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Social media interactions and responses on consumers’ expenditures

Thirty Sixth International Conference on Information Systems, Fort Worth 2015 1

Quantifying the Effects of Social MediaMarketing Interactions and Responses on

Consumers’ Purchase BehaviorsCompleted Research Paper

Introduction

There are now over a billion people on Facebook with at least 48% of the users logging on to theplatform every day (Statisticbrain.com, 2015). Attracted to the high number of eyeballs, marketerscreate social media brand community (SMBC) to reach out to their consumers on SNS. Such platformalso allows consumers to react to marketers’ content. What we have described is the ongoingparadigm shift in marketing communication where consumers are no longer passive receptacle andare able to respond to marketers contents (Deighton and Kornfeld, 2009; Malthouse et al. 2013).

While some marketers welcome the changes, others choose to be cautious after witnessing companies becoming casualties of a social-media botch when consumers’ responses go wrong (Gellman 2015).

The central theme of our research investigates if consumers’ responses add any economic value to acompany’s marketing SNS communication strategies. We investigate if posts with good responses

(e.g., receiving a lot of likes and comments) are actually more effective at inducing expenditure. Priorresearch has quantified the role of marketer generated content (MGC)/ user generated content (UGC)contents in inducing expenditure and increasing store visit frequency (e.g., Chevalier and Mayzlin2006; Dhar and Chang 2007; Goh et al. 2013; Rishika et al. 2013). However, these papers examinedMGC/UGC as an aggregated whole and do not put into perspective the effects of the responses ofconsumers to the original contents. In addition, we are also interested in investigating thephenomenon of “Social Tagging”, which is a unique type of consumers’ response where a consumermay like a marketer’s content and direct it to his/her friends. We find a lack of research literatures inexamining the effectiveness of such directed UGC recommendations on consumers’ expenditure. Wealso want to investigate how different contents type, as coded by natural language processing (NLP),actually have differing effects on an individual’s expenditure. There are papers which studies on theeffects on consumers’ engagement or sales performance from exposure of different content context(Lee et.al 2014; Gopinath et al. 2014). These researches find that not all contents are created equal,and different contents type have different effects on the consumers. However, the gap is these papersusually have the dependent variables as either engagement (e.g., comments and ‘likes’ ) or aggregated

sales level. Our research bridges the gap by taking it to the next level and examines the effects onexpenditure.

Thus, we attempt to answer the following research questions: Does brand community fans’ responses(e.g., “ Likes”, Comments) add value to the content leading to greater influence of the originatingcontent in increasing expenditure of an individual? Is expenditure of a consumer influenced by thenumber of tags he/she has received? Is expenditure also affected by an individual observing tags fromothers even though it is not directed at himself/herself? How is the influence on expenditure differentfrom various type of contents (e.g., promotion-orientated vs light-hearted) an individual observes?

By using data from a leading SNS platform and transaction data of loyalty program consumers of aleading kids’ apparel, we find evidence that MGC contents are further enhanced by comments fromconsumers in inducing expenditure. As for UGC, “Likes” were found to enhance the original content inconvincing increased expenditure. Next, receiving a “Social Tag” was found to be effective in leading toconsumers’ increased consumptions and finally different contents types were found to have different

effects on increasing expenditures. Specifically, MGC contents which are more light-hearted wereactually found to be more effective than promotion-orientated contents while contest contents werefound to have no realistic impact on near-term purchases. In doing this research, we ensuredrobustness of results by comparing against a heckman selection model and controlled for selection

bias of online SNS brand community member.

Our theoretical contributions come in three ways. (1) We demonstrated the successful use of NLPanalytics to decompose information stored in unstructured data content. (2) We reveal new insightson the benefits of brand community members adding value to conversations through comments andcomputer mediated gesture like “Likes”. (3) Finally, we find evidence of the positive role of socialtagging as a customers’ way of endorsing the brand and the products to their relevant friends.

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Literature Review

Brand Community

Our research centered on SMBC which is a brand community. A brand community is defined as “afabric of relationships in which the customer is situated” (McAlexander, Schouten & Koenig, 2002).

According to Alexander, community-integrated customers serve as missionaries, carrying WOM into

other communities. And it leads to longevity of the customers’ relationship. According to Muniz et. al(2001), the essence of a brand community is a “Consciousness of kind” where member feel animportant connection to the brand as well as to each member. Fans also demarcate themselves fromusers of other brands. In Algesheimer et al. (2005) paper, it was found brand community members’identification with the brand community is related to the consumer’s relationship with a brand. Also,it was found that brand community’s influence can affect a brand profitability. Brodie et al. (2013) hasshown that engaged consumers in a brand community exhibit “greater consumer loyalty, satisfaction,connection, emotional bonding, trust and commitment”. It is also mentioned that people areinterested in the social links between customers as a result from the brand affiliations and brandmembers typically play a few roles like Mentor, learner and guide (Fournier and Lee 2009). Theseliterature underscore the benefits of marketers in starting and nurturing a brand community.

Co-participating roles of consumers

They are a plenty of research which focused on the co-creation of value phenomenon. Consumers

have an eager urge to interact with firms and “co-create” in order to extract greater value for bettersatisfactions. (Prahalad and Ramaswamy 2004; Lusch and Vargo 2006). Consumers also actively co-create brand identities with marketers and shape brand meanings making a brand more relevant and

valuable (Bendapudi and Leone 2003; Payne et al. 2009). According to Mcwilliam (2000), Co-participation of consumers also benefit other consumers as the complementary content of consumersand potential for relationships with other brand members “act as a magnet, drawing consumers to the

business on a frequent and regular basis”.

