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Advertising on Social Network Sites: A Structural Equation Modelling Approach

Anant Saxena Uday Khanna

AbstractSocial networking sites (SNSs) emerged as one of the most powerful media for advertising across the globe. Globally, companies are shifting a larger pie of their advertising budgets towards social networking sites for better reach and interactive platform. The companies are also looking at it as a low-cost model, which could reap results in minimum time possible for the targeted ‘Facebook generation’. These very facts motivate researchers to study the value of advertisements on social networking sites like Facebook, LinkedIn, Twitter and others. The article is an empirical study to understand the implications of different variables in advertisements on the delivery of advertising value to the respondents. Confirmatory factor analysis (CFA) has been conducted to test the reliability of instrument being used for data collection. Further, a model has been proposed for measuring advertising value through structural equation modelling. The predicted results confirm the roles of different variables, namely, information, entertainment and irritation, in accessing value of advertisements displayed on social networking sites.

Key WordsAdvertising Value, Social Networking Sites, Structural Equation Modelling

IntroductionSocial networkingwebsites (SNSs) have emerged as the‘needofanhour’.Theirjourneystartedwiththelaunchofsixdegrees.comintheyear1997,whichattractedmillionsofusersat that time.Thesiteallowedtheuserstocreateprofiles listing their friends with the ability to surf thefriends list (Boyd and Ellison, 2007). This has beenfollowed by an array of SNSs like Facebook, Orkut,LinkedinandMySpace in theyear2003–2004.Withinashortspanoftime,thesewebsitesbecomeanaddictionforyoungstersasthesegivethemopportunityandplatformtoexpresstheirfeelingsandemotionsinthesociety.WebsiteslikeFacebook,Orkut,TwitterandMySpacehavebecomehouseholdnames and an integral part of people’s life somuchthatithasbecometoughforregularuserstoimaginea lifewithout them.Globally, Internet users spendmorethanfourandahalfhoursperweekonSNSs,moretimethan they spend on e-mail (Anderson et al., 2011). Asmoreandmoreofwhatpeoplethinkanddoendsupgettingexpressed on SNSs, it is expected that SNSs affect thebuying decisions greatly. In addition, the huge viewer’sbaseofthesewebsitesmakesthemafavourablemediaforadvertisements by different companies. According to astudy done by comScore, Inc., a market research firm,SNSsaccountedformorethan20percent,thatis,onein

five, display ads of all display ads viewed online, withFacebookandMySpacecombiningtodelivermorethan80per cent of ads among sites in the social networkingcategory (comScore, 2009). According to Rizavi et al.(2011)socialnetworkingwebsitesactasagoodplatformforadvertisingthatattractmillionsofusersfromdifferentcountries, speaking multiple languages belonging todifferentdemographics.AccordingtoTrusovetal.(2009)referralsandrecommendationsonSNSshaveasignificantimpact on new customer acquisition and retention. ThisfactledmarketerstoturntoInternetplatformslikeSNSs,blogsandothersocialmediaasanavenueforcost-effectivemarketing, employing e-mail campaigns, website adver-tisements and viral marketing. Also from a marketingperspective, these websites give potential customers theopportunity to virtually explore a business, encouragethemtovisitandatlastsharetheirviewsandexperienceswith their friends (Phillips et al., 2010). Understandingthe effectiveness of SNSs in promoting product andservices through advertisements, companies across theglobe have increased their advertising budget for SNSswhichhasledtoincreaseinrevenuegenerationforsocialnetworking website companies. According to a reportreleased by Interactive Advertising Bureau (IAB),Internetadvertisingrevenuestotaled$14.9billionin2011,up 23 per cent from the $12.1 billion reported in 2010

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18 Advertising on Social Network Sites

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(PricewaterhouseCoopers LLP., 2011). India shares thesamestoryintermsofInternetadvertisingrevenues.

According to a report, the sizeof Internet advertisingindustry was INR 7.7 billion in 2010 registering agrowth of 28.3 per cent over INR 6 billion in 2009(PricewaterhouseCoopers Private Limited, 2011). Thesame report highlights that in India SNSs have shown aremarkable growth of 43 per cent in 2010 over 2009,witha54percentgrowthinadvertisingonSNSsin2010–2011 (PricewaterhouseCoopers Private Limited, 2011).ConsideringthefactthatadvertisingonSNSsisonanewhigh, this research focus on studying the value ofadvertisementsbeingdisplayedonSNSs.

