A typology of internet users basedon comparative affective states:evidence from eight countries

George Christodoulides and Nina MichaelidouBirmingham Business School, The University of Birmingham,

Birmingham, UK, and

Nikoletta Theofania SiamagkaHull University Business School, University of Hull, Hull, UK

Abstract

Purpose The role of affective states in consumer behaviour is well established. However, no studyto date has examined online affective states empirically as a basis for constructing typologies ofinternet users and for assessing the invariance of clusters across national cultures. This paper aims toaddress this issue.

Design/methodology/approach Four focus groups were carried out with internet users to adapta set of affective states identified from the literature to the online environment. An online survey wasthen designed to collect data from internet users in four Western and four East Asian countries.

Findings Based on a cluster analysis, six cross-national market segments are identified andlabelled Positive Online Affectivists, Offline Affectivists, On/Off-line Negative Affectivists,Online Affectivists, Indistinguishable Affectivists, and Negative Offline Affectivists. Theresulting clusters discriminate on the basis of national culture, gender, working status and perceptionstowards online brands.

Practical implications Marketers may use this typology to segment internet users in order topredict their perceptions towards online brands. Also, a standardised approach to e-marketing is notrecommended on the basis of affective state-based segmentation.

Originality/value This is the first study proposing affective state-based typologies of internetusers using comparable samples from four Western and four East Asian countries.

Keywords Typology, Internet users, Affect, Cluster analysis, Behaviour, Attitudes

Paper type Research paper

IntroductionRecent statistics indicate that the number of people going online is now close to twobillion (27 per cent of the worlds population). Indicative of the enormous growth ofinternet usage is the fact that 2000-09 saw an increase of almost 400 per cent in internetusage (Internet World Stats, 2009). With the growing number of internet usersworldwide, it is essential for marketers to develop a solid understanding of onlineconsumer behaviour. The internet has helped firms overcome geographical barriers bymaking it easier for them to offer their products and services to internationalaudiences. From the consumers perspective, the internet has reduced customerslocational dependence (Sheth and Sharma, 2005, p. 620). As Wilson (1999) suggestedover a decade ago, Once you are on the web, you are immediately a global company(in Barnes et al., 2007, p. 72). One of the key challenges for firms wishing to establish anonline presence is therefore how to address the needs and wants of international

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Received 8 February 2010Revised 29 July 2010

7 December 2010Accepted 4 February 2011

European Journal of MarketingVol. 47 No. 1/2, 2013

pp. 153-173q Emerald Group Publishing Limited

0309-0566DOI 10.1108/03090561311285493

consumers as they are likely to be different from those of local consumers.Cross-national research indicates that online usage (e.g. Singh et al., 2006) as well asonline buying behaviour (e.g. Kuhlmeier and Knight, 2005; Park and Jun, 2003) differsfrom country to country, suggesting that a standardised internet marketing strategymay not be appropriate for all consumers.

For several years marketers have dealt with consumer heterogeneity by segmentingthe market to identify and address different consumer clusters. Such an approach hasenabled marketers to capitalise on the specific needs of consumers in every cluster so asto increase the likelihood of satisfying consumers more effectively. The wealth ofinformation that marketers are able to collect about their customers online means thatthey can more precisely segment the market and better match their products andservices to consumer clusters (Dibb and Stern, 1999). A number of relevantpsychographic and behavioural bases have been advocated in the literature in orderto segment internet users and shoppers, including online shopping motives such asvariety seeking and convenience (Rohm and Swaminathan, 2004), personality variables,trust, attitude, perceived risk and shopping enjoyment (Barnes et al., 2007). Yet, albeit theinternational nature of the internet, it is surprising that only a small number of studies(e.g. Barnes et al., 2007; Brengman et al., 2005; Shiu and Dawson, 2002) have developedtheir segmentation typologies using data from more than one country.

A second gap in the literature concerns the examination of online users affectivestates and their use as a basis for segmenting internet users. While the role of emotionsand feelings in consumer decision making is well documented in the literature (e.g. Hanet al., 2007; Kwortnik and Ross, 2007; Ruth et al., 2002) and despite research highlightingthe importance of isolated affective dimensions for internet usage and shopping(e.g. Davis et al., 1992; Elliot and Fowell, 2000; Lim et al., 2008), to date no segmentationtypology exists based on internet users affective states as a whole. For instance,previous research suggests that consumers often feel more insecure online than offlinedue to a higher perceived risk (Ko et al., 2004). In addition, evidence shows that internetusage and online shopping are negatively related to perceived risk (Elliot and Fowell,2000; Lim et al., 2008; Miyazaki and Fernandez, 2001) and positively related to perceivedtrust (Bart et al., 2005; Lim et al., 2008) and enjoyment (Davis et al., 1992; Lim et al., 2008).It is therefore evident that context-specific affective states could play a critical role ininternet usage levels and e-shopping behaviour. In line with previous research (Joneset al., 2008, p. 419), all media are potentially emotive (and not always in a positivemanner). However, characteristics specific to the internet such as interactivity andengagement . . .confer emotion-evoking advantages that offline media lacks.

To address the two gaps outlined above (i.e. the lack of research on cross-nationalsegmentation of internet users and the limited knowledge with regard to onlineaffective states) this study develops a typology of internet users based on theircomparative online affective states. Specifically, it focuses on the emotions and feelingsthat users experience online compared to offline, using data derived from four Westernand four East Asian countries. The paper opens with a literature review oninternational and internet segmentation, followed by a discussion of past research onaffective states. It then explains the methodology adopted to develop a set of affectivestates pertinent to online environments, and outlines the procedures to derive clustersof internet users from data collected in four Western (UK, USA, Australia and Canada)and four East Asian countries (China, South Korea, Japan and Singapore). The findings

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are then discussed, and the implications for marketers are highlighted. Consistent withprior literature (e.g. Blackwell et al., 2001; Barnes et al., 2007), in this study marketsegmentation and consumer typologies are considered synonymous.