Effects of MGC/UGC on business goals

Scholars have written about two main purposes of MGC: awareness generation as according toKleindorfer and Wind (2009) and persuasion purpose which can be argued to exert informationalinfluence as described in Deutsch and Geread (1955) paper. Other suggested classifications includesattribute-focused versus emotional-focused by Gopinath (2014) and persuasive versus informative

content (Goh et al, 2013; Lee et al. 2014).Scholars have also studied the impact of social networks interactions on both short-term and long-term influence on business goals. For instance, Goh et al. (2013) studied the effect of Social Networkmarketers generated contents (MGC) and users generated contents (UGC) on consumers’ expendituredata and found that exposure to these contents are significantly linked to increase in consumers’expenditure. Another scholar attributed higher customer visit frequency and customer profitability totheir participation in a firms’ social media activities (Rishika et al. 2013). Another paper asserted thatsocial media activities have predictive power on equity value due to their impact on the long-termperformance of the firm (Luo, Zhang and Duan, 2013).

Various aspects of MGC/UGC were being studied. The simplest form is the volume metric which is asimple count of number of posts, were found to be positively correlated with sales (e.g., Chevalier andMayzlin 2006; Dellarocas, et al. 2007).However, research papers have gone a step ahead. In Gopinathet al. (2014) research, volume of content was further broken down to number of attribute-focused

message and emotion-focused message and it was found that attribute-focused message have ashorter effectiveness than emotion-focused messages in invoking sales. Similarly, Lee et al. (2014) broke down SNS contents into informative contents versus persuasive contents via NLP techniquesand found that persuasive contents, typically status that share an interesting facts and tap into readeremotion, are better received by users. Goh et al. (2013) meanwhile adopted another approach inintroducing the concept of information richness which is the amount of useful words in MGC/UGCand this factor was found to be significant in inducing expenditure. Besides just the measure of theamount of content, Goh goes one step further and investigate the effect of directionality onexpenditure. The paper also discovered that UGC content has a stronger effect than MGC and thatUGC functions through informative and persuasive interactions while MGC is only through persuasiveinteractions. Similarly, Valence is commonly being studied and was found to affect book sales and

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children apparel sales (Chevalier and Mayzlin, 2006; Goh et al. 2013). Readability and complexity ofUGC were also found to affect demand (Goes et al. 2014; Ghose et al. 2011). In Ghose et al. (2012)paper, it was found that in hotel booking, a type of experience products, reviews that are less complex,have shorter words and have fewer spelling errors influence demand positively as compared toreviews with much characters and/or written in simpler terms. Meanwhile, in another paper,reputation of the WOM giver was found to be more impactful in terms of usefulness of a word ofmouth (Forman, Ghose and Wiesenfeld, 2008). Even writing style was found to affect demand (Ghose

and Ipeirotis , 2011).

Research Model and Hypotheses

We categorized MGC into promotion, contest, and general categories whereby general categories arenon-promotional and non-contest posts. To start off, repeated exposure to promotional content has

been argued to reinforce brand preference (Tellis, 1998). Thus increased exposure to promotionalcontent would lead to greater individual’s expenditure. Meanwhile, contest posts may help build thecommunity and contribute to the “community identification” process as according to McAlexander etal. (2002). This integrates the brand community and increases their attachment to the brand. Even ifthe consumers choose not to participate in the contest, the mere presence of these posts will send amessage to the consumer on the cohesion level of the community. Thus, contest posts will lead to anincrease in expenditure. Meanwhile, we argue that general MGC falls into the categories of“Persuasive Content” as they are generally light-hearted and appeal to the consumers. We use Freitas& Higgins (2002) regulatory focus framework and argue that consumers are probably on a prevention

focus stance when exposed to promotional posts as someone is trying to sell them something butexposure to general MGC posts may soften their stance and make them more open to the information.Thus exposure to general MGC is predicted to lead to increase in expenditure. Overall, we predict thatthe volume of MGC content is related to higher individual’s expenditure.

Hypothesis 1 (H1): A higher volume of MGC posts is related to a higher level of consumerexpenditure.

In terms of UGC, we categorized UGC to complaints category and general category whereby generalcategories are UGC posts which are not complaints. We expect more complaint posts to relatenegatively to an individual’s future expenditure as these are posts which acts as negative word ofmouth. In fact, we expect the negativity effect to be strong as past research shows that consumers tendto react more to negative content commonly defined by the term negativity-bias (Sen & Lerman,2007). Meanwhile for UGC general posts, we expect it to relate to increase in an individualexpenditure since they are UGC without the negative WOM and are thus majority WOM contents.

Hypothesis 2A (H2A): A higher volume of UGC posts about complaints is related to a lower level ofconsumer expenditure.

Hypothesis 2B (H2B): A higher volume of UGC general posts is related to higher level of consumerexpenditure.

