to want all forms of avatar help. In contrast, over 70 percent of both the North American and the Oceania samples want either friendly help or all types of help. As this is not an anticipated result, weightings were applied to the samples to compensate for differences in gender ratios, the only significant difference between the regional samples. These adjustments made little difference to the overall result, which discounts the possibility that these differences may be largely gender-related, rather than in some way related to regional differences.

In the analyses of role scale means, it was possible to include also the sub-samples from the rest of Europe (50) and Asia (96), while accepting that these cannot represent adequately such large and diverse regions. Again, the UK sample is significantly out of line with the others, but with smaller differences on the personal shopper scale. In that the study was not specifically designed to test for these differences, such results should be treated with caution, thus their presentation within appendix 4. They may however prompt future investigation into how general service expectations in different countries or regions might influence preferences for avatar choices, types and roles.

CONCLUSIONS AND IMPLICATIONS

The HCI literature on help systems (e.g. Grayling 1998, 2002) suggests an acute need for new approaches to assisting users, especially in contexts such as online shopping, where usage is discretionary rather than work-related. Here, the key term


and focal point of any system design must be “customers”, rather than “users”, as customers have many choices between alternative vendors and retail channels. The lack of social interaction has been cited as a major inhibitor to more widespread adoption of online shopping (e.g. Barlow et al. 2004; Holtzwarth et al. 2006), and this may be especially relevant to females (Li 2006) and to those with less online buying experience (Spiekermann and Paraschiv 2002). As Artificial Intelligence technology improves towards the point that avatars could resemble the experience of talking with real people (Qui and Benbasat 2005), there is every possibility that avatars will indeed become virtual salespersons, potentially very adaptable in their appearance, personality and roles.

Our results show that most respondents perceived some roles for avatars, although a minority was classified as “No help wanted”. The evidence of this study and the work of Fogg (2003) stress the importance of providing choice in whether or not, and how, to interact with an avatar. The majority perceives some positive roles for them, which comprise three of the four clusters within the typology. Of these, two clusters emphasise the friendly roles, accounting for over half of the respondents. This suggests that, as customers switch to the Internet for many of their purchases, they do not invariably lose their liking for some form of (para)social interaction, observed to have major relational benefits offline (e.g. Mittal and Lassar 1996; Gremler and Gwinner 2000). Others do appear to lean towards a more functional set of interaction preferences, in which the avatar can adopt a more specialist sales role as a ‘virtual personal shopper’.

Fogg (2003) argues that internet users in general will be more likely to reject avatars, as they gain in experience. This may relate especially to the rather trivial roles that characterised some early avatars. As they are developed to provide more expert roles on transactional sites, they are likely to be valued by many experienced online buyers but for different roles. Furthermore, in online retailing, parallels with offline experiences will remain far more salient than on non-transactional sites, whatever the experience level of the e-shopper. It is generally accepted that multi-channel shopping will become the norm for the majority of people (e.g. Gulati and Garino 2000; Johnson et al. 2004; Dholakia et al. 2005). Our panel data suggested that the earlier adopters of internet shopping, now the experienced users, are more likely to be amongst the category classified as self-service prone by Bateson (1985). As internet shopping becomes more universal, this self-selection bias towards a more impersonal shopping experience is likely to reduce, maybe reverse in the case of the later adopters of online shopping.

We hypothesised that women would be more appreciative of avatars, due to a greater liking for rapport (Colley and Todd 2002) and expressive orientation (Rodgers and Harris 2003), plus a higher perception of risk in online transactions (Sheehan 1999). This supposition was supported by our data, with males being significantly more likely to reject an avatar and the relational benefits being of greater importance to the females. This can however distract from the more important observation that the vast majority of the male sample do perceive roles for avatars, albeit more biased toward the functional roles. Here the data are given further support from the qualitative comments supplied by male members of the sample. Prior evidence with respect to age differences was less indicative. Our data indicate that users in the 55 – 64 age group are most likely to reject all avatar roles, while the younger age groups are more likely to focus on the functional roles.

