Social Media Analytics in Retail: Exploring Consumer Engagement on Facebook

This study is to investigate the relationship between retailers’ Facebook activities (e.g., type of post they use, time they upload) and the resulting consumer engagement behavior on the Facebook fan pages. A total of 2,695 postings on Facebook fan pages for Macy’s, Target, Anthropologie, and Gap were collected. A three-way Analysis of Variance (ANOVA) and series of ANOVAs were conducted to examine how content type, publisher type and timeframe affect consumer engagement on Facebook fan pages. Posting photos or videos attracted more engagement than status posts did. Results showed that marketer-generated content (MGC) was more effective in engaging users than user-generated content (UGC). This exploratory study provides retailers with effective strategies to enhance consumer engagement on their Facebook fan pages.

Keywords: Social media; Consumer engagement; Facebook; Social media analytics; Retailer; Marketing

Over the past two decades, marketers have experienced a major transformation of marketing with the development of social media platforms such as Facebook, Twitter, and Instagram. The growth of social media marketing has changed the ways that marketers and consumers behave across all type of marketing channels. Social media marketing has facilitated new ways for consumers to interact with marketers. The interactive nature of social media allows consumers not only to find information but also to share information with one another [1]. Moreover, consumers act as agents who amplify the effect of marketing actions, providing marketers with large-scale electronic Word-of-Mouth (eWOM). Therefore, social media has become an indispensable tool for marketers to use to enhance consumer engagement.

Among the various social media platforms, this study particularly focuses on Facebook, as the social platform that has been most widely used by consumers and marketers. On average 1.52 billion people logged onto Facebook daily in December 2018, representing a 9% year-over-year increase [2]. Almost 80% of Americans use Facebook to share information and experiences with the second largest platform being Instagram at 32% [3]. Most of social media advertisers (93%) use Facebook ads and 24% use Instagram. Marketers have developed their own brand pages on Facebook, publishing content to engage users [4]. Almost half of the Facebook users ‘like’ a Facebook fan page to support a favorite brand [5].

Facebook remains one of the top social media platforms for use by retailers as 85% of consumer purchases from social media sites come from Facebook [6]. Seventy-eight percent of American consumers have discovered retail products to buy on Facebook and more and more Facebook users are clicking on e- commerce ads [7]. Facebook is also one of the leading social media networks used to manage relationships with consumers in the retail industry. Facebook supports conversations among users with a variety of features such as writing on friends’ walls, commenting on links, and participating in forum discussions [8]. Thus, retailers are increasing their involvement in the Facebook community. In 2013, 96% of Fortune 500 specialty retailers used Facebook [9].

The increased importance of social media has resulted in a substantial body of research focusing on social media marketing. Although these studies have advanced understanding of social media marketing, there is lack of empirical research on measuring marketers’ performance and maximizing consumer engagement in social media. A few studies have investigated the relationship between retailer’s social media marketing activities and consumer engagement. However, there has been no consistent conceptualization and operationalization of the consumer engagement on social media. Most studies used behavioral intentions instead of actual behaviors, limiting accurate understanding of the consumer engagement phenomenon.

Social media analytics are used to analyze Facebook content in this study. Social media analytics is an emerging research approach for investigating communication patterns and significant topics in social media content or big data generated from social media [10,11]. These methods have been integrated with other research methods in statistics and geographic information systems to test effects [12,13]. Big data can be a useful tool to investigate retailerconsumer interaction in social media as it shows both retailer and consumer activities [14]. Using big data, this study captures both retailers’ social media activities and consumers’ behavioral engagement (e.g., like, comment, share). Therefore, the purpose of this study is to investigate the relationship between retailers’ Facebook activities (e.g., type of post they use, time they upload) and the resulting consumer engagement behavior on the Facebook fan pages.

This study contributes to the development of a new research direction to advance the literature of social media marketing, particularly for the retail industry by using behavioral data of retailers and consumers. Retailers may benefit from this study as it provides more accurate understanding of consumer engagement on social media, utilizing big data. The research findings will help retailers to maximize the use of social media to influence customer engagement behaviors.

