Volatility Spillovers Across User-Generated Content and Stock Market Performance

Volatility is an important metric of financial performance, indicating uncertainty or risk. So, predicting and managing volatility is of interest to both company managers and investors. This study investigates whether volatility in user-generated content (UGC) can spill over to volatility in stock returns and vice versa. Sources for user-generated content include tweets, blog posts, and Google searches. The authors test the presence of these spillover effects by a multivariate GARCH model. Further, the authors use multivariate regressions to reveal which type of company-related events increase volatility in user-generated content. Results for two studies in different markets show significant volatility spillovers between the growth rates of user-generated content and stock returns. Further, specific marketing events drive the volatility in user-generated content. In particular, new product launches significantly increase the volatility in the growth rates of user-generated content, which in turn can spill over to volatility in stock returns. Moreover, the spillover effects differ in sign depending on the valence of the user- generated content in Twitter. The authors discuss the managerial implications

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice

From Marketer-Generated Content to User-Generated Content: Evidence from Online Health Communities

How should marketers engage with social media features in online communities to shape knowledge contributions from customers in their potential markets? This is an important question because customer contributions are important drivers of business value. We examine the effect of marketer generated content in online health communities on user-generated content, using longitudinal data from a leading online health community. We focus on the firm’s practice of knowledge investment, in which its marketers provide product information or share life experience by posting in the social interaction section of online health platforms. The results demonstrate that because of knowledge investment in healthcare markets, the use of platform’s social media feature by marketer influence both the quantity and linguistics features of customer-generated content

Limitations of Nonfinancial Metrics Reported by Social Media Companies

Publicly traded companies in the U.S. are required by the Securities and Exchange Commission (SEC) to file annual and quarterly financial statements (form 10-K and form 10-Q respectively). The Management Discussion and Analysis (MD&A) section of these reports, as per SEC requirements, should include the identification and discussion of nonfinancial performance metrics that are critical to management and important to investors. This paper examines a set of common nonfinancial metrics reported by some well-known social media companies. These metrics include such quantities as number of registered users, monthly average users, and number of unique visitors. The definition and use of metrics such as these have gained increased importance as the recent stratospheric market valuations of a number of social media companies seem to be supported by them, as opposed to more traditional measures, such as profitability. This paper points to a number of limitations of these reported metrics, including that: What a metric actually measures may lie in the details of how it’s calculated; that is, relying on the name of the metric to indicate its meaning may be an error. Many of the metrics reported are inexact but the companies reporting them do not specify ranges of uncertainty around these point estimates. Important nonfinancial metrics (e.g., user demographics and customer churn rates) may simply not be reported at all. Typically, corporate nonfinancial metrics are not audited as is financial data. The contribution of this paper is in providing investors and other interested parties with a better understanding of the meaning and limitations of nonfinancial metrics reported by social media companies. Further, in highlighting some problematic issues in the current reporting of these nonfinancial metrics, we hope to raise interest in improving MD&A reporting standards

It’s more about the Content than the Users! The Influence of Social Broadcasting on Stock Markets

Social broadcasting networks facilitate the public exchange of information and contain a large amount of stock-related information. This data is increasingly analyzed by research and practice to predict stock market developments. Insights from social broadcasting networks are used to support the decision-making process of investors and are integrated into automatic trading algorithms to react quickly to broadcasted information. However, a comprehensive understanding about the influence of social broadcasting networks on stock markets is missing. In this study, we address this gap by conceptualizing and empirically testing a model incorporating three dimensions of social broadcasting networks: users, messages, and discussion. We analyze 1.84 million stock-related Twitter messages concerning the S&P 100 companies between January and April 2014 and corresponding intraday stock market data from NYSE and NASDAQ. Our research model is constructed applying factor analyses and tested using a fixed effects panel analysis. The results show that the influence of social broadcasting on stock markets is driven by the message and discussion dimensions whereas the user dimension has no significant influence. Specifically, the influence of user mentions, financial sentiment, discussion reach, and discussion volume has the largest impact and should carefully be considered by investors making trading decisions

