Informatik und Kommunikation
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The slow progress of digital transformation in public organizations has been a source of concern, not least due to a lack of digital government competences (DGCs). We approach the persisting issue by drawing on resource-based theory and human capital. We conceptualize DGCs as knowledge, skills, abilities, and other characteristics (KSAOs), which compose digital government-related human capital resources. We further posit that a lack of such resources on the individual level negatively impacts transformation capabilities on the organizational level. To substantiate our claims, we perform a competence assessment based on job advertisements. We analyzed 2,869 job ads from local governments in Germany, Australia, and New Zealand using a job-mining approach that combines large language models and topic modeling. Our findings reveal that DGCs are notably scarce in these job ads, which helps explain the slow progress of digital government pursuit. Our study contributes to research by providing a novel theoretical foundation for the literature stream on DGCs, framing them as KSAOs as the foundation for human capital resources. We also share our job-mining pipeline for replication and adaptation by other researchers. Finally, we propose four action points to address the challenges from a hiring perspective.
The General Data Protection Regulation (GDPR) has been in force since May 2018. Organizations and individuals must comply with this legislation if they collect or process the personal information of residents of the European Union. Prior research has focused on the examination of the privacy policies of the most frequently visited websites or mobile applications with the highest number of installations. The present study assesses the privacy policies of a less explored field: medium-sized town administrations. For this purpose, we analyzed and evaluated 644 privacy policies collected in Austria, Germany, and Ireland, focusing on their coverage of different data practice categories and GDPR-related dictionary phrases. We employed semi-automated data collection methods, deep learning and NLP techniques, and manual labor to perform this analysis. Our findings provide insight into the privacy policy landscape of medium-sized town administrations, where Austria and Germany exhibit a highe r average coverage of GDPR data practice categories than Ireland.
Four decades of security and privacy research: evolution of topics, impact, and the community
(2026)
As digital technologies become increasingly embedded in societal infrastructure, IT security and privacy (S&P) have become critical for protecting sensitive information and preserving trust. These domains have evolved from foundational security measures to address complex challenges introduced by artificial intelligence, regulatory frameworks, and decentralized technologies. This paper presents a longitudinal analysis of the evolution of IT S&P research from 1980 to 2023, analyzing over 13k papers from the most relevant venues. Employing the frameworks of established theories from social sciences, i.e. Latour’s actor–network theory, and Bourdieu’s forms of capital, along with Leydesdorff’s key dimensions in scientometrics, we discuss the evolution of research topics and highlight research priorities in the past and today. We apply modern natural language processing techniques to build a taxonomy of research topics within the S&P community. Using this taxonomy, we analyze the community’s thematic development, tracing its growth from 5 topics in the 1980s to 100 distinct research topics, reflecting the field’s expanding scope and complexity. Analyzing 0.5M authors, we demonstrate strong collaboration networks in the IT S&P community. We also demonstrate that the proportion of female authors in this community has remained relatively constant over the decades, despite an increase in their research activity in recent years. Finally, we assess factors impacting paper citations, author networks, and the linguistic evolution of the community. This study enhances the understanding of the S&P research community, providing valuable insights into future directions. The data underlying this article, including the analysis code and data processing pipeline, are available in the repository at: https://pulse-of-cybersecurity.com/, which also provides an interactive webpage for exploring our results.
Using AI adequately is necessary for user companies to remain competitive. Studies show that nevertheless many companies are hesitant in this regard. In relation to the assumption that people’s ability to act is influenced by a lack of trust, particularly in the context of AI, we conducted a study as part of the TrustKI research project to analyze which factors are relevant to documenting trustworthiness in the context of AI. Our evaluation revealed that users demand holistic transparency; the provision of relevant information on the AI solution and proof of technical expertise is not sufficient to build trust, but there is a demand from users for specific information about the respective company. Based on the generally recognized components, we were able to identify further dimensions to provide the required information even more precisely. Thus, the study allows us to propose a preliminary set of information requirements for AI providers.
Anthropomorphism and trust are two variables that are measured in a multitude of HRI studies. These two variables are often treated as dependent variables influenced by a particular robot design or behavior. However, psychological literature suggests that people have individual tendencies to anthropomorphize, influencing these two variables. We ran an experiment in which 70 participants were confronted with two robots, a humanoid and a non-humanoid robot. Before exposing them to the robots, they were screened for their individual tendency to anthropomorphize. After exposure, they rated the robots in terms of anthropomorphism and trust. With this experiment, we show that the individual tendency to anthropomorphize has a clear impact on measuring anthropomorphism and trust with frequently used questionnaires. Given our results, we emphasize that future studies should screen participants for their tendency to anthropomorphize to avoid unwanted biases.
