This short paper addresses the complex relationship dynamics between a municipal website’s gamified IS artifact design and the desired goal states of municipal trust. To better understand this relationship, a mixed-methods study is proposed that identifies relevant gamification features in a three-step process in Study 1 and then explores the relationship between gamification and municipal trust using a survey in Study 2. The study results promise added value for (a) user-centered technology design and (b) novel feedback mechanisms to strengthen municipal trust. Additionally, we will contribute to existing gamification literature by identifying relevant gamification features in relation to the design of municipal websites.

Urban digital transformation has the potential to improve quality of life by connecting data generated by sensors, mobile phones and networked everyday objects to enhance services, participation and sustainability. However, when such technologies are implemented in a purely technocratic manner, they often deepen existing social and digital divides, this meaning that only well-connected groups benefit from smart-city innovations. The objective of this paper is to apply a combination of citizen-generated data combining methodologies such as the WIN (Wishes, Interests and Needs) methodology of diverse residents to co-design and an Urban Living Lab approach that test solutions in real urban settings, emphasizing inclusion from the outset to the innovation process with User-generated Data and Volunteered Urban Sensing. By centering local knowledge and everyday experiences, the approach seeks to develop more sustainable, fair and holistic smart-city configurations in which a broad majority of citizens can meaningfully shape and share the benefits of urban digital transformation.

GovTech is emerging as a policy-relevant frame at the intersection of digital government, public procurement, and startup innovation. Yet research remains fragmented and often disconnected from the institutional conditions that shape implementable solutions. This paper develops a stakeholder-validated, dimensional publicness theory informed research agenda for GovTech in the European context using a multiround agenda-setting Delphi study with experts from public administrations, industry, and academia. Synthesizing qualitative inputs and quantitative prioritization, we identify thematic clusters and topic areas, formulate exemplary research questions, and provide rationales that connect each area to concrete governance and implementation challenges. To assess dynamism, we additionally examine perceived relevance trends across two measurement points (2023 and 2025), highlighting where urgency is increasing and where research sequencing is needed. The resulting agenda informs scholarship by clarifying GovTech as a hybrid, regulation-shaped domain and offers entry points for empirical and design-oriented research on ecosystem formation, procurement, regulation, and strategic scaling.

Public administrations increasingly explore the use of artificial intelligence (AI), yet little is known about the organizational capabilities required for effective and responsible AI adoption. This study examines the development of AI-specific dynamic capabilities (DCs) in nineteen German municipalities and investigates whether these organizational capability patterns are reflected in employee-level perceptions. Drawing on a mixed-methods design, we combine qualitative focus group data with an employee survey (n = 5,332). The qualitative findings show that AI-specific sensing, seizing, and transforming capabilities are not yet systematically embedded across municipalities, with notable variation in their degree of implementation. The quantitative results show that perceived organizational innovativeness is moderately associated with several AI-specific capability dimensions, whereas AI literacy shows weaker and statistically non-significant relationships. By triangulating expert-based capability assessments with employee-level indicators, the study provides a granular empirical account of the current development of AI-specific DCs in public administration and highlights implications for both research and practice.

Generative AI systems are increasingly used for cognitively demanding tasks, yet little is known about how psychological factors shape user prompting behavior. This study investigates the role of individual satisficing tendencies in maximizing behavior when selecting prompt strategies across different task domains. In an online vignette experiment with 132 participants, individuals selected between satisficing and maximizing prompt options in five problem-solving scenarios. Satisficing tendencies were assessed using the Short Maximization Inventory, with algorithm aversion and prompt-writing competence included as controls. Linear mixed models showed that stronger satisficing tendencies were associated with reduced maximizing behavior, while higher self-reported competence predicted more maximizing. Participants maximized more in job-related and creative tasks, but satisficed more in writing and technical support tasks, suggesting that task characteristics shape prompting strategies. The results demonstrate that individual differences systematically affect interactions with generative AI. This highlights the importance of considering psychological dispositions in future research on human–AI collaboration.

