Electronic Markets

Lopez-Lopez, David; Fondevila-Gascón, Joan-Francesc; Torres-Peregrina, Belén-María; Marco-Simó, Josep-Maria

doi: 10.1007/s12525-026-00892-1pmid: N/A

Artificial intelligence (AI) is increasingly integrated into electronic marketplaces and digital platforms, where customer service functions as a critical interaction layer mediating trust, value creation, and user retention. While the strategic potential of AI is well recognized, most adoption models focus narrowly on organizational or user-centric perspectives, overlooking the stakeholder frictions that arise in real-world service environments. To address this limitation, we examine AI adoption in customer service through a multi-actor lens that captures the views of end users, frontline agents, and supervisors. Grounded in stakeholder theory and classical technology adoption models, the study employs a multi-stakeholder, exploratory design, combining quantitative online surveys (n = 198) with illustrative qualitative insights from semi-structured interviews. Results reveal persistent misalignments across stakeholder groups. Users support AI when paired with human fallback, emphasizing empathy and trust. Agents express skepticism due to increased workload, limited training, and unclear communication. Supervisors prioritize efficiency, often underestimating operational frictions. These tensions are particularly relevant in AI-enabled marketplaces, where customer service operates as a key interface between platforms and users rather than as a standalone organizational function. Our findings reframe AI adoption as an organizational transformation that requires aligning technological efficiency with human experience. Rather than treating implementation as a purely technical upgrade, we highlight the need for human-centric strategies that balance automation with empathy and workforce inclusion. The study offers actionable insights for platform operators seeking sustainable, trust-based AI integration.

Viljoen, Altus; Hein, Andreas; Krcmar, Helmut

doi: 10.1007/s12525-025-00848-xpmid: N/A

Low-code development platforms (LCDPs) such as Mendix, OutSystems, and Microsoft Power Platform are reshaping software development. These platforms enable non-technical users to develop applications by reusing and configuring out-of-the-box components in visual, user-friendly environments. While LCDPs are typically portrayed as new standalone development tools, they also exhibit many characteristics of digital platform ecosystems. For example, similar to traditional app stores, they also offer marketplaces where external developers and business users can contribute what they have developed. Yet, despite these conceptual similarities, the “platform” dimension of LCDPs has received little attention. Accordingly, this Fundamentals paper conceptualizes LCDPs as a distinct type of digital platform ecosystem. We build on research in digital platform ecosystems, boundary resources, packaged software reuse, and platform governance, and explain how LCDPs align with and differ from conventional platforms. Specifically, we show how LCDPs combine infrastructure and reuse, embed design constraints directly into components, and orchestrate ecosystem participation through a logic of “curated enablement” rather than open contribution. We conclude by outlining a research agenda that highlights LCDPs not as marginal tools for end-user development, but as rich, theory-relevant settings for further exploration.

Dolch, Darleen; Lasch, Rainer

doi: 10.1007/s12525-026-00880-5pmid: N/A

The rapid growth of e-commerce has intensified packaging waste, highlighting the need for sustainable alternatives. Reusable transport packaging (RTP), rooted in circular economy principles, presents a promising solution but faces challenges in adoption within the German online retail market. Addressing a gap in theory-driven research on RTP, this study extends the theory of planned behavior (TPB) by integrating environmental concern, personal innovativeness, perceived usefulness, return convenience, and shopping frequency to explain consumers’ intention to adopt RTP. Survey data from 792 German online shoppers were analyzed using partial least squares structural equation modeling. The results show that environmental concern and perceived usefulness are key drivers of adoption intention, whereas personal innovativeness and return convenience have no effect. Mediation analyses reveal that attitude and perceived usefulness fully mediate the impact of return convenience on intention and partially mediate the effect of environmental concern. Shopping frequency does not impact the relationship between attitude or perceived usefulness and intention, but it moderates the environmental concern-intention relationship, weakening its influence among frequent shoppers. Theoretically, the study advances TPB by incorporating underexplored psychological and situational factors relevant to low-complexity sustainable innovations. Managerially, it suggests that retailers and packaging providers should emphasize the functional and environmental benefits of RTP to target both environmentally conscious and habitual online shoppers.

Alam, Syed Shah

doi: 10.1007/s12525-025-00865-wpmid: N/A

The study builds on established theories, including the Technology Acceptance Model (TAM), Trust Theory, and Perceived Risk Theory, to investigate how personalization, interactivity, and cultural factors impact AI trust and how this trust mediates willingness to buy, particularly in the context of performance risk. Utilizing a cross-sectional quantitative research design, data were collected from 410 respondents. The findings reveal that personalization and interactivity significantly enhance AI trust, which, in turn, positively influences willingness to buy. Performance risk is shown to have a dual role, sometimes acting as a deterrent and other times enhancing willingness to buy when the AI systems are perceived as innovative. Cultural dimensions also significantly shape willingness to buy, although they have a more direct impact on willingness to buy rather than on trust formation. These insights provide theoretical advancements in understanding AI adoption and practical implications for businesses looking to optimize AI-powered platforms.

Chung Chai Man, Anthony

doi: 10.1007/s12525-026-00902-2pmid: N/A

This paper explores the impact of decentralized autonomous organizations (DAOs) on relationship marketing compared to traditional investor-owned businesses (IOBs) and member-owned cooperatives. Through a relationship marketing lens, we investigate the influence of DAOs on loyalty, drawing on concepts of psychological ownership, customer empowerment, and commitment. Using a mixed-methods approach that includes a quantitative online experiment with 300 respondents and an exploratory qualitative study involving 14 participants, our findings suggest that DAOs foster stronger psychological ownership, empowerment, and commitment compared to traditional companies, albeit to a lesser extent than cooperatives. The qualitative analysis highlights two key factors contributing to this phenomenon: direct participation and influence, as well as company values. This research contributes to the literature by elucidating the transformative potential of DAOs in relationship marketing and the challenges and opportunities they present for businesses.

Bichler, Martin

doi: 10.1007/s12525-026-00906-ypmid: 42261461

Generative AI enables autonomous software agents that can search, compare, and transact across digital marketplaces, promising large reductions in consumer search costs and improved matching between buyers and sellers. This paper argues that such gains are not automatic. Drawing on economic search theory, we first discuss the impact of reduced search costs on markets. Then, we show how the behavior of current AI agents introduces frictions that limit competitive outcomes. Empirical studies reveal persistent deviations of AI agents from optimal search behavior that function as behavioral search costs even when technical search costs approach zero. At the same time, AI-generated content contributes to signal dilution, reducing the informativeness of offers by AI agents and weakening effective product differentiation. These forces interact with low entry costs, which encourage excessive and often low-value entry. Together, they can trap agentic markets in inefficient equilibria. We outline key implications for electronic marketplaces and highlight promising directions for future research on agentic markets.

Straub, Lisa; Zeiß, Christian; Pehl, Justus; Greiner, Maximilian; Winkelmann, Axel; Lechner, Ulrike

doi: 10.1007/s12525-025-00863-ypmid: N/A

Where established financial literacy initiatives fail to keep pace with rapidly evolving digital asset markets, new customer learning environments emerge. In the context of crypto assets and the blockchain ecosystem, platform providers, ranging from trading platforms to specialized educational portals, have taken on a central role in promoting knowledge-building among users. However, most providers lack educational expertise. In line with affordance theory, which focuses on possibilities for action emerging from the interplay between users and technology, it is not enough for platforms to provide merely learning opportunities. To promote broader crypto adoption, educational offerings should align with users’ needs. Therefore, this study employs a data-driven persona development approach and analyzes 20 crypto education platforms concerning their implemented learning components. In addition, we apply topic modeling to explore the educational content. These analyses aim to assess the current state of crypto education platforms and to identify the opportunities (affordances) embedded in the design. Based on the insights and an accompanying user survey, we examine which educational offerings are perceived as valuable by different user groups and derive four distinct types of crypto learners: Cautious Strategist, Critical Observer, Curious Gamified Explorer, and Hands-On Practical Experimenter. Our findings reveal a misalignment between current platform design and learner preferences. Across all types, users tend to favor dynamic formats over static, article-based content. Based on affordance theory, we highlight design implications for crypto learning and present data-supported personas to foster more differentiated and user-centered educational strategies.

