Unlocking the value from car data: A taxonomy and archetypes of connected car business models

Sterk, Felix; Stocker, Alexander; Heinz, Daniel; Weinhardt, Christof Article — Published Version Unlocking the value from car data: A taxonomy and archetypes of connected car business models Electronic Markets Provided in Cooperation with: Springer Nature Suggested Citation: Sterk, Felix; Stocker, Alexander; Heinz, Daniel; Weinhardt, Christof (2024) : Unlocking the value from car data: A taxonomy and archetypes of connected car business models, Electronic Markets, ISSN 1422-8890, Springer, Berlin, Heidelberg, Vol. 34, Iss. 1, https://doi.org/10.1007/s12525-024-00692-5 This Version is available at: https://hdl.handle.net/10419/315774 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. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Electronic Markets (2024) 34:13 https://doi.org/10.1007/s12525-024-00692-5 RESEARCH PAPER Unlocking thevalue fromcar data: Ataxonomy andarchetypes ofconnected car business models FelixSterk1 · AlexanderStocker2· DanielHeinz1· ChristofWeinhardt1 Received: 24 February 2023 / Accepted: 8 January 2024 / Published online: 13 February 2024 © The Author(s) 2024 Abstract The automotive industry is relocating from viewing cars as standalone products to an all-encompassing ecosystem built around connected cars enabling data-driven business models. The vastly increasing amount of data collected by connected cars grants a unique driving experience for its users while providing companies operating in the automotive industry access to valuable information and, ultimately, cost and revenue benefits. In this article, we develop an empirically and theoretically grounded taxonomy of data-driven business models in the connected car domain to explore the impact of car connectivity and data availability on business models. Building on this, we conduct a cluster analysis revealing seven business model archetypes for the connected car domain: data platforms, location-based services, fleet management, diagnostics and maintenance, driving analytics, cyber-physical protection, and connected infotainment. Our findings advance the theoretical knowledge of data-driven business models, provide researchers with a systematic analysis of connected car-enabled business models, and enable decision-makers to identify strategic opportunities for leveraging connected car technology to enrich their business portfolios. Keywords Business models· Connected cars· Data monetization· Taxonomy· Archetypes· Cluster analysis JEL Classification O3· L8 Introduction The transition toward increased vehicle connectivity, autonomous driving, powertrain electrification, and shared mobility mutually reinforces advances in the automotive landscape (Burkacky etal., 2023). Taken together, they not only reshape the automotive value chain by attracting newcomers from various industries but also critically drive business innovation in the mobility space (Kaiser etal., 2021; Stocker etal., 2017). As pioneers of Internet of Things (IoT) technologies, automotive original equipment manufacturers (OEMs) have invested heavily in equipping vehicles with telematic control units and related capabilities to ensure connectivity and facilitate additional service offerings (Cichy etal., 2021; Svahn etal., 2017). Consultancies with deep industry expertise (i.e., Bertoncello etal., 2016; Seiberth & Gruendinger, 2018) argue that despite the long-term decline in car sales, monetizing car data will compensate for this and even increase OEM’s revenues by leveraging data-based services. However, many players in the connected car space struggle to capitalize on the potential of data monetization and connected services (Hood etal., 2019; Martens & Schneiderbauer, 2021), leading to numerous companies ceasing operations (e.g., Automatic Labs or Dash Labs). Nonetheless, the industry is currently at an inflection point that could create $250 billion to $400 billion in annual incremental value, enabled using vehicle data by 2030 (Martens & Schneiderbauer, 2021). The automotive sector is a technological frontrunner for IoT applications and connected products (Cichy etal., 2021), as OEMs began equipping vehicles with connectivity many years ago to establish vehicle-to-vehicle and Responsible Editor: Christine Legner * Felix Sterk f[email protected] 1 Institute ofInformation Systems andMarketing (IISM) attheKarlsruhe Institute ofTechnology (KIT), Kaiserstraße 12, 76131Karlsruhe, Germany 2 Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010Graz, Austria Electronic Markets (2024) 34:1313 Page 2 of 24 vehicle-to-infrastructure communications and to enable cooperative intelligent transportation systems (Kerber & Gill, 2019; Sterk etal., 2022a). Car data is personal, highvolume, high-velocity, and highly diverse data that is often combined with contextual data such as weather or location data to develop new services (Kaiser etal., 2021; Soley etal., 2018). The integration of digital technologies into cars as physical products is gradually changing the dynamics of the automotive sector (Bohnsack etal., 2021) and driving the formation of organizational and technological ecosystems aimed at sharing and leveraging data (Heinz etal., 2022). Google, for example, enables smartphone-like in-vehicle applications with its open-source “Android Automotive”1 operating system, to which numerous OEMs have signed up, including Volvo, Renault, GM, and Ford (Legenvre etal., 2022). Connected cars provide a unique setting to test and extend existing theories and empirical insights on business models (Cichy etal., 2021). Equipped with telematics control units and connected to OEM data centers, they generate continuous streams of data through multiple powerful sensors, making them a central component of innovative datadriven business models (DDBMs) (Cichy etal., 2021; Kaiser etal., 2021; Koester etal., 2022). Regardless of make and model, they already generate massive amounts of valuable data, not only about the cars themselves, but also about their environment through various sensors (e.g., to measure temperature, humidity, or position), which are also of interest to various ecosystem representatives (e.g., suppliers, repair shops, or insurers) (Sterk etal., 2023a). Although research directions related to connected cars, and in particular data sharing mechanisms and associated privacy concerns, have recently received attention (e.g., Cichy etal., 2021; Kaiser etal., 2021; Koester etal., 2022), the information systems (IS) literature has not adequately explored the topic of connected cars so far. Particularly, the current literature lacks a structural analysis that explicitly examines the anatomy, such as stereotypical patterns (i.e., archetypes), of connected car-enabled business models, hereafter referred to as connected car business models (Sterk etal., 2022b). Indeed, the specifics of designing empirical business model classifications require further research (Groth & Nielsen, 2015; Lambert, 2015). Classifying digital business models in the automotive industry is pivotal as the car itself cannot be fully digitized (Piccinini