Next we dwell on the computer mediated gesture “Likes” . de Vries et al. (2012) has proposed that“Likes” is a public way of expressing opinion and thus it can be regarded as a form of word of mouth.Despite having no text-content in “Likes”, we speculate that it will still be effective in drivingexpenditure, as twitter tweets with limited text content are able to move movie sales as an aggregate

whole according to Hennig-Thurau et al. (2014) research. According to Pletikosa Cvijikj andMichahelles (2013), “Likes” proxies for how engaged consumers are and thus a high number is

beneficial for brand communities. Perhaps, number of “Likes” relates to higher expenditure as “ Likes”convey a sense of approval from other consumers and this is especially important for the fashionretailer in our context as everyone wants to buy clothes which people approve of. Thus we proposehypothesis 3:

Hypothesis 3A (H3A): A higher number of “Likes” on MGC posts is related to higher level ofconsumer expenditure.

Hypothesis 3B (H3B): A higher number of “Likes” on UGC posts is related to higher level ofconsumer expenditure.

We also hypothesize that brand community members serves to help other brand fans (UGC and MGC)find information that are useful. This process is done through computer-mediated social gesturesavailable in most popular SNS platform. Specifically we propose that No of “Likes” serves as signalsfor other consumers to establish the usefulness, relevancy and authenticity of the posts. Consumers

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would be more likely to pay attention to a post that has good responses in terms of number of “likes” and replies. A “like” gesture could also serve as a stamp of approval for the original MGC/UGC topic.These may ultimately boost the persuasiveness of the original MGC/UGC post, increasing theirinfluence in inducing expenditure. In addition, we hypothesize that brand fans add value toMGC/UGC contents by providing relevant and useful comments that builds the original content. Wecan draw some inference from Onishi and Manchanda (2012) paper, where it was found thatcustomers’ contents like word-of-mouth has a synergetic role with TV advertising. Thus, we speculate

that comments/replies may also play a synergistic role on enhancing the original content’spersuasiveness.

Overall, these behaviors of brand fans help serve the whole brand community better. These behaviorsare probably driven by the same factors inducing generation of WOM: altruism, a desire for socialinteraction, concerns for others and an opportunity to enhance self-worth as according to Hennig-Thurau paper (Hennig-Thurau et al. 2004). Thus, these co-participating behaviors of the brandmembers have a synergetic effect with the marketer’s communication strategies. We hereby introducehypothesis 4 as interaction variables test on MGC:

Hypothesis 4A (H4A): There is an interaction effect between the volumes of MGC posts and MGC“likes” on consumer expenditure, such that as the volume of “likes” on MGC posts increases, thepositive relationship between the volume of MGC posts and consumer expenditure is strengthened.

Hypothesis 4B (H4B): There is an interaction effect between the volumes of MGC posts and MGCcomments on consumer expenditure, such that as the volume of MGC comments increases, the

positive relationship between the volume of MGC posts and consumer expenditure is strengthened. And hypothesis 5, which are the same interaction tests on UGC:

Hypothesis 5A (H5A): There is an interaction effect between the volumes of UGC posts and UGC“likes” on consumer expenditure, such that as the volume of “likes” on UGC posts increases, thepositive relationship between the volume of UGC posts and consumer expenditure is strengthened.

Hypothesis 5B (H5B): There is an interaction effect between the volumes of UGC posts and UGCcomments on consumer expenditure, such that as the volume of UGC comments increases, thepositive relationship between the volume of UGC posts and consumer expenditure is strengthened.

We also postulate that brand members direct useful contents to people who could benefit from theinformation. This indirectly create an information organization phenomenon in brand community.Such phenomenon is enabled through “Social Tagging”, a common form of computer-mediatedgestures in most SNS. Research has shown that the informational value of these social tags help

organize content (Nam & Kannan, 2014). It can also be argued that tagging is a form of narrowcastingand helps deliver useful information to the recipient. This is based on Barasch and Berger (2014)research which talks about the benefits of narrowcasting as one-to-one communication whichheightens the psychological focus on the receiving personnel. Similarly, Goh et al. (2013) paper hasprovided empirical evidence that directed contents are effective in inducing expenditure. In addition,

we theorize that consumers have an urge to reciprocate to tags as a result of peer pressure.

Hypothesis 6 (H6A): A higher volume of MGC posts tagged to a focal consumer through socialtagging on SMBC is related to a higher level of consumer expenditure by the same focal consumer.

On the flip side, we are curious and ask if tagging also influences others when they are not theintended recipients. People may adopt a herding behavior and assume that since others people arerecommending their friends through tags then the content must be good. This may also be argued as aform of informational conformity where a person who lacks knowledge about a topic will look tosignals from a group (conversation participants) for guidance.

Hypothesis 6 (H6B): A higher volume of MGC posts tagged to other non-focal consumers throughsocial tagging on SMBC is related to a higher level of consumer expenditure by a focal consumer.

Research Methodology

Research Context

Our research context is a business fan page brand community on a top SNS site set up in July 2009, atrendy kids casual wear apparel retailer in a small Asian market. According to the company, socialmedia platform is the only tool the company uses to engage their customers. The retailer has providedus with customer information from their customer loyalty program. Figure 1 presents a realistic

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mockup of the brand community. The retailer setup the community with the goal of better engagingcustomers and allowing social bonds to be formed within brand fans. Unfortunately, due to NDA, weare not allowed to disclose the full identity of the company who sponsored our data.

Figure 1. Mockup of the actual SNS site

Consumers can “Like” the page which allows them to receive subsequent new posts on theirpersonalized feeds. They can also share interesting posts from the page and tag their friends into postsin which their friends will get a private notification about the tag. Whatever posts they have started onthe fanpage will also appear on their friends’ newsfeed. Posts are created in the page and when userreplies to the post, they are considered comment. The front page only contains marketer’s posts due tothe SSN inherent design and consumers’ UGC appear in a separate portion on the fan page. We defineMGC as posts authored by the company and posted as a new conversation. Furthermore, we defineeach photo album and the individual photos as a single MGC post as usually there would be only 1status update on the whole album instead of each individual photo. This means we consider photos

and the album one entity, meaning that replies to the photo is coded as replying to the album. We donot consider status updates (e.g., Company has changed their profile photo) as a MGC posts.