These differences are statistically significant and contribute to the body of knowledge within the HCI and marketing literature on age, gender and experience

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differences within internet shopping. However, it is important to note that over half within all the age groups, experience groups and both gender groups perceived the value of the friendly avatar roles. Furthermore, the vast majorities within all of these groups welcome the more basic forms of help and facilitation. Designers can therefore place only limited reliance upon some of these traditional segmentation variables. They may have value in the absence of other preference data but online shoppers can easily demonstrate their requirements of help and interaction, and avatars of the future could be increasingly flexible in providing this. While retail stores have some scope to recruit and train their staff to match typical customer requirements (Churchill et al. 1975, 1985), online and multichannel retailers have at their disposal an almost infinitely adaptable service resource.

Our findings suggest particular points in the internet shopping process when an avatar would be beneficial and specific functions that it could perform. Participants most wanted one to draw attention to possible errors in selection and to point out special offers. They least wanted it to make recommendations based on other people’s purchases. Customers need to have the choice about interacting with an avatar, and must not feel alienated from the site by meeting one they perceive as inappropriate or even offensive. However, some types of internet shopper may be attracted to a site with a helpful, appropriate and task-orientated avatar, just as these attributes are important in offline salespersons (e.g. Brown et al. 1993). Distinct role types for an avatar include being a helpful, friendly and/or proactive online assistant: amongst these attributes, helpful was most widely preferred, followed by friendly roles. These findings auger well for the potential of avatars to help overcome the lack of social interaction in offline retailing (Barlow et al. 2004).

Behind these general trends, there is a huge diversity of specific requirements and preferences, expressed by panel members and those adding their own suggestions to the main survey questionnaire. Here there is an opportunity for avatars to overcome a limitation long recognised in many retail salespeople, the inability or unwillingness to adapt to customer preferences or choice criteria (e.g. O’Shaughnessy 1971/2; Churchill et al. 1975). Firstly, the very nature of the avatar can be adapted to individual preferences, such as the ‘gender’ or level of anthropomorphism. Secondly, the extent of presence or intervention can be set to match the style and difficulty of the consumers’ choice task. Thirdly, a potentially huge database can inform the actions and recommendations of an avatar, rivalling those available in the best offline systems for relationship marketing.

The preferences and search style of even the most infrequent shopper can be learnt, without visible recourse to a company’s database. The information gained from customer-avatar interactions can also inform marketing activity offline, such as the content and communication style of direct mailings, or cues available to salespersons in point-of-sale systems. Of course, some current avatars, especially those with more ‘human-like’ characteristics, run the risk of failing to match customers’ interaction expectations (Dehn and van Mulken 2000). On the other hand, customers may accept more readily the need to supply an avatar with an explicit list of their search requirements, which they would not necessarily provide in such detail to a human salesperson. This presents the ideal opportunity to match these needs and criteria (O’Shaughnessy 1971, p.2), resulting in both profits and satisfied, loyal customers. Clearly, these and future findings on avatar roles can contribute a new dimension and insights to the sales management literature.


LIMITATIONS AND EXTENSIONS

This study has demonstrated the scope for differentiating the roles undertaken by avatars, either as a bespoke interface for the individual e-shopper or, at least, as an interface targeted to groups with known demographic, internet usage or possibly national characteristics. Although the 14 roles examined in the main study were derived from a wide-ranging examination of avatars in different contexts, the responses within the main study may have been influenced by the two introductory examples, namely, online bookshops and travel insurance sites. There are many different e-shopping and e-service situations, which are likely to suggest a different mix of roles for avatars. Similarly, there can be fundamentally different shopping scenarios even within the same shopping context. For example, Van Kenhove et al. (1999) demonstrated five scenarios motivating visits to home improvement stores, each with very different priorities in terms of service and other attributes sought. A worthwhile extension of this study would examine avatar role requirements across a much wider range of internet transactional contexts and specific shopping scenarios.

Although the survey website was accessible to respondents across five continents, exploration of international or regional differences was not an initial objective of the study. The findings nonetheless suggest that there may be very significant geographical differences in the preferred nature of the interaction with avatars. Drawing upon studies of differences in cultures and consumer behaviour (e.g. Aaker and Williams 1998; Steenkamp et al. 1999), a study that focussed upon this dimension could broaden further the applicability of these findings, while contributing to our knowledge of international and regional differences in service expectations. A study with this primary aim would clearly seek to ensure that each national sub-sample was as representative as possible of the national adult populations. This would require more rigid systems of stratified sampling than were appropriate to the objectives of this study.