etal., 2015), and the emergence and impact of digital business models and ecosystems in the non-digital context of large, complex products (e.g., automobiles) remain to be elucidated to fully understand digital transformation (Hanelt etal., 2021). As a step toward operationalizing this issue, our research responds to recent calls to better understand data-driven business models (DDBM) in the mobility domain (Ketter etal., 2022) and better specify their key features, ultimately supporting decision-makers in their entrepreneurial activities to leverage connected car data. Therefore, we pose the following research question: What are the key characteristics and archetypal patterns of data‑driven business models in the connected car domain? To address this question, we follow a sequential research design comprising two phases. In the first phase, we follow the taxonomy development process of Nickerson etal. (2013) by conducting a structured literature review (SLR) on connected car business models and analyzing 70 real-world examples of connected car companies to empirically verify and revise our findings, ensuring both theoretical rigor and practical relevance. We evaluate the taxonomy by conducting twelve expert interviews, applying it to 154 connected car business models, and having four raters classify a subset of these cases to compare their ratings. Our final taxonomy is structured along Al-Debei and Avison’s (2010) four business model perspectives (i.e., value proposition, value architecture, value network, and value finance) and includes a total of ten dimensions and 48 corresponding characteristics. In the second phase, we use the taxonomy to re-classify the set of 154 real-world business models and perform a cluster analysis (Kaufman & Rousseeuw, 1990) to derive seven cluster groups of business models that share similar characteristics across the taxonomy dimensions. By comparing the respective cases within each cluster, we derive archetypes as qualitative interpretations that describe and distinguish ideal configurations of connected car business models. Finally, we evaluate the structural strength and quality of each cluster using silhouette width as a measure of cluster validity (Rousseeuw, 1987). The contribution of our work is threefold. First, we provide a systematically analyzed dataset of connected car business models that gives an overview of how companies use digital technologies in the connected car domain. Second, we introduce a taxonomy and a set of archetypes that extend existing literature DDBMs and establish a unified language for analyzing, classifying, and configuring connected car business models. These tools, which represent high-level business model configurations, structure the body of knowledge in this emerging field and facilitate a detailed and systemic exploration of the diverse business models present in the connected car landscape (Glass & Vessey, 1995). Our contributions are also of practical significance, as decision-makers can use our taxonomy and archetypes as strategic management tools for developing, evaluating, and benchmarking business models in the connected car sector, 1 https:// devel opers. google. com/ cars/ design Electronic Markets (2024) 34:13 Page 3 of 24 13 thereby fostering innovation and strategic advancement within this industry. In conclusion, our work not only fills a highly relevant research gap but also acts as a catalyst for future research and development in this domain. This article is structured as follows: In the next section, we review related work on business models, associated taxonomies and archetypes, and their application in the connected car field. Subsequently, we describe our research approach. The “Results” section presents a business model taxonomy and corresponding archetypes for the connected car domain. The “Discussion” section discusses implications, limitations, and future research opportunities. Finally, the “Conclusion” section provides a summary and conclusion of our work. Related work Taxonomies andarchetypes ofdata‑driven business models In the prevailing literature, the term taxonomy is often used as a synonym for other classification concepts, such as typology (Gimpel etal., 2018; Paukstadt etal., 2019). However, while typologies are conceptually derived through a topdown approach with predefined dimensions, taxonomies are obtained through an empirical bottom-up approach by observing real-world objects, with categories being designed retrospectively (Baden-Fuller & Morgan, 2010; Fiedler etal., 1996). The taxonomies at the core of our research are intended to guide researchers and practitioners in understanding, analyzing, and structuring knowledge in emerging research areas (Nickerson etal., 2013). Although data monetization is still a largely unexplored area in current research (Parvinen etal., 2020), various taxonomies of data-driven business models exist in the literature, which can be divided into generally applicable and industry-specific taxonomies (Dehnert etal., 2021). In total, we identified 28 DDBM-related taxonomies, which we categorized in Table1. For example, Hartmann etal. (2016) provided one of the first generally applicable DDBM taxonomies deductively derived from a structured literature review. Conversely, Engelbrecht etal. (2016) designed an industry-agnostic DDBM taxonomy based on questioning experts. Several publications combine both conceptual and empirical approaches in a conceptualempirical procedure to classify DDBMs (Passlick etal., 2021; Schüritz etal., 2017). In addition to generally applicable DDBM taxonomies, the body of existing literature also contains several DDBM taxonomies that focus on specific industries and address the monetization of more specific types of data, such as logistics data (Möller etal., 2020), manufacturing data (Müller & Buliga, 2019), or FinTech data (Gimpel etal., 2018). In addition to these existing DDBM-related taxonomies, we also acknowledge the existence of several other taxonomies that bear the label “digital”; however, given the scope of our research, our focus has remained on taxonomies that are characterized as data-driven, in line with our research objectives focused on DDBMs in the connected car domain. Several articles (Gimpel etal., 2018; Müller & Buliga, 2019; e.g., Passlick etal., 2021) go beyond merely designing taxonomies and identify so-called business model archetypes by performing a cluster analysis and interpreting the findings to identify typical combinations of characteristics across all included dimensions. These archetypes serve as stereotypical patterns for business development and empirical work in their respective research areas. For example, Hunke etal. (2022) identify and conceptualize four generic archetypes of analytics-based services: (1) making data usable to customers, (2) delivering data-based insights, (3) providing data-based recommendations, and (4) enabling novel ways to conduct business. Despite the substantial progress in DDBM taxonomy development and archetype generation, there exists a notable gap in the context of connected car business models. Current taxonomies and