Literature Review and HypothesisWebadvertisingcontinuestobeamajorareaofadvertisingresearchfromalongtime.Anumberofstudieshavebeendone discussing advertisements on the Web and theireffects. Berthon et al. (1996) have discussed the role ofWorldWideWeb as an advertisingmedium in themar-keting communicationmix and proved thatWorldWideWeb is a new medium for advertising characterized byease-of-entry,relativelylowset-upcosts,globalness,timeindependenceandinteractivity.InspiteoftheacceptanceofWorldWideWebasaneffectivemediaforadvertising,fewstudieshave focusedon thevalueofadvertisementsdisplayed on this medium. R.H. Ducoffe introduced theconcept of advertisement value in 1995. According toDucoffe(1995)advertisingvalueisdefinedastheutilityorworthoftheadvertisement.Ducoffe(1996),inhisanotherstudyonWorldWideWeb,provedthesignificantimpact(either +ve or –ve) of entertainment, information andirritationonadvertisementvalue.BrackettandCarr(2001)in their study on cyberspace advertising reports thatinformation, entertainment, irritation and credibilitysignificantly affect advertisement value which in turnaffects attitude towards advertisements. Discussion ondifferentpredictorsofadvertisementvaluewithreferencetoSNSsadvertisementsisherebyillustrated:

1. Information: Information content is an importantdeterminantofadvertisementeffectiveness.Comp-anies advertise for one main reason—providinginformationabouttheirproduct,servicesandbrandto consumers. Consumers reported that supplyinginformationistheprimaryreasonwhytheyapproveadvertising(Baueretal.,1968).AccordingtoNorris(1984) information in advertisements enables thecustomerstoevaluatetheproductsmorerationallyleading to improvedmarkets with low prices andhighqualityoftheproduct.InformationcontentonInternet can be delivered better in comparison to

televisionmedium,reasonbeingshorttimespanoftelevision advertisements. Yoon and Kim (2001)mentionedthatInternetadvertisingdiffersfromtra-ditionaladvertisingasitdeliversunlimitedinforma-tionbeyond timeandspaceand itgivesunlimitedamountandsourcesofinformation.Webadvertise-mentsprovideinformationandgenerateawarenesswithout interactive involvement (Berthon et al.,1996). On the contrary, information deliveredthroughSNSsadvertisementsisdifferentfromtra-ditionalWebadvertisementsbecauseSNSsprovidea medium that is interactive in nature. A personcouldscanandshareinformationwithonlinefriendsandfollowers,thusmakingtheadvertisementinfor-mationviralinnature.LargemediacompanieshaverealizedthepotentialofSNSstoreachanddeepenrelationshipswith the ‘subscribed’ audience (Jhih-Syuan and Pena, 2011). This specialty of SNSsadvertisementmakes it themost competitive plat-form for sharing information about products andservices.As the delivery and importanceof infor-mation for SNSs advertisements is different fromotherformsofadvertisements,itisimportanttonoteits effect on advertisement value. Based on thisrationale,thehypothesistestedis:

H1:Thereisasignificantpositiveimpactofinfor-mation content of advertisements on the value ofadvertisements displayed on social networkingwebsites.

2. Entertainment: An advertisement that is full ofinformationbutnil inentertainmentcontent isnotworthy.AccordingtoMcQuail(1994)anadvertise-ment entertainswhen it fulfils the audience needsfor escapism, diversion, aesthetic enjoyment oremotional release. The ability of advertising toentertaincanenhancetheexperienceofadvertising.Inaddition,anadvertisementcouldbeinformationfor one and entertainment for other person at thesametime(AlwittandPrabhaker,1992).Consumerswhofoundadvertisingtobeentertainingalsoevalu-ate it as informative (Ducoffe, 1995).This showsthatentertainmentand informationare interrelatedconcepts when talking about advertisements.SNSsplatform is interactive innatureanddisplaybanner advertisements of different brands at thesameplatformandsametime;theyhavethepowerto entertain the audience. Kim and Lee (2010)noted that college students use SNSs for sixmain reasons: entertainment, passing time, socialinteraction,informationseeking,informationprovi-ding, andprofessional advancement.According to