The next section provides a literature review and hypotheses, followed by section 3, which describes the methodology. Section 4 presents the results, while Section 5 discusses the research findings and derives the implications. Section 6 addresses limitations and suggests future research. The whole paper is concluded with Section 7.

Social media marketing

Social media has evolved as an important marketing tool to promote a product, service, or brand.

Social media allows complex interactions between consumers and marketers as marketers can share information with consumers, receive feedback, and reply to consumers via social media platforms [15]. With the aid of information and mobile technology, marketers can reach many consumers simultaneously and further build relationships with social media users [16,17]. Social media also plays a crucial role in maintaining consumer engagement. It has been found that consumers visit stores more frequently after becoming Facebook fans of a retailer and spread more positive word of mouth than nonfans [18,19]. In addition, social media empowers consumers to share their views and influence other consumers. It enables consumers to not only gather information but also share opinions. Communications between consumers via social media vastly affects decision-making since many consumers trust these communications [20]. Thus, marketers are investing significant efforts in developing social media features to increase consumer communications on their social media websites and in turn, generate the eWOM marketing [21]. eWOM has been found to have higher credibility, empathy, and relevance for customers than marketer-generated sources [22]. eWOM marketing is a form of viral marketing, described as “any strategy that encourages individuals to propagate a message, thus, creating the potential for exponential growth in the message’s exposure and influence” [22]. This means that consumer engagement is the major determinant of eWOM’s success. The effect of eWOM has been found to be greater when the level of social media activities in the social media site is high. However, more than 80% of marketers are concerned about measuring the returns on investment from social media [23]. Since Facebook implemented policy changes to filter out unpaid businesses’ promotional postings in users’ news feeds a few years ago, effective Facebook posting activities should be of interest to marketers. According to Wozniak, et al. [24], measuring the performance qualities of consumer engagement could indicate the amount of return on investment in marketing efforts. Social media has been studied from different perspectives such as usage patterns, motivations, and characteristics of communities. For example, Cvijikj, et al. [25] examined consumers’ usage patterns such as topics of conversation, intentions for posting and type of interactions on a Facebook fan page. They identified product, sales, brand, competitor, Facebook contest, company and environment as the seven major topics of the posts on the Facebook fan page. Consumers’ intentions for posting were suggestions & requests, affect expression, sharing, information inquiry, complains & criticism, gratitude, competitor companions, and praise. They also found that consumers rather responded to existing posts by commenting and liking than creating their own posts. Jansen, et al. [26] argued that consumers’ posts generate information dissemination processes on Twitter, which include branding comments, sentiments, and opinions. A few studies have given attention to consumers who “like” brands (e.g., [8,27]) or share brand-related content on social media (e.g., [28, 29]). However, little has been investigated regarding the marketers’ performance related to consumer engagement.

Consumer engagement in social media

The act of “engaging” can be defined as interactive consumer experiences that are co-created with others [11]. Vivek, et al. [30], 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.” Specifically, in online contexts, consumers’ engagement could be described as “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” [31]. Consumer engagement can result in positive consumer attitudes and experiences such as trust, satisfaction, commitment, emotional attachment, empowerment and loyalty [22,32-36]. Particularly, consumer engagement activities (i.e., likes, shares, or comments) in social media, forward content to other members of online communities. The propagation of information driven by consumer engagement is one of the most desirable outcomes of social media marketing. Further, consumer engagement does not necessarily result in actual purchases since the important role of consumers in this process is influencing potential customers and the general public [37,38].

Several researchers have attempted to classify consumer engagement behaviors. Brodie, et al. [4] distinguished between sharing, learning, co-developing, advocating, and socializing. Jaakkola, et al. [39] identified four types of consumer behaviors: co-developing, augmenting, influencing, and mobilizing. Muntinga, et al. [40] recognized three levels of consumer engagement on social media: (1) consuming (least active), (2) contributing, and (3) creating (most active). Consuming represents a minimum level on engagement, including viewing contents and following Facebook pages. Contributing signifies user-to-content and user-to-user interactions such as commenting on posts.

Creating involves producing and publishing brand-related content for others to consume and contribute. Among Muntinga’s three levels of consumer engagement on social media, this study examined the medium- level of consumer engagement, contributing such as liking, commenting and sharing.

