@article{64863,
  abstract     = {{Traditional dyadic customer-provider interactions are being shifted to polyadic interactions involving diverse participants in digital service ecosystems. Especially, artificial intelligence (AI) is increasingly integrated into these ecosystems, so that they comprise non-human participants (e.g., AI-based chatbots)—fundamentally altering the nature of value (co-)creation. While existing literature examines human-to-human interactions, knowledge of service interactions between human actors and AI-based systems is still underexplored. To address this research gap, we develop a taxonomy, comprising six iterations, that explores the peculiarities of AI as either a resource or a (non-human) agent in digital service ecosystems. We evaluate our taxonomy using a multiple case study and derive the four archetypes of AI in digital service ecosystems: (1) discriminative experience enhancer, (2) protective ecosystem orchestrator, (3) ecosystem innovation companion, and (4) personalized service composer. Our results extend the knowledge on service science by showing how AI-based systems—discriminative or generative, and focusing on the interaction in the ecosystem or the individual service encounter—assume the role of resources and non-human agents. Researchers and practitioners can utilize our results to augment their ecosystems with AI.</jats:p>}},
  author       = {{Hansmeier, Philipp and Schäfer, Jannika Marie and zur Heiden, Philipp}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Artificial intelligence in digital service ecosystems—Towards a taxonomy and archetypes}}},
  doi          = {{10.1007/s12525-026-00879-y}},
  volume       = {{36}},
  year         = {{2026}},
}

@article{65476,
  abstract     = {{Service research has evolved into an interdisciplinary research field that bridges diverse disciplines, including information systems (IS) and marketing. Nearly two decades ago, the service system concept was introduced as a foundational abstraction in service research, drawing on ideas from the service-dominant logic (S-D logic) of marketing. Despite its widespread adoption in service research, particularly in the IS discipline, the service system concept lacks a solid theoretical foundation. This has resulted in conceptual ambiguity and overlap with related constructs, such as service ecosystems. Moreover, it has largely remained a static analytical lens, insufficiently capturing dynamic service phenomena, including value co-creation and co-destruction, as well as the emergence of institutional arrangements. To address these limitations, we propose Luhmann’s systems theory (LST) as a robust framework for conceptualizing service systems as autopoietic (self-creating) systems, in which communication serves as the fundamental mechanism that drives value co-creation. We derive five theoretical propositions from this re-conceptualization that clarify conceptual ambiguity and allow researchers to explore dynamic service phenomena in greater depth. Given LST’s general approach, our conceptualization provides a theoretically grounded, interdisciplinary foundation for advancing service research.}},
  author       = {{Beverungen, Daniel and Poeppelbuss, Jens and Hemmrich, Simon and Iqbal, Taskeen}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Service system, Service ecosystem, Systems theory, Service research}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Service through communication—Conceptualizing service systems with Luhmann’s systems theory}}},
  doi          = {{10.1007/s12525-026-00889-w}},
  volume       = {{36}},
  year         = {{2026}},
}

@article{59335,
  abstract     = {{Technological advancements and evolving value orientations reshape future value creation and pose new requirements for service innovation. While a variety of disciplines are developing new approaches to drive service innovation, this is primarily done in isolation and generates only fragmented solutions. Sociological theory has proposed “boundary objects” as an effective umbrella for communication and cooperation among communities. Therefore, we introduce continuous value shaping (CVS) as a boundary object describing service innovation approaches along five principles. We reflect on this concept through the different disciplinary lenses of researchers in service marketing, information systems, service engineering, sociology of work, and innovation management. These perspectives highlight how the CVS principles already connect to discourses within the individual disciplines. However, the CVS concept will not only provide an umbrella to embrace existing activities in different academic disciplines. It also assists to identify research themes that will benefit from uniting the power of these disciplines, and it can serve as an integrating framework to conceptualize complex service innovation approaches. Thus, the CVS concept should guide both researchers and practitioners to develop and implement novel innovation and transformation efforts—in and across organizations.}},
  author       = {{Böhmann, Tilo and Roth, Angela and Satzger, Gerhard and Benz, Carina and Beverungen, Daniel and Boes, Andreas and Breidbach, Christoph and Gersch, Martin and Gudergan, Gerhard and Hogreve, Jens and Kurtz, Christian and Langes, Barbara and Leimeister, Jan Marco and Lewandowski, Tom and Meiren, Thomas and Nägele, Rainer and Paluch, Stefanie and Peters, Christoph and Poeppelbuss, Jens and Robra-Bissantz, Susanne and Schultz, Carsten and Schumann, Jan H. and Wirtz, Jochen and Wünderlich, Nancy V.}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Continuous value shaping (CVS), Service research, Service innovation, Digitalization, Sustainability, Interdisciplinary research}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Continuous value shaping: A boundary concept for innovating service innovation approaches}}},
  doi          = {{10.1007/s12525-025-00771-1}},
  volume       = {{35}},
  year         = {{2025}},
}

@article{60169,
  abstract     = {{<jats:title>Abstract</jats:title>
          <jats:p>Data ecosystems can generate valuable business opportunities, but research on their emergence within specific industries is limited. The cultural event industry is characterized by a multifaceted cultural landscape and a fragmented and heterogeneous market of cultural event platforms. The emerging German cultural data ecosystem, envisioned to share event data in a data space, could foster data-driven innovation and enhance value creation in the cultural event industry. Yet, following the ecosystem-as-structure view, the platforms’ willingness to participate in the cultural data ecosystem depends on whether their business model aligns with at least one of the focal value propositions of the cultural data ecosystem. In this paper, we develop a taxonomy of cultural event platform business models, and derive six archetypes. Additionally, we interview industry representatives of these archetypes to shed light on the benefits and obstacles when participating in the cultural data ecosystem, and to identify potential focal value propositions, corresponding actor roles, and activities. Our work contributes to the discussion on taxonomies of data-sharing business models and the emergence of data ecosystems in the cultural event industry.</jats:p>}},
  author       = {{Althaus, Maike and Vorbohle, Christian and Müller, Michelle and Kundisch, Dennis}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Setting the stage for a flourishing cultural data ecosystem: A spotlight on business models of cultural event platforms}}},
  doi          = {{10.1007/s12525-025-00790-y}},
  volume       = {{35}},
  year         = {{2025}},
}

@article{56659,
  abstract     = {{<jats:title>Abstract
</jats:title><jats:p>A citizen-centric view is key to channeling technological affordances into the development of future cities in which improvements are made with the quality of citizens’ life in mind. This paper proposes City 5.0 as a new citizen-centric design paradigm for future cities, in which cities can be seen as markets connecting service providers with citizens as consumers. City 5.0 is dedicated to eliminating restrictions that citizens face when utilizing city services. Our design paradigm focuses on smart consumption and extends the technology-centric concept of smart city with a stronger view on citizens’ roadblocks to service usage. Through a series of design workshops, we conceptualized the City 5.0 paradigm and formalized it in a semi-formal model. The applicability of the model is demonstrated using the case of a telemedical service offered by a Spanish public healthcare service provider. The usefulness of the model is validated by qualitative interviews with public organizations involved in the development of technology-based city solutions. Our contribution lies in the advancement of citizen-centric analysis and the development of city solutions for both academic and professional communities.</jats:p>}},
  author       = {{Becker, Jörg and Chasin, Friedrich and Rosemann, Michael and Beverungen, Daniel and Priefer, Jennifer and Brocke, Jan vom and Matzner, Martin and del Rio Ortega, Adela and Resinas, Manuel and Santoro, Flavia and Song, Minseok and Park, Kangah and Di Ciccio, Claudio}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{City 5.0: Citizen involvement in the design of future cities}}},
  doi          = {{10.1007/s12525-023-00621-y}},
  volume       = {{33}},
  year         = {{2023}},
}

@article{35740,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.</jats:p>}},
  author       = {{Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Management of Technology and Innovation, Marketing, Computer Science Applications, Economics and Econometrics, Business and International Management}},
  number       = {{1}},
  pages        = {{375--396}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Systematizing the lexicon of platforms in information systems: a data-driven study}}},
  doi          = {{10.1007/s12525-022-00530-6}},
  volume       = {{32}},
  year         = {{2022}},
}

