Leveraging Big Data for M&A: Towards Designing Process Mining Analyses for Process Assessment in IT Due Diligence

The success of mergers & acquisitions (M&A) depends on the buyer\u27s adequate due diligence (DD) assessment of the target firm. Assessing the target\u27s IT-enabled processes recently emerged as a novel information technology DD (IT DD) responsibility. However, it remains unclear how to operationalize and conduct the process assessment in IT DD. To address this challenge, we propose the big data analytics technology process mining (PM) and follow a design science research approach, based on literature and 12 interviews, to reveal and operationalize requirements for process assessment in IT DD, demonstrate PM to measure the operationalized requirements, and derive design principles and enabling factors to guide the design, implementation, and use of PM for process assessment in IT DD. Consequently, our study contributes to research on IT DD, M&A, and PM and provides practitioners with design knowledge and a prototypical PM artifact to leverage PM for process assessment in IT DD

Recorded Work Meetings and Algorithmic Tools: Anticipated Boundary Turbulence

Meeting recordings and algorithmic tools that process and evaluate recorded meeting data may provide many new opportunities for employees, teams, and organizations. Yet, the use of this data raises important consent, data use, and privacy issues. The purpose of this research is to identify key tensions that should be addressed in organizational policymaking about data use from recorded work meetings. Based on interviews with 50 professionals in the United States, China, and Germany, we identify the following five key tensions (anticipated boundary turbulence) that should be addressed in a social contract approach to organizational policymaking for data use of recorded work meetings: disruption versus help in relationships, privacy versus transparency, employee control versus management control, learning versus evaluation, and trust in AI versus trust in people

Who is Influencing the #GDPR Discussion on Twitter: Implications for Public Relations

On May 25, 2018, the European Union (EU) implemented the General Data Protection Regulation (GDPR) to protect individuals’ privacy and data. This regulation has far-reaching implications as it applies to any organization that deals with data of EU residents. By studying the discussion about this regulation on Twitter, our goal is to examine public opinions and organizational public relations (PR) strategies about GDPR. The results show that the regulation is being actively discussed by a variety of stakeholders, but especially by cybersecurity and IT-related firms and consultants. At the same time, some of the stakeholders that were expected to have a more active role were less involved, including companies that store or process personal data, government and regulatory bodies, mainstream media, and academics. The results also show that the stakeholders mostly have one-way rather than two-way communication with their audiences, thus fulfilling the rhetorical than relational function of PR

Innovation and Challenges of Blockchain in Banking: A Scientometric View

Blockchain has been gaining focus in research and development for diverse industries in recent years. Nevertheless, innovations that impact to the banking nurture a potential for disruptive impact globally for economic reasons; however it has received less scholarly attention. Hence the effect of blockchain technologies on banking industry is systematically reviewed. The relevant literature is extracted from Scopus, Web of Science and bibliometric techniques are applied. While a bulk of earlier papers focuses only on bit coins, a broader framework is envisaged that synthesizes interdisciplinary thematic areas for advancement; hence novelty in current work. A few practical and theoretical implications for stakeholders in view of technology, law and management are discussed

Urban Outbreak 2019 Pre-Analytic “Quick Look”

From 17-18 September 2019, the Uniformed Services University of the Health Sciences (USUHS) - National Center for Disaster Medicine and Public Health (NCDMPH) and the United States Naval War College (NWC) conducted a game at Johns Hopkins University’s Applied Physics Lab (JHU-APL) in Laurel, Maryland. Titled “Urban Outbreak 2019,” this two-day, three-move analytic game was internally developed by the NWC’s Humanitarian Response Program (HRP) and emerged as an output from their 2018 Civilian-Military Humanitarian Response Workshop.https://digital-commons.usnwc.edu/civmilresponse-program-sims-uo-2019/1000/thumbnail.jp

Data Sovereignty in Data Donation Cycles - Requirements and Enabling Technologies for the Data-driven Development of Health Applications

Personalized healthcare is expected to increase the efficiency and the effectiveness of health services using different kinds of algorithms on existing data. This approach is currently confronted with the lack of digital data and the desire for self-determined personal data handling. However, the issue of health data donation is on the political agenda of some governments. Within this work, a knowledge base will be created by reviewing existing approaches and technologies regarding this topic with the focus on chronic diseases. A list of requirements will be derived from which we conceptualize a data donation cycle to demonstrate the challenges and opportunities of health data sovereignty and its future possibilities concerning data-driven health application development. By linking the requirements to technological approaches, the baseline for future open ecosystems will be presented

Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature

The booming adoption of Artificial Intelligence (AI) likewise poses benefits and challenges. In this paper, we particularly focus on the bright side of AI and its promising potential to face our society’s grand challenges. Given this potential, different studies have already conducted valuable work by conceptualizing specific facets of AI and sustainability, including reviews on AI and Information Systems (IS) research or AI and business values. Nonetheless, there is still little holistic knowledge at the intersection of IS, AI, and sustainability. This is problematic because the IS discipline, with its socio-technical nature, has the ability to integrate perspectives beyond the currently dominant technological one as well as can advance both theory and the development of purposeful artifacts. To bridge this gap, we disclose how IS research currently makes use of AI to boost sustainable development. Based on a systematically collected corpus of 95 articles, we examine sustainability goals, data inputs, technologies and algorithms, and evaluation approaches that coin the current state of the art within the IS discipline. This comprehensive overview enables us to make more informed investments (e.g., policy and practice) as well as to discuss blind spots and possible directions for future research

Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning

Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained. As data is distributed across several legal units, potentially even across countries, the fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions -- all while still reaching superior prediction performance. In this work, we propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network. We follow a design science research approach and evaluate our method with respect to feasibility and performance in an industrial use case. First, we show that it is feasible to perform network-wide analyses that preserve data confidentiality as well as limit data transfer volume. Second, we demonstrate that our method outperforms a conventional isolated analysis and even gets close to a (hypothetical) scenario where all data could be shared within the network. Thus, we provide a fundamental contribution for making business networks more effective, as we remove a key obstacle to tap the huge potential of learning from data that is scattered throughout the network.Comment: Preprint, forthcoming at Information Technology and Managemen