David Antons

Patent classifications are central to technology and innovation research, providing structured yet detailed technological information. However, the hierarchical design of widely used systems such as IPC and CPC imposes three long-standing constraints: limited horizontal comparability within domains, the separation of technologies into siloed domains, and inconsistencies in classification across patent office jurisdictions. The contribution of this paper is threefold: Firstly, we address these constraints conceptually and methodologically by developing a machine learning method, based on Meta’s Llama 3, that assigns Japanese F-terms - granular, attribute-level descriptors - to patents worldwide, and simultaneously generates high-dimensional vector representations that map all F-terms into a unified technological space. Secondly, we operationalize this capability by releasing both the trained classifier and the complete vector dataset, enabling researchers and practitioners to apply F-term–based methods beyond Japan, to conduct consistent cross-national analyses, and to measure technological distances across the entire innovation landscape. Thirdly, we outline a research agenda that leverages these new resources to advance the study of technological change, innovation systems, and cross-domain analysis. While the classifier’s performance can be further enhanced, this work provides a new empirical basis for studying technological change and innovation with greater granularity, cross-domain comparability, and consistency across jurisdictions.

There is an extensive body of research exploring the role of external partners in the innovation process. Various research concepts such as open innovation, supplier involvement, strategic alliances, or university–industry collaboration approach this phenomenon from different angles. They all share a common focus on collaboration with external partners to facilitate the generation of innovation. However, despite this common interest, links and differences between these concepts are not clearly outlined, leading to a fragmented research landscape where different concepts examine similar research questions in a parallel manner without cross-fertilising insights. To address this challenge, a computational literature review of 17 research concepts about external partners in innovation is conducted in this study, analysing 7,356 full-text articles. Our study provides an integrated research landscape of 97 topics, revealing a predominance of phenomenological research and underrepresentation of conceptual and methodological studies. Using network analysis, it organises the research field into four clusters: Collaborative Ventures & their Management, Supply Chain Collaboration, Collaboration in Regional Clusters, and Open Innovation Approaches & Ecosystems. Each cluster exhibits distinct thematic focuses, bibliometric characteristics, and temporal trajectories. In particular, our review shows the distinct position of the Cluster about Open Innovation Approaches and Ecosystems. While this cluster attracts high degrees of attention, it also faces the risk of becoming a fragmented research field. Lastly, our study uncovers points of convergence and significant overlaps between the four clusters, especially between the clusters on Collaborative Ventures & their and Open Innovation Approaches & Ecosystems, and derives directions for future research.

What is the point in hacking computer systems when organizations voluntarily disclose personal data to anyone who asks convincingly? We show that the European GDPR is paradoxically exploitable for identity theft despite being designed to protect personal data. Subject access requests (SARs) according to its “right of access” (Article 15) can be weaponized by impersonating a victim and submitting fraudulent SARs in their name. We task attackers with stealing the personal data of three volunteers (highly privacy aware person, average user, and semipublic figure) in a real-world setting. These attacks could be replicated by just about anyone. Yet, they obtained sensitive personal data, including addresses, phone numbers, national ID and bank account information, and insurance data. Based on 718 submitted SARs and 21 interviews with data protection officers, we tell a frightening, yet fascinating story of how these identity thefts unfold, expose flaws in how organizations process SARs, and uncover a systemic weakness in the GDPR. We analyze the underlying factors enabling such attacks, assess their real-world impact, and explore mitigation options for individuals, organizations, and lawmakers. Our insights have important implications for how data privacy and data security interrelate and how we manage and regulate them.

Transitioning from a linear to a circular bioeconomic system is crucial for sustainable development. Among political priorities and a thriving bioeconomy, consumer acceptance is vital. This article explores how consumers perceive innovative biobased products, emphasizing attitudes and acceptance. Drawing from established theories and employing a two-stage research approach, it uncovers broad trends and focused insights. Embarking on the shift from a linear economic model to a circular bioeconomic system, this article delves into sustainability—specifically, consumer acceptance of biobased products. Grounded in the Theory of Planned behaviour, the research focuses on attitudes, revealing their complex role in decision-making. In Study 1, factors like contact sensitivity, safety, emotions, and environmental impact interconnect, exposing complex correlations. Study 2 unveils consumer archetypes—rational evaluators, risk minimizers, and fatalistic consumers—emphasizing the role of trust, safety, emotions, and plausibility in shaping attitudes. This nuanced understanding of consumer evaluations serves as a strategic guide for navigating toward a circular bioeconomy. The article urges future research, emphasizing sustained behaviour changes and the lasting impact of marketing strategies, laying the groundwork for a sustainable and environmentally conscious future.