Literature reviewInternational segmentationIn response to increased internationalisation of markets, scholars have turned theirattention to identifying efficient and effective strategies for segmenting consumersacross international markets. Segmentation strategies and bases were investigatedwithin international contexts in an effort to form cross-national consumer segments(e.g. Barnes et al., 2007; Boote, 1983; Lesser and Hughes, 1986; Steenkamp and Hofstede,2002). Central to international segmentation is the benefit of . . . structuring theheterogeneity that exists among consumers and nations (Steenkamp and Hofstede,2002, p. 186). By identifying segments across national borders, practitioners can addressheterogeneity more effectively. In this way, businesses can develop strategies tailored tothe needs of different consumer segments that cut across surpassing national borders.Firms embarking on international segmentation may achieve significant benefitsincluding cost reductions through economies of scale, enhanced product quality andincreased market competitiveness (Steenkamp and Hofstede, 2002). The generalisabilityof consumer segments has been attested to in several studies which have developedtypologies or segments of consumers, based on cross-national data (Boote, 1983;Moskowitz and Rabino, 1994; Hofstede et al., 1999). For example, Brengman et al. (2005)found that web-usage lifestyle has the same structure in Belgium and in the USA, whileHofstede et al. (1999) identified four consumer segments, one of which consisted ofconsumers from eleven European countries. The results of such studies substantiate theuse of international segmentation as an effective strategy within a highlyinternationalised business environment.

The increasing penetration of the internet as a retail channel provides businesseswith opportunities to internationalise their marketing activities. By abolishinggeographical boundaries, online markets are more accessible to consumers from allaround the world. On the internet, consumers can browse through the different productaisles without any effort, having full control over the information they seek and theweb sites they visit (Menon and Kahn, 2002). In view of the rapid increase in internetuse (Chinn and Fairlie, 2006), understanding the needs of online consumers has becomevital (Bhatnagar and Ghose, 2004a).

Segmentation of internet users/shoppersScholarly inquiry within the online context has focused on developing segmentationstrategies for online consumers (Allred et al., 2006; Barnes et al., 2007; Bhatnagar andGhose, 2004a; Brengman et al., 2005; Gonzalez and Paliwoda, 2006; Shiu and Dawson,2002; Swinyard and Smith, 2003; Vellido et al., 1999). Demographic variables, such asgender and age (Shiu and Dawson, 2002) or web usage statistics (Assael, 2005; Bonnet al., 1999) have been considered as segmentation bases for online shoppers. Yet, thereis some evidence suggesting that demographics fail to discriminate between differentweb buyers/non-buyers (Bhatnagar and Ghose, 2004a; Brashear et al. 2009).

To better understand online consumers, researchers have also examined consumerspsychographics. The benefits and risks of internet shopping (Bhatnagar and Ghose,

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2004a, b), lifestyles (Allred et al., 2006; Brengman et al., 2005; Swinyard and Smith,2003), shopping motivations (Rohm and Swaminathan, 2004), motivation to adoptinternet banking (Lee et al., 2005), web users behaviour (Kau et al., 2003), extraversionand neuroticism, trust, perceived risk, attitudes towards online shopping, shoppingenjoyment and willingness to buy (Barnes et al., 2007) constitute some of thesegmentation bases proposed in the literature.

Empirical evidence sheds light on the relationship between different consumersegments and their respective online buying behaviour (Allred et al., 2006; Barnes et al.,2007; Bhatnagar and Ghose, 2004a; Rohm and Swaminathan, 2004). For example,Allred et al. (2006) identified consumer segments, which are reluctant to purchasegoods online due to security concerns and technological incompetence. Significantvariations in the online behaviour of clusters have also been observed in the literaturefor different product categories. In particular, Bhatnagar and Ghose (2004a) foundvariations between clusters in terms of attitude towards online purchases.

Although various segmentation bases have been proposed to suit the onlineenvironment, including isolated affective states such as trust and enjoyment (Barneset al., 2007), no typology to date has considered internet users affective statesholistically. However, affective states, including emotions and feelings, have asignificant impact on consumers behaviour (Bigne` and Andreu, 2004; Donovan et al.,1994; Foxall, 1997; Han et al., 2007; Kwortnik and Ross, 2007; Menon and Kahn, 2002).In particular, internet-specific affective states (e.g. Bart et al., 2005; Cho, 2004; Daviset al., 1992; Elliot and Fowell, 2000; Forsythe and Shi, 2003; Lim et al., 2008; Miyazakiand Fernandez, 2001) have been shown to affect marketing outcomes (e.g. Jones et al.2008; White, 2010). On this basis, this study proposes a new segmentation basis basedon users comparative affective states.

Affective states in marketingThe terms affect, feelings, emotions and moods have been used inconsistently in theliterature (Bagozzi et al., 1999). For example, Bagozzi et al. (1999) and Ajzen (2001) useaffect to capture both emotions and moods, while Cohen and Areni (1991) refer to affectas feeling states that include emotions and moods. While there is no unified definitionof affect and what it constitutes, the term has been loosely used in the literature to referto emotions and moods, albeit that the two are different mental processes (Bagozzi et al.,1999; Scherer, 2005; Beedie et al., 2005; Davidson, 1994; Parkinson et al., 1996).Specifically, emotions are defined as . . .mental states of readiness that arise fromcognitive appraisals of events or thoughts (Bagozzi et al., 1999, p.184) and refer tospecific objects or stimulus events (Scherer, 2005). By contrast, moods are unfocused,low intensity mental states which last longer than emotions, and lack intentionalcapacity and action tendencies (Bagozzi et al., 1999; Beedie et al., 2005; Parkinson et al.,1996). Furthermore, emotions and feelings have also been used interchangeably inresearch, compounding their conceptual boundaries (Damasio, 2004; Scherer, 2004).Feelings are generally more quickly acting than emotions, and tend to be closer tosensory stimulation (Pettinelli, 2008). They are also unintentional in that they lack theevaluative, cognitive and motivational components which are typical of emotions(Ben-Zeev, 1987). Previous research has used emotions and/or feelings as a proxy tomeasure affect (e.g. Lavine et al., 1999).