Data Description

Our data combine four sources: (1) a longitudinal dataset on SSN posts contents as obtained from theSSN API calls, (2) an internal SSN panel dataset that give SSN account details such as the number offriends, (3) Panel Transaction data from company and (4) a cross-sectional slice of customer loyaltyprogram database containing demographic data of customers. We adopted a weekly convention forour measures. In total we have 1,585 users who are both loyalty program customers and also SSNfanpage members.

For the purpose of removing sample selection bias, we performed propensity score matching on our1,585 users to identify a separate set of 1,585 users who are loyalty customers but did not like the SSNfanpage. They have similar selected attributes with the 1,585 fan members and these total 3170customers data is used in our heckman selection model for robustness check.

Again for robustness purposes, we also have an extended dataset without the internal account SSNpanel dataset as the source has a shorter length. As a result, there are some factors that cannot becalculated in this longer timespan dataset. The purpose of this dataset is to run a model across alonger time span, again to check for the robustness of coefficients in our regression.

Propensity score matching (PSM) methodology

Using a propensity score matching package in R, we matched 1,585 customers who are non-fanpage but customer loyalty program member. The matching was according to variables such as the

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willingness to accept phone call,mail, emails and demographic factors as we deem these variable aspossibily affecting fan member fanpage join decision. The rationale of doing PSM is to help us remove

bias across the groups (Rosenbaum & Rubin, 1983). We have also conducted a t-test to ensure that thedifference of the mean of variables are insignificant after the matching procedure. As you can observein the next table, the p-value of t-test of difference of mean is no longer significant after PSM as mostp-value are >0.3. This means we have minimized the difference in characteristic between controlgroup and treated group and emulated a randomized trial setting.

VariablesBefore PSMMean Value

After PSMMean Value

T-statistics ofdiff

T-testp-value

Control Treated Control Treated Before After Before After

can_phone 0.46 0.43 0.42 0.43 1.74 -0.72 0.08* 0.47

can_mail 0.09 0.08 0.08 0.08 1.12 -0.26 0.26 0.80

can_email 1.00 1.00 1.00 1.00 1.00 1.00 0.32 0.32

age 32.01 31.21 31.31 31.21 4.22 0.37 0.00*** 0.71

LoyaltyAge 3.30 3.41 3.40 3.41 -11.83 -0.19 0.00*** 0.85

avgexpbeforejoining 3.043.26

3.323.26

-1.180.40

0.240.69

ExpenditureBeforeJoining 0.070.08

0.080.08

-4.020.68

0.00***0.49

MonthlyIncomeLevel 2863.752184.78

2207.822184.78

12.900.31

0.00***0.75

genderISMale 0.11 0.11 0.11 0.11 0.48 0.17 0.63 0.86

NLP text classifications methodology

We have decomposed our MGC contents into three categories: promotional, contest, and general

categories. These categories have resemblance to “attribute-focused” versus “emotional Focus” inGopinath et al. (2014) paper or “informative Content” versus “persuasive Content” in Lee et al. (2014)paper. In some sense, promotional content could be regarded as “informative Content” and “attribute-focused” content, while contest and general posts could be classified as “persuasive Content” as theyare generally light-hearted posts with little references to the products and brand. As for UGCcontents, we classified them to complaint UGC versus general. Generally, most UGC are complimentsand WOM from consumers and complaints are a small proportion of UGC in our dataset. Each ofthese categories 1 are mutually exclusive and are determined using our NLP binary classifieralgorithms and the choice of categories are decided because they were found to be generic patterns ofconversations in most SMBC. The screenshot in table 1 shows modified samples which we have beenclassified via our NLP Boosting classifiers algorithm. MGC promotion and contest both have a binaryclassifier and if both classifier reports negative results, we put them into general category. Similarly,for UGC, complaints has a binary classifier and if it doesn’t fall into that category, it is considered asgeneralUGC.

MGC Promotional Post: Calling all students! Spend just $30 nett (usual $150) to jointhe loyalty program for exclusive deals and event entries! For a limited period only, checkin-store for more details now!

MGC Contest Post: Ohh! We see excitement building up as we count down to theModel Search Auditions this Thursday, 17 December! We are all just as egggg-cited to

1 We have no way to ensure total mutual exclusiveness of the binary classifiers. Only one case of MGCposts were being classified as both promotional and contest post but most cases are mutually exclusive

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meet the kids!! :D

MGC General Post: Say Hi! to Hello Kitty! Isn't that floral pop-out just darling?

UGC Complaints Post: I think the company has the best summer collections in 2005 but the standard drops year by year... so sad

UGC General Post: 5 dollars to be a member! thats great...

Table 1 Illustration of modified actual content classification by NLPalgorithm

To add more details, we trained boosting binary classifier to classify the posts. We created some testdataset through manual tagging following the pre-defined heuristic to train our classifiers:

MGC Promotion: Post created by the marketer and contains price information aboutnew products or product attribute information. The end motive should be to leadconsumers to increase purchase or increase their frequency of visit to the store.