The interdisciplinary nature of digital transformation requires a shift from theorizing within disciplinary silos to leveraging theory in a way that helps bridge disciplinary boundaries. This study seeks to contribute to this objective by demonstrating that service-dominant logic (S-D logic) as a meta-theory has strong potential to connect IS, service, innovation, and marketing scholars as well as practitioners in understanding and shaping digital transformation. For this purpose, we conduct a computational literature review and create a map of the S-D logic research landscape. Based on this map, we assess how the S-D logic can help address some of the key gaps in our understanding of digital transformation leading to a cross-disciplinary agenda for future research. We argue that an S-D logic perspective on digital transformation can also be informative for practitioners involved in digital transformation activities, whom we equip with an actionable roadmap for implementation.

When common‐pool resources such as freshwater, clean air, and fisheries span national borders, the collective action problems encountered are particularly severe. This study explores the role of polycentric governance systems in overcoming these pressing problems, which frequently underlie global grand challenges. Integrating political economy and management research, we hypothesize about how two governance mechanisms – international treaties and multi‐stakeholder organizations – shape the likelihood of cooperation and conflict between countries. Leveraging unique, longitudinal data capturing interactions of countries bordering international river basins, our empirical analysis reveals two main findings. First, we find that the specification of multi‐stakeholder organizations enhances water‐related cooperation and reduces water conflict among countries, while the specification of international treaties enhances cooperation but does not affect conflict. Second, we find that leaving one of these governance mechanisms less specified than the other actually improves, rather than harms, relationships between countries. Our findings point to a superior governance configuration that simultaneously enhances cooperation and constrains conflict. This configuration combines: (1) treaties that establish property rights but leave procedural rules and uncertainty management provisions less established with (2) multi‐stakeholder organizations that define processes for making decisions, sharing information, engaging the public, and resolving disputes.

In this study, we draw on signalling theory to examine the organization‐level relationship between reputation and employee movement in and out of organizations. Focusing on organizations’ media reputations relative to their peers, we map all employee flows between organizational dyads in a population of English hospitals. We find support for our main argument that an organization's relative reputation predicts employee mobility above and beyond what can be explained by its absolute level of reputation. In particular, our findings suggest that the number of employees moving between two organizations is highest when both organizations have a high reputation. We refer to this as the elites’ mutual attraction effect (EMA‐effect). We also find that the motives of voluntary leavers and geographical distance between two organizations influence the strength of this EMA‐effect. Overall, this study shows that developing and testing dyadic theory can extend research on organizational reputation and collective turnover in meaningful ways.

The Internet of Things (IoT) is permeating all areas of life. However, connected devices are associated with substantial risks to users’ privacy, as they rely on the collection and exploitation of personal data. The case of connected cars demonstrates that these risks may be more profound in the IoT than in extant contexts, as both a user's informational and physical space are intruded. We leverage this unique setting to collect rich context-immersive interview (n = 33) and large-scale survey data (n = 791). Our work extends prior theory by providing a better understanding of the formation of users’ privacy risk perceptions, the effect such perceptions have on users’ willingness to share data, and how these relationships in turn are affected by inter-individual differences in individuals’ regulatory focus, thinking style, and institutional trust.

Topic modeling is a popular technique for exploring large document collections. It has proven useful for this task, but its application poses a number of challenges. First, the comparison of available algorithms is anything but simple, as researchers use many different datasets and criteria for their evaluation. A second challenge is the choice of a suitable metric for evaluating the calculated results. The metrics used so far provide a mixed picture, making it difficult to verify the accuracy of topic modeling outputs. Altogether, the choice of an appropriate algorithm and the evaluation of the results remain unresolved issues. Although many studies have reported promising performance by various topic models, prior research has not yet systematically investigated the validity of the outcomes in a comprehensive manner, that is, using more than a small number of the available algorithms and metrics. Consequently, our study has two main objectives. First, we compare all commonly used, non-application-specific topic modeling algorithms and assess their relative performance. The comparison is made against a known clustering and thus enables an unbiased evaluation of results. Our findings show a clear ranking of the algorithms in terms of accuracy. Secondly, we analyze the relationship between existing metrics and the known clustering, and thus objectively determine under what conditions these algorithms may be utilized effectively. This way, we enable readers to gain a deeper understanding of the performance of topic modeling techniques and the interplay of performance and evaluation metrics.