A number of studies have addressed antecedents of consumer engagement. For example, Hennig- Thurau, et al. [41] have identified eight specific factors, which motivate consumers to engage with online communities, including (1) venting negative feelings, (2) concern for other consumers, (3) self-enhancement, (4) advice-seeking, (5) social benefits, (6) economic benefits (e.g. cost savings), (7) platform assistance, and (8) helping the company. Some studies have examined the motivations that underlie people’s decisions to contribute content to social media (e.g., [30]). In this study, the effect of specific attributes (i.e., content type, publisher type and timeframe) of social media post on consumer engagement was investigated.

Determinants enhancing consumer engagement on social media websites

Recent studies have recognized that specific attributes of social media posts could influence consumer engagement on social media websites. DeVries, et al. [42] have tested variables that enhance brand post popularity (i.e., the number of likes and the number of comments) on social media websites. They found that vividness of images, interactivity (e.g., asking a question, providing an interactive link), position of post (e.g., locating a post at the top of a social media page), and valence of content (e.g., positive, negative, or neutral) effectively increase the numbers of likes or comments. In addition, Malhotra, et al. [43] have suggested that marketers should make content shorter, more personal, and more focused on the topic to increase consumer engagement on social media websites. Kwok, et al. [44] revealed that photos or links in content were more effective in increasing the number of likes and comments.

Content type: A post can be in a variety of forms such as text message, images, videos, or links.

Social media users are motivated to find useful information in specialized communities, so the quality of content (e.g., information quality or message length) can significantly increase engagement. Photos or images in online content can have significant influence on increasing the usefulness of that content [45]. Thus, visual content like images or videos can be a powerful aid to consumer engagement. Alboqami, et al. [46] have found that pictures attached to tweets increase eWOM effects and facilitate favorable reactions on Twitter. Kwok, et al. [44] compared four content types of Facebook (link, status, video, and photo) and concluded that photo and status posts increase the number of likes and comments. In social media, users are likely to circulate content when they perceive the post interesting or fun [47,48] and content type could influence the perception. Therefore, the following hypothesis is proposed.

H1: Content type is associated with the number of reactions, comments, and shares.

Publisher types: On Facebook fan pages, both users and marketers can publish posts, however, publishers have different purposes in posting [46]. Ding, et al. [49] noted that previous literature generally ignored the different effects of marketergenerated content (MGC) from user-generated content (UGC). Specifically, the goal of MGC is to spread information about products, services, and brands; it is designed to facilitate conversations among users in a community. MGC helps increase awareness of a brand or product and attract more users to the community page [50]. UGC includes “the various forms of media content that are publicly available and created by end-users” [51]. Consumers find UGC more credible and trustworthy than content posted by marketers [1]. Therefore, the following hypothesis is proposed.

H2: Publisher type is associate with the number of reactions, comments, and shares.

Timeframe: Ample content is generated in social media every day, ranging from simple stories about sports to posts on critical social issues [52]. Social media transmits information about events in real time, which is its unique feature [53]. A few studies have investigated that consumers’ reactions (e.g., sentiment, the number of comments and likes) to posts by timeframe (weekdays/ weekends or forenoon/afternoon) when marketers post content [54,55] but their findings are inconsistent. Therefore, the following hypothesis is proposed.

H3: Timeframe is associate with the number of reactions, comments, and shares.

Data collection

This study was designed to examine the effect of specific attributes (i.e., content type, publisher type and timeframe) of social media posts on consumer engagement. To meet the objectives of the study, the Facebook fan pages of Macy’s, Target, Anthropologie, and Gap were chosen for analysis. These four retailers were selected because they are among the top retailers based on the number of Facebook followers of each company [56]. Three different types of retail outlets were tested to reflect different uses of social media by retail type: 1) Department stores (i.e., Macy’s), selling a large assortment of clothing, shoes, and housewares, 2) Discount stores (i.e., Target), selling basic merchandise including clothing, and 3) Specialty stores (i.e., Anthropologies and Gap), selling a limited assortment of clothing and accessories [57]. To collect the data, Netvizz v1.44 (https://apps.facebook.com/netvizz/) was used. Netvizz is a tool that can specify type of data to be collected, postings, users, etc. Posts on the Facebook fan pages from January 1, 2017 to December 31, 2017 were used for this study. The approach of examining Facebook engagement used in this study was validated by previous studies (e.g., [31]). Due to the nature of secondary data set, the researchers were not able to measure the reliability of the measurements. However, the face validity of the measurement and methodology was achieved by semi-structured interviews with five local social media managers in retail stores.