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Taylor et al. (2011) SNSs advertisements provideentertainmentvaluetotheaudience.Thesamestudyreported that entertainment exhibits almost fourtimesmorestrengthofinfluenceonfavourablecon-sumers’attitudetowardsadvertisementsthaninfor-mation.Withreferencetotheexistingliterature,itisimportant to find the impact of entertainment onadvertisementvalueofSNSsadvertisements.Inthesamevein,thehypothesistestedis:

H2:Thereisasignificantpositiveimpactofenter-tainmentcontentofadvertisementsonthevalueofadvertisements displayed on social networkingwebsites.

3. Irritation: Irritation from advertisements ariseswhenwefeeldiscomfortinwatchingadvertisementdue to any reason.The reason can be personal orsocial.Apersonalreasoncouldbedistractionwhilefocusingonaparticular taskonWorldWideWeb.According toWells et al. (1971) irritation is oneamongst six dimensions of personal reactionstowards advertising. It is the degree towhich theviewerdisliked thecontents thathehadseen.Thewordsthatcameintothemindoftheviewerattimeof getting irritated from an advertisement are‘terrible’, ‘stupid’, ‘ridiculous’, ‘irritating’ and‘phony’. An advertisement can be rewarding forsomeviewersandyetbeanirritantandunrewardingforothers(AlwittandPrabhaker,1992).AccordingtoAakerandBruzzone(1985),increaseinirritationcanleadtogeneralreductionintheeffectivenessofadvertisement.IncaseofInternetadvertising,italsogenerates considerable irritation (Schlosser et al.,1999).Asonlinebehaviour includinguseofSNSsis highly goal oriented, advertisements on SNSsmightirritatetheuser(Tayloretal.,2011).Thelit-eraturesuggestedthatirritationhasanegativeeffectontheeffectivenessofadvertisementirrespectiveofthemedia. Based on this rationale the hypothesistestedis:

H3:Thereisasignificantnegativeimpactofirrita-tioncontentofadvertisementsonthevalueofadver-tisementsdisplayedonsocialnetworkingwebsites.

Aconsiderableamountof researchondeterminantsofWeb advertising effectiveness and value has been done(Berthonetal.,1996;Brownetal.,2007;Ducoffe,1995;Lei, 2000; Schlosser et al., 1999;Yoon andKim, 2001);however, these studies were more focused on traditionalwebsites rather than SNSs.Advertising through SNSs isdifferent fromtraditionalwebsitesdue toseveral reasons.

First,advertisementsonSNSsaredifferentnotonlyinformand substance but also in deliverymethod. Some of themessagesare‘pushed’uponconsumerswhileothersrelyonconsumersto‘pull’content;somegeneraterevenuewhereassomearenon-paidcontentdeliveredthroughmediacontent(Tayloretal.,2011).Second,SNSshavetheirownuniqueuser-to-userinterface(SafkoandBrake,2009).Third,SNSsusersareincreasingdaybydayallovertheworld,whichmakes this medium suitable for advertising. As SNSsadvertisingisdifferentfromtraditionalWebadvertisingandalittleisknownaboutvalueofSNSsadvertisements,thisstudy tries to fill this researchgapbyprovidingamodel,which tests the interrelationships between differentdeterminantsofadvertisementvalue.

Model TestingThe importance of advertisements displayed on SNSs isincreasingdaybyday.AccordingtoStelzner(2011)88percentof the marketers have reported that their social mediaadvertisements have generated more exposure for theirbusinesses.ThisleadstheauthorstotestamodelforaccessingthevalueofadvertisementsdisplayedonSNSsbyemployingstructuralequationmodelling (SEM)approach.UseofSEMtechnique gives us the opportunity to examine multipledependence techniques simultaneously. SEM approach is astatistical methodology that combines the strength of factoranalysisandpathanalysis.AccordingtoSingh(2009)SEMisconsideredasamoreadvancedtechniquethanothermultivariatetechniques because it can estimate a series of interrelateddependence relationship simultaneously.According toByrne(1998)SEMtechniqueisbetterbecause:

