The many faces of social media in business and economics research: Taking stock of the literature and looking into the future

Tumasjan, Andranik Article — Published Version The many faces of social media in business and economics research: Taking stock of the literature and looking into the future Journal of Economic Surveys Provided in Cooperation with: John Wiley & Sons Suggested Citation: Tumasjan, Andranik (2023) : The many faces of social media in business and economics research: Taking stock of the literature and looking into the future, Journal of Economic Surveys, ISSN 1467-6419, Wiley, Hoboken, NJ, Vol. 38, Iss. 2, pp. 389-426, https://doi.org/10.1111/joes.12570 This Version is available at: https://hdl.handle.net/10419/290353 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ DOI: 10.1111/joes.12570 SPECIAL ISSUE ARTICLE The many faces of social media in business and economics research: Taking stock of the literature and looking into the future Andranik Tumasjan Gutenberg School of Management and Economics, Johannes Gutenberg University Mainz, Mainz, Germany Correspondence Andranik Tumasjan, Johannes Gutenberg University Mainz, Jakob-Welder-Weg 9, 55128 Mainz, Germany. Email: [email protected] Abstract Since their inception more than 15 years ago, social media have become a vibrant research topic in business and economics research. This article presents an integrative literature review taking stock of and showing the many faces of social media in extant research. Based on N=1419 articles published in the leading peer-reviewed business and economics journals in the years 2008–2022, we identify and describe seven overarching research themes, namely, social media as a: (1) market-oriented interaction hub, (2) resource-oriented interaction hub, (3) information market, (4) innovation and business venturing hub, (5) societal challenge, (6) political hub, and (7) data source. Finally, we derive a research agenda to stimulate future research on this increasingly important topic. KEYWORDS social media, social network, social networking, Facebook, Twitter, Weibo This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2023 The Authors. Journal of Economic Surveys published by John Wiley & Sons Ltd. JEconSurv.2024;38:389–426. wileyonlinelibrary.com/journal/joes 389 390 TUMASJAN 1 INTRODUCTION Over the past more than 15 years, social media (i.e., “platforms on which people build networks and share information and/or sentiments”, Li et al., 2021, p. 52; e.g., Twitter, Facebook, TikTok, and Weibo) have transformed into a global phenomenon. Social media now have more than 4.76 billion active users, representing over 92% of the 5.16 billion worldwide internet users and more than 59% of the world’s human population (Statista, 2023). Social media have also become an essential part of many people’s lives in terms of time spent, with a daily average use time of 147 min (Statista, 2022). Conceptualized initially as social networks for connecting with friends, social media today serve many more functions, including sharing personal information with followers, discussing products and services, and engaging in political deliberation. Accordingly, in recent years, business and economics scholars have been increasingly engaging in social media research to understand the business and economic implications of this phenomenon. While business and economics research in the early days of social media focused on the social networking and marketing aspects of social media, today, we witness an enormous bandwidth of functional perspectives that scholars have employed in this context. Thus, the variety of social media research is wide, spanning from obvious and well-researched topics, such as wordof-mouth in social media marketing (Dost et al., 2019), to niche applications, such as using the timing of the US president’s Twitter activity to proxy his sleeping behavior as a predictor of his performance (Almond & Du, 2020). This article aims to provide a broad multidisciplinary overview of the current social media research landscape in business and economics and to showcase the many faces of social media research, including research in finance, accounting, marketing, information systems, innovation, entrepreneurship, and human resource management. Since extant social media-related reviews have focused on surveying the literature within selected sub-fields and taken a narrow disciplinary approach – for example, concentrating on marketing (Cartwright et al., 2021; Lamberton & Stephen, 2016), innovation (Bhimani et al., 2019), trust (Håkansson & Witmer, 2014), business process improvement (Nascimento & Silveira, 2017), and fake news research (George et al., 2021) – the present article contributes to the literature by deliberately taking a broad and integrative approach to identify the many different facets of social media research (Cronin & George, 2023). Thus, this integrative review differs from the extant disciplinary and narrow review articles by zooming out and providing an inclusive perspective on the social media research landscape to account for the phenomenon’s multifacetedness. Thereby, this review advances the field by connecting insights from multiple disciplines and highlighting the roads less traveled to inspire novel empirical and conceptual research across disciplines. The remainder of the article is structured as follows. First, we describe our literature review method. Second, we provide a bibliometric overview of the current literature. Third, the narrative review part identifies seven main research themes and reviews the major contributions and topics within these themes. Finally, the article concludes by highlighting under-researched aspects cutting across the themes and, in closing, deriving directions for future research. 2 METHOD Our literature review follows an established three-step iterative process (e.g., Theurer et al., 2018). First, we define and identify the relevant literature. Second, we perform structural and content TUMASJAN 391 analyses of the literature. Third, we map and integrate the literature by clustering it into research themes. 2.1 Step 1: Definition and identification of the relevant literature In line with common practice in the field, our literature review includes only English-language articles published in peer-reviewed academic business and economics journals. To capture the relevant peer-reviewed literature in both business and economic research, we relied on two eminent journal lists – namely, (1) for business research, the Academic Journal Guide 2021 by the Chartered Association of Business Schools (“ABS list”; N=1703 journals), and (2) for economics research, the Tinbergen Journal list (“TI journal list”; N=399 journals) in its latest 2016 version. Whereas both lists contain business and economics journals, the focus of the former is on business journals, whereas the focus of the latter is on economics journals. Due to the large number of journals on both lists, the number of journals needed to be restricted to arrive at a manageable number of articles. Therefore, on the ABS list, only journals ranked as 4* or 4, and on the TI journal list only journals with a score of at least 0.3 were selected. Moreover, since the ABS list also contains journals going farther beyond business and economics (e.g., general experimental psychology), we further limited our journal selection to the research area “business economics” in the Web of Science (WoS; i.e., the literature database for this literature review, see below). Due to a partial overlap between the two lists, some journals ranked below 4 on the ABS list are also included in the sample. To identify social media research broadly and cover the most important social media sites worldwide, we used the following search terms in Clarivate’s WoS database: “social media,” “social networking sit*,” “Facebook,” “YouTube,” “Instagram,” “TikTok,” “Douyin,” “Snapchat,” “Weibo,” “Qzone,” “Kuaishou,” “Pinterest,” “Reddit,” “Twitter,” “Tweets,” “Quora,” “Tieba,” “LinkedIn,” “Picsart,” “Likee,” “Stack Exchange.” One of the search terms needed to be mentioned in at least one of the following fields: “Title,” “Abstract,” “Author Keywords,” and “Keywords Plus” (i.e., reflecting the overarching search category “Topic” in WoS). We used the journals’ ISSN (print) to identify articles in the respective journals. The search was finalized in March 2023 and included all articles published until December 31st, 2022 (i.e., with the publication year 2022 and excluding “early access” articles without a publication year). Using this search procedure and excluding duplicate articles resulting from the combination of both lists, our final sample consists of N=1419 articles. These articles span a time period of 14 years, from 2008 to 2022. Our integrative review sample deliberately contains all articles resulting from the abovementioned search procedure, including, for example, editorials, comments, and articles where social media plays an important role but is not necessarily the sole or primary focus. 2.2 Step 2: Structural and automated content analyses To get an overview of the structural properties and general content of the identified research, the articles were analyzed regarding their main contents (word cloud analysis), number of publications over time, total and per year citation numbers, and the journals with the most social media articles. Figure 1displays a word cloud based on all keywords – except “social media” – of the identified articles, where the size of each word reflects its frequency among the keywords (WordClouds.com, 2023). 392 TUMASJAN FIGURE 1 Word cloud based on all articles’ keywords. 2 2 14 12 28 39 57 76 97 117 131 160 188 246 250 0 50 100 150 200 250 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 FIGURE 2 Number of social media articles per year over time. TUMASJAN 393 As evident in Figure 1, the most important words include information, management, communication, performance, technology, content, networks, data, and behavior, reflecting the focus of most social media research in business and economics. Notably, many of the largest words are related to marketing and communication (e.g., customer, product, marketing, brand), giving an indication of the share of research on this topic. Further important words include investor, sentiment, returns, communities, knowledge, news, Facebook, and Twitter, indicating further well-researched topics. In sum, the word cloud documents the variety of topics in social media research in business and economics, while revealing the well-researched focus areas. To examine the publication frequency over time, Figure 2plots the number of articles published per year. As expected, overall, there has been a steady growth in the number of articles, especially in the past few years. The most frequently cited articles are displayed in Table 1(total number of citations) and Table 2 (citations per year). Reflecting the picture of the word cloud, many of the most frequently cited articles are in the field of marketing. However, articles on social media in the context of politics, entrepreneurship and innovation, financial markets, and even recent research in the context of the COVID-19 pandemic are among the most cited publications. Finally, Table 3displays the journals with the highest number of articles on social media. This table shows the broad bandwidth of business and economics fields, reflecting the variety of research perspectives scholars have taken when investigating the phenomenon of social media. 