Store-based retailers face the challenge of meeting increased customer demands and compete with online marketplaces. Big data (BD) can support store-based retail and engage customers locally. We are therefore conducted a three-year mixed-method-study to identify relevant factors for the German store-based retail in three successive phases. Firstly, we qualitatively identify effectiveness-factors using BD through 13 semi-structured-interviews. Secondly, we quantitatively evaluate the relevance of the identified effectiveness-factors (i.a., over 2.8 million data points) using the 7P-Marketing-Mix, and thirdly analyzed significant factors. Our findings show that many store-based retailers lack knowledge about smart town and BD-use, e.g., by creating a network or running joint retail campaigns to increase the towns attractiveness. We provide an overview and guidance on how BD-analysis can effectively influence the store-based retail transformation in smart towns. Using a mixed-method-study in a German town, we were able to identify which factors influence store-based retail in a smart town and suggest promising interventions.

This study investigates how self-esteem and gender influence the potential of interactive augmented reality (AR)-based virtual try-on systems (VTOs) to improve the virtual product experience (VPE). In particular, we examine the impact of self-esteem and gender on the influence of varying levels of interactivity in virtual product presentation formats on two key determinants of VPE, i.e., user engagement and product enticement. In a between-subjects online experiment, 265 participants were randomly assigned to one of three interactivity conditions: no AR, low AR VTO, and high AR VTO. For each condition, they evaluated three products (eyewear) within an online store setting and indicated their subjective engagement and enticement. Results show that high AR interactivity significantly increased both engagement and enticement. Furthermore, self-esteem was positively associated with both VPE outcomes, and engagement increased with interactivity more strongly among individuals with lower self-esteem than among those with higher self-esteem. These findings extend current VPE frameworks by integrating psychological user characteristics and offer practical implications for the design of inclusive and user-centered product presentation formats.

This study investigates the potential role of diversity in shaping innovation outcomes within the GovTech sector, an emerging domain where startups collaborate with public administrations to drive digital transformation. While GovTech is positioned as a vehicle for more agile, inclusive, and citizen-oriented public services, its effectiveness hinges on the extent to which it mirrors the populations it serves. Grounded in theories of diversity management and diffusion of innovation, this research explores how the demographic composition of GovTech founders compares to public administration employees, the broader startup ecosystem, and the general citizenry in Germany. The study employs a mixed-methods approach, combining 108 expert interviews with GovTech founders and comparative analysis using secondary data from national datasets. It focuses on three diversity dimensions: gender, migration background, and socio-economic (labor vs. academic) background. The findings reveal substantial misalignments. GovTech startups are heavily male-dominated (85.7% male founders), starkly contrasting with the higher female representation in public administration (58.6%) and the near gender parity in the citizenry. Migration background is also underrepresented among GovTech founders (22.7%) relative to citizens (28.6%), though aligned with general startup trends. GovTech shows relatively strong socio-economic inclusivity, with 62.8% of founders from labor backgrounds, exceedingboth startup and citizen benchmarks. These demographic mismatches raise concerns about the representational legitimacy and inclusiveness of GovTech solutions, which may limit their relevance, adoption, and impact. Public administrations, while more gender-diverse, also exhibit gaps, particularly in migration and socio-economic representation, potentially compounding the dis-connect between technology providers and end-users. The study proposes strategic responses, including inclusive procurement policies, support for diverse founders, and cross-sector alignment initiatives to strengthen equity in digital public services.

GovTech is propelled by collaboration between the public and private sectors, aiming to foster innovation and enhance the delivery of public services. However, the integration of GovTech solutions remains complex, particularly due to challenges in aligning startup-driven innovations with public sector demands and overarching digital governance strategies. This study examines the strategic alignment of GovTech initiatives by proposing a conceptual model grounded in public strategic management processes to analyze the determinants that shape such initiatives. The research centers on the Madrid GovTech program, which is part of the broader Madrid Digital Capital strategy, and evaluates how the alignment between information systems and government-wide strategies influences the success of GovTech initiatives. The findings contribute to the expanding body of GovTech scholarship by offering theoretical insights into governance paradigms and providing empirical evidence on the supply-and-demand dynamics within the GovTech ecosystem.

PurposeKnowledge contribution in community-driven knowledge-sharing platforms is crucial for fostering innovation and collaboration. However, designing an effective framework to ensure both contribution quality and quantity remains a challenge. To address this, our study explores the impact of a holistic portfolio of gamification features and user motivation on knowledge contribution, focusing on the widely used knowledge-sharing platform Stack Overflow.Design/methodology/approachWe conducted a cross-sectional survey informed by a preparatory analysis identifying Stack Overflow's gamification features through literature review and expert validation. The survey was then distributed to Stack Overflow users (n = 236) to test the relationships between the motivational dimensions of gamification features, the motivational state of inspiration, and knowledge contribution using a structural equation model.FindingsThe results of our study show that the two motivational dimensions achievement-related features and social-related features showed a significant influence on "inspired by"; "inspired by" is a positive predictor of "inspired to"; "inspired to" is a positive predictor of quality and quantity of contribution; and quantity and quality of contributions show a significant relationship.Originality/valueThe study highlights the potential of inspiration as a motivational state to explain user behavior in Information Systems research. Therefore, the study illuminates the crucial context of knowledge-sharing platforms. In this regard, the study extends existing gamification theories by identifying motivational dimensions of gamification that evoke inspiration and knowledge contributions.

Evidence-based policy-making in public procurement depends on high-quality data. This case study evaluates 14 months (Nov 2023–Jan 2025) of procurement records from the EU’s Tenders Electronic Daily (TED) to assess the reliability of this key data source. Our analysis uncovers significant anomalies in data accuracy and governance, raising concerns about the integrity of procurement processes and the policies built on them. We highlight the urgent need for improved data governance to support transparency, accountability, and innovation-driven procurement.

We report preliminary findings from a study investigating the potential of AI-generated images to inspire people similarly to user-generated content, such as photographs shared on social media. We conducted an online experiment involving 171 participants who were randomly assigned to one of two groups. In one group, they indicated their level of being inspired by AI-generated images; in the other group, the level of being inspired by photographs. In each group, stimuli were embedded in the theme domains of dinner dishes, room design, and beauty and style. For specific domains, we found that people are more inspired by AI-generated images than by photographs. Based on these results, we propose a follow-up study employing a mixed-methods approach to delve deeper into the domain-specific variations of AI as an inspirational technology, contributing to the broader discourse on human-AI collaboration across Information Systems and related fields.

To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician–AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations. Moreover, the results suggest that this fear is driven by little or moderate knowledge about AI. Posts related to the third theme focused on factual discussions about how AI and medicine have to be designed to become broadly adopted in health care. Study 2 quantitatively examined the relationship between the fear of AI, knowledge about AI, and medical professionals' intention to use AI-enabled technologies in more detail. Results based on a sample of 223 medical professionals who participated in the online survey revealed that the intention to use AI technologies increases with increasing knowledge about AI and that this effect is moderated by the fear of being replaced by AI.

A new decision system introduction gone wrong can be costly and have further negative consequences. One of the reasons why IT introductions can go wrong is user resistance - often caused by a status quo bias of the users towards the old system. An ironic aspect as decision systems are often especially designed to counter decision biases. This case study presents evidence that targeted countermeasures to status quo bias can help. Based on a review of the available literature, research-based countermeasures were identified and later complemented with practice-based countermeasures from a case study at DB Schenker. With the help of these countermeasures, DB Schenker managed to turn around a new financial reporting system introduction gone wrong.