Introduction to Machine Learning and Network Analytics in Finance Minitrack

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The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Research has extensively demonstrated that healthcare industry has rapidly implemented and adopted information technology in recent years. Research in health information technology (HIT), which represents a major component of the Hawaii International Conference on System Sciences, demonstrates similar findings. In this paper, review the literature to better understand the work on HIT that researchers have conducted in HICSS from 2008 to 2017. In doing so, we identify themes, methods, technology types, research populations, context, and emerged research gaps from the reviewed literature. With much change and development in the HIT field and varying levels of adoption, this review uncovers, catalogs, and analyzes the research in HIT at HICSS in this ten-year period and provides future directions for research in the field

Your Sentiment Matters: A Machine Learning Approach for Predicting Regime Changes in the Cryptocurrency Market

Research suggests that a significant number of those investing in cryptocurrencies do not follow what we might call rational, profit-maximizing behavior. We also know that with the progressive lowering of entry barriers to online trading platforms, an increasing number of inexperienced investors are investing in cryptocurrencies. Increasingly, the behavior of investors contradicts the predictions made by traditional financial models and challenges the assumptions on which such models have previously relied when anticipating returns on cryptocurrency investments. To overcome this issue we develop a random forest model which we train with features stemming from a sentiment analysis performed on data generated by cryptocurrency enthusiasts using Twitter, Google Trends, and Reddit. Our findings show that such features have an important role to play in capturing the behavior of cryptocurrency investors and increase our model’s ability to anticipate regime changes in the cryptocurrency market. Our model outperforms the predictive ability of the Log-Periodic Power Law model—currently, the model most widely-used to predict regime changes in financial markets. These results imply that scholars and practitioners aiming to understand and predict the development of cryptocurrency markets stand to benefit from analyzing social media data generated by cryptocurrency enthusiasts

Institutionalizing Analytic Data Sharing in SME Ecosystems – A Role-Based Perspective

There is a variety of reasons that sharing data among Small and Medium-Sized Enterprises (SMEs) carries business potential, particularly for analyti-cal applications. But outside a few niche domains, the number of success stories for data sharing is rather modest. Based on a qualitative study and first experiences from a research project with pilot im-plementations, we argue that this is mainly due to a lack of an institutionalized governance structure: Founding a separate legal entity for data sharing and analysis can address core concerns regarding sharing valuable data assets. However, this requires a well-calibrated set of defined roles for the in-volved partners. Based on our results we propose a first concept on delineating and mapping out those roles

Rebuilding Evolution: A Service Science Perspective

This paper explores a simple idea and asks a simple question: What determines the speed limit of evolutionary processes, and might there be ways to speed up those processes for certain types of systems under certain conditions? Or even more simply, how rapidly can complex systems be rebuilt? To begin with, the universe can be viewed as an evolving ecology of entities. Entities correspond to types of systems - from atoms in stars to organisms on Earth to ideas in the heads of people. Service science is the study of the evolving ecology of service system entities, complex socio-technical systems with rights and responsibilities – such as people, businesses, and nations. We can only scratch the surface in this paper, but our explorations suggest this is an important research question and direction, especially as we enter the cognitive era of smart and wise service systems. For example, it takes a child multiple years of experience to learn language and basic social interactions skills, but could machine learning algorithms with the proper data sets learn those capabilities in a fraction of the time

Boosting Innovation for the Development of Smart-Service Factories of the Future: The Cases of the Federal State of Vorarlberg and its Neighbouring Regions

Factory of the Future is an initiative of the European Commission. It is highly narrative and describes the transformation of “ordinary” manufacturing operations and structures to fully-integrated cyber-physical manufacturing systems. Basing on case study research performed in the greater area of Vorarlberg, this article aims to explore how Small and Medium Enterprises (SMEs) in the field of manufacturing can evolve to smart-service Factories of the Future. It takes a mixed-methods approach with quantitative research (questionnaire) and qualitative case study interviews and provides findings about three main topics in service system engineering: “transformation of an operational need into a description of system performance parameters”, “integration of related technical parameters and assurance of compatibility of all physical, functional and program interfaces” and “integration of reliability, maintainability, safety, survivability, human and other such factors”. As it turns out, increased servitization measures, service management, service performance and service quality by development of service-oriented architectures (SOA) are key to evolve to a smart-service Factory of the Future

Innovative Concepts within Knowledge Management

In our increasingly knowledge-based society the need for innovative concepts within the discipline of Knowledge Management (KM) becomes clear. Therefore, this article aims to shed light on current and uprising innovative technologies and concepts within the discipline of KM. This study conveys recent and previous scientific literature on the relevance of uprising innovative concepts within the various dimensions of KM. We conducted a systematic literature review (SLR) on various literature sources to cover the whole spectrum of innovative KM approaches. All 37 reviewed articles originate from acknowledged sources and were written in English. The findings show, which innovative concepts show relevance within KM, how they are classified into the three innovation categories social, technological, and organizational, how they manifest within KM and what to expect from future KM innovations