2.3 Step 3: Identification of research themes After a detailed content analysis of the literature base, the articles were classified according to their content focus to identify the main research themes. This iterative process resulted in the identification of seven main research themes that reflect the many faces of social media research: 1. Social media as a market-oriented interaction hub: Engaging with consumers and other stakeholders, focusing on marketing and sales topics, such as word-of-mouth, branding, consumer engagement, sales promotion, and market intelligence. 2. Social media as a resource-oriented interaction hub: Engaging with (potential) employees and other professionals, including research on human resources management (e.g., applicant screening and recruitment), professional discussion forums, corporate blogging, and enterprise social media (e.g., knowledge management). 3. Social media as an information market: Predicting financial market and firm performance outcomes, focusing on finance topics, such as investor and general population sentiment and financial market outcomes (e.g., stock returns). 4. Social media for innovation and business venturing, including innovation management (e.g., idea generation communities) and entrepreneurship topics (e.g., crowdfunding). 5. Social media as a societal challenge, focusing on the societal problems associated with social media, such as cyberbullying, social media addiction, and fake news. 6. Social media as a hub for political deliberation and action, including research on social media in the context of public organizations and political processes (e.g., elections). 7. Social media as a data source, including research viewing social media primarily from a data collection perspective, allowing scholars to measure otherwise hard-to-assess phenomena (e.g., personality characteristics of executives and relationships between countries). 394 TUMASJAN TABLE 1 Most frequently cited articles (total). Authors Year Title Journal Total citations Allcott, H.; Gentzkow, M. 2017 Social media and fake news in the 2016 election Journal of Economic Perspectives 1780 Xiang, Z.; Gretzel, U. 2010 Role of social media in online travel information search Tourism Management 1464 Trusov, M.; Bucklin, R.E.; Pauwels, K. 2009 Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site Journal of Marketing 1201 Verhoef, P.C.; Kannan, P.K.; Inman, J.J. 2015 From multi-channel retailing to omni-channel retailing: Introduction to the special issue on multi-channel retailing Journal of Retailing 1026 Kozinets, R.V.; de Valck, K.; Wojnicki, A.C.; Wilner, S.J.S. 2010 Networked narratives: Understanding word-of-mouth marketing in online communities Journal of Marketing 970 Nambisan, S.; Lyytinen, K.; Majchrzak, A.; Song, M. 2017 Digital innovation management: Reinventing innovation management research in a digital world MIS Quarterly 796 Stieglitz, S.; Dang-Xuan, L. 2013 Emotions and information diffusion in social media-sentiment of microblogs and sharing behavior Journal of Management Information Systems 751 Belk, R.W. 2013 Extended self in a digital world Journal of Consumer Research 709 Goh, K.Y.; Heng, C.S.; Lin, Z.J. 2013 Social media brand community and consumer behavior: Quantifying the relative impact of userand marketer-generated content Information Systems Research 693 Nambisan, S. 2017 Digital entrepreneurship: Toward a digital technology perspective of entrepreneurship Entrepreneurship Theory and Practice 676 Hennig-Thurau, T.; Malthouse, E.C.; Friege, C.; Gensler, S.; Lobschat, L.; Rangaswamy, A.; Skiera, B. 2010 The impact of new media on customer relationships Journal of Service Research 643 (Continues) TUMASJAN 395 TABLE 1 (Continued) Authors Year Title Journal Total citations Tilson, D.; Lyytinen, K.; Sorensen, C. 2010 Digital infrastructures: The missing IS research agenda Information Systems Research 574 Munar, A.M.; Jacobsen, J.K.S. 2014 Motivations for sharing tourism experiences through social media Tourism Management 531 Liu, Z.W.; Park, S. 2015 What makes a useful online review? Implication for travel product websites Tourism Management 516 Krasnova, H.; Spiekermann, S.; Koroleva, K.; Hildebrand, T. 2010 Online social networks: Why we disclose Journal of Information Technology 496 Nambisan, S.; Wright, M.; Feldman, M. 2019 The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes Research Policy 442 Aral, S.; Walker, D. 2011 Creating social contagion through viral product design: A randomized trial of peer influence in networks Management Science 435 Hollebeek, L.D.; Srivastava, R.K.; Chen, T. 2019 S-D logic-informed customer engagement: Integrative framework, revised fundamental propositions, and application to CRM Journal of the Academy of Marketing Science 430 Kumar, A.; Bezawada, R.; Rishika, R.; Janakiraman, R.; Kannan, P.K. 2016 From social to sale: The effects of firm-generated content in social media on customer behavior Journal of Marketing 413 Parasuraman, A.; Colby, C.L. 2015 An updated and streamlined technology readiness index: TRI 2.0 Journal of Service Research 408 396 TUMASJAN TABLE 2 Most frequently cited articles (citations per year). Authors Year Title Journal Citations per year Allcott, H.; Gentzkow, M. 2017 Social media and fake news in the 2016 election Journal of Economic Perspectives 296.7 Nambisan, S.; Lyytinen, K.; Majchrzak, A.; Song, M. 2017 Digital innovation management: Reinventing innovation management research in a digital world MIS Quarterly 132.7 Verhoef, P.C.; Kannan, P.K.; Inman, J.J. 2015 From multi-channel retailing to omni-channel retailing: Introduction to the special issue on multi-channel retailing Journal of Retailing 128.3 Nambisan, S. 2017 Digital entrepreneurship: Toward a digital technology perspective of entrepreneurship Entrepreneurship Theory and Practice 112.7 Xiang, Z.; Gretzel, U. 2010 Role of social media in online travel information search Tourism Management 112.6 Nambisan, S.; Wright, M.; Feldman, M. 2019 The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes Research Policy 110.5 Hollebeek, L.D.; Srivastava, R.K.; Chen, T. 2019 S-D logic-informed customer engagement: Integrative framework, revised fundamental propositions, and application to CRM Journal of the Academy of Marketing Science 107.5 Kellogg, K.C.; Valentine, M.A.; Christin, A. 2020 Algorithms at work: The new contested terrain of control Academy of Management Annals 103.3 Altig, D.; Baker, S.; Barrero, J.M.; Bloom, N.; Bunn, P.; Chen, S.; Davis, S.J.; Leather, J.; Meyer, B.;Mihaylov,E.;Mizen,P.; Parker,N.;Renault,T.; Smietanka, P.; Thwaites, G. 2020 Economic uncertainty before and during the COVID-19 pandemic Journal of Public Economics 98.7 Appel, G.; Grewal, L.; Hadi, R.; Stephen, A.T. 2020 The future of social media in marketing Journal of the Academy of Marketing Science 94.0 (Continues) TUMASJAN 403 2015), idea generation (van Osch & Bulgurcu, 2020), boundary spanning (Van Osch & Steinfield, 2016,2018), and new hire socialization (Koch et al., 2012; Leidner et al., 2018). 3.3 Theme 3: Social media as an information market: Predicting financial market and firm performance outcomes A large body of research has investigated social media as an information hub that can be used to predict financial market outcomes, such as stock prices, market indices, and firm performance (e.g., Tobin’s q). This research can be broadly divided into three strands using three different kinds of information signals to predict financial market outcomes: (1) research focusing on investor sentiment; (2) research focusing on information posted by firms’ and firm executives’ social media accounts; and (3) research focusing on using social media data as a measure of the general populations’ sentiment (e.g., general mood) as information signals to predict financial market outcomes. Investor sentiment as an information signal. Most research by far has focused on predicting financial market indicators, such as stock returns, trading volume, and volatility, using investor sentiment as an information signal (e.g., Affuso & Lahtinen, 2019; Cookson & Niessner, 2020;Farrell et al., 2022;Jiaoetal.,2020; Rakowski et al., 2021;Renault,2017; Sprenger, Sandner et al., 2014; Sprenger, Tumasjan et al., 2014). The empirical evidence mainly demonstrates positive associations between social media (e.g., Twitter, used by most studies) message sentiment and further message characteristics (e.g., volume and disagreement) and financial market indicators. For instance, based on 250,000 stockrelated Tweets from 2010, an early study by Sprenger, Tumasjan, et al. (2014) finds significant relationships between the Twitter message characteristics of message sentiment, volume, and disagreement on the one hand and the stock characteristics of stock returns, trading volume, and volatility on the other hand. Similarly, Affuso and Lahtinen (2019) find economically significant effects of sentiment on daily returns, such that negative sentiment has a larger impact than positive sentiment. Although microblog sentiment seems to be mostly influenced by market movements, there is evidence for a bidirectional causality between sentiment and stock returns (Deng et al., 2018). Likewise, in a quasi-natural experiment using Twitter outages, Rakowski et al. (2021) find that Twitter influences stock trading. However, other studies do not provide evidence in favor of a causal interpretation (e.g., Jiao et al., 2020). Whereas some research suggests that traditional news media contain genuine news and precede social media signals that, in turn, seem to repeat and discuss the traditional news’ information (Jiao et al., 2020; Peng, Zhang, et al., 2022), other research finds a two-way flow whereby the influence of social media information on traditional media is stronger than vice versa (Milas et al., 2021). However, in the former evidence, certain traders view repeated social media signals as genuine new information and trade on them, which is consistent with a “correlation neglect” or “echo chamber” effect interpretation (Jiao et al., 2020, p. 64). Relatedly, Fan et al. (2020)examine Tweets posted by automated social media bots, finding significant associations between such bot Tweets and stock returns, trading volume, and volatility in the context of FTSE 100 firms. Their findings suggest that bots play a seeding role in enhancing noise trading. Furthermore, they point to the importance of establishing social media codes of practice to prevent the potential spread of misinformation that retail investors are especially sensitive to. However, there is also research finding no economically meaningful relationships between Twitter information and stock outcomes (e.g., Behrendt & Schmidt, 2018; Lachanski & Pav, 2017). 