MGC Contest: Posts created by marketer to enhance brand recall and foster community building through organizing events (offline and online) that involve participation of thecommunity and sometimes involve some prize money and are non-recurring activities ofthe company.

UGC Complaint: Posts created by other users to express dissatisfaction, anger at bad

products/ services or company policies. Table 2 Heuristic of classification of data for training dataset

Our training dataset has a balance of both true cases and false cases to prevent creation of biased binary classifier. For each category, we trained a classifier each and used the 20 n-fold cross validationmethod (Hastie et al. 2009). We used the average accuracy from the n-fold validation to determine the

best analytical model to use among the models that managed to converge. Boosting ensemble modelended up as our final model as it has the highest accuracy of 93.8% and a true positive rate of 91.2%and true negative rate of 88.2%.

Algorithm Average

Sensitivity

Average

Specificity

Average

Accuracy

Bagging 91.9% 71.4% 84.3%

Boosting 91.2% 88.2% 93.8%RandomForest 94.6% 64.3% 86.3%

SVM 94.1% 83.3% 92.3%

TREE 82.4% 84.6% 83.3%

Table 3 Results of 20-fold cross validation

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Variables and Measures

Continuing on, we have prepared the following independent variables in our models:

Categories Variable Calculation Method

MGCPASSIVE

MGCPostsVol Sum of MGCPromo + MGCContestVol +MGCGeneralVolMGCPromotionVol Count of promo posts

MGCContestVol Count of Contest posts

MGCGeneralVol Count of post not promo and not contestMGCTagNonFocal Count of tags excludes tag from marketer.

MGCLikeCountNumber of likes on MGC post as well as MGC comments andreplies to comments on MGC

MGCCommentVolNumber of comments and replies to comments on a MGCpost excluding marketer replies

MGCPostsReadibility Average of all MGC posts’ automated readability index. Usedas a control.

MGCPostValence Average of all MGC post valence

UGCPassive

UGCPostsVol Sum of UGCComplaintVol and UGCGeneralVolUGCComplaintVol Count of complaint post

UGCGeneralVol Count of UGC which is not complaint

UGCLikeCountNumber of likes on UGC post as well as UGC replies andreplies to comments on UGC

UGCCommentVolNumber of comments and replies to comments on a UGCpost excluding author replies

UGCPostValence Average of all UGC post valenceDirected

variablesTagForFocal

The number of tag received by individual i in a week for bothMGC/UGC

SSNFeatures

MGCVelocity Reciprocal of the average interval time of MGC post in a weekUGCVelocity Reciprocal of the average interval time of UGC post in a week

OtherControls

Variables

SSN_Age Duration of the user SSN accountFriendCount Number of friend in a week

NetworkFriendCount Number of friends who are also in the brand community Age Age of the person

BirthdayMonth Dummy to indicate if it is the month of birthdayLoyaltyAge Duration of person loyalty membership

PeriodDiscount Average discount of that period versus prices in the lastmonth. VoucherUsed Number indicating total voucher used in a week

BenefitUsed Number indicating total benefit used in a weekProgramUsed Number indicating total program used in a week

PastExp Average expenditure per week

MonthlyIncomeLevelTime invariant income level when user sign up for loyaltymembership

genderMale Time invariant dummy to indicate gender AvgPrice Average price of products in a week

AdditionalProbit

Selection Variables

PhoneDisDummy indicating whether customer is opened to receivingcall for promotional deals

MailDisDummy indicating if a customer is opened to receive emailpromotional content

Tripcountperweek Total trip count divided by last observed week. A Constant.

Avgexpb4join Average expenditure for customer who did not join fan page. Average expenditure prior to joining fan page for eventual brand member.

Table 4 Independent variables used

Meanwhile, our dependent variable is “Expend” which is the average expenditure of an individual in acertain week. For weeks in which the consumers do not buy anything, the value is coded as zero.

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Empirical Model

We present a specification that has at most two-way interactions. Social network variables andparticipation in them are all lagged by 1 week.

T represents the week no, represents monthly dummy to control for seasonality and representsindividual specific effect. MGCPostsVol= MGCPromotionVol + MGCContestVol + MGCGeneralVol.

UGCPostsVol=UGCComplaintVol + UGCGeneralVol.For our Heckman selection model, we first introduced a probit selection equation and then combine it

with the main model.

Heckman selection model is typically used for truncation and censoring of data of the dependent variable, and for our case it is truncation of data as we could not observe non-fanpage members’ SSNactivity. Heckman model is one of the most-used econometric models that correct for selection biasand infuse randomness in non-experimental settings (Heckman, 1979).

In specifying the models, we made a few assumptions. Our first assumption is that we are expectingconsumers to read all content in the social network whenever a new post appears. Secondly, weassume that all fan responses (e.g., likes, comments and tags) occur within the same week as thecreation of original post. Finally, we are assuming that our text-categories classifiers are perfect in thepredictions of categories.

Data analyses and Results

Descriptive Analysis

To begin, we first started doing our descriptive analysis. Our final dataset spans 118 weeks from8/9/2009 to 13/12/2011 for our main data and for our extended data it is until 18/6/2013 (197 weeks).