Time is also a key element in shaping expectations and preferences for avatar interaction, as illustrated through differences by internet experience. Furthermore, the use of avatars within the e-tailing context is still at an introductory stage, so their knowledge bases and interactive capability will continue to develop (Qui and Benbasat 2005). It is therefore worthwhile to continue evaluating the current and potential roles of avatars, in helping to encourage e-transactions and in generating loyalty to websites. It must also be remembered that screen interfaces are not limited to online selling through the Internet: kiosks are becoming a “channel within a channel” in many stores, airports, etc. (Keeling et al. 2006). As multichannel shopping becomes the norm (McGoldrick and Collins, 2007), avatars will become a part of the integrated sales and relationship marketing function. No doubt the selling roles and potential of avatars will attract increased attention from sales management researchers in the future.

ACKNOWLEDGEMENTS

The authors would like to thank the many participants in the panels and survey for their involvement. They are also most grateful for the support of the Manchester Retail Research Forum and the Engineering and Physical Sciences Research Council (EPSRC grant number GR/R66890/01).

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McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing


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APPENDIX 1

Wording of Role Preference Questions

I would like an online assistant:

1. Welcoming me to the internet shopping site.

2. Greeting me by name.

3. Offering to give me a guided tour of the site.

4. Asking if I need help finding what I want.

5. Pointing out special offers.

6. Making recommendations based on my previous purchases.

7. Making recommendations based on other people’s purchases.

8. Suggesting items to match things I have already chosen, e.g., accessories for clothing.

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9. Drawing attention to possible errors in my selection e.g. when buying multipacks of food items.

10. Guiding me through the payment process.

11. Reassuring me about security aspects of the site.

12. Concluding my visit to the internet shopping site by saying “goodbye”.

13. Searching the Internet for other items I ask for.

14. Pointing out sources of further information such as brochures, catalogues and telephone numbers.

APPENDIX 2

Regional Distribution of Additional Comments on Avatar Roles

Region Sub-Sample n

% Adding Comments

Number ofComments

Comments/Respondent

United Kingdom 1203 10.8 205 0.17

Rest of Europe 50 16.0 14 0.28

North America 440 21.1 171 0.38

South America 18 22.2 10 0.55

Oceania 278 21.6 116 0.42

Asia 96 31.3 69 0.72

Africa 29 24.1 12 0.41

Total 2114 15.7 587 0.28

APPENDIX 3

Examples of Additional Comments on Avatar Roles

HELPER ROLES

Make an effort to assure customer of next shipment delivery if sold out

(Male, Age 55-64, Asia, <1 Year Shopping Online)

Asking if a language translation is needed for the site (M, 45-54, Oceania, 1-2)An assistant would be very helpful for new internet buyers (F, 35-44, Oceania, <1)Helping with sites we have not visited before (F, 55-64, S. America, <1)Sometimes I get lost in complicated sites – then I would love someone to pop up and help (F, 35-44, Oceania, <1)Convert money values where you come from (F, 45, N. America, >5)Payment site – repeating numbers back – easier to check the accuracy

(F, 35-44, N. America, 1-2)

Locating special deals on what I am interested in (F, 35-44, Europe, <1)Cont’d...



Coming to the rescue if confused or lost (F, 35-44, UK, 3-5)Where there seems to be an excessive amount of time deciding, asking if some help is needed (M, 45-54, Oceania, 3-5)Comparing the prices and quality with other competitors, if possible (M, 18-24, Asia, <1)Advising that what I had done was correct (M, 45-54, Africa, 1-2)Pointing out technical details (e.g., shipping fees and time, payment methods) non intrusively (F,18-24, Asia, <1)Guided tours of specific parts of the site, not just one of the whole site (M, 25-34, UK, 1-2)