404 TUMASJAN For instance, Behrendt and Schmidt (2018) take an intraday perspective to study individual-level stock return volatility and also find significant co-movements of intraday volatility and Tweet information for all Dow Jones Industrial Average firms. However, they also find that the effects are economically negligible. Thus, professional investors may not benefit from using Twitter information for their forecasting in intraday and individual-level stock trading. There is now considerable evidence that the social media sentiment effect (a) is driven by retail and novice investor activity and (b) matters more for firms with higher levels of information asymmetry, such as small firms and firms with less coverage (e.g., Bartov et al., 2018;Dingetal.,2020; Peng, Zhang, et al., 2022; Rakowski et al., 2021;Renault,2017). For instance, in the context of the microblogging platform StockTwits, Renault (2017, p. 25) finds that “the first half-hour change in investor sentiment predicts the last half-hour S&P 500 index ETF return”, and, having investigated users’ investment approach, he concludes that there is direct evidence of intraday level noise trading driven by sentiment. Notably, the pattern whereby social media sentiment matters more for smaller firms and firms with higher levels of information asymmetry is also evident in the context of firm-initiated social media activity which will be reviewed in the following paragraphs. Information posted by firms and firm executives as an information signal. Fewer studies focus on the effects of firms’ and firm executives’ social media activity on financial market outcomes (e.g., Al Guindy, 2021; Cao et al., 2021; Chung et al., 2020; Feng & Johansson, 2019; Kim & Youm, 2017; Lacka et al., 2022). Overall, these studies document significant relationships between firmgenerated content and financial market indicators. For example, Lacka et al. (2022) demonstrate that firm-generated Twitter content impacts stock prices in the context of S&P 500 information technology firms. Interestingly, Tweets comprising both valence (positive or negative) and subject matter information regarding consumer or competitor orientation had permanent price impacts, with negative-valence Tweets regarding competitors having the largest permanent price impact. In contrast, Tweets comprising only one type of information exhibited only temporary price information (Lacka et al., 2022). Echoing this finding, Cao et al. (2021) show that firms disclosing negative news about their industry peers on Twitter experience abnormal returns over the market and industry, suggesting that negative disclosures about competitors function as positive self-disclosures for the publishing firms. Studying firm responses on Facebook business pages, Chung et al. (2020)findthatfirms’ responses to negative customer messages (but not firms’ self-initiated posts and responses to positive customer messages) are positively related to firm performance, as indicated by abnormal returns and Tobin’s q. Similar to the findings in the context of investor sentiment and investor attention, research focusing on firm and firm executive information finds that social media sentiment especially matters in the context of small firms and firms characterized by comparatively large information asymmetry (e.g., Al Guindy, 2021; Blankespoor et al., 2014; Feng & Johansson, 2019). In this vein, using all NYSE, AMEX, and NASDAQ listed firms, Al Guindy (2021) demonstrates that while firms that post (versus do not post) information on social media have a lower cost of equity capital, this effect is even more pronounced for firms with high levels of information asymmetry—that is, smaller firms and firms with few analyst followings and few institutional holdings. Similarly, in the context of the Chinese microblogging platform Sina Weibo, Feng and Johansson (2019) show that for firms wherein a board chair possesses a Weibo account, there is more firm-specific information contained in stock prices, and this effect is stronger for firms that are smaller, newly listed, and have less analyst coverage. Generally, research has established that firms strategically use social media as a channel for financial information disclosure and dissemination TUMASJAN 405 and that investors indeed pay attention to this information (Elliott et al., 2018;Heetal.,2022; Jung et al., 2018; Mazboudi & Khalil, 2017;Nekrasovetal2022.;Yang&Liu,2017). Social media data measuring the general population’s and consumer sentiment as an information signal. Several studies have used social media sentiment as an indicator reflecting information about external events or entities (e.g., Abu Bakar et al., 2014; Danbolt et al., 2015; Huang, 2018; Lehrer et al., 2021;Nguyenetal.,2020). For instance, Abu Bakar et al. (2014) studied the “Monday effect” (i.e., that returns are, on average, negative on Mondays) using mood data from Facebook (i.e., the Gross National Happiness index; GNH) and demonstrated that the aggregated mood contained in Facebook users’ status updates significantly predicts Monday returns. Moreover, they show that the Monday effect disappears when controlling for mood, providing further evidence that mood may drive the Monday effect. Using the same sentiment indicator from Facebook, Danbolt et al. (2015) find a positive relationship between Facebook’s GNH and bidder announcement returns. Focusing on consumer sentiment, Huang (2018) shows that more positive consumer opinions sourced from 14.5 million Amazon.com product reviews from 2004 to 2015 are positively associated with abnormal stock returns, revenues, and earnings surprises. Regarding consumer confidence measurement, Lehrer et al. (2021) provide evidence that Twitter-based consumer confidence indeed improves the out-ofsample forecast of the traditional Conference Board’s consumer confidence index (CCI). Together, these and related studies provide substantial support for the notion that social media-based consumer sentiment measures can forecast financial market outcomes. 3.4 Theme 4: Social media for innovation and business venturing There has also been substantial interest in social media as a platform for facilitating innovation (Candi et al., 2018;Marionetal.,2014; Roberts et al., 2016; Safadi et al., 2021; Schlagwein & Hu, 2017) and business venturing (Fischer & Reuber, 2014; Greenwood & Gopal, 2015;Meureretal., 2022; Schou et al., 2022; Smith et al., 2017; Veer et al., 2022). In the research stream on innovation, most research has concentrated on investigating social media-based idea-generation communities and co-creation of new products and services with consumers, while in the research stream on business venturing, studies have focused on social media in the context of crowdfunding and as a facilitator of entrepreneurial processes. Innovation. In sum, the empirical evidence suggests that social media use is positively related to firms’ innovation performance (Lam et al., 2016; Roberts et al., 2016) and absorptive capacity (Schlagwein & Hu, 2017). In this vein, a range of studies has investigated firms’ social media use in new product development (Du et al., 2016; Katona, 2015), idea generation (Stanko, 2016;Zhu et al., 2019), and customer co-creation (Bosch-Sijtsema & Bosch, 2015; Dong & Wu, 2015; Suseno et al., 2018), finding positive effects overall, while some earlier research had suggested that social media were not helpful for new product development (Marion et al., 2014). Notably, we know much less about the dark side of social media-based idea-generation activities and co-creation. For instance, Gatzweiler et al. (2017), representing one of the few studies investigating negative aspects in social-media-based idea generation activities, conceptualize and examine “destructive deviant co-creation” (e.g., malicious protests, ridicule, and mocking), which poses a substantial risk for the hosting firm. However, they also shed light on “constructive deviant content” (e.g., provocative, humorous, and norm-violating content), which, in turn, may lead to positive outcomes, such as challenging and further developing initial ideas. 406 TUMASJAN Business venturing. Studies within this theme have often investigated the role of social media in the context of crowdfunding (Hong et al., 2018;Lietal.,2017; Thies et al., 2016). The empirical evidence shows that social media may act as an amplifier of crowdfunding projects, leading to higher levels of funding success (Li et al., 2017; Thies et al., 2016). Moreover, social media have also been shown to be helpful in different stages of the entrepreneurial process. For example, growth-oriented entrepreneurial firms’ communication on Twitter (e.g., conveying positive affect and relational orientation) can increase potential customers’ perceptions of quality and distinctiveness (Fischer & Reuber, 2014). Moreover, using Reddit as an empirical context, Meurer et al. (2022) identify four affordances in the realm of social support that social media provide to entrepreneurs: resolving problems, reframing problems, reflecting on situations, and refocusing thinking and efforts. Likewise examining Reddit, Schou et al. (2022) show that social media may act as learning spaces for entrepreneurs where they get the opportunity to develop skills and knowledge, challenge each other, collect ideas, and talk about their fears and uncertainties. In the context of new venture internationalization, research demonstrates that social media help entrepreneurial ventures position themselves in strategic networks, which in turn helps them to address the liabilities of newness, smallness, and foreignness (Fraccastoro et al., 2021). Moreover, social networking sites may help firms become more internationally oriented (Williams et al., 2020). Social media have also been shown to act as a signal in the context of entrepreneurial finance. For instance, Tumasjan et al. (2021) show that Twitter sentiment about technologies (e.g., artificial intelligence, 5G, and blockchain technology) predicts VCs’ valuations of startups whose business models build on these technologies. However, Twitter sentiment does not predict longterm startup success in terms of acquisitions and initial public offerings (IPO), whereas patents predict both valuations and long-term startup success. Similarly, startup social media activity has been shown to be related to venture capital financing (Aggarwal & Singh, 2013; Nigam et al., 2020) and IPO value, with social media use being even more effective for B2C firms and firms with more coverage by traditional media (Mumi et al., 2019). Interestingly, the social media presence of entrepreneurs also reflects entrepreneurial failure, with Twitter messages becoming less emotional in tone and indicating more psychological distress but also more self-assurance and reflection after business failure (Fisch & Block, 2021). 