Exploiting the impact of user-generated content on brand coolness and consumer brand engagement: A text-mining approach

This dissertation aims to comprehend the impact of deploying user-generated content (UGC) campaigns on consumers’ perceptions of brand coolness and consumer brand engagement. The trending concept of coolness in the beauty industry is studied through electronic word of mouth to understand if brands encouraging their users to post about their brand experiences leads to consumers perceiving them as cool and engaging more positively through those publications. The methodology in use is a netnography, along with a sentiment analysis technique. The analysis consisted in observing the interactions, incited by a user-generated content campaign led by a prestigious beauty brand - Drunk Elephant, between the brand and its online brand community on the social network Instagram for one year to avoid seasonal phenomena. The comments were retrieved using a text-mining tool and analyzed through Natural Language Processing according to their sentiment polarity, and trending topics identified. The data retrieved from the year of 2019 amounted to 67 321 interactions. Results show consumers’ perceptions of coolness can be positively influenced by adopting UGC campaigns, which can also lead to positive consumer brand engagement. Not only do these campaigns generate brand awareness, but they stimulate brand community’s expansion and influence consumers’ perceptions towards the brand. Beauty brands seeking to grow their status of coolness and consumer interactions should consider implementing user-generated content campaigns, as keeping up with the trends in the market is not only regarded as cool but is necessary to remain relevant in the ever-changing marketplace beauty has proven itself to be.Esta dissertação visa entender o impacto da utilização de campanhas de conteúdo gerado pelos utilizadores nas perceções dos consumidores da coolness de uma marca e interações entre marca e consumidores. A tendência coolness na indústria da beleza é examinada através de electronic word-of-mouth para compreender se encorajar os utilizadores a partilhar conteúdo sobre as suas experiências com as marcas, os leva a pensar na marca como cool e a interagir mais com essas publicações. A metodologia usada é uma análise netnográfica em conjunto com uma técnica de análise sentimental. A análise foi conduzida sob interações textuais, incitadas pela campanha da marca de prestígio de beleza – Drunk Elephant, entre a marca e a sua comunidade online na rede social Instagram durante um ano para evitar fenómenos sazonais. Os comentários foram extraídos por text mining e analisados através de processamento de linguagem natural, tendo em conta a polaridade do seu sentimento, e tópicos mais frequentes identificados. Os dados retirados do ano de 2019 totalizaram 67 321 interações. Os resultados demonstram que as perceções de coolness do consumidor podem ser positivamente influenciadas adotando o uso destas campanhas e podem conduzir a interações positivas. Não só estas campanhas criam visibilidade para a marca, como encorajam a expansão da comunidade da marca e influenciam as perceções dos seus consumidores. Marcas na indústria da beleza que procuram aumentar a sua coolness e interações com os consumidores devem considerar implementar campanhas de conteúdo gerado pelos utilizadores, de maneira a manter-se atuais num mercado em constante transformação

