Development and evaluation of a taxonomy for platform revenue models

Bartels, Nedo; Koch, Matthias; Heß, Anne; Gordijn, Jaap Article — Published Version Development and evaluation of a taxonomy for platform revenue models Electronic Markets Provided in Cooperation with: Springer Nature Suggested Citation: Bartels, Nedo; Koch, Matthias; Heß, Anne; Gordijn, Jaap (2025) : Development and evaluation of a taxonomy for platform revenue models, Electronic Markets, ISSN 1422-8890, Springer, Berlin, Heidelberg, Vol. 35, Iss. 1, https://doi.org/10.1007/s12525-025-00841-4 This Version is available at: https://hdl.handle.net/10419/330912 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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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. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Electronic Markets (2025) 35:88 https://doi.org/10.1007/s12525-025-00841-4 RESEARCH PAPER Development andevaluation ofataxonomy forplatform revenue models NedoBartels1,2· MatthiasKoch1· AnneHeß1,3· JaapGordijn2,4 Received: 19 May 2025 / Accepted: 15 September 2025 © The Author(s) 2025 Abstract A critical challenge in launching successful platform business models is the design of viable revenue models. While existing frameworks and taxonomies address platform business models more broadly, conceptual clarity regarding platform revenue models remains limited, particularly in terms of how value is captured among platform actors. The absence of a consistent taxonomy leaves dimensions and characteristics fragmented across studies, thereby constraining theory-building and limiting actionable guidance for managerial decisions on value-capture mechanisms and pricing strategies. This study proposes a taxonomy of platform revenue models comprising 15 dimensions and 64 characteristics. Following a taxonomy design methodology grounded in design science research, we apply iterative design cycles and principles. A controlled experiment is conducted to empirically assess the usefulness of the taxonomy. Our results show that the taxonomy significantly improves the completeness and accuracy of the designed platform revenue models. This research advances platform business model theory by offering an evaluated taxonomy that supports the conceptualization of platform revenue models. Keywords Taxonomy evaluation· Taxonomy design· Design science research· Business model· Revenue model· Digital platform JEL classification L86· M15· O3· L10 Introduction The platform economy has emerged as a transformative force (McAfee & Brynjolfsson, 2017; Parker etal., 2016), attracting scholarly interest from diverse fields seeking to examine the disruptive impact of platform businesses, such as Airbnb in hospitality (Zervas etal., 2017), Uber in mobility (Clarke, 2022; Eckert etal., 2024), or Spotify in the music industry (Fleischer, 2021; Vonderau, 2019). Platforms enhance trading efficiency by increasing transaction frequency and reducing search, replication, and verification costs (Xue etal., 2020), while their rapid growth, driven by network effects, often leads to market dominance through winner-take-it-all dynamics (Armstrong, 2006; Hagiu & Wright, 2015; Rochet & Tirole, 2003). To gain a deeper understanding of their role in shaping economic interactions, it is necessary to look beyond their technical infrastructure and examine their underlying business logic (Guggenberger etal., 2020; Täuscher & Laudien, 2018), which is conceptualized through a business model that describes how value is created, delivered, and captured (Teece, 2010). Unlike Responsible Editor: Juho Lindman * Nedo Bartels nedo.bar[email protected]er.de Matthias Koch matthias.koc[email protected]er.de Anne Heß [email protected] Jaap Gordijn j.gordi[email protected] 1 Fraunhofer IESE, Kaiserslautern, Germany 2 Vrije Universiteit Amsterdam, Amsterdam, TheNetherlands 3 Technical University ofApplied Sciences Würzburg-Schweinfurt, Würzburg, Germany 4 The Value Engineers, Soest, TheNetherlands Electronic Markets (2025) 35:88 88 Page 2 of 23 value chain or pipeline-oriented business models, which are characterized by traditional firm-customer value delivery, platform business models rely on peer-to-peer exchanges in which value is co-created within actor-to-actor networks, as exemplified by platforms such as Airbnb and Uber (Fehrer etal., 2018; Täuscher & Laudien, 2018; Wirtz etal., 2019). Platform business models transform not only value creation and delivery but also redefine value capture, as they employ revenue models, i.e., the mechanisms by which a firm generates revenue from its value creation (Osterwalder & Pigneur, 2013), that extract value across multiple market sides rather than from a single customer segment (Daxhammer etal., 2019; Kenney & Zysman, 2016; Täuscher & Laudien, 2018). While prior research has offered broad insights into the value creation and delivery dimensions of platform business models (Fu etal., 2017; Rohn etal., 2021; Täuscher & Laudien, 2018), value capture remains underexplored (Fehrer etal., 2018; Hein etal., 2020). In platform contexts, designing revenue models is particularly challenging, as business model designers must make complex decisions about whom and what to charge to ensure scalability (Kim, 2016; Madanaguli etal., 2023; Pidun etal., 2020). Flawed revenue model design is a central factor in platform failure, particularly due to inadequate surplus sharing and insufficient protection of monetization opportunities (Mancha & Gordon, 2022; Parker etal., 2016). Accordingly, information systems (IS) researchers have emphasized the importance of gaining deeper insights into the value capture and revenue model design of digital platforms (cf. Hein etal., 2020; Madanaguli etal., 2023; Veile etal., 2022). This study contributes to this call by proposing a taxonomy that structures platform revenue models and