Data analysis

Two different types of data analysis were used in this study. Microsoft Excel 2016 was used for descriptive statistics and graphic illustrations based on the results of the text mining analysis. A series of ANOVAs and t-test were then applied to evaluate significant differences in engagement levels for the proposed variables (i.e., content type, publisher type and timeframe) using SPSS 24.

Descriptive analysis regarding Facebook engagement

During the study period of one year, Macy’s had 961 posts (641 MGC and 320 UGC), Target had 528 posts (169 MGC and 359 UGC), Gap had 541 posts (190 MGC and 351 UGC) and Anthropologie had 664 posts (606 MGC and 58 UGC) resulting a total of 2,694 posts (Table 1). Users actively posted on the Target’s and Gap’s Facebook fan pages, while most of the posts on the Anthlopologie’s Facebook fan page were published by marketers. Target received the most comments (295 per post on average), while reactions (1,625 per post on average) were most numerous on Macy’s Facebook fan page. Anthropologie’s posts were shared the least (13 shares per post on average). A total of 326 videos, 2,089 photos, 170 status posts, and 109 links were posted during the one-year study period (Table 1).

Hypotheses testing

ANOVA results show that there are significant differences in the number of comments (F = 3.766, α= .01), reactions (F = 4.824, α< .01), and shares (F = 8.318, α < .001) across different types of posts. Overall, photos attracted more comments, reactions, and shares than status or links, while videos attracted more reactions than the other types (Table 2). Thus, hypothesis 1 is supported. T-test results show that MGCs (1,606 posts) attracted more comments (t = 2.995, α< .01), reactions (t = 9.111, α< .001), and shares (t = 2.992, α< .01) than UGCs (1,088 posts). Thus, hypothesis 2 is supported. In addition, posts published in February, March, and May received more comments (F = 2.155, α = .014), reactions (F = 2.292, α < .01), and shares (F = 2.293, α< .01) than the other months. Thus, hypothesis 3 is supported. There was no statistical significance in engagement across posts published on the different days of the week, while relatively higher number of posts are published on Tuesdays and Thursdays than other days.

Table 1:Overall descriptive for stores.

Table 2:Post-hoc test for posting type.

In addition, a series of ANOVAs conducted show that the number of comments is significantly different across different retail brands (F = 5.068, α< .01), while reactions and shares remain insignificant. Post-hoc analysis results show that posts on Macy’s and Gap’s Facebook fan pages attracted more comments than on Anthropologie’s page (Table 2).

Consistent with previous studies [58, 46, 44], the findings of this study suggest that visual content leads to more interactions with social media users. There are countless posts in news feeds on Facebook, so consumers are likely to skip most of them, particularly from marketers. In social media, entertainment is a key motivator for consumers to share user-generated content [59]. Retailers need to post interesting images or short videos that can grab consumers’ attention within a short time frame which encourages consumers to see, react and share the post. In addition, while research shows people retain only 10 to 20% of the information they read online, retention could be increased up to 65% with the addition of visual images [6]. Providing relevant visuals that helps consumers understand the marketing information easily and effectively would also be an important consideration for retailers.

Previous studies have reported that consumers find UGCs more credible and relevant than MGCs [1,45]. However, the findings of this study show that MGCs are much more effective in creating engagement than UGCs. Although numerous users published content on Facebook pages, MGCs drew significantly more comments, reactions and shares. Retailers should aim to find effective ways to showcase UGCs. Encouraging users to post images or videos instead of texts could be a way to increase engagement with UGCs. Retailers could also share UGCs in Facebook stories to make UGCs gain more attention. Further, retailers could use Facebook as a platform for value co-creation between consumers and the retailer, or consumers and consumers by running collaborative campaigns/events. For example, social media ad contests could be an opportunity for retailers to attract more engagement with both MGCs and UGCs and co-create value with consumers. Consumers can play a central role in the process of creating an ad campaign by providing ideas and voting for winners or reacting to the posts.