archetypes, whether general or industryspecific, do not adequately provide a clear analytical frame for understanding and developing business models in the connected car domain, given its unique data characteristics and specific industry dynamics. Recognizing this research gap, our article extends the existing corpus by creating a taxonomy and corresponding archetypes specifically designed for DDBMs in the connected car domain. In the next subsection, we elaborate on the specifics of data-driven business models in the connected car domain and link our research to related work in this area. Data‑driven business models intheconnected car domain The term connected car, as used in this article, refers to a vehicle with the ability to access the internet, communicate with its ecosystem, and generate and transmit real-time data, which aligns with previous definitions (Bosler etal., 2017; Coppola & Morisio, 2016). The combination of built-in cameras, radars, ultrasonic sensors, and actuators of a connected car is causing the amount of data generated by modern cars to increase exponentially (Karmanska, 2021). As a result, industry incumbents (e.g., legacy OEMs) and new entrants (e.g., startups) in the evolving automotive ecosystem are seeking to transform the data generated by cars into valuable information and, ultimately, to innovate products, services, and business models that leverage this information (Kaiser etal., 2021; Nischak & Hanelt, 2019). Electronic Markets (2024) 34:1313 Page 4 of 24 Accessing vehicle data is critical for implementing data-driven business models, and researchers from different disciplines have recently begun to identify early approaches to monetizing these valuable data assets. For example, Kaiser etal., (2017a, 2017b) investigate OEMs’ digital service strategies and the novel business models established by connected car startups. OEMs offer services such as remote car (un-)locking, real-time traffic information, and intelligent emergency calls, which are integrated into digital service platforms such as BMW ConnectedDrive, Mercedes me connect, and VW Car-Net. Since OEMs typically retain exclusive access to car data, third-party service providers (e.g., startups, insurers, suppliers) are forced to find alternative technical gateways that offer equal access options. To address this issue, Martens and Mueller-Langer (2020) identified four alternative data access options for independent service providers. A number of startups, including Mojio, Vinli, and Zubie, have chosen to use telematics-equipped “dongles” that plug into the onboard diagnostics (OBD) interface for remote data access (Coppola & Morisio, 2016; Pütz etal., 2019; Soley etal., 2018). However, because such OBD dongles entail time-consuming installations, expensive hardware purchases, and limited data quality, another option for third-party data access has recently emerged that does not require additional hardware and is directly supported by OEMs (Sterk etal., 2023a). Emerging data marketplaces, such as Caruso Dataplace or Otonomo, serve as neutral intermediaries that enable OEMs to sell multi-brand car data to independent service providers (Kaiser etal., 2021; Table 1 Overview and categorization of existing DDBM-related taxonomies *Methodological approach: C, conceptual; E, empirical; C&E, conceptual and empirical Industry Focus of the developed artifacts Authors Methodol. approach* Archetype development Industry-agnostic IioT platforms’ architectural features Arnold etal. (2022) C&E Yes Digital business models Bock and Wiener (2017) C&E No Data-based value creation in companies Baecker etal. (2021) C&E No Smart product-service systems and value proposition types in B2C Dehnert and Bürkle (2020) C&E No Data-driven business models Dehnert etal. (2021) C&E No Data-driven business models Engelbrecht etal. (2016) E No Data-driven business models used by startup firms Hartmann etal. (2016) C&E Yes Analytics-based services Hunke etal., (2019, 2022) C&E Yes Data-based value creation in information-intensive services Lim etal. (2018) C&E No Analytics as a Service Naous etal. (2017) C&E Yes Predictive maintenance as an IoT-enabled business model Passlick etal. (2021) C&E Yes Smart services Paukstadt etal. (2019) C&E No Proactive services Rau etal. (2020) C&E No Data-driven services Rizk etal. (2018) C&E Yes Big data business models Schroeder (2016) C&E No Data-infused business model innovation Schüritz and Satzger (2016) C&E Yes Revenue models for data-driven services Schüritz etal (2017) C&E No Smart interactive services Wünderlich etal. (2013) E No Manufacturing Data-driven services in manufacturing industries Azkan etal. (2020) C&E No Industrial service systems enabled by digital product innovation Herterich etal. (2016) C&E No Data-driven business models for manufacturing companies in Industry 4.0 Müller and Buliga (2019) C&E Yes Data-driven industrial services Schuh and Kloz (2017) C&E No Smart machines in the mechanical engineering industry Scharfe and Wiener (2020) C&E No FinTech Service offerings of consumer-oriented FinTech startups Gimpel etal. (2018) C&E Yes Smart living Smart services for smart living Fischer etal. (2020) C&E Yes Logistics Data-driven business models in logistics Möller etal. (2020) C&E No Smart city Urban data business models McLoughlin etal. (2019) C&E No Car data marketplaces Data marketplaces in the automotive industry Bergman etal. (2022) C&E Yes Electronic Markets (2024) 34:13 Page 5 of 24 13 Martens & Mueller-Langer, 2020). Bergman etal. (2022) explore business model archetypes of such data marketplaces, ranging from private to independent ownership and from a hierarchical to a market orientation. Our research addresses a noticeable gap in the existing body of knowledge regarding data-driven business models (DDBMs) in the connected car domain. In this work, we refer to these “connected car business models” as the rationale for leveraging modern vehicles’ advanced connectivity capabilities and collected data to offer innovative solutions (e.g., real-time data analytics, remote vehicle management, and personalized in-car services) and generate new revenue streams and customer value. Although various aspects of the automotive data value chain have been explored in previous research, a synthesized consolidation of existing knowledge in this area is still lacking. We aim to fill this gap by introducing a specifically designed taxonomy and archetypes. These tools will effectively synthesize the prevailing knowledge and facilitate a structured pathway for future research efforts in this area. In line with recent calls within information systems (IS) research for more dynamic participation in discussions surrounding the emergence of connected, autonomous, shared, and electric (CASE) vehicles, our research seeks to make a meaningful contribution (Ketter etal., 2022). Through our research, we aim to help shape a smart, sustainable mobility ecosystem that benefits users, mobility providers, and the broader environment. Our contribution is positioned as a first but important step in this evolutionary trajectory of IS research, aiming to cultivate and