Our dataset has 20,852 customers of which we used only 1585 of them as we were able to trace themto their social network accounts. We also used an additional 1585 customers coming from the PSMprocedure for our heckman selection model dataset. These additional 1585 customers are non-fanpagecustomers. In the 118 week that we examined, we have about 276 MGC posts of which 175 was albumupdates. There were also 183 UGC posts and total of 21 comments on these UGC excluding marketerreplies and author own replies and zero shares on UGC. You may refer to the next table for selecteddescriptive statistics:

Statistic Mean St. Dev. Min MaxFriendCount 253.513 256.731 0 3,998

LoyaltyAge 0.969 0.536 0.003 2.414

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MGCCommentVol 5.786 14.012 0 92

MGCContestVol 0.477 1.016 0 5

MGCGeneralVol 0.724 1.191 0 15

MGCLikeCount 46.233 110.592 0 837

MGCPostsVol 2.362 2.271 0 16

MGCPostValence 0.047 0.079 -0.016 0.429

MGCPromotionVol 1.162 1.23 0 4 MGCTagNonFocal 0.067 0.395 0 4

MonthlyIncomeLevel 2,196.77 1,870.34 0 7,501.00

NetworkFriendCount 7.224 9.902 0 145

RevPerTrans 39.831 32.107 0.13 501.67

SSNAge 2.433 0.912 0.005 6.633

TagForFocal 0.001 0.098 0 9

UGCCommentVoL 0.4 1.241 0 5

UGCComplaintVol 0.255 0.591 0 3

UGCGeneralVol 1.726 2.2 0 10

UGCLikeCount 5.892 21.972 0 116

UGCPostsVol 1.98 2.507 0 12

UGCPostValence 0.03 0.062 -0.093 0.259 Table 5 Descriptive statistics of selected variables

Model Estimation Procedures

After the descriptive analysis, we carried out a correlation analysis followed by a variance inflationfactor (VIF) analysis. There was a pair of variables with correlation greater than 0.7. However, thesetwo variables (MGCCommentVol and MGCLikeCount) were not causing collinear problem asaccording to our GVIF value which is explained later. Following the correlation test, we used thegeneralized VIF values and created a pooled OLS model excluding interaction variables and found thatall variables have GVIF of less than 10 indicating no presence of multi-colinearity problem.

Our first model was a pooled ordinary least square (Model 1) followed by a Random Effect (Model 2)and then Fixed Effect using a within-estimator (Model 3). A Hausman test was conducted and itsuggests that the random effect model is inconsistent ( ).Thus we picked

the FE model over RE. Model 3 is our final empirical model. It is a good model to predict next weekexpenditure (Adj-Rsq: 15.7%, F=515.969***).

Robustness Checks

To check for sample selection bias, we created a fixed effect Heckman selection model (Model 4). Themodel has roughly the same beta as our fixed effect model while the significance is about the same.Thus, we conclude that our model is not severely affected by selection bias significantly. We furtherchecked our model for heteroskedasticity issues by conducting the Breusch Pagan test ( =657,646.9, ). Our result shows that our data suffers from heteroskedasticity problemand the p-value estimates are not reliable. As a response, we ran a Arellano standard error procedureto correct for the biasness and cross-sectional dependence in our fixed effect model (Arellano, 1987).Model 3 coefficients significance is then updated as according to the robust standard error.Furthermore, we used the extended dataset to construct Model 5 alongside with robust error. This setof data have roughly the same beta and significance as our main model and it shows that our findingsare not time dependent.

Finally, we can be reasonably confident to analyze with our empirical model after correcting forheteroskedasticity and checked for sample selection bias problem.

Model Estimation Results

Table 6’s columns (1) to (6) contains results for each of our models as described before and column 3is our final model. Hypothesis 1 is supported since MGCPromotionVol and MGCGeneral are found to

be significantly positive. The beta coefficient of MGCPromotionVol is 0.153 (p<0.05). MGCPromotion is also found significant across all our models, demonstrating the robustness of the

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variable. MGCContestVol is however insignificant in our main model with. Perhaps, this may be dueto only 46 contests topic post being detected by our analytical text classifier out of 276 MGC posts(~16%) and one can observe the beta coefficient of contest post in the longer timespan is actuallypositive and significant as there are more contests posts in the extended timespan data. Meanwhile,for MGCGeneralVol, it is at 0.286 (p<0.01). It is significant across all models.

Hypothesis 2A on UGC complaint is not supported. The UGCComplaintVol is not of statisticalsignificance, hence indicating the inability of users’ complaints in reducing other customers’

expenditure. Hypothesis 2b is also not supported and instead the opposite effect is found at-0.129 (p<0.01) for the factor UGCGeneralVol. However, as we have interaction variables which aresignificant, we cannot conclude that complaints have no negative impact on expenditure and UGCgeneral has negative impact on expenditure without more sophisticated modeling.

Hypothesis 3A is not supported as MGCLikeCount is statistically insignificant. Hypothesis 3Bsimilarly is not supported as the coefficient of -0.108(p<0.01) for UGCLikeCount contradicted thehypothesis. Again, it is important to point out that the variables are tested in the presence ofmoderating variables and they may be insignificant/contradictory under the presence of moderation

variables.

Hypothesis 4A is not supported as “likes” were not found to interact with MGC topics as evident fromMGCPostsVol * MGCLikeCount. Hypothesis 4B is supported as we find statistical evidence ofinteraction effect on comments on marketers MGC posts. The coefficient of the interaction variable isat 0.012 (p<0.05) for MGCPostsVol * MGCCommentsVol and is found to be positive and significant in

all models except model 5. As for hypothesis 5A, it is supported as there is statistical evidence that No of “ Likes” boosted theeffectiveness of UGC topics. The coefficient of UGCPostsVol * UGCLikeCount is at 0.012 (p<0.05).This variable is also significant and positive across all models. For hypothesis 5B, it is not supportedas the interaction effect of UGC comment on UGCPostsVol was found to be insignificant.

