The increasing availability of open data promises transparency and economic growth. Although several studies have highlighted the value of open data for businesses, the private sector has been slow to adopt it, falling short of expectations. This is due to a lack of knowledge about the real benefits of using open data as a business and few use cases for commercial reuse of open data. Against this backdrop, our study aims to shed light on the commercial use of open data by developing a comprehensive taxonomy consisting of 14 dimensions and deriving five commercial open data user archetypes. Our study contributes to the literature by providing a nuanced understanding of the commercial use of open data, as well as a conceptual framework that structures successful open data use cases, which can be used by businesses and practitioners to identify opportunities for potential open data use scenarios in their respective organizations.

The increasing capabilities of Artificial Intelligence (AI) raise concerns about the risks associated with the technology. The European Union, therefore, proposed the Artificial Intelligence Act aiming to mitigate the risks of AI by fostering their safety and transparency. However, there is controversial debate about its impact on AI innovation. While the AI Act aims to provide legal certainty guiding innovation, the criticism refers to exaggerated bureaucratic burden such as transparency requirements impeding innovation. Based on a multivocal literature review, we examine the impact of the AI Act’s transparency requirements on patenting as a means for AI innovation. Our results indicate that the transparency requirements do not necessarily hinder the patentability of AI innovations. Instead, existing concerns primarily rely on uncertainties within key terms of the AI Act. Accordingly, we propose an improvement suggestion focusing on resolving existing uncertainties.

Federated Learning presents a way to revolutionize AI applications by eliminating the necessity for data sharing. Yet, research has shown that information can still be extracted during training, making additional privacy-preserving measures such as differential privacy imperative. To implement real-world federated learning applications, fairness, ranging from a fair distribution of performance to non-discriminative behavior, must be considered. Particularly in high-risk applications (e.g. healthcare), avoiding the repetition of past discriminatory errors is paramount. As recent research has demonstrated an inherent tension between privacy and fairness, we conduct a multivocal literature review to examine the current methods to integrate privacy and fairness in federated learning. Our analyses illustrate that the relationship between privacy and fairness has been neglected, posing a critical risk for real-world applications. We highlight the need to explore the relationship between privacy, fairness, and performance, advocating for the creation of integrated federated learning frameworks.

The automotive industry is undergoing a profound shift driven by digitalization, prompting the emergence of data-driven business models (DDBMs). As the original equipment manufacturers (OEMs) have already realised a number of DDBMs, their role in the traditional automotive industry is of great interest. This study investigates DDBMs within the European automotive sector, addressing two key objectives: a categorization of existing internal OEM DDBMs and internal OEM challenges. Interviews were made with sixteen automotive experts from four OEMs and two OEM suppliers, working in DDBM-related departments. Hence, five internal OEM DDBM categories were identified: Technical, Product Optimization, Marketing Analysis, Selling Raw Data, and Customer Services. The seven detected challenges that hinder DDBM development include legal constraints, technical complexities, organizational culture, and data knowledge gaps. These findings were guided by theoretical contributions to DDBMs in Information Systems (IS) and practical contributions such as DDBM advices for OEMs.

In today's working world, driven by technological progress, there is a growing need to adapt and update skills to remain competitive. Employers need qualified employees, and employees want to develop their job-related skills, so skills profiles are becoming more prevalent. These profiles comprise skills that individuals bring to their jobs and develop over employment. When used effectively, they offer benefits in attracting, retaining, and developing talent, as well as in staffing and performance management. This paper proposes a data model for such profiles. Drawing upon a systematic literature review and an interview with employees, we derived the critical aspects of a skill profile data model. We further demonstrate the complexity and the need for a structured approach to in-clude a bottom-up perspective. This research contributes to the theoretical understanding of skills profile data models by including the employee perspectives. It further provides insights for organizations to develop a skilled workforce.

Organizations are compelled by a variety of strategic and contextual shifts, as well as the pervasive nature of digital changes, to undertake enterprise transformation (ET). Such endeavors typically entail profound and emergent changes across multiple organizational levels and key constituencies, with business processes being a particularly sensitive aspect due to their ongoing dynamics. Despite the practical significance of business processes within enterprise-level change, there is only little research on the dynamic interplay between business processes and the ET. This study aims filling the gap by proposing an integrated, data-driven ET and business process steering. The designed method framework is based on the utilization of existing data sources of an enterprise. It is informed by a real-world case, dynamic capabilities as kernel theory and the existing literature on business process dynamics, ET, and digital twins in context of processual phenomena. We validated the proposal using a focus group of technology-related executives.

Effective collaboration between humans and artificial intelligence (AI) results in superior decision-making outcomes if human reliance on AI is appropriately calibrated. The emerging research area of human-AI decision-making focuses on empirical methods to explore how humans perceive and act in collaborative environments. While previous studies provide promising insights into reliance on AI systems, the multitude of studies has made it challenging to compare and generalize outcomes. To address this complexity, we use the theoretical lens of task technology fit theory and synthesize study design choices in four meta-characteristics: collaboration, agent, task, and precondition. Our goal is to develop a taxonomy on AI reliance experiment design choices that helps structure research efforts and supports producing generalizable scientific knowledge. Thus, our research has notable contributions to both empirical science in information systems and practical implications for designing AI systems.