3.5 Theme 5: Social media as a societal challenge Increasingly, especially in recent years, research has come to see social media as a challenge for society and, accordingly, focused on topics such as well-being, cyberbullying, harassment, addiction, loneliness, privacy issues, social engineering, fake news, and misinformation. These studies can be grouped into two broader research streams, namely (1) societal challenges at the individual level (e.g., cyberbullying, well-being, and addiction) and (2) societal challenges at the network level (e.g., fake news). Individual level. Studies investigating social media as a societal challenge at the individual level have concentrated on cyberbullying (Chan et al., 2019; Wong et al., 2021), well-being (Allcott et al., 2020; Krasnova et al., 2015), addiction (Allcott et al., 2022; Bhargava & Velasquez, 2021; Osatuyi & Turel, 2020), and privacy issues (Acquisti et al., 2020; Cascavilla et al., 2018; Ong et al., 2022;Quach et al., 2022). Overall, research on cyberbullying has focused more on individual-level psychological predictors (e.g., cognitive processes of bullies) and macro-level predictors (e.g., environmental factors) than on IT artifacts; however, the latter have been the focus of more recent research (Lowry TUMASJAN 407 et al., 2016). For instance, in the field of cyberbullying, studies have investigated social media affordances (e.g., information retrieval about users, editability to deny bullying acts) and design features (e.g., identifiability and monitoring awareness) as predictors of bullying (Chan et al., 2019; Lowryetal.,2017). Regarding well-being, most studies show adverse effects of social media on individuals’ wellbeing(Allcottetal.,2020; Bao et al., 2021; Braghieri et al., 2022; McDool et al., 2020), while only a few studies show positive effects (Wenninger et al., 2019). For instance, in a randomized experiment in the United States, Allcott et al. (2020) find that deactivating one’s Facebook account led to higher levels of subjective well-being. Braghieri et al. (2022) demonstrate in a quasi-experimental setting that the introduction of Facebook negatively impacted student mental health, identifying the facilitation of unfavorable social comparisons as a mechanism. Likewise, in China, Bao et al. (2021) find that social media browsing led to decreased subjective well-being, driven mainly by relative income and social comparison. Similarly, envy was identified as a predictor of reduced well-being (Krasnova et al., 2015). In contrast, Wenninger et al. (2019) show that social media can also positively affect well-being, specifically as a result of targeted reciprocity-evoking activities (e.g., chatting, giving and receiving feedback). On the other hand, Castellacci and Tveito (2018), drawing on a comprehensive literature review, suggest that it is the interaction between personal characteristics and individuals’ activities in different life domains that explains the differential effect of a more positive versus negative impact of the Internet in general, and social media in particular, on individuals’ well-being. Importantly, recent research has increasingly attended to social media addiction, noting that social media are intentionally designed to be addictive, creating ethical concerns (Bhargava & Velasquez, 2021). For instance, Chapman et al. (2021) show how Foursquare seduces its users to gain control over users’ behavior in order to extract economic value, and Hoong (2021) demonstrates that individuals indeed overuse social media (i.e., use them more than they desire) because of limited self-control. Therefore, scant but emerging research has focused on designing interventions to reduce excessive social media use (Osatuyi & Turel, 2020). Finally, in the area of privacy research, the extant findings suggest that rather than being careless, most users are not aware of privacy issues (Ozdemir et al., 2017; Tow et al., 2010)andarenot capable of attaining the desired levels of privacy (Acquisti et al., 2020). In this regard, research has developed systems to detect unintentional information leakage (e.g., in the context of Facebook comments; Cascavilla et al., 2018). Network level. The scholarly interest in the mechanisms underlying fake news has been skyrocketing in the past years. Accordingly, many studies have investigated the triggers, actions, and outcomes related to fake news (for a recent multi-disciplinary review, see George et al., 2021). In business and economics, fake news research has focused on the spread, detection, and mitigation of fake news (Wang et al., 2022; Wei et al., 2022). For instance, Horner et al. (2021) demonstrate that strong emotional reactions toward fake news headlines were predictive of increased interaction and sharing behavior related to the fake news. Moreover, the participants were more likely to believe those (fake) news stories that aligned with their own political views (see also Kim & Dennis, 2019). This pattern was also found in related research documenting increased sharing of fake news conforming to one’s political views (Turel & Osatuyi, 2021). Increasingly, not only human actors but also bots (Salge et al., 2022) are spreading fake news (Hajli et al., 2022; Ross et al., 2019), thereby creating additional challenges in fake news mitigation interventions. In this regard, several mitigation interventions have been examined, including measures such as fact-checking, fake news reporting, automatic fake news detection, flagging and correcting false news, forwarding restrictions, identity verification, verification badges, and 408 TUMASJAN different kinds of ratings, such as article or source ratings by users and experts (Gimpel et al., 2021; King et al., 2021; Moravec et al., 2019;Ngetal.,2021; Schuetz et al., 2021;Wang,Pang,etal.,2021; Zhang et al., 2019). Although some of the measures may successfully mitigate the spread of fake news (e.g., expert and user ratings), other measures may work only under specific circumstances (e.g., correction messages) or create additional challenges and side effects (e.g., increasing echo chambers). For instance, one notable side effect is the “implied truth effect,” reflecting that fake headlines that are not flagged as fake news are considered validated and, therefore, are seen as more accurate than similar headlines in a control group where no tagging was used at all (Pennycook et al., 2020). Overall, as yet, there appears to be no agreed-upon or optimal solution for combatting fake news spread in social media. Interestingly, financial markets seem to price fake news correctly, such that the abnormal trading volume and stock price reaction are less pronounced for fake news than for legitimate news (Clarke et al., 2021). 3.6 Theme 6: Social media as a hub for political deliberation and action In this theme, business and economics scholars have investigated social media in the context of public sector organizations (e.g., social media use by public organizations), political processes (e.g., government communication, elections, movements, and revolutions), and public sentiment (e.g., polarization). Public sector organizations. The extant studies have focused on the predictors of social media use by public sector organizations, such as administrative culture, organizational capacities, and municipal resources (Ma, 2014; Zhang & Feeney, 2020). Moreover, there is evidence regarding the conditions under which social media use by public organizations is beneficial (Kim et al., 2015;Ma, 2014;Parketal.,2016; Zhang & Feeney, 2020). For instance, government use of Twitter is related to enhanced trust in the government due to higher levels of transparency, directness of contact, and participation (Kim et al., 2015;Parketal.,2016; Porumbescu, 2016). Furthermore, social media also seem to enhance public service delivery quality (Young, 2021) and public engagement (Agostino & Arnaboldi, 2016). Political processes. Several studies investigated the role of social media in social and political movements (Selander & Jarvenpaa, 2016), such as in the context of the Egyptian revolution (Acemoglu et al., 2018;Ohetal.,2015; Venkatesan et al., 2021) and the Russian protest movements (Enikolopov et al., 2020), and found that social media seem to play a positive role in supporting such movements (Leong et al., 2019). In turn, fewer studies have focused on how the government can use social media to build collective identity and mobilize citizens. For instance, in the context of the COVID-19 outbreak, La Torre et al. (2022) show how the Italian government engaged citizens to support social distancing using a hashtag-based campaign (#istayathome). Beneficial effects of social media have also been found for reducing corruption (Enikolopov et al., 2018; Jha & Sarangi, 2017), reducing electoral irregularities (Garbiras-Diaz & Montenegro, 2022), enhancing the alignment between democratic representatives and their constituents (Mousavi & Gu, 2019), gathering community intelligence to cope with social crises (Oh et al., 2013), and intensifying political competition and thereby reducing the barriers to entering politics (Petrova et al., 2021). Former US President Donald Trump’s heavy Twitter activity provided researchers with the opportunity to study the effect of presidential Tweets on economic outcomes. Scholars indeed found significant effects, such as negative sentiment toward Russia being associated with ruble depreciation (Afanasyev et al., 2021) and statements on the Fed (in the TUMASJAN 409 context of Trump’s pressure on the Fed to cut interest rates) being related to lower long-term interest rates. Public sentiment. Assessing public sentiment and investigating whether and how it may influence political discourse or even help predict political outcomes has long been at the heart of social media research (Tumasjan et al., 2010). In this vein, there has been mixed evidence on whether and how social media sentiment can predict political outcomes, such as election results (Cerina & Duch, 2020; Huberty, 2015), or shape public discourse (Miranda et al., 2016). On the other hand, whereas there is evidence of the polarization of public opinion in social media (“echo chambers”, “filter bubbles”), more nuanced research also documents opinion diversification and increased moderation (Kitchens et al., 2020; Shore et al., 2018), partly fueled by social media bots (Gorodnichenko et al., 2021). For example, Levy (2021) demonstrates that Facebook algorithms lead to lower exposure to counter-attitudinal news, thereby increasing polarization. Similarly, in terms of Facebook likes, users engage with congenial rather than uncongenial posts regarding politicians (Garz et al., 2020). In a highly cited study in the context of the 2016 presidential election, Allcott and Gentzkow (2017) show that ideologically segregated social media networks enhance the effect of believing stories favoring the preferred presidential candidate. 3.7 Theme 7: Social media as a data source Research in this theme has viewed social media primarily as a data source to assess phenomena that are otherwise difficult or impossible to measure. Thus, in this research strand, the focus of the investigation is not social media as a phenomenon per se but rather the data that social media provide to study other phenomena (e.g., Barbos & Kaisen, 2022; Boegershausen et al., 2022; Bourreau et al., 2022; Chau et al., 2020; Dugoua et al., 2022;Geetal.,2016; Jiang et al., 2018; Kuchler et al., 2022; Lee et al., 2022; Makridis, 2022; Peng, Teoh, et al., 2022;Sinclairetal.,2022;Tambe, 2014;Zhang&Ram,2020). Scholars have used social media data for a large variety of research purposes, such as measuring public sentiment and awareness regarding specific topics (e.g., attitude toward working from home via aggregate Twitter posts; Zhang et al., 2021); intra-firm employment-related information (e.g., working conditions via Glassdoor reviews; Hope et al., 2021); personality traits, often of samples that are hard to survey (e.g., Big Five personality traits of executives via their Twitter posts; Winkler et al., 2020); individuals’ behaviors (e.g., presidential sleeping behavior via his Twitter account activity; Almond & Du, 2020); group behaviors (e.g., sleeplessness of city inhabitants via aggregate Weibo keyword analysis; Heyes & Zhu, 2019); relationships between individuals and between aggregated geographical entities, such as countries (e.g., social connectedness via Facebook connections; Bailey et al., 2021); and relationships between other entities (e.g., brand relationships via Facebook users’ brand engagement; Yang et al., 2022). For instance, based on text data analysis, Hope et al. (2021) use Glassdoor employee reviews to measure financial analysts’ perceived work-life balance. In a similar vein, Min et al. (2021)use Twitter data to measure the daily emotions related to working from home at the state level in the United States. Using Twitter accounts, Winkler et al. (2020) examined the personality traits of Chief Marketing Officers to examine whether the Big Five traits moderate the relationship between venture maturity and web traffic. Based on keywords in Tweets following a TV show about teen pregnancy, Kearney and Levine (2015) assessed the interest in birth control as a result of the show. 410 TUMASJAN Rather than assessing text data, Bailey et al. (2021) use Facebook network data to construct a measure of the pairwise social connectedness between 170 countries and 332 European regions to predict the extent to which there is trade between two countries. Furthermore, observing the timing of the then-US President’s Twitter account activity, Almond and Du (2020) proxied his sleep duration, finding a negative relationship between sleep duration and public performance. Finally, using brand engagement data from Facebook users, Yang et al. (2022) constructed a novel market structure measure representing brand relationships that exist beyond traditional product–market boundaries as defined in the standard industry classification systems (e.g., North American Industry Classification System). Thus, overall, social media has been shown to provide a rich pool of digital trace data that can be used by business and economicsresearchers to validly assess different existing and new phenomena and constructs. 