1. It accounts for measurement errors in course ofmodeltesting.

2. Itcanincorporateobserved(indicator)variablesaswellas latent (unobserved)variablesat same timeduringmodeltesting,

3. Ittestsapriori relationshipsratherthanallowingthetechniqueordatatodefinethenatureofrelationshipbetweenthevariables.

In the present study, SEM analysis is conducted intwo major steps; first, to test the measurement modeland second, a structural model. Measurement modelprovides the series of relationships that suggests howobserved variables represent latent variables (Figure 1),tested by means of confirmatory factor analysis (CFA).Structuralmodelteststheconceptualrepresentationoftherelationshipsbetweenthelatentvariables.Ittellswhetherthe proposed model is eligible to represent a proposedconceptandconceptualrelationshipsbetweenthevariablesornot(Figure2).



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Figure 1. Measurement Model

ENTERTAINMENT

INFORMATION

IRRITATION

ADD.VALUE

ENTERTAIN 3

ENTERTAIN 2

ENTERTAIN 1

INFO 1

INFO 2

INFO 3

IRRITATION 1

IRRITATION 2

IRRITATION 3

ADDVALUE 1

ADDVALUE 2

ADDVALUE 3

0.57

0.17

0.19

0.47

0.40

0.51

0.45

0.80

0.39

0.80

0.86

0.52

0.23

0.75

0.65

0.76

0.77

0.73



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Figure 2. Structural Model

MethodSample Design

The research focuses on social networking websites withcollegestudentsassamplerespondents.Thecollegestudentswere selected as sample for two basic reasons. First,student sample is more homogeneous (less variable) interms of socio-economic background, demographics andeducation (Peterson, 2001). Second, a number of studieshave reported that students are the main users of socialnetworking websites (Dwyer et al., 2007; Pempek et al.,2009; Subrahmanyam et al., 2008). With this rationale,present study sample includes postgraduate managementstudents of a reputed college based in India. 276 studentshave responded to an online questionnaire mailed to 300students. The questionnaires were mailed with Googledocuments facility to form and mail online forms/questionnaires.After removing incomplete questionnaires,only189questionnaireswerefoundtobeuseableforanalysisandfurtherstudy.Resultingsampleconsistsof71percentmalesand29percentfemales.Subjectswereaskedtoreporttheirreactionstoinstrumentstatementsbyconsideringtheir

perceptions of advertisements on SNSs in general, not asingle advertisement or advertisement for any particularproduct.Theobjectiveofthisgeneralizationistoassessthevalueofadvertisementonsocialnetworkingwebsitesacrossdifferentadvertisementsofproductandservicecategories.

Sample Size and SEM Analysis

SamplesizeisakeyissuewhenperformingSEManalysis.According to Bentler and Bonett (1980) and Hair et al.(2007)chi-squarevalueissensitivetoincreaseinsamplesize,whileitlackspowertodiscriminatebetweengoodfitand poor fitmodelswith small sample size (Kenny andMcCoach,2003).Hairetal.(2007)mentionedthat15res-ponses per parameter is an appropriate ratio for samplesize.Goingonwiththisapproachasamplesizeof189res-pondentsformeasuring12parameterswasappropriate.

Research Instrument

For measuring the advertisement value of advertisementsdisplayed on social media, a 12 item scale developed by

ADD. VALUE

ADDVALUE 3

ADDVALUE 2

ADDVALUE 1

IN1 IN2 IN3

e3 e2 e1

IRR1 IRR2 IRR3

e9 e8 e7

EN1 EN2 EN3

e6 e5 e4

INFORMATION

IRRITATION

ENTERTAINMENT

e10

e11

e13

e12

0.55 0.84 0.38

0.27

0.15

0.87 0.58

0.77 0.90 0.51

0.40

0.360.38

0.75

0.76

0.72

0.25



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Ducoffe(1995)wasused.Theinstrumentwasmodifiedaspertheneedofthestudy.Afive-itemLikertscalewasusedasaresponsescale,fromstronglydisagreestostronglyagree.

Measurement ModelMeasurementmodelisaspecificationofthemeasurementtheory that shows how constructs are operationalized bysets of measured items. Confirmatory factor analysis isusedtotestthereliabilityofameasurementmodel.Unlikeexploratoryfactoranalysis,CFAallows theresearcher totelltheSEMprogrammewhichvariablebelongstowhichfactorbeforetheanalysis(Hairetal.,2007).AccordingtoSalisburyetal.(2001)CFAallowstheresearchertospecifythe actual relationship between the items and factors aswellaslinkagesbetweenthem.