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The body of research knowledge on emotions and feelings within the marketingdiscipline has been drawn from environmental psychology (Mehrabian and Russell,1974; Penz and Hogg, 2011), social psychology (e.g. Parrott, 2001), neurobiology(e.g. Damasio, 2004), cognitive theories (e.g. Lazarus, 1984) and even philosophy(e.g. Platos Republic in 360 B.C. in Allen, 2006). In addition, the development of thePositive Affect (PA) and Negative Affect (NA) scales as incorporated within thePANAS scale (Watson et al., 1988), have enhanced researchers abilities to measure PAand NA reliably, showing stability over a significant time period. To add to theexisting research on emotions, marketing scholars have presented a number ofemotional responses (Laros and Steenkamp, 2005; Richins, 1997). On the lower level ofspecificity, emotions are distinguished between negative and positive affect (Laros andSteenkamp, 2005). A hierarchical model by Laros and Steenkamp (2005) draws ondifferent levels of specificity to introduce three levels of emotions. Starting with generalpositive and negative affect in the first level, and moving from one level to another, thespecificity increases to 42 specific emotions at the third level. A narrower distinctionbetween different emotions within the consumption context has been introduced byRichins (1997), who developed the Consumption emotions set (CES) based on 13emotions. This set encapsulates both positive and negative emotions, and wasempirically tested for its ability to distinguish between emotions associated withdifferent product classes. Variations in the emotions evoked by different consumptionsituations were evidenced; sentimental objects were the least likely to evoke negativeemotions such as anger and fear, while automobiles were likely to evoke feelings ofguilt. The findings illustrated that the nature of emotions experienced by consumersdepends on the specific consumption situation, and should not be isolated from thecontext in which they occur.

The marketing literature has established a significant impact in terms of affectivestates on consumer behaviour and attitudes (Bagozzi et al., 1999; Bigne` and Andreu,2004; Donovan et al., 1994; Foxall, 1997; Han et al., 2007; Kwortnik and Ross, 2007;Menon and Kahn, 2002; Vanhamme and Lindgreen, 2001; Williams and Aaker, 2002),positive word of mouth intentions (White, 2010) and viral marketing effectiveness(Dobele et al., 2007). Bagozzi et al. (1999, p. 202) provide a comprehensive account of theimpact of emotions on consumer responses and reach the conclusion that . . .emotionsare ubiquitous throughout marketing. Furthermore, numerous studies haveillustrated the impact of affective states on attitudes towards advertisments (Batraand Ray, 1986), customer satisfaction (Westbrook and Oliver, 1991; Vanhamme andLindgreen, 2001) and customer retention (Vanhamme and Lindgreen, 2001), consumermistrust of firms (Vanhamme and Lindgreen, 2001), actual purchase behaviour(Donovan et al., 1994), consumers approach and/or avoidance behaviour (Foxall, 1997;Penz and Hogg, 2011) and browsing and shopping behaviour (Menon and Kahn, 2002).

The impact of emotions on consumer behaviour cannot be generalised acrossdifferent consumption situations and channels. Richins (1997) highlights that emotionstriggered by exposure to advertising are different from emotions triggered inconsumption situations, in that they encompass different affective responses. Previousresearch has shown that although, in some situations, consumers act in such a way asto trigger positive affect (Kwortnik and Ross, 2007), in other situations positive feelingssuch as pleasure become less significant (Celsi et al., 1993). Within a consumptionsetting, consumers are often confronted by mixed emotions (Penz and Hogg, 2011; Ruth

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et al., 2002; Williams and Aaker, 2002). Empirical evidence suggests that those with ahigher propensity to accept duality feel more comfortable when exposed to mixedemotional appeals, than those with a lower propensity (Williams and Aaker, 2002).

Further research has examined the role of various affective states within theinternet retail channel (e.g. Jones et al., 2008; Menon and Kahn, 2002; Penz and Hogg,2011). Jones et al. (2008) suggest that triggering positive emotions online could benefit abrand through increased loyalty, trust and ultimately through market share. Specificemotions such as enjoyment have been found to positively affect intentions and actualpurchases online (Davis et al., 1992; Lim et al., 2008; Vijayasarathy, 2003). In particular,Menon and Kahn (2002) suggest that the sequence or order in which consumers visitweb sites can have a significant impact on their purchase behaviour. For example, ifconsumers initially visit a pleasurable web site, they are more likely to shop online.Also, the peak or the last emotion experienced is often the one that is best remembered,and consequently has considerable impact on overall evaluations ( Jones et al., 2008).These findings suggest that the emotions or feelings induced by online firms play animportant role in shaping consumer behaviour. In the light of this, affective statescould be applied as a segmentation base to target consumers more effectively, and topredict the behaviour of each of the affective state-based segments. Despite therelevance and significance of emotions and feelings in marketing, and in particularwithin online contexts (E`thier et al., 2006), very few studies use emotions or feelings asa segmentation base (e.g. Bigne` and Andreu, 2004).