Effect of brand social media adoption on brand performance

The continuous emergence and decline of social media platforms present challenges for businesses in planning, investing, and justifying their investments in these platforms. Observations have noted that social media often underperforms compared to firm expectations. While existing academic marketing research typically assumes social media adoption and focuses on the deployment of tactical decisions (e.g., when to post, what to post, achieving virality, or managing brand firestorms), the causal impact of social media adoption on firm performance as a strategic decision has not been addressed. Drawing on theories such as the resource-based view (RBV), and organizational learning, this study aims to address three questions related to a firm's strategic decisions: (1) What is the causal impact of social media adoption on short- and long-term firm performance (i.e., financial performance, including abnormal stock returns, sales growth, ROE, Tobin's Q, total Q, and non-financial performance, such as firm innovativeness)? (2) What are the mechanisms that drive short- and long-term performance? (3) What factors influence the effectiveness of a company's social media adoption? Utilizing event studies in both short-term and long-term windows, this research examines stock market performance at the time of social media adoption by firms. Additionally, the causal impacts of social media adoption on firm performance are investigated through an instrumental variable fixed effect, where the number of social media adoptions is considered treatment intensity, and the instruments include peer effects on social media adoption and platform popularity. Drawing on a unique dataset specifically curated for these research questions, this study discovered a positive long-term impact of social media adoption on firm performance. However, this effect materializes only after a firm has adopted multiple platforms, more specifically, after the third adoption. This result can be attributed to the learning effect and risk diversification that firms must endure to experience the reversion of the adoption effect (from negative to positive), in line with the organizational learning theory and RBV. Furthermore, the findings reveal that in the short run, regardless of the number of platforms adopted, firms consistently yield positive returns. The differential results between the long-term and short-term effects help explain the social media paradox, wherein firms expect positive results from social media adoption but often face underperformance. Lastly, an intriguing finding emerged that B2C firms do not experience the initial negative adoption effect of social media (compared to B2B firms), but the final adoption effect magnitude (i.e., the fourth adoption) is smaller than that of B2B firms. This study offers valuable insights into the strategic decision-making process of firms regarding social media adoption and its effects on firm performance.Includes bibliographical references

The power of prediction with social media

Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importanc

Social Media Networks: The Social Influence of Sentiment Content in Online Conversations on Dynamic Patterns of Adoption and Diffusion

The current study is focusing on diffusion and adoption of new digital artifacts. The goal is to explore the social role of user-generated content (UGC) during the diffusion process of digital artifacts in the context of online social networks. The study spans a wide range of analytics methods and tools such as predictive modeling, latent sentiment analysis, data retrieval, and other tools of time-series analysis & visualization. Data collection is conducted on 260 new digital products and more than 105 thousand social network nodes. Results of the study provide a deeper insight into the influence of textual UGC sentiment on new product diffusion and how such a web system (i.e.: online social networks) can help to enable a process of value co-creation. The overall finding shows that Volume of Post and UGC Sentiment have a dynamic impact on Diffusion (Adoption Rate) of digital products. But, the relationships among them depend on certain situations. Specifically, UGC Sentiment has a dynamic impact on Adoption Rate in the early stage of the diffusion process. That is UGC Sentiment and Adoption Rate have a reciprocal relationship during the early stage. However, this relationship was faded out in the later stage. Volume of Post has a positive impact on Adoption Rate throughout the process. Both UGC Sentiment and Volume of Post are also more likely to influence on a single-generation and successful product than a multiple-generation product. Surprisingly, Depth of Post and Ratings did not play a significant role in the diffusion process. The study sheds light on the crowding power and the long-tail effect in online social networks. Findings also offer valuable implications for organizations to set up their strategic vision in terms of targeted marketing, customer relationship management, and information dissemination

Taking Stock of the Digital Revolution: A Critical Analysis and Agenda for Digital, Social Media, and Mobile Marketing Research

Marketing has been revolutionized due to the rise of digital media and new forms of electronic communication. In response, academic researchers have attempted to explain consumer- and firm-related phenomena related to digital, social media, and mobile marketing (DSMM). This paper presents a critical historical analysis of, and forward-looking agenda for, this work. First, we assess marketing’s contribution to understanding DSMM since 2000. Extant research falls under three eras, and a fourth era currently underway. Era 1 focused on digital tools and platforms as consumer and marketer decision aids. Era 2 studied online communications channels (e.g., online forums) as word of mouth marketing “laboratories,” capturing the potential of DSMM for social information transmission. Era 3 embraced the notion of “connected consumers” by considering various antecedents and consequences of socially interconnected consumers in marketplaces. Era 4, currently starting, considers mobile marketing and brings psychological and social theories to bear on emergent DSMM issues. Second, we critique the DSMM literature and advance a series of recommendations for future research. While we find much to applaud, we argue that several problems limit the relevance of this research moving forward and suggest ways to alleviate these concerns moving forward