Construct Validity

According toHair et al. (2007) construct validity is theextenttowhichasetofmeasureditemsactuallyrepresentstheoretical latent construct; those items are designed tomeasure.ThereliabilityofadvertisementvaluescalewasexaminedbyspecifyingamodelinCFAusingAMOS19.Reliability of an instrument can also be calculated byCronbach’s alpha, but use of SEM technique makessuch a practice unnecessary and redundant (Bagozziand Yi, 2012). The results (see Table 1) confirm theoverall fit of a measurement model when employed toCFA.

AccordingtoHairetal.(2007)oneincrementalfitindex(CFI), one goodness of fit index (GFI), one absolute fitindex(GFI,SRMR)andonebadnessoffitindex(SRMR),withchi-squarestatisticshouldbeusedtoassessamodel’sgoodness of fit. Our study results show all the differenttypesofindicesintheacceptablerange.

Convergent and Discriminant Validity

Convergent validity exists when the items that areindicatorsofaspecificconstructconvergeorshareahighproportion of variance in common. In general, ‘factorloading’ and ‘variance extracted’ measures are used tomeasureconvergentvalidity.Wehaveusedfactorloadingmeasureinourstudytomeasureconvergentvalidity(Hairetal.,2007;Salisburyetal.,2001).Allthefactorloadingsare statistically significant, a minimum requirement forconvergence(Hairetal.,2007).Furthermore,exceptitems‘Info3’and‘Irritation1’allfactorloadingsareintherangeof 0.50 to 0.80,which ismore than acceptable value of0.50(Hairetal.,2007)(seeFigure1).AccordingtoChinetal.(1997)discriminantvalidityexistsifthecorrelationbetweentheconstructsisnotequalto1.Followingtherule,our study shows the discriminant validity between theconstructs(seeFigure1).

Structural ModelAfter assessing the eligibility of scale for measuringdifferentvariablesinthestudy,thenextstepistotestthehypothesizedrelationshipsinastructuralmodel.Ducoffe(1996) has proved the respective role of information,entertainmentandirritationonadvertisementvaluefortheadvertisementsontheWeb.Inourstudy,wetrytoexploretheimpactoftheserespectivevariablesonadvertisementvaluevis-à-visSNSs.

Performance of the Model

Hypothesized relationships are supported by the overallmodelfitindicesobtained.Allofthefitindicesareabovethe recommended values. The c2/df value 2.31 met therecommendedvalueoflessthan3(CarminesandMcIver,1981).Hair et al. (2007) argues that chi-square value issensitivetosamplesizeandnumberofvariables;therefore,c2/df value is not taken as a sole indicator ofmodel fit.Other model fit indicators taken are also within therecommendedrange(seeTable2).Insum,variousmodelfit indices indicates that the proposed model fitted wellwiththepresentdataset.

Table 1. Model Fit Indices for Measurement Model

StatisticRecommended

Value Obtained Value

Chi-square c2 92.616Df 48c2/df (Hinkin, 1995),

(Carmines and McIver, 1981)

< 3.00 1.93

GFI (Hooper et al., 2008), (Hair et al., 2007)

> 0.90 0.92

AGFI (Muenjohn and Armstrong, 2008)

> 0.80 0.88

SRMR (Hu and Bentler, 1999)

< 0.08 0.06

CFI (Watchravesringkan et al., 2008)

> 0.80 0.92

Note: AGFI: Adjusted goodness of fit index; SRMR: Standardized root mean square residual; CFI: Comparative fit index



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Table 2. Model Fit Indices for Structural Model

StatisticRecommended

Value Obtained Value

Chi-square c2 115.539Df 50c2/df (Hinkin, 1995),

(Carmines and McIver, 1981)

< 3.00 2.31

GFI (Hooper et al., 2008), (Hair et al., 2007)

> 0.90 0.91

AGFI (Muenjohn and Armstrong, 2008)

> 0.80 0.86

RMSEA (MacCallum et al., 1996)

< 0.10 0.08

CFI (Watchravesringkan et al., 2008)