MethodologyIdentifying affective statesFour UK focus groups made up of internet users were initially used to adapt a set ofaffective states based on the literature to the online environment. The first focus groupconsisted of younger internet users, mainly students (18-24 year of age); the secondfocus group comprised young adults (34-45 years of age); the third focus group wasmade up of middle aged users (45-54) and the fourth focus group consisted of olderusers (60 ). The deliberations of each focus group were recorded and lasted anaverage of 1.5 hours. A total of 12 participants attended the first focus group (six maleand six female); nine the second (four male and five female); ten the third (four male andsix female); and 12 the fourth (five male and seven female). The transcripts of the focusgroups were analysed separately by each of the researchers. Based on the literature, alist of a priori codes was developed. However, additional codes were also allowed toemerge from the transcripts (Miles and Huberman, 1994). What was evident in all thefocus groups was that although our questions addressed the affective states thatinternet users experience when they are online (i.e. in absolute terms), our participantskept referring to their affective states online versus offline (i.e. in relative terms). Aresultant list of 19 adjectives, both positive (e.g. happy) and negative (e.g. stressed) wasproduced to form a set of online affective states (see Table I).

Given the nature of the subject, an internet questionnaire was developed includingthe 19 items measured on a five point comparative scale such as emerged from thefocus groups (1 much more online, 2 slightly more online, 3 about the sameboth online and offline, 4 slightly more offline, 5 much more offline). Thequestionnaire was pre-tested using a sample of 50 students in the UK. Revisions weremade to the phrasing of the question to read Thinking now about your feelings and

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emotions, where do you feel most . . . In order to avoid the systematic order effect, theinternet survey was designed to rotate the affective states. The main data collectionwas conducted in four Western (UK, USA, Australia and Canada) and four East Asiancountries (China, South Korea, Japan and Singapore) by a large market researchcompany using panel data sets and quota sampling to control for respondent selectionbias. Many cross-cultural researchers have identified significant differences betweenWestern and East Asian consumers, especially so with regard to online affective states(e.g. Jarvenpaa and Tractinsky, 1999; Teo and Liu, 2007), rendering the countriesselected interesting for comparison. All respondents were internet users and membersof online research panels. The questionnaire was back-translated and pre-tested by theagency in each country. Quotas were further imposed for gender, age, location(urban/rural) and working status (working/not working) to ensure that the samplefrom each country was representative of that countrys internet population at the timeof the data collection. For each country a gift voucher worth $200 was used as a prize ina draw.

Discriminating variablesThe questionnaire also included demographic (country, gender, age, working status)and attitudinal variables (general online brand perceptions) to be used for the externalvalidation of the clusters. Segmentation studies commonly utilise relateddiscriminating variables to perform external validity checks which validate theircluster solutions. Ketchen and Shook (1996) report that 22 per cent of the studiesreviewed in their paper assessed criterion-related validity via ANOVA tests on

ClustersAffective states 1 2 3 4 5 6

Happy 1.81 4.12 2.71 1.61 2.95 2.90Fulfilled 2.04 4.47 3.02 1.56 3.08 3.29Beautiful 2.13 4.20 2.91 1.57 3.07 2.97Deceitful 3.11 2.91 2.52 2.01 2.94 2.79Playful 2.11 3.73 2.26 1.66 2.92 2.79Comfortable 1.86 4.32 2.74 1.66 3.04 2.99Anxious 3.67 3.42 3.21 2.15 2.94 3.61Confident 1.92 4.03 2.53 1.56 3.00 2.75Stressed 3.98 3.61 3.57 2.47 2.96 4.19Sexy 2.45 3.88 2.76 1.60 3.00 2.96Powerful 2.29 3.83 2.69 1.69 3.03 3.00Adventurous 2.05 3.71 2.27 1.61 2.94 2.52Conservative 3.63 3.40 3.37 2.16 2.99 3.48Invisible 3.45 2.58 1.89 1.95 2.85 3.24Brave 2.07 3.62 2.20 1.69 2.99 2.75Anonymous 2.43 2.34 1.58 1.67 2.82 2.18Expressive 1.82 3.99 2.23 1.71 3.02 2.65Self-conscious 2.62 3.70 3.24 1.99 3.02 3.64Imaginative 1.82 3.73 2.36 1.57 2.91 2.86Cases 196 161 271 87 636 242Percentage of sample 12.3 10.2 17 5.4 40 15.1

Note: Cluster descriptors range from 1: much more online to 5: much more offlineTable I.

Cluster solution

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non-clustering variables. Online brand perceptions refer to consumers attitudestowards online brands in general and, consistent with the branding literature(Christodoulides et al., 2006; Keller, 2003), are measured through five items such asOnline brands help me develop my identity and personality and Online brands areuseful as they allow me to communicate with others on a five-point Likert scale.Previous research implies that consumers emotions influence their perceptions ofbrands (Gobe, 2001; Thompson et al., 2006).

Analysis and findingsDemographic characteristicsThe sample consisted of 1,593 internet users from 8 countries. Table II shows thedemographic characteristics of the sample, indicating a similar set of respondents fromeach country.

Cluster analysisAlong similar lines to Richins (1997), the affective states were not subjected to factoranalysis to produce composite affective states. Instead, cluster analysis was performedto partition the sample of internet users into segments using the 19 discrete states.Cluster analysis is an exploratory technique which aims to uncover groups or clustersof observations that are homogeneous and separated from other groups (Everitt et al.,2001). The cluster analysis was conducted in two stages using a combination of clusterprocedures involving hierarchical and K-means methods in order to perform aninternal validation to ensure the stability of the result (Punj and Stewart, 1983).