supports business model designers in developing viable revenue model strategies. It is guided by the following two questions. • RQ1: Which dimensions and characteristics constitute a taxonomy for describing platform revenue models? • RQ2: How useful is a developed taxonomy in supporting the design of platform revenue models? This study follows the taxonomy design methodology of Kundisch etal. (2022) within a design science research (Hevner & Chatterjee, 2010) context. As a first step toward addressing RQ1, we synthesize existing research on platform revenue models and incorporate insights from the analysis of seven platform cases. To answer RQ2, we conduct a controlled experiment with ten practitioners, equally divided into test and control groups, to evaluate the taxonomy’s usefulness in designing platform revenue models. Expert reviewers assessed the resulting descriptions in terms of completeness (i.e., coverage of relevant components) and accuracy (i.e., clarity, structure, and logical coherence), allowing for a measurable comparison of outcomes. The contribution of this study is twofold. The first contribution is a taxonomy of platform revenue models, comprising 15 dimensions and 64 characteristics. The taxonomy distinguishes between two perspectives that together form a comprehensive logic of a platform revenue model: the platform operator, with eight dimensions, and the supply-side actors, who offer products and services via the platform, with seven dimensions. By enabling a differentiated perspective on revenue model design across platform actors, this taxonomy contributes to addressing a key unresolved issue in platform research––namely, how value is captured between platform owners and supply-side actors (Hein etal., 2020; Helfat & Raubitschek, 2018). The usefulness of the proposed taxonomy is evaluated in a controlled experiment, which shows that participants applying it designed more comprehensive and accurate models than those in the control group. The second contribution results from applying the taxonomy to seven platform cases during its development. This application identified 26 distinct revenue model types and illustrates the simultaneous use of multiple revenue strategies within the observed cases. Our results extend prior research on classified platform business models (Staub etal., 2021; Täuscher & Laudien, 2018) and underscore the need for a deeper understanding of how different revenue model types can be combined within a single platform business model. Building on these contributions, we offer practitioners a structured framework for designing their own platform revenue models. For researchers, the taxonomy provides a foundation for developing ex post theories (Bapna etal.,2004) and fostering a deeper understanding of platform revenue model design. Theoretical context Platforms can be analyzed from a market-oriented, sociotechnical, technical, or business-oriented perspective (Hein etal., 2020; Täuscher & Laudien, 2018). This study takes a business-oriented perspective, focusing on the business model aspects of digital platforms. Platform business models Interest in business models is growing in the field of IS, leading to a rich body of definitions and perspectives on the concept (Massa etal., 2017; Möller etal., 2022; Zott etal., 2011). The conceptualizations proposed by Teece (2010) and Osterwalder and Pigneur (2010) are widely recognized and have become foundational references (Amit & Zott, 2020; Massa etal., 2017). In this study, we follow the definition of Teece (2010), who describes a business model as “the design Electronic Markets (2025) 35:88 Page 3 of 23 88 or architecture of the value creation, delivery, and capture mechanisms” employed. Similar to the broader concept of a business model, the term platform business model lacks a widely accepted definition and is often used interchangeably with related terms such as “multi-sided platforms”, “multisided markets”, “platform-based markets”, and “platform ecosystems” (Fehrer etal., 2018). A platform business model reduces transaction costs by providing an infrastructure through which multiple transactions can take place efficiently (Fehrer etal., 2018; Rohn etal., 2021). Traditional business models rely on a centralized exchange of value by managing a linear series of activities from input to output, resembling a pipeline or value chain (Wirtz etal., 2019). In contrast, platform business models create value by facilitating interactions between different stakeholders through a digital platform, creating a value network and often resulting in co-created value (Ceccagnoli etal.,2012; Smedlund etal., 2018; R. Wieringa & Gordijn, 2023). This study examines platform business models that rely on digital infrastructures to enable transaction-based value creation, commonly referred to as “transaction platforms” (Cusumano etal., 2019; Evans & Gawer, 2016). In contrast to innovation platforms that provide a technological foundation for complementary innovations (Cusumano etal., 2019), transaction platforms create value by facilitating interactions between distinct participants engaged in the exchange of assets (Dushnitsky etal., 2022; Koch etal., 2022). In this context, an “asset” can be defined as any good––material or immaterial, such as products, services, or data––that is considered valuable by both providers and consumers (Koch etal., 2022). Such transactions typically involve collaboration between providers and consumers mediated by a platform operator who does not own these assets themselves (Hein etal., 2020; Koch etal., 2022; Subramaniam etal., 2019). Acknowledging the varying terminologies in the literature (Beverungen etal., 2021; Hein etal., 2020; Parker etal., 2016), we adopt the framework proposed by Koch etal. (2022) to describe the triadic relationship in which a digital platform facilitates brokering activities