Neubauer, Maria; Schick, Doreen; Schreiter, Melina; Stark, Jeannette; Eymann, Torsten; Schlieter, Hannes

doi: 10.1007/s12525-025-00868-7pmid: N/A

The rapid digitalization of public health systems demands a framework to ensure the responsible implementation of digital technologies. The Digital Responsibility (DR) framework, proposed by Trier et al. (2023), can serve this purpose but requires further contextualization — an objective pursued in this paper. We apply a content analysis approach to map the DR principles and levels to the Public Health Agency Maturity Model (PHAMM), which is widely used in the German public health service (PHS) to assess digital maturity and derive actions for its advancements. By mapping 354 PHAMM criteria to the DR framework, we identified areas for improvement in both models, contextualized DR for the PHS by identifying 16 subthemes, and extended the DR framework. The extended framework was discussed and evaluated in a focus group with nine experts. This process led to complementing the existing DR levels with an inter- and intracorporate relation and introduces an additional DR principle (Security). Moreover, the study draws crosscutting lessons learned.

Li, Jiaxuan; Han, Mingxing; Yuan, Qinjian

doi: 10.1007/s12525-026-00872-5pmid: N/A

With the rapid integration of artificial intelligence (AI) into online knowledge communities (OKCs), understanding how AI feedback style shapes users’ cognitive responses and knowledge-related behaviors has become increasingly important. This study examines how AI dissent—AI-generated feedback that challenges user viewpoints—affects user knowledge innovation within OKC interactions. Grounded in the Stimulus-Organism-Response (SOR) framework, we propose a serial mediation process in which AI dissent functions as a cognitive stimulus that elicits cognitive dissonance, thereby enhancing cognitive flexibility and ultimately fostering knowledge innovation. In addition, we investigate the moderating role of AI anthropomorphism in this process. Across two experimental studies and a qualitative inquiry, the findings show that AI dissent significantly promotes user knowledge innovation through sequential effects on dissonance and flexibility. Importantly, this effect varies by anthropomorphism level. This study advances theoretical understanding of AI communication behaviors in OKCs, enriches research on anthropomorphism and cognitive dissonance in user-AI knowledge exchange, and offers actionable design implications for intelligent knowledge systems aiming to cultivate user learning and innovation.

Holldack, Florian; Banh, Leonardo; Strobel, Gero

doi: 10.1007/s12525-025-00861-0pmid: N/A

Recent advancements in artificial intelligence (AI) have catalyzed the emergence of agentic information systems (IS), which exhibit autonomous behavior and advanced cognitive capabilities. Unlike traditional IS, which functioned primarily as reactive tools supporting humans, agentic IS can make decisions independently, act in unstructured environments, and even delegate tasks to humans. This paradigm shift fundamentally transforms the human-IS relationship, questioning the long-standing assumption of human agentic primacy in IS research and practice. In this article, we provide a conceptual overview of agentic IS, delineating their defining characteristics and situating them within the broader evolution of IS. We introduce key archetypes of agentic IS, explore novel patterns of delegation and interaction between humans and machines, and discuss the socio-technical implications of these developments. Furthermore, we highlight the challenges and risks associated with integrating agentic IS from an individual, organizational, and societal perspective, emphasizing the need for nuanced understanding to harness the potential while addressing emerging complexities.