In line with Punj and Stewart (1983), in stage 1 the data was randomly divided intotwo subsets (e.g. Lockshin et al., 1997). The first subset was used to generate thepossible alternative cluster solutions using hierarchical cluster analysis with Wards

UK USA Australia Canada ChinaSouthKorea Japan Singapore

n % n % n % n % n % n % n % n %

GenderMale 100 50 100 49.5 98 47.1 96 48.7 127 63.2 98 56 101 49 108 53Female 100 50 102 50.5 110 52.0 101 51.3 74 36.8 77 44 105 51 96 47Age16-30 42 21 50 24.8 55 26.4 49 24.8 76 37.8 80 45.7 53 25.7 70 34.331-40 50 25 48 23.8 39 18.7 48 24.3 48 23.8 58 33.1 63 30.5 61 29.941-50 54 27 52 25.7 40 19.2 49 24.8 22 10.9 21 12 35 16.9 38 18.651 54 27 52 25.7 74 35.5 51 25.8 55 27.3 16 9.1 55 26.6 35 17.1Working statusUnemployed 30 15 62 30.6 54 25.9 49 24.8 24 11.9 23 13.1 63 30.5 51 25Retired 60 30 38 18.8 44 21.1 47 23.8 45 22.3 11 6.2 10 4.8 16 7.8PT student 7 3 3 1.5 3 1.4 3 1.5 8 3.9 12 6.8 4 1.9 2 0.9FT student 15 7.5 21 10.3 10 4.8 14 7.1 18 8.9 19 10.8 8 3.8 17 8.3PT job 19 9.5 12 5.9 31 14.9 19 9.6 12 5.9 11 6.2 22 10.6 15 7.3FT job 50 25 50 24.7 48 23 50 25.3 89 44.2 83 47.4 88 42.7 96 47Other 19 9.5 16 7.9 18 13.4 15 7.6 5 2.4 17 9.7 11 5.3 7 3.4Total 200 202 208 197 201 175 206 204

Table II.Characteristics of thesamples

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method (Rohm and Swaminathan, 2004; Singh, 1990). Hierarchical clustering does notpartition the data into a particular number of clusters at a single step. Instead, theclassification consists of a series of partitions that may run from a single clustersolution to an n cluster solution (Everitt et al., 2001). The hierarchical cluster analysisconducted on the first subset indicated four different possible solutions: four, five, sixand seven clusters. Subsequently, stage 2 of the procedure involved conductingK-means cluster analysis on the second subset of the data. The K-means methodpartitions the data into a specified number of groups, in our case four, five, six andseven clusters, as indicated by the hierarchical cluster analysis on the first subset of thedata. The initial centroids provided by the hierarchical analysis on the first subset werealso used to parameterise the K-means analysis in the second subset, in line with Punjand Stewart (1983). To establish the best clustering solution, memberships fromK-means analysis were then compared with memberships produced from thehierarchical cluster analysis. The degree of agreement between the K-meansassignments (of the second subset) and the results of the hierarchical analysis providedan indication of the solutions stability (Punj and Stewart, 1983). The six clustersolution was chosen as the most appropriate solution in terms of stability andreproducibility. The datasets were then combined, and a final K-means cluster analysiswas conducted in line with Everitt et al. (2001). The final cluster solution is presented inTable I.

ClustersClusters are described on the basis of the level of specific emotions and feelings thatthey entail. Consistent with the previous literature which indicates that emotions andfeelings (Lavine et al., 1999) capture affect, the clusters were labelled, based on overallpositive or negative affect.

. Cluster 1. This cluster consists of 196 internet users representing 12 per cent ofthe sample. Compared to the other clusters, members in cluster one experiencednegative affective states offline (anxious, stressed, conservative and invisible)compared to online, where they felt comparatively more happy, comfortable,confident, expressive and imaginative. This cluster is labelled positive onlineaffectivists.

. Cluster 2. This cluster includes 161 internet users (10 per cent of the sample).Members of this cluster experienced more intense affective states offline. Withthe exception of one feeling (i.e. anonymous), they seemed not to experience anyother to a greater extent online which might suggest that the real world is muchmore important to them. Compared to the other clusters, they have more positiveemotions and feelings offline rather than online. Feeling anonymous online maybe the reason why members of these cluster experience more intense emotionsand feelings offline. Internet users in this cluster are labelled offline affectivists.

. Cluster 3. This cluster comprised 17 per cent of the respondents. Members of thiscluster were more anxious, stressed, conservative and self-conscious offline andfelt more invisible and anonymous online. However, compared to the otherclusters, they seemed to experience the same level of positive emotions andfeelings both online and offline (e.g. powerful, sexy, confident). They aretherefore labelled on/off-line negative affectivists.

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. Cluster 4. This cluster is the smallest cluster with 87 members representing 5.5per cent of the sample. The members of this cluster had the highest scores amongthe clusters on all affective states. They are the opposite of cluster 2, and in factlive their life online, experiencing more intense emotions and feelings onlinecompared to offline. This cluster is labelled online affectivists.

. Cluster 5. This cluster comprises the highest number of respondents (40 percent). Members in this cluster assigned the same affective intensity to the realworld and to the online world (with a mean of around 3). They are thereforenamed indistinguishable affectivists.

. Cluster 6. This cluster includes 242 internet users (15 per cent). Members of thiscluster felt more anxious, stressed, self conscious and conservative offline andmore anonymous, brave, adventurous, expressive and imaginative online. Incontrast to the other clusters, members in this cluster felt much more stressedoffline compared to online. On this basis, this cluster is labelled negative offlineaffectivists.

