Speak Up! Examining Voice as a Construct in Information Systems Literature

Voice research has traditionally used a deliberative perspective, in which individuals carefully calculate individual and situational facts to determine whether or not to speak up. Voice offers information systems scholars many avenues for research as prior literature indicates its benefits such as reducing cyberbullying in online settings, advocating for equal rights, and engaging in social movements through social media. These studies examine how individuals can express their concerns to induce change. To date, IS scholars rarely study the construct of voice directly but discuss the importance of reporting wrongdoing or advocating for others. Besides, the majority of studies examining voice are situated in organizational contexts and little research has examined voice in technology-mediated settings. As a result, there is a lack of systematic understanding of the underlying factors that facilitate or inhibit voice behavior when information and communication technologies are used (ICTs). This paper synthesizes current research on voice, making connections between divergent literature (i.e., management and information systems) to develop a framework for studying voice in online settings

Exploring the Relationship between Influencers’ Sentiment and Cryptocurrency Fluctuation through Microblogs

Scholars and practitioners increasingly recognize the importance of microblogs in capturing eWord of Mouth (eWoM) and their predictive power for cryptocurrency markets. This research in progress paper examines the extent to which microblog messages are related to bitcoin fluctuation. Building on information systems and finance literature, we examine the interactions between influencers’ extreme sentiment and the bitcoin fluctuation using natural language processing techniques and hypothesis testing. Our preliminary results show when influencers express extreme sentiment, in favour or against bitcoin, it is less likely that their tweets are related to future bitcoin fluctuation. However, when their extreme tweets are in-depth and unique, this negative relationship is moderated. Overall, our findings reveal that influencers’ sentiment is an important predictor in determining bitcoin fluctuation, but not all tweets are of equal impact. This study offers new insights into social media and its role in the cryptocurrency market

Information or noise: How Twitter facilitates stock market information aggregation

We assess the relevance of Twitter for stock-relevant information dissemination in financial markets on the single stock level. We use a unique dataset including more than 12 million Twitter feeds linked to specific firms. Using intraday data for the computation of advanced trading metrics, such as effective spreads, intraday volatility, and a daily version of the microstructure variable probability of informed trading (PIN), we measure the impact of Twitter activity on trading and information dissemination. The PIN model indicates that more uninformed than informed traders rush to the market along with rising Twitter activity. These results indicate that Twitter serves as an excellent indicator of news that is relevant for the stock market. However, we show that Twitter does not lead traditional news channels. In contrast, Twitter activity follows the market and has no predictive power with regard to future stock trading volume or volatility on the single stock level

FINFLUENCERS: OPINION MAKERS OR OPINION FOLLOWERS?

This paper explores the concept of Finfluencers: financial social network actors with high potential social influence. Our research aims to clarify whether Finfluencers drive or are influenced by the broader social network sentiment, thereby establishing their role as either opinion makers or opinion followers. Using a dataset of 71 million tweets focusing on stocks and cryptocurrencies, we grouped actors by their social networking potential (SNP). Next, we derived sentiment time series using state-ofthe- art sentiment models and applied the technique of Granger causality. Our findings suggest that the sentiment of Finfluencer actors on Twitter has short-term predictive power for the sentiment of the larger group of actors. We found stronger support for cryptocurrencies in comparison to stocks. From the perspective of financial market regulation, this study emphasizes the relevance of understanding sentiment on social networks and high social influence actors to anticipate scams and fraud

FinTech in Information Systems Research: A 2010-2020 Review of the AIS Senior Scholars’ Basket

Financial technology (FinTech) has emerged as a significant innovative and transformative force where the primary drivers are disruptive information systems technologies. As a result of the amplified role of FinTech, this article presents a review of FinTech research published in the top Information Systems (IS) journals over the 2010-2020 time period to assess the FinTech contributions made during the 10-year period by IS researchers. There is a unique opportunity for FinTech researchers to learn from and extend the work that has already been published in the highly correlated IS field. Our analysis reviewed 74 articles on a variety of FinTech topics published in the “Association for Information Systems Senior Scholars’ Basket of the top eight ranked IS academic journals. Across the selected IS publications, our findings compared research methodologies, topic areas investigated, and research trends. Our findings demonstrate that several methodologies are understudied or absent and a variety of FinTech topic areas require further exploration

Trading on Cryptocurrency Markets: Analyzing the Behavior of Bitcoin Investors

Driven by innovative information technologies, the financial industry is facing a recent disruptive fintech revolution. One emerging technology within this field is cryptocurrency, aiming to change the future means of payment. In this paper, we study Bitcoin exchange trading and examine what factors influence the behavior of different cryptocurrency investor types. To answer this question, market bids are considered in form of investors\u27 offers and orders as a proxy for their trading behavior. First, an unsupervised clustering technique is applied in order to group different types of investors based on similarities in trading behavior. Second, a supervised classification mechanism is used on social media news to measure the sentiment influencing trading decisions. Among other indicators this bullishness is integrated in an autoregressive distributed lag (ARDL) model to identify the factors influencing the trading behavior of investor types. Besides large investors, foreign traders and speculators, cryptocurrency-specific market participants are characterized in the form of miners. With identifying indicators driving investors\u27 actions (i.e., macro-financial fundamentals, technical trading indicators, technological measures and market sentiment), this study contributes to recent research by explaining the trading behavior on cryptocurrency markets and its impact on exchange rates

Unleashing the Potential of Argument Mining for IS Research: A Systematic Review and Research Agenda

Argument mining (AM) represents the unique use of natural language processing (NLP) techniques to extract arguments from unstructured data automatically. Despite expanding on commonly used NLP techniques, such as sentiment analysis, AM has hardly been applied in information systems (IS) research yet. Consequentially, knowledge about the potentials for the usage of AM on IS use cases appears to be still limited. First, we introduce AM and its current usage in fields beyond IS. To address this research gap, we conducted a systematic literature review on IS literature to identify IS use cases that can potentially be extended with AM. We develop eleven text-based IS research topics that provide structure and context to the use cases and their AM potentials. Finally, we formulate a novel research agenda to guide both researchers and practitioners to design, compare and evaluate the use of AM for text-based applications and research streams in IS

Dialectic tensions in the financial markets: a longitudinal study of pre- and post-crisis regulatory technology

This article presents the findings from a longitudinal research study on regulatory technology in the UK financial services industry. The financial crisis with serious corporate and mutual fund scandals raised the profile of compliance as governmental bodies, institutional and private investors introduced a ‘tsunami’ of financial regulations. Adopting a multi-level analysis, this study examines how regulatory technology was used by financial firms to meet their compliance obligations, pre- and post-crisis. Empirical data collected over 12 years examine the deployment of an investment management system in eight financial firms. Interviews with public regulatory bodies, financial institutions and technology providers reveal a culture of compliance with increased transparency, surveillance and accountability. Findings show that dialectic tensions arise as the pursuit of transparency, surveillance and accountability in compliance mandates is simultaneously rationalized, facilitated and obscured by regulatory technology. Responding to these challenges, regulatory bodies continue to impose revised compliance mandates on financial firms to force them to adapt their financial technologies in an ever-changing multi-jurisdictional regulatory landscape