Examining the information value of virtual communities: Factual versus opinion-based message content

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Examining the information value ofvirtual communities: Factual versusopinion-based message contentChris Archer-Brown a , Niall Piercy b & Adam Joinson ca University of Bath , UKb Cardiff Metropolitan University , UKc University of the West of England , UKPublished online: 02 Nov 2012.

To cite this article: Chris Archer-Brown , Niall Piercy & Adam Joinson (2013) Examining theinformation value of virtual communities: Factual versus opinion-based message content, Journal ofMarketing Management, 29:3-4, 421-438, DOI: 10.1080/0267257X.2012.732599

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Journal of Marketing Management, 2013Vol. 29, Nos. 3–4, 421–438, http://dx.doi.org/10.1080/0267257X.2012.732599

Examining the information value of virtualcommunities: Factual versus opinion-basedmessage content

Chris Archer-Brown, University of Bath, UKNiall Piercy, Cardiff Metropolitan University, UKAdam Joinson, University of the West of England, UK

Abstract Social customer relationship management (SCRM) is an evolvingdiscipline which uses technology as a communication channel. It isunder-researched and is lacking in its use of traditional marketing principlesto inform practice. We shed light on the type of content that is most valued invirtual communities (VCs), where search for information is a primary sourceof gratification. Contrary to findings from advertising research, our findingsindicate that informational content is more highly valued. This is the firsttime such measures have been used to evaluate effectiveness of new mediatechniques. We discuss critical implications for brands hoping to develop orenhance customer relationships in these increasingly important channels.Specifically, we offer practical advice on how best to create content which maybe valued by consumers seeking information.

Keywords new media; social networks; customer relationship management;performance accountability; viral marketing; marketing management

Introduction

The mass adoption of social networking as a frequent activity by increasingly largesections of the population and by ever-widening social groups (Dutton & Blank,2011; Pew Research Centre, 2010) has changed Internet usage habits and, with it,expectations of where information is acquired about products, services, and brands(Valos, Ewing, & Powell, 2010). The present research focuses on the confidenceconsumers place on information available in virtual communities (VCs), which areprimarily designed for people to engage in peer-to-peer dialogue (Rheingold, 2000).However, VCs are also important because they offer brands an important platform onwhich to engage consumers as part of their social customer relationship management(SCRM) strategy (Faase, Helms, & Spruit, 2011).

Three related conceptual areas frame the present study. First, the search forinformation is an important factor for individuals to join and continue to be amember of a VC (Ridings & Geffen, 2004; Wellman, Salaff, Dimitrova, Garton,Gulia, & Haythornthwaite, 1996). Second, participation in consumer discussion

© 2013 Westburn Publishers Ltd.

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422 Journal of Marketing Management, Volume 29

forums, which are part of many VCs, is an effective way for consumers to meetthis aim (Pitta & Fowler, 2005). This means that VCs have become an importantchannel for brands to engage customers in dialogue and share information (Cova& White, 2010; Godes & Mayzlin, 2009). Finally, social media channels haveoffered brands opportunities to engage with consumers in exciting new ways.However, to become more adept at exploiting these channels, tactics need tomature, and marketers need to draw from traditional marketing theory (Barwise &Meehan, 2010).

These are important factors for brands wishing to communicate with theircustomers using these channels. Therefore, understanding the ways they interact iscritical for the development of theory supporting SCRM.

CRM and social networks

SCRM is about ‘creating a two-way interaction between the customer and thefirm. It is a CRM strategy that uses Web 2.0 services to encourage active customerengagement and involvement’ (Faase et al., 2011, p. 9). Another definition highlightsthe key distinction between ‘social’ and ‘traditional’ CRM: that it is ‘designedto engage the customer in a collaborative interaction . . . It’s the company’sprogrammatic response to the customer’s ownership of the conversation’ (Greenberg,2010, p. 414).

SCRM provides opportunities to gather intelligence, develop dialogue, andinteract in innovative ways (Meadows-Klue, 2008). Empirical studies indicate that,where SCRM is successfully executed, it can deliver incremental benefits (Faase et al.,2011).

So why do consumers need to be treated in a different way online? ‘Socialconsumers’ behave differently; they are highly connected and expect informationon demand; they share information and socialise online, and choose to engage withpeers and brands whom they consider to be transparent and authentic and whom theyreward with loyalty and public statements of support. ‘If things go well, they becomeadvocates. The core driver of this relationship is trust.’ (Greenberg, 2009, p. 411).In other words, if brands wish to provide information through these channels, itsdistribution must meet with these norms and expectations be considered valuable.

Existing CRM strategies are argued to have been adapted to exploit socialmedia. Analyst firm Forrester offers case-study evidence which suggests thatsuccessful outcomes can be achieved by brands playing an active role and engagingwith members of various types of VCs. By engaging users, relationships areenhanced, resulting in increased loyalty and encouraging customers to become activeparticipants in the product development process (Forrester, 2010). In other words,to be successful in SCRM, brands must actively participate in VCs and become avalued member, offering advice and information which other members will considerbeneficial.

The key theme of the literature appears to be that SCRM is different fromtraditional forms of relationship marketing due to the enhanced opportunity tocreate dialogue. However, the creation of meaningful dialogue has always been acore tenet of CRM (Little & Mirandi, 2003; Payne & Frow, 2005) with the aim ofunderstanding customer needs and delivering value (Grönroos, 1990). Therefore, wesuggest that the key difference between traditional CRM and its ‘social’ descendant is

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Archer-Brown et al. Examining the information value of virtual communities 423

simply the new technology that provides the opportunity to collect data and interactin different ways.