Posts published in February, March, and May drew more engagement than other months. This time of year, might be when more consumers are visiting retailers’ Facebook pages. Spring is the time for renewing and refreshing. Consumers are looking for inspirations to update their wardrobe, home etc. on social media. Thus, retailers should take advantage of this opportunity to highlight their new products and social media fans’ engagement may maximize the exposure of the new products through eWOM.

An interesting finding is that there are different levels of engagement across different retail brands. That is, the social media’s fan behavior may vary by retail brand. The difference in engagement level might be also due to the difference in retailers’ activities on social media. Findings of this study show that Target received more shares and comments than the other retailers studied. This might be due to Target’s active use of Facebook apps such as “Give with Friends” and “Give with Target”. Theses apps encourage shares since fans need to team up with Facebook friends to give a gift to their friends, family or local schools. The numbers of comments and shares are lower on Anthropologie’s Facebook page. Anthropologie is known for their visual designs so their fans could be engaging more with the image-based social media platforms such as Instagram and Pinterest rather than Facebook [60]. Thus, retailers may want to analyze how their consumers respond to their social media marketing activities. Instead of following general tips on social media marketing, each retailer should design their own strategies that best fit their brand.

This study explored social media data collected from the selected retailers over a limited one-year period, which could limit any generalizations from this study. Researchers may want to consider studies of other social media platforms to further examine the effects of the various factors on consumer engagement. In addition, this research only used a quantitative analysis method (e.g., ANOVAs and t-test). Further investigation of marketers’ perspectives, using in-depth interviews is recommended to better understand the current needs of retail marketers. There were differences in the average number of comments, reactions and shares per post by retailer. It would be worthwhile to investigate the differences in posting activities (e.g., content type, timeframe) by retailer and how those differences affect the average number of comments, reactions and shares per post.

This study examines the relationship between retailers’ social media management activities and the consumer engagement behavior on the Facebook fan pages. The study findings suggest that visual postings (photos and videos) attracted more engagement than status posts did. In addition, MGC was more effective in engaging users than UGC. In this regard, this study contributes to the literature of social media marketing by confirming the effect of retailers’ Facebook activities on consumer engagement behaviors. Further, big data enables the measurement of actual consumer behaviors (e.g., liking, commenting, sharing) on Facebook fan pages and accurate understanding of consumer engagement phenomenon in social media. Thus, this study suggests specific strategies for retailers to increase consumer engagement activities on their Facebook fan pages.

None.

Authors declare no conflict of interest.