4 DISCUSSION AND AVENUES FOR FUTURE RESEARCH Social media continue to be a pervasive phenomenon influencing billions of people’s daily lives. As a result, business and economics research on social media has increasingly grown over the past 15 years. Based on a comprehensive literature review, this article reveals the many faces of social media research in business and economics by identifying seven key research themes covering almost all sub-disciplines. Although, as can be expected from the nature of social media, marketing and communication topics have been most prominently investigated in extant research, ample studies cover the full range of social media use in the economy and society. Building on the extant findings and recent developments in social media, we distilled eight overarching research topics for future investigation that cut across the identified research themes (Tables 4and 4). These eight research topics represent, on the one hand, avenues less taken or neglected in prior research. On the other hand, these research topics reflect recent general developments in social media that scholars have paid less attention to thus far. In this vein, Tables 4 and 4summarize the overarching eight research topics for future investigation and provide sample research avenues for each of the seven key research themes identified in this review. The following paragraphs describe the eight research topics and resulting research avenues. Adverse effects of social media. Although, as this review has shown, studies have increasingly addressed the adverse effects of social media in terms of societal challenges (e.g., cyberbullying, addiction, fake news), the adverse effects for companies, such as in the context of customer engagement, financial market outcomes, innovation, business venturing, and data source use have been investigated to a lesser extent. Thus, we see a predominance of studies on the positive contribution and potential of social media, which calls for a better balance in examining the why, when, and how of adverse effects. Accordingly, we need more research shedding light on the reasons and circumstances under which social media use may backfire for companies and individuals (e.g., adverse effects of customer engagement, social overload in the context of ESM, and financial market manipulations; see Table 4). In this vein, we also need more intervention studies testing concrete measures to combat these adverse effects. Beyond text data. The overwhelming majority of research to date has clearly relied on text data, which is reasonable given that text has been the dominant communication form in the past years. However, videos and pictures increasingly complement or even replace text in “traditional” social media (e.g., Twitter posts increasingly contain pictures and videos rather than only text). Moreover, “traditional” video-based social media like YouTube offer ample data and opportunities for investigation (e.g., regarding fake news spread) but have been relatively underused, probably also due to the more complex data analyses required for videos (Zhou et al., 2021). More recently TUMASJAN 411 TABLE 4 Future research directions. Research topics Research theme Sample research avenues within research themes Adverse effects of social media (1) Caveats and backfiring effects of customer engagement, sales promotion, and market intelligence (2) Dark side of social media in HRM (e.g., privacy, social engineering, manipulation of employer image) (2) Negative effects of ESM engagement (e.g., oversharing, social overload) (3) Financial market manipulations through social media (e.g., short squeezes) (5) Testing interventions to combat negative effects (e.g., cyberbullying) Beyond text data (1) Role of picture and video applications for customer engagement, branding, word-of-mouth, market intelligence (2) Validity of applicant screening based on video and picture data (e.g., personality assessment) (3) Predictive validity of investor sentiment measures via videos, pictures, memes, emojis, likes, re-post numbers and other data (5) Comparing the spread of fake news, addictive behaviors, cyberbullying in the context of videos, pictures, and other features (7) Testing videos, pictures, memes, emojis, likes, and meta-data as further data sources beyond text analyses Differences between social media (1) Comparison of word-of-mouth and engagement strategies across different social media (e.g., Twitter versus TikTok) (2) Antecedents of recruitment success across different social media (3) Comparison of investor sentiment’s and firm postings’ effects across different types of social media (5) Differential effects across social media designs in fostering and combating negative behaviors (e.g., addiction) (6) Comparison of different social media platforms’ functionality for political processes Non-mainstream and non-US social media (1) Social media management strategies (e.g., engagement, seeding) on non-mainstream social media (e.g., Mastodon) (2) Applicant screening and recruitment activities on non-mainstream and non-US social media (e.g., Weibo) (3) Role of investor sentiment and firm executive posts on non-mainstream and non-US social media (e.g., Steemit) (5) Fake news diffusion and combating interventions under alternative social media designs (e.g., Mastodon) (6) Comparing political processes in main stream versus non-mainstream and non-US social media Changes in social media over time (1) User base and feature changes’ effects on engagement, branding, word-of-mouth, market intelligence, and other outcomes (Continues) 412 TUMASJAN TABLE 4 (Continued) Research topics Research theme Sample research avenues within research themes (3) Changes in the predictive validity of investor sentiment over time (e.g., depending on feature and user base changes) (5) Changes in adverse societal effects over time (e.g., addiction) based on features, user base, and further characteristics (6) Changes in character of political deliberations depending on features and user base (7) Changes in predictive validity of social media data over time depending on feature and user base changes Differentiating effects between segments (1) User engagement differences across customer groups (e.g., gender, vulnerable customers) (2) Differential effects of social media in HRM (e.g., recruitment) across segments (e.g., employee groups, industries, firm types) (2) Differential effects of ESM (e.g., knowledge sharing) across segments (e.g., employee groups, industries, firm types) (4) Differences in innovation performance across segments (e.g., industries, products versus services) (5) Identifying risk groups and testing customized mitigation strategies (e.g., addiction) Beyond desktop use (1) Influence of mobile social media use on customer and other stakeholder interactions (2) Effect of mobile social media use on HRM applications (e.g., recruitment) and ESM (4) Idea generation quality and quantity in idea communities on mobile devices and in virtual/augmented reality (5) Testing interventions’ functionality in the context of mobile devices (6) Effect of mobile device use on quality and quantity of political deliberation and action Contribution to sustainable development (1) Customer engagement and branding in the context of transitioning to sustainable products and services (2) Recruitment strategies for sustainability-driven businesses (5) Social media management strategies to contribute to sustainable development goals (6) Organizing political processes to support sustainable development goals (7) Social media data use to inform research and development of sustainability-driven businesses Note. Numbering of research themes: (1) Social media as a market-oriented interaction hub: Engaging with consumers and other stakeholders (2) Social media as a resource-oriented interaction hub: Engaging with (potential) employees and other professionals (3) Social media as an information market: Predicting financial market and firm performance outcomes (4) Social media for innovation and business venturing (5) Social media as a societal challenge (6) Social media as a political hub (7) Social media as a data source TUMASJAN 419 He, F., Feng, Y., & Hao, J. (2022). Information disclosure source, investors’ searching and stock price crash risk. Economics Letters,210, 110202. Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015). Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies. Journal of the Academy of Marketing Science,43(3), 375–394. Herhausen, D., Ludwig, S., Grewal, D., Wulf, J., & Schoegel, M. (2019). Detecting, preventing, and mitigating online firestorms in brand communities. Journal of Marketing,83(3), 1–21. Hernandez-Ortega, B. I., Stanko, M. A., Rishika, R., Molina-Castillo, F. J., & Franco, J. (2022). Brand-generated social media content and its differential impact on loyalty program members. Journal of the Academy of Marketing Science,50(5), 1071–1090. Heyes, A., & Zhu, M. Y. (2019). Air pollution as a cause of sleeplessness: Social media evidence from a panel of Chinese cities. Journal of Environmental Economics and Management,98, 102247. Hildebrand, C., & Schlager, T. (2019). Focusing on others before you shop: Exposure to Facebook promotes conventional product configurations. Journal of the Academy of Marketing Science,47(2), 291–307. Hogreve, J., Bilstein, N., & Hoerner, K. (2019). Service recovery on stage: Effects of social media recovery on virtually present others. Journal of Service Research,22(4), 421–439. Hong, Y. L., Hu, Y. H., & Burtch, G. (2018). Embeddedness, prosociality, and social influence: Evidence from online crowdfunding. MIS Quarterly,42(4), 1211–1224. Hoong, R. (2021). Self control and smartphone use: An experimental study of soft commitment devices. European Economic Review,140,103924. Hope, O. K., Li, C. C., Lin, A. P., & Rabier, M. (2021). Happy analysts. Accounting Organizations and Society,90, 101199. Horner, C. G., Galletta, D., Crawford, J., & Shirsat, A. (2021). Emotions: The unexplored fuel of fake news on social media. Journal of Management Information Systems,38(4), 1039–1066. Hu, Y. H., Xu, A. B., Hong, Y. L., Gal, D., Sinha, V., & Akkiraju, R. (2019). Generating business intelligence through social media analytics: Measuring brand personality with consumer-, employee-, and firm-generated content. Journal of Management Information Systems,36(3), 893–930. Huang, J. K. (2018). The customer knows best: The investment value of consumer opinions. Journal of Financial Economics,128(1), 164–182. Huang, N., Hong, Y. L., & Burtch, G. (2017). Social network integration and user content generation: Evidence from natural experiments. MIS Quarterly,41(4), 1035–1058. Huang, Y., Singh, P. V., & Ghose, A. (2015). A structural model of employee behavioral dynamics in enterprise social media. Management Science,61(12), 2825–2844. Huberty, M. (2015). Can we vote with our tweet? On the perennial difficulty of election forecasting with social media. International Journal of Forecasting,31(3), 992–1007. Jalali, N. Y., & Papatla, P. (2016). The palette that stands out: Color compositions of online curated visual UGC that attracts higher consumer interaction. Quantitative Marketing and Economics,14(4), 353–384. Jeske, D., & Shultz, K. S. (2016). Using social media content for screening in recruitment and selection: Pros and cons. Work Employment and Society,30(3), 535–546. Jha, C. K., & Sarangi, S. (2017). Does social media reduce corruption? Information Economics and Policy,39, 60–71. Jiang, J., Wang, I. Y., & Wang, K. P. (2018). Revolving ratina analysts and ratings of mortgage-backed and assetbacked securities: Evidence from LinkedIn. Management Science,64(12), 5832–5854. Jiao, P. R., Veiga, A., & Walther, A. (2020). Social media, news media and the stock market. Journal of Economic Behavior & Organization,176, 63–90. Johnen, M., & Schnittka, O. (2019). When pushing back is good: The effectiveness of brand responses to social media complaints. Journal of the Academy of Marketing Science,47(5), 858–878. Johnson, M. A., & Leo, C. (2020). The inefficacy of LinkedIn? A latent change model and experimental test of using LinkedIn for job search. Journal of Applied Psychology,105(11), 1262–1280. Jung, M. J., Naughton, J. P., Tahoun, A., & Wang, C. (2018). Do firms strategically disseminate? Evidence from corporate use of social media. Accounting Review,93(4), 225–252. Karaguer, Z., Becker, J. M., Klein, K., & Edeling, A. (2022). How, why, and when disclosure type matters for influencer marketing. International Journal of Research in Marketing,39(2), 313–335. Kaschig, A., Maier, R., & Sandow, A. (2016). The effects of collecting and connecting activities on knowledge creation in organizations. Journal of Strategic Information Systems,25(4), 243–258. 420 TUMASJAN Katona, Z. (2015). Democracy in product design: Consumer participation and differentiation strategies. Quantitative Marketing and Economics,13(4), 359–394. Kearney, M. S., & Levine, P. B. (2015). Media influences on social outcomes: The impact of MTV’s 16 and pregnant on teen childbearing. American Economic Review,105(12), 3597–3632. Kim, A., & Dennis, A. R. (2019). Says who? The effects of presentation format and source rating on fake news in social media. MIS Quarterly,43(3), 1025–1039. Kim, E. H., & Youm, Y. N. (2017). How do social media affect analyst stock recommendations? Evidence from S&P 500 electric power companies’ Twitter accounts. Strategic Management Journal,38(13), 2599–2622. Kim, S. K., Park, M. J., & Rho, J. J. (2015). Effect of the government’s use of social media on the reliability of the government: Focus on Twitter. Public Management Review,17(3), 328–355. King, K. K., Wang, B., Escobari, D., & Oraby, T. (2021). Dynamic effects of falsehoods and corrections on social media: A theoretical modeling and empirical evidence. Journal of Management Information Systems,38(4), 989– 1010. Kitchens, B., Johnson, S. L., & Gray, P. (2020). Understanding echo chambers and filter bubbles: The impact of social media on diversification and partisan shifts in news consumption. MIS Quarterly,44(4), 1619–1649. Klostermann, J., Plumeyer, A., Boger, D., & Decker, R. (2018). Extracting brand information from social networks: Integrating image, text, and social tagging data. International Journal of Research in Marketing,35(4), 538–556. Koch, H., Gonzalez, E., & Leidner, D. (2012). Bridging the work/social divide: The emotional response to organizational social networking sites. European Journal of Information Systems,21(6), 699–717. Kokkodis, M., Lappas, T., & Ransbotham, S. (2020). From lurkers to workers: Predicting voluntary contribution and community welfare. Information Systems Research,31(2), 607–626. Krasnova, H., Widjaja, T., Buxmann, P., Wenninger, H., & Benbasat, I. (2015). Why following friends can hurt you: An exploratory investigation of the effects of envy on social networking sites among college-age users. Information Systems Research,26(3), 585–605. Kuchler, T., Russel, D., & Stroebel, J. (2022). JUE insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook. Journal of Urban Economics,127,103314. Kuem, J., Ray, S., Siponen, M., & Kim, S. S. (2017). What leads to prosocial behaviors on social networking services: A tripartite model. Journal of Management Information Systems,34(1), 40–70. Kumar, V., Bhaskaran, V., Mirchandani, R., & Shah, M. (2013). Practice prize winner creating a measurable social media marketing strategy: Increasing the value and ROI of intangibles and tangibles for Hokey Pokey. Marketing Science,32(2), 194–212. La Torre, M., Di Tullio, P., Tamburro, P., Massaro, M., & Rea, M. A. (2022). Calculative practices, social movements and the rise of collective identity: How #istayathome mobilised a nation. Accounting, Auditing & Accountability Journal,35(9), 1–27. Lachanski, M., & Pav, S. (2017). Shy of the character limit: “Twitter mood predicts the stock market” revisited. Econ Journal Watch,14(3), 302–345. Lacka, E., Boyd, D. E., Ibikunle, G., & Kannan, P. K. (2022). Measuring the real-time stock market impact of firmgenerated content. Journal of Marketing,86(5), 58–78. Lam, H. K. S., Yeung, A. C. L., & Cheng, T. C. E. (2016). The impact of firms’ social media initiatives on operational efficiency and innovativeness. Journal of Operations Management,47–48,28–43. Lamberton, C., & Stephen, A. T. (2016). A thematic exploration of digital, social media, and mobile marketing: Research evolution from 2000 to 2015 and an agenda for future inquiry. Journal of Marketing,80(6), 146–172. Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on social media: Evidence from Facebook. Management Science,64(11), 5105–5131. Lee, G., Naiker, V., & Stewart, C. R. (2022). Audit office labor market proximity and audit quality. Accounting Review, 97(2), 317–347. Lee, S. Y., Rui, H. X., & Whinston, A. B. (2019). Is best answer really the best answer? The politeness bias. MIS Quarterly,43(2), 579–600. Lehrer, S., Xie, T., & Zeng, T. (2021). Does high-frequency social media data improve forecasts of low-frequency consumer confidence measures? Journal of Financial Econometrics,19(5), 910–933. Leidner, D. E., Gonzalez, E., & Koch, H. (2018). An affordance perspective of enterprise social media and organizational socialization. Journal of Strategic Information Systems,27(2), 117–138. TUMASJAN 421 Leonardi, P. M. (2014). Social media, knowledge sharing, and innovation: Toward a theory of communication visibility. Information Systems Research,25(4), 796–816. Leonardi, P. M. (2015). Ambient awareness and knowledge acquisition: Using social media to learn “who knows what” and “who knows whom.” MIS Quarterly,39(4), 747–762. Leonardi, P. M. (2018). Social media and the development of shared cognition: The roles of network expansion, content integration, and triggered recalling. Organization Science,29(4), 547–568. Leong, C., Pan, S. L., Bahri, S., & Fauzi, A. (2019). Social media empowerment in social movements: Power activation and power accrual in digital activism. European Journal of Information Systems,28(2), 173–204. Leung, F. F., Gu, F. F., Li, Y. W., Zhang, J. Z., & Palmatier, R. W. (2022). Influencer marketing effectiveness. Journal of Marketing,86(6), 93–115. Levy, R. (2021). Social media, news consumption, and polarization: Evidence from a field experiment. American Economic Review,111(3), 831–870. Li, F., Larimo, J., & Leonidou, L. C. (2021). Social media marketing strategy: Definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science,49(1), 51–70. Li, J., Chen, X. P., Kotha, S., & Fisher, G. (2017). Catching fire and spreading it: A glimpse into displayed entrepreneurial passion in crowdfunding campaigns. Journal of Applied Psychology,102(7), 1075–1090. Li, Y. Y., & Xie, Y. (2020). Is a picture worth a thousand words? An empirical study of image content and social media engagement. Journal of Marketing Research,57(1), 1–19. Liu, A. X., Steenkamp, J., & Zhang, J. R. (2018). Agglomeration as a driver of the volume of electronic word of mouth in the restaurant industry. Journal of Marketing Research,55(4), 507–523. Liu, L., Dzyabura, D., & Mizik, N. (2020). Visual listening in: Extracting brand image portrayed on social media. Marketing Science,39(4), 669–686. Lowry, P. B., Moody, G. D., & Chatterjee, S. (2017). Using IT design to prevent cyberbullying. Journal of Management Information Systems,34(3), 863–901. Lowry, P. B., Zhang, J., Wang, C., & Siponen, M. (2016). Why do adults engage in cyberbullying on social media? An integration of online disinhibition and deindividuation effects with the social structure and social learning model. Information Systems Research,27(4), 962–986. Lu, B. J., Guo, X. H., Luo, N. L., & Chen, G. Q. (2015). Corporate blogging and job performance: Effects of workrelated and nonwork-related participation. Journal of Management Information Systems,32(4), 285–314. Lukyanenko, R., Parsons, J., & Wiersma, Y. F. (2014). The IQ of the crowd: Understanding and improving information quality in structured user-generated content. Information Systems Research,25(4), 669–689. Lukyanenko, R., Parsons, J., Wiersma, Y. F., & Maddah, M. (2019). Expecting the unexpected: Effects of data collection design choices on the quality of crowdsourced user-generated content. MIS Quarterly,43(2), 623–647. Ma, L. (2014). Diffusion and assimilation of government microblogging. Public Management Review,16(2), 274–295. Ma, L. Y., Sun, B. H., & Kekre, S. (2015). The squeaky wheel gets the grease – An empirical analysis of customer voice and firm intervention on Twitter. Marketing Science,34(5), 627–645. Maffie, M. D. (2020). The role of digital communities in organizing gig workers. Industrial Relations,59(1), 123–149. Makridis, C. A. (2022). The social transmission of economic sentiment on consumption. European Economic Review,148, 104232. Malhotra, P., & Bhattacharyya, S. (2022). Leveraging cofollowership patterns on social media to identify brand alliance opportunities. Journal of Marketing,86(4), 17–36. Mallipeddi, R. R., Kumar, S., Sriskandarajah, C., & Zhu, Y. (2022). A framework for analyzing influencer marketing in social networks: Selection and scheduling of influencers. Management Science,68(1), 75–104. Marchand, A., Hennig-Thurau, T., & Flemming, J. (2021). Social media resources and capabilities as strategic determinants of social media performance. International Journal of Research in Marketing,38(3), 549–571. Marion, T. J., Barczak, G., & Hultink, E. J. (2014). Do social media tools impact the development phase? An exploratory study. Journal of Product Innovation Management,31,18–29. Martin, G., Parry, E., & Flowers, P. (2015). Do social media enhance constructive employee voice all of the time or just some of the time? Human Resource Management Journal,25(4), 541–562. Matook, S., Dennis, A. R., & Wang, Y. M. (2022). User comments in social media firestorms: A mixed-method study of purpose, tone, and motivation. Journal of Management Information Systems,39(3), 673–705. Mazboudi, M., & Khalil, S. (2017). The attenuation effect of social media: Evidence from acquisitions by large firms. Journal of Financial Stability,28,115–124. 