External validity of the clustersIn line with the previous clustering research, to establish the external validity of thecluster solution, criterion-related validity was assessed using attitudinal (online brandperceptions) and demographic variables (country, gender, age, working status) notincluded in the development of the clusters. Reliability analysis was established for theonline brand perceptions scale at a 0.63. The overall score was subsequently used inthe cluster validation procedure, which involved Chi-square tests and ANOVA. Thefindings of the x 2 tests for demographic variables show significant differences acrossclusters in terms of working status (x 2 50:987, df 30, p , 0:05), gender(x 2 30:877, df 5, p , 0:000) and national culture (x 2 207:5, df 35,p , 0:000). Age does not discriminate the clusters. Furthermore, ANOVA indicatessignificant differences in online brand perceptions (F 64:978, df 3, p , 0:000).

Table III shows significant (p , 0:05) pairwise differences between clusters interms of their online brand perceptions. Specifically, clusters vary in terms of theirpositive or negative perceptions about online brands usefulness and expressive value.Table IV shows the demographic descriptions of the clusters in terms of nationalculture, gender, age and working status, although age does not discriminate theclusters, while Table V shows the mean and standard deviation values reported foreach cluster in terms of online brand perceptions. In particular, positive onlineaffectivists have less positive online brand perceptions compared to clusters ofon/off-line negative affectivists and online affectivists. As such, these internet usersbelieve that online brands are less useful and not value expressive.

DiscussionThis study adds to existing knowledge by using affective states to segment internetusers from different countries into different and distinct clusters. Our studydifferentiates from that of Rohm and Swaminathan (2004) and Barnes et al. (2007) inthat we use a new base for segmenting internet users, that of affective states, as well asusing a sample of internet users from four Western (i.e. UK, USA, Australia andCanada) and four East Asian (i.e. China, South Korea, Japan and Singapore) countries.It also contributes to the existing literature by integrating the notion of affective states

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in the spectrum of segmentation bases used in both online and offline contexts. To thebest of our knowledge, no previous research has examined online affective statesholistically and in a comparative manner. This study has built on the extant literatureand has developed a set of the affective states that individuals experience in an onlineenvironment. Although a primary contribution of this research is the identification ofclusters of internet users based on their affective states, a side contribution is theidentification of 19 affective states that individuals experience online compared to theoffline world. This set of affective states will help market researchers obtain a betterunderstanding of the dual nature of contemporary consumers who increasingly switchbetween online and offline channels, and will provide a point of departure forpractitioners who wish to identify a set of online affective states pertinent to theirspecific business context (e.g. gambling sites).

The findings of this study suggest six distinct clusters profiled using demographicand attitudinal variables. Providing support to findings regarding thecontext-specificity of emotions (Richins, 1997), clusters are found to differ in termsof the emotions and feelings experienced online versus offline. Interestingly, the largest

Clusters Mean difference SE Sig. Lower bound Upper bound

1-2 20.55342 * 0.05933 0.000 20.7227 20.38411-3 20.16463 * 0.05230 0.021 20.3139 20.01541-4 0.51806 * 0.07186 0.000 0.3130 0.72311-5 20.40638 * 0.04557 0.000 20.5364 20.27641-6 20.41641 * 0.05360 0.000 20.5693 20.26352-1 0.55342 * 0.05933 0.000 0.3841 0.72272-3 0.38879 * 0.05550 0.000 0.2304 0.54712-4 1.07148 * 0.07422 0.000 0.8597 1.28322-5 0.14704 * 0.04921 0.034 0.0066 0.28742-6 0.13701 0.05673 0.152 20.0248 0.29893-1 0.16463 * 0.05230 0.021 0.0154 0.31393-2 20.38879 * 0.05550 0.000 20.5471 20.23043-4 0.68269 * 0.06873 0.000 0.4866 0.87883-5 20.24175 * 0.04046 0.000 20.3572 20.12633-6 20.25178 * 0.04933 0.000 20.3925 20.11104-1 20.51806 * 0.07186 0.000 20.7231 20.31304-2 21.07148 * 0.07422 0.000 21.2832 20.85974-3 20.68269 * 0.06873 0.000 20.8788 20.48664-5 20.92444 * 0.06376 0.000 21.1064 20.74254-6 20.93447 * 0.06973 0.000 21.1334 20.73555-1 0.40638 * 0.04557 0.000 0.2764 0.53645-2 20.14704 * 0.04921 0.034 20.2874 20.00665-3 0.24175 * 0.04046 0.000 0.1263 0.35725-4 0.92444 * 0.06376 0.000 0.7425 1.10645-6 20.01003 0.04213 1.000 20.1302 0.11026-1 0.41641 * 0.05360 0.000 0.2635 0.56936-2 20.13701 0.05673 0.152 20.2989 0.02486-3 0.25178 * 0.04933 0.000 0.1110 0.39256-4 0.93447 * 0.06973 0.000 0.7355 1.13346-5 0.01003 0.04213 1.000 20.1102 0.1302

Note: * The mean difference is significant at 0.05Table III.

Multiple comparisons

A typology ofinternet users

163

Pos

itiv

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ts

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Neg

ativ

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tsn

%n

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%n

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%

Country

UK

2613

.223

14.2

3814

78

7912

.427

11.1

Au

stra

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2412

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11.1

4115

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13.8

8413

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SA

147.

132

19.9

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85

5.7

8613

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16.9

Can

ada

157.

637

2318

6.6

89.

290

14.2

2912

Ch

ina

6432

.611

6.8

186.

622

26.2

6610

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8.3

Sou

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178.

711

6.8

4817

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865

10.2

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168.

217

10.5

259.

