1. JOURNAL OF
MARKETING
MANAGEMENT
A typology of roles for avatars in online retailing
Peter J. McGoldrick, MBS, University of Manchester, UK*
Kathleen A. Keeling, MBS, University of Manchester, UK
Susan 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.
INTRODUCTION
In offline retailing, the salesperson plays an important role in forming buyerseller 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
*Correspondence details and biographies for the authors are located at the end of the article
JOURNAL OF MARKETING MANAGEMENT, 2008, Vol. 24, No. 3-4, pp. 433-461
ISSN0267-257X print /ISSN1472-1376 online © Westburn Publishers Ltd.
doi: 10.1362/026725708X306176

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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 (humanlike), 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

3. McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing
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.
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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);

5. McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing
• 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 electronic 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’ communication 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 (Garbarino 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
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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.

7. McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing
FIGURE 1 Conceptual and operational framework
SALESPERSON
ROLES
SYNTHETIC
CHARACTERS
TYPE OF
Assisting
Persuading
Relationship
building
Reducing risk
Image
Service vs
self-service
Usage in
advertising
Brand building
Relational
interaction
Para-social
relationships
ECA design
AVATAR
ROLES OF
AVATAR
FRIEND
ROLE
PERSONAL
SHOPPER
PREFERENCES
TYPOLOGY
No help wanted
Functional help
Friendly help
All help
HELPER
ROLE
INTERNET USER
SEGMENTS
Age
Gender
Online buying
how long?
how often?
Regional
GUIDELINES FOR USE/DESIGN OF AVATARS
Appropriateness of avatar to context
Offer option of avatar or not
Choice of avatar: gender, format
Interaction style: friendly, functional
Adapt to demonstrated preferences
Adapt to browsing/buying context
Points in process at which to assist
Types of assistance to offer
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.
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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.

9. McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing
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 wordof-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
Male
Female
Household income
Less than £5,000
£5,000 - £9,999
£10,000 - £19,999
£20,000 - £29,999
£30,000 - £39,999
£40,000 - £49,999
£50,000 - £59,999
£60,000 or over
On-Line Shopper?
Yes
No
Total sample 2,114
%
37.8
62.2
%
5.3
6.3
20.0
23.0
17.2
12.6
7.1
8.5
%
91.7
8.3
Shop how long?*
Less than a month
1 – 6 months
7 – 12 months
1 – 2 years
3 – 5 years
Over 5 years
Shop how often?*
Several times a week
About weekly
2 – 3 times a month
About monthly
6 – 10 times a year
1 – 5 times a year
Less than yearly
%
2.7
6.6
7.9
25.5
38.2
19.1
4.2
11.5
24.0
21.7
17.1
18.7
2.8
Age group
18 – 24
25 – 34
35 – 44
45 – 54
55 – 64
65 or over
Location
U.K.
Rest of Europe
North America
South America
Africa
Asia
Oceania
*Percentages of online shoppers
%
12.6
29.7
27.5
20.4
8.8
1.0
56.9
2.4
20.8
0.9
1.4
4.5
13.2
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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,
TABLE 2 Preferred roles of an avatar
Types of Role Specific potential roles
Mean
3.87
SD
0.88
Loadings
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
.730 .825
Helper, solving problems and labour saving
Re-assuring about security aspects of the site.
3.69
1.14
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.

11. McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing
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
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Journal of Marketing Management, Volume 24
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. Kmeans 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
TABLE 3 Cluster centres – whole and split sample
Clusters
Split Sample 1
Cluster
1
2
3
4
Split Sample 2
Cluster
1
2
3
4
Whole Sample
Cluster
1
2
3
4
F Ratio (p = )
Friend
Role
Mean
1.44
2.74
3.89
4.29
Personal Shopper
Role
Mean
1.31
3.10
2.83
4.06
Helper
Role
Mean
1.77
3.47
3.95
4.50
Cluster
Size
N
79
277
284
429
1.48
2.98
3.98
4.35
1.43
3.25
2.75
4.06
1.95
3.62
3.99
4.50
107
312
255
371
1.49
2.88
3.96
4.29
1.40
3.16
2.77
4.06
1.89
3.53
4.00
4.48
191
592
514
817
2101.71 (.000)
1593.15 (.000)
1578.39 (.000)

13. McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing
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”.
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TABLE 4 Differences by gender and age
% in Cluster
Respondent
Gender
Male
Female
Chi-sq (p = )
Age
18 – 24
25 – 34
35 – 44
45 – 54
55 – 64
Chi-sq (p = )
No Help
Wanted
Functional
Help
Friendly
Help
13.5
6.3
42.37
30.6
26.4
22.4
25.5
33.5
41.8
(.000)
6.4
7.2
10.0
9.5
15.7
34.6
32.5
27.0
23.2
18.9
18.8
22.6
24.7
26.7
29.7
40.2
37.7
38.3
40.6
35.7
3df
40.41
12df
(.000)
Mean Age
41.3
Brown-Forsythe (p = )
11.95
Friend Role
Mean
3df
Personal Shopper
Role Mean
(.000)
Helper Role
Mean
Gender
Male
Female
3.38
3.67
3.13
3.33
3.68
3.97
T-test (p = )
(.000)
(.000)
(.000)
3.49
3.55
3.55
3.67
3.47
3.67
3.38
3.32
3.21
3.22
3.00
3.17
3.85
3.84
3.83
3.97
3.78
3.92
.061 (.005)
-.084 (.000)
.063 (.004)
Avatar Role Scales
36.7
All Help
Appreciated
40.2
38.7
Age
18 – 24
25 – 34
35 – 44
45 – 54
55 – 64
65 and over
Spearman Corr (p = )
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

15. McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing
TABLE 5 Differences by online buying experience
% in Cluster
Experience Buying Online
How Long Bought Online
Less than a month
1 – 6 months
7 – 12 months
1 – 2 years
3 – 5 years
Over 5 years
Chi-sq (p = )
No Help
Wanted
Mean Years
Brown-Forsythe (p = )
How Often Buy Online
Several times a week
About weekly
2 – 3 times a month
About monthly
6 – 10 times a year
1 – 5 times a year
Less than yearly
Chi-sq (p = )
Mean Buying per Month
Brown-Forsythe (p = )
TOTAL
Avatar Role Scales
Experience Buying Online
How Long Bought Online
Less than a month
1 - 6 months
7 - 12 months
1 - 2 years
3 - 5 years
Over 5 years
ANOVA F Ratio (p = )
Spearman Corr. (p = )
Functional
Help
Friendly
Help
1.9
0.8
4.6
5.5
12.3
15.4
94.56
19.2
21.1
26.1
25.5
29.6
32.4
21.2
21.9
20.9
30.7
24.3
18.4
4.4
3.6
24.69
15df
3.1
3df
16.0
32.1
14.8
19.4
32.0
12.2
23.2
26.7
9.2
23.5
29.5
9.0
27.2
24.8
9.4
26.2
28.4
8.8
41.8
21.8
1.8
27.09
18df
2.2
1.9
1.6
3.93
3df
9.0
28.0
24.3
Friend Role
Personal Shopper
Mean
Role Mean
All Help
Appreciated
57.7
56.3
48.4
38.4
33.8
33.8
(.000)
3.0
(.000)
37.0
36.5
40.9
38.0
38.7
36.6
34.5
(.077)
1.9
(.008)
38.6
Helper Role
Mean
3.99
3.98
3.67
3.71
3.42
3.31
16.50 (.000)
-.182 (.000)
3.71
3.58
3.44
3.28
3.14
3.13
10.12 (.000)
-.117 (.000)
4.30
4.21
4.02
3.99
3.74
3.64
17.29 (.000)
-.191 (.000)
Partial Corr. [control for age]
How Often Buy Online
Several times a week
About weekly
2 - 3 times a month
About monthly
6 - 10 times a year
1 - 5 times a year
Less than yearly
ANOVA F Ratio (p = )
-.193 (.000)
-.117 (.000)
-.200 (.000)
3.35
3.41
3.59
3.53
3.56
3.58
3.78
1.86 (.083)
3.24
3.21
3.29
3.26
3.24
3.17
3.27
0.67 (n.s.)
3.63
3.74
3.87
3.86
3.88
3.88
4.18
2.88 (.008)
Spearman Corr. (p = )
-.045 (.047)
.035 (n.s.)
-.061 (.007)
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16. JMM
Journal of Marketing Management, Volume 24
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
FIGURE 2 Role scales and customer segments
4.5
4
F rie n d
R o le
3.5
Personal
Shopper
3
H e lp e r
R o le
Gender
Age Range
>5 Yrs
3-5 Yrs
1-2 Yrs
1-6 Mon
7-12 Mon
<1 Months
55-64
45-54
35-44
25-34
18-24
Male
2.5
Female
SCALE MEANS
448
How Long Using

17. McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing
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 subsamples 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
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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 multichannel 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

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

21. 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.

25. McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing
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
SubSample n
% Adding
Comments
Number of
Comments
Comments/
Respondent
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
United Kingdom
APPENDIX 3
Examples of Additional Comments on Avatar Roles
HELPER ROLES
Make an effort to assure customer of next shipment
delivery if sold out
Asking if a language translation is needed for the site
An assistant would be very helpful for new internet
buyers
Helping with sites we have not visited before
Sometimes I get lost in complicated sites – then I would
love someone to pop up and help
Convert money values where you come from
Payment site – repeating numbers back – easier to
check the accuracy
Locating special deals on what I am interested in
(Male, Age 55-64, Asia, <1 Year
Shopping Online)
(M, 45-54, Oceania, 1-2)
(F, 35-44, Oceania, <1)
(F, 55-64, S. America, <1)
(F, 35-44, Oceania, <1)
(F, 45, N. America, >5)
(F, 35-44, N. America, 1-2)
(F, 35-44, Europe, <1)
Cont’d...
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Coming to the rescue if confused or lost
Where there seems to be an excessive amount of time
deciding, asking if some help is needed
Comparing the prices and quality with other
competitors, if possible
Advising that what I had done was correct
Pointing out technical details (e.g., shipping fees and
time, payment methods) non intrusively
Guided tours of specific parts of the site, not just one of
the whole site
SOCIAL ROLES
Congratulating me on my purchase and asking me to
come back soon
All aspects appeal to me using a buddy, so to speak
Interaction is a positive approach … can make
purchasing fun
Thanking you when you have made the purchase
Asking for the birthday of the purchasers – sending
birthday greetings
When it is your birthday greeting you
Enlightening the experience
During payment process, a friendly face to ease the
frustration
Maybe dressed in season costume, e.g., an elf at
Christmas
I would like one to conclude my visit (with a purchase
made) by saying goodbye AND thank you
Maybe include the occasional joke of the relevant site
Only in places where it is used solely for humour
Throughout – a sense of humour – sadly lacking in real
shop assistants
PERSONAL SHOPPER ROLES
When anything out of stock, suggest alternatives
Defining and describing the product features
Ask purpose for which I need a particular product and
suggest the best option, for example
Suggestions for a substitute if what I wanted was not in
stock
Matching other items and advising what ones would be
suitable
Offer alternative options based on information given
(e.g., different gift ideas)
Showing items that match my favourite colors and for
my family ages
Asking for a ballpark spend figure to speed up selection
Point out shortcomings and advantages associated with
the product
(F, 35-44, UK, 3-5)
(M, 45-54, Oceania, 3-5)
(M, 18-24, Asia, <1)
(M, 45-54, Africa, 1-2)
(F,18-24, Asia, <1)
(M, 25-34, UK, 1-2)
(F, 18-24, N. America, <1)
(F, 55-64, UK, 305)
(F, 55-64, UK, 3-5)
(F, 18-24, UK, <1)
(M, 35-44, Asia, <1)
(F, 35-44, Oceania, <1)
(F, 35-44, S. America, 3-5)
(F, 35-44, N. America, 3-5)
(M, 25-34, UK, >5)
(F, 18-24, N. America, 3-5)
(F, 25-34, Oceania, <1)
(M, 45-54, UK, >5)
(F, 45-54, UK >5)
(F, 55-64, Africa, 3-5)
(F, 35-44, Asia, 3-5)
(M, 25-34, Asia, 1-2)
(F, 45-54, N. America, 3-5)
(F, 35-44, Europe, <1)
(F, 45-54, UK, 3-5)
(F, 35-44, N. America, 3-5)
(M, 45-54, UK, 3-5)
(F, 45-54, Africa, 1-2)
Cont’d...

27. McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing
Explaining products I maybe haven’t seen before and
giving ideas on what they are for
Offer suggestions if first choice was not available
Suggested purchases for particular occasions, e.g.,
birthdays
At log-on, “What are we looking for today?”
NEGATIVE COMMENTS
I would find an online assistant annoying and suspect it
would slow down the purchase process
I don’t really like them to be honest!
No online assistant … it slows down the website
Make no assumption that the user has broadband
access!
If it takes too much time to load onto a browser, people
will leave
I wouldn’t – it is one reason why internet shopping is so
appealing, no nagging sales clerks
I hate them popping up everywhere and just getting in
the way slowing things down
Needs to be time efficient though as often in lunch hour
I find online assistants a bit patronising unless they are
linked to a real person in a call centre somewhere
Personally I find them distracting and often annoying
There seems to be nothing that an online assistant can
do that cannot be adequately done with plain text
(F, 25-34, Asia, 1-2)
(F, 35-44, UK, 3-5)
(F, 18-24, UK, 3-5)
(M, 35-44, UK, <1)
(F, 45-54, Asia, <5)
(F, 25-34, Oceania, 3-5)
(M, 45-54, N. America, <5)
M, 45-54, Europe, 3-5)
(F, 18-24, N. America, 3-5)
(F, 45-54, N. America, 3-5)
(M, 55-64, UK, 3-5)
(F, 25-34, UK, 3-5)
(F, 25-34, UK, >5)
(F, 35-44, UK, 3-5)
(M, 35-44, UK, 3-5)
AVATAR OPTIONS
I 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
(F, 45-54, N. America, 3-5)
gender etc. you could have the option
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.
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APPENDIX 4
Exploratory analysis of regional differences
% in Clusters
Main Sample Regions
UK
North America
Oceania
Chi-sq
(p = )
No Help
Wanted
12.7
5.7
2.9
63.48
Functional
Help
31.7
22.7
24.1
Friendly
Help
22.1
28.2
27.7
6 df
All Help
Appreciated
33.5
43.4
45.3
(.000)
Avatar Role Scales
(# cases)
(1203)
UK
Rest of Europe (50)
North America (440)
(278)
Oceania
(96)
Asia
Friend Role
Mean
3.37
3.80
3.78
3.77
3.99
Personnel Shopper
Role Mean
3.13
3.37
3.37
3.37
3.68
Helper Role
Mean
3.72
3.97
3.98
4.12
4.24
ANOVA F Ratio
24.00 (.000)
13.12 (.000)
20.81 (.000)
(p= )
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 (McGrawHill), 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 MBSUMIST 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 peter.mcgoldrick@manchester.ac.uk
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

29. McGoldrick, Keeling and Beatty A typology of roles for avatars in online retailing
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 3519
E kathy.keeling@manchester.ac.uk
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 “HumanComputer Relationships and Persuasiveness in E-retailing”. She developed two user
panels and three large, web-based experimental studies to investigate the relationshipbuilding 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 7666
E susan.beatty@manchester.ac.uk
461