performed by an asset broker (platform operator) to match asset providers (organizations or individuals offering information, goods, or services) with asset consumers (organizations or individuals consuming those offerings). Platform revenue models We define a platform revenue model as an economic concept within the value-capture dimension of a business model, specifying the monetization mechanisms through which a digital platform generates revenue from its intermediation activities between market sides (Kim, 2016; Osterwalder, 2004; Täuscher & Laudien, 2018). It highlights the mechanisms by which value is captured by the platform, often through subscription fees, transaction fees, or advertising revenue (Täuscher & Laudien, 2018), using monetization mechanisms that preserve and enhance rather than undermine network effects (Parker etal., 2016). Network effects and their creation are particularly important for platforms (Kim, 2016; Trischler & Meier, 2021) as illustrated in Fig.1. Prior research has extensively shown that platform pricing and revenue model design play a key role in fostering direct and indirect network effects (Caillaud & Jullien, 2003; Hagiu, 2006; Parker & van Alstyne, 2005; Rochet & Tirole, 2003). In the case of direct network effects, the value to each user increases as the number of users on the Fig. 1 Illustrated interaction between platform actors, brokered assets, and network effects Digital platform Asset broker (platform operator) Asset providers (supply side) Asset consumers (demand side) Indirect network effects Direct network effects Legend Asset (e.g., product or service) Actor (e.g., individual or organization) Electronic Markets (2025) 35:88 88 Page 4 of 23 same side of the market increases. Indirect network effects occur when the value of users on one side of the market increases as the size of the opposite side of the market increases. These externalities influence user behavior and are a driver of platform scalability (Cusumano, 2015; Katz & Shapiro, 1994; Sorri etal., 2019; Tussyadiah & Pesonen, 2016). The literature increasingly emphasizes the importance of choosing which side of the market to serve as a revenue source within a platform revenue model (Eisenmann etal., 2006; Kim, 2016; Omarini, 2017; Wirtz etal., 2019). According to Eisenmann etal. (2006), platform operators need to consider the price sensitivity of each market side to determine the money and the subsidy side. Participants on the money side, who pay for platform services, usually exhibit low price sensitivity, whereas the subsidy side is more price sensitive (Omarini, 2017). Platforms (e.g., app stores) subsidize the side with higher demand elasticity (e.g., users) to attract participation, while charging the money side (e.g., developers) (Kim, 2016; Rochet & Tirole, 2003). While some platform operators subsidize one or more market sides, others may charge a single price for all market sides, differentiate between market sides, or even vary fees within a market side (Daxhammer etal., 2019). Taxonomies forplatform revenue models A taxonomy classifies concepts or objects, aiding structured insights and comprehension of complex domains. It provides researchers with a means to analyze, structure, and understand complex domains (Nickerson etal., 2013). The IS community has proposed various taxonomies for business models and digital platforms, as exemplified in the works of Bergman etal. (2022), Duparc etal. (2022), Lage etal. (2022), Möller etal. (2022), Tessmann and Elbert (2022), and Weking etal. (2020a). The literature on platform business models has also identified a variety of dimensions for value capture, reflecting both general principles and contextspecific nuances, as exemplified by selected studies shown in the concept map in Fig.2. The studies included in the concept map were selected for their relevance to platform revenue models and their scholarly visibility (each cited more than ten times). The selection is not comprehensive but serves to highlight the fragmentation of research on platform revenue models and should be understood as illustrative rather than exhaustive. Existing frameworks emphasize common dimensions such as key revenue streams, price discrimination (Staub etal., 2021; Täuscher & Laudien, 2018), price discovery (Staub etal., 2021; Täuscher & Laudien, 2018; van de Ven etal., 2021), and pricing models (Springer & Petrik, 2021; van de Ven etal., 2021). Sector-specific dimensions, such as pie-splitting in industrial platforms (Springer & Petrik, 2021) and smart contracts in data marketplaces (van de Ven etal., 2021), underscore the adaptability of value-capture strategies to different sectors. A taxonomy incorporating the value-capture perspectives of asset brokers and providers could enable a more nuanced and structured approach to platform revenue model design (Fehrer etal., 2018; Hein etal., 2020; Helfat & Raubitschek, 2018). Fig. 2 Concept map of selected platform revenue model dimensions Electronic Markets (2025) 35:88 Page 5 of 23 88 Fig. 3 Applied ETDP research design Electronic Markets (2025) 35:88 88 Page 6 of 23 Extended taxonomy design process Our research approach follows the extended taxonomy design process (ETDP) with six phases1 as proposed by Kundisch etal. (2022) and is illustrated in Fig.3. Kundisch etal. (2022) extend the original taxonomy development process introduced by Nickerson etal. (2013) by emphasizing ex-post evaluation, which assesses the usefulness of a taxonomy after its development. In addition to the applied ETDP approach, the 26 taxonomy design recommendations (TDRs) outlined by Kundisch etal. (2022) were also applied. These recommendations provide researchers with design guidelines for each of the six ETDP phases and are systematically referenced in the corresponding steps of this study (see steps 1–18 in Fig.3) and are presented in ESM1-Supplement