critically evaluate DDBMs that embody a harmonized balance of profitability, customer value, and overarching sustainability within the mobility domain (Ketter etal., 2022). Lastly, the connected car is a key facilitator of DDBMs in general for several reasons: (1) the established data sharing mechanisms and data marketplaces facilitate the rapid deployment of DDBMs, (2) the expected mass market entry of connected cars points to significant scalability potential for new DDBMs, (3) connected cars have a broader range of sensors and actuators compared to other mass connected products like smartphones or smart meters, and (4) within the mobility and transportation sector, connected cars are the focal point of most DDBMs. Therefore, a better understanding of connected car business models could also serve as a leading indicator of future DDBMs in other domains. Research design Our research follows a sequential research design to provide a taxonomy and archetypes of data-driven business models in the connected car domain. This procedure allows us to generate rich insights by combining qualitative and quantitative methods in the same inquiry, which is particularly suitable for the relatively unexplored area of classifying connected car business models (Gimpel etal., 2018; Hunke etal., 2022; Weking etal., 2020). Our research design comprises two major sequential phases (Fig.1), each with three steps, adopting the structure of previous studies (e.g., Weking etal., 2020). In the first phase, we design and evaluate the taxonomy by adapting Nickerson etal.’s (2013) taxonomy development method and supplementary evaluation guidelines (Kundisch etal., 2022; Szopinski etal., 2020). In the second phase, we build on the results of the first phase to identify and evaluate archetypes by conducting a cluster analysis (Kaufman & Rousseeuw, 1990) and interpreting the results. Phase 1: Taxonomy development In the first phase of our research design, we focused on developing a taxonomy for the connected car domain, using the methodological guidelines of Nickerson etal. (2013). We defined our meta-characteristic as “key distinguishing features of connected car-enabled business models” representing a specialized subset of data-driven business models. This definition reflects the overarching purpose of the taxonomy and forms the basis for its components. All proposed dimensions must be a consequence of this meta-characteristic Structured literature review (characteristics & dimensions) Analyzing real -world objects (n=70) Expert interviews (n=12) & applying realworld objects (n=154) Interim resultsEvaluated resultsChange of taxonomy or archetypes Without change of taxonomy or archetypes Structured literature review (business model archetypes) Cluster analysis & interpretation Silhouette coefficient Phase 2: Archetype DevelopmentPhase1: Taxonomy Development Iteration1: Conceptual Iteration2: Empirical Iteration3: Evaluation Step 1: Conceptual Step 2: EmpiricalStep3: Evaluation Connected Car DDBM Taxonomy Connected Car DDBM Archetypes Fig. 1 Research design of the two consecutive phases Electronic Markets (2024) 34:1313 Page 6 of 24 and help describe the structural differences observed in such business models. Guided by this meta-characteristic, we adopted Al-Debei and Avison’s (2010) “V4 business model framework” and used the V4 concepts (i.e., value proposition, value architecture, value network, and value finance) as meta-dimensions in our taxonomy. Ensuring that each dimension in the taxonomy corresponded to one of these concepts facilitated a structured taxonomy formulation closely aligned with our meta-characteristic. We also established ending conditions to define when the iterative taxonomy development process would end, following the conditions suggested by Nickerson etal. (2013) (Table8 in the Supplementary information). With these foundations in place, we proceeded with the taxonomy development and evaluation process in three iterations. Iteration 1: Structured literature review (conceptual) In our initial iteration, we adopt the conceptual-toempirical approach of Nickerson etal.’s (2013) taxonomy development method. Thereby, we build on the existing literature by conducting a structured literature review, following the methodological suggestions of Webster and Watson (2002) and Vom Brocke etal. (2009). The literature base is established by querying various interdisciplinary research databases2 to identify articles that match our search term3 in title, abstract, or keywords. Our initial search yielded a total of 787 studies, of which 547 remained after removing duplicates (see Fig.2). To assess their relevance to our study, we analyzed the title and abstract of each article, applying four inclusion criteria: the study must (1) examine at least one of the four business model dimensions represented by the taxonomy’s meta-characteristics, (2) focus on the connected car domain, (3) be available in English, and (4) be peer-reviewed. Subsequently, a thorough review of the full texts of the 133 remaining articles was conducted based on these criteria. This process yielded 29 relevant articles, and 16 additional articles were included through forward and backward searching, resulting in a total set of 45 articles. Next, we used the 45 articles identified to derive an initial set of taxonomy dimensions and characteristics. The selected articles were analyzed for recurring themes, frameworks, or models to categorize the subject matter. This led us to twelve articles that were most suitable to guide concrete concepts for our taxonomy, such as dimensions, their definitions, and the associated characteristics. The remaining articles helped throughout the work to contextualize the research field concerning connected car business models and to situate our findings in the existing literature. We uniformly summarized and named the identified taxonomy dimensions and characteristics and mapped them to the superordinate dimensions of Al-Debei and Avison (2010). Overall, we discovered a total of four primary taxonomy dimensions during our analysis: value for customer (Coppola & Morisio, 2016; De, 2018), data access (Bosler etal., 2017; Coppola & Morisio, 2016; Martens Total (Forward & backward search) Full text screening † Title & abstract screening † Removing duplicates 787 Database search 29 †Inclusion criteria: company must (1) examine at least one business model dimension, (2) focus on the connected car domain, (3) be available in En g lish, and (3) be peer -reviewed 4594547 AIS Electronic Library 16 Emeral Insight 9 IEEE Xplore Digital Library 140 ProQuest 22 ScienceDirect/ Scopus 452 Web of Science 148 Search term: “business model*” AND (connected OR data* OR digital*) AND (car* OR vehicle* OR automotive*) Fig. 2 Literature search process 2 AIS Electronic Library, Emerald Insight, IEEEXplore Digital Library, ProQuest, ScienceDirect/Scopus, Web of Science. 