Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

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 5 million 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

IS IT REALLY ABOUT FACTS? THE POSITIVE SIDE OF MEFORMING FOR TURNING SELF-DISCLOSURE INTO SOCIAL CAPITAL IN ENTERPRISE SOCIAL MEDIA

Through the way in which people communicate they can affect their relationships, social network structures and ultimately the social capital acquired through their connections. We understand social capital from an ego-centric point of view as the resources obtained through the relationships among people within social networks. Social capital is a key factor for the performance of individuals and organizations. Therefore, companies increasingly implement social media platforms to facilitate communication among employees and to leverage the social capital benefits. To enhance our understanding of successfully building social capital through communication in enterprise social media, we apply human coding and quantitative analysis to the content and tone of 6,306 enterprise microblogging messages created by 136 employees of an international financial service provider. In accordance with existing information systems (IS) literature, we develop a model-based operationalization of communication styles and empirically abstract the two general communication types of In- and Meformers. Analysing the individual network structures of these communication types, we identify differences in the effectiveness building of social capital. Contrary to preliminary findings in public social media, our results suggest that a more self-disclosing communication type benefits from a higher efficacy in building social capital compared to a primarily factual-oriented communication type

The side effect of scrutinising traders in social trading platforms

Increased transparency in investor/trader relationships induces behavioural biases, write Florian Glaser and Marten Risiu

TOWARDS AN UNDERSTANDING OF CONSPIRACY ECHO CHAMBERS ON FACEBOOK

Selective online exposure to information that serves to only affirm people’s opinions or is strongly aligned with their interests is considered to be a major issue in modern societies. Echo chambers, for example, are online environments in which users are only exposed to confirming opinions and alternative voices are excluded or discredited. Echo chambers are considered to be particularly dangerous, because they may lead to polarization and even radicalization. Social media facilitate the formation of echo chambers as described in the Social Identity Theory by means of homophily and depersonalization. This can be especially harmful in the case of conspiracy beliefs, where particularly extreme opinions lead to a stronger seclusion from society, encourage socially destructive actions, and curate Fake News. In our research we will assess different echo chambers in terms of actively established common patterns of consumed online information sources. To that end, we analyse the news source Likes from over 7,000 users with their approximately 1,450,000 Likes on Facebook. We intend to identify different types of Facebook echo chambers with a focus on conspiracy groups, understand distinguishing characteristics in communicative behaviour of the conspiracy groups on Facebook and explore unique characteristics of users in conspiracy echo chambers

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

Social Media Management Strategies for Organizational Impression Management and their Effect on Public Perception

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 15 million 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. (C) 2015 Elsevier B.V. All rights reserved

IS IT WORTH IT? DISMANTLING THE PROCESS OF SOCIAL MEDIA RELATED SALES PERFORMANCE

Social media platforms present unique possibilities for companies to interact with their customers and take up a key role in building relationships. A substantial body of research has demonstrated the impact of social media regarding, for example, brand awareness and corporate reputation. However, little is known concerning the financial Return on Investment from social media engagement and specific strategies to leverage it. To this end, the study draws on relationship marketing theory to develop and operationalise a research model, which understands objective firm performance in terms of sales as a result of relationship antecedents (i.e., corporate investment and dyadic similarity) mediated through the customer-perceived relationship strength. To test the assumed research model, we collect and analyse a dataset of over 1.5 million Twitter messages revolving around ten car manufacturers and measure the impact on new car registration volumes. The results of this study suggest that companies can increase their sales volume through greater relationship investment (i.e., by providing interest group-specific information) and by adopting a social media strategy that promotes the users’ relationship satisfaction (i.e., raises the share of voice within user messages)

Blockchain Decision Path: When to Use Blockchains? Which blockchains do you mean?

Many organizations are looking at blockchain technologies. However, the drawbacks of blockchain databases (e.g., scalability, capacity, latency, privacy) mean that the technology is not always appropriate. This article presents a ten-step decision path that can help determine whether the application of blockchain is justified and, if so, which kind of blockchain technology to use. We describe how this decision path was used to develop a blockchain prototype for the Danish maritime shipping industry

Understanding the Current State of Knowledge and Future Directions of Doxing Research: A Social Cognitive Theory Perspective

Doxing is the public release of personal information with harmful intentions. It is an emergent online practice that is used in social protest movements, for personal revenge, or even as a means of cyber-warfare. To amalgamate the ambiguous multi-disciplinary research, we summarize the current state of knowledge and identify directions for doxing research. To that end, this study applies social cognitive theory in a systematic review of 28 doxing papers and provides an overview of current doxing research trends. The review shows that doxing research has been primarily focused on the environmental perspective, particularly the legal regulation of doxing while neglecting personal and behavioral factors. We identify a series of research questions to guide and inspire future research on the role of digital technologies in this emerging issue

A Review of Hate Speech Detection: Challenges and Innovations

Hate speech on social media platforms has severe impacts on individuals, online communities, and society. Platforms are criticized for shirking their responsibilities to effectively moderate hate speech on their platforms. However, Various challenges, including implicit expressions, complicate the task of detecting hate speech. Consequently, developing and tuning algorithms for improving the automated detection of hate speech has emerged as a crucial research topic. This paper aims to contribute to this rapidly emerging field by outlining how the adoption of natural language processing and machine learning technologies has helped hate speech detection, delving into the latest mainstream detection techniques and their performance, and offering a comprehensive review of the literature on hate speech detection online including the notable challenges and respective mitigating efforts. This paper proposes the integration of interdisciplinary perspectives into deep learning models to enhance the generalization of models, providing a new agenda for future research