Effects of Promotion Tweets on the Number of Followers in Micro-Blogging Site

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1 Effects of Promotion Tweets on the Number of Followers in Micro-Blogging Site Yi LIU ESC Rennes School of Business 2 Rue Robert d'arbrissel Rennes, France yi.liu@esc-rennes.com Bernadetta Tarigan ETH Zurich Weinbergstrasse 56/ Zurich, Switzerland btarigan@ethz.ch ABSTRACT The effectiveness of social media marketing highly depends on the number of people who follow their accounts on social media. Thus, merchants are eager to attract more followers in order to maximize the marketing effect on social media platforms. In this study, we examine how to increase the number of followers through promotion tweets in micro-blogging site. Drawing on Expectancy theory, we argue that the number of followers would increase significantly if the promotion tweets from the merchant facilitate users with more attractive deal and require lower level of efforts. We contextualize our study on the micro-blogging account of a daily-deal group-buying merchant. The number of followers was recorded on hourly basis and the promotion tweets were collected. The results indicate the attractiveness of lucky draw product from the promotion tweet has significant effect on the increase of the follower number. CCS Concepts Information systems Online shopping. Keywords Follower number; social media; micro-blogging; social commerce; group-buying. 1. INTRODUCTION The rise of social media offers companies an effective and costfree channel to advertise their products and maintain relationships with consumers [19]. In order to gain higher popularity of their posts on social media, companies would like to have more followers [18]. Some companies have successfully gained large number of followers on social media, such as 9 million followers for Samsung Mobile and 6 million followers for Starbucks Coffee, both on Twitter. However, vast number of companies suffer from the limited number of followers which restraints their social media campaigns. In practice, how to effectively attract more followers is unclear and some companies even buy followers on social media via certain methods. In academia, social media has been investigated from many perspectives, such as the adoption of social media by enterprises [3], social media content management [1], word-of-mouth (WOM) communication through social media Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. ICEC '16, August 17-19, 2016, Suwon, Republic of Korea Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM /16/08 $15.00 DOI: [8], and social media [12]. Concerning companies activities on social media, consumer participation on companies micro-blogs [19], and post popularity from companies accounts have been studied [18]. It has been found that the popularity of the post is affected by the number of followers [18]. However, the understanding of how to manage the social media contents to increase follower number remains limited. We aim to address this concern by contextualizing the research in group-buying that use micro-blogging site. We choose this context for two reasons. First, group-buying websites typically reveal the number of coupons sold for all the deals. These deals are used in promotion tweets to boost follower number on microblogging site. For example, in promotion tweets (or called lucky draw tweets), group-buying merchant can encourage social media users to follow their micro-blogging account and recommend to their social media friends. A certain number of users among them may have the chance to get a free deal. Moreover, the number of coupons sold can represent the attractiveness of the promotion tweet. Second, micro-blogging messages can be tracked and crawled from API of micro-blogging site. In our study, we analyzed a group-buying website hosted in China that utilizes a Chinese micro-blogging site (i.e., Weibo). Group-buying business in China is the most competitive in the world with hundreds of group-buying websites existing in the market [10]. Attracting a number of local merchants who wish to publish deals on their platforms is critical for group-buying websites. This study contributes to researchers in several ways. First, this study extends and tests Expectancy Theory in the social media context. Existing studies employing Expectancy Theory have centered in Information Systems research by studying the motivation to use the information system [15]. As social media and e-commerce become popular research topics in Information Systems, this study explain the behavior of social media users from motivation aspect. Second, this study adds to the existing social media literature which lacks the studies on follower number of social media users. The findings of this study could inform the social media marketers on how to boost the number of followers on social media sites via promotion contents in order to gain higher popularity of their posts on social media. 2. LITERATURE REVIEW As social media become popular platforms for both users and merchants, researchers in Information Systems and Marketing disciplines have studied the behavior of users and merchants to examine the economic values on social media. Merchants can also actively deliver social media messages users in order to increase sales. It has been found that the information richness of social media communication generated by restaurant business on Facebook positively affects its sales [4]. Micro-blogging sites, as one of the popular social media platforms which facilitates