3 “business model*” AND (connected OR data* OR digital*) AND (car* OR vehicle* OR automotive*). Electronic Markets (2024) 34:13 Page 7 of 24 13 & Mueller-Langer, 2020), role in ecosystem (Kaiser etal., 2021; Kaiser etal., 2017a, b; Rahman & Tadayoni, 2018; Riasanow etal., 2017; Stocker etal., 2017), and revenue model (De, 2018; Kukkamalla etal., 2020; Mikusz & Herter, 2016; Mikusz etal., 2015). These dimensions were supported by 16 corresponding characteristics representing different manifestations within these dimensions for connected car business models. Iteration 2: Analysis ofreal‑world objects (empirical) In the next iteration, we used an empirical-to-conceptual approach to examine real business models in the connected car domain and aimed to link our conceptual findings to real-world phenomena. To build a comprehensive and representative dataset of connected car business models, we decided to query different sources and examine the sample in a sequential analytical procedure. First, we extracted 18 real-world examples (i.e., companies) from six out of the 45 articles from the previously conducted SLR (i.e., Bosler etal., 2017; Kaiser etal., 2017a, 2017b, 2021; Rahman & Tadayoni, 2018; Stocker etal., 2017). Next, we expanded our sample using two practitioner-oriented business reports published by leading consulting firms: Capgemini’s report (Arif etal., 2019) helped us identify 27 emerging startups, and PwC’s report (PwC, 2020) added 27 leading companies. Finally, we also queried Crunchbase, the world’s largest startup database, and obtained 147 companies using the search term “connected car”. After removing duplicates, we were left with 204 companies for further review. Third, we reviewed the companies’ websites and applied three inclusion criteria—the company must (1) still be active, (2) provide an English website, and (3) focus on the connected car domain—resulting in a set of 130 potentially relevant companies. However, we only included 70 of the 130 identified companies in the further taxonomy development process to avoid overrepresenting the startup share within the sample. To achieve this balance, we applied a purposive sampling approach (Bryman, 2016) to the 80 companies sourced from Crunchbase. Our screening aimed to select a diverse subset of 20 connected car companies based on the variety of their business model descriptions and the comprehensiveness of their website information, which led to the exclusion of 60 companies. Figure3 gives a detailed overview of our company selection approach, and Table8 in the Supplementary information shows the sample with the name and references of each company. We subsequently scanned the company websites for dimensions and characteristics to add to the preliminary taxonomy artifact. By analyzing the companies that emerged from the SLR, we identified three additional dimensions (i.e., customer segment, vehicle ownership, and data monetization) and added 13 characteristics to our taxonomy. We also examined the websites of the consulting sub-sample, which revealed seven characteristics and three further dimensions, namely data personalization, influence of car data, and influence of autonomy. Finally, we analyzed the Crunchbase sub-sample but did not identify any further dimensions or characteristics, confirming the existing dimensions and characteristics of the taxonomy and suggesting theoretical saturation. According to Nickerson etal. (2013), the ending conditions were met (Table8 in Final business model sample Business model split Final company Sample Purposeful sampling Applying inclusion criteria* Remove duplicates 18 Structured Literature Review 1 42 144 80 20 30 31 1545075 27 Capgemini report (Startups) 27 PWC report 147 Crunchbase 2 45 Capgemini report (Incumbents) 50 Accenture report 128 Crunchbase 3 32 1 Databases: AIS Electronic Library, Emerald Insight, IEEEXploreDigital Library, ProQuest, ScienceDirect/Scopus, Web of Science 1 Search term: “business model*” AND (connected OR data* OR digital*) AND (car* OR vehicle* OR automotive*) 2 Search term: “connected car” 3 Search term: “connected car” AND “connected vehicle” AND “connected mobility” AND “car data” AND “vehicle data” * Inclusion criteria: compan y must (1) still be active, (2) provide an En g lish website, and (3) focus on the connected car domain 113 30 3535 70 61 73 Phase1, Iteration 3 (Applyingreal-world objects) Phase1, Iteration 2 (Analysis of real-world objects) 1818 Excluding companies that pivoted or ceased Fig. 3 Company and business model selection process in Iterations 2 and 3 of the first phase Electronic Markets (2024) 34:1313 Page 8 of 24 the Supplementary information), and the taxonomy development process was terminated. Iteration 3: Expert interviews andapplying real‑world objects (evaluation) We extended Nickerson etal.’s (2013) original taxonomy development process considering recent suggestions (Kundisch etal., 2022; Szopinski etal., 2019). Therefore, as the final iteration of the first phase, we evaluated the taxonomy by applying Szopinski etal.’s (2019) taxonomy evaluation framework. We performed two successive episodes using both qualitative and quantitative methods. In the first episode, we conducted twelve expert interviews, six with practitioners and six with academic researchers with extensive experience in data-driven business models, connected cars, and/or taxonomy building (Table2). We used a semi-structured approach based on the suggestions of Myers and Newman (2007) and asked questions about the taxonomy’s adequacy, completeness, and relevance, encouraging an open discussion. We also solicited suggestions to modify the taxonomy, such as adding, renaming, or removing dimensions or characteristics based on Kundisch etal.’s (2022) basic taxonomy operations on taxonomy elements. All 12 interviews were conducted by two authors using video-conferencing software, lasted on average 38min, and were recorded and transcribed, and then analyzed. With this process, we qualitatively evaluated the taxonomy on the criteria of comprehensibility, completeness, perceived usefulness, and the level of abstraction of characteristics and dimensions. In the next step, we used MaxQDA software to employ Mayring’s (2000) qualitative content analysis as a flexible research technique to analyze and interpret the qualitative interview data (Krippendorff, 2019). In doing so, we conducted a deductive coding approach, employing the previously defined meta-characteristics and tentative taxonomy dimensions and characteristics as our coding scheme to analyze the interview data in a structured manner. Based on the resulting codes, we applied taxonomy operations such as adding, renaming, swapping, splitting, or deleting dimensions or characteristics (Kundisch etal., 2022). To ensure the validity and robustness of the coding process, we independently analyzed the data with two authors and critically reviewed and discussed it with a third author. Finally, we compared the identified codes with the initial version of the taxonomy, incorporated them, and produced the final version of the taxonomy artifact (Table3). More details on the taxonomy operations on specific elements and the taxonomy’s changes after the evaluation can be found in Table9 in the Supplementary information. After redesigning the taxonomy, we conducted a second evaluation episode to assess its practical