28

9.2

9314

.647

19.4

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2010

.212

7.4

5921

.818

20.7

7311

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9

Gender

Mal

e12

262

.263

39.1

147

54.2

5259

.834

153

.610

342

.6F

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e74

37.8

9860

.912

445

.835

40.2

295

46.4

139

57.4

Age

16-3

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30.6

4427

.382

30.2

2832

.217

227

8936

.831

-40

4623

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23.6

7326

.925

28.7

166

26.1

6727

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-50

4020

.437

2357

2116

18.4

119

18.7

4217

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5025

.542

2659

21.8

1820

.717

928

.144

18.2

Workingstatus

Un

emp

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ed44

22.4

2918

6122

.524

27.6

138

21.7

6024

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19.4

3421

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1112

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115

9.3

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617

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426.

620

8.3

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196

12.3

161

10.1

271

1787

5.5

636

39.9

242

15.2

Table IV.Cluster demographicdescriptions

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cluster of consumers (40 per cent) (i.e. indistinguishable affectivists) experience aboutthe same level of intensity of affect, both online and offline. This may be due to thechanging landscape of the online environment, where advances in interactivetechnologies and the popularisation of the social web now allow consumers to developmeaningful relationships with both individuals and communities, and consequently toexperience a virtual life which is more akin to real life.

Internet users within the offline affectivists cluster are more traditional in that theyassign more affective gravity to the offline world by experiencing more intenseemotions offline, both positive and negative, compared to online. Among members ofthis cluster, the internet is perhaps perceived as being more cognitive than affective orsocial; thus, it would be associated with functional motivations and uses such asinformation seeking, convenience shopping and price comparison (Christodoulides andMichaelidou, 2010). Online affectivists constitute the smallest cluster (5.5 per cent) andare the opposite of offline affectivists in that its members gravitate towards the onlineworld and experience stronger emotions regardless of valence (positive or negative)online compared to the real world. This cluster refers to digital mavens who live asecond life in virtual space, and whose real life experiences are not as intense in termsof affective states experienced, as online. The positive online affectivists clustercomprises of internet users who experience stronger negative affective states offlinethan online. However, these users experience stronger positive affective states onlinethan offline. These people feel more invisible, and experience more stress and anxietyin the real world, and seem to use the online space for escapism and fantasy. Thisallows them to be happier, more confident, comfortable, expressive and imaginativecompared to their situation in the offline world. Members of the on/off-line negativeaffectivists cluster (17 per cent) are characterised by stronger negative affective states,some of which are experienced more intensely online and some offline. For instance,internet users in this cluster feel more anxious, stressed, conservative andself-conscious in the offline world, but more invisible and anonymous online. Finallyinternet users within the negative offline affectivists cluster share the same negativeaffective states as on/off-line negative affectivists in the offline world (anxiety, stressetc.) but experience both positive (e.g. brave, expressive, imaginative) and negativeaffective states (e.g. anonymous) online.

Additionally, findings show that national culture discriminates the resultingclusters, corroborating Barnes et al. (2007) who also report variations in terms ofbehaviour based on national culture. In our study, offline affectivists are mainly NorthAmericans (USA and Canada) whereas positive online affectivists have a higher

Clusters M SD N

Positive online affectivists 2.55 0.549 196Offline affectivists 3.11 0.671 161On/off-line negative affectivists 2.72 0.513 271Online affectivists 2.03 0.649 87Indistinguishable affectivists 2.96 0.530 636Negative offline affectivists 2.83 0.563 242

Note: 1: positive online brand perceptions, 5: negative online brand perceptionsTable V.

Online brand perceptions

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percentage of Chinese respondents compared to the other clusters. Going a step further,it is observed that offline affectivists are mainly Western internet users (68.2 per cent),while most of the online affectivists come from East Asia (64.1 per cent). One possibleexplanation alludes to the individualism/collectivism dimension of culture (Hofstede,1980) which is found to affect the ways people form trust (Lim et al., 2004). East Asiansare typically collectivist and more trusting of the online environment compared to theirmore individualist Western counterparts (Park and Jun, 2003). In addition to this, thepopularisation of social media potentially makes the internet more suited to thecommunity needs of collectivist cultures. This means that dissimilar affective statesmay be experienced online versus offline by individuals from different nationalcultures. This is an interesting finding which provides support for the notion that theinternet, although an international medium, is not culturally neutral. While previousresearch has identified cross-national differences in emotions displayed byindividualist versus collectivist people in the offline world (Matsumoto et al., 2008),no study to date has examined cross-national differences in internet users affectivestates.

Furthermore, findings show that clusters discriminate in terms of gender andworking status. For example, offline affectivists and negative offline affectivistsinclude more females than males in contrast to the other clusters where malesdominate. This finding underlines the discriminant role of gender in online affectivestates, and is in line with previous research showing that disgust-based and fear-basedviral marketing campaigns are more likely to be forwarded by male than femalerecipients (Dobele et al., 2007). In terms of working status, positive online affectivists,on/off-line negative affectivists and indistinguishable affectivists have the highestpercentage (around 38 per cent) of respondents in full-time employment. Additionallyclusters of on/off-line negative affectivists, online affectivists and negative offlineaffectivists have the lowest percentage of retired members (around 13 per cent). Almosta quarter (23-24 per cent) of members in all clusters are unemployed, with the exceptionof online affectivists, where unemployed respondents reach 28 per cent. Clusters alsodiffer in terms of perceptions towards online brands. Online affectivists have the mostpositive online brand perceptions, whereas offline affectivists have the least positive.This means that, comparatively, online affectivists value the role of online brands inshaping identity and personality and allowing users to interact and communicateonline. This is an interesting finding which validates the clusters further since theresults show that online affectivists experience more positive affective states onlinecompared to the other clusters.