1. Cheong HJ, Morrison MA (2018) Consumers’ reliance on product Information and recommendations found in UGC. Journal of Interactive Advertising 8(2): 38-49.
2. Facebook (2019) Company info.
3. Donnelly G (2018) 75 Super-useful Facebook statistics for 2018.
4. Brodie RJ, Ilic A, Juric B, Hollebeek L (2013) Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research 66(1): 105-114.
5. Smith A, Anderson M (2018) Social media use in 2018. Pew Research Center.
6. Hatch C (2018) Be in the know: 2018 ecommerce statistics you should know. Disruptive Advertising.
7. Cooper P (2018) 41 Facebook stats that matter to marketers in 2019.
8. Wallace E, Buil I, DeChernatony L, Hogan M (2014) Who ‘likes’ you ... and why? A typology of Facebook fans: From ‘fan’-atics and self-expressives to utilitarians and authentics. Journal of Advertising Research 54(1): 92-109.
9. Barnes N, Lescault AM, Wright S (2013) Fortune 500 are bullish on social media: Big companies get excited about Googleþ, Instagram, Foursquare and Pinterest.
10. Bruns A , Burgess J (2012) Researching news discussion on Twitter: New methodologies. Journalism Studies 13(5/6): 801-814.
11. Luo Q , Zhong D (2015) Using social network analysis to explain communication characteristics of travel-related electronic word-of-mouth on social networking sites. Tourism Management 46: 274- 282.
12. Park SB, Jang J, Ok CM (2016) Analyzing Twitter to explore perceptions of Asian restaurants. Journal of Hospitality and Tourism Technology 7(4): 405-422.
13. Park SB, Kim HJ, Ok CM (2017) Linking emotion and place on Twitter at Disneyland. Journal of Travel & Tourism Marketing 35(5): 664-677.
14. Kunz W, Aksoy L, Bart Y, Heinonen K, Kabadayi S, et al. (2017) Customer engagement in a Big Data world. Journal of Services Marketing 31(2): 161-171.
15. Cvijikj JP, Michahelles F (2013) Online engagement factors on Facebook brand pages. Social Network Analysis and Mining 3(4): 843-861.
16. Boyd DM, Ellison NB (2007) Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication 13(1): 210-230.
17. Hudson S, Roth MS, Madden TJ, Hudson R (2015) The effects of social media on emotions, brand relationship quality, and word of mouth: An empirical study of music festival attendees. Tourism Management 47: 68-76.
18. Dholakia UM, Durham E (2010) One cafe´. Facebook experiment. Harvard Business Review 88(3): 26.
19. Rishika R, Kumar A, Janakiraman R, Bezawada R (2013) The effect of customers’ social media participation on customer visit frequency and profitability: An empirical investigation. Information Systems Research 24(1):108–127.
20. Cheung CMK, Lee MKO (2012) What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems 53(1): 218-225.
21. Stankov U, Lazic L, Dragicevic V (2010) The extent of use of basic Facebook user-generated content by the national tourism organizations in Europe. European Journal of Tourism Research 3(2): 105- 113.
22. Gruen TW, Osmonbekov T, Czaplewski AJ (2006) eWOM: The impact of customer-to-customer online know-how exchange on customer value and loyalty. Journal of Business Research 59(4): 449-456.
23. eMarketer (2015) Social network ad spending to hit $68 billion worldwide in 2015. eMarketer.
24. Wozniak T, Stangl B, Schegg R, Liebrich A (2017) The return on tourism organizations’ social media investments: Preliminary evidence from Belgium, France and Switzerland. Information Technology & Tourism 17(1): 75-100.
25. Cvijikj I, Michahelles F (2013) Understanding the user generated content and interaction on a Facebook brand page. International Journal of social and Humanistic Computing 2(1-2): 118.
26. Jansen BJ, Zhang M, Sobel K, Chowdury A (2009) Twitter power: Tweets as electronic word-of-mouth. Journal of the American Society for Information Science and Technology 60(11): 2169-2188.
27. Nelson-Field K, Riebe E, Sharp B (2012) What’s not to ‘like?’ Can a Facebook fan base give a brand the advertising reach it needs? Journal of Advertising Research 52(2): 262-269.
28. Belk R (2014) You are what you can access: Sharing and collaborative consumption online. Journal of Business Research 67(8): 1595-1600.
29. Shi A, Rui h, Whinston AB (2014) Content sharing in a social broadcasting environment: Evidence from Twitter. MIS Quarterly 38(1): 123-142.
30. Toubia O, Stephen AT (2013) Intrinsic vs. image-related utility in social media: Why do people contribute content to Twitter? Marketing Science 32(3): 368-392.
31. Mariani MM, Felice DM, Mura M (2016) Facebook as a destination marketing tool: Evidence from Italian regional destination management organizations. Tourism Management 54: 321-343.
32. Casaló L, Flaviá n C, Guinalíu M (2007) The impact of participation in virtual brand communities on consumer trust and loyalty: The case of free software. Online Information Review 34(6): 775-792.
33. Hollebeek LD (2011) Demystifying customer brand engagement: Exploring the loyalty nexus. Journal of Marketing Management 27(7/8) 785-807.
34. Bowden JLH (2009) The process of customer engagement: a conceptual framework. Journal of Marketing Theory and Practice 71(1): 63-74.
35. Chan KW, Li SY (2010) Understanding consumer-to-consumer interactions in virtual communities: The salience of reciprocity. Journal of Business Research 63(9): 1033-1040.
36. Schau HJ, Muñizn AM, Arnould EJ (2009) How brand community practices create value. Journal of Marketing 73(5): 30-51.
37. Lemon KN, Verhoef PC (2016) Understanding customer experience throughout the customer journey. Journal of Marketing 80(6): 69-96.
38. Vivek SD, Beatty SE, Morgan RM (2012) Customer engagement: Exploring customer relationships beyond purchase. The Journal of Marketing Theory and Practice 20(2): 122-146.
39. Jaakkola E, Alexander M (2014) The role of customer engagement behavior in value co-creation: A service system perspective. Journal of Service Research 17(3): 247-261.
40. Muntinga DG, Moorman M, Smit EG (2011) Introducing COBRAs: Exploring motivations for brand related social media use. International Journal of Advertising 30(1): 13-46.
41. Hennig-Thurau T, Gwinner KP, Walsh G, Gremler DD (2004) Electronic word-of-mouth via consumer opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing 18(1): 38-52.
42. DeVries L, Gensler S, Leeflang PS (2012) Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing. Journal of Interactive Marketing 26(2): 83-91.
43. Malhotra A, Malhotra CK, See A (2012) How to get your messages retweeted. MIT Sloan Management Review 53(2): 61-66.
44. Kwok L, Yu B (2013) Spreading social media messages on Facebook: An analysis of restaurant business-to-consumer communications. Cornell Hospitality Quarterly 54(1): 84-94.
45. Gutierrez K (2014) Studies confirm the power of visuals in elearning.
46. Alboqami H, Al-Karaghouli W, Baeshen Y, Erkan I, Evans C, Ghoneim A (2015) Electronic word of mouth in social media: The common characteristics of retweeted and favourited marketer-generated content posted on Twitter. International Journal of Internet Marketing and Advertising 9(4): 338- 358.
47. Dobele A, Lindgreen A, Beverland M, Vanhamme J, Van Wijk R (2007) Why pass on viral messages? Because they connect emotionally. Business Horizons 50(4): 291-304.
48. Golan GJ, Zaidner L (2008) Creative strategies in viral advertising: An application of Taylor's six- segment message strategy wheel. Journal of Computer-Mediated Communication 13(4): 959–972.
49. Ding Y, Phang CW, Lu X, Tan C-H, Sutanto J (2014) The role of marketer-and user-generated content in sustaining the growth of a social media brand community. The 47^th Hawaii International Conference on System Sciences, Waikoloa, HI, USA.
50. Scholz M, Dorner V, Landherr A, Probst F (2013) Awareness, interest, and final decision: The effects of user-and marketer-generated content on consumers’ purchase decisions. The 34^th International Conference on Information Systems, Milan, Italy.
51. Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of social media. Business Horizons 53(1): 59-68.
52. Becker H, Naaman M, Gravano L (2011) Beyond trending topics: Real-world event identification on Twitter. Proceedings Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain, pp. 438-441.
53. Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes Twitter users: Real-time event detection by social sensors. The 19^th International Conference on World Wide Web. Raleigh, NC, USA.
54. Ibrahim NF, Wang X, Bourne H (2017) Exploring the effect of user engagement in online brand communities: Evidence from Twitter. Computers in Human Behavior 72: 321-338.
55. Sabate F, Berbegal-Mirabent J, Cañabate A, Lebherz PR (2014) Factors influencing popularity of branded content in Facebook fan pages. European Management Journal 32(6): 1001-1011.
56. Retail innovation (2017) The number of followers top retailers have on social media.
57. Fowler K, Bridges E (2010) Consumer innovativeness: Impact on expectations, perceptions, and choice among retail formats. Journal of Retailing and Consumer Services 17(6): 492-500.
58. Lee I (2017) Big data: Dimensions, evolution, impacts, and challenges. Business Horizons 60(3): 293- 303.
59. Phelps JE, Lewis R, Mobilio L, Perry D, Raman N (2004) Viral marketing or electronic word-of-mouth advertising: Examining consumer responses and motivations to pass along email. Journal of Advertising Research 44(4): 333–348.
60. Zog Digital (2013) Anthropologie stylizes social media marketing.