Consumer Engagement on Social Media:
The Conditional Mediator Effect of Post Type and Electronic Word-of-Mouth
Thesis Supervisor: Prof. J.J. Ebbers
Willemijn Jongbloed
Master of Science in Business Administration
Entrepreneurship and Management of the Creative Industries August 4th, 2016
Statement of Originality
This document is written by Student Willemijn Jongbloed who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion
Abstract
Due to the widely used social media platforms, consumers have become the dominant selectors of experience products. Therefore, firms face the objective of incorporating
consumer engagement in their marketing objectives. However, basing the value of consumer engagement solely on the transactions with the firm is insufficient. Therefore, this thesis focusses on the consumer engagement as the outcome. Three different products related information post on Facebook are investigated; ticket posts, sampling posts and engagement posts. Dividing consumer engagement in two behavioural types: electronic word-of-mouth (eWOM) and online consumption of the post, the results show that a unit increase effect of eWOM affects online post consumption with 1.03, compared to .0207 due to direct post exposure, suggesting the strength of consumer-to-consumer interaction in the social media environment. Although significant for all post types, the direct effect of a unit increase of post exposure shows the greatest effect on consumption of the posted content for engagement post with an increase of .0397, suggesting that consumers do not feel the need to engage in eWOM behaviour when the purchase decision is made. Interesting is that for sampling posts, the effect of a unit increase in post exposure actually decreases consumer eWOM with -.0112. Managers, thus, should focus on post exposure for both engagement and sampling post, and on generating eWOM for ticket posts.
Table of Contents
I. Introduction 5
II. Literature Review 8
a. Value Experience Products
b. Social Media 9
c. Electronic Word-of-Mouth 10
d. Engagement 11
e. New Selection System 12
f. Exposure of a Product Informative Post 15
g. Electronic Word-of-Mouth on a Product Informative Post 17
h. Post Type 20
III. Methodology 25
a. Empirical Context
b. Sample and Procedure 28
c. Coding 29 d. Variables 30 e. Analytical Strategy 31 IV. Results 34 V. Conclusion 43 VI. Discussion 45 a. Empirical Findings b. Implications
c. Limitations and Future Directions 46
VII. Bibliography 49
VIII. Appendix A 56
I. Introduction
Social media platforms have substantially changed the marketing landscape as consumers are no longer passive recipients of marketing messages (Saboo et al., 2015). Hence, the traditional one-to-many marketing approach is no longer a feasible strategy (Gillin, 2010). According to Ashley and Tuten (2015) “marketers are expected to increase social media advertising spending to 5 billion in 2014, up from 4.1 billion in 2013” (Ashley and Tuten, 2015, p.15). Especially in the market for experience products, defined by products whose true value cannot be revealed to the consumer prior to the purchase (Wlomert and Papies, 2016), consumers face uncertainty about a product’s value if they have not used it before (Wang and Zhang, 2009), ranging from functional risk, economic risk, to social and psychological risk (Kohli, 1999). As consumers’ have become the dominant selectors, thereby deciding on the value of experience products, firms face the challenge of incorporating consumer engagement in their strategies to pursue their firm’s performance goals (Gensler et al, 2013). On the one hand, a firm can post a product informative post in the online social environment, and, on the other hand, that post can include a sample (Kim et al., 2014). Whereby, Saboo et al. (2015) argue that “consumers identifying with brands manifest both in-role behaviour, which is related to product consumption, and extra-role behaviour, which is related to word-of-mouth” (Saboo et al. 2015, p. 3). Although the different consumer engagement behaviours are
highlighted by Saboo et al. (2015), the inter-relationship of these engagements have received limited scholarly attention. Yet the resulting product or brand value is an important decision-making tool for consumers as it reduces risk and saves time (Gensler, et al., 2013).
Therefore, the objective of this study is to examine if, whether and how extra-role engagement, electronic word-of-mouth (eWOM) mediates the relationship between post exposure and in-role online consumption. The empirical context of the study are offline experience products, music festivals, whereby firms engage consumers with ticket, sampling or engagement posts. In addition, by adopting the distinction of consumer engagement by Saboo et al. (2015), this thesis argues that extra-role engagement refers to eWOM and in-role engagement refers to consumption behaviour, whereby the proposed relationship between eWOM and online consumption behaviour depends on the type of firm-initiated product informative post. Hence, the research question is; does consumer eWOM mediate the relationship between post exposure of a firm-initiated post and online consumption behaviour, and, does this relationship depend on the type of post?
This thesis focusses on consumer engagement as the outcome. By combining literature on eWOM with sampling literature, a conceptual model framework is developed to quantify the inter-relationship of online consumer behaviour. The outcome variable measures online consumer consumption, which is engagement not visible to other consumers, with a firm-initiated product informative post and is operationalised by total post clicks. The outcome of the dependent variable online consumption, thereby, differs per post type. For ticket posts, the variable measures how many consumers click on the ticket link, for sampling posts this is the amount of clicks on the sample provided, which is a limited experience of the full product experience, and for engagement posts the variable measures photo album clicks.
Nevertheless, all outcomes are the consumers’ online consumption of the provided post content and hence are gathered conceptually as ‘online consumption’.
Selection system theory argues that the opinion of dominant selectors in the market for experience products is an important quality signals for consumers. Historically, the
empirically established positive effect of quality signals in the form of reviews on a firm’s performance outcomes is extensively studied and the decreased studies addressing the phenomenon suggests that a full understanding has been reached (Kumar et al., 2015). Although the established positive effect of eWOM on consumption behaviour (Godes and Mayzlin, 2006) shows the importance of quality signals for the consumer decision process, these studies focus on an expert selection system whereby reviews are written by
gatekeepers. However, in the social media environment consumers have become the dominant selectors by engaging in a range of online activities. In addition, construal level theory argues that consumers weight evaluative criteria in their decision-making process differently depending on whether the behaviour is closer of more distant in time (Arts et al., 2011). Thus, while, consumers have become the dominant selectors and consumer look at the opinion of their peers, empirical literature is scarce on the inter-relationship of engagement (Saboo et al., 2015).
Moreover, in the market for experience products, sampling is common practice (Nejad et al., 2015), where, for example, the book industry releases free copies for a limited of time and music industry implements the freemium business model where part of the music content is free. 85% of package goods firms engage in sampling (Schlereth et al., 2013). Literature on sampling has mainly focussed on the strategic business problem of how much of the product should be freely available or on the proportion of consumers that try the sample, ranging from 40-80% (Heiman et al., 2001). While it has been found that providing samples increases immediate sales with 42% (Lammers, 1991), other literature finds that the positive effect
depends on the product price where it consumers might interpret it as a signal of bad quality (Scott, 1976). Yet, sampling remains an under-investigated marketing tool.