422 TUMASJAN McDonald, S., Damarin, A. K., McQueen, H., & Grether, S. T. (2022). The hunt for red flags: Cybervetting as morally performative practice. Socio-Economic Review,20(3), 915–936. McDool, E., Powell, P., Roberts, J., & Taylor, K. (2020). The internet and children’s psychological wellbeing. Journal of Health Economics,69, 102274. Melumad, S., Inman, J. J., & Pham, M. T. (2019). Selectively emotional: How smartphone use changes user-generated content. Journal of Marketing Research,56(2), 259–275. Melumad, S., & Meyer, R. (2020). Full disclosure: How smartphones enhance consumer self-disclosure. Journal of Marketing,84(3), 28–45. Meurer, M. M., Waldkirch, M., & Schou, P. K. et al. (2022). Digital affordances: How entrepreneurs access support in online communities during the COVID-19 pandemic. Small Business Economics,58,637–663. Milas, C., Panagiotidis, T., & Dergiades, T. (2021). Does it matter where you search? Twitter versus traditional news media. Journal of Money Credit and Banking,53(7), 1757–1795. Min, H. Y., Peng, Y. S., Shoss, M., & Yang, B. J. (2021). Using machine learning to investigate the public’s emotional responses to work from home during the COVID-19 pandemic. Journal of Applied Psychology,106(2), 214–229. Miranda, S. M., Young, A., & Yetgin, E. (2016). Are social media emancipatory or hegemonic? Societal effects of mass media digitization in the case of the SOPA discourse. MIS Quarterly,40(2), 303–330. Moravec, P. L., Minas, R. K., & Dennis, A. R. (2019). Fake news on social media: People believe what they want to believe when it makes no sense at all. MIS Quarterly,43(4), 1343–1360. Mousavi, R., & Gu, B. (2019). The impact of Twitter adoption on lawmakers’ voting orientations. Information Systems Research,30(1), 133–153. Mousavi, R., Johar, M., & Mookerjee, V. S. (2020). The voice of the customer: Managing customer care in Twitter. Information Systems Research,31(2), 340–360. Mumi, A., Obal, M., & Yang, Y. (2019). Investigating social media as a firm’s signaling strategy through an IPO. Small Business Economics,53(3), 631–645. Nascimento, A. M., & Silveira, D. S. d. (2017). A systematic mapping study on using social media for business process improvement. Computers in Human Behavior,73,670–675. Neeley, T. B., & Leonardi, P. M. (2018). Enacting knowledge strategy through social media: Passable trust and the paradox of nonwork interactions. Strategic Management Journal,39(3), 922–946. Nekrasov, A., Teoh, S. H., & Wu, S. J. (2022). Visuals and attention to earnings news on Twitter. Review of Accounting Studies,27(4), 1233–1275. Ng, K. C., Tang, J., & Lee, D. (2021). The effect of platform intervention policies on fake news dissemination and survival: An empirical examination. Journal of Management Information Systems,38(4), 898–930. Nguyen, H., Calantone, R., & Krishnan, R. (2020). Influence of social media emotional word of mouth on institutional investors’ decisions and firm value. Management Science,66(2), 887–910. Nigam, N., Benetti, C., & Johan, S. A. (2020). Digital start-up access to venture capital financing: What signals quality? Emerging Markets Review,45,100743. Oh, O., Agrawal, M., & Rao, H. R. (2013). Community intelligence and social media services: A rumor theoretic analysis of tweets during social crises. MIS Quarterly,37(2), 407–426. Oh, O., Eom, C., & Rao, H. R. (2015). Role of social media in social change: An analysis of collective sense making during the 2011 Egypt revolution. Information Systems Research,26(1), 210–223. Ong, L. L., Fox, A. K., Cook, L. A., Bessant, C., Gan, P. P., Hoy, M. G., Nottingham, E., Pereira, B., & Steinberg, S. B. (2022). Sharenting in an evolving digital world: Increasing online connection and consumer vulnerability. Journal of Consumer Affairs,56(3), 1106–1126. Osatuyi, B., & Turel, O. (2020). Conceptualisation and validation of system use reduction as a self-regulatory IS use behaviour. European Journal of Information Systems,29(1), 44–64. Overgoor, G., Rand, W., van Dolen, W., & Mazloom, M. (2022). Simplicity is not key: Understanding firm-generated social media images and consumer liking. International Journal of Research in Marketing,39(3), 639–655. Ozdemir, Z. D., Smith, H. J., & Benamati, J. H. (2017). Antecedents and outcomes of information privacy concerns in a peer context: An exploratory study. European Journal of Information Systems,26(6), 642–660. Panagiotopoulos, P., & Barnett, J. (2015). Social media in union communications: An international study with UNI Global Union affiliates. British Journal of Industrial Relations,53(3), 508–532. Pancer, E., Chandler, V., Poole, M., & Noseworthy, T. J. (2019). How readability shapes social media engagement. Journal of Consumer Psychology,29(2), 262–270. TUMASJAN 423 Park, M. J., Kang, D., Rho, J. J., & Lee, D. H. (2016). Policy role of social media in developing public trust: Twitter communication with government leaders. Public Management Review,18(9), 1265–1288. Pasquier, V., Daudigeos, T., & Barros, M. (2020). Towards a new flashmob unionism: The case of the fight for 15 movement. British Journal of Industrial Relations,58(2), 336–363. Patrick-Thomson, H., & Kranert, M. (2021). Don’t work for free: Online discursive resistance to precarity in commercial photography. Work Employment and Society,35(6), 1034–1052. Pei, A., & Mayzlin, D. (2022). Influencing social media influencers through affiliation. Marketing Science,41(3), 593–615. Peng, J., Agarwal, A., Hosanagar, K., & Iyengar, R. (2018). Network overlap and content sharing on social media platforms. Journal of Marketing Research,55(4), 571–585. Peng, J., Zhang, J. H., & Gopal, R. (2022). The good, the bad, and the social media: Financial implications of social media reactions to firm-related news. Journal of Management Information Systems,39(3), 706–732. Peng, L., Teoh, S. H., Wang, Y. K., & Yan, J. W. (2022). Face value: Trait impressions, performance characteristics, and market outcomes for financial analysts. Journal of Accounting Research,60(2), 653–705. Pennycook, G., Bear, A., Collins, E. T., & Rand, D. G. (2020). The implied truth effect: Attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings. Management Science,66(11), 4944–4957. Petrova, M., Sen, A., & Yildirim, P. (2021). Social media and political contributions: The impact of new technology on political competition. Management Science,67(5), 2997–3021. Porumbescu, G. A. (2016). Comparing the effects of e-government and social media use on trust in government: Evidence from Seoul, South Korea. Public Management Review,18(9), 1308–1334. Quach, S., Thaichon, P., Martin, K. D., Weaven, S., & Palmatier, R. W. (2022). Digital technologies: Tensions in privacy and data. Journal of the Academy of Marketing Science,50(6), 1299–1323. Rakowski, D., Shirley, S. E., & Stark, J. R. (2021). Twitter activity, investor attention, and the diffusion of information. Financial Management,50(1), 3–46. Renault, T. (2017). Intraday online investor sentiment and return patterns in the US stock market. Journal of Banking & Finance,84,25–40. Rishika, R., & Ramaprasad, J. (2019). The effects of asymmetric social ties, structural embeddedness, and tie strength on online content contribution behavior. Management Science,65(7), 3398–3422. Roberts, D. L., Piller, F. T., & Luttgens, D. (2016). Mapping the impact of social media for innovation: The role of social media in explaining innovation performance in the PDMA comparative performance assessment study. Journal of Product Innovation Management,33(S1), 117–135. Rode, H. (2016). To share or not to share: The effects of extrinsic and intrinsic motivations on knowledge-sharing in enterprise social media platforms. Journal of Information Technology,31(2), 152–165. Ross, B., Pilz, L., Cabrera, B., Brachten, F., Neubaum, G., & Stieglitz, S. (2019). Are social bots a real threat? An agentbased model of the spiral of silence to analyse the impact of manipulative actors in social networks. European Journal of Information Systems,28(4), 394–412. Roth, P. L., Thatcher, J. B., Bobko, P., Matthews, K. D., Ellingson, J. E., & Goldberg, C. B. (2020). Political affiliation and employment screening decisions: The role of similarity and identification processes. Journal of Applied Psychology,105(5), 472–486. Roulin, N., & Levashina, J. (2019). LinkedIn as a new selection method: Psychometric properties and assessment approach. Personnel Psychology,72(2), 187–211. Rust, R. T., Rand, W., Huang, M. H., Stephen, A. T., Brooks, G., & Chabuk, T. (2021). Real-time brand reputation tracking using social media. Journal of Marketing,85(4), 21–43. Safadi, H., Johnson, S. L., & Faraj, S. (2021). Who contributes knowledge? Core-periphery tension in online innovation communities. Organization Science,32(3), 752–775. Salge, C. A. D., Karahanna, E., & Thatcher, J. B. (2022). Algorithmic processes of social alertness and social transmission: How bots disseminate information on Twitter. MIS Quarterly,46(1), 229–260. Sarbu, M. (2017). Does social media increase labour productivity? Jahrbücher für Nationalökonomie und Statistik, 237(2), 81–113. Schlagwein, D., & Hu, M. (2017). How and why organisations use social media: Five use types and their relation to absorptive capacity. Journal of Information Technology,32(2), 194–209. 424 TUMASJAN Schou, P. K., Bucher, E., & Waldkirch, M. (2022). Entrepreneurial learning in online communities. Small Business Economics,58, 2087–2108. Schuetz, S. W., Sykes, T. A., & Venkatesh, V. (2021). Combating COVID-19 fake news on social media through fact checking: Antecedents and consequences. European Journal of Information Systems,30(4), 376–388. Schweidel, D. A., & Moe, W. W. (2014). Listening in on social media: A joint model of sentiment and venue format choice. JournalofMarketingResearch,51(4), 387–402. Seiler, S., Yao, S., & Wang, W. B. (2017). Does online word of mouth increase demand? (And how?) Evidence from a natural experiment. Marketing Science,36(6), 838–861. Selander, L., & Jarvenpaa, S. L. (2016). Digital action repertoires and transforming a social movement organization. MIS Quarterly,40(2), 331–352. Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., & Lee, K. C. (2020). Enhancing social media analysis with visual data analytics: A deep learning approach. MIS Quarterly,44(4), 1459–1492. Shore, J., Baek, J., & Dellarocas, C. (2018). Network structure and patterns of information diversity on Twitter. MIS Quarterly,42(3), 849–872. Sinclair, M., Ghermandi, A., Signorello, G., Giuffrida, L., & De Salvo, M. (2022). Valuing recreation in Italy’s protected areas using spatial big data. Ecological Economics, 200, 107526. Smith, C., Smith, J. B., & Shaw, E. (2017). Embracing digital networks: Entrepreneurs’ social capital online. Journal of Business Venturing,32(1), 18–34. Song, T. T., Huang, J. H., Tan, Y., & Yu, Y. F. (2019). Using userand marketer-generated content for box office revenue prediction: Differences between microblogging and third-party platforms. Information Systems Research, 30(1), 191–203. Sonnentag, S., Reinecke, L., Mata, J., & Vorderer, P. (2018). Feeling interrupted – being responsive: How online messages relate to affect at work. Journal of Organizational Behavior,39(3), 369–383. Sprenger, T. O., Sandner, P. G., Tumasjan, A., & Welpe, I. M. (2014). News or noise? Using Twitter to identify and understand company-specific news flow. Journal of Business Finance & Accounting,41(7-8), 791–830. Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2014). Tweets and trades: The information content of stock microblogs. European Financial Management,20(5), 926–957. Stanko, M. A. (2016). Toward a theory of remixing in online innovation communities. Information Systems Research, 27(4), 773–791. Statista. (2022). Daily time spent on social networking by internet users worldwide from 2012 to 2022. Retrieved from https://www.statista.com/statistics/433871/daily-social-media-usage-worldwide/ Statista. (2023). Number of internet and social media users worldwide as of January 2023. Retrieved from https:// www.statista.com/statistics/617136/digital-population-worldwide/ Steinbach, A. L., Kautz, J., & Korsgaard, M. A. (2021). Caring for their own: How firm actions to protect essential workers and CEO benevolence influenced stakeholder sentiment during the COVID-19 pandemic. Journal of Applied Psychology,106(6), 811–824. Stephen, A. T., & Galak, J. (2012). The effects of traditional and social earned media on sales: A study of a microlending marketplace. Journal of Marketing Research,49(5), 624–639. Sun, S. J., Gao, Y., & Rui, H. X. (2021). Does active service intervention drive more complaints on social media? The roles of service quality and awareness. Journal of Management Information Systems,38(3), 579–611. Susarla, A., Oh, J. H., & Tan, Y. (2012). Social networks and the diffusion of user-generated content: Evidence from Youtube. Information Systems Research,23(1), 23–41. Suseno, Y., Laurell, C., & Sick, N. (2018). Assessing value creation in digital innovation ecosystems: A social media analytics approach. Journal of Strategic Information Systems,27(4), 335–349. Tambe, P. (2014). Big data investment, skills, and firm value. Management Science,60(6), 1452–1469. Tan, X., Jin, F. J., & Dennis, A. R. (2022). How appreciation and attention affect contributions to electronic networks of practice. Journal of Management Information Systems,39(4), 1037–1063. Tellis, G. J., MacInnis, D. J., Tirunillai, S., & Zhang, Y. W. (2019). What drives virality (sharing) of online digital content? The critical role of information, emotion, and brand prominence. Journal of Marketing,83(4), 1–20. Theurer, C. P., Schäpers, P., Tumasjan, A., Welpe, I., & Lievens, F. (2022). What you see is what you get? Measuring companies’ projected employer image attributes via companies’ employment webpages. Human Resource Management,61(5), 543–561. TUMASJAN 425 Theurer, C. P., Tumasjan, A., Welpe, I. M., & Lievens, F. (2018). Employer branding: A brand equity-based literature review and research agenda. International Journal of Management Reviews,20(1), 155–179. Thies, F., Wessel, M., & Benlian, A. (2016). Effects of social interaction dynamics on platforms. Journal of Management Information Systems,33(3), 843–873. Thompson, P., McDonald, P., & O’Connor, P. (2020). Employee dissent on social media and organizational discipline. Human Relations,73(5), 631–652. Tim, Y., Hallikainen, P., Pan, S. L., & Tamm, T. (2020). Actualizing business analytics for organizational transformation: A case study of Rovio Entertainment. European Journal of Operational Research,281(3), 642–655. Todri, V., Adamopoulos, P., & Andrews, M. (2022). Is distance really dead in the online world? The moderating role of geographical distance on the effectiveness of electronic word of mouth. Journal of Marketing,86(4), 118–140. Toubiana, M., & Zietsma, C. (2017). The message is on the wall? Emotions, social media and the dynamics of institutional complexity. Academy of Management Journal,60(3), 922–953. Tow, W., Dell, P., & Venable, J. (2010). Understanding information disclosure behaviour in Australian Facebook users. Journal of Information Technology,25(2), 126–136. Tumasjan, A., Braun, R., & Stolz, B. (2021). Twitter sentiment as a weak signal in venture capital financing. Journal of Business Venturing,36(2), 106062. Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Election forecasts with Twitter: How 140 characters reflect the political landscape. Social Science Computer Review,29(4), 402–418. Turel, O., & Osatuyi, B. (2021). Biased credibility and sharing of fake news on social media: Considering peer context and self-objectivity state. Journal of Management Information Systems,38(4), 931–958. Van Iddekinge, C. H., Lanivich, S. E., Roth, P. L., & Junco, E. (2016). Social media for selection? Validity and adverse impact potential of a Facebook-based assessment. Journal of Management,42(7), 1811–1835. van Osch, W., & Bulgurcu, B. (2020). Idea generation in enterprise social media: Open versus closed groups and their network structures. Journal of Management Information Systems,37(4), 904–932. Van Osch, W., & Steinfield, C. W. (2016). Team boundary spanning: Strategic implications for the implementation and use of enterprise social media. Journal of Information Technology,31(2), 207–225. Van Osch, W., & Steinfield, C. W. (2018). Strategic visibility in enterprise social media: Implications for network formation and boundary spanning. Journal of Management Information Systems,35(2), 647–682. Veer, T., Yang, P., & Riepe, J. (2022). Ventures’ conscious knowledge transfer to close partners, and beyond: A framework of performance, complementarity, knowledge disclosure, and knowledge broadcasting. Journal of Business Venturing, 37(3), 106191. Venkatesan, S., Valecha, R., Yaraghi, N., Oh, O., & Rao, H. R. (2021). Influence in social media: An investigation of tweets spanning the 2011 Egyptian Revolution. MIS Quarterly,45(4), 1679–1714. Vujic, S., & Zhang, X. Y. (2018). Does Twitter chatter matter? Online reviews and box office revenues. Applied Economics,50(34-35), 3702–3717. Wade, J. T., Roth, P. L., Thatcher, J. B., & Dinger, M. (2020). Social media and selection: Political issue similarity, liking, and the moderating effect of social media platform. MIS Quarterly,44(3), 1301–1358. Wang, S. T., Greenwood, B. N., & Pavlou, P. A. (2020). Tempting fate: Social media posts, unfollowing, and long-term sales. MIS Quarterly,44(4), 1521–1571. Wang, S. T., Pang, M. S., & Pavlou, P. A. (2021). Cure or poison? Identity verification and the posting of fake news on social media. Journal of Management Information Systems,38(4), 1011–1038. Wang, S. T., Pang, M. S., & Pavlou, P. A. (2022). Seeing is believing? How including a video in fake news influences users’ reporting of fake news to social media platforms. MIS Quarterly,46(3), 1323–1354. Wang, Y. Y., Guo, C. H., Susarla, A., & Sambamurthy, V. (2021). Online to offline: The impact of social media on offline sales in the automobile industry. Information Systems Research,32(2), 582–604. Wei, X., Zhang, Z., Zhang, M. Y., Chen, W. Y., & Zeng, D. D. (2022). Combining crowd and machine intelligence to detect false news on social media. MIS Quarterly,46(2), 977–1008. Wenninger, H., Krasnova, H., & Buxmann, P. (2019). Understanding the role of social networking sites in the subjective well-being of users: A diary study. European Journal of Information Systems,28(2), 126–148. Williams, C., Du, J. N., & Zhang, H. Z. (2020). International orientation of Chinese internet SMEs: Direct and indirect effects of foreign and indigenous social networking site use. Journal of World Business,55(3), 101051. Winkler, H. J., Rieger, V., & Engelen, A. (2020). Does the CMO’s personality matter for web traffic? Evidence from technology-based new ventures. Journal of the Academy of Marketing Science,48(2), 308–330. 426 TUMASJAN Wong, R. Y. M., Cheung, C. M. K., Xiao, B., & Thatcher, J. B. (2021). Standing up or standing by: Understanding bystanders’ proactive reporting responses to social media harassment. Information Systems Research,32(2), 561– 581. WordClouds.com. (2023). Word cloud created on https://www.wordclouds.com/ Yang, J. H., & Liu, S. W. (2017). Accounting narratives and impression management on social media. Accounting and Business Research,47(6), 673–694. Yang, Y., Zhang, K. P., & Kannan, P. K. (2022). Identifying market structure: A deep network representation learning of social engagement. Journal of Marketing,86(4), 37–56. You, Y., Vadakkepatt, G. G., & Joshi, A. M. (2015). A meta-analysis of electronic word-of-mouth elasticity. Journal of Marketing,79(2), 19–39. Young, M. M. (2021). The impact of technological innovation on service delivery: Social media and smartphone integration in a 311 system. Public Management Review,24(6), 926–950. Yu, K. Y. T., Goh, K. H., & Kawasaki, S. (2022). Investigating electronic word-of-mouth on social media: An eyetracking approach. Human Resource Management,61(5), 599–616. Zhang, C., Yu, M. C., & Marin, S. (2021). Exploring public sentiment on enforced remote work during COVID-19. Journal of Applied Psychology,106(6), 797–810. Zhang, C. W., Gupta, A., Kauten, C., Deokar, A. V., & Qin, X. (2019). Detecting fake news for reducing misinformation risks using analytics approaches. European Journal of Operational Research,279(3), 1036–1052. Zhang, F. X., & Feeney, M. K. (2020). Engaging through technology: The role of administrative culture and mandates. Public Management Review,22(10), 1423–1442. Zhang, K., & Moe, W. (2021). Measuring brand favorability using large-scale social media data. Information Systems Research,32(4), 1128–1139. Zhang, L. W., Van Iddekinge, C. H., Arnold, J. D., Roth, P. L., Lievens, F., Lanivich, S. E., & Jordan, S. L. (2020). What’s on job seekers’ social media sites? A content analysis and effects of structure on recruiter judgments and predictive validity. Journal of Applied Psychology,105(12), 1530–1546. Zhang, W. L., & Ram, S. (2020). A comprehensive analysis of triggers and risk factors for asthma based on machine learning and large heterogeneous data sources. MIS Quarterly,44(1), 305–349. Zhang, Y. C., Moe, W. W., & Schweidel, D. A. (2017). Modeling the role of message content and influencers in social media rebroadcasting. International Journal of Research in Marketing,34(1), 100–119. Zhou, M., Chen, G. H., Ferreira, P., & Smith, M. D. (2021). Consumer behavior in the online classroom: Using video analytics and machine learning to understand the consumption of video courseware. Journal of Marketing Research,58(6), 1079–1100. Zhu, H. Z., Kock, A., Wentker, M., & Leker, J. (2019). How does online interaction affect idea quality? The effect of feedback in firm-internal idea competitions. Journal of Product Innovation Management,36(1), 24–40. Zor, O., Kim, K. H., & Monga, A. (2022). Tweets we like aren’t alike: Time of day affects engagement with vice and virtue tweets. Journal of Consumer Research,49(3), 473–495. How to cite this article: Tumasjan, A. (2024). The many faces of social media in business and economics research: Taking stock of the literature and looking into the future. Journal of Economic Surveys,38, 389–426. https://doi.org/10.1111/joes.12570