applicability and usefulness in classifying, differentiating, and comparing real-world objects, using the evaluation criteria robustness, utility, efficacy, stability, and completeness. As we did not want to base the evaluation only on objects already used in the previous taxonomy development process in Iteration 2, we expanded our sample (n = 70) to include more connected car companies that had not previously been involved. However, nine companies were excluded from our initial sample for changing their business focus or exiting the market. To find more established companies in the connected car domain, we referred to practitioner-oriented reports from consulting firms such as the Capgemini report (Arif etal., 2019), which listed 45 incumbent firms, and an Accenture report (Seiberth & Gruendinger, 2018), which identified 50 additional incumbent firms. We also queried Crunchbase with an extended search term4 and obtained 351 startups, of which we excluded 223 that had not received funding. Table 2 Overview of interviewees with background, role, institution, and expertise Background Role Institution Expertise Corporate Managing Director Consulting Firm DDBMs, Connected Cars Managing Director Consulting Firm DDBMs, Connected Cars Lead Software Developer Tier 1 Supplier DDBMs, Connected Cars Business Developer Tech Company DDBMs, Connected Cars Head of Sales Data Marketplace DDBMs, Connected Cars Product Owner Car Manufacturer DDBMs, Connected Cars Academia Postdoctoral Researcher University DDBMs, Connected Cars Postdoctoral Researcher Research Center DDBMs, Connected Cars PhD Candidate University DDBMs, Taxonomies PhD Candidate University DDBMs, Taxonomies PhD Candidate University DDBMs, Taxonomies PhD Candidate University DDBMs, Taxonomies 4 “connected car*” OR “connected vehicle*” OR “connected mobility” OR “car data” OR “vehicle data”. Electronic Markets (2024) 34:13 Page 15 of 24 13 Table 5 Characteristics’ frequency distribution for each archetype DimensionCharacteristic Archetype A1: Data platforms A2: Locationbased services A3: Fleet management A4: Diagnostics and maintenance A5: Driving analytics A6: Cyber-physical protection A7: Connected infotainment Number of cases per cluster12162619213129 Va lue for car owner or driver Safety & security 8 %0 % 81 % 100 % 48 % 100 % 17 % Cost reduction8 % 44 % 100 % 95 % 100 % 6 %0 % Traffic efficiency 33 % 100 % 77 %0 %5 % 29 % 66 % Infotainment 0 %0 %0 %0 %0 %3 % 100 % Environmental sustainability0 %6 % 38 %5 %5 %0 %0 % Convenience 0 % 50 % 19 % 63 % 24 %0 % 100 % Indirect value100 % 0 %0 %0 %0 %0 %0 % Car data impact on value Car data core business model100 % 100 % 100 % 100 % 100 % 0 %0 % Car data-enabled business model0 %0 %0 %0 %0 % 100 % 100 % Car autonomy impact on value Enhanced value by autonomy 0 % 100 % 0 % 68 %0 % 90 % 100 % Reduced value by autonomy 0 %0 %0 %0 % 24 %6 %0 % Autonomy not relevant 100 % 0 % 100 % 32 % 76 %3 %0 % Data category PII0 %0 %0 %0 %0 %0 % 69 % Contextual data 42 % 100 % 23 %0 %0 % 61 % 21 % Diagnostic data 67 %0 % 92 %100 %5 %0 %0 % Usage data 67 %0 % 100 % 0 % 100 % 97 % 100 % ADAS data 0 %0 %4 %0 %5 % 100 % 10 % Application data 0 % 38 % 12 %0 % 43 % 19 % 86 % Data access OEM proprietary access 0 %0 % 15 % 21 % 10 % 10 % 79 % OEM-specific cloud or neutral server 83 % 0 % 15 % 53 % 24 % 42 %3 % OBD2-dongle 17 %0 % 65 % 16 % 62 %0 %0 % Other retrofit devices8 % 25 % 12 % 16 % 24 % 55 %7 % Smartphone or another non -invehicle sources17 % 100 % 12 %0 % 29 %3 % 14 % Enabler technology Blockchain 58 %0 %0 %0 %0 % 39 %0 % Augmented reality0 %0 %0 %0 %0 %0 % 100 % Over-the-air architectures0 % 50 %4 % 100 % 0 % 100 % 76 % ADAS technology 0 % 100 % 0 %0 %5 % 100 % 0 % Artificial intelligence 0 %6 % 100 % 100 % 19 %3 %0 % Cellular networks 100 % 0 % 23 % 100 % 100 % 3 %0 % Role in ecosystem End-customer solution provider 25 % 100 % 100 % 100 % 100 % 23 % 66 % Platform provider100 % 0 %4 %0 %5 %3 % 24 % Technology provider 0 %0 %0 %0 %0 % 100 % 55 % Customer segment Private individuals (B2C)0 % 44 %0 % 16 % 57 % 16 % 100 % Fleet providers (B2B)0 %6 % 100 % 5 % 10 %6 %0 % OEMs (B2B)100 % 44 %0 % 53 % 33 % 74 % 45 % Third-party providers (B2B)100 % 13 %0 % 26 % 10 % 10 %0 % Governments (B2G) 17 %0 %4 %5 %0 %6 %0 % Data monetization strategy Data source & data provision 100 % 0 %0 %0 %0 % 100 % 59 % Data aggregation & data exchange 100 % 0 %0 %0 %0 %0 %0 % Data analysis & data insights0 % 38 % 12 % 84 % 100 % 6 %0 % Data application & data service0 % 100 % 100 % 47 % 67 %0 % 66 % 0 %-20 % 21 %-50 % 51 %-80 % 81 %-100 % Electronic Markets (2024) 34:1313 Page 16 of 24 reporting service needs, including remote onboard diagnostics or predictive maintenance. For example, Preteckt (BM143) offers cloud-based vehicle diagnostics to identify technical issues early before they progress to expensive repairs. Similarly, Pitstop (BM142) provides fleet managers with predictive insights to increase and balance fleet uptime and minimize maintenance costs by anticipating vehicle issues in advance and recommending appropriate actions. Other business models reduce the need for on-site vehicle service through incremental updates using over-the-air (OTA) technology. For example, T-Systems (BM100) offers network-based, OEM-independent solutions for OTA updates to improve recall rates, which can be implemented virtually without disturbing the driver. Table 6 Summary of the identified archetypes ID Archetype Distinguishing characteristics Typical applications A1 Data platforms • Operating as a data platform provider in the automotive ecosystem • Making car data available for third-party service providers at scale • Accessing data directly from the OEM and acting as a neutral server Car data marketplaces, contextual data providers A2 Location-based services • Delivering end-customer value through navigation or parking services enhancing overall transportation efficiency • Collecting data by mobile mapping vehicles equipped with remote sensing systems • Exploiting contextual data such as external road and environmental conditions to create real-time maps Navigation systems, parking applications A3 Fleet management • Addressing fleet providers, logistics service providers, and mobility service providers as customer segment • Delivering end-customer value primarily in the areas of driving safety, cost reduction, and traffic efficiency • Accessing data commonly by means of retrofitted OBD2-dongles Fleet management systems, digital driver logbooks A4 Diagnostic and maintenance • Analyzing diagnostic data such as trouble codes to offer proactive and predictive maintenance • Providing value not only to end-customers but also to aftermarket players such as car dealers or workshops • Utilizing over-the-air architectures to do incremental remote updates and repairs in future scenarios Remote diagnostics services, predictive maintenance services A5 Driving analytics • Analyzing car usage data for monitoring driving patterns and behavior • Reducing cost through pay-as-you-drive tariffs or recommendations for fuel-efficient driving • Providing driving assistance and driver tutoring to increase road safety Usage-based