Research aims

Recently, practitioners and theorists have sought to establish a framework thatencompasses activities aimed at developing and enhancing relationships withconsumers in social media. We argue that, in common with many CRM initiatives inthe past (Wilson, Daniel, & McDonald, 2002), SCRM is largely technology led andthat today’s marketers risk the same mistakes of their predecessors if they neglecttraditional marketing principles while experimenting with different strategies (Bull,2003; Rigby & Ledingham, 2004; Forrester, 2010). To this end, the measurementof efficacy of communications in VCs should include scales that have been usedby marketers to establish the effectiveness of advertising over a number of years.Specifically, we argue that these should be the predisposition to the medium asa reliable channel of information and the believability of the message itself. Theprimary aim of the present study has been to investigate the relationship betweenthese constructs – the extent to which they interact and affect consumers’ perceptionof the value of information.

Of particular relevance, especially in the context of discussion forums, where thewritten word is paramount, is the relative value of fact-based information versusopinion-based posts. This subject has been the topic of much discussion in theadvertising world for a number of years (Heath & Nairn, 2005) but has receivedscant attention in the SCRM literature. The call by Barwise and Meehan (2010) forsocial media to move out of the experimental phase and into maturity applies toSCRM, and the aim of this paper is to contribute to this initiative.

Hypotheses development

The central premise of our study is that, under the guise of SCRM, brands areengaged in dialogue with consumers within discussion forums in VCs and that, incases where they provide information, it should be of value to the consumer inorder to achieve positive outcomes (e.g. relationship enhancement or positive wordof mouth). Extant research in advertising suggests that consumers who have a generaldisposition towards being sceptical are generally less likely to value the informationin this medium, although this is mitigated in cases where the content of the messageis considered believable (Obermiller & Spangenberg, 2005).

Our hypotheses allow us to test whether this phenomenon also exists in VCs andwhether the theory can be extended to computer-mediated communication. Thisleads to the measurement and interaction of three specific constructs: informationvalue, forum scepticism, and believability. These are discussed in further detail in thefollowing sections.

Information value

In the modern world, consumers are inundated with information from a wide rangeof sources, and they become adept at organising the information based on its value

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to them (Hiltz & Turoff, 1985). The Internet is considered an important source ofinformation on products, brands, and services (Dutton & Blank, 2011), and thereappears to be an inherent link between the value people place on information soughtfrom communities and the way they perceive the community itself (Brown et al.,2007). Of particular relevance, online discussion forums are considered to be avaluable resource for marketers to communicate and develop meaningful dialoguewith consumers (Pitta & Fowler, 2005).

Petty, Cacioppo, and Goldman (1981) found that where the subject of a messagewas of value to the receiver, the perceived factual quality of the message was ofprimary importance; where the subject was of less value to the receiver, the perceivedquality of the source was the more important factor. Product involvement has beenfound in other studies to be of primary importance in the way messages are receivedby consumers and the extent to which they place value on the information leadingto changes in the relationship and perception of the brand (Fang-Ping & Jun-Der,2011).

Five attributes contribute to the perception of value in information: (1) the extentto which it resolves uncertainty; (2) its ease of distribution; (3) the extent to which itcan be applied by the receiver; (4) the nature of the content; and (5) its relevance toany decisions to be made by the receiver (Hirshleifer, 1973). These have formed thebasis of our operationalisation of the information value construct.

The value of the information as perceived by the receiver is an antecedent tothe development of relationships, which are key to the development of communitiesonline as well as with brands (Rheingold, 2000). Further, Brown, Broderick, and Lee(2007) argue that the value of information is the key antecedent to the developmentof word of mouth online. For these reasons, we chose information value as theoutcome variable for our model.

Forum scepticism

Advertising scepticism, which is defined as ‘the general tendency toward disbeliefof advertising claims’ (Obermiller & Spangenberg, 1998, p. 160), has been acore element in the measurement of advertising effectiveness since the 1990s. Theimportance of this element is that, in general, sceptics value the information sharedin advertising less and are more likely to disregard it (Obermiller & Spangenberg,2005). However, sceptics do not remain unaffected by advertising claims: theyappear more prone to emotional arguments, particularly related to a productwhere they demonstrate high levels of involvement (Obermiller & Spangenberg,2005).

In evaluating the validity of including this construct outside the immediate domainof advertising, two factors need to be taken into consideration. First, advertising isa non-personal form of communication and, while many posts in Internet forumsmay be specifically addressed to another member, for the majority of readers ofpublic posts they are non-personal and in this way conform with this categorisation.Second, Obermiller and Spangenberg (1998) contended that the aim of all advertising‘generally and ultimately is to persuade people to buy the advertised product’(p. 164). This, clearly, is not the case with posts in VCs. However, we elaborate on thisargument subtly by proposing that advertising, along with a significant proportion ofinstrumental posts within many VCs, has the ultimate aim of persuading people of

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an argument, an opinion on a product, a political perspective, or a general piece ofadvice.

While we consider there to be similarities in the context, we also anticipate somedifferences. The end result of the diffusion of information within VCs contributes toword of mouth, which has been argued to be more effective in sharing informationthan advertising (Day, 1969; Engel, Kegerreis, & Blackwell, 1969). Further, thepresence of a discussion thread where it is easy to view others’ comments and toarticulate a counter-argument (Kietzmann et al., 2011) may lead to a difference inthe way scepticism affects message processing by members.

While significant research has been carried out into the effects of scepticism ofadvertising as a moderating factor in evaluating advertising effectiveness, to ourknowledge no such studies have been conducted into members’ scepticism of theinformation posted by other members within VCs. This leads us to develop our firsthypothesis:

H1: Respondents who are more sceptical of VCs in general are less likely to perceivevalue in information accessed from this source.