We have positive statistical results to support hypothesis 6A. When customers receive a tag from theirfriends on content, they will have higher tendency to be persuaded and spend more on the products.The beta is at 6.272 (p<0.01) for TagForFocal. This means for each tag a person receive, he/she isgoing to spend $6 more. For Hypothesis 6B, we do not find statistical evidence that observing othertags (directed to someone else) will have an impact on the third party observer. Hence H6B is notsupported from the inference of MGCTagNonFocal beta coefficient.

Table 6 Main Results

Models’ dependent Variable=RevPerTrans

Full OLS RandomEffect

FixedEffects

HeckmanPSM Fixed

LongerTimes an

(1) (2) (3) (4) (5)

MGCPromotionVol 0.271*** 0.266*** 0.153** 0.154** 0.135***

(-0.067) (-0.067) (-0.071) (0.067) (-0.029)

MGCContestVol 0.008 0.007 -0.023 -0.022 0.174***

(-0.109) (-0.109) (-0.098) (0.108) (-0.036)

MGCGeneralVol 0.415*** 0.409*** 0.286*** 0.287*** 0.254***

(-0.081) (-0.081) (-0.084) (0.08) (-0.024)

MGCTagNonFocal -0.212** -0.205** -0.017 -0.018 0.046***

(-0.097) (-0.097) (-0.09) (0.096) (-0.008)

MGCCommentVol -0.114*** 0.112*** -0.065** -0.066** -0.020**

(-0.033) (-0.033) (-0.031) (0.033) (-0.009)

MGCLikeCount 0.004 0.004 0.004 0.004 0.001**

(-0.004) (-0.004) (-0.004) (0.004) (-0.0002)

TagForFocal 6.381*** 6.374*** 6.272*** 6.272*** 6.198***

(-1.263) (-1.262) (-0.543) (1.25) (-0.635)

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MGCPostsReadibility -0.003 -0.004 -0.032* -0.032* -0.014*

(-0.018) (-0.018) (-0.017) (0.017) (-0.009)

MGCPostValence 2.468*** 2.484*** 2.690*** 2.69*** 1.284**

(-0.888) (-0.887) (-0.913) (0.876) (-0.499)

MGCVelocity -0.0004 -0.0004 -0.001 -0.001 -0.0005***

(-0.0005) (-0.0005) (-0.0004) (0.0005) (-0.0001)

UGCVelocity -0.00001** -0.00001** -0.00001** -0.00001* -0.00001***

(-0.00001) (-0.00001) (0) (0.00001) (0)

UGCComplaintVol -0.158 -0.156 -0.115 -0.115 -0.114

(-0.119) (-0.119) (-0.119) (0.118) (-0.073)

UGCGeneralVol -0.161*** -0.160*** -0.129*** -0.13*** -0.127***

(-0.039) (-0.039) (-0.038) (0.039) (-0.02)

UGCPostValence 3.312*** 3.302*** 2.923*** 2.923*** 1.331***

(-0.858) (-0.858) (-0.796) (0.852) (-0.316)

UGCCommentVol -0.409** -0.402** -0.205* -0.207 -0.175**

(-0.159) (-0.159) (-0.121) (0.158) (-0.089)

UGCLikeCount -0.134*** -0.134*** -0.108*** -0.108*** -0.184***

(-0.042) (-0.042) (-0.038) (0.041) (-0.021)

MGCCommentVol*MGCLikeCount 0.0001 0.0001 0.00001 0.00001 0.00003***

(-0.0001) (-0.0001) (-0.0001) (0.0001) (0)

MGCCommentVol*MGCPostsVol 0.018*** 0.018*** 0.012** 0.012** -0.003**

(-0.005) (-0.005) (-0.005) (0.005) (-0.001)

MGCLikeCount*MGCPostsVol -0.003** -0.003** -0.002 -0.002 -0.0002***

(-0.001) (-0.001) (-0.001) (0.001) (-0.00003)

UGCCommentVol*UGCLikeCount 0.031*** 0.031*** 0.027*** 0.027*** 0.038***

(-0.006) (-0.006) (-0.006) (0.006) (-0.003)

UGCCommentVol*UGCPostsVol 0.02 0.019 -0.004 -0.003 -0.019

(-0.029) (-0.029) (-0.026) (0.029) (-0.018)

UGCLikeCount*UGCPostsVol 0.016*** 0.016*** 0.012** 0.012** 0.022***

(-0.005) (-0.005) (-0.005) (0.005) (-0.003)

Constant 5.210*** 5.199***

(-0.556) (-0.558)

Time Dummies+ Control variables included

Observations 124,062 124,062 124,062 243,445 247,884

R 2 0.172 0.171 0.159 0.198 0.232

Ad usted R 2 0.1 2 0.1 1 0.1 0.18 0.2Individuals 1585 1585 1585 3170 1585

WeekNo 2/118 2/118 2/118 2/118 2/197

ote:*p<0.1; **p<0.05; ***p<0.01

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Discussions and Implications

Discussion of Findings

Our study empirically showed that not all social media contents are equal. In our research, MGC withpromotional content was found to affect future expenditure while marketing contest was not found to

be effective in inducing near-term expenditure. Interestingly, MGCGeneralVol was found to also

increase expenditure despite the lack of promotional content. In fact, it is actually more effective thanpromotional MGC content. Posts in the general category do not usually have brand/productsinformation and are typically light-hearted and entertaining in nature. This actually draws parallel

with Lee et.al (2014) research in which he found that persuasive content – Status updates that sharean interesting fact and tap into reader’s emotion- are better received by users than informativecontent. Gopinath et al. (2014) also classified contents into the various categories and found that notall contents are created equal. Thus, it is imperative that future research go beyond the traditionalaggregated measures of the amount of contents.