insurance tariffs, applications for driving style suggestions A6 Cyber-physical protection • Delivering end-customer value by ensuring occupant safety and cybersecurity • Enabling data-driven business models by providing crucial and secure connected car technologies • Harboring huge future potentials, for instance through over-the-air driver assistance system updates Cybersecurity solutions, driver assistance systems A7 Connected infotainment • Delivering end-customer value via in-car infotainment applications • Providing a standardized platform that enables incar third-party applications on the head unit • Increasing value through car autonomy, e.g., by watching virtual reality films or playing video games Digital cockpit solutions, infotainment operating systems Electronic Markets (2024) 34:13 Page 17 of 24 13 Archetype 5: Driving analytics The fifth archetype covers driving analytics aiming to reduce usage costs incurred by the end-customers by monitoring and profiling actual driving patterns. Insurance companies have been early adopters of this archetype, offering usage-based insurance (UBI) programs that utilize dynamic behavioral data collected via OBD2-dongles, other retrofit devices (e.g., black boxes), or modern smartphones to calculate premiums (Coppola & Morisio, 2016). For example, KOBA Insurance (BM133) and Metromile (BM17) offer pay-as-you-drive insurance plans in which the vehicle owner pays a monthly rate plus a set amount for each mile driven. Other companies, including Allianz with its BonusDrive app (BM47), expand this approach to pay-how-you-drive models by monitoring and analyzing not only mileage but also riskrelated data (such as braking, acceleration, or speeding) to assess driving behavior. These driving scores can be calculated for individual drivers, specific vehicles, or entire fleets. Insurance companies also use telematics data to obtain accident reports for better claims processing. For example, IMS (BM129) provides “connected claims” that enable early detection of theft or accidents and reduce claims processing costs through data-driven decision-making. In the future, with the possibility of autonomous driving, even more comprehensive data-driven insurance tariffs can be offered. For example, Koop (BM135) sells next-generation insurance products that focus on the risks of autonomous vehicles, robotics, and automation. In addition to insurance use cases, some companies are developing applications that monitor driver behavior to provide driving assistance, such as Michelin’s Ideal Driver Pro app (BM69), which allows drivers to access a continuous analysis of their driving behavior. The results are reflected in an overall score and sub-scores (i.e., pace, adaptability, anticipation), serving as a connected driving coach. Archetype 6: Cyber‑physical protection The sixth archetype refers to cyber-physical protection aiming to improve the physical safety of drivers and passengers, as well as the cybersecurity of the vehicle using hardware and software solutions. These business models are mainly targeted at OEMs, such as Innoviz Technologies (BM66), which provides them with hardware technologies like advanced driver assistance systems (ADAS). In addition, some startups like Nauto (BM21) offer retrofit solutions like dashcams directly to drivers or fleet managers, while others like Owlcam (BM73) use existing smartphone sensors (e.g., GPS, accelerometer, or luminance) to collect and analyze safety-related driving data. However, both retrofitted and built-in sensor technologies raise security concerns, as decisions based on available driving data can become vulnerable targets for hackers. For this reason, newly developed or upgraded software components undergo rigorous testing procedures to ensure a high level of cybersecurity. For example, Fescaro (BM125) offers cybersecurity testing to OEMs to detect and handle vulnerabilities. Despite this, there is still a possibility that vulnerabilities could be exploited by attackers. Therefore, the component vendor’s software must be integrated into the vehicle’s central cybersecurity management system to be informed and able to be fixed through OTA updates. Bosch (BM52), for example, provides regular software and firmware OTA updates to ensure that connected vehicles are always up to date, protected from hacker attacks, and vulnerabilities are resolved. Archetype 7: Connected infotainment The seventh archetype, connected infotainment, represents business models that contribute to a personalized in-car experience through touchscreens or display-equipped head units. These infotainment systems seamlessly integrate automotive features, interfaces, and applications, and can go beyond displaying relevant vehicle information to providing interactive content for increased safety, traffic efficiency, and convenience. Overall, infotainment systems consist of several layers (Sivakumar etal., 2020) that should not be considered isolated systems, but rather have supporting, alternating, or substituting relationships. The first two layers comprise the operating system (OS) and middleware, which enable rapid development and deployment of data-based applications for the car. For example, BlackBerry’s QNX (BM82) provides a comprehensive white-label service package that can be customized by OEMs. However, the traditional proprietary approach to development and functionality is increasingly being replaced by open-source models, such as Google’s Android Automotive OS (AAOS) (BM10). Second, the human–machine interface and application layers encompass everything the driver sees. To this end, automotive suppliers provide frameworks for OEMs to develop digital cockpits, such as the TomTom Digital Cockpit (BM103), which supports the development of highly integrated applications based on AAOS. These applications can come from third-party vendors or directly from the OEM, although OEMs have historically encapsulated infotainment features (e.g., remote vehicle access, real-time traffic information) under their own sub-brands, such as BMW ConnectedDrive (BM4). As vehicles become autonomous, passengers are likely to demand more infotainment services that are currently more typical of smartphones, such as media streaming and video games. As a result, the world of smartphones is already making its way into the cockpit with mirroring capabilities that allow seamless projection of smartphone interfaces into Electronic Markets (2024) 34:1313 Page 18 of 24 the digital cockpit. The most prominent example is Apple CarPlay (BM1), where the operating system (i.e., iOS) and applications (e.g., the voice assistant) still run through the smartphone. Discussion Driven by the growing importance of connected cars, OEMs as technical pioneers in the IoT and established technology players as experienced orchestrators of digital ecosystems are competing to deliver a “smartphone on wheels.” Existing research on business models in the connected car domain has mainly focused on topics such as privacy concerns (Cichy etal., 2021), ecosystem conceptualization (Kaiser etal., 2021), or path dependence (Bohnsack etal., 2021). While there