A. Phase I: Identify problem andmotivate Step 1: Observed phenomenon The phenomenon under consideration is the dimensions and characteristics specific to revenue models of transactional platforms that are critical to understanding the value-capture aspect of platform business models (TDR1). While existing taxonomies and frameworks broadly address platform business models (Täuscher & Laudien, 2018), there is still limited conceptual clarity regarding revenue model design, particularly concerning how value is captured between platform actors (Hein etal., 2020; Helfat & Raubitschek, 2018). Steps 2 and3: Target user groups andintended purposes The primary purpose of this taxonomy is to support researchers and practitioners, including managers, business analysts, and digital innovation designers (TDR 3), by providing a framework for describing, designing and analyzing platform revenue models (TDR 2). Phase II: Define objectives ofasolution Step 4: Determine meta‑characteristics The design aspects of platform revenue models are established as the meta-characteristic, providing a structured basis for identifying and categorizing the key dimensions and characteristics of platform revenue models (TDR 4). The choice of this meta-characteristic is driven by the need for a more precise understanding of platform revenue models. Our meta-characteristic includes, for example, the configuration of revenue sources and streams––specifically, whether an asset broker generates revenue from asset consumers through transaction fees, subscriptions, or other mechanisms––and how asset providers monetize their offerings through the platform. During the development of the taxonomy, no changes were made to this meta-characteristic (TDR 5). Step 5: Determine ending conditions andevaluation goal The ending conditions are divided into objective criteria: generalizable, inclusive, conclusive, unique, and subjective criteria: concise, robust, comprehensive, extendible, explanatory (Nickerson etal., 2013). All conditions are described in Table5 of phase IV. Beyond these conditions, TDR 6 also emphasizes the importance of anticipating an evaluation objective, which is defined in step 15. Phase III: Design anddevelopment The development of the taxonomy followed an iterative approach, comprising two conceptual-to-empirical (C2E) and two empirical-to-conceptual (E2C) iterations, in line with TDR 9, which requires at least one of each (Kundisch etal., 2022). Detailed information about the two C2E iterations and the literature review is provided in Bartels etal. (2023), while the two E2C iterations are detailed in Bartels etal. (2024). Step 6: Building approach? Motivated by TDR 7, we began the process with two C2E iterations to build a theoretical foundation, as sufficient insights were available from the literature. Once the conceptual structure was in place, the development continued with two E2C iterations, in line with TDR 8, to incorporate empirical insights from seven case studies. Steps 7c‑10: Conceptual‑to‑empirical iterations (7c) conceptualize characteristics anddimensions ofobjects To ensure robust C2E iterations (TDR 10), a literature review, as detailed in ESM4, is conducted across the fields of IS and Business Management. Inclusion was based on whether the paper contributes to the conceptualization of 1 Whereas the original ETDP distinguishes between objective ending conditions in phase IV and subjective ones in phase V (Kundisch etal; 2022), this study presents all ending conditions collectively in phase IV. Electronic Markets (2025) 35:88 Page 7 of 23 88 platform revenue models by addressing their dimensions or characteristics (e.g., revenue strategies, pricing logic). A total of 930 papers were retrieved using the search term: (ecosystem OR platform) AND (business model OR valuecapture OR revenue model OR profit model). These papers were sourced from six databases: Scopus (259), Web of Science (149), IEEE Xplore (23), ACM (11), Google Scholar (133), and Dimensions (355). As an additional step, five papers were manually included based on their conceptual relevance to platform revenue models, which were not fully captured by the initial search: Derave etal. (2022), Freichel, Fieger, and Winkelmann (2021), Springer and Petrik (2021), van de Ven etal. (2021), and Weking etal., (2020b). As shown in Fig.4, from a total of 935 papers, 34 papers are selected as relevant, with 68 dimensions and 258 characteristics extracted. The remaining 901 papers were excluded based on the following criteria: 204 were duplicates (EC1), 30 were not written in English (EC2), six were less than three pages (EC3), typically abstracts or summaries lacking sufficient depth for analysis, 13 were not research papers (EC4) as they lacked a clear methodology, 41 were not accessible (EC5) even after contacting the authors, and 607 did not meet the inclusion criteria (EC6) for extracting dimensions and characteristics for platform revenue models. The review of the 34 papers revealed 68 dimensions and 258 characteristics relevant to platform revenue models. To synthesize these data, a concept matrix was developed following Webster and Watson (2002). The definitions provided by the authors in the analyzed papers were extracted and documented in Excel. Nine dimensions were unclassifiable and therefore labeled “n/a”. The remaining 59 dimensions were sorted and categorized based on identified commonalities and then discussed among three authors, resulting in eight self-coded dimensions as shown in Table1. The full coding procedure is documented in ESM4. Each study is categorized based on whether it presents a classification (e.g., a taxonomy) and its alignment with TDR 11, which emphasizes the importance of referencing existing Fig. 4 Summary of the search results Electronic Markets (2025) 35:88 88 Page 8 of 23 Table 1 Concept matrix of the 34 articles