2 information sharing and dissemination, attracts researchers to study its user behavior in recent years. It has been found that the capabilities of micro-blogs to share unique content, strengthen positive emotion, maintain interconnectivity, and enhance unidirectional relationships influence status updates of users [17]. On micro-blogging sites, companies also update statuses through their official accounts and expect the engagement with consumers. It has been found that self-congruence and partner quality affect consumers trust and commitment toward companies brands, which in turn influence participation on company micro-blogs [19]. The popularity of company posts is affected by the occurrence time and number of followers [18]. Thus, how to increase the follower number on social media is potentially to be examined. Group-buying phenomenon, especially daily deal businesses, has also attracted research interests recently. Daily deal websites feature several deals from merchants at the same time and each deal only has a relatively short span of time. By collaborating with daily deal websites, merchants could gain advertising effects and sell a large quantity of products/services in short time [10]. Consumers may regularly check the website to choose the deals. Group-buying websites could also push the information of new deals to consumers using promotions. A number of researchers focus on consumer purchasing behavior in group-buying in order to induce more buyers, such as incentive mechanisms for participations [5], purchasing satisfaction [14], altruism and reciprocity [13], herding behavior [9], the starting and ending effects of purchasing [20], and the redemption time of the deal [11]. Social media could also facilitate group-buying websites to induce more buyers. For instance, within the deal pages on groupbuying websites, the presence of the number of existing buyers does not always increase purchase intention of buyers, whereas the presence of like information from social media has a positive influence on purchase intention [6]. Thus, it is critical for group-buying merchants to have larger number of followers on social media which could lead more sales due to the active social media activities. And this study aims to investigate the increase of follower number on social media. 3. THEORETICAL BACKGROUND We propose that promotion (lucky draw) tweets from microblogging account of the company are processed by consumers, and how consumers are motivated determines the increase of follower number, based on Expectancy Theory. Expectancy Theory is intended to explain how an individual chooses between alternative forms of behavior [16]. It proposes that people will be motivated to exert effort to do things which are expected to lead to valuable or attractive outcomes [16]. According to Expectancy Theory, expectancy, instrumentality, and valence are identified as three key elements to assess the motivation degree of the person. Expectancy represents the probability that the effort will lead to acceptable performance. If the desired performance is extremely difficult to achieve, the expectancy of the people would be low. Instrumentality indicates people s estimation of the probability that the achieved performance will result in reward. And valence represents the value of the reward for the people. A person is motivated to take an action since that the person believes the action will lead to acceptable performance which could result in valuable reward. The increase of expectancy, instrumentality and value results in the increase of motivation. Expectancy Theory has been utilized in Information Systems field to explain the use of information systems. Concerning the use of information systems, Snead and Harrell [15] examine managers motivation to use Decision Support Systems (DSS) and find that the valence of the outcome associated with using DSS and associated probabilities affect the attractiveness of using DSS. In this study, we adapt expectancy theory to examine social media users action towards promotion (lucky draw) tweets from microblogging account of the company which affects the effectiveness of promotion tweets. The company expects consumers to share the promotion tweet in order to attract other consumers to follow its micro-blogging account. Thus, we use two variables, the number of shares and the number of follower increase, to represent the effectiveness of promotion tweets. In the promotion tweet, the company presents a deal which the lucky draw winner can get after finishing the specified task. Consumers normally need to follow the account and share the promotion tweet to a number of friends. In each promotion tweet, the number of lucky draw winners and the valence of the deal may be specified by the company differently. Thus, these factors affect the motivation of consumers. Our thesis states that the effectiveness of promotion tweet depends on the number of friends to be shared (expectation), the number of lucky draw winner (instrumentality), and the valence of the deal (valence). In order to attract followers, the company encourages consumers to share the promotion tweets to a number of friends on microblogging site. The more friends receive the promotion tweet shared by consumers, the higher possibility that the follower number would increase. However, the higher number of friends to be shared would also increase the task difficulty for consumers. Although consumers would like to be the lucky draw winner, a higher number of friends required to be shared increases the effort for consumers to achieve the desired performance in the task (expectancy). As a result, consumers would give up the task and not share the promotion tweets with friends, which decreases the effectiveness of promotion tweets. Thus, we use two variables to represent the effectiveness of promotion tweet, the number of retweet and the number of follower increased, and we hypothesize: H1a: The number of friends consumers should refer has negative effect on the increase of retweet number. H1b: The number of friends consumers should refer has negative effect on the follower increase. When a consumer achieves the desired task, the belief (instrumentality) that the consumer will receive the reward (lucky draw deal) also affects the effectiveness of promotion tweets. In the promotional tweet, the company indicates the number of lucky draw winners. A higher number of lucky draw winners indicated in the promotion tweet could motivate consumers to perform the task which increases the effectiveness of the promotion tweet. Thus, we hypothesize: H2a: The number of luck draw winners has positive effect on the increase of retweet number. H2b: The number of luck draw winners has positive effect on the follower increase. In the promotion tweet, the company indicates a deal from one of its current group-buying products for lucky draw winners. Consumers could be motivated by the high value and the popularity of the rewards (valence) and perform the task by sharing the promotion tweet to other friends. Accordingly, we hypothesize:

3 H3a: The valence of lucky draw deal has positive effect on the increase of retweet number. H3b: The valence of lucky draw deal has positive effect on the follower increase. 4. RESEARCH METHODOLOGY To test our hypotheses, we collected data from a daily-deal groupbuying website which regularly has promotion tweets on one micro-blogging site. Daily-deal group-buying website displays the information (e.g., discounted price, value, real-time number of coupons sold, etc.) of deals in each city which are used in promotion tweets. This information facilitates us to measure the valence of the deals. Data were collected via API of group-buying website and micro-blogging site. On micro-blogging site, we crawled the follower number of group-buying website s account on hourly basis. All tweets including contents and posting time were also crawled and saved. On group-buying website, the information (e.g., discounted price, value, real-time number of coupons sold, etc.) of all deals in that period was crawled. On micro-blogging site, we observe 295 promotion tweets from group-buying website s account. These promotion tweets have similar structure as follows: #Lucky draw promotion# XXX menu for free at XXX restaurant. You only need to retweet this promotion X number of friends. X winners will be drafted on the date of XX. In these promotion tweets, the number of friends to be shared and the number of winners are extracted. In addition, the time of the promotion tweets are also extracted, in order to check the number of followers at that specific time. Along with the main text of these promotion tweets, the number of shares for each promotion tweet is also extracted. It reflects the effectiveness of promotion tweets. Thus, the valence (i.e. the value) of the lucky draw can be measured. As we crawled the follower number on hourly basis, we could calculate the increase of follower number after and before the promotion tweet. Table 1 shows the descriptive statistics of key variables in the data. 5. RESULTS We ran regression analysis separately on two variables, the number of retweets (Table 2) and the number of followers increased (Tables 3, and 4). As we captured the number of followers on hourly basis, we ran analysis on the number of followers increased in one hour (Table 3), and two hours (Table 4), respectively. As micro-blogging users, who follow a number of accounts, may only notice the latest tweets which are shown on their first page of micro-blogging sites, we only consider the time till two hours after the tweet. In each analysis, we control the time of promotion tweet and we ran four models for different time period, model 1 for all time period, model 2 for the morning time (09:00-12:00), model 3 for the afternoon time (12:00-18:00) and model 4 for the evening time (18:00-24:00). Besides the three main variables described in the hypotheses, the number of friends referred, the number of winners and value of the deal, we also control the quantity sold and expiration time of the deal, and the number of comments for promotion tweet. Considering the results for the number of rewteets (Table 2), the number of winners has negative effect for all time period (Model 1, coef=-3.25, p<0.01) and evening period (Model 4, coef=-3.97, p<0.05). The results are not significant for the variables of value and the number of friends referred, However, the variable of the quantity sold of the deal, has positive effect for all time period (Model 1, coef=0.01, p<0.10) and evening period (Model 4, coef=0.01, p<0.05). The quantity sold of the deal indicates the popularity of the deal and micro-blogging users are likely to retweet the promotion tweet when the deal is popular. Thus, H1a and H2a are not supported, and H3a is partially supported. Considering the results for the number of followers increased, the variable of the number of friends referred has positive effect for one hour period (Table 3, Model 1, coef=2.04, p<0.05), and two hours period (Table 4, Model 1, coef=4.50, p<0.05). Thus, H1b is not supported. The possible explanation could be the maximum number of friends referred in our dataset is four, which could still be seen as a reasonable number for micro-blogging users to handle with. As it has no effect on the number of retweet, the number of followers increases more when micro-blogging users refer to more people. The number of winners weakly affects the number of followers increased in one hour period (Table 3, Model 1, coef=0.00, p<0.10). Thus, H2b is partially supported. The quantity sold of the deal positively affects the number of followers increased in the morning for one hour period (Table 3, Model 2, coef=0.01, p<0.05), and two hours period (Table 4, Model 2, coef=0.02, p<0.01). Thus H3b is partially supported. 6. DISCUSSION This study offers several implications for researchers. First, it extends and tests Expectancy Theory in social media context. The existing literature in Information Systems employing Expectancy Theory mainly investigates the motivation of employees to use new information systems [2,15]. This paper adopts Expectancy Theory to explain how to motivate social media users to start participating the social commerce by following company s social media account. Second, this study enriches social media literature. Although social media platform is attracting more attention, empirical studies, especially theoretical frameworks anchored studies, still lack. In addition, this study adds to the existing social media literature which lacks the studies on examining the follower number. Merchants expect to use social media to improve their business performance and extant social media literature examines how to manage social media to engage with social media users. For example, it has been found that social support and relationship quality affect the user s intention of future participation in social commerce[7]. However, in order to engage with users, merchants need to first reach a certain number of social media users which can be reflected by the number of followers. Moreover, the popularity of companies posts on social media depends on their number of followers [18]. This study tries to address this research gap and focus on how to increase the number of followers on social media. Third, this study contributes to the group-buying literature. The daily-deal group-buying phenomenon has attracted researchers from different fields, such as information systems and marketing. However, the existing literature on group-buying is still limited and the sustainability of group-buying business model needs to be examined [10]. Group-Buying is highly related with social commerce and the usage of social media facilitates group-buying websites to reach and engage more consumers, and finally influence purchase intention of consumers [6]. Existing studies on group-buying mostly focus on data derived from group-buying websites, such as the number of sold coupons, to determine consumer purchasing behavior on such websites [10]. This study extends group-buying research into social media context and collect data from multiple sources (sales data of group-buying deals and its social media activities).

4 This study also contributes to practitioners, especially for the merchants who manage social media for marketing and customer relationship management purposes. First, this study reveals that in order to increase the number of followers on social media platform, the company should provide attractive lucky draw products for promotion tweets. It is not necessary to provide the products with high value. Thus, the company could choose lower valued products with good sales records to motivate social media users to retweet the promotion tweets and follow the account. Second, in promotion tweets, the company could ask users to refer four friends instead of one or two friends. The increased number of friends to be referred up to four people could not affect user s motivation to retweet the promotion tweets, but increase the number of followers. Third, it finds some better time to motivate consumers to follow companies accounts on social media more effectively. The promotions tweeted around 11 am in the morning could gain better promotion effect and have the number of followers increased. 7. REFERENCES 1 Aladwani, Adel M. The 6As model of social content management. International Journal of Information Management, 34 (2014), DeSanctis, G. Expectancy Theory as an Explanation of Voluntary Use of a Decision-Support System. Psychological Reports, 52 (1983), Fosso Wamba, Samuel and Carter, Lemuria. Social Media Tools Adoption and Use by SMES: An Empirical Study. Journal of Organizational and End User Computing, 26, 2 (2014), Goh, K.Y., Heng, C.S., and Lin, Z. Social media brand community and consumer behavior: Quantifying the relative impact of user- and marketer-generated content. Information Systems Research, 24, 1 (2013), Kauffman, R.J., Lai, H., and Ho, C.T. Incentive Mechanisms, fairness and participation in online group-buying auctions. Electronic Commerce Research Applications, 9, 3 (2010), Kuan, K.K.Y., Zhong, Y., and Chau, P.Y.K. Informational and Normative Social Influence in Group-Buying: Evidence from Self-Reported and EEG Data. Journal of Management Information Systems, 30, 4 (2014), Liang, T.P., Ho, Y.T., Li, Y.W., and Turban, E. What Drives Social Commerce: The Role of Social Support and Relationship Quality. International Journal of Electronic Commerce, 16, 2 (2012), Liu, Y. Word of mouth for movies: its dynamics and impact on box office revenue. Journal of Marketing, 70, 3 (2006), Liu, Y. and Sutanto, J. Buyers purchasing time and herd behavior on deal-of-the-day group-buying websites. Electronic Markets, 22, 2 (2012), Liu, Y. and Sutanto, J. Online Group-Buying: Literature Review and Directions for Future Research. DATA BASE for Advances in Information Systems, 46, 1 (2015). 11 Luo, X., Andrews, M., Song, Y., and Aspara, J. Group-Buying Deal Popularity. Journal of Marketing, 78, 6 (2014), Luo, X., Zhang, J., and Duan, W. Social media and firm equity value. Information Systems Research, 24, 1 (2013), Shiau, W.L. and Chau, P.Y.K. Does altruism matter on online group buying: Perspectives from egotistic and altruistic motivation. Information Technology & People, 28, 3 (2015), Shiau, W.L. and Luo, M.M. Factors affecting online group buying intention and satisfaction: A social exchange theory perspective. Computers in Human Behavior, 28, 6 (2012), Snead, Ken C. and Harrell, Adrian M. An Application of Expectancy Theory to Explain a Manager's Intention to Use a Decision Support System. Decision Sciences, 25, 4 (1994), Vroom, V.H. Work and Motivation. John Wiley and Sons, New York, London, and Sydney, Wang, C., Jin, X., Zhou, Z., Fang, Y., Lee, M.K.O., and Hua, Z. Effect of perceived media capability on status updates in microblogs. Electronic Commerce Research and Applications, 14 (2015), Zadeh, A.H. and Sharda, R. Modeling brand post popularity dynamics in online social networks. Decision Support Systems, 65 (2014), Zhang, K.Z.K., Benyoucef, M., and Zhao, S.J. Consumer participation and gender differences on companies microblogs: A brand attachment process perspective. Computers in Human Behavior, 44 (2015), Zhou, G., Xu, K., and Liao, S.Y. Do starting and ending effects in fixed-price group-buying differ. Electronic Commerce research and Applications, 12, 2 (2013),

5 Table 1 Descriptive statistics Variable Min. Max. Mean S.D. Lucky draw tweet info Number of retweet Number of comment Follower number info Follower number (at the time of tweet) Lucky draw tweet content info Number of friends shared Number of winners Lucky draw deal info Quantity sold Value Expiration time of coupon

6 Table 2 Results for Number of Retweets Model 1 Model 2 Model 3 Model 4 (all) (morning) (afternoon) (evening) Variables Coef. SE Coef. SE Coef. SE Coef. SE (Intercept) ns ns ns ns factor(time) ns ns factor(time) * ns factor(time) * factor(time) ** factor(time) * factor(time) ** factor(time) ** ns factor(time) * ns factor(time) * ns factor(time) * ns factor(time) ** ns factor(time) *** ns factor(time) * ns factor(time) ** ns factor(time) * ns factor(time) ** ns factor(time) ** factor(time) ** ns factor(time) * ns factor(time) ** ns factor(time) ns factor(time) ** ns factor(time) ** ns factor(time) * ns factor(time) * ns factor(time) ** ns factor(time) * ns factor(time) ** ns follower number ns ns ns ns value ns ns ns ns expiration_time ns ns ns ns number_of_friends_referred ns ns ns ns number_of_winners ** ns ns * quantity_sold ns ns * new_number_of_comment *** *** *** *** R-squared ***p<0.001, **p<0.01, *p<0.05,. p<0.1, ns = not significant

7 Table 3 Results for Number of Followers in One Hour Model 1 (all) Model 2 (morning) Model 3 (afternoon) Model 4 (evening) Variables Coef. SE Coef. SE Coef. SE Coef. SE (Intercept) ns ns ns ns factor(time) ns factor(time) ns ns factor(time) factor(time) * factor(time) ns factor(time) ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns * factor(time) ns ns factor(time) ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns new_number_of_comment ns ns ns ns new_number_of_retweet ns ns ns ns value ns ns ns ns expiration_time ns ns number_of_friends_referred * ns ** ns number_of_winners ns ns ns quantity_sold ns * ns ns R-squared ***p<0.001, **p<0.01, *p<0.05,. p<0.1, ns = not significant

8 Table 4 Results for Number of Followers in Two Hours Model 1 (all) Model 2 (morning) Model 3 (afternoon) Model 4 (evening) Variables Coef. SE Coef. SE Coef. SE Coef. SE (Intercept) ns ns ns ns factor(time) * factor(time) ns ns factor(time) * * factor(time) * * factor(time) * factor(time) factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns * factor(time) ns ns factor(time) ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns ns factor(time) ns * factor(time) ns ns new_number_of_comment ns * ns ns new_number_of_retweet ns * ns ns value ns ns ns ns expiration_time ns * ** ns number_of_friends_referred * ** ns number_of_winners ns ns ns ns quantity_sold ns ** ns ns R-squared ***p<0.001, **p<0.01, *p<0.05,. p<0.1, ns = not significant