have been efforts to create taxonomies for data-driven business models and data monetization in general (Bock & Wiener, 2017; Hartmann etal., 2016; Passlick etal., 2021), there is little conceptual or empirical evidence on the specifics of the connected car phenomenon (Sterk etal., 2022b). As a result, research to date does not explain the potential impact of vehicle data on automotive business models and lacks in-depth empirical investigations. Moreover, in practice, there is a gap between the potential business value of car data monetization and the actual value delivered. The objective of this study is to bridge this gap through two successive phases. First, following Nickerson etal.’s (2013) methodological guidance, we developed a business model taxonomy based on a structured literature review and an analysis of 154 connected car business models, which was evaluated both qualitatively and quantitatively. In the second phase, a cluster analysis (Kaufman & Rousseeuw, 1990) was performed to identify seven connected car business model archetypes by interpreting and evaluating the corresponding clusters. To the best of our knowledge, this work represents the first industry-specific taxonomy on the subject and complements existing industry-agnostic classifications (e.g., Passlick etal., 2021; Schüritz etal., 2017). While generally applicable taxonomies help distinguish connected car companies based on industry-agnostic dimensions such as role in ecosystem, data monetization strategy, or revenue model, they are insufficient to fully comprehend the connected car landscape and the configuration of underlying business models. Instead, our proposed taxonomy captures the peculiarities of the connected car, which is highly complex and not fully digitizable, by introducing novel dimensions such as value for car owner or driver (e.g., infotainment), car autonomy impact on value (e.g., enhanced value by autonomy), or data access (e.g., OBD2-dongle). In the second part of our study, we developed a systematic understanding of business model configurations and derived seven archetypes from real use cases, summarized in Fig.5. Fundamentally, our archetypes can be divided into three overarching categories. Category 1 consists of contextrelated business models (A2–A5) providing direct value to drivers or vehicle owners through data-driven applications. In contrast, categories 2 and 3 represent cross-contextual business models that enable further business models through either in-vehicle (Category 2, A6–A7) or off-vehicle (Category 3, A1) infrastructure solutions. Category 2 archetypes (A6–A7) rely on in-vehicle software architecture (e.g., infotainment systems or ADAS) assembled by OEMs from various software vendor components, creating enabler technologies and valuable data sources for implementing the data-driven applications summarized in Category 1 (A2–A5). Category 3 encompasses a single archetype (A1), which operates entirely outside the vehicle and acts as a Fig. 5 Visualization of archetypes and their relationships Electronic Markets (2024) 34:13 Page 19 of 24 13 marketplace for data exchange between car manufacturers and third-parties, facilitating independent service providers to implement business models in the first category (A2–A5). Theoretical implications Our research ties into the descriptive knowledge of connected cars and associated business models, an emerging and still-developing domain (Kaiser etal., 2018). Although vehicle connectivity is a major trend, the connected car is a relatively new topic in IS research, with most of the available work focused on exploring privacy concerns rather than business potentials (Cichy etal., 2021; Koester etal., 2022; Lechte etal., 2023). The outcome of our study resulted in a theoretically sound and empirically validated taxonomy summarizing the critical characteristics of connected car business models, along with seven archetypes representing recurring patterns across all characteristics. We contribute to comprehend this domain and provide theoretical and empirical implications to inform future research. The connected car provides a unique setting to examine and extend existing theory and evidence on business models for connected device data (Cichy etal., 2021). Unlike currently existing taxonomies that focus primarily on data, connectivity, or the IoT in general, our taxonomy stands out as the first to focus specifically on the connected car. First, our taxonomy provides theoretical insights in the form of a common language and structure for analyzing, classifying, and configuring connected car business models, paving the way for further research, and helping scholars position their work within it. Throughout its development, we ensured both theoretical rigor and practical relevance by using input from the existing literature corpus and industry-specific real-world objects (i.e., 154 connected car business models). Our taxonomy represents an analytical theory, following Gregor (2006), that classifies the specific dimensions and characteristics of connected car business models by summarizing commonalities in discrete observations. Thus, our research contributes to structuring the body of knowledge in the emerging field of connected cars and smart mobility in IS research and enables a more systematic description and analysis of such business models (Glass & Vessey, 1995). Second, the corresponding archetypes can serve as a starting point for understanding higher-level business model configurations in the connected car domain. Moreover, the empirical findings of our study extend the knowledge of data monetization by presenting seven established configuration options for business models in the connected car domain. Based on typical configurations of characteristics across all dimensions included in our taxonomy (Table5), these archetypes provide initial insights into the technical prerequisites (i.e., value architecture) required by ecosystem actors, their potential roles (i.e., value network), and the data-driven services they can offer (i.e., value proposition) to successfully monetize vehicle data (i.e., value finance). Hence, our research responds to recent calls for a better understanding of “the role that data aggregators and refiners play in data monetization, how they create value, and how different parties can capture it” (Parvinen etal., 2020, p. 44). Third, empirically we provide a systematically analyzed dataset of connected car business models that demonstrates how companies leverage digital technologies in the mobility sector. Our data collection process primarily relied on publicly available sources such as company websites and industry-specific business reports, making the dataset easily reproducible and extendable to reflect future developments in the automotive industry. Therefore, this dataset serves as a valuable resource for guiding further studies on digital innovation in the connected car domain. Finally, the success of connected car business models, as represented by our developed taxonomy and archetypes, is increasingly tied to ensuring data privacy and security (Wiener etal., 2020). Unