Setting the stage for a flourishing cultural data ecosystem: A spotlight on business models of cultural event platforms

Althaus, Maike; Vorbohle, Christian; Müller, Michelle; Kundisch, Dennis Article — Published Version Setting the stage for a flourishing cultural data ecosystem: A spotlight on business models of cultural event platforms Electronic Markets Provided in Cooperation with: Springer Nature Suggested Citation: Althaus, Maike; Vorbohle, Christian; Müller, Michelle; Kundisch, Dennis (2025) : Setting the stage for a flourishing cultural data ecosystem: A spotlight on business models of cultural event platforms, Electronic Markets, ISSN 1422-8890, Springer, Berlin, Heidelberg, Vol. 35, Iss. 1, https://doi.org/10.1007/s12525-025-00790-y This Version is available at: https://hdl.handle.net/10419/323629 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Electronic Markets (2025) 35:47 https://doi.org/10.1007/s12525-025-00790-y RESEARCH PAPER Setting thestage foraflourishing cultural data ecosystem: Aspotlight onbusiness models ofcultural event platforms MaikeAlthaus1 · ChristianVorbohle1· MichelleMüller1· DennisKundisch1 Received: 24 February 2024 / Accepted: 6 May 2025 © The Author(s) 2025 Abstract 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. Keywords Emerging data ecosystem· Business model· Cultural event platform· Taxonomy· Archetypes· Interview JEL Classification M10 Introduction In today’s networked business world, economic value is not only created by leveraging data from a single organization but, increasingly, by combining and enriching data from various sources, organized in so-called data ecosystems (Fassnacht etal., 2024; Gelhaar etal., 2021). By facilitating new ways of value co-creation and innovation based on complementary data-sharing (Oliveira etal., 2019), data ecosystems are emerging across various sectors, such as mobility or health care (Acatech, 2024; Health-X, 2024). While data ecosystems are gaining in importance, the development of flourishing data ecosystems rarely happens spontaneously—rather, it requires considerable efforts invested in the design and collaboration between participating entities (Gelhaar & Otto, 2020; Karst etal., 2025; Möller etal., 2024). As a result, the practical implementations of data ecosystems are often still in their infancy (Fassnacht etal., 2024). Moreover, few organizations are aware of the latent benefits of cross-organizational data-sharing, highlighting the need to establish focal value propositions for potential actors (Azkan etal., 2020; Gelhaar & Otto, 2020). The emerging phase of a data ecosystem is particularly crucial, as the conditions determined at the outset strongly influence the subsequent development process (Gelhaar etal., 2021). However, while the ecosystem literature mostly provides general, cross-industry insights into relevant data ecosystem concepts, we currently lack industry-specific Responsible Editor: Frederik Oliver Möller * Maike Althaus [email protected] Christian Vorbohle christian.v[email protected] Michelle Müller [email protected] Dennis Kundisch [email protected] 1 Chair ofInformation Systems, Esp. Digital Markets, Paderborn University, Warburger Str. 100, 33098Paderborn, Germany Electronic Markets (2025) 35:47 47 Page 2 of 22 knowledge for the development of focal value propositions in the emerging phase (Toorajipour etal., 2024). A pertinent example for an industry where a data ecosystem is currently in the emerging phase is the cultural event industry in Germany (Acatech, 2023). The industry is characterized by a multifaceted offering comprising a wide range of genres, e.g., performing arts, music, and film (Angelo etal., 2018). Hundreds of cultural event platforms (CEPs), such as Eventbrite (eventbrite.com/), Billetto (billetto.eu/), or Rausgegangen (rausgegangen.de/en/), promote different partially overlapping subsets of these events at the local, regional, national, or international level, mostly unconnectedly, and with some platforms focusing on a specific genre. Moreover, some platforms are publicly funded, while others operate privately. Together, they form a highly heterogeneous and fragmented platform landscape. Given the number of CEPs, however, cultural event providers (i.e., the organizers of cultural events such as theatres, operas, cultural events agencies, amateur theatre groups, sports clubs, or arthouse cinemas) often lack the required editorial capacity to enter data about upcoming cultural events on more than one CEP—although it would be preferable to publish them on multiple CEPs as each has its own, limited visibility and reach (Jarke, 2023). Hence, the emerging cultural data ecosystem for the cultural event industry would offer new opportunities for sovereign data-sharing that could benefit all the stakeholders in the cultural event sector (Gieß etal., 2023; Hansmeier etal., 2024; Jarke, 2023). The emerging cultural data ecosystem is also intended to help lesser-known creative artists, by promoting a diverse cultural landscape, and preventing the consolidation of monopolistic structures (Acatech, 2023). Further, data-sharing among CEPs could enhance other value-adding service initiatives in the cultural sector, such as the German Digital Library1 or the KulturPass.2Therefore, the cultural data ecosystem has the potential to substantially change the way CEPs operate, affecting their processes, services, and business models (Wiesböck & Hess, 2020). Subject to the business models of the CEPs, their willingness to participate in the cultural data ecosystem highly depends on whether their own business model value creation mechanisms align with at least one of the focal value propositions of the cultural data ecosystem (Heinz etal., 2022; Kohtamäki etal., 2019). However, given the diversity of CEPs and the significant differences in the data they could contribute to the cultural data ecosystem, analyzing each business model separately would be complex and impractical. To overcome this obstacle, and to develop a framework that enables a more systematic understanding of the CEP landscape, it is essential to reduce the current complexity of the different CEPs by identifying their key characteristics and by clustering business models into archetypes based on common attributes. This would allow to make meaningful comparisons, and to generate insights into the different roles that CEPs could play in the emerging cultural data ecosystem, in accordance with their respective business models and data ecosystem readiness (i.e., the maturity of a CEP to share and integrate data through the cultural data ecosystem) (Adner, 2017; Kraemer etal., 2023). Taking the ecosystem-as-structure view as a foundation (Adner, 2017; Hou & Shi, 2021), this study combines data ecosystem (Oliveira etal., 2019; Otto etal., 2019) and business model research (Massa etal., 2017; Möller etal., 2022), focusing on the archetypal business models of CEPs in the emerging cultural data ecosystem. This combination allows insights to be derived from the perspective of different business models, the potential benefits and obstacles they face, the focal value propositions of the cultural data ecosystem that align with their business model, and the CEP’s potential actor role and corresponding activities within the cultural data ecosystem. Hence, the aim of this study is to answer the following two research questions: RQ1: What are the archetypal business models for CEPs that couldparticipate in the emerging cultural data ecosystem? RQ2: Which potential actor roles and activities would CEPs with different archetypal business models fulfill in the cultural data ecosystem? To answer these research questions, we propose a multiple-method approach. First, we develop a business model taxonomy for CEPs in the emerging cultural data ecosystem by using well-established general taxonomy development methods (Kundisch etal., 2022; Nickerson etal., 2013) and specific guidance for business model taxonomies (Möller etal., 2022). Drawing upon the literature on data ecosystems, business model taxonomies, and the cultural event industry, we iteratively combine the derived insights with empirical data from 151 existing CEPs from German-speaking countries. Based on this, we identify 18 dimensions and 83 characteristics of CEP business models, which are validated by applying the taxonomy to out-of-sample CEPs. Second, we perform a cluster analysis on the 151 CEPs classified in the taxonomy to uncover six CEP business model archetypes. Third, using these archetypes as a foundation, and based on a workshop and interviews with representatives from eight CEPs, we derive insights about the benefits and obstacles of these archetypes when participating in the cultural data ecosystem. This allows us to derive potential focal value 1 https:// www. deuts chedigit alebibli othek. de/? lang= en 2 The KulturPass is a state-funded €100 voucher for all 18-year-olds in Germany to be used for a range of cultural offers and events from their 18.th birthday in the registration year (https:// www. kultu rpass. de/ en/ for18yearolds). Electronic Markets (2025) 35:47 Page 3 of 22 47 propositions of the cultural data ecosystem and corresponding actor roles and activities for each of these archetypes. This paper offers several theoretical and practical contributions that lie at the periphery of data ecosystem research. First, we contribute to the literature on data-sharing business models, and provide a detailed taxonomy of possible CEP business models, thereby offering valuable insights for upcoming or existing CEPs about how their business model could be designed and/or enhanced in data ecosystems. In addition, we expand the understanding of CEP business model archetypes. While CEPs exhibit significant diversity—from small local event information providers to global ticketing platforms—our study reveals how their business models shape their incentives and ability to engage in data-sharing. By synthesizing these insights, we offer a structured understanding for evaluating CEPs’ data ecosystem readiness and identify factors influencing their willingness to participate in data-sharing initiatives. Second, we extend data ecosystem research by applying the ecosystemas-structure view (Adner, 2017) to the emergence of the cultural data ecosystem. Unlike traditional data ecosystem research, which often focuses on industries with established value capture mechanisms, we examine an underexplored industry where financial models, public–private interactions, and data-sharing incentives remain ambiguous. By leveraging the structuralist approach, we provide insights for analyzing and designing data ecosystems in fragmented industries, addressing calls for further research on data ecosystem emergence (Gelhaar & Otto, 2020; Heinz etal., 2022). By that, we provide strategic guidance for managers and data ecosystem designers, identifying key challenges, opportunities, and structural requirements for integrating diverse CEPs into the cultural data ecosystem. Background Platforms anddata space‑enabled data ecosystems While platforms differ from ecosystems, the two concepts are closely connected (Jacobides etal.,2024b). Platforms provide the technical infrastructure that facilitates interactions between two sides of a market (Parker etal., 2016)—in the case of CEPs, interactions between event seekers (i.e., people interested in attending cultural events) and event providers. Hence, platforms—such as CEPs—enable value-creating interactions between event seekers and event providers, leveraging network effects to enhance event discovery (for seekers) and event visibility (for providers). Ecosystems, by contrast, are concerned with inter-organizational collaboration for the coordination of complementary resources (Jacobides etal., 2018) and are defined (in general terms) “as groups of actors that must collaborate intensively to achieve a joint outcome” (Jacobides etal., 2024a, p. 107).Ecosystems can emerge around a platform that provides the technological infrastructure for collaboration (i.e., platform ecosystems). However, ecosystems do not require platforms in order to emerge and function (Jacobides etal., 2024b). Data ecosystems represent a specific form of an ecosystem for the coordination of complementary data assets (Legner & Otto, 2023; Oliveira etal., 2019), where data serve as the key resource for the development and success of data-driven innovations (Beverungen etal., 2022; Otto etal., 2019). Data spaces provide a decentralized infrastructure for data ecosystems by realizing secured and trusted datasharing through data space connectors (Hutterer & Krumay, 2022; Möller etal., 2024; Otto, 2022). Data spaces do not require a central data store and thereby enable data sovereignty for data providers (Möller etal., 2024). Data spaceenabled data ecosystems “emerge around one or multiple (federated) data spaces. They represent the sum of collaborative data-sharing activities built on the secure and trustworthy data-sharing paradigm of data spaces to realize shared goals (e.g., innovation, compliance, optimization) for their members.” (Möller etal., 2024, p. 41). Emergence ofdata ecosystems Ideally, all actors within a data ecosystem ought to benefit from participation and align their business models to create a multilateral win–win situation (Azkan etal., 2020; Heinz etal., 2022). However, two different views can be applied to analyze how data ecosystems emerge (Adner, 2017; Hou & Shi, 2021). First, the ecosystem-as-coevolution view refers to the ability of an ecosystem and its actors to collectively evolve from one stage to another and facilitate innovations that can address multiple value propositions at once or value propositions evolving over time (Hou & Shi, 2021; Moore, 1993). Recent data ecosystem definitions (Oliveira etal.,2019; Möller etal.,2024) build on this view and apply it on a macro level, for example, to understand the process of complementary alignment among multiple actors over time (Hou & Shi, 2021). Second, the ecosystem-as-structure view3 posits that a data ecosystem forms around one focal value proposition, which functions as a reference point for other organizations 3 The ecosystem-as-structure view is associated with the terms innovation ecosystem (e.g., Adner, 2017) and business ecosystem (e.g., Jacobides etal., 2018; Kapoor, 2018). The terminological distinction between business and innovation ecosystems may lead to more confusion than clarity (Thomas & Autio, 2020). Thus, we follow Hou and Shi (2021) in distinguishing only between the structure view and the coevolution view. For a visualization of our conceptual perspective, see AppendixFigureA1. Electronic Markets (2025) 35:47 47 Page 4 of 22 to contribute resources and activities and creates an ecosystem boundary (Adner, 2017; Hou & Shi, 2021). This structuralist approach proposes four elements (activities, actors, positions, and links) that collectively characterize the configuration required for a value proposition to materialize (Adner, 2017). According to this view, the most critical challenge for an emerging ecosystem is the search for partner alignment, particularly with regard to the focal value proposition. As Adner (2017) explains, “different actors may have different views on the value proposition, [and] an analysis of an ecosystem must account not only for divergence in interests […], but also divergence in perspectives […]” (Adner, 2017, p. 43). This implies that data ecosystems do not emerge spontaneously but are at least partly the outcome of a design process (Jacobides etal., 2018). If multiple actors share their data in a data space but create different focal (ecosystem) value propositions, this implies that multiple data ecosystems emerge and that the emerging cultural data ecosystem would be, adopting a structuralist view, a set of separate but intertwined data ecosystems (Adner, 2017). We refer to each entity of the set of separate data ecosystems as “value proposition-specific cultural data ecosystem” (VPS cultural data ecosystem), while the term “(emerging) cultural data ecosystem” serves as the umbrella term for the overall set of VPS cultural data ecosystems. If the ecosystem has no inherent ecosystem orchestrator, or is not built on an existing alliance—like in the case of the German cultural data ecosystem—it emerges bottom-up (Hoffmann etal., 2022). For this case, the ecosystem-as-structure view can be useful to identify one or several focal value propositions ex-ante and then design a respective data ecosystem structure around each identified focal value proposition (Adner, 2017; Hou & Shi, 2021; Weber etal., 2024). Research on the emergence of data ecosystems lies still in its infancy and calls for further investigation, especially for specific industries (Gelhaar & Otto, 2020; Heinz etal., 2022). The cultural event industry presents a case of a data ecosystem that requires a bottom-up design approach and a joint effort among CEPs to find alignment in a fragmented and heterogeneous landscape. In this study, we build on the ecosystem-as-structure view to develop an in-depth understanding of the emerging cultural data ecosystem in Germany, and outline how it can be designed in a desirable manner (Hou & Shi, 2021). Taxonomies ofbusiness models indata ecosystems In line with the ecosystem-as-structure view, and to identify the focal value propositions of the cultural data ecosystem for our case, it is crucial to first understand the organizational landscape and the corresponding CEP business models. Business models are defined as the basic mechanisms through which a company creates value for the customer, delivers its products and/or services to the market, generates profit, and collaborates with its stakeholders (Osterwalder & Pigneur, 2010; Teece, 2010). Accordingly, a business model can be structured into four major building blocks: (1) value proposition, (2) value creation, (3) value delivery, and (4) value capture (Günzel & Holm, 2013). This structure has been adopted by scholars to discover business model archetypes of data marketplaces (Fruhwirth etal., 2020), and to analyze similarities and differences between business models with the help of taxonomies for various industries (Möller etal., 2022; Remane etal., 2017). Taxonomies are a well-established classification tool for business models. The advantage of taxonomies is that existing or newly acquired knowledge can be transferred at the concept level to all objects that belong to the same class (here: CEP business model archetype). This is important as otherwise each object (here: each CEP business model) would have to be treated as unique. Taxonomies have already been usefully deployed to explore different archetypes of business models (Schoormann etal., 2023). Möller etal. (2022) provide a comprehensive overview and guidance of business model taxonomies, summarizing their prevalence and differences, and identifying five taxonomies dealing with generic digital platform business models. In addition, recent research by Sterk etal. (2024) identifies 28 datadriven business model–related taxonomies, focusing on internal data usage for business model design. Data-sharing business models in data ecosystems present a subset of data-driven business models, concentrating on the process of data transfer (Schweihoff etal., 2023). Most existing taxonomies of data-sharing business models in ecosystems focus on general (crossindustry) business model characteristics rather than on specific industries (see Table1). We identify three taxonomies offering a general systematization of datasharing initiatives with high-level aspects of business models in data ecosystems. For instance, Gelhaar etal. (2021) and Gieß etal. (2023) include an economic metadimension, and adopt a business model perspective on data ecosystems. Other cross-industry research examines data-sharing business models (Schweihoff etal., 2023) and data-driven business models in ecosystems (Schweihoff etal., 2022)—which we drew on when developing our taxonomy. In terms of industry-specific taxonomies, the analysis by Möller etal. (2020) of data-driven business models in the logistics industry, with dimensions for inter-organizational data-sharing, provides an interesting perspective on data ecosystems, relevant to the development of our taxonomy. Moreover, Sterk etal. (2024) and Bergman etal. (2022) analyzed the automotive industry, focusing on business models in the connected car domain and data marketplaces in the B2B domain. Electronic Markets (2025) 35:47 Page 5 of 22 47 We note that taxonomies of data-sharing business models are predominantly concerned with adopting a more general, cross-industry perspective (see Table1). Industry-specific taxonomies provide deeper insights into a specific industry (Kamprath & Halecker, 2012; Möller etal., 2020). This type of analysis is particularly important in industries that are significantly different from others, such as cultural event industry. Moreover, only the taxonomy developed by Gieß etal. (2023) focuses on data space-enabled data ecosystems. Therefore, the burgeoning research field on data ecosystems would benefit from (1) an industry-specific business model taxonomy and identified archetypes focusing on the cultural event industry, and (2) a specific perspective on data spaces, as a promising form of data-sharing infrastructure (Möller etal., 2024). Research environment: The cultural event industry andcultural event platforms The cultural event industry facilitates access to cultural events and thereby contributes to enrich the cultural life of a population (Hesmondhalgh & Pratt, 2005). Cultural events range from small local theatre productions or regional dance shows to art exhibitions of international importance (Hosagrahar, 2019). These events are characterized by their spatial concentration and performative-interactive manner, offering attendees the chance to enrich their life with meaningful experiences (Geus etal., 2016). Cultural event providers, whether they operate on a for-profit or a not-for-profit business model, deal with budgetary limitations and funding challenges to different degrees (Borin etal., 2018). Within the cultural event industry, CEPs have reached a significant role and have increasingly affected the processes of promoting and disseminating events (Hansmeier etal., 2024). CEPs fundamentally reduce search costs (Parker etal., 2016) for event seekers by providing relevant information on cultural events, sometimes offering additional services such as ticketing systems and social networking options. In addition, CEPs can enable event seekers to filter local events based on their preferences, such as expected travel time, to ensure that they receive event suggestions that are realistically accessible (Man Li etal., 2023). However, event seekers are currently limited to the offerings of individual CEPs they visit regularly or have signed up to, displaying the type of events that matches their stated preferences (or those within a selected geographic distance), and, therefore, they may not fully exploit the entire range of all the available and potentially relevant cultural events (Acatech, 2023). Research on data-sharing in the cultural event industry (and for CEPs in particular) remains scant to date. To the best of our knowledge, only that of Given etal. (2024) is closely related to our research. In their study, the authors analyze cultural data-sharing initiatives and practices. They suggest that the emergence of cultural data initiatives rely on interdisciplinary collaboration among creative artists, cultural event providers, and technology providers to share digitized content (e.g., recorded performances of poetry readings), particularly by leveraging data assets from galleries, libraries, archives, and museums. Persisting challenges are related to (i) resource constraints, mainly due to a funding landscape that often prioritizes short-term projects over long-term support; (ii) structural and policy limitations; and (iii) the ethical handling of copyright and data governance (Given etal., 2024). For a healthy data ecosystem, creative artists and cultural event providers have to navigate these challenges by adopting a more user-focused design and trusted data-sharing practices (Terras etal., 2021). While Given etal. (2024) highlight the importance of data-sharing among CEPs, they address the sharing of digitized cultural Table 1 Overview and categorization of taxonomies of data-sharing business models in data ecosystems Taxonomy for Authors Industry Perspective on data-sharing (acc. to Möller etal., 2024) Archetypes Data ecosystem characteristics Gelhaar etal. (2021) Cross-industry Not specified No Data collaboratives Susha etal. (2017) Cross-industry Data intermediaries No Design options for data spaces Gieß etal. (2023) Cross-industry Data Spaces No Data-driven business models in ecosystems Schweihoff etal. (2022) Cross-industry Not specified No Data-sharing business models Schweihoff etal. (2023) Cross-industry Not specified No Data marketplaces from a business model perspective Fruhwirth etal. (2020) Cross-industry Data intermediaries Yes Data marketplace business models Van de Ven etal. (2021) Cross-industry Data intermediaries No Data-driven business models in logistics Möller etal. (2020) Logistics Not specified No Data-driven business models in the connected car domain Sterk etal. (2024) Automotive Data intermediaries Yes Business models of data marketplaces in the B2B automotive industry Bergman etal. (2022) Automotive Data intermediaries Yes Business model for CEPs in an emerging data space-enabled data ecosystem This paper Cultural events Data space Yes Electronic Markets (2025) 35:47 47 Page 6 of 22 content rather than event data (Given etal., 2024) and focus on different stakeholders in cultural data initiatives. Hence, we identify the need for further research with a specific focus on CEPs as actors in the emerging cultural data ecosystem. Research method To address our research questions, we conducted a three-step research approach (see Fig.1): In Phase 1, we focus on the development and evaluation of the CEP business model taxonomy for the emerging cultural data ecosystem. In Phase 2, we conduct a cluster analysis based on the classification of real-world objects using the developed taxonomy. In Phase 3, we conduct interviews about the benefits and obstacles of the emerging cultural data ecosystem, with at least one CEP platform provider from each identified cluster, and interpret the obtained data. Phase 1: Taxonomy development andevaluation Drawing on established methods for developing taxonomies in IS (Kundisch etal., 2022; Nickerson etal., 2013) and specific guidance for designing business model taxonomies (Möller etal., 2022), our steps are as follows: 1a) Defining the problem and target user groups Following prior literature that developed a platform business model taxonomy, we designated the conceptual representation of a business model for CEPs in the emerging cultural data ecosystem as the meta-characteristic (Massa etal., 2017; Weber etal., 2022). We then deduced dimensions or characteristics essential to describing the four established building blocks of a business model (value proposition, value creation, value delivery, and value capture) to broadly structure the specific dimensions identified in our analysis (Günzel & Holm, 2013; Remane etal., 2017). Two target user groups emerged for our taxonomy: (1) researchers interested in CEP business models in general, and in emerging data ecosystems, specifically, and (2) practitioners with the same interests. 1b) Taxonomy development We conducted three deductive conceptual-to-empirical and four inductive empirical-to-conceptual iterations. The conceptual-to-empirical (C2E) approach helps to identify prior knowledge in the literature, while the empirical-toconceptual (E2C) approach facilitates the transition from observable data and objects toward abstract concepts and principles (Nickerson etal., 2013). Since there is currently no data ecosystem for the cultural event industry, and hence, no CEP that can participate in such an ecosystem, the first five iterations focus on the current state of CEPs and their business models. Iterations 6 and 7 include preliminary considerations of how CEPs could participate in the emerging cultural data ecosystem, as well as some initial steps that have already been taken in this direction. Figure2 provides an overview of all the iterations of the taxonomy Phase 1: Taxonomy Development and Evaluation Phase 2: Cluster Development Phase 3: Interviews with Platform Providers 1a. Define Problem and Specific Target Objects 1b. Taxonomy Development 1c. Taxonomy Evaluation 2a. Identify Clusters 2b. Analyze Clusters 3a. Define Interview Guide 3b. Select Interviewees and Conducting Interviews3c. Analysis and Interpretation I.Conduct three conceptual-toempirical iterations II.Conduct four empirical-toconceptual iterations I. Define meta-characteristic: Business models for CEPs in a data ecosystem II.Define target user groups: researchers and practitioners I.Check objective ending conditions II.Perform ex-ante evaluation III.Check design science research criteria I.Use ward’s minimum variance method II.Determine optimal clusters (elbow rule) I.Select Interveweesfrom representative CEPs II.Conduct interviews (n=8) I.Evaluation of cluster solutions: two non-profit, four for-profit II.Focus on six-cluster solution for deeper insights I.Perform transcription II.Conduct coding and thematic analysis I. Define semi-structured interview guide Fig. 1 Overview of the research approach and its individual steps Electronic Markets (2025) 35:47 Page 7 of 22 47 development process and the corresponding dimensions. In the following, we describe each of the iterations in detail. Iteration 1 (C2E) As there already exists a structured overview of business model taxonomies for different industries and technologies in the IS literature (Möller etal., 2022), we focused our first iteration on deriving initial dimensions and characteristics based on the literature covered by this study. However, as the research by Möller etal. (2022) only covers business model taxonomy studies published before 2021 in IS conference or journal proceedings, we additionally searched for literature in the IS domain from 2021 to 2023 using the same search criteria as Möller etal. (2022)4—this resulted in nine additional papers, which we analyzed. The initial dimensions and characteristics were derived by meticulously reviewing the identified business model taxonomies relevant to our meta-characteristic (see Fig.2). Iteration 2 (C2E) To further refine the dimensions and characteristics, we conducted a second literature review specializing on CEP business models using the database Web of Science. First, we developed a search string5 dealing with the cultural context (e.g., culture, event, creative), the area of interest (marketplace, platform, or industry), and business models (e.g., business model, value creation, value Legend: The grey boxes depict the added or renamed dimensions in the specific iteration, and the dotted line shows the iterations made to include preliminary considerations for an emerging cultural data ecosystem. Iteration 1 Iteration 2 Iteration 3 Iteration 4 Iteration 5 Iteration 6 Iteration 7 C2EE2C Value Proposition Cultural Domain Range Value Creation Value Delivery Value Capture Key Value Proposition Data Origin Price Discovery Review System Design Options forSpecial Needs Geographic Functions Community Functions Ticketing Technology Device Geographic Scope Registration Options Customer Relationship User Segment Offered Services Supply Segment Revenue Streams Revenue Source Business Objective Cost Structure C2EE2CE2C C2EE2C Additional Services Data Ecosystem Cost Cultural Domain Range Cultural Domain Range Cultural Domain Range Cultural Domain Range Cultural Domain Range Cultural Domain Range Key Value Proposition Key Value Proposition Key Value Proposition Key Value Proposition Key Value Proposition Key Value Proposition Data Origin Data Origin Event Data Origin Event Data Origin Event Data Origin Event Data Origin Price DiscoveryPrice DiscoveryPrice DiscoveryPrice DiscoveryPrice DiscoveryPrice Discovery Review System Design Options forSpecial Needs Geographic Functions Geographic Functions Geographic Functions Geographic Functions Geographic Functions Geographic Functions TicketingTicketingTicketingTicketing Ticketing Ticketing Community Functions Community Functions Community Functions Community Functions Community Functions Community Functions Offered Services Data Services Data Services Data Services Data Services Additional Services Additional Services Additional Services Options for Data Sharing Options for Data Sharing Technology Device Technology Device Geographic Scope Geographic Scope Geographic Scope Geographic Scope Geographic Scope Geographic Scope Registration Options Registration Options Registration Options Registration Options Registration Options Registration Options Customer Relationship Customer Relationship Customer Relationship Customer Relationship Customer Relationship Customer Relationship User Segment Supply Segment Revenue Streams Revenue Streams Revenue Streams Revenue Streams Revenue Streams Revenue Streams Revenue Source Revenue Source Revenue Source Revenue Source Revenue Source Revenue Source Business Objective Business Objective Business Objective Business Objective Business Objective Business Objective Cost Structure Cost Structure Cost Structure Maintenance Cost Structure Maintenance Cost Structure Data Ecosystem Cost Fig. 2 Development of dimensions for CEP business models 4 The search string includes: “Business Model Taxonomy” OR “Business Model Classification” in the AISeL database and TITLEABS-KEY (“business model” AND “Taxonomy”) in the Scopus database. 5 The search string includes: (“cultural” OR “event*” OR “creative” OR “culture” OR “music” OR “theater” OR “museum” OR “promoter” OR “festival” OR “concert” OR “show” OR “exhibition”) AND (“platform” OR “marketplace” OR “industr*”) AND (“business model*” OR “value creation” OR “value proposition*” OR “success factor*” OR “revenue”)). Electronic Markets (2025) 35:47 47 Page 8 of 22 proposition), which we applied to title, abstract, and keywords. Thus, we identified and analyzed 32 publications (conferenceand journal papers) in depth. We then refined the taxonomy by adding three dimensions. Iteration 3 (E2C) In this first E2C iteration, aimed at refining our taxonomy and making it more robust, we consulted two experts from our own network in the field of CEPs that are involved in the development of the emerging cultural data ecosystem. We asked them about CEPs from the Germanspeaking market that they expect to have the widest range of business model elements. We focused on the German-speaking market because Western industrialized nations experienced an impressive cultural boom since the 1970s, resulting in a remarkable diversity of cultural offerings (Burton & Scott, 2003). With the input of the experts, we identified 23 CEPs (see Appendix TableA3). Examining their business models in greater detail allowed us to refine our taxonomy, focusing primarily on characteristics. Iteration 4 (E2C) We engaged with a third expert in the field who is familiar with a specific region (Southern Lower Saxony) and lesser-known, niche CEPs. With the help of this expert, who is also involved in the development of the emerging cultural data ecosystem, we were able to identify and analyze an additional 14 CEPs. This allowed us to further refine several dimensions and characteristics. Iteration 5 (E2C) We expanded our list with the inclusion of 114 additional CEPs in Germany (see Appendix TableA3). This expansion was carried out through a thorough examination of online sources and internet searches (e.g., searching for events in various regions). To balance the cost–benefit analysis of our study, i.e., the effort of analyzing CEPs against new insights gained on dimensions and characteristics, we initially selected a random sample of 21 CEPs for this iteration. This approach allowed us to manage resources effectively while leaving enough CEPs for potential further iterations. The resulting modifications of our taxonomy primarily centered on linguistic refinements, such as the renaming of dimensions and characteristics. Iteration 6 (C2E) With our business model taxonomy, we aim to support the transformation of CEP business models in the emerging cultural data ecosystem. As this cultural data ecosystem is only just beginning to take shape, many data ecosystem-relevant dimensions and characteristics can only be determined conceptually. Nonetheless, it is important to investigate the initial steps already taken by some CEPs to participate in such an ecosystem (e.g., regardless of whether they provide options for data-sharing), to assess their data ecosystem readiness. Therefore, we conducted another C2E iteration for our taxonomy development process. To identify relevant literature in this area, we created a search string6 dealing with data ecosystems plus various terms referring to business models and applied it to title, abstract, and keywords of the results of this search. This procedure yielded 21 relevant publications, which were analyzed and discussed among the author team against the background of CEPs in the emerging cultural data ecosystem. This allowed us to derive two more dimensions (i.e., options for data-sharing and data ecosystem cost) and 11 additional characteristics (i.e., data service provider, API, download, specialized software, no options, selling data, data receivers, transactionbased data costs, membership fee, service costs, and none/ not disclosed). Iteration 7 (E2C) We revisited our CEPs based on the new knowledge gained from iteration 6. We classified all CEPs according to the new dimensions and characteristics, aiming to derive further insights from this refined categorization. However, since such a cultural data ecosystem does not yet exist, and the considerations from iteration 6 are mostly conceptual, CEPs cannot always meet the specific criteria. For example, questions about “data ecosystem costs”—such as whether costs are based on data transactions, a fixed membership fee, data services—cannot yet be answered, either because they are not in place or are undisclosed. Consequently, the classification of the CEPs did not reveal any new significant insights, allowing us to conclude this iteration. An overview of all iterations and their corresponding resources for the taxonomy development process can be found in Table2. 1c) Taxonomy evaluation Following Kundisch etal. (2022), we developed a threestep strategy aimed at establishing the validity of our taxonomy, while also assessing its practicality and usefulness. First, we checked the criteria of the objective ending conditions (Nickerson etal., 2013, p. 344). All these criteria were successfully met, with the exception of one condition: “Each characteristic is unique in its dimension.” In this specific case, we chose a nonand mutually exclusive approach (following, e.g., Püschel etal., 2016), deviating from strict uniqueness, to better capture the nuances of the business model strategy we were aiming to describe. Second, we conducted an ex-ante evaluation to ensure that the taxonomy is applicable. This evaluation involved verifying that the subjective ending conditions met five key criteria (Nickerson etal., 2013): (1) The number of 6 The search string includes: (“data ecosystems” AND (“business models” OR “value creation” OR “taxonomy” OR “data sharing”)). Electronic Markets (2025) 35:47 Page 15 of 22 47 metadata evaluation that would enable the event providers using a CEP to assess the popularity of their events, and that could help promote smaller cultural event providers. In the value creation building block, our interviewees listed data standardization and the reduction in editorial workload as potential benefits of the cultural data ecosystem. The networking between CEPs was also perceived as beneficial: “If I have understood this correctly, it offers certain opportunities, perhaps also to initiate business relationships” (C6.1). When it comes to the value capture building block, some CEPs suggested the idea to use the cultural data ecosystem for cross-platform advertising, giving event providers the opportunity to advertise their events on multiple platforms to increase their audience. Next, we present the perceived obstacles of the cultural data ecosystem mentioned in the CEP interviews. In the building block value proposition, the CEPs mentioned aspects such as the concern about losing their market status. One interviewee stated that their event data is their USP which would be lost when their event data is transferred and made accessible to multiple CEPs: “We put much effort into ensuring that all events are available on our platform” (C3.1). Some interviewees feared increased competition for users between CEPs, if all CEPs on the data ecosystem hold similar event data from the ecosystem. Our interviewees also cited potential drawbacks, such as that recommendation systems might suggest events that are not of interest to their users, that the customer experience might suffer from too many events being offered, and that this oversupply might ultimately harm smaller event providers. From a value creation perspective, nearly all interview partners stated that amongst the main obstacles they perceived would be the high technical effort required for the infrastructure development of the cultural data ecosystem, including difficulties in data mapping: “I think one of our biggest challenges is to ensure the compatibility of the individual systems” (C4.1). Moreover, our interviewees were concerned that it might require too much effort for them to transfer their data onto the ecosystem, and that lack of documentation about how to upload data may hinder some CEPs to share their data properly. Lack of clarity regarding the licensing of the shared data and data protection issues was also seen by the CEP interviewees as a potential problem: “It’s quite difficult to share customer data because of data protection” (C5.1). Furthermore, our interviewees stated the sunk cost effect as a potential drawback, as their own efforts in event data acquisition would be overlooked when data is shared through an ecosystem. Another aspect mentioned was the critical mass of event data that a data ecosystem needs to acquire to become sustainable and attractive for a sufficient number of CEPs to join. Next, from a value capture perspective, when being asked about who should fund the cultural data ecosystem, the majority of the interviewed CEPs agreed that it should be publicly funded due to a lack of own financial resources: “To be honest, […] since we are currently struggling to secure the budget for this regional platform, it will be difficult for us to make a financial contribution to the data ecosystem. [Because] our financial resources are very limited” (C2.1). The remaining interviewed CEPs agreed that they would participate financially in the data ecosystem if added value for them could be clearly demonstrated. Lastly, according to one interviewee, event providers and event seekers could not be expected to share the cost of participation in a data ecosystem: “Event providers will not pay, you are lucky if they enter the events onto your platform […]. End users want everything free of charge, anyway” (C3.1). Finally, when comparing the interviewees’ perspective from different CEP archetypes on the data ecosystem, it becomes apparent that non-profit CEPs (i.e., clusters 1 and 2) perceive the most potential drawbacks to arise from the technical implementation of a data ecosystem (i.e., ensuring system compatibility, data quality, data transfer, data protection), indicating a lower level of data ecosystem readiness. On the other hand, potential benefits are assumed to arise from data standardization and the expansion of their own focal value proposition to their users by making more relevant cultural events available to them on their platform. Upcoming event platforms (i.e., cluster 3), by contrast, identify potential threats, such as loss of market status and increased competition between CEPs, as the greatest challenges of the cultural data ecosystem and, therefore, adopt a rather negative mindset towards sharing data over a data ecosystem. Lastly, ticket provider CEPs (i.e., clusters 5 and 6) focus more on the higher-level potential of the cultural data ecosystem, such as the possibility to create new data services, like personalization or recommendations for users, or networking with other CEPs, indicating a higher level of data ecosystem readiness. As most of the CEPs in this archetype already have a functioning business model, they had fewer concerns about a potential loss of market status or increased competition among CEPs. Discussion andimplications Due to the heterogeneous and fragmented landscape of cultural event data spread across numerous CEPs, data ecosystems hold great potential for the cultural event industry more generally, and for CEPs in particular (Acatech, 2023; Jarke, 2023). It is still unclear, though, how the cultural data ecosystem could flourish and work satisfactorily from the perspective of CEPs. Our results reveal that event platforms differ significantly, as revealed by an analysis of their business models. These differences result in six distinct business model archetypes, ranging from publicly funded CEPs, Electronic Markets (2025) 35:47 47 Page 16 of 22 mainly providing local event information, to profit-oriented ticket providers who offer advanced ticketing and various data services. The interviews indicate that CEPs from different archetypes favor different benefits of the emerging cultural data ecosystem, suggesting that the emerging cultural data ecosystem should offer multiple focal value propositions. Consequently, and in line with the conceptual lens of data ecosystems-as-structure, the emerging cultural data ecosystem consists of separate but intertwined data ecosystems created around these different focal value propositions, referred to as VPS cultural data ecosystems. Based on the business model analysis of each cluster (see Table4), our interview analysis (see Table5) and the discussion in the digital workshop, two focal value propositions that could appeal to different business model archetypes stand out. Following Kraemer etal. (2023), these two focal value propositions are discussed with the aid of the two primary structural elements (activities and actors) of the ecosystem-as-structure view that characterize the main configuration required for each value proposition to materialize (Adner, 2017). Focal Value Proposition I For publicly funded non-profit CEPs, our results suggest that the biggest advantage is associated with data standardization and data quality. This enables CEPs, especially those without direct ticketing options, to increase the number of listed events on their platform while, at the same time, reducing costs by cutting editorial overhead of event providers. Cultural events only need to be entered on one CEP as they can be shared in the VPS cultural data ecosystem, improving the event search experience and information quality for event seekers. This also increases the visibility of cultural events and benefits creative artists, especially currently lesser-known creative artists. Hence, CEPs could extend the cultural domain focus as well as the geographic scope. We label this VPS cultural data ecosystem ‘Event data visibility for cultural enrichment.’ Activities to be undertaken for this value proposition are event data provision, event data aggregation and standardization, seamless event data exchange, and data integration on platforms. Event data can be provided through data input by event seekers, creative artists, and event providers, or by the CEP gathering data through inquiry (see “dimension event data origin” in the taxonomy). Event data aggregation and standardization refers to the process of integrating and organizing event data in a way that creates a unified and consistent dataset for data exchange. Seamless event data exchange refers to the efficient and automated sharing of event data between different CEPs. Lastly, data integration on platforms means that CEPs integrate event data from the VPS cultural data ecosystem into their platform so that event seekers can discover these additional events with no extra effort. Actors for this value proposition include CEP archetypes and the data space provider. A data ecosystem encompasses the following main business model roles: data consumer (here: actors that acquire data via the data ecosystem), data provider (here: actors that offer data via the data ecosystem), and a federator (here: the actor that provides federation services such as the secure transmission of data and the cataloging of data offerings) (Kraemer etal., 2023). Table6 designates each activity to one of these business model roles and lists actors that could fulfill these roles needed for the value proposition to materialize. Based on our cluster analysis, we argue that all CEP archetypes could perform the activity of event data provision (see Appendix TableA4). Based on our interview and digital workshop results, we conclude that all CEP archetypes would fulfill the role of event data provision except for upcoming event platforms (cluster 3). This archetype (cluster 3) voiced concerns toward sharing data via a data ecosystem due to the fear of losing their competitive advantage. If these concerns were indeed representative of this archetype, it would be hard to convince the CEPs in this archetype to provide their event data. Further, the event data for cultural enrichment (without direct ticketing) would mainly be provided by clusters 1, 2, and 4, and only to a limited extent by clusters 5 and 6, due to their ticketing-focused business model. The federator (i.e., the data space provider) could be responsible for the activities of event data aggregation and standardization and seamless event data exchange. However, it is also possible that a third-party provider or a CEP with existing data-sharing services (i.e., cluster 6) provides the aggregation and standardization activity. Publicly funded non-profit CEPs (cluster 1 and 2) do not have the capabilities to provide the aggregation and standardization activity, Table 6 Activities and actors for the VPS cultural data ecosystem Event data visibility for cultural enrichment Note: Potential actors shown in parentheses would only fulfil a limited role Activities Data ecosystem business model actor role Potential actors Event data provision Data provider Clusters 1, 2, and 4 (clusters 5, 6) Event data aggregation and standardization Federator Data space provider, third party provider, cluster 6 Seamless event data exchange Federator Data space provider Event data platform integration Data consumer Clusters 1, 2 (Clusters 3, 4, 5, 6) Electronic Markets (2025) 35:47 Page 17 of 22 47 as they lack the technical resources for, and experience of, data-sharing (see Appendix, TableA4). Data consumers would mainly be publicly funded non-profit event platforms (cluster 1 and 2) because they have limited data available on their CEP. Additional event data—especially for local and regional events or lesser-known creative artists—would support their non-profit business model. For-profit platforms with ticketing as their main revenue source (clusters 4, 5, and 6) would only acquire event data if their business model aligns with that of the VPS cultural data ecosystem. This elaboration on the activities and business model roles fulfilled by potential actors reveals several interesting insights and challenges that must be addressed before focal value proposition I can be materialized. First, there are multiple CEPs that would act as data providers. Hence, there is a sufficient amount of data that could be provided to this data ecosystem. Second, the activity of aggregation and standardization of event data is crucial for seamless data exchange and data integration (Immonen etal., 2014; Jarke, 2023). However, especially the data integration procedure comes with substantial technical and operational challenges for the primary data consumer archetypes of clusters 1 and 2. Third, the actors most suited to undertake the activity of event data aggregation and standardization (i.e., CEPs with advanced data-processing capabilities like those in cluster 6, the data space provider, or a third-party provider) do not inherently benefit from it and, thus, would have to be adequately compensated. Fourth, the data exchange activity would need to be managed by a data space provider. However, this actor does also not directly benefit from focal value proposition I and would require financial compensation for the data space provision. This raises fundamental financing issues, as the primary data consumers— publicly funded, non-profit CEPs (cluster 1 and 2)—would benefit from improved event data availability and quality but lack the financial resources to compensate actors that conduct the necessary data ecosystem activities. Consequently, the envisioned VPS cultural data ecosystem is currently unlikely to flourish without external funding because actors needed for crucial activities are not the primary beneficiaries. Either public subsidies or alternative financing models (e.g., corporate sponsoring) could be considered to financially support essential actors who are not direct beneficiaries. Otherwise, despite its societal value, this VPS cultural data ecosystem would struggle to function effectively. Focal Value Proposition II For ticket provider CEPs, our results indicate that additional data services, in particular personalized recommendations, have the potential to be the most important benefit derived from data-sharing. If user and event data from multiple CEPs were shared in this data ecosystem, it would be possible to provide a recommender system for event seekers with similar preferences. This would create additional value for event seekers by improving their search experience and ultimately helping them find the cultural events that best match their personal preferences in a one-stop shop fashion via their preferred CEP. We label this VPS cultural data ecosystem ‘Personalized recommendations for event discoverability.’ Activities to be undertaken for this value proposition are user data provision, secure user data exchange, user data analysis and recommendation model provision, and recommendation model integration. User data can be provided because CEPs collect user data based on registration options, ticket sales, or community functions. Secure user data exchange refers to the secured and trusted data-sharing of user data between different CEPs in accordance with user data protection regulations. User data analysis and recommendation model provision involves processing and analyzing user data to generate a recommendation service that benefits CEPs. Lastly, recommendation model integration means that CEPs integrate the recommendation service into their platform to enhance event discoverability. Actors include CEP archetypes, the data space provider, and the data analysis provider. In addition to the business model roles of data provider and federator described for focal value proposition I, this VPS cultural data ecosystem comprises two additional business model roles: data analysis provider (i.e., actors that provide data analysis as a service) and data analysis consumer (i.e., actors that request data analysis services) (Kraemer etal., 2023). Table7 designates each activity to a business model role and to actors that could fulfill these roles needed for the focal value proposition II to materialize. Table 7 Activities and actors for the VPS cultural data ecosystem Personalized recommendations for event discoverability Note: Potential actors shown in parentheses would only fulfil a limited role Activities Data ecosystem business model actor role Potential actors User data provision Data provider Clusters 5, 6 (clusters 1, 2, 4) Seamless user data exchange Federator Data space provider User data analysis and recommendation model provision Data analysis provider Third-party provider, cluster 6 Recommendation model integration Data analysis consumer Clusters 4, 5, 6 (clusters 1, 2, 3) Electronic Markets (2025) 35:47 47 Page 18 of 22 Ticket providers (clusters 5 and 6) could fulfill the role of user data providers, as they collect user data through registration, ticket purchases, and community functions such as user reviews (see Appendix TableA4). By contrast, most CEPs of archetype cluster 1, 2, and 4 collect only limited user data—if any—as they neither require user registration for using platform services nor provide direct ticketing. The role of upcoming event platforms (cluster 3) remains uncertain, as they are still in the process of identifying their business models. These platforms are probably reluctant to share user data but may be potential data analysis consumers, willing to purchase a personalized recommendation service. The data space provider would again act as a federator. The data analysis provider plays a key role in this ecosystem by processing user and event data to develop personalized recommendations. This role could be fulfilled either by a third-party provider specializing in recommendation services or by a technically advanced CEP from the cluster 6 archetype. These CEPs would act as a data analysis provider if the recommendation service could be financially monetized, either through direct usage fees or indirect benefits such as increased ticketing. We note that there could be a potential conflict of interest for cluster 6 as the CEPs in this archetype might have an incentive to prioritize their own events in the recommendation algorithm. The digital workshop results indicate a broad interest in integrating a personalized recommendation service by all CEPs from different archetypes. However, while clusters 4, 5, and 6 have the financial resources to purchase the service, clusters 1, 2, and 3 mostly lack these resources to participate in this VPS cultural data ecosystem (see Table5). This leads to a crucial societal concern: while profit-driven ticketing platforms (cluster 4, 5 and 6) stand to gain substantial benefits, publicly funded, non-profit CEPs (cluster 1 and cluster 2) and upcoming CEPs (cluster 3) could be excluded due to financial limitations. In contrast to focal value proposition I, focal value proposition II could create a financially self-sustaining VPS cultural data ecosystem with the potential to enhance event discoverability. However, if the emerging cultural data ecosystem was primarily intended to help lesser-known creative artists, by promoting a diverse cultural landscape, and preventing the consolidation of monopolistic structures in the CEP landscape (Acatech, 2023), the impact of this VPS cultural data ecosystem may be undesirable from a societal point of view. If left purely to market forces, this VPS cultural data ecosystem would risk amplifying the dominance of large ticketing providers while sidelining publicly funded non-profit CEPs that lack the financial means of participation. Thus, focal value proposition II necessitates careful design and governance considerations to balance additional value for event seekers with the promotion of cultural diversity. Conclusion Our study provides a comprehensive analysis of CEPs, offering a novel taxonomy and clustering of business models to better understand their role in the emerging cultural data ecosystem. Through a multiple-methods approach, we identified six distinct business model archetypes, reflecting the diverse ways in which CEPs create, deliver, and capture value. Our qualitative insights from platform provider further highlight the varying perspectives on the opportunities and challenges of data-sharing in this industry. Our findings emphasize that CEPs are highly heterogeneous, ranging from local event information providers to global platforms with extensive service offerings. This diversity directly influences their actor roles and activities within the emerging cultural data ecosystem as different business models entail different benefits and obstacles when engaging in data-sharing. Rather than assuming a uniform data ecosystem, our results reveal that the cultural data ecosystem is more accurately conceptualized as a network of intertwined data ecosystems, each structured around a specific focal value proposition that aligns with different business model archetypes (i.e., a VPS cultural data ecosystem). Based on our business model taxonomy, cluster analysis, and qualitative insights, we identify two primary focal value propositions that could serve as structural pillars for the emerging cultural data ecosystem. The first, Event data visibility for cultural enrichment, primarily benefits publicly funded, non-profit CEPs, enabling data standardization and cost-efficient content aggregation to enhance event discoverability and increase the representation of less-well-known creative artists. However, this data ecosystem faces significant financial and structural challenges, as the key actors required to sustain data standardization and exchange lack intrinsic financial incentives, and therefore would require external funding or subsidies. The second, Personalized recommendations for event discoverability, caters to profit-driven ticketing platforms and offers data-driven services such as personalized event recommendations by leveraging shared event and user data. While this data ecosystem has the potential to be financially self-sustaining, it risks reinforcing the dominance of large commercial CEPs, potentially excluding publicly funded platforms that lack the financial resources to participate in advanced data-sharing initiatives. Theoretical contributions This study makes two important theoretical contributions at the periphery of data ecosystem research: First, we have developed a taxonomy and deduced archetypes that extend the body of scientific knowledge on taxonomies and classifications of data-sharing business models (Möller etal., Electronic Markets (2025) 35:47 Page 19 of 22 47 2022). We synthesize existing knowledge and enable the establishment of a shared understanding of CEP business models in emerging data ecosystems, thus offering a “theory for analyzing” (Gregor, 2006). By identifying six archetypes that represent diverse approaches to value creation, datasharing readiness, and financial sustainability, we provide a foundation that enables researchers to analyze the role of business models in the emerging cultural data ecosystem. Second, we extend the literature on data ecosystems by introducing a structurally relevant perspective on their emergence, thus answering calls for further research on this topic (Gelhaar & Otto, 2020; Heinz etal., 2022). Most studies on data ecosystems focus on highly commercialized and technologically mature industries, where value capture mechanisms are well established. By contrast, our study applies the ecosystem-as-structure view (Adner, 2017) to a heterogeneous and fragmented industry, demonstrating how emerging data ecosystems must be understood through the interplay of actors, activities, and focal value propositions. Rather than viewing data ecosystems as singular entities, our findings indicate that multiple intertwined data ecosystems could co-exist within the same industry, each structured around a distinct value proposition that attracts different actor groups. Managerial contributions The heterogeneous nature of the CEP market makes it difficult for platform managers to navigate business model decisions and identify strategic opportunities for datadriven innovation. Our study assists platform managers by providing a structured business model taxonomy and six archetypes, helping them select appropriate business model characteristics or archetypes, thereby enhancing the business model innovation process (Schoormann etal., 2023). Moreover, resource disparities among CEPs create significant barriers to data-sharing participation in the cultural data ecosystem. CEPs with high financial resources and advanced digital skills can easily collect, enter, and manage event data, whereas those with low financial resources struggle with these tasks due to limited time, budget, or technical expertise (Borin etal., 2018). Additionally, the technological infrastructure required to process and disseminate event information remains a major challenge (Jarke, 2023). These insights highlight the structural imbalances in the CEP market and emphasize the need for targeted support mechanisms to enable broader participation in data ecosystems. Our study also provides key insights for data ecosystem designers by analyzing the practical challenges of data-sharing in the cultural event industry. Our findings indicate that, while the concept of the cultural data ecosystem holds strong potential, financial resource allocation remains a decisive factor for its success. Key enabling activities—such as data standardization, integration, and governance—are essential for a functioning data ecosystem but lack inherent financial incentives for the actors best positioned to undertake them. As a result, our study highlights the structural and economic barriers that ought to be overcome, emphasizing that without external financial support mechanisms (e.g., subsidies, funding programs), an inclusive and sustainable cultural data ecosystem is unlikely to emerge. Limitations andfuture research We acknowledge that our study is not free from limitations. As highlighted by Nickerson etal. (2013), taxonomies, however valuable, inherently suffer from biases due to researchers’ subjective interpretations and decisions. The development of our taxonomy relies on information available on the respective CEPs’ websites. It is crucial to note that our examination was limited to publicly accessible information. Any data concealed beyond public access, such as content exclusively available to cultural artists, remained beyond our purview for analysis. To overcome this limitation, future research could involve different approaches like participant observation or case studies to gain deeper insights into the inner workings of CEPs. Such a holistic approach would allow for a more nuanced understanding of the factors shaping the business models and practices of these platforms, including any hidden or exclusive content accessible only to cultural artists. Similarly, our cluster analysis is subject to variability owing to the diverse algorithms available for determining optimal cluster numbers. However, the identification of appropriate clusters remains instrumental in gaining a deeper understanding of CEPs and their chosen business models. Our results serve as a starting point for further investigation. Consequently, future research can delve into alternative algorithms to assess the robustness of our analysis. Additionally, limitations may arise from our sample selection process, which mainly consists of 110 CEPs in Germany along with 41 from Austria and Switzerland. It is worth noting that the selection process involved a combination of random internet searches and leveraging the network of experts we consulted, particularly those who were familiar with smaller, lesser-known CEPs. Nevertheless, we encourage future research to expand the dataset to include other countries, beyond the German-speaking cultural sphere. Furthermore, it is important to note that our interviews were conducted with a relatively limited number of participants, selected on the basis of specific characteristics and backgrounds aligning with the respective clusters. To enhance the broader applicability of our findings, we suggest that future research strives to employ larger and more diverse samples to provide a more comprehensive understanding of the subject matter. Electronic Markets (2025) 35:47 47 Page 20 of 22 Finally, we propose two potential focal value propositions (and therefore, two distinct, but intertwined data ecosystems). However, these considerations are only the first step in the emergence of the cultural data ecosystem, and further investigations are needed. Future research could investigate in more depth the positions, links, and further potential actors (such as cultural event providers) within ecosystems, based on Adner’s framework (Adner, 2017). This could involve utilizing appropriate modeling languages, such as business model modelling languages (Szopinski etal., 2022; Vorbohle & Kundisch, 2022), and designing a minimum viable data ecosystem with an adequate number of first-mover CEPs (Adner, 2012). A question that arises from our results is whether the identified and significant number of publicly funded CEPs should be “introduced” to the cultural data ecosystem by legislation. On the one hand, this would create a setting where a sufficient number of participants attend. However, it would be left to political decision-makers to decide who should bear the data ecosystem participation costs (e.g., cities, districts, regions, or federal states). On the other hand, the economic and monopolistic effects of this legislation need to be further investigated. After establishing and validating a minimum viable data ecosystem with actively participating CEPs, future research could then analyze the influence of data-sharing on other cultural actors, such as event providers, and include further actors from closely related cultural domains to realize additional value propositions (Weber etal., 2024). Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1252502500790-y. Acknowledgements The authors would like to thank all student assistants from our chair for their excellent assistance. This work was partially supported by the Bundesregierung für Kultur und Medien (BKM) within the project Datenraum Kultur (https:// www. acate ch. de/ proje kt/ daten raumkultur/). Funding Open Access funding enabled and organized by Projekt DEAL. Data Availability All data supporting the findings of this study are included in the manuscript and/or theOnline Appendix. Declarations Competing Interest statement The authors have no conflicts of interest to declare that are relevant to the content of this article. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References Acatech. (2023). The culture data space. Retrieved February 22, 2024, from https:// en. acate ch. de/ proje ct/ thecultu redataspace/ Acatech. (2024). Mobility dataspace: the data marketplace for the mobility sector. Retrieved February 22, 2024, from https:// mobil itydatas pace. eu/ mobil itydataspace Adner, R. (2012). The wide lens: A new strategy for innovation (9th ed.). Penguin. Adner, R. (2017). Ecosystem as structure. Journal of Management, 43(1), 39–58. https:// doi. org/ 10. 1177/ 01492 06316 678451 Al-Debei, M. M., & Avison, D. (2010). Developing a unified framework of the business model concept. European Journal of Information Systems, 19(3), 359–376. https:// doi. org/ 10. 1057/ ejis. 2010. 21 Angelo, C., Mariangela, L., Manuela, M., & Lorenzo, Q. (2018). Innovating performing arts management through a product lifecycle management approach. Product Lifecycle Management to Support Industry, 4, 420–431. Azkan, C., Möller, F., Meisel, L., & Otto, B. (2020). Service dominant logic perspective on data ecosystems: a case study based morphology. ECIS 2020 Proceedings(pp. 1–19). Bergman, R., Abbas, A. E., Jung, S., Werker, C., & De Reuver, M. (2022). Business model archetypes for data marketplaces in the automotive industry. Electronic Markets, 32(2), 747–765. https:// doi. org/ 10. 1007/ s1252502200547-x Beverungen, D., Hess, T., Köster, A., & Lehrer, C. (2022). From private digital platforms to public data spaces: Implications for the digital transformation. Electronic Markets, 32(2), 493–501. https:// doi. org/ 10. 1007/ s1252502200553-z Borin, E., Donato, F., spsampsps Sinapi, C. (2018). Financial sustainability of smalland medium-sized enterprises in the cultural and creative sector: the role of funding. E. Innerhofer, H. Pechlaner, spsampsps E. Borin (Eds.), FGF Studies in Small Business and Entrepreneurship. Entrepreneurship in Culture and Creative Industries (pp. 45–62). Springer International Publishing. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https:// doi. org/ 10. 1191/ 14780 88706 qp063 oa Burton, C., & Scott, C. (2003). Museums: challenges for the 21st century. International Journal of Arts Management, 5(2), 55–68. 6473. de Geus, S., Richards, G., & Toepoel, V. (2016). Conceptualisation and operationalisation of event and festival experiences: Creation of an event experience scale. Scandinavian Journal of Hospitality and Tourism, 16(3), 274–296. https:// doi. org/ 10. 1080/ 15022 250. 2015. 11019 33 Fassnacht, M., Leimstoll, J., Benz, C., Heinz, D., & Satzger, G. (2024). Data sharing practices: The interplay of data, organizational structures, and network dynamics. Electronic Markets, 34(1), 1–25. https:// doi. org/ 10. 1007/ s1252502400732-0 Fruhwirth, M., Rachinger, M., & Prlja, E. (2020). Discovering business models of data marketplaces. HICSS 2020 Proceedings(pp. 5738–5747). Gelhaar, J., Groß, T., & Otto, B. (2021). A taxonomy for data ecosystems. HICSS 2021 Proceedings(pp. 6113–6122). Electronic Markets (2025) 35:47 Page 21 of 22 47 Gelhaar, J., & Otto, B. (2020). Challenges in the emergence of data ecosystems. PACIS 2020 Proceedings(pp. 1–14). Gieß, A., Möller, F., Schoormann, T., & Otto, B. (2023). Design options for data spaces. ECIS 2023 Proceedings(pp. 1–18). Given, L. M., Polkinghorne, S., & Cattlin, J. (2024). Structural elements and spheres of expertise: Creating a healthy ecosystem for cultural data initiatives. Journal of the Association for Information Science and Technology, 75(10), 1070–1086. https:// doi. org/ 10. 1002/ asi. 24849 Gregor. (2006). The nature of theory in information systems. MIS Quarterly, 30(3), 611–642. https:// doi. org/ 10. 2307/ 25148 742 Günzel, F., & Holm, A. B. (2013). One size does not fit all: Understanding the front-end and back-end of business model innovation. International Journal of Innovation Management, 17(1), 1340002. https:// doi. org/ 10. 1142/ S1363 91961 34000 21 Hagerty, M. R. (1985). Improving the predictive power of conjoint analysis: The use of factor analysis and cluster analysis. Journal of Marketing Research, 22(2), 168–184. https:// doi. org/ 10. 1177/ 00222 43785 02200 206 Hansmeier, P., zur Heiden, P., & Beverungen, D. (2024). Modeling customer journeys in digital data ecosystems: a domain-specific modeling language. ECIS 2024 Proceedings(pp. 1–17). Health-X. (2024). dataLOFT Platform. Retrieved February 22, 2024, from https:// www. healthx. org/ en/ platf orm Heinz, D., Benz, C., Fassnacht, M. K., & Satzger, G. (2022). Past, present and future of data ecosystems research: a systematic literature review. PACIS 2022 Proceedings(pp. 1–17). Hesmondhalgh, D., & Pratt, A. C. (2005). Cultural industries and cultural policy. International Journal of Cultural Policy, 11(1), 1–13. https:// doi. org/ 10. 1080/ 10286 63050 00675 98 Hoffmann, M. G., Murad, E. P., Da Lemos, D. C., Farias, J. S., & Sanches, B. L. (2022). Characteristics of innovation ecosystems’ governance: An integrative literature review. International Journal of Innovation Management, 26(8), 1–25. https:// doi. org/ 10. 1142/ S1363 91962 25006 21 Hosagrahar, J. (2019). Culture 2030 indicators. Sustainable development goals. United Nations Educational, Scientific and Cultural Organization. Hou, H., & Shi, Y. (2021). Ecosystem-as-structure and ecosystem-ascoevolution: A constructive examination. Technovation, 100, Article 102193. https:// doi. org/ 10. 1016/j. techn ovati on. 2020. 102193 Hutterer, A., & Krumay, B. (2022). Integrating heterogeneous data in dataspaces: a systematic mapping study. PACIS 2022 Proceedings(pp. 1–17). Immonen, A., Palviainen, M., & Ovaska, E. (2014). Requirements of an open data based business ecosystem. IEEE Access, 2, 88–103. https:// doi. org/ 10. 1109/ ACCESS. 2014. 23028 72 Jacobides, M. G., Candelon, F., Krayer, L., Round, K., & Chen, W. (2024a). Building synthetic worlds: Lessons from the excessive infatuation and oversold disillusionment with the metaverse. Industry and Innovation, 31(1), 105–129. https:// doi. org/ 10. 1080 / 13662 716. 2023. 22790 51 Jacobides, M. G., Cennamo, C., & Gawer, A. (2018). Towards a theory of ecosystems. Strategic Management Journal, 39(8), 2255–2276. https:// doi. org/ 10. 1002/ smj. 2904 Jacobides, M. G., Cennamo, C., & Gawer, A. (2024b). Externalities and complementarities in platforms and ecosystems: From structural solutions to endogenous failures. Research Policy, 53(1), Article 104906. https:// doi. org/ 10. 1016/j. respol. 2023. 104906 Jarke, M. (2023). Culture data space: a case study in federated data ecosystems. Retrieved from VLDB Workshops website: https:// publi catio ns. rwthaachen. de/ record/ 974337/ files/ 974337. pdf.Accessed 01.04.2025. Kamprath, M., & Halecker, B. (2012). A systematic approach for business model taxonomy - How to operationalizeand compare large quantities of business models?. In Contribution at the 5th ISPIM Innovation symposium – stimulatinginnovation: Challenges for management, science & technology. Kapoor, R. (2018). Ecosystems: Broadening the locus of value creation. Journal of Organization Design, 7(1), 1–16. https:// doi. org/ 10. 1186/ s414690180035-4 Karst, F. S., Li, M. M., & Leimeister, J. M. (2025). SynDEc: A synthetic data ecosystem. Electronic Markets, 35(1), 1–28. https:// doi. org/ 10. 1007/ s1252502400746-8 Kodinariya, T. M., Makwana, & Prashant R. (2013). Review on determining number of cluster in K-Means clustering. International Journal of Advance Research in Computer Science and Management Studies, 1(6), 90–95. Kohtamäki, M., Parida, V., Oghazi, P., Gebauer, H., & Baines, T. (2019). Digital servitization business models in ecosystems: A theory of the firm. Journal of Business Research, 104, 380–392. https:// doi. org/ 10. 1016/j. jbusr es. 2019. 06. 027 Kraemer, P., Niebel, C., & Reiberg, A. (2023). Gaia-x and business models. Retrieved from https:// gaia-xhub. de/ wpconte nt/ uploa ds/ 2023/ 02/ White paperGaia-XBusin essModels. pdf.Accessed 01.04.2025. Kundisch, D., Muntermann, J., Oberländer, A. M., Rau, D., Röglinger, M., Schoormann, T., & Szopinski, D. (2022). An update for taxonomy designers. Business & Information Systems Engineering, 64(4), 421–439. https:// doi. org/ 10. 1007/ s1259902100723-x Landwehr, M. auf der, Schoormann, T., Viebahn, C. von, & Trott, M. (2023). From purchase to pantry – exploring archetypes and strategies in the context of e-grocery fulfilment. European Journal of Information Systems, 1–39. https:// doi. org/ 10. 1080/ 09600 85X. 2023. 21804 46 Legner, C., & Otto, B. (2023). Editorial to the special issue on Management of data ecosystems and platforms. Die Unternehmung, 77(1), 1–3. https:// doi. org/ 10. 5771/ 0042059x2023-1-1 Man Li, S., Lam, A. H. C., & Chiu, D. K. W. (2023). Digital transformation of ticketing services. Management and marketing for improved retail competitiveness and performance(pp. 156–179). Mangiameli, P., Chen, S. K., & West, D. (1996). A comparison of SOM neural network and hierarchical clustering methods. European Journal of Operational Research, 93(2), 402–417. https:// doi. org/ 10. 1016/ 03772217(96) 00038-0 Massa, L., Tucci, C. L., & Afuah, A. (2017). A critical assessment of business model research. Academy of Management Annals, 11(1), 73–104. https:// doi. org/ 10. 5465/ annals. 2014. 0072 Merton, R. K. (1990). Focused interview. Free Press. Möller, F., Jussen, I., Springer, V., Gieß, A., Schweihoff, J. C., Gelhaar, J.,Guggenberger T.,Otto, B. (2024). Industrial data ecosystems and data spaces. Electronic Markets, 34(1), 1–17. https:// doi. org/ 10. 1007/ s1252502400724-0 Möller, F., Stachon, M., Azkan, C., Schoormann, T., & Otto, B. (2022). Designing business model taxonomies: Synthesis and guidance from information systems research. Electronic Markets, 32(2), 701–726. https:// doi. org/ 10. 1007/ s1252502100507-x Möller, F., Stachon, M., Hoffmann, C., Bauhaus, H., & Otto, B. (2020). Data-driven business models in logistics: a taxonomy of optimization and visibility services. HICSS 2020 Proceedings(pp. 5379–5388). Moore, J. F. (1993). Predators and prey: A new ecology of competition. Harvard Business Review, 71(3), 75–86. Nickerson, R. C., Varshney, U., & Muntermann, J. (2013). A method for taxonomy development and its application in information systems. European Journal of Information Systems, 22(3), 336–359. https:// doi. org/ 10. 1057/ ejis. 2012. 26 Oliveira, S., Iury, M., Barros Lima, G. d. F., & Farias Lóscio, B. (2019). Investigations into data ecosystems: A systematic mapping study. Knowledge and Information Systems, 61(2), 589–630. https:// doi. org/ 10. 1007/ s101150181323-6 Electronic Markets (2025) 35:47 47 Page 22 of 22 Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook of visionaries, game changers, and challengers. John Wiley & Sons. Otto, B. (2022). A federated infrastructure for European data spaces. Communications of the ACM, 65(4), 44–45. https:// doi. org/ 10. 1145/ 35123 41 Otto, B., Lis, D., Jürjens, J., Cirullies, J., Opriel, S., Howar, F.,Sven, M., Möller, F. (2019). Data ecosystems: conceptual foundations, constituents and recommendations for action. Retrieved from https:// publi ca. fraun hofer. de/ entit ies/ publi cation/ ed37b c8ec5c243f89e80ec825 145bc e3/ detai ls.Accessed 01.04.2025. Parker, G., van Alstyne, M., & Choudary, S. P. (2016). Platform revolution: how networked markets are transforming the economy - and how to make them work for you. W. W. Norton & Company. Püschel, L., Röglinger, M., & Schlott, H. (2016). What’s in a smart thing? Development of a multi-layer taxonomy. ICIS 2016 Proceedings(pp. 1–19). Remane, G., Hanelt, A., Tesch, J. F., & Kolbe, L. M. (2017). The business model pattern database: A tool for systematic business model innovation. International Journal of Innovation Management, 21(01), 1750004. https:// doi. org/ 10. 1142/ S1363 91961 75000 49 Rowley, J. (2012). Conducting research interviews. Management Research Review, 35(3/4), 260–271. https:// doi. org/ 10. 1108/ 01409 17121 12101 54 Schoormann, T., Schweihoff, J., Jussen, I., & Möller, F. (2023). Classification tools for business models: Status quo, comparison, and agenda. Electronic Markets, 33(1), 1–36. https:// doi. org/ 10. 1007/ s1252502300639-2 Schweihoff, J., Jussen, I., Dahms, V., Möller, F., & Otto, B. (2023). How to share data online (fast) – a taxonomy of data sharing business models. HICSS 2023 Proceedings(pp. 607–616). Schweihoff, J. C., Jussen, I., Stachon, M., & Möller, F. (2022). Design options for data-driven business models in data-ecosystems. Lecture Notes in Informatics(pp. 331–345). Sonnenberg, C., spsampsps vom Brocke, J. (2012). Evaluations in the science of the artificial: reconsidering the build-evaluate pattern in design science research. DESRIST 2012 Proceedings(pp. 381–397). Sterk, F., Stocker, A., Heinz, D., & Weinhardt, C. (2024). Unlocking the value from car data: A taxonomy and archetypes of connected car business models. Electronic Markets, 34(1), 1–24. https:// doi. org/ 10. 1007/ s1252502400692-5 Susha, I., Janssen, M., & Verhulst, S. (2017). Data collaboratives as a new frontier of cross-sector partnerships in the age of open data: Taxonomy development. In HICSS 2017 Proceedings, pp. 2691–2700. Szopinski, D., Massa, L., John, T., Kundisch, D., & Tucci, C. (2022). Modeling business models: a cross-disciplinary analysis of business model modeling languages and directions for future research. Communications of the Association for Information Systems, 51, 774–841. https:// doi. org/ 10. 17705/ 1cais. 05133 Teece, D. J. (2010). Business models, business strategy and innovation. Long Range Planning, 43(2–3), 172–194. https:// doi. org/ 10. 1016/j. lrp. 2009. 07. 003 Terras, M., Coleman, S., Drost, S., Elsden, C., Helgason, I., Lechelt, S.,Osborne, N.,& Speed, C. (2021). The value of massdigitised cultural heritage content in creative contexts. Big Data & Society, 8(1), 1–15. https:// doi. org/ 10. 1177/ 20539 51721 10061 65 Thomas, L. D. W., & Autio, E. (2020). Innovation ecosystems in management: an organizing typology. Retrieved from https:// papers. ssr n. com/ sol3/ papers. cfm? abstr act_ id= 34769 25. Accessed 01.04.2025. Toorajipour, R., Oghazi, P., & Palmié, M. (2024). Data ecosystem business models: Value propositions and value capture with artificial intelligence of things. International Journal of Information Management, 78, Article 102804. https:// doi. org/ 10. 1016/j. ijinf omgt. 2024. 102804 van de Ven, M., Abbas, A. E., Roosenboom-Kwee, Z., & de Reuver, G. A. (2021). Creating a taxonomy of business models for data marketplaces. In Proceedings of the 34th bled econference – digital support from crisis to progressive change (pp. 313–325). University of Maribor Press. https:// doi. org/ 10. 18690/ 978961g286385-9. 23 Vorbohle, C., & Kundisch, D. (2022). Overcoming silos: a review of business model modeling languages for business ecosystems. ECIS 2022 Proceedings(pp. 1–11). Weber, M., Beutter, M., Weking, J., Böhm, M., & Krcmar, H. (2022). AI startup business models. Business & Information Systems Engineering, 64(1), 91–109. https:// doi. org/ 10. 1007/ s1259902100732-w Weber, P., Hiller, S., Kurrle, S., Werling, M., & Werth, D. (2024). Evolutionary milestones in the development of data ecosystems. AMCIS 2024 Proceedings(pp. 1–11). Wiesböck, F., & Hess, T. (2020). Digital innovations. Electronic Markets, 30(1), 75–86. https:// doi. org/ 10. 1007/ s1252501900364-9 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.