In contrast, examining the effect of providing information on consumer decision-making process did had a long tradition in marketing research (Burke et al., 1990). Product related announcements are expected to affect consumers brand perceptions and purchase intentions. In addition, announcing enables consumers to be prepared for the adoption of a new product (Kohli, 1999). While unfavourable product-relation information influences consumer
decision-making stronger than favourable, as individual’s pay more attention to negative information, the same ‘negativity bias’ that is established in eWOM literature (Godes and Mayzlin, 2009). Nevertheless, literature on product announcements have either focussed on the firm’s strategies to deter entry, retaliation or signalling strategies (Jung, 2010) thus game theoretical models (Schatzel and Calantone, 2006) or are based on surveys to establish the cost and benefits of providing product announcements (Eliashberg and Robertson, 1988). In sum, literature on eWOM, sampling as well as preannouncements have empirically established that all three positively affect a firm’s sales outcomes or consumer purchase intentions (Bapna and Umyarov, 2015). However, the focus on sales as a performance indicator explains the phenomenon only partially as consumer engagement on social media may take time to influence customer behaviour, Kumar et al. (2010) argue that “assessing the value of consumers based solely upon their transactions with the firm may not be sufficient” (Kumar et al., 2010, p. 297).
Due to the empirical context of this study, this thesis focusses on post types that include samples, which provide a limited experience of the full product. Hence, the results of the thesis can be extended to other experience product industries such as movies, software, or test driving a car, where sampling also provides the consumer with a limited experience. In addition, the quickly changing many-to-many marketing on social media asks for more empirical insights in this phenomenon, whereby this study contributes valuable insights on consumer’ engagement behaviour on promotional tools of firms. This is supported by Brodie et al. (2013) who suggests that “the existence of any interactions among the dimensions of consumer engagement needs further attention” (Brodie et al., 2013, p.112). Moreover, the results of the thesis can help both scholars as well as marketing professionals understand the role of social media in consumer engagement behaviour, as consumer engagement in the social media environment impacts their decision-making and hence marketing strategies (Wang et al., 2012).
II. Literature Review
In order to study the inter-relationship of different consumer engagements, this section firstly provides a theoretical overview of the value of experience products, followed by online engagement literature, introducing several concepts and conclusions, as well as the definitions for engagement and electronic word-of-mouth (eWOM). Thereafter the building blocks for the hypotheses are subsequently proposed.
2.1 The Value of Experience Products
The value of experience products is determined by the dominant selectors in the market. Selection system theory distinguished three types of selectors; market selection, when consumers themselves are the dominant selectors, peer selection, when other producers are the dominants selectors, and expert selection, whenever experience products are evaluated by neither consumers or producers but a third party of experts (Wijnberg and Ebbers, 2012). In order to compete in the market for experience products, a firm must convince the dominant selectors of the value of their product. These dominant selectors may change. Firstly, in the creative industries, characterized by high uncertainty, these selectors may change in the competitive stages of the product lifecycle. It is possible that the selection system consists of multiple stages, each with its own set of selectors. For example, before a firm can even compete in the actual market, there is a competition for financing, thereafter, when the product enters the market, another competition between firms occur in order to compete for consumers. Both stages can have different selectors. Moreover, the selection system may be a mix of market, peer and expert selection.
As experience products in creative industries lack complete information between producer and consumer, these products are dependent on quality signals from the dominant selectors in the market. Only recently, firms have embraced social media to increase their product value among consumers (Kumar et al., 2015). However, before the adoption social media applications, the success of experience products historically depended on the opinion of gatekeepers in the online environment. (Salganik and Watts, 2008). Reviews of (credible) expert selectors were perceived as a signal of quality. The most fundamental assumption regarding gatekeeper reviews is that it indeed affects consumer behaviour (Godes et al., 2005), illustrating the importance of quality signals from dominant selectors in the market. Most often, literature studies the relationship between reviews and the outcome variable sales and finds a positive relation between reviews and sales (Godes and Mayzlin, 2009). One of
the important studies is of Godes and Mayzlin (2006) who find that online book reviews tend to be positive and in return positive reviews affected sales (Godes and Mayzlin, 2006).
However, negative comments are more powerful in decreasing sales than positive reviews are in increasing sales, suggesting the relative importance of negative reviews (Godes and
Mayzlin, 2009). The same study finds that the amount of readers of reviews affect book sales. Moreover, when consumers lack information and experience with the product or brand, they are more likely to search for and accept negative reviews (Brodie et al., 2013). Some scholars find evidence that the number of online reviews affect product sales (Liu, 2006), while other argue that the valence of reviews is a more important factor influencing product sales
(Dellarocas et al., 2007). In addition to the outcome variable sales, the influence of
gatekeepers is find to increase box office revenue, where Liu et al. (2006) find that most of the explanatory power of eWOM on revenue comes from the volume of eWOM not the valence (Liu et al., 2006); television ratings (Godes and Mayzlin, 2004) and new customer profitability (Villabueva et al., 2008 in Libai et al., 2012).
3.2 Social Media
However, the influence of gatekeepers on the perceived value of experience products has decreased significantly. Due to the diffusion of high-speed internet, both mobile as well as on desktop, social media applications have growth significantly (Chueng and Lee, 2010). Social media has opened up the possibility of large-scale consumer-to-consumer interaction where consumers interact with the firm or with other consumers about a product (Nejad et al., 2015). This change in communication from one-to-many to a direct interaction of consumers-to-consumer as well as consumer-to-producer has changed the dominant selection system from expert selection, in the form of gatekeepers, to market selection by consumers. Thus consumers pay attention to quality signals from their peers when evaluating a new product (Nejad et al., 2015). While literature has acknowledged that user-generated brand stories either predicts or driving managerial outcomes like sales (Gensler, 2013; Smith et al., 2012), suggesting the importance of consumers for the value of experience products, this thesis focusses on firm-initiated consumer interactions in the social media environment. The latter is conceptually explained in the part hereafter.
Social media applications are characterized by direct social interaction and connectivity. On these online spaces, for example Facebook, LinkedIn or Instagram, individuals can create a profile, connect to other profiles and by that create a personal online network. These
profiles on the social networking site. Brands offer identity extensions and symbolic value to their customers by introducing brand communities. Individuals connect with brands by following or liking the brand fan pages. Those consumers who become followers (hereafter fans) on these brand fan pages tend to be loyal and committed to the company, and are more open to receiving information about the brand (Enginkaya and Yilmaz, 2014). In addition, followers of a brand community share interests which is found to product affinity and create a bond (De Valck et al., 2009). Fans are sent new content from the brand automatically, which enables consumers to directly interact with a brand (de Vries and Carlson, 2014). Content refers to the information delivered by the brand, which can either be functional or hedonic (Ashley and Tuten, 2015). Brands can thereby inform their ‘fans’ of product information on their brand fan page. Such a post then reaches individuals referred to as post exposure. The individuals’ exposed to the post have the option to engage with the content, by eWOM behaviour and/or online consumption of the posted content.
Social interaction is driven when consumers are able to submit feedback, which an important character of social media (Mangold and Faulds, 2009). And the reversed; people are likely to communicate through social media whenever they see new content about the product, service or company. While traditionally brands were associated with a product or service, in today’s social media environment, brands refer to a persona that is the subject of communication efforts (Saboo et al., 2015). Consequently, brands engage directly with their consumers which creates an emotional bond, resembles more closely a normal interpersonal relationship than traditional marketing efforts, while consumers view brands as a friend (Saboo et al., 2015).
3.3 Electronic Word-of-Mouth
One of the important means through which consumers express themselves and
communicate with brands or other users in the social media environment is electronic word-of-mouth (hereafter referred to as eWOM) (Enginkaya and Yilmaz, 2014; Nejad et al., 2015). Participation in online social networks represents a social phenomenon that depends largely on the interactions with other consumers in the network (Chueng and Lee, 2010). Aral and Walker (2014) state “traditionally, word-of-mouth has been specified as information (often about opinions, preferences, or choices) deliberately exchanged through face-to-face
interactions, though more recently the term has been applied to online or technology-enabled information exchange between individuals or from one individual to a group of others (as in the case of consumer product reviews)” (Aral and Walker, 2014, p. 1356). Mangold and
Faulds (2009) state that eWOM is the online exchange of information and opinions about products, services or events. Whereas Smith et al. (2012) define eWOM as “any positive or negative statement made by potential, actual, or former customers about a product or
company, which is made available to a multitude of people and institutions via the Internet” (Smith et al., 2012, p. 39; Khan and Vong, 2014). In accordance to the proposed definitions, this study will use the definition of Smith et al. (2012) for eWOM: “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet”. Important to note is the relevant distinction that Godes and Mayzlin (2009) make between endogenous and exogenous eWOM. While the first refers to natural occurring conversations between consumers in the online environment, the latter is eWOM created as the result of a firm’s actions, which is, as mentioned before, the focus for this thesis (Godes and Mayzlin, 2009). Godes and Mayzlin (2009) argue that firm-created eWOM can be thought of as a hybrid between traditional advertising and customer eWOM.
3.4 Engagement
However, eWOM is only one behavioural outcome of consumer engagement. The concept engagement has been used to describe the broader nature of an individual’s specific interaction with the product or firm (Brodie et al., 2013). Some of the definitions of the concept that have been proposed, are discussed by Brodie et al. (2013) starting by Patterson et al. (2006) who define consumer engagement as “the level of a customer's physical,
cognitive and emotional presence in their relationship with a service organization” (Patterson et al., 2006). In contrast, Vivek at al. (2012) define consumer engagement as “the intensity of an individual's participation and connection with the organization's offerings and activities initiated by either the customer or the organization” (Vivek et al., 2012). While Hollebeek (2011) states customer brand engagement as “the level of a customer's motivational, brand-related and context-dependent state of mind characterized by specific levels of cognitive, emotional and behavioural activity in brand interactions” (Brodie et al., 2013, p.106). According to Mollen and Wilson (2010) engagement is "the cognitive and affective commitment to an active relationship with the brand as personified by the website or other computer-mediated entities designed to communicate brand value". In addition, Van Doorn et al. (2010) highlight the behavioural nature of engagement, defined as “a customer’s
behavioural manifestation toward a brand or firm, beyond purchase, resulting from
literature, this study will adopt the definition of consumer engagement proposed by Vivek et al. (2012) as “the intensity of an individual’s participation and connection with the
organization’s offerings and activities initiated by either the consumer or the organization”. Thereby, this thesis addresses engagement as a dynamic concept of consumer interaction with the firm.
By combining both the accepted definition of engagement with consumers’ identification theory, which argue that identification with a product or brand has powerful impact on
consumer behaviour (Saboo et al., 2015; Wang et al., 2015), this thesis explicitly disentangles different levels of online consumer engagement. On the one hand, consumers can engage in line with their perceived identity in active behaviour, which requires an opinion statement from the focal consumer, and on the other hand, in passive behaviour, which does not require an opinion statement of the consumer. By extending the framework of Saboo et al. (2015), who state that consumers identifying with brands manifest both in-role behaviour related to product consumption and extra-role behaviour related to eWOM, this thesis disentangles extra-role behaviour, hereafter eWOM from in-role behaviour, hereafter online consumption. While the first is visible to other consumers, the latter is only known by the focal consumer.
Moreover, online consumption engagement includes sampling engagement, thereby this thesis proposes that a consumer can manifest their engagement with a brand by engaging in three ways; (1) consumption behaviour, (2) sampling behaviour and/or (3) eWOM behaviour. Labelling the latter one as engagement is supported by Brodie et al. (2013) who argue that although eWOM is generally a broader concept than engagement, in the more focused context of brand communities eWOM and engagement tend to considerably overlap each other (Brodie et al., 2013). In conclusion, both engagement behaviours of sampling and consumption are considered passive engagement behaviour, while eWOM behaviour is active engagement behaviour (Saboo et al., 2015). The distinction is important as the hypotheses, subsequently introduced in the next part, propose how active eWOM influences online consumption of the posted content and how these levels of behavioural consumer engagement depend on the type of post.
3.5 New Selection System
More importantly, as consumers are ever more influential in the success of experience products, being the new dominant selectors, firms are increasingly dependent on consumer opinions on social media as the channel to influence potential consumers (Kumar et al., 2015; Bao and Chang, 2014). In accordance to selection system theory, literature on eWOM has
recognized the significance of eWOM in affecting the performance outcomes of firms as eWOM is considered a more trustworthy source of information about products than firm-initiated messages (Chu and Kim, 2011). In addition to the role of eWOM as a signal of quality, it has been widely acknowledged by literature on social contagion that eWOM also creates a "buzz" about products (Aral and Walker, 2011). Social contagion is defined as “those individuals that influence other individuals in their network, not already actively engaged by the brands message, to engage with the brand posts by liking, sharing or
commenting (Godes et al., 2005). This “buzz” suggests that eWOM does not solely affect the performance of the firm but also other consumers’ online engagement behaviour with the firm. The resulting social contagion and peer influence leads to the diffusion of information between consumers and, consequently, the probability of adoption (Bapna and Umyarov, 2015). Although, Van Den Bulte and Lilien (2001) argue that social contagion does not play much of a role and marketing efforts are more important. Thus, consumer engagement in the social media environment has a dual outcome; both affecting the focal consumer’s behaviour as well as (other) consumers’ behaviour in the online environment.
How, then, does eWOM affects other consumers’ behaviour? Literature on the
determinants of eWOM is plentiful and discusses the effects of tie strength, homophily, trust and interpersonal influence on information diffusion via eWOM (Godes et al., 2005; Chu and Kim, 2011), where, for example, strong ties are found to be better suited when risk is
involved, because strong ties are characterized by high trust and hence facilitate transactions and coordination (Ebbers, 2013), whereas weak ties are valuable when risk is minimal to spread new information and opportunities (Gensler et al., 2012). However, Burt (2005) has argued that weak ties are more likely to transmit novel information and thus may have a bigger effect on peer influence. While literature on entrepreneurship focus on the content of these social networks and find that, for example, strong ties that are entrepreneurs is
positively related to the likelihood of becoming an entrepreneur (Ebbers, 2013). Other literature focusses on the cognitive antecedents of eWOM (Wang et al., 2015) and find that the four main reasons why consumers engage in eWOM are to seek social interaction, care for other consumers, strive for self-worth or respond to economic incentives.
These determinants allow firms to try to “engineer” campaigns that attract eWOM behaviour among their targeted consumers (Godes and Mayzlin, 2009) by identifying key influential consumers in the network and/or identify network characteristics that facilitate eWOM. The design of word-of-mouth campaign that includes consumer influence metrics has shown a 49% increase in brand awareness and a similarly impressive gain in sales and
return-on-investment (Kumar et al., 2013). In accordance, seeding literature discusses whom and when to seed the product announcement in order to expose the product information to the most influential and/or important selectors. An important distinction made by Libai et al. (2012) is to decompose seeding programs into two mechanisms; (1) market expansion, where the information reaches consumers that would have otherwise not been aware of the product, and (2) market acceleration, where consumers are informed that were already aware of the product.
In conclusion, in the market for experience products social media has become an important tool for firms to directly interact with their consumers and catalyse consumer-to-consumer interaction (Wang and Zhang, 2009). As firms face the objective of diminishing information asymmetry, they try to convince the dominant selectors of the value of their brand or product by directly engage with their consumers by posting product informative posts. These posts can either be posted prior to purchase or after purchase, and, in addition, they could include a sample. As shown in Figure 1 below, within the context of the model, four distinct paths –direct, mediated and conditional mediated- are proposed to show the effects of a product informative post on consumer engagement behaviour. Whereby, post exposure of a product informative post is proposed to influence consumers to engage in visible eWOM and/or invisible online post consumption. Thus, first, a consumer can evaluate the product based on their own experience or it can base their evaluation on the experience of other consumers in the social media environment (Kohli, 1999). Figure 1 below shows the conceptual model of the thesis, including the hypotheses subsequently proposed assigned as ‘H1’, ‘H2’, ‘H3’, ‘H4’, and ‘H5’.
Figure 1: Conceptual Model
H5 Ticket Post Sampling Post Engagement Post H2 H4 H3 H1 Online Consumption Post Reach eWOM Post Post Type
2.6 Exposure of a Product Informative Post
Parallel to the change to many-to-many marketing efforts consumers purchase decision process has evolved as well (Wang et al., 2012). Whereby the study of Wang et al. (2012) suggest that due to social media, consumers extend their identity by engaging with brands on their fan pages, which influences their usage and purchase behaviour. Consumer engagement represents one of the most significant developments in consumer purchase decision. Where a consumer’s purchase decision is represented by a process of multiple stages through which a consumer passes, from awareness to adoption. During the decision-making process the consumer forms perceptions and evaluations of the characteristics of the product and makes a choice decision (Arts et al., 2011).
The theory of mere exposure argues that the frequency of consumer exposure to product information positively influences consumers’ attitudes toward the product or brand (Wang, 2015). The theory of mere exposure is supported by the theory of planned behaviour which argue that intention is a strong predictor of behaviour (Wang et al., 2015; Ajzen, 1985). While traditionally, consumers were influenced by offline marketing efforts, the majority of consumers spends a significant amount of time on social media (Cheung and Lee, 2010). In addition, this awareness, due to post exposure as consumer spend significant time on social media, impacts consumers’ behavioural outcomes and affects their preferences (Kumar et al., 2015). This positive attitude-intention-behaviour relationship has received ample empirical support (Wang et al., 2015). Although, Arts et al (2011) argue that because consumers’ evaluative criteria of changes during the decision-making process, intention is a poor predictor of actual consumer behaviour (Arts et al., 2011). Nevertheless, the evaluation of a product or service is a goal-directed process, whereby consumers have an incentive to lowering their uncertainty about the product or service, suggesting the importance of exposure on consuming the provided content (Arts et al., 2011).
In accordance with the theory of mere exposure, recent literature on advertisement in the social media environment has shown there is a “dose-exposure response” which is explained by Gainsbury et al. (2016) who study addictive behaviour, as “increased exposure to
gambling marketing has a cumulative influence and results in greater gambling engagement”. Post exposure refers to the amount of consumers that are shown the post. In accordance to advertisement literature, De Vries et al. (2012) find that the position of a post in the social media environment increases the probability of clicking, implying that a well-positioned post is exposed to more consumers. Moreover, not only current, also prior exposure to a post
positively influences online consumption of the posted content in the form of post clicks (De Vries et al., 2012). Thus, both advertisement as well as content-related literature, argument in favour of the positive effect of consumers’ exposed to a post, either by the position of the post or by prior exposure, and the likelihood of consumers clicking on a certain post.
Moreover, combining the theory of mere exposure with the distinction between market expansion and market exposure (Libai et al., 2010), the amount of post exposure both affects existing consumers as well as new consumers to engage in behavioural outcomes through attitude and intention. Market expansion refers to a product informative posts that is exposed to consumers that would have otherwise not been aware of the product, while market
acceleration refers to post exposure to existing consumers. In accordance to market
acceleration, Saboo et al. (2015) find that social following, or fans, has a positive effect on the attractiveness of a brand, although at a decreasing rate, suggesting the importance of existing consumers for the firm (Saboo et al., 2015). Yet, new consumers need extra
information to form an opinion about the product or service provided by the firm. Whereby these consumers need to know the existence of the product (Reinhardt and Gurtner, 2015). Thereafter, online consumption provides these consumers access to post content and helps them in their decision-making process. Moreover, online consumption does not need an opinion statement, allowing consumers to engage with the provided content without having to form an opinion. Thereby, online consumption of the firm-provided content is an attractive means for these new consumers in their first stages of product awareness, when uncertainty about the product or brand is high.
Consequently, combining the importance of fans with the theory of mere exposure, an increase in consumers exposed to a firm-initiated social media post positively impacts new consumers’ as well as fans’, attitude towards the product or service, which leads to
behavioural outcomes and thus resulting in more online consumption of consumers with the post content. Thus, exposure to a product (pre-) announcement might make a consumer aware of a product they had not been aware of before, or it might persuade them to positively
change their attitude, intention and behaviour towards a product they had been aware of by increasing the utility of that product (Godes and Mayzlin, 2009). Therefore, the following hypothesis is proposed;
2.7 Electronic Word-of-Mouth on a Product Informative Post
Thus, whilst post exposure of the product informative post increases the likelihood of consumers to consume the posted content, how post exposure is related to online
consumption may be of more interest. First, however, it is important to note why consumers engage in the social media environment. Henning-Thurau et al. (2004) find eight factors that motivate consumers to engage within the online social network, ranging from venting negative feelings, to concern for other consumers, self-enhancement, advice seeing, social benefits, economic benefits, platform assistance, to helping the company (Henning-Thurau et al., 2004). Of these determinants to engage in online behaviour, social benefits influence consumers to strongest to engage with a post. As the amount of eWOM on a product informative post shows the level of interaction between consumers (Oestreicher-Singer and Zalmason, 2013 a firm-initiated product informative post is expected to attract eWOM. Moreover, engaging in eWOM can occur at virtually no cost for the consumer. Therefore the following hypothesis is proposed;
Hypothesis 2. Post exposure leads to eWOM.
As mentioned before, in the absence of objective quality information, individuals rely on others for the evaluation of experience products, which is referred to as ‘observational learning’ (Salganik and Watts, 2008). Active consumer engagement, manifested by eWOM, includes consumer opinion statements, which enhances the attractiveness of the brand and decreases information asymmetry thereby positively influencing online performance outcomes of firms (Kumar et al., 2015). Moreover, the exchange of information between consumers through eWOM positively influences empathy and positive feelings towards the brand among consumers (De Vries et al., 2012) suggesting that positive consumer referrals are perceived as a signal of quality. Not only positive referrals, also popularity is an
important signal of quality, even when the information is not true. The belief in a particular outcome shaped by the perception of success, might cause that outcome to be realized, but only when this perception is credible (Salganik and Watts, 2008). Popularity is showed by the opinion of other consumers in the network, thus a form of eWOM. In addition, not only the perceived success of an experience product, also earlier success leads to future success and thus consumer eWOM not only influences the outcome of the particular informative post but also the outcome of future posts.
However, eWOM is not only used as a decision-making tool for consumers in the social environment -as discussed by selection system theory-, it also causes information to spread within a network. While post exposure mainly affects the first stage of the consumer decision-making process, eWOM triggers an automated notification message to other consumers. Thereby causing a behavioural opinion to spread within a network (Aral and Walker, 2011; Brodie et al., 2013). Consequently, active consumer engagement builds awareness among friends of the focal consumer and influences other potential consumers to also engage with the product informative post. Consequently, eWOM on a product
announcement enables consumers’ to influence each other’s online consumption behaviour (Oestreicher-Singer and Zalmason, 2013). Therefore, consumers’ who do not purchase heavily from a brand may still be of substantial interest to the firm if the consumer exerts significant influence on the behaviour of her connections (Gensler et al., 2013). Thus, in the online environment, a consumer’s value no longer comes from just her purchase value, but also from her influence value through engaging in in eWOM (Ho et al., 2012).
The resulting bandwagon effect, where large numbers of individuals engage with a brand in rapid succession, influences more individuals to engage with the brand (Saboo et al., 2015). In addition, consumer participation in the online environment is more positively linked to community participation than to the volume of content (Oestreicher-Singer and Zalmason, 2013). This positive effect of social contagion and peer influence on behaviour has received ample empirical support as eWOM exponentially increases the reach and visibility of the product announcement, as it pushed post exposure to a much larger network (Gensler et al., 2013). Firstly, it has been found that active-personalized viral messaging capabilities generates only a 98% increase of peer influence and contagion, in contrast to the passive-broadcast viral messaging capabilities which generates a 246% increase in local peer
influence (Aral and Walker, 2011). Secondly, scholars have argued that by providing product information, firms can gain from the peer-to-peer networks, as the influence value of a consumer through eWOM increases the odds of product adoption with 60% (Bapna and Umyarov, 2015), showing the importance of a focal consumer’s engagement on the behaviour of other consumers in the network.
However, the effect of eWOM on consumers in the network depends on whether the affected consumers are existing or new consumers, suggesting again the importance of the distinction argued by Libai et al. (2012) New consumers infected by the announcement may have an even more important effect of online consumption through eWOM, as new
some individuals experience high similarity to others as unpleasant, and therefore seek ways to be unique. Existing consumers of new products have a need of uniqueness, defined as a positive need to be different from others and seek ways to manifest their uniqueness and specialness (Moldovan et al., 2015), hereby attracting even more new consumers, resulting in a bandwagon or ‘butterfly’ effect. To summarize, eWOM not only affects other consumers’ by positively influencing their perceptions of the value of the product, but also by increasing their likelihood of engaging in eWOM behaviour (De Vries et al., 2012). Thereby increasing the attractiveness of the firm-generated post.
However, the bandwagon effect that increases the attractiveness of the product announcement due to eWOM does not always increase with an increase post exposure. Where eWOM is hypothesized to increase the market of non-consumers, existing consumers face a dilemma between their need to show their uniqueness and knowledge about a product and their need for uniqueness. Optimal distinctiveness theory suggests that consumers that are associated with a group both want to be part of that group by showing behaviour
engagement in the form of eWOM, as well as the need to distinguish themselves from other group members of the same group (Moldovan et al, 2015). Existing consumers, who have already engaged with the product informative post and recommend it to later or new consumers, are central in the diffusion process of information but may become reluctant to engage in eWOM behaviour. Accordingly, existing consumers may become unwilling to engage in eWOM behaviour compared to new consumers, for posts that attract more eWOM. Moreover, those consumers that want to distinguish themselves might strategically engage in negative eWOM behaviour to make the product or post less attractive to other consumers (Moldovan et al., 2015). Negative eWOM causes consumer to form a negative attitude towards the post, have a strong negative effect on intention to click on a post, and decrease the attractiveness of the post (De Vries et al., 2012).
In addition to the theory of distinctiveness, that describes one effect of market expansion for existing consumers, the bandwagon effect also accelerates existing consumers in their decision-making process, also referred to as the market acceleration effect (Ho et al., 2012). This acceleration effect has to competing effects; on the one hand, each consumer evolves through the various different stages of the decision-making process quicker, which is beneficial for the firm, on the other hand, the acceleration of this process decreases the pool of influential consumers, thereby decreasing the amount of eWOM generated. This suggests that marketing efforts of the firm, leading to more post exposure, might in fact decrease WOM generated by the post. This is supported by Van Den Bulte and Lilien (2001) who
argue that in the case of intensive marketing efforts by the firm not much eWOM is needed for consumer to become aware of the product, suggesting the relative unimportance of eWOM for attracting new consumers.
Nevertheless, the arguments above show that eWOM plays an important role in
consumers decision-making process, from being exposed to a firm-initiated post to engage in online consumption (Ho et al., 2012). In conclusion, the bandwagon effect, through eWOM, makes a brand or product not only more attractive to its existing customers but also to non-customers. Thereby it has the benefits of both expanding and accelerating market for the experience product by (1) influencing consumers who otherwise might not have bought the product, and (2) a positive feedback between consumers, which may decrease the timing of adoption (Ho et al., 2012). In accordance, while post exposure is important to make
consumers aware of the product, eWOM causes the post to be exposed to different consumers, resulting in more consumption of the posted content and thus mediating the relationship between exposure and online consumption. Thus, the effect of post exposure on online consumption is facilitated by eWOM, proposed by the following hypothesis:
Hypothesis 3. eWOM mediates the effect of post exposure on online post consumption. 2.8 Post Type
However, to catalyze quality signals through consumer engagement, a firm can post a product pre-announcements in the online social environment, and/or that product
informational post can include a sample (Kim et al., 2014). Thus, to reduce uncertainty among consumers and increase market efficiencies, firms both provide their consumers with (1) the option to make a purchase, (2) the option to sample a limited experience of the full product, or (3) the option to engage with the brand after purchase. These posts allow
consumers to evaluate the offered product or service. While ticket posts and sampling posts are posted prior to purchase, engagement posts are posted after purchase but does include a limited experience of the full product. All post types are considered product informative posts, however ticket posts and sampling posts are product pre-announcement posts, which provide new information to consumers about the product’s availability, features and content (Burke et al., 1990).
Table 1: Post Types
Post Type Product Announcement Sampling Online Consumption
Ticket Post Yes No Purchase Intention
Sampling Post Yes Yes Sampling Post Content
Engagement Post No Yes Sampling Post Content
Literature has focussed on the effect of post content on consumer engagement and find that more interactive post receive more engagement. De Vries et al. (2012) define
interactivity as “the degree to which two or more communication parties can act on each other, on the communication medium, and on the messages and the degree to which such influences are synchronized. Interactivity is characterized by two-wat communications between companies and customers, as well as between customers themselves: put differently, it characterizes many-to-many communication” (De Vries et al., 2012, p. 85). Their study finds that posts with a higher degree of interactivity receive more consumer eWOM,
therefore it can be expected that for posts that allow a consumer to sample, both sampling as well as engagement posts. However, no empirical support is found for either informative or entertaining post and the likelihood of consumers engaging with the post or the content, arguing against a bigger influence of posts that allow a consumer to sample and eWOM (De Vries et al., 2012). But, theory is inconclusive on the effect of post type and consumer engagement.
Literature on innovation adoption typically studies perceptions and characteristics of consumers that have already made a purchase compared to those who have not. Importantly, literature on innovation adoption describes, in accordance to Wang et al. (2012), the multiple stages through which an individual pass from awareness to product adoption for experience goods, suggesting that various stages encompass different consumer criteria for valuing the product. In addition, construal level theory argues that individual’s weight evaluative criteria differently to future behaviour. While behaviour that are more distant in time are affected by more abstract or general considerations, behaviour more close in time is affected by concrete, specific and context dependent considerations (Arts et al., 2011). Moreover, during the complex consumer purchase decision process, consumers form perceptions of the characteristics of the product and the brand.
The success of a product strongly depends on the knowledge of consumers, especially in the early stages of the purchase decision process (Reinhardt and Gurtner, 2015). In addition,
evaluative criteria of consumers differ in different stages of the purchase decision process (Arts, et al., 2011), where more recent studies find empirical support for the dynamic effects of these criteria. While, the study of Kumar and Gilovich (2016) on preferred consumption profiles, show that consumers have a higher utility from delaying the consumption of
experience goods. This effect is driven by the tendency for the pre-purchase period to provide enjoyment (Kumar and Gildovich, 2016).
Providing potential consumer with a sample is commonly done in various experience market industries (Nejad et al., 2015). Offering free samples is a way for firms to disclose their content quality, allowing consumers to have an actual experience with the good before purchase (Halbheer et al., 2014). Where free samples stimulate the trial of a new product, encourage new use of an improved brand and it may attract consumers who have just entered the product category (Kim et al., 2014). Therefore, these non-price promotions may increase consumer behaviour (Bawa and Schoemaker, 2004). Samples are effective in increasing sales for experience products (Kim et al., 2014) by providing the consumer with information before purchase (Schlereth and Skiera, 2013). Therefore, as a result of sampling; (1) some consumers’ purchase decision is not affected because they are unaffected by the sample, (2) some consumer’s buy the product anyway but at a different time, and (3) some consumers engage with the product who would otherwise have not. Thus, either sampling is used to introduce consumer to a new product or to introduce a product to new consumers (Heiman et al., 2001).
Literature on sampling have mainly focussed on the performance outcomes of samples. While literature on adoption finds that 20% to 60% of consumers that sample, eventually purchase the product (McGuiness et al., 1992) and Lammers (1991) finds that sampling increases immediate sales with 42%, other literature argues that this effect is dependent on the price of the product as consumers can perceive providing a sample as a signal of bad quality (Heiman et al., 2001). Nevertheless, these studies focus on tangible samples provided in stores, where the product is immediately available, while this study focus on sampling as part of the marketing tools a firm has in the online environment. Heiman et al. (2001) argue that when the sample is consumed in a different place than the place of purchase, the effect on immediate sales is lost, although it may affect the consumers’ preferences for the product.
In contrast to the outcome variable of short-term sales, the study of Scott (1976) shows that of those consumers given a free 2-week trial subscription, only 4% subscribed, compared to 9% without a free trial (Bawa and Schoemaker, 2004). While, other literature that has incorporated social network characteristics, show that the effect of sampling on profit is more
determined by whom to target than how many to target (Schlereth et al., 2013). While, more recent studies have focused the trade-off of providing a sample, where either providing a sample can inform consumers in their purchase decision process and thereby functions as a promotional tool, providing a sample could also lead to a substitution effect of free
consumption (Kretschmer and Peukert, 2014).
Although this thesis does not focus on the performance outcomes of sales, the above described arguments suggests that consumers do consume a sample to make an informed purchase decision. Moreover, posts that allow a consumer to sample affects consumers in their first stage of their purchase decision, where their need to make a valuable judgement is high (Arts et al., 2011) and uncertainty about the quality is high. Where Heiman et al. (2001) find that 40-80% of consumers that are provided a sample try the sample (Heiman et al., 2001). Combining the argument that samples affects consumers in the first stages of their purchase decision process with construal level theory, it can be expected that post that include a sample are consumed when behaviour is more distance in time and thus influenced by more abstract considerations. As consumers’ look at other consumers in the social media environment when actually making the purchase decision, it is thus expected that posts including a sample directly influences consumer online post consumption.
In conclusion, the amount of consumers’ exposed to a posts affects consumers in different stages of their purchase decision process. In these various stages, consumers have different criteria in valuing experience products. Whereas, in the first stages of their decision-process uncertainty about the quality of the product is high and their need to make a valuable judgement is high, sampling posts are expected to show a strong effect of post exposure on online consumption. Based on the above it is hypothesized that;
Hypothesis 4. The, at hypothesize 1, proposed effect of post exposure on online consumption
is strongest for posts including a sample.
Moreover, providing a sample has shown to increase other outcomes such as belief strength and attitude, brand perception, and, more importantly, the initiation of consumer-to-consumer communication. Giving away free products is common practice for producers in the experience market industry to harness the full benefits of referrals (Nejad et al., 2015). In accordance to construal level theory, which argues that behaviour more distant in time is affected by more abstract or general criteria, while behaviour more close in time is affected by specific and context dependent criteria (Arts et al., 2011). As the dominant selection
system has changed from expert selection in the form of reviews to market selection by consumer engagement, consumers are influenced in later stages of their decision-process by other consumers’ opinion. This more concrete and context dependent signals of quality refers to eWOM. Moreover, consumers will make an opinion statement only when the risk, ranging from functional risk, economic risk, to social and psychological risk (Kohli, 1999), of making a wrong valuable judgement about the product value is low. Whereby Wang and Zhang (2009) argue that uncertainty is lower when consumers have knowledge about the product (Wang and Zhang, 2009).
In addition, ticket posts are used to stimulate sales (Kohli, 1999). While, free samples not only benefits existing consumer but also attracted new customers by reducing their risk of trial (Kim et al., 2014), both new and existing customers may postpone the purchase of an experience product to increase the enjoyment of consumption (Kohli, 1999). Kohli (1999) argues that such a product informative posts persuades consumers to wait for the upcoming product rather than buying from a competitor. Corbin (1980) states that consumers decision-making process may be ‘delayed’ by waiting for the best alternative. While, ticket posts affect consumer behaviour more close in time and is, according to construal level theory, affected by concrete and context dependent considerations. Selection system theory
suggested that consumers have become the dominants selectors in the market for experience products, especially relevant in the social media environment, and thus consumers look at eWOM as concrete criteria. Therefore, this thesis proposes the following hypothesis;
Hypothesis 5. The, at hypothesis 3, proposed indirect effect of eWOM on the relation between
post exposure and online consumption is strongest for ticket posts.
Table 2: Overview Hypotheses
Hypothesis Description
1 Post exposure leads to online consumption 2 Post exposure leads to eWOM
3 eWOM mediates the effect of post exposure on online consumption 4 Post exposure on online consumption strongest for sampling posts
III. Methodology
This chapter provides a description of the data and the regression models used to estimate the mediating role of eWOM in the relationship between post reach and online consumption, conditioned post type. In line with the objective of the thesis, the level of analysis is firm-generated Facebook post where aggregate consumer engagements per post is measured. The study focusses on the first-stage of information diffusion. Firstly, the sample and procedure is discussed. Secondly, the variables and measurements are discussed. Third, the analytical strategy and models are discussed, necessary to interpret the results in the part thereafter. However, the empirical context, including both Facebook as well as the product, is discussed hereafter.
3.1 Empirical Context 3.1.1 Facebook
The data provided is collected by Facebook and provided to brands that have an online band community on the social media platform. As shown in picture 1, these communities provide consumers the possibility to ‘Like’ the fan page, thereby informing them for future events, photo albums and more. Moreover, the consumer is also given the possibility to post a ‘status’, ‘photo’, or ‘video’ in the event.
Picture 1: Brand Community Electronic Family
Picture 2 shows the event of Electronic Family, which is connected to their brand fan page as is seen by ‘Hosted by’. An event allows consumers to engage with the product by
clicking either ‘interested’, ‘going’, or ‘not going’, thereby showing to other consumers’ their engagement with the product. The event also informs the consumer about general information on the product, for example, date time and location. In addition, consumers are given the opportunity to post a text, photo, video or create a poll in the event. These posts are
consumer-generated and hence not the focus of the thesis. However, the brand itself can also either post in the event or in the brand fan page, where consumers are given the opportunity to engage with the firm-initiated product information, as shown in picture 3.
Picture 2: Event Electronic Family July 23, 2016
3.1.2 Music Festivals
Data is provided by the company Fanalist which analyses the online strategy of ALDA events. ALDA events “develops next level technical productions and complete, high-quality brands from scratch. ALDA events always works from an ‘integrated concept perspective’ to create next level technical production” (ALDA events). Their website states that they product 160 shows worldwide in 40 countries, which are visited by 1,5 million people from 120 countries. While their live broadcasts are viewed by 5 million viewers with 100 million video content views from over 170 countries. The company is set-up by Allan Hardenberg and David Lewis and has started in 1997 with big events like ‘Armin Only’, ‘Tiesto Solo’ and ‘Three DJs in a Boat’.
The research setting is large Dutch music festivals of the music genre EDM. The festivals analysed are ‘The Flying Dutch’ (TFD), ‘Electronic Family’ (EF), and ‘Amsterdam Music Festival’ (AMF). All three are productions of ALDA Events. The music festival event industry is characterized by music festivals (brands), offering a one-time music festival experience (product) with different music genres (sample) by combining various DJ’s (artists) to visitors (consumers). Music is an experience good, whereby consumers do not have full information about the product beforehand. In addition, the event of music festivals is an experience good as well. Hereby, sampling is an effective tool to reduce uncertainty which allows for the research on various post types. Moreover, the high visibility of both music and event consumption makes social media important due to the public nature of the consumption process (Gensler et al., 2013).
The choice of the music festival industry is driven by numerous considerations. Firstly, the music industry is both socially and economically relevant, where music festivals contribute to a large income stream for the society (Saboo et al., 2015), approximately close to 200 million according to the ING Dance Music Survey 2015. Secondly, the industry is characterized by a large social media fan base where interactions have a significant influence on consumer online engagement and product preferences. Music festival brands build an emotionally significant relationship with their fans and consumers experience a close
relationship with these brands (Saboo et al., 2015). Thirdly, the industry has changed rapidly in the past decade. Most importantly, the integration of music consumption in social
networking sites, where both free samples as well as promotional videos are posted alongside ticket information and product information. Fourth, the importance of artists is significant where the earnings of the top 10 DJ’s worldwide total about 208 million (ING Dance Survey, 2015). In addition, the choice for the three festivals is motivated by their size. All three
festivals are approximately visited by 100,000 people, making them the largest festivals in the Netherlands. These numbers are reflected by their online fan base, where the Flying Dutch has 192,504 fans on July 4, 2016, Electronic 68,049 and Amsterdam Music Festival 325,100. Moreover, the events of the particular festivals do not overlap one another and thus fans will not have to choose between any of them. Where TFD takes place on June 4, 2016 and July 30, 2016, EF takes place on July 23, 2016 and AMF on October 22 and 23, 2016. 3.2 Sample and Procedure
Since the year 2011, Facebook collects consumer insights on Facebook posts for businesses. This database is retrieved of off Facebook by exporting post level measurements. Hence, each observation is a single post by a festival. The sample contains 1230 unique post observations, including general post information Post ID, Permalink, Post Message, Type, and Posted. The variable Post ID is the unique ID of each individual post, Permalink links the data to the actual post on Facebook, Post Message shows what is typed in the status of the post, Posted shows the data and time of the post and Type shows the category of the post; (1) Video, (2) Photo, (3) Link or (4) Status. From approximately 2013 on the database can be considered fairly complete in terms of measured variables. However, the festival TFD started its Fan Page in 2015 and, hence, the observations of TFD range from February 1, 2015 until June 28, 2016. The amount of observations of TFD in this database is 446. The post
observations of EF starts earlier and ranges from July 17, 2013 until June 28, 2016. The amount of observations of EF posts in the database is 400. The post observations of AMF range from July 23, 2013 until June 28, 2016. The amount of observations is 446.
The database provided includes the tabs; (1) Key Metrics, (2) Lifetime Talking About This, defined as “the number of unique people who created a story about your Page post by interacting with it (unique users)”, (3) Lifetime Post Consumers by Type, defined as “the number of unique people who created a story about your Page post (unique users)”, and (4) Lifetime Negative Feedback, defined as “the number of people who have given negative feedback to your post, by type (unique users). From the four tabs, the variables Reach Unique, Like, Comment, Share, Link Click and Other Clicks per unique post observation for the three brands are retrieved and combined. Due to sample size requirements and to
3.2.1 Coding and Conditioning of the Sample
Over 1230 individual posts were identified and coded. Although the database shows the category of the post –Video, Photo, Link or Status-, the database does not provide information on whether or not observations contain a link and where the link leads the consumer to. While it is clear that Link contain a link, Photo or Video post are also able to contain a link. Moreover, posts that do contain a link differ in whereto they link the
consumer. Therefore, all observed posts are identified and coded. Firstly, posts with a link are coded 1 for the variable ‘Link in Status’, posts without a link are coded 0 for ‘Link in Status’. Secondly, as shown in Table 1, posts are categorized based on a standardized scheme ranging from 0 to 5. Coding was executed by one independent coder. Any remaining post issues were categorized according to rational logic of the coder.
Posts that allow the consumer to purchase a ticket or to subscribe to earlier ticket access are coded Type=1 and post that allow the consumer to sample music of the artists performing are coded Type=2. The latter category, posts that are coded 2 (Type = 2), are
posted prior to the event, and allows consumers to sample a sub-set of the music of DJ’s that
have been booked by the festival. In addition, posts which do not link the consumer to the music of the DJ but mention the DJ which link to the DJ’s Fan page are also coded 2 (Type = 2). Post posts that allow the consumer to sample after the event are coded Type = 3,
including photo album posts (Type = 3, Link = 0), after movie and/or livestream post (Type = 3, Link = 1). Type 2 and 3 are coded with either rational interpretability of the post content and date or by a unique hashtag, for example, the hashtag ‘#AMF2015’. Rule of thumb is that sampling post (Type = 2) must be posted prior to the event, whereas engagement post (Type = 3) are posted after the event. Posts that inform the consumer about merchandise of the music festival and/or those that are a blog post are coded 4 (Type = 4). Lastly, posts that inform the consumer about the product are coded 5 (Type = 5).
In conclusion, post which allow the consumer to either purchase a ticket or provide them with additional information on ticket sales or that allow the consumer to pre-subscribe to ticket sales are coded 1. Posts that either mention a DJ, introduce a DJ or show one of their own music sampling channels, Souncloud, Spotify, Beatport or YouTube provide the
consumer with a non-price promotional sample intended to lower information asymmetry. Posts that fall into this category are coded 2. Lastly, posts that initiated after the event has taken place and are intended to increase engagement are coded 3. The latter contains photo album posts, livestream or after sales information.
Table 1: Code Specifics Post Type
Variable Specification
Type Description Posts Included
0 Brand Info Post Update Fan Page (banner, logo), Mentions to Other Brands, Mentions Other Events
1 Ticket Post Information on Ticket Sales and Subscription
2 Sampling Post DJ introduction (Soundcloud, Spotify, Beatport, YouTube) and DJ Mention
3 Engagement Post Photo Album, After movie, Livestream 4 Additional Sales Post Merchandise, Blog Posts
5 Product Info Post Promotions, Event, Own Page Mentions
3.3 Variables and Measures
3.3.1 Dependent Variable: Total Clicks
The dependent variable of the study is online post consumption. The variable is
calculated as the sum of ‘Link Clicks’ and ‘Other Clicks’ from the retrieved databade. Link Clicks are retrieved from the Excel tab ‘Lifetime post consumers by type’, which Facebook defines as “Lifetime: The number of people who clicked anywhere in your post, by type. (Unique Users)”. The variable measures the aggregated amount of unique clicks on a link in a firm-generated Facebook post. As ‘Link Clicks’ only measures “the number of clicks on links appearing on your ad or Page that direct people off Facebook as a result of your post”, for the post which allow a consumer to (after-) sample the variable does not measure these clicks. Therefore, the variable ‘Other Clicks’ is added, which is retrieved from the same excel tab. This variable measures the aggregate unique clicks on a firm-generated Facebook posts other without directing consumers off of Facebook as a result of the click.
3.3.2 Independent Variable: Exposure
The independent variable exposure is operationalised by ‘Unique Reach’, retrieved from the tab ‘Key Metrics’. The variable measures the aggregated amount of unique people that have been reached by a certain Facebook posts, which is named as “Lifetime Post Total Reach” and defined as “Lifetime: The Total Number of People your Page Post was served to. (Unique Users)”. Thereby including both organic, viral and paid reach.
3.3.3 Mediator Variable: eWOM
The mediator variable for this study is electronic word-of-mouth (eWOM), which is in the empirical context of brand communities the nature of an individual’s interaction with the firm (Brodie et al., 2013). As the sample measures aggregate post likes, comments and shares a new variable eWOM is created by calculating a weighted average as eWOM = likes + 2 comments + 3 shares. On the one hand, incorporating eWOM as a weighter average is statistically necessary due to multicollinearity, on the other hand, incorporating eWOM is theoretically useful as the thesis focusses on eWOM volume defined as “the total amount of consumer interaction” (Liu, 2006, p.75). Results have shown that the volume of eWOM provides consumers with information about how many others have experienced the product and how popular the product is in the market, thus thereby affecting consumers’ awareness and reduce uncertainty (Cheng, Wang and Xie, 2011), the measure includes unique eWOM. In addition, like is given the least weight and shares the most as according to consumer behavioural theory, eWOM is consumer engagement behavioural manifestations which contains hierarchical levels of which comments require a higher level of eWOM than likes 3.3.4 Moderator Variable: Post Type
As discussed before, all posts are coded for post type. All posts that are not either a product related post or including a sample are excluded from the sample. The three remaining post are the dummy for ticket posts, sampling posts or engagement posts.
3.4 Analytical Strategy: Multiple Moderated Mediation Model 3.4.1 Conceptual Model
The analysis involves the estimation of the indirect effect of post Reach and Total Clicks through eWOM, moderated by the type of post. According to the seminal paper of Baron and Kenny (1986) “a variable functions as a mediator when it meets the following conditions: (a) variations in the levels of the independent variable significantly accounts for variations in the presumed mediator, (b) variations in the mediator significantly account for variations in the dependent variable, and (c) when both (a) and (b) are controlled, a previous significant relation between the independent and the dependent variables is no longer
significant, with the strongest demonstration of mediation occurring in path (c) is zero” (Baron and Kenny, 1986, p. 1176). To test for the mediator effect, three conditions must hold; (1) the independent variable Reach in the first model must affect the mediator eWOM,
(2) the independent variable Reach has to show an effect on the dependent variable Total Clicks, and (3) the mediator eWOM has to affect the dependent variable Total Clicks. Although the causal steps approach as defined by Baron and Kenny (1986) does not address the hypothesis of mediation directly (Little et al., 2007), it allows for the comparison of the indirect and direct effect of post Reach on Total Clicks.
Figure 2: Mediator Effect (Baron and Kenny, 1986) Figure 3: Types of Mediation (Little et al., 2007)
As shown in Figure 4, the indirect effect of Reach on Total Clicks through eWOM is hypothesized to be moderated by the type of post.
Figure 4: Conceptual Diagram Conditional Mediator Model
H4 Ticket Post Sampling Post Engagement Post H5 H2 H3 H1 Online Consumption Post Exposure eWOM Post Post Type