SOCIAL ROLESCongratulating me on my purchase and asking me to come back soon (F, 18-24, N. America, <1)All aspects appeal to me using a buddy, so to speak (F, 55-64, UK, 305)Interaction is a positive approach … can make purchasing fun (F, 55-64, UK, 3-5)Thanking you when you have made the purchase (F, 18-24, UK, <1)Asking for the birthday of the purchasers – sending birthday greetings (M, 35-44, Asia, <1)When it is your birthday greeting you (F, 35-44, Oceania, <1)Enlightening the experience (F, 35-44, S. America, 3-5)During payment process, a friendly face to ease the frustration (F, 35-44, N. America, 3-5)Maybe dressed in season costume, e.g., an elf at Christmas (M, 25-34, UK, >5)I would like one to conclude my visit (with a purchase made) by saying goodbye AND thank you (F, 18-24, N. America, 3-5)Maybe include the occasional joke of the relevant site (F, 25-34, Oceania, <1)Only in places where it is used solely for humour (M, 45-54, UK, >5)Throughout – a sense of humour – sadly lacking in real shop assistants (F, 45-54, UK >5)

PERSONAL SHOPPER ROLESWhen anything out of stock, suggest alternatives (F, 55-64, Africa, 3-5)Defining and describing the product features (F, 35-44, Asia, 3-5)Ask purpose for which I need a particular product and suggest the best option, for example (M, 25-34, Asia, 1-2)Suggestions for a substitute if what I wanted was not in stock (F, 45-54, N. America, 3-5)Matching other items and advising what ones would be suitable (F, 35-44, Europe, <1)Offer alternative options based on information given (e.g., different gift ideas) (F, 45-54, UK, 3-5)Showing items that match my favourite colors and for my family ages

(F, 35-44, N. America, 3-5)

Asking for a ballpark spend figure to speed up selection (M, 45-54, UK, 3-5)Point out shortcomings and advantages associated with the product

(F, 45-54, Africa, 1-2)Cont’d...

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Explaining products I maybe haven’t seen before and giving ideas on what they are for (F, 25-34, Asia, 1-2)Offer suggestions if first choice was not available (F, 35-44, UK, 3-5)Suggested purchases for particular occasions, e.g., birthdays (F, 18-24, UK, 3-5)At log-on, “What are we looking for today?” (M, 35-44, UK, <1)

NEGATIVE COMMENTSI would find an online assistant annoying and suspect it would slow down the purchase process (F, 45-54, Asia, <5)I don’t really like them to be honest! (F, 25-34, Oceania, 3-5)No online assistant … it slows down the website (M, 45-54, N. America, <5)Make no assumption that the user has broadband access! M, 45-54, Europe, 3-5)If it takes too much time to load onto a browser, people will leave (F, 18-24, N. America, 3-5)I wouldn’t – it is one reason why internet shopping is so appealing, no nagging sales clerks (F, 45-54, N. America, 3-5)I hate them popping up everywhere and just getting in the way slowing things down (M, 55-64, UK, 3-5)Needs to be time efficient though as often in lunch hour (F, 25-34, UK, 3-5)I find online assistants a bit patronising unless they are linked to a real person in a call centre somewhere (F, 25-34, UK, >5)Personally I find them distracting and often annoying (F, 35-44, UK, 3-5)There seems to be nothing that an online assistant can do that cannot be adequately done with plain text (M, 35-44, UK, 3-5)

AVATAR OPTIONSI would like the option of reducing the amount of help as familiarity with the site increases (F, 35-44, Oceania, 3-5)In some situations, you may want to shop quickly and wouldn’t need an assistant M, 45-54, N. America, 3-5)Would like the ability to turn it off if it become irritating (M, 55-64, UK, 1-2)If my connection is slow it could offer to become text only (F, 25-34, UK, 3-5)Having a close button on the guide (F, 18-24, N. America, 3-5)Being context sensitive and only coming up when requested (M, 35-44, UK, 3-5)Perhaps you want to talk or be assisted by another gender etc. you could have the option

(F, 45-54, N. America, 3-5)Cont’d...

Choosing own assistant on homepage (F, 55-64, Oceania, <1)Always available in a corner and easily opened when needed (M, 45-54, Africa, 3-5)Ability to turn off if desire (F, 35-44, N. America, 1-2)There must be a balance between neglect and excessive advice to make it helpful and not a nuisance (F, 18-24, N. America, 3-5)Not fond of one following me around; would be good if similar toMS Office Assistant who you can bring forward if needed (M, 35-44, Europe, >5)In parentheses are gender, age range, region, and number of years buying online.



APPENDIX 4

Exploratory analysis of regional differences

% in ClustersMain Sample Regions

No HelpWanted

FunctionalHelp

FriendlyHelp

All HelpAppreciated

UKNorth AmericaOceania

12.75.72.9

31.722.724.1

22.128.227.7

33.543.445.3

Chi-sq (p = ) 63.48 6 df (.000)

Avatar Role Scales(# cases)

Friend RoleMean

Personnel Shopper Role Mean

Helper RoleMean

UK Rest of EuropeNorth America Oceania Asia

(1203) (50)(440)(278)(96)

3.373.803.783.773.99

3.133.373.373.373.68

3.723.973.984.124.24

ANOVA F Ratio (p= ) 24.00 (.000) 13.12 (.000) 20.81 (.000)

ABOUT THE AUTHORS AND CORRESPONDENCE

Professor Peter McGoldrick has researched retail marketing and shopper behaviour for over thirty years, publishing seven books, including Retail Marketing (McGraw-Hill), numerous research reports and over 150 research papers. In 1991 he was appointed to the first joint chair between MBS and UMIST, and now holds the first Tesco Chair in Retailing. For several years he was Co-Director of the joint MBS-UMIST Executive MBA program and has held external examining appointments in many universities. He has been Director of the Manchester Retail Research Forum since 1998, a group of blue-chip companies that helps to identify, facilitate and support innovative research. With his colleagues at MBS and UMIST he has been awarded several major research grants from the ESRC, EPSRC, DTI, OFT and other funding bodies.

Corresponding author: Professor Peter J. McGoldrick, Tesco Professor of Retailing, Manchester Business School, Booth Street West, Manchester, M15 6PB, UK.

T +44 161 306 3467 E [email protected]

Dr Kathy Keeling is a senior lecturer at Manchester Business School, UK, in Research Methods, Data Analysis, and E-marketing at undergraduate and postgraduate level. For the last 10 years she has worked closely with, and gained research support from, many large UK and international retail and computer software organisations, as well as major funding from UK and European research bodies. Resulting papers have been published in retailing, marketing, e-commerce, human-computer interaction and universal access to IT fields. Her core research interests are in human issues in design

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and adoption of e-commerce and online retail communities, focusing at present on the design and nature and strength of “virtual” relationships with onscreen characters and the potential for persuasiveness in e-retailing.

Dr Kathleen A. Keeling, Senior Lecturer in Marketing Research Methods, Manchester Business School, Booth Street West, Manchester, M15 6PB, UK.

T +44 161 306 3519E [email protected]

Dr Susan Beatty holds degrees in Microbiology, Educational Research and a Ph.D. in Psychology, the thesis entitled “Psychological Factors and Stages of Change in Drivers’ Willingness to Reduce their Car Use”. She then worked at the Drug Misuse Research Unit at Manchester University, evaluating the Arrest Referral Scheme for drug-misusing offenders. From 2002 – 2005 she held a post-doctoral fellowship at UMIST and MBS, on an EPSRC and Retail Forum funded study entitled “Human-Computer Relationships and Persuasiveness in E-retailing”. She developed two user panels and three large, web-based experimental studies to investigate the relationship-building potential of on-screen characters (embodied conversational agents) on internet shopping sites. She is presently conducting research into factors that predict accident risk in older pedestrians, based in the School of Psychological Sciences at the University of Manchester.

Dr Susan F. Beatty, Research Associate, Faculty of Medical and Human Sciences, University Place, The University of Manchester, Manchester, M13 9PL, UK.

T +44 161 306 7666E [email protected]


document6

Technology

term avatars

ukabstract avatars

appropriateness of avatars

keywords avatars

online assistants

online survey

online retailers

online situations