Marten Risius

Effectiveness of corporate social media activities in increasing relational outcomes

We assess three social media activities of 28 companies on nested hierarchical levels.We apply relationship marketing theory to develop novel social media success measures.Using a social media management tool improves attitudinal loyalty and word of mouth.Operating a broadcasting account type will increase the average word of mouth valence.A conversational communicative approach improves the word of mouth volume and valence. This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception.

Social media management strategies for organizational impression management and their effect on public perception

Social media management strategies are modeled to influence the public perception.Distinguish a tool- and a web-based strategy based on company-generated messages.The public perception is measured based on 15.5million user-generated messages.A mixed method approach is used to qualitatively explore the quantitative results.A tool-based social media management improves word of mouth and attitudinal loyalty. With the growing importance of social media, companies increasingly rely on social media management tools to analyze social media activities and to professionalize their social media engagement. In this study, we evaluate how social media management tools, as part of an overarching social media strategy, help companies to positively influence the public perception among social media users. A mixed methods approach is applied, where we quantitatively analyze 15million user-generated Twitter messages containing information about 45 large global companies highly active on Twitter, as well as almost 160 thousand corresponding messages sent from these companies via their corporate Twitter accounts. Additionally, we conducted interviews with six social media experts to gain complementary insights. By these means, we are able to identify significant differences between different social media management strategies and measure the corresponding effects on the public perception.

A Blockchain Research Framework

While blockchain technology is commonly considered potentially disruptive in various regards, there is a lack of understanding where and how blockchain technology is effectively applicable and where it has mentionable practical effects. This issue has given rise to critical voices that judge the technology as over-hyped. Against this backdrop, this study adapts an established research framework to structure the insights of the current body of research on blockchain technology, outline the present research scope as well as disregarded topics, and sketch out multidisciplinary research approaches. The framework differentiates three groups of activities (design and features, measurement and value, management and organization) at four levels of analysis (users and society, intermediaries, platforms, firms and industry). The review shows that research has predominantly focused on technological questions of design and features, while neglecting application, value creation, and governance. In order to foster substantial blockchain research that addresses meaningful questions, this study identifies several avenues for future studies. Given the breadth of open questions, it shows where research can benefit from multidisciplinary collaborations and presents data sources as starting points for empirical investigations.

You Reap What You Sow? How Knowledge Exchange Effectiveness is Affected by Different Types of Communication in Enterprise Social Media

The way in which people communicate affects their relationship building, social network structures and ultimately the knowledge they receive through their connections. For organizations, an effective knowledge exchange among employees is crucial for the competitive performance. Therefore, companies rely increasingly on social media platforms to facilitate communication and collaboration. To enhance our understanding of successful communication in enterprise social media, we apply human coding and quantitative analysis to the content and tone of 15,505 enterprise microblogging messages created by 1,166 employees of an international financial service provider. Specifically, we develop a model-based operationalization of communication styles and empirically derive two general communication types. Analyzing the quality and number of answers to questions from these communication types, we identify differences in the effectiveness of knowledge exchange. Our results suggest that a more factual-oriented communication type benefits from a higher knowledge exchange effectiveness compared to a primarily self-disclosing communication type.

Effects of transparency: analyzing social biases on trader performance in social trading

Social Trading platforms combine the trading functionalities of classical online broker services with the communication and interaction features of social networks. Next to following other users’ profiles, a main characteristic of social trading platforms is the possibility to follow other users by automatically copying their trades. By being a technologically based financial intermediary that enables individual profit maximization, social trading platforms constitute a contemporary example of financialization. Our empirical analysis of the behavior of traders on a social trading platform provides new insights on financialization related questions regarding the influence of transparency and interaction in delegated investment environments. The disposition effect is a well-studied behavioral bias of investors and traders. Human investors tend to realize returns of their winning positions too early and let unfavorable positions accumulate losses for too long. We find that on social trading platforms the traders’ sensitivity to the disposition effect is influenced by the amount of attention they receive from their followers who invested capital into the traders’ strategy. These novel insights propose a link between principal-agent theory and the disposition effect induced by transparency mechanisms. We extend the literature on trader-investor interaction channels in social trading networks. The results obtained in a social network environment are of high relevance for regulators who have a strong focus on customer protection and financial services regulation. They also provide guidelines for platform designers, traders, investors and social trading platform operators.

A Blockchain-Based Approach Towards Overcoming Financial Fraud in Public Sector Services

In financial markets it is common for companies and individuals to invest into foreign companies. To avoid the double taxation of investors on dividend payment – both in the country where the profit is generated as well as the country of residence – most governments have entered into bilateral double taxation treaties, whereby investors can claim a tax refund in the country where the profit is generated. Due to easily forgeable documents and insufficient international exchange of information between tax authorities, investors illegitimately apply for these tax returns causing an estimated damage of 1.8 billion USD, for example, in Denmark alone. This paper assesses the potential of a blockchain database to provide a feasible solution for overcoming this problem against the backdrop of recent advances in the public sector and the unique set of blockchain capacities. Towards this end, we develop and evaluate a blockchain-based prototype system aimed at eliminating this type of tax fraud and increasing transparency regarding the flow of dividends. While the prototype is based on the specific context of the Danish tax authority, we discuss how it can be generalized for tracking international and interorganizational transactions.

Differential Emotions and the Stock Market - The Case of Company-specific Trading

Practitioners and researchers alike increasingly use social media messages as an additional source of information to analyse stock price movements. In this regard, previous preliminary findings demonstrate the incremental value of considering the multi-dimensional structure of human emotions in sentiment analysis instead of the predominant assessment of the binary positive-negative valence of emotions. Therefore, based on emotion theory and an established sentiment lexicon, we develop and apply an open source dictionary for the analysis of seven different emotions (affection, happiness, satisfaction, fear, anger, depression, and contempt).To investigate the connection between the differential emotions and stock movements we analyse approximately 5.5 million Twitter messages on 33 S&P 100 companies and their respective NYSE stock prices from Yahoo!Finance over a period of three months. Subsequently, we conduct a lagged fixed-effects panel regression on the daily closing value differences. The results generally support the assumption of the necessity of considering a more differentiated sentiment. Moreover, comparing positive and negative valence, we find that only the average negative emotionality strength has a significant connection with company-specific stock price movements. The emotion specific analysis reveals that an increase in depression and happiness strength isassociated with a significant decrease in company-specific stock prices.

Developing and Evaluating a Readability Measure for Microblogging Communication

Especially due to the recent expansion of social media platforms, researchers and practitioners exert ever growing efforts to advance big data analytics techniques to derive actionable insights from social networks. Although a substantial body of research has shown that readability is of vital importance for the success of text-based communication, currently it is rarely considered in social media research or especially microblogging. In this research project, we intend to develop a readability measure for microblogging messages that is applicable to large scale data analysis and also provides concrete formulation recommendations for single messages. We will combine text mining and machine learning techniques to analyze a sample of approximately 6.8 million Twitter messages from and about 33 large S&P 100 companies to develop a respective readability measure.

Mit Social-Media-Stimmungen Börsenkursbewegungen vorhersagen

Nachrichten aus sozialen Medien werden von Forschern und Praktikern zunehmend als zusätzliche Informationsquelle zur Vorhersage von Börsenkursveränderungen verwendet. Basierend auf Erkenntnissen der psychologischen Emotionslehre und einem etablierten Tool zur Messung von Stimmungen (englisch Sentiment) in Textnachrichten, haben wir ein Sentiment-Tool entwickelt, mittels dessen man die Stärke von sieben verschiedenen Emotionen in Textdokumenten erfassen kann.