In supply chain management of port operations querying and visualizing network data often requires complex joins, nested queries and predefined reporting templates, which can hinder exploratory analysis and decision-making. When relational databases become too rigid and cumbersome for transactional processing, we envision an alternative approach using graph data models, provided by transforming entities (e.g. containers, vessels, terminals) into nodes and their relationships (e.g. arrival, loading, handling) into edges. To investigate the potential benefits of transforming relational supply chain data into a graph-based model, we designed and implemented a structured approach that integrated data processing, transformation and performance analysis across both relational and graph databases.
Sustainability communication as an increasingly circumscribable research field grounds in communication, management and marketing concepts. This chapter focuses on communication of sustainability, sustainable consumption and green claiming and discusses the effects of non-transparent sustainability communication and greenwashing. It specifically presents a study on how the suspicion of greenwashing in green advertising affects attitudes towards the ad and the brand, as well as the purchase intention. The affect transfer hypothesis is used as a model to understand advertising effects in this context. Additionally, this study delves into potential influencing factors on suspicion of greenwashing, such as the type of green advertising and the “lifestyle of health and sustainability” (LOHAS). The research question is addressed through a quantitative experimental online survey. The results demonstrate that the suspicion of greenwashing has a negative effect on attitude towards the ad and on attitude towards the brand, as well as on purchase intention. The chapter therefore offers new insights into one of the critical aspects of strategic sustainable communication and lays the groundwork for future research on “washing” (green, pink, rainbow, etc.) and the need for re-framing sustainability in business-consumer discourses.
Phishing is an increasing threat to the security of end-users, networks, and organizations. Phishing simulations via email are a widespread tool used to measure user awareness, especially in workplace settings. However, current studies focusing on large-scale analysis of phishing simulations often have issues: The phishing simulations were conducted using a small sample size (mostly one or two organizations), or while many emails are sent, the analysis focuses only on specific companies. This study analyzes phishing simulations conducted over three years at 36 organizations with over 68 000 delivered emails. We compare different dimensions of the organizations where these simulations were conducted, such as the economic sector and departments. Furthermore, we evaluate various dimensions of phishing simulation campaigns, such as detection difficulty and the scenario under which the simulation occurs. Our findings indicate significant disparities in the results, such as the industry sector in which the company operates. Moreover, we find substantial differences between the success rates of varying scenarios used for phishing emails.
Hybrid broadcast broadband television (HbbTV) is an evolving technology that connects linear TV with modern HTML5 applications, delivering extras like games, videos, and online shopping. However, its bidirectional transmission functionality raises privacy concerns, as it introduces new tracking methods for TV channels. While previous studies focused on security issues or user awareness of HbbTV privacy challenges, a detailed examination of the tracking and transparency mechanisms of the HbbTV ecosystem is still missing. This study fills this gap by extensively analyzing these features within the European HbbTV ecosystem, and in particular within German-language TV channels. We monitored more than 350 TV channels for over 400 hours, evaluating 1) prevalent HbbTV tracking methods, 2) consent notice prevalence and user interactions, and 3) privacy policy disclosures. Our findings indicate that the HbbTV tracking system operates independently of the Web, consent notices exploit system constraints to influence users, and privacy policies often do not align with actual data practices.
KI-Lösungen und -Systemen wird im wirtschaftlichen sowie gesellschaftlichen Kontext zunehmend Bedeutung beigemessen. Doch aufgrund damit verbundener Implikationen darf die Diskussion hinsichtlich der Verantwortung beispielsweise mit Blick auf die Haftung für die Verursachung von Schäden nicht ausbleiben. Die Frage ist jedoch: Wer trägt die Verantwortung wofür? Insbesondere unter dem Aspekt, dass sich Verantwortung nicht umfassend reglementieren lässt. Zur Beantwortung und entsprechender Handlungsweise bedarf es hier eines gemeinsamen Spielverständnisses aller Beteiligten – denn nur so lässt sich gewährleisten, dass KI verantwortungsvoll eingesetzt werden kann, auch mit Blick auf die Zukunft. Im Beitrag wird hierfür ein Lösungsansatz vorgestellt.