> 0.80 0.88

Note: SMSEA: Root Mean Square Error of Approximation

Estimated Standardized Path Coefficients

Figure 2 shows the standardized path coefficients ofthe four constructs under investigation. All the pathcoefficientsweresignificantat the levelof0.01with thedirectionofinfluenceashypothesized(+or−).Informationand entertainment were positively associated withadvertisementvaluewhereasirritationisnegativelyasso-ciatedwith advertisement value; thus all the hypothesesframedarestatisticallysupported.Asignificantcorrelationbetweeninformationandentertainmentalsoindicatesthattheconsumerswhofindadvertisement tobeentertainingaremorelikelytoevaluateitasinformative.Theseresultsareconsistentwithanotherstudy(Ducoffe,1995).Finally,the squared multiple correlations (R2) indicates that thepresent model explains 38 per cent of the variance inadvertisementvalue.

Discussion and ImplicationThestudyyieldedimportantnewinsightsaboutatopicthatis important for both industry practitioners and aca-demicians.Theconceptofadvertisementvalueandfactorsaffecting it had been widely tested for various typesofadvertisementsinanumberofstudiesbutlackofworkforadvertisementsdisplayedonsocialnetworkingwebsiteswasthemotivatingfactortodoresearchintheparticulardomain.Thestudyteststhemodeltoassessadvertisementvalue by employing SEM approach. SEM combines thestrengthoffactoranalysisandpathanalysis.Itenablesusto test whether observed variables completely describeslatent variables or not. In addition, SEM is a moresuccessfultechniquethanothermultivariatetechniquesasit can estimate a series of interrelated dependencerelationshipsimultaneously.It tellswhether theproposed

model is eligible to represent a proposed concept andconceptualrelationshipsbetweenthevariablesornot.Theresults of CFA suggest that the observed variables aresuitableenoughtorepresentdifferentlatentvariables,thatis,information,entertainment,irritationandadvertisementvalue in the particular domain of social networkingadvertising.

The findingsof structuralmodelanalysis suggest thatthe proposed model for accessing the value ofadvertisements displayed on SNSs fitswell. In addition,the proposed hypotheses assessing the relationshipsbetween the variables are statistically supported. Thefindings suggest that when advertisements displayed onSNSs provide entertainment and information content orimpressions, it increases theworth of the advertisement.Ontheonehand,ashasbeenprovedtrueforothertypesofmedia advertising, consumers derive utility fromadvertisements that provide some useful or functionalinformation and increase hedonic value by entertainingthem.Ontheotherhand,irritationdecreasesthenetworthof the advertisements displayed on SNSs. This suggeststhatthecompaniesusingSNSsmediaforadvertisingtheirproducts and services should reduce the contents,whichirritatetheviewers’base.

It isworth noting that ‘information’ exhibited around1.6 times more strength of influence on advertisementvalue than entertainment. This suggests that companiesshould firstly focus on providing information content intheir advertisements tomake their advertisements worthfor consumers. In addition, it is interesting to note thatfindings of this study show a significant correlationbetween information and entertainment, which indicatesthatconsumerswhofindadvertisementtobeentertainingaremorelikelytoevaluateitasinformative.

LimitationsAlthoughthestudyhasbeendonetakingintoaccountthemethodologicalrigour,somelimitationsremain.First,thesampling used is convenience sampling. Second,exploration of other variables that affects the value ofadvertisementisneeded.

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Anant Saxena ([email protected]) is working as a ResearchAssociate at IMT Ghaziabad, UP, India. He is currentlyresearchingtheroleofcommonservicecenter(CSC)projectinIndian governance and also working on the impact of greenmarketing on consumer purchase decision in India. Hehas published research papers in national and internationaljournalsofrepute.Hisresearchinterestsaremarketingthroughsocial media, information technology & government policiesande-marketing.

Uday Khanna ([email protected]) is an AssistantProfessorattheFacultyofManagementStudiesatGraphicEraUniversity,Dehradun,India.HisareasofinterestareMarketing,MarketingResearchandSalesandDistribution.Heiscurrentlyresearching the quality of corporate governance of Indiancompanies.Hehaspublishedsomegoodpapersinnationalandinternationaljournalsofrepute.Hehasrichindustryexperiencein FMCG companies of repute like Gillette India Ltd andHindustanPencilsLtd.


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