Implications for marketersThe findings reported in this study have significant implications for online marketers.Our study shows that internet users experience different affective states online versusoffline. This is likely to affect their perceptions of online brands. For example, offlineaffectivists and online affectivists experience stronger emotions and feelings(regardless of valence) off- and on-line respectively. Unsurprisingly consumers whoexperience higher intensity of emotions and feelings online (offline affectivists) havethe most positive perceptions towards online brands. In contrast, consumers whoexperience more intense negative and positive affective states in the real world, havethe least positive perceptions towards online brands. Rather than focussing solely on

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the functional aspects of the internet, marketers should strive to create onlineexperiences which generate strong affective responses, as these are likely to translateinto more favourable perceptions towards online brands.

It is also interesting that our clusters discriminate on the basis of national cultures,thus contributing to the large body of research showing differences in respect ofvarious aspects of online consumer behaviour. In the light of this finding, astandardised approach to e-marketing that cuts across national cultures may not beappropriate, specifically so in terms of consumers affective states. The findings of thisstudy indicate significant differences in the comparative affective states experiencedby the consumers of different national cultures. Although the internet is aninternational medium, the findings of this study suggest that online marketers servingboth Western and East Asian consumers should avoid a mass marketing approach,and should develop different marketing activities to generate positive affectivelyintense experiences. In addition to this, our findings provide initial empirical supportfor the value of emotional branding (Gobe, 2001; Thompson et al., 2006) both online aswell as in a cross-national context. Online marketers are encouraged to engage internetusers from across countries at the level of affective states. However, managers couldpotentially benefit more from using emotional branding as an online tool in East Asiarather than in Western countries, as most online affectivists are East Asians. Thismight also suggest that pure internet-based companies are more likely to prosper inEast Asia (than in the West). In particular, East Asians are found to experience moreintense (positive) emotions and feelings online than offline, indicating a significantopportunity for managers engaging in emotional branding in this region. This also hasimplications in terms of marketing budget allocation. For instance, bricks-and-clicksbrands are encouraged to invest more in promotional activities in the physical worldwhich aim to create strong and favourable affective responses, while in East Asia,managers should consider spending more on activities geared to enhancing theirconsumers online experience. As the literature suggests, there is a plethora ofopportunities on the internet to express the affective component of a brand (Gobe, 2001;Dobele et al., 2007).

Limitations and directions for future researchNo study comes without limitations. Our study utilises a comparative scale to captureonline affective states (offline versus online). However, affective states addressed inabsolute terms would have further enhanced academic knowledge with respect to therole of emotions and feelings in an online context. Although this is a cross-nationalstudy, exploratory research was only undertaken in the UK based on which a set ofaffective states identified from the literature was adapted to the online environment.Ideally further exploratory research should have taken place in each of the remainingcountries studied in order to establish that the meaning of online emotions and feelingsderived from the UK sample is consistent across countries.

Drawing on past evidence suggesting that emotions and feelings arecontext-specific (Bart et al., 2005; Celsi et al., 1993; Kwortnik and Ross, 2007;Richins, 1997), the research on the affect-based segmentation of internet users couldbenefit from an investigation of different consumption situations. In particular,researchers are encouraged to study segmentation of internet users through differentproduct classes. This will allow a comparison of the affective responses evoked across

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different consumption situations. Furthermore, future research may investigate howcountry level factors such as internet penetration and infrastructure might affect thevalence or intensity of emotions and feelings experienced by users online. Valuableconclusions are expected to be drawn from the study of these clusters over time. Alongitudinal study would allow researchers to make safe conclusions regarding thestability of the identified clusters over time (Barnes et al., 2007). A detailed analysis ofthese results will shed more light on the underlying differentiating factors of theclusters. If the clusters are found to be unstable over time, researchers should turn theirattention to other factors such as levels of economic development, to identify the basisof these online user distinctions.

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About the authorsGeorge Christodoulides has recently joined Henley Business School at the University of Readingin the UK as a Professor of Marketing. His research interests lie in the areas of brandmanagement and e-marketing, particularly consumer-based brand equity conceptualisation and

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measurement and the impact of interactive/social media on consumer-brand relationships.Georges research has attracted funding from prestigious external bodies including the Economicand Social Research Council (ESRC), the British Academy and the Chartered Institute ofMarketing. He is a regular presenter at international conferences, and his research has appearedin various scientific journals, including Journal of Advertising Research, Industrial MarketingManagement, European Journal of Marketing, and Journal of Marketing Management. Georgehas guest-edited a book and special issues of journals on various aspects of brand management.George Christodoulides is the corresponding author and can be contacted at:G.Christodoulides@henley.reading.ac.uk

Dr Nina Michaelidou is a Reader in Marketing at Loughborough University School ofBusiness and Economics. Her research interests lie in the area of consumer behaviour andspecifically personality traits such as variety seeking and innovativeness, emotions and healthbehaviours as well as social media usage and consumers responses to advertising appeals andpromotions on social media. She is the leader of the Academy of Marketing Special InterestGroup on Consumer Research, and has published papers in various journals including Journal ofMarketing Management, European Journal of Marketing, Journal of Strategic Marketing, Journalof Business Research, Industrial Marketing Management, Food Policy, Journal of ConsumerAffairs, International Journal of Advertising and Journal of Consumer Behaviour.

Nikoletta Theofania Siamagka is a Lecturer in Marketing at Henley Business School,University of Reading. Her main research interests lie in the area of consumer behaviour andinclude consumer ethnocentrism, consumer responses to product placements and emotions.Nikoletta is also interested in social media usage, both in a B2B and B2C context. Nikolettasresearch has appeared in leading scientific journals including Industrial Marketing Managementand European Journal of Marketing. She is a regular presenter at international conferences.

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