Design Theory for Market Surveillance Systems

Watchers, Watched, and Watching in the Digital Age: Reconceptualization of Information Technology Monitoring as Complex Action Nets

Despite increasing studies of information technology (IT) monitoring, our understanding of how IT-mediates relations between the watcher and watched remains limited in two areas. First, either traditional actor-centric frameworks assuming predefined watcher-watched relationships (e.g., panopticon or synopticon) are adopted or monitoring actors are removed to focus on data flows (e.g., dataveillance, assemblages, panspectron). Second, IT monitoring research predominantly assumes IT artifacts to be stable, bounded, designed objects, with prescribed uses which provides an oversimplified view of actor relationships. To redress these limitations, a conceptual framework of veillance applicable to a variety of possible IT or non-IT-mediated relationships between watcher and watched is developed. Using the framework, we conduct a conceptual review of the literature, identifying IT-enabled monitoring and transformations of actors, goals, mechanisms and foci and develop an action net model of IT veillance where IT artifacts are theorized as equivocal, distributable and open for diverse use, open to edits and contributions by unbounded sets of heterogenous actors characterized by diverse goals and capabilities. The action net of IT veillance is defined as a flexible decentralized interconnected web shaped by multidirectional watcher-watched relationships, enabling multiple dynamic goals and foci. Cumulative contributions by heterogenous participants organize and manipulate the net, having an impact through influencing dispositions, visibilities and the inclusion/exclusion of self and others. The model makes three important theoretical contributions to our understanding of IT monitoring of watchers and watched and their relationships. We discuss implications and avenues for future studies on IT veillance.

Exploring Signals for a Nuclear Future Using Social Big Data

Since the start of the new Korean government in 2017, the Korean nuclear energy system has undergone a major change. This change in national energy policy can be forecasted by analyzing social big data. This study verifies whether future forecasting methodologies using weak signals can be applied to Korean nuclear energy through text mining the data of web news between 2005 and 2018, comparing and applying the methodology to notable events (i.e., the UAE nuclear power plant (NPP) contract and nuclear phase-out). In addition, we predict what changes will be made in the Korean nuclear energy system post-2019. Keywords extracted through text mining were quantitatively classified into a weak signal or a strong signal using a Keyword Emergence Map (KEM) and a Keyword Issue Map (KIM). The extracted keywords predicted the contract of the UAE NPPs in 2009 and nuclear phase-out in 2017. Furthermore, keywords revealing future signals beyond 2019 were found to be ‘nuclear phase-out’ and ‘wind energy’. The weak-signal methodology can be applied as a tool to predict future energy trends during the current circumstance of the rapidly changing world energy market.

An Innovative Management Perspective for Organizations through a Reputation Intelligence Management Model

Banking companies aiming to maintain their sustainability in financial markets need to develop an integrated management based on the most important intangibles assets of relational capital. Decision- makers need to analyze and understand a huge volume of opinions continuously generated in digital ecosystems about emotions and feelings that their stakeholders associate with the performance and communication of the brand. Current tools of management fail to consider transversal and holistic models, which study the frequency and value of existing relationships between the relational capital and intangible assets. In this research, an innovative management model based on reputation intelligence is proposed. This model incorporates methodology from business intelligence models, through OLAP and data mining techniques, to analyses the complex relationships among intangible assets experience, emotion and attitude. The proposed model was applied to companies in the banking sector and the results obtained permit a conclusion about the kinds of relationships for these intangibles in each bank.

A Penny Is Worth a Thousand? Investigating the Relationship Between Social Media and Penny Stocks

Increasingly more investors are seeking information from social media to help make investment decisions. Considering that information on penny stocks is often less reported in traditional media, investors may rely more on social media to obtain such information for investment advice. Although previous research has shown that stock opinions in traditional media is a possible predictor of stock returns, no previous research has considered the effect of the stock opinions in social media on these stocks in terms of future stock performance and the moderation effect of penny stocks. In this research, we studied the relationship between social media and the financial performance of penny stocks. We used the net proportion of positive words in stock articles in social media to help predict the future stock performance for penny stocks. The moderation effect of penny stocks on the net fraction of positive words was found to be significant in short terms, revealing a stronger relationship between social media and stock performance at lower price and market capitalization (MC) levels. Based on the findings, we proposed simple strategies utilizing social media and our measure. The results of our applications will be of interest to individual and institutional investors, shareholders, and regulators.