identified *Note:Studies without coding entries could not be unambiguously assigned to any dimension No Authors Classification provided (e.g., taxonomy)? Revenue model Revenue stream Revenue source Payment frequency Pricing model Price mechanism Price discovery Price discrimination 1 Curtis and Mont (2020)Yes x x x x 2 Derave etal. (2022)Yes x x x x x 3 El Sawy and Pereira (2013)Yes x x 4 Enders etal. (2008)No x 5 Freichel, Hofmann, etal. (2021)Yes x x 6 Freichel, Fieger, and Winkelmann (2021)Yes x x x x 7* Ghezzi (2012)No 8 Giessmann etal. (2014)Yes x 9* Helfat and Raubitschek (2018)No 10* Hoyer and Stanoevska-Slabeva (2009)No 11 Hyrynsalmi etal. (2012)No x 12 Immonen etal. (2014)No x 13 Janssen and Zuiderwijk (2014)No x 14 Kim (2016)No x 15 Kohler (2015)No x 16 Kübel and Zarnekow (2014)Yes x x 17 Laczko etal. (2019)No x 18 Lin etal. (2020)No x 19 Mancha and Gordon (2022)No x 20 Park etal. (2021)No x 21 Rohn etal. (2021)Yes x x x x 22 Ruggieri etal. (2018)No x 23* Schreieck etal. (2017)No 24 Springer and Petrik (2021)Yes x x 25 Staub etal. (2021)Yes x x x 26 Still etal. (2017)Yes x 27 Täuscher and Laudien (2017)Yes x x x x x 28 Täuscher and Laudien (2018)Yes x x x x x 29* Teece and Linden (2017)No 30* Teece (2010)No 31 van de Ven etal. (2021)Yes x x x 32* Verstegen and Doorneweert (2017)No 33 Weking etal. (2020a)No x x 34 Weking etal., ( 2020b)Yes x x x x Sum 10 12 11 4 8 5 5 5 Electronic Markets (2025) 35:88 Page 15 of 23 88 Participants received presentations, a use case description, and the proposed taxonomy (test group only). The control group followed the same protocol, but without the taxonomy presentation and interview. Participants were given a use case description. All participants used virtual Miro boards to design platform revenue models with freedom to make assumptions. Three experts independently rated all ten neutral descriptions on Miro boards using a structured feedback template, blinded to taxonomy application and participant identity. The protocol (see ESM2 for more information) included a briefing on the use case and rating task, and a presentation of the platform revenue model theory, with expert 1 in session one and experts 2 and 3 in session two. Expert rating criteria included clarity (ease of understanding of the description), completeness (presence of all essential information), and appropriateness (relevance of the platform revenue model concept described). Each criterion was rated on a three-point scale: “ + ” (3 points) for positive, “0” (2 points) for neutral, and “ − ” (1 point) for negative, with optional notes for comments. Participant composition andexpertise The evaluation involved ten digital innovation designers with software and business skills from Fraunhofer IESE, whose expertise varied from students to seniors. In recruiting participants for the experiment, we sought individuals who possessed a blend of technical and business acumen, essential for grasping the intricate connections between a digital platform’s technology and its business model. Figure6 illustrates the average competencies of participants through a spider chart, where a score of five in each category represents an ideal profile, while a score of zero indicates unsuitability for the experiment. Since the scores in each category were around four and approaching the maximum of five, it was concluded that the profiles of the ten candidates sufficiently met the requirements for participation in the evaluation. All ten participants were divided into two groups: a test group equipped with the taxonomy (WI) and a control group without it (WO), each consisting of one student, three designers, and one senior designer, as seen in Table6. All participants worked individually, without collaboration within or across groups. Over the course of ten individual sessions, the participants created unique revenue model descriptions. These were evaluated by three experts: one internal expert from Fraunhofer IESE, identified as expert 1, with specific domain knowledge in ecosystems from the MSP project, and two external experts (expert 2 and expert 3), who possess years of research experience and have entrepreneurial insights from running their own startup in the platform business sector. We recruited only participants who were not involved in the taxonomy development process, in accordance with TDR 23, to ensure an unbiased perspective in the evaluation. Step 17: Evaluation goal met? To assess whether the evaluation goal was met for hypotheses 1 and 2, the evaluation results are presented in Table7. The Likert scores from test subjects regarding their experience with the taxonomy application for hypothesis 3 are shown in Table8. These results are accompanied by a discussion of each hypothesis. A detailed explanation of the results for each metric is provided in ESM1-Supplement E, with all calculations available in ESM3 . Hypothesis 1 suggests that using the proposed taxonomy improves the completeness of platform revenue model descriptions. This hypothesis was evaluated using two metrics: average coverage rate (M1.1) and average completeness grade (M1.2). Metric M1.1 shows significantly higher coverage rates for descriptions that employed the taxonomy (WI) than those that did not (WO), with rates of 89% vs. 39% for asset brokers, and 82% vs. 29% for asset providers. Fig. 6 Average software and business profile scores for experiment participants (n = 10) Table 6 Demographic profile (n = 10) Demographic profile Number Gender Female 7 Male 3 Age 21–25 1 26–30 8 31–35 1 Job status Student 2 Digital innovation designer 6 Senior digital innovation designer 2 Electronic Markets (2025) 35:88 88 Page 16 of 23 As seen in Table7, a Mann–Whitney U-test confirmed these differences as statistically significant with a p value of 0.008 and a strong effect size of 0.83. Metric M1.2 reveals that WI descriptions were graded higher by experts for completeness compared to WO descriptions. Statistical significance is found with a p value of 0.008 and an effect size of 0.8. Given these statistical results, hypothesis 1 is supported: the proposed taxonomy demonstrably enhances the completeness of designed platform revenue models. Hypothesis 2suggests that the employment of the proposed taxonomy will yield more accurate descriptions of platform revenue models than those generated without its guidance. To assess the validity of this hypothesis, two metrics are considered: average expert grade (M2.1) and expert feedback (M2.2). For metric M2.1, the analysis of the data reflects a more favorable outcome for the group using the taxonomy (WI), with an average grade of 6 points out of a possible 9, against the 4-point average grade for the group without the taxonomy (WO). Both averages show a difference in the perceived accuracy of the platform revenue model descriptions. The reported p value of 0.008 from the Mann–Whitney U-test and the effect size (r) of 0.84, as seen in Table7, indicate that this difference is not only statistically significant but also represents a robust effect size, lending strong support to the hypothesis. Metric M2.2 provides qualitative insights through expert feedback, which underscores the clarity and precision achieved by group WI in their descriptions. We attribute this enhancement to the use of the proposed taxonomy. Despite the inherent complexities, the descriptions from group WI appear to be more accurate. In contrast, group WO’s descriptions suffered from problems with clarity and logical flow, which negatively affected completeness. Criticism directed at both groups regarding the lack of detail on pricing mechanisms and money flow details highlights an area for improvement but does not detract from the overall findings. The linear regression analysis between “average expert grade” (M2.1) and “average coverage rate” (M1.1) underlined this finding, showing a strong positive correlation (R = 0.85), thus suggesting that the more complete the descriptions, the higher their accuracy. In light of the findings from both metrics, hypothesis 2 is supported: the proposed taxonomy demonstrably enhances the accuracy of designed platform revenue models. Hypothesis 3 evaluates whether users find the proposed taxonomy a useful tool in the design of platform revenue models. This evaluation is informed by analyzing the observed results of the taxonomy’s application (M3.1) and the user feedback received (M3.2). Regarding metric M3.1, participants created seven revenue models for asset brokers and five for asset providers, adhering to the taxonomy’s structure. Table 7 Descriptive statistics and test results Dimensions Descriptive statistic Mann–Whitney U-test Mean SD U p r M1.1: average coverage rate Without (WO) 35 10 0 0.008 0.83 With (WI) 89 11 M1.2: average completeness grade Without (WO) 1 0.3 0.5 0.008 0.80 With (WI) 2 0.4 M2.1: average expert grade Without (WO) 4 0.4 0 0.008 0.84 With (WI) 6 1.3 Table 8 Descriptive statistics of Likert scale responses (five participants, 4-point Likert scale: 1 = disagree to 4 = agree) *Note: One participant did not provide a response to S10 Statements Mean SD (S1) The taxonomy covers all aspects of platform revenue models 3.6 0.5 (S2) The taxonomy’s structure is logical and intuitive 3.2 1.3 (S3) The taxonomy is clear and easy to understand 2.8 0.8 (S4) The taxonomy is presented in a straightforward manner, avoiding unnecessary complexity 3.2 0.8 (S5) The taxonomy feels overwhelming 2.2 1.3 (S6) The taxonomy is easy to apply 3.6 0.5 (S7) The taxonomy allows for the representation of various platform revenue model types 3.8 0.4 (S8) The taxonomy is useful for analyzing platform revenue models 3.4 0.9 (S9) The taxonomy is beneficial for designing new platform revenue models 3.6 0.9 (S10)* The taxonomy has the potential to advance platform business model research 3.8 0.5 Electronic Markets (2025) 35:88 Page 17 of 23 88 However, an analysis of the 12 models by the authors revealed 11 issues, highlighting confusion in model selection, difficulties in switching perspectives between asset brokers and asset providers, and missing aspects (e.g., lack of specified pricing mechanisms). Further details can be found in ESM3. For metric M3.2, the taxonomy received mixed feedback across statements (Table8). While participants considered it a useful tool for designing platform revenue models (S9) and for representing various model types (S7), some perceived it as overwhelming (S5). The taxonomy was also viewed as covering all relevant aspects (S1), having a logical structure (S2), and being easy to apply (S6). However, fewer participants agreed that it is clear and easy to understand (S3), indicating potential issues with clarity. Several participants reported difficulties evaluating certain statements (S1, S7, and S10). Inconsistencies also emerged, as the taxonomy was rated only moderately for understandability (S3) but simultaneously as easy to apply (S6). This divergence suggests that participant responses may not fully reflect a coherent assessment. This is a known issue with user feedback, which can be imprecise or misleading regarding an artefact’s actual utility or efficacy (Venable etal., 2016). Qualitative feedback praised the taxonomy for providing a structured checklist that aids in covering all necessary Fig. 7 Finalized taxonomy for platform revenue models Electronic Markets (2025) 35:88 88 Page 18 of 23 aspects and facilitating idea generation. Yet, the participants identified challenges with the taxonomy’s format and navigation, suggesting that enhancements such as sentence templates and a more intuitive structure could make it more user-friendly. The participants advocated a design overhaul to optimize the taxonomy for practical application. In conclusion, hypothesis 3 is partially supported as participants found the taxonomy useful, but also highlighted challenges and recommended that it be developed into a more practical tool for designing platform revenue models. Phase VI: Communication The final taxonomy, developed through the ETDP approach, includes 15 dimensions and 64 characteristics (TDR 25), visualized in Fig.7 and detailed in ESM1-Supplement F. Clear descriptions of dimensions and characteristics ensure usability (TDR 26). Following TDR 24, the iterative development process is transparently documented in the ESM1Supplement C, detailing changes and ensuring traceability. Step 18: Report taxonomy As seen in Fig.7, the first dimension of the asset broker (DB1) outlines the asset broker’s revenue model type. The revenue stream (DB2) details monetization strategies, including access fees, listing fees, advertising fees, commission fees, and donations and sponsorships. The revenue source (DB3) specifies who is monetized, whether asset consumers, asset providers, or third parties. The payment trigger (DB4) addresses the timing, e.g., pay per access, while the payment frequency (DB5) defines the frequency of charges, i.e., one-time or recurring. Price discovery (DB6) delves into the platform price setting, potentially by asset brokers, asset providers, asset consumers, or through negotiations. The price mechanism (DB7) examines how supply and demand influence platform pricing, be it fixed, variable, or negotiable, and price discrimination (DB8) explores pricing strategies for the platform price, such as user type, location, or tariff options like basic or premium. The first dimension of the asset providers (DP1) describes the asset provider’s revenue model type. The revenue stream (DP2) focuses on monetization strategies, such as sales of assets, rentals, usage-based charges, and donations or sponsorships. The revenue source (DP3) defines who is monetized by the asset providers, including asset consumers, the asset broker, or third parties. Payment frequency (DP4) details payment regularity, i.e., one-time, subscription, usage, or rental-based. Price discovery (DP5) discusses asset price determination, involving brokers, providers, consumers, or negotiations. The price mechanism (DP6) analyzes the influence of market forces on prices, which can be fixed or variable. Price discrimination (DP7) considers price variations based on factors like quantity or user location. Limitations This study has some limitations, which are structured according to the framework proposed by Wohlin etal. (2024), covering construct, internal, external, and conclusion threats to validity. Construct validity concerns whether the taxonomy accurately captures the concept of platform revenue models. During the 4th iteration of development, two project use cases (SLR and MSP) were analyzed in a single empiricalto-conceptual (E2C) iteration. Combining both cases may have affected construct validity. However, no further changes emerged in the final case (MSP), so we consider the taxonomy to be stable. Still, due to the evolving nature of platform business models, future iterations may uncover additional relevant dimensions. Furthermore, there is some potential overlap between dimensions, which may affect robustness. “Revenue model type” (DB1) and “revenue stream” (DB2) both relate to the revenue mechanism but capture different levels of abstraction and are well supported in the literature (cf. Table4), so they were retained separately. A similar case applies to “payment trigger” (DB4) and “payment frequency” (DB5): in SLR’s listing model, asset providers pay both a one-time and a recurring fee per listed solution, making it necessary to distinguish the trigger “pay per asset listing” from the frequency dimension (“pay once” or “recurring”). Finally, while the taxonomy comprises 15 dimensions, this number exceeds the heuristic of seven plus or minus (Nickerson etal., 2013). Internal validity addresses whether the observed effects in the experiment can be attributed to the use of the taxonomy rather than other factors. The taxonomy was used as a normative model by the authors to assess the completeness of the descriptions created by the test and control groups (see M1.1). This may have contributed to the higher completeness observed in the test group. However, expert evaluation (M1.2) supports the usefulness of the taxonomy and indicates that it covers the essential components of platform revenue models. In addition, potential bias in participants’ subjective assessments of the taxonomy’s usefulness (M3.2) must be acknowledged. Similarly, author bias during qualitative data analysis cannot be ruled out, despite mitigation efforts such as detailed documentation to support external verification. External validity concerns the extent to which the findings can be generalized beyond the study context. The controlled experiment involved a small sample of ten participants with specific backgrounds. Although the results were statistically significant, the limited sample size and expertise constrain generalizability. Subject profiles were documented Electronic Markets (2025) 35:88 Page 19 of 23 88 to increase transparency. Moreover, the artificial setting of the experiment lacked real-world business pressures, which may have affected participant engagement and reduced the practical robustness of the resulting models. The absence of evaluation by industry platform managers constrains the practical generalizability of the findings. Finally, the taxonomy was applied to transaction platforms operating in Germany, which may limit its applicability to other platform types, such as innovation platforms, and different regional or institutional contexts. Conclusion validity refers to the extent to which the observed effects can be attributed to the treatment rather than to chance. Despite the small sample size, the experiment yielded statistically significant results across all measured variables. Mann–Whitney U-tests revealed significant differences between the test and control groups in terms of coverage rate (U = 0, p = 0.008, r = 0.83), completeness grade (U = 0.5, p = 0.008, r = 0.80), and expert evaluation (U = 0, p = 0.008, r = 0.84). These values indicate large effect sizes, supporting the robustness of the observed differences. In addition, expert ratings substantiate the practical value of the taxonomy. Future work Building on the identified threats to validity, future research should replicate the experiment with larger and more diverse samples to confirm the robustness and generalizability of the results. Second, evaluations in more realistic, business-relevant settings could enhance external validity and practical applicability. Future research should incorporate evaluations with platform managers to enhance practical applicability. Third, to improve construct validity, further studies could examine whether the distinction between closely related dimensions is meaningful and consistent across different platform contexts. Fourth, to reduce potential researcher bias and strengthen internal validity, automated classification techniques could be integrated to support qualitative analysis. In addition, future research could also investigate business model archetypes (cf. Bergman etal., 2022; Duparc etal., 2022). Conclusion While existing research provides valuable insights into the architecture and design of platform business models (Fehrer etal., 2018; Kim, 2016; Täuscher & Laudien, 2018), a systematic understanding of how platform revenue models can be classified and designed remains underdeveloped. To address this gap, we apply the enhanced taxonomy development process (Kundisch etal., 2022) to develop and evaluate a taxonomy for platform revenue models. The taxonomy consists of 15 dimensions and 64 characteristics, directly addressing RQ1. Furthermore, we demonstrate the taxonomy’s usefulness by evaluating its applicability in a controlled experiment, thereby addressing RQ2. This study makes two main contributions: first, we present the taxonomy along with detailed descriptions of all characteristics, and its dimensions reflect the perspectives of both asset brokers and asset providers. This distinction responds to calls for a clearer separation in the value-capture logic of platforms (Hein etal., 2020; Helfat & Raubitschek, 2018). From a practical standpoint, the taxonomy offers a structured framework for designing and analyzing platform revenue models. It enables practitioners to align revenue strategies with platform operational roles and asset offerings. From a theoretical perspective, our taxonomy advances research by conceptualizing revenue-related design choices (e.g., revenue streams and price discovery) for platform operators and asset providers, thereby contributing to a more nuanced understanding of multi-sided value capture across different platform roles (Hein, 2020; Helfat & Raubitschek, 2018). This study also extends prior research on the interplay of multiple revenue model strategies (Daxhammer etal., 2019; Li, 2023), as illustrated by the analyzed cases––for example, Tyre24’s combination of access and commission models with interdependent pricing structures that mutually influence each other. Second, the seven platform cases observed during the taxonomy development phase illustrate the complexity of realworld platform revenue models, resulting in the identification of 26 distinct revenue model types. In line with prior research by Täuscher and Laudien (2018), who report that commission models are used by asset brokers in 72% of observed platform cases, our analysis reveals a comparable pattern, with commission models present in 71% of the platforms examined. However, when analyzing all 26 revenue model types identified across the seven platforms, commission-based models occur only 7 times (27%), while access-based models appear 9 times (35%). Although the number of cases in our study is limited, the findings reveal an important insight: several platforms employ multiple revenue models simultaneously to capture value, such as Tyre24 (five revenue model types) and Vinted (six types), whereas others follow a more narrowly focused approach, such as empto (two types) or MyHammer (one type). This discrepancy underscores the prevalence of mixed monetization strategies in platform contexts and opens up new avenues for future research on the interplay between complementary and mutually exclusive platform revenue model types. In conclusion, by drawing on a theoretical foundation developed through a literature review, together with empirical grounding through the analysis of existing platform cases and evaluation in a controlled experiment, this study integrates current insights from both research and practice on platform revenue models. Accordingly, this research lays the groundwork for future studies and promotes the development of platform revenue models as a focused line of inquiry within the broader field of business models. Electronic Markets (2025) 35:88 88 Page 20 of 23 Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1252502500841-4. Acknowledgements We thank the Editor and the anonymous referees for their helpful comments and suggestions, whichgreatly improved the paper. We sincerely thank Christian Vorbohle for his constructive feedback andfriendly prereview of our manuscript. Nedo Bartels and Matthias Koch acknowledge financial supportfrom the Digital Europe Programme (DIGITAL) of the European Union under Grant Agreement No.101123121 (EURIDICE). Funding Open Access funding enabled and organized by Projekt DEAL. Declarations Competing interest The authors declare no competing interests. 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. 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