Believability

In his study of consumers’ responses to advertising, Maloney (1963) concluded that‘believability is not an inherent property of the advertisement itself . . . Believabilitydepends upon the interaction of each advertisement with the consumer’s attitudes’(p. 2). Believability is also linked to action; an advertisement that is consideredcompletely unbelievable is unable to elicit the desired response, whereas one thatis considered believable is more likely to prompt a reaction (Beltramini & Evans,1985; Maloney, 1963). For this reason, believability has been linked with advertisingeffectiveness (Berlo et al., 1969).

Advertising believability was argued to be an important component in theevaluation of advertising in the 1980s (Beltramini, 1988). It has been tested in anumber of contexts: claims about product effectiveness (Beltramini & Evans, 1985),cigarette warning labels (Beltramini, 1988), and political advertising (O’Cass, 2002).These studies led to overarching conclusions about the way consumers derivedmeaning from advertising messages (Atkins & Beltramini, 2007; Maloney, 1963).

Clearly, there are some differences in the context of the present study comparedto the advertising research from which we drew our inspiration. However, in theirstudy of the believability of the US press, Robinson and Kohut (1988) found thatthe majority of respondents believed what they read, and the authors found this tobe broadly similar across a range of demographic groups or technological sources,leading them to conclude that ‘technology . . . is not the hook upon which opinionshang’ (p. 188).

The inclusion of this construct is based upon the concept that if users arepredisposed towards scepticism of information presented in a VC, the likelihoodthat they will perceive value in the information provided is low, unless they find theparticular post to be inherently believable in itself. Thus we suggest that believabilityhas a mediating role in the relationship between forum scepticism and informationvalue. This is reflected in our next hypothesis:

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H2: If a sceptical reader finds the content of a post in a VC believable, the informationcontained in the post is more likely to be considered valuable.

The role of message content

The attention we pay to an advertising message is also seen to be a key factor in itseffectiveness (Heath & Nairn, 2005) and has been seen to have a positive effect onbrand perceptions and relationships with customers (Fang-Ping & Juan-Dur, 2011).Informative messages (particularly in a subject in which the reader is particularlyinterested) have been noted to require central, rather than peripheral, cognitiveprocessing, meaning that the subject may be more likely to derive value from thecontent and elaborate on it for future discussion (Petty et al., 1981). Focussing onmembers of a special-interest community to respond to our survey, we inferred thatthe respondents would have an equal level of product involvement and excluded thisfrom our analysis.

The nature of the content of messages was also found to affect the way peopleperceive the value of the message in the context of advertising; for instance,emotional messages were found to mitigate the sceptics’ general nature (Obermiller& Spangenberg, 2005). It was concluded that this was due to informational messagesgiving the sceptics a basis to question the facts being communicated (Obermiller &Spangenberg, 2005), which we suggest is compounded by the lack of a platformto reply in the context of advertising. However, this is where we hypothesise thatthe differences in the medium may affect the relationship between believabilityand information value differently. The ability to seek clarification, offer a counter-argument, or share a supporting point of view in discussion forums may reduce theextent to which sceptics are inclined to reject an informational message, leading toour final hypothesis:

H3: The content of the message moderates the relationship between believability andinformation value, where informational messages will cause a greater effect.

The hypotheses outlined above are represented in the conceptual model detailedin Figure 1.

Figure 1 Conceptual model.

ForumScepticism H1

H2

H3

InformationValue

Believability

MessageContent

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Research methods

Although the primary method was quantitative, the subject of information value inonline forums was discussed in two focus groups (n = 19). The purpose of thisqualitative pilot step was to expand our understanding of the constructs and theirrelationships in addition to confirming that our chosen measures were relevant.

A questionnaire survey was developed to measure our constructs. Where possible,existing scales were deployed. Where none was available, questions were developed

Table 1 Operationalisation table.

Information value (IV): Please state the extent to which you agree or disagree with thefollowing.IV1 – The information is useful to menow.IV2 – The information will be useful to mein the future.IV3 – I think the post makes goodsuggestions.IV4 – I think the post contains valuableideas.

The questions were developed broadlyfollowing Hirshleifer’s (1973) attributesof information outlined in a previoussection.1 = ‘strongly disagree’; 7 = ‘stronglyagree’.

Believability of the post (B): I find the content of the post to be:B1 – Believable /unbelievable (R).B2 – Trustworthy/untrustworthy (R).B3 – Not convincing/convincing.B4 – Not credible/credible.B5 – Unreasonable/reasonable.B6 – Dishonest/honest.B7 – Questionable/unquestionable.B8 – Inconclusive/conclusive.B9 – Not authentic/authentic.B10 – Unlikely/likely.

Adopted Beltramini’s (1988) 10-item scalewhich was originally developed to testbelievability of cigarette warning labelsin advertising and has subsequentlybeen adopted in a range of contexts.Semantic differential.

Forum Scepticism (FS): What is your view of online communities?FS1 – We can depend on getting the truthin posts in online forums.FS2 – The aim of posts in online forums isto inform other members.FS3 – I believe posts in online forums aregenerally informative.FS4 – Online forums are generallytruthful.FS5 – Online forums are a reliable sourceof information.FS6 – Online posts are truth well told.FS7 – In general, posts in online forumspresent a true picture of any productmentioned.FS8 – I feel I’ve been accurately informedafter viewing posts in an online forum.

Adapted Obermiller and Spangenberg’s(1998) SKEP scale for measuringadvertising scepticism.1 = ‘strongly disagree’; 7 = ‘stronglyagree’.

R, item was reverse coded in the SPSS file.

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based on extant literature and tested for face validity using a small pilot group (n =15). Standard demographic questions were asked at the end of the survey.

The survey was deployed with the co-operation of the hosts of a large-scalecommunity (digital photography) who posted links to the survey throughout theforum. The incentive of a nominal cash prize draw for those completing the surveywas offered, and the survey itself was hosted online over a two-week period in thespring of 2011.

To prompt a range of responses and to allow for investigation of different typesof message content, two real posts were chosen from the forum which discussedtheir authors’ opinions on products associated with digital photography but throughtwo different conversant approaches (categorised in Table 2). The posts were firstscreened to ensure that they were representative of the type of subject discussedin the forum and were selected initially based upon receiving a large response(>100 each).

Members of the two focus groups validated the categorisation, where confirmationof their being considered factual or opinion based was obtained. Permission was givenby the authors to use a copy of the actual post and their profile page in the survey.

An invitation to participate in the survey was posted on the front page of theparticipant site by the website owners. The wording of the invitation was ‘Participatein our survey for a chance to win a prize’. When users clicked on the link, a newpage opened with an overview of the study, explaining that the project was part ofa research study being undertaken at the University of Bath and included some rulesof the prize draw. If they gave consent by clicking ‘begin the survey’, they were thenled through a multi-page online survey. On page one, they were shown a sample post(either post A or post B, which were served sequentially to respondents). In addition,the respective poster’s profile was shown in the survey in order to establish in therespondents’ minds that the posters were regular, long-term contributors to thecommunity to establish similarity and avoid confounds. Respondents were asked toconsider this information when answering questions on believability and the value ofthe information.

At the end of the study, participants were given the opportunity to provideadditional feedback to the researchers and to enter their e-mail address in order toenter the prize draw. These data were not stored with their study responses.

Steps were taken to minimise missing data during data collection, including theuse of forced choice questions. All data were screened for missing data, and nonewas found. Further, measures were taken to avoid common method variance inthe design of the survey following the guidance of Podsakoff, MacKenzie, Lee, andPodsakoff (2003). These included: (1) ordering of questions to interchange between

Table 2 Message content summary.

Post A – factual Post B – opinion basedContent Purposive and direct. Sharing and discursive.

Arguments are justified and theimportance is stated.

Arguments presented andnot justified.

Profile details shown Five years tenure in community. Five years tenure incommunity.

Active participant. Active participant.Purpose of post Discussion starter. Discussion starter.

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response formats (seven-point Likert scales and semantic differential scales) andreverse scored items; (2) designing questions to be clear and unambiguous, usingcommonly used language; (3) collecting data from a globally available Internet forumso the respondents would come from a range of backgrounds; and (4) pilot-testingthe questionnaire across 15 members of different VCs and incorporating all feedbackwas. As indicated in Table 1, data were manipulated to account for reverse-codeditems.

Data integrity

Data analysis was performed with SPSS v18 and AMOS 18. A large sample wasobtained for testing (n = 1135). Due to the sample size, the statistical tests ofnormality are inappropriate for drawing firm conclusions (Pallant, 2007; Tabachnick,Fidell, & Osterlind, 2001). Inspection of the histograms showed that some itemsdisplayed the characteristics of positive skew highlighted by Peterson and Wilson(1992) as is common in measuring customer self-reported data. The data were testedfor kurtosis and skewness, and all the data were found to be within acceptable limitsof +1/−1. However, both Kolmogorov–Smirnov and Shapiro–Wilk tests indicatedthat the data exhibited positive skew, which would be remedied using the Bollen–Stein bootstrapping method (Byrne, 2001). Given the size of the sample, the datawere considered suitable for further analysis.

The data were analysed for the presence of outliers using a range of tests: Z-scores were calculated, and none was found to be in excess of 3.29, although theinstances of observations with scores between 1.96 and 3.01 were marginally inexcess of 5% of the total, indicating the presence of outliers (Field, 2009). With theexception of two variables, the difference between the trimmed mean and the meanwas found to be below 5%. Using the procedure outlined by Pallant (2007), boxplotswere produced, and a small number of outliers was identified by case. To assess theappropriate remedy, the procedure of profiling each case was followed, as outlinedin Hair Jr et al. (1995), and one data case was removed. While it was noted that thismay adversely affect the statistical results, the remainder were retained on the basisof increasing the generalisability of the sample, given that overall they did not differfrom the sample.

To validate the reliability of the multi-item constructs being measured, Cronbach’sco-efficient alpha tests were applied to the scales (n = 1134), giving the followingresults: forum scepticism α = .897; believability α = .942; information value α =.877. Confirmatory factor analysis (CFA) was performed in order to validate theinclusion of the variables in the structural model, indicating the need for minorpost-hoc amendments to the model which are outlined in Table 3 (comparative fitindex [CFI] = .958; goodness of fit index [GFI] = .930; root mean square error ofapproximation [RMESA] = .054; χ2 = 955.868; p = .000).

All other items in the survey were used in the structural model following CFA, asshown in Table 1.

Findings and discussion

The model was formulated with two groups (post A and post B) in order to conductthe moderation analysis (Byrne, 2001), and 2000 bootstrap samples were specified

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Table 3 Summary of amendments to model following CFA.

Code Basis for post-hoc amendmentB1 Problematic with a number of other items in the scale, which was considered to

be due to it measuring the construct directly (believable/unbelievable).B8 Removed on the basis that neither post was intended to draw conclusions; on the

contrary, both were ‘thread starters’ in the forum.FS7 Found to be problematic with a number of other items; this was concluded to be

due to its duplication of ‘truthfulness’ (product specific in this case).

Table 4 Fit statistics.

Fit index Acronym ScoreGoodness of fit GFI .927Adjusted goodness of fit AGFI .905Normed fit index (Bentler & Bonett, 1980) NFI .943Relative fit index (Bollen, 1986) RFI .934Incremental fit index (Bollen, 1989) IFI .962Comparative fit index (Bentler, 1990) CFI .962Root mean square error of approximation (Brown & Cudeck, 1993) RMSEA .041

in order to mitigate the non-normality of the data where the Bollen–Stein bootstrapwas found to be significant (p = .000). The chi-square of the model was 933.018 with330 degrees of freedom (df ).

A commonly accepted threshold for acceptability of structural models is a GFIof .900 (Bagozzi & Yi, 1988), and our model exceeds this threshold. However, weunderstand the most comprehensive method of assessing acceptability of the fit ofthe model to be Hu and Bentler’s (1998) proposed combination of CFI (≥.960) andRMSEA (≤.050), and note that our model is also within these thresholds.

Scepticism and information value

Significant relationships between forum scepticism and information value were notedin both posts, thereby supporting H1. However, in comparison with the other pathsin the model, we note that the size of the relationship is moderate, explainingaround 13% of the variance in information value alone. Further, in comparison withprior tests in advertising, the explanatory power of this construct is roughly half(Obermiller & Spangenberg, 2005). This indicates that other factors are at play inthe overall perception of value, suggesting that the extent to which the individualacts both in terms of long-term behaviour (reflected in their profile statistics) and thecontent they present are important factors. From comparison of the means, it is clearthat the information contained in post A was considered more valuable (4.427) thanthat in post B (3.258).

Both tests of reliability (Cronbach and CFA) indicate that the scale is equally asrobust in this context as in the world of advertising and has the potential to beestablished as a control measure in tests for effectiveness of SCRM activities by brandswithin VCs.

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Figure 2 Key results from the structural model.

ForumScepticism H1

H2

H3

InformationValue

Post A Mean: 4.427 (n = 621)Post B Mean: 3.258 (n = 513)

Post A Mean: 4.857 (n = 621)Post B Mean: 4.457 (n = 513)

p < .05* , p < .01** , p < .001***

Post A : SRW 0.138***Post B : SRW 0.132**

Post A : SRW 0.300***Post B : SRW 0.253***

Post A : SRW 0.713***

SRW = Standardized Regression Weights

Post B : SRW 0.582***

Believability

MessageContent

Although we presented no hypothesis related to the differences in effectbetween the two posts, we have presented these figures for completeness andin order to establish moderation at the model level which is discussed in alater section. However, it is interesting to note that the relationship is onlymarginally different between post A (SRW = .138) and post B (SRW = .132).This is contrary to our expectations, which were based upon findings in theadvertising world where informational advertisements were broadly rejected bymore sceptical respondents, whereas emotional appeals received an improvedresponse by them (Obermiller & Spangenberg, 2005). Similarly, our modelquestions this finding in the path between scepticism and believability, where theinformational post tended to appeal more to the sceptical respondents than didpost B.

The role of believability

In establishing the presence of mediation by believability between forum scepticismand information value, Baron and Kenny’s (1986) four steps were followed. Formodels with latent effects (such as ours), it is advisable to use the total effect forpath c. Results, as outlined in Table 5, indicate that believability partially mediates

Table 5 Direct and indirect effects.

Indirect (ab) Direct (c′) Total effect (c)Post A .214 .138 .352Post B .147 .132 .279

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the relationship between forum scepticism and information value. For completeness,a Sobel test was carried out, and evidence of partial mediation was present for bothpost A and post B (p = .000; Sobel, 1982).

Interpretation of the findings in Table 5 suggests that while partial mediation ispresent in both factual and opinion-based posts, the effect is magnified in the former(post A), thereby supporting H2.

Therefore, we conclude that where a message is considered believable, the valueplaced on the information is greater and mitigates the individual’s scepticism towardsthe medium. This is broadly in support of literature on advertising effectiveness whichsuggests that, where messages are built on pre-existing perceptions, they are morelikely to be believed and considered to be effective in persuasion (Maloney, 1963).

The role of message content

We suggested earlier that the content of the post will affect the nature of thevalue perceived in the information, hypothesising that the effect would exist in therelationship between the believability construct and the dependent variable (H3).Following the procedure outlined by Byrne (2001), we analysed the differencesin chi-square and degrees of freedom to assess the whether the post moderatedat the model level and, if so, which paths were affected. Results are outlined inTable 6.

The content of the post moderates at the model level and on the path betweenbelievability and information value, thereby supporting H3, which we interpret toprovide further support to the notion that consumers process messages differentlydepending on the nature of the message itself.

This does not lead us to suggest that post A was more believable per se. In fact,comparison of means for this construct by post (difference of .4 on a seven-pointscale) indicates that they were perceived to be equally believable. However, thisdoes suggest that where the more informative post was believed, this led to anoverall greater perception of value. This is a further indication that there is aninteraction between believability and information value; in other words, not allbelievable messages are valuable, but they are much more likely to be consideredso if they are considered believable.

Table 6 Results of moderation tests.

Model description χ2 df χ2∗ df∗∗ pCombined baseline model (Post A and B) 855.738 294 − − −Factor loadings and variances constrainedto equal

936.795 313 81.057 19 .000

Variance between forum scepticism andbelievability constrained

856.502 295 .764 1 NS

Variance between believability andinformation value constrained

859.900 295 4.162 1 <.050

Variance between forum scepticism andinformation value constrained

855.859 295 .121 1 NS

∗χ2, difference in chi-square; ∗∗df, difference in degrees of freedom.

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Conclusions

In the case of establishing the behaviour of sceptics in relation to VCs, we havebroadly supported findings of tests conducted in the context of advertising: scepticalmembers are more difficult to convince of the value of information from the forumsource. There is some variation, which can be explained by the medium, and wehave concluded that the ability for members to engage in on-going dialogue withthe author of a post underpin the differences. This, we believe, is a significantcontribution to the body of research in the important area of evaluating brands’engagement with customers using social media.

Further, we found that the extent to which the message is considered believablemitigates an individual’s own scepticism. If sceptics tended to believe a message, theextent to which they found the information valuable was increased in the case ofboth post A and post B. This finding is line with previous tests on the evaluation ofadvertisements. However, in comparison with these studies, the effect is appreciablysmaller, which we suspect may be due to the members’ involvement with the forumand their role in developing and policing norms within the community itself. Thisis markedly different from advertising, where communication is one way from thebrand to the consumer and does not include a ‘membership’ element.

However, our next conclusion is contrary to the extant literature in the advertisingcontext: the informational post appeared to reassure the sceptical users. In trying tounderstand this nuance, we consulted the literature on word of mouth, where it isoften argued that information about products is often considered more trustworthyand less biased than that received from the firm (Day, 1969). We suggest thatthis may explain why the effect between the two is relatively small, and suggeststhat perceptions of information received offline versus that presented in onlinecommunities may be different, perhaps due to the interactivity in the latter.

Both sample posts in our test were judged by the respondents to be broadly equalin terms of believability, yet the post which was interpreted as using a direct styleof communication, where any claims were justified, was judged to be considerablymore valuable in terms of the information provided. We have suggested that thismay be a result of members being more predisposed to trust the direct provision ofinformation in this forum, or perhaps it is the presence of a mechanism for them topresent a counter-argument that contributes to this effect.

The overall theme of this research is that the evaluation of messages being diffusedthrough an online community demonstrates some similarities in principle with whatone would expect in the communication of information through the advertisingmedium. This being said, there are some marked differences which are outlined inthe previous section and which we conclude are principally due to two key factors:involvement in the community itself and the right to reply. We believe this is a uniquecontribution to the literature on this subject, as outlined in the following sections.

Theoretical contribution

This paper contributes to the understanding of brands’ interaction with consumersin computer-mediated communication. Currently, the theoretical focus in socialmedia has been upon the micro-level interactions measured through network nodesand their behaviour, but these do not inform theorists on consumer attitudes orperceptions that are behind the diffusion of information in cascades. The present

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study sheds light on how important elements on their predisposition to the mediumand the effect different styles of communication can have upon them. This addsto our understanding of how information is judged, leading to enhanced customerrelationships and co-production of value.

In addition, we have validated the Obermiller and Spangenberg’s (1998) SKEPscale in the context of information provided within VCs. The purpose was toestablish it as a measure to be included in tests of effectiveness of informationalexchanges, particularly in discussion forums which are a prominent feature of manyVCs. The reliability of the scale in this context was strong in both tests, suggestingits applicability for this purpose. The exclusion of one variable was specific to thecircumstances of this study (where a reference to ‘product truthfulness’ was notsupported by our sample messages).

Further, we wanted to test the extent to which informational versus opinion-based material would appeal to members of this forum. Here we found significantdifferences in the extent to which informational messages tended to reassure moresceptical respondents. This, we believe, contextualises the theory to social media.

We support recent calls for marketing initiatives to evolve and mature, learningfrom well-established rules to evaluate efforts to influence members of VCs.We believe our contextualisation of the SKEP scale (Obermiller & Spangenberg,1998) and the believability scale (Beltraminin, 1998) contribute to these efforts.

Practical contributions

Marketing managers are presented with great opportunities to establish and developrelationships with their customers within social media, but in comparison with otherchannels, their communication is less sophisticated (Barwise & Meehan, 2010).Adaptions of existing scales from the world of advertising have been shown in thisstudy to add value to the measurement of communication effectiveness in VCs whichcan be applied in practice.

Further, we have indicated that the direct provision of information is paramountin communicating with members of these types of communities. The use of emotionas a tool to engage and inform consumers is less effective than in advertising. Wherebrands are engaging in SCRM initiatives and communicating directly with consumers,the nature and tone of content should reflect this conclusion. Where a message isfound to be believable, the perception of the information is enhanced, but this effectis amplified where the style is direct and any claims are well justified.

While research into the effects of social media in marketing in recent years hasfocused on its ‘newness’, the time is now right to focus on a phase of maturity. In noother sphere is this more important than in cases where brands are aiming to engagewith their customers in order to increase the effectiveness of their SCRM initiatives.

Limitations and future research

There are some limitations to this study, which we acknowledge when we considerthe implications of our findings. First, we collected data from one special-interestcommunity, offering one of only two post options, and in the future suggest that arange of different posts should be used, perhaps including other categories in additionto the (factual/opinion-based) categories we tested. Second, a range of different

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product categories would enable us to take account of product category and testother factors such as involvement in more depth.

When designing the study, we chose information value as our dependent variablebased upon its importance to the development of trusted relationships and its effecton word of mouth. We did not consider a more determinate dependent variable, suchas brand trust or purchase intentions. Other researchers may wish to extend the scopeof our model and include these in the future.

In addition, we propose that future research extends tests to a wider range ofVCs, establishing the extent to which these initial findings can be generalised to awider range of product categories. Further, it would be valuable to experiment onthe differences between online versus offline communication attempts in order toestablish the extent of any difference.

References

Atkins, J.A., & Beltramini, R.F. (2007). Exploring the perceived believability of DTCadvertising in the US. Journal of Marketing Communications, 13(3), 169–180.

Bagozzi, R.P., & Yi, Y. (1988). On the evaluation of structural equations models. Journal of theAcademy of Marketing Science, 1, 6(1), 74–94.

Baron, R.M., & Kenny, D.A. (1986). The moderator–mediator variable distinction in socialpsychological research: Conceptual, strategic and statistical considerations. Journal ofPersonality and Social Psychology, 51, 1173–1182.

Barwise, P., & Meehan, S. (2010). The one thing you must get right when building a brand.Harvard Business Review, 88(12), 80–84.

Beltramini, R. (1988). Perceived believability of warning label information presented incigarette advertising. Journal of Advertising, 17, 26–32.

Beltramini, R.F., & Evans, K.R. (1985). Perceived believability of research results informationin advertising. Journal of Advertising, 14(3), 18–24, 31.

Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107,238–246.

Bentler, P.M., & Bonett, D.G. (1980). Significance tests and goodness of fit in the analysis ofcovariance structures. Psychological Bulletin, 88, 588–606.

Berlo, D., Lemert, J., & Mertz, R. (1969). Dimensions for evaluating the acceptability ofmessage sources. Public Opinion Quarterly, 33, 563–576.

Bollen, K.A. (1986). Sample size and Bentler and Bonnett’s nonnormed fit index.Psychometrika, 51, 375–377.

Bollen, K.A. (1989). A new incremental fit index for general structural equation models.Sociological Methods and Research, 17, 303–316.

Brown, J., Broderick, A.J., & Lee, N. (2007). Word of mouth communication within onlinecommunities: Conceptualizing the online social network. Journal of Interactive Marketing,21, 2.

Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K.A. Bollen& J.S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA:Sage.

Bull, C. (2003). Strategic Issues in customer relationship management (CRM) implementation.Business Process Management Journal, 9(5), 592–602.

Byrne, B. (2001). Structural Equation Modeling with AMOS basic concepts, applications andprogramming. Mahwah, NJ: Lawrence Erlbaum.

Cova, B., & White, T. (2010). Working consumers: The next step in marketing theory? Journalof Marketing Management, 26(3/4), 256–270.

Day, G.S. (1969). Attitude change, media and word of mouth. Journal of Advertising Research,11(6), 31–39.

Dow

nloa

ded

by [

Her

iot-

Wat

t Uni

vers

ity]

at 0

6:34

08

Oct

ober

201

4


Dutton, W.H., & Blank, G. (2011). Next generation users: The Internet in Britain. OxfordInternet Survey 2011. Oxford Internet Institute, University of Oxford.

Engel, J.F., Kegerreis, R.J., & Blackwell, R.D. (1969). Word-of-mouth communication by theinnovator. The Journal of Marketing. 33(3), 15–19.

Faase, R., Helms, R., & Spruit, M. (2011). Web 2.0 in the CRM domain: Defining social CRM.International Journal of Electronic Customer Relationship Management, 5(1), 1–22.

Fang-Ping, C., & Jun-Der, L. (2011). Product involvement in the link between scepticismtoward advertising and its effects. Social Behaviour and Personality, 39(2), 153–160.

Field, A. (2009). Discovering statistics using SPSS. London: Sage.Forrester Research Inc (2010). Topic Overview: Social CRM Goes Mainstream.Godes, D., & Mayzlin, D. (2009). Firm-created word-of-mouth communication: Evidence

from a field test. Marketing Science, 28(4), 721–739.Greenberg, P. (2010). The impact of CRM 2.0 on customer insight. Journal of Business and

Industrial Marketing, 25(6), 410–419.Grönroos, C. (1990). Relationship approach to the marketing function in service contexts:

The marketing and organizational behavior interface. Journal of Business Research, 20(1),3–12.

Hair, J.F., Jr., Anderson, R.E., Tatham, R.L., & Black, W.C. (1995). Multivariate data analysis:With readings. Upper Saddle River, NJ: Prentice-Hall.

Heath, R., & Nairn, A. (2005). Measuring affective advertising: Implications of low attentionprocessing on recall. Journal of Advertising Research, 45(2), 269–281.

Hiltz, S.R., & Turoff, M. (1985). Structuring computer-mediated communication systems toavoid information overload. Communications of the ACM, 28, 689.

Hirshleifer, J. (1973, May). Where are we in the theory of information? The AmericanEconomic Review, 63(2). Papers and Proceedings of the Eighty-fifth Annual Meeting of theAmerican Economic Association (pp. 31–39).

Hu, L., & Bentler, P.M. (1998). Fit indices in covariance structural modeling: Sensitivity tounderparamererized model misspecification. Psychological Methods, 3(4), 428–453.

Kietzmann, J.H., Hermkens, K., McCarthy, I., & Silvestre, B.S. (2011). Social media? Getserious! Understanding the functional building blocks of social media. Business Horizons,54, 341–251.

Little, & Mirandi, (2003). Relationship marketing management. Derby, England: Thompson.Maloney, J.C. (1963). Is advertising believability really important? The Journal of Marketing,

27, 1–8.Meadows-Klue, D. (2008). Falling in love 2.0: Relationship marketing for the Facebook

generation. Journal of Direct, Data and Digital Marketing Practice, 9(3), 245–250.Obermiller, C., & Spangenberg, E.R. (1998). Development of a scale to measure consumer

scepticism toward advertising. Journal of Consumer Psychology, 7, 159–186.Obermiller, C., & Spangenberg, E.R. (2005). Ad scepticism: The consequences of disbelief.

Journal of Advertising, 34(3), 7–17.O’Cass, A. (2002). Political advertising believability and information source value during

elections. Journal of Advertising, 31(1), 63–74.Pallant, J. (2007). SPSS survival manual. Milton Keynes, England: Open University Press.Payne, A., & Frow, P. (2005). Customer relationship management: From strategy to

implementation. Journal of Marketing Management, 22, 135–168.Peterson, R.A. and Wilson, W.R. (1992) Measuring Consumer Satisfaction: fact and artefact.

Journal of the Academy of Marketing Science. 20, 61–67.Petty, R.E., Cacioppo, J.T., & Goldman, R. (1981). Personal involvement as a determinant of

argument based persuasion. Journal of Personality and Social Psychology, 41, 847–855.Pew Research Centre (2010). Generations. Retrieved September 20, 2011, from http://

pewinternet.org/Reports/2010/Generations-2010.aspxPitta, D.A., & Fowler, D. (2005). Internet community forums: An untapped resource for

consumer marketers. Journal of Consumer Marketing, 22(5), 265–274.

Dow

nloa

ded

by [

Her

iot-

Wat

t Uni

vers

ity]

at 0

6:34

08

Oct

ober

201

4

http://pewinternet.org/Reports/2010/Generations-2010.aspx

http://pewinternet.org/Reports/2010/Generations-2010.aspx


Podsakoff, P.M., MacKenzie, S.B., Lee, J., & Podsakoff, N.P. (2003). Common method biases inbehavioral research: A critical review of the literature and recommended remedies. Journalof Applied Psychology, 88(5), 879–903.

Rheingold, H. (2000). The virtual community: Homesteading on the electronic frontier.Cambridge, MA: MIT Press.

Ridings, C.M., & Geffen, D. (2004). Virtual community attraction: Why people hang outonline. Journal of Computer-Mediated Communication, 10(1).

Rigby, D.K., & Ledingham, D. (2004). CRM done right. Harvard Business Review, 82(11),118–129.

Robinson, M.J., & Kohut, A. (1988). Believability and the press. Public Opinion Quarterly,52, 174.

Sobel, M.E. (1982). Asymptotic confidence intervals for indirect effects in structural equationmodels. In S. Leinhardt (Ed.), Sociological methodology 1982 (pp. 290–312). Washington,DC: American Sociological Association.

Tabachnick, B.G., Fidell, L.S., & Osterlind, S.J. (2001). Using multivariate statistics. Boston:Allyn & Bacon.

Valos, M.J., Ewing, M.T., & Powell, I.H. (2010). Practitioner prognostications of the future ofonline marketing. Journal of Marketing Management, 26(3–4), 361–376.

Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., & Haythornthwaite, C.(1996). Computer networks as social networks: Collaborative work, telework, and virtualcommunity. Annual Review of Sociology, 22(1), 213–238.

Wilson, H., Daniel, E., & McDonald, M. (2002). Factors for success in customer relationshipmanagement (CRM) systems. Journal of Marketing Management, 18, 193–219.

About the authors

Chris Archer-Brown is a Lecturer in Information Systems at the School of Management atthe University of Bath, UK, where he is studying the behaviour of consumers in virtualcommunities, which is the subject of his PhD Thesis. He holds a masters in marketing, wherehis dissertation focussed on consumer perceptions of brands’ attempts to develop relationships.Prior to committing to the PhD, he was managing partner of CRM Consultancy ClarityBlueworking on a range of CRM challenges with large consumer brands.

Corresponding author: Chris Archer-Brown, University of Bath, Claverton Down, Bath,BA2 7AY, UK.

E [email protected]

Niall Piercy’s research is focused on the interface between marketing and operationsmanagement and how this relates to customer value or service delivery. His work has appearedin publications such as Journal of Marketing Management, Journal of Strategic Marketing,European Journal of Marketing, The Marketing Review, Journal of Marketing Education,International Journal of Operations and Production Management, Production Planning andControl, Business History, and many others. His research is supported by a range of funders,including the Economic and Social Research Council, the Engineering and Physical ScienceResearch Council, The British Academy, the European Operations Management Association,the Welsh Assembly Government, and the Ministry of Defence. He has served as adviseron organisational integration to local and national government on issues from service andcorporate management through to counterterrorism operations, as well as to private sectororganisations around the world in industries as diverse as pharmaceutical to power generation.

E [email protected]

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mailto:[email protected]





Adam Joinson holds the post of Professor of Behavioural Change at Bristol Business School,UWE, Bristol, and has a BSc and PhD in psychology. He is director of the Interactions Labin the School and conducts research into social media, privacy, security, communication, andorganisations. His work has been published in Human–Computer Interaction, InternationalJournal of Human–Computer Studies, Journal of the American Society for Information Scienceand Technology, European Journal of Social Psychology, Behavior Research Methods, and atthe ACM Computer–Human Interaction (CHI) conference. His research is funded by the UKresearch councils ESRC and EPSRC, UK Government, and European Union. He wrote (withMonica Whitty) Truth, Lies and Trust on the Internet (Routledge, 2008) and Understanding thePsychology of Internet Behaviour (Palgrave, 2003), and led the editing of the Oxford Handbookof Internet Psychology (OUP, 2007). His website is http://www.joinson.com.

E [email protected]

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http://www.joinson.com

Documents

Examining the information value of virtual communities: Factual versus opinion-based message content