More importantly, through this research, we gained new insights of the roles of brand communitymembers. Brand community plays the role of helping other members find relevant information fasterthrough social tagging. In our results, we found consumers who received a social tag spend almost$6.00 more in subsequent week. This hints that the tagged content is useful to the consumer to allowhim/her to make an informed decision.

In addition, brand community members help each other identify useful contents through computer-mediated gesture like “ Likes” on SSN. From our empirical results, we observe that posts with highnumber of UGC “ Likes” are actually more influential in inducing expenditure than UGC posts with lownumber of “Likes” . This hints that “Likes” serves as endorsement and useful signals to help othermembers focus on relevant posts.

Also, brand fan replies were found to actually add-value to MGC contents. Our model showed thatMGC posts with more comments is more effective than MGC posts with lesser comments in inducingconsumers’ expenditure. This hints that the comments have informational value and complementedthe original content making the overall conversation more persuasive in inducing expenditure. Also,these comments may act as referral and stamp of approval for the original MGC content.

It is interesting to observe that MGC and UGC have different effects on these moderation processes.“ Likes” were found to boost UGC influence on expenditure while number of comments were found to

boost MGC topics. Perhaps, “Likes” functions like a popularity vote and users post is all about gettingpopularity. Perhaps for MGC, comments help validate marketing contents in the posts, thus making

the original MGC post more convincing.

Theoretical Contributions

Our research highlighted the problem of how aggregated data like volume of posts leads to loss ofinformation. Using NLP with machine learning algorithms, we demonstrated how posts can beclassified effectively and accurately. By decomposing contents and classifying them according to theircontext, we also empirically captured more information and these decomposition predictors wouldfare better than aggregated volume measures found in other SNS literatures.

The biggest contribution though is that our research revealed new insights on the dynamics of brandcommunity. We peeked into the mechanism of brand community and realize that consumers’comments and gestures actually value-add to the marketers contents. We discovered the synergeticprocess, whereby brand fans actually contribute replies to help other customers make a betterdecisions on the marketers’ posts. They also leave signals through “ Likes” to tell other brand fans

about good deals and interesting posts. Thus, consumers would be more confident to make a purchasedecision under certainty condition. We observe consumers having a benevolent desire to help theirfriends by tagging them into posts whenever they think the information is useful to the other party.Our research revealed the mechanism in which brand members build one another alongside with themarketers in a brand community.

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Practical Contributions

By categorizing MGC/UGC contents into different categories using NLP techniques, we offer a noveldiagnostic tools for marketers to optimize their message contents. Marketers could use anexperimental approach to fine-tune their marketing contents so as to maximize their effectiveness indriving revenue. Furthermore, perhaps marketers could do this in real-time, in another words: doingsome form of real-time analytics to reap real-time benefit. This would have beneficial effect as firms

face a budget constraint of resources in multichannel settings (Chu et al. 2007). Marketers could alsoencourage their brand fans to recommend their friends posts through tagging if they think theinformation is helpful to their friends and in return give them some incentives , for exampleparticipating in a lucky draw. This will actually lead to their consumers to spend more.

Our research provide empirical evidence of the appeal of marketing through brand community andhelp to dispel some irrational fear of social media marketing in some marketers. Marketers shouldthus relax and grant some autonomy over the flow of contents in the brand community instead ofavoiding using a SMBC totally. They should focus more on creating the brand community experienceon social network and encourage consumers to interact with each other as well as with the marketers.Marketers usually have fears that social networks may generally attract bad and/or cynical commentsabout their brand, but our research actually showed that comments from brand fans on average valueadd to the marketers’ original content. Marketers should thus embrace a model of co-creation ofmarketing contents on their online social network community.

Limitations

Despite highlighting several interesting findings, we have to acknowledge that our research is notperfect. Firstly, our NLP classification algorithms may be prone to error as it is not 100% accurate.Secondly, our research is based on observational data and does not come from randomized trials orfield experimentations. Although, we did do a cross-check against a Heckman’s selection model, ourHeckman selection does not come with correction for heteroskedasticity and panel data structure.

Also, we are unable to completely decouple correlation and causation and show that some of ourpredictors cause future expenditure, but our research do offer some hints that they may be somecausal relationship going over. Furthermore, the data comes from a single industry and single retailerand may not be sufficient to represents the effect in any brand community.

Conclusion

Summing up, our paper offered evidences of some potential roles of brand community members in the brand community. Brand fans play the role of organizing useful information by tagging helpfulinformation to other brand fans. They also contribute useful comments to add value to marketer’scontent and help other brand fans identify focus on useful information by using gesture such as“ LIKES ” on the useful posts. Also, through our NLP decomposition methods on contents, we showedthat not all contents are equal and it is imperative for marketers to fine-tune their contents tooptimize profitability of their marketing initiatives instead of annoying consumers with a constant

blast of the same type of contents.

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