@article{35728,
  abstract     = {{Technological developments such as Cloud Computing, the Internet of Things, Big Data and Artificial Intelligence continue to drive the digital transformation of business and society. With the advent of platform-based ecosystems and their potential to address complex challenges, there is a trend towards greater interconnectedness between different stakeholders to co-create services based on the provision and use of data. While previous research on digital transformation mainly focused on digital transformation within organizations, it is of growing importance to understand the implications for digital transformation on different layers (e.g., interorganizational cooperation and platform ecosystems). In particular, the conceptualization and implications of public data spaces and related ecosystems provide promising research opportunities. This special issue contains five papers on the topic of digital transformation and, with the editorial, further contributes by providing an initial conceptualization of public data spaces' potential to foster innovative progress and digital transformation from a management perspective.}},
  author       = {{Beverungen, Daniel and Hess, Thomas and Köster, Antonia and Lehrer, Christiane}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Digital transformation, Public data spaces, Digital platforms, GAIA-X}},
  pages        = {{493--501}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{From private digital platforms to public data spaces: implications for the digital transformation}}},
  doi          = {{10.1007/s12525-022-00553-z}},
  volume       = {{32}},
  year         = {{2022}},
}

@article{35732,
  abstract     = {{While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.}},
  author       = {{Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Platform, Text mining, Machine learning, Data communications, Interpretive research, Systems design and implementation}},
  pages        = {{375--396}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Systematizing the lexicon of platforms in information systems: a data-driven study}}},
  doi          = {{10.1007/s12525-022-00530-6}},
  volume       = {{32}},
  year         = {{2022}},
}

@article{37138,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Assuming that potential biases of Artificial Intelligence (AI)-based systems can be identified and controlled for (e.g., by providing high quality training data), employing such systems to augment human resource (HR)-decision makers in candidate selection provides an opportunity to make selection processes more objective. However, as the final hiring decision is likely to remain with humans, prevalent human biases could still cause discrimination. This work investigates the impact of an AI-based system’s candidate recommendations on humans’ hiring decisions and how this relation could be moderated by an Explainable AI (XAI) approach. We used a self-developed platform and conducted an online experiment with 194 participants. Our quantitative and qualitative findings suggest that the recommendations of an AI-based system can reduce discrimination against older and female candidates but appear to cause fewer selections of foreign-race candidates. Contrary to our expectations, the same XAI approach moderated these effects differently depending on the context.</jats:p>}},
  author       = {{Hofeditz, Lennart and Clausen, Sünje and Rieß, Alexander and Mirbabaie, Milad and Stieglitz, Stefan}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets (ELMA)}},
  keywords     = {{Management of Technology and Innovation, Marketing, Computer Science Applications, Economics and Econometrics, Business and International Management}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring}}},
  doi          = {{10.1007/s12525-022-00600-9}},
  year         = {{2022}},
}

@article{30735,
  abstract     = {{While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.}},
  author       = {{Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Management of Technology and Innovation, Marketing, Computer Science Applications, Economics and Econometrics, Business and International Management}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Systematizing the lexicon of platforms in information systems: a data-driven study}}},
  doi          = {{10.1007/s12525-022-00530-6}},
  year         = {{2022}},
}

@article{37144,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Artificial intelligence (AI) is being increasingly integrated into enterprises to foster collaboration within humanmachine teams and assist employees with work-related tasks. However, introducing AI may negatively impact employees’ identifications with their jobs as AI is expected to fundamentally change workplaces and professions, feeding into individuals’ fears of being replaced. To broaden the understanding of the AI identity threat, the findings of this study reveal three central predictors for AI identity threat in the workplace: changes to work, loss of status position, and AI identity predicting AI identity threat in the workplace. This study enriches information systems literature by extending our understanding of collaboration with AI in the workplace to drive future research in this field. Researchers and practitioners understand the implications of employees’ identity when collaborating with AI and comprehend which factors are relevant when introducing AI in the workplace.</jats:p>}},
  author       = {{Mirbabaie, Milad and Brünker, Felix and Möllmann Frick, Nicholas R. J. and Stieglitz, Stefan}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Management of Technology and Innovation, Marketing, Computer Science Applications, Economics and Econometrics, Business and International Management}},
  number       = {{1}},
  pages        = {{73--99}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{The rise of artificial intelligence – understanding the AI identity threat at the workplace}}},
  doi          = {{10.1007/s12525-021-00496-x}},
  volume       = {{32}},
  year         = {{2021}},
}

@article{17158,
  author       = {{Beverungen, Daniel and Müller, Oliver and Matzner, Martin and Mendling, Jan and vom Brocke, Jan}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  pages        = {{7--18}},
  title        = {{{Conceptualizing smart service systems}}},
  doi          = {{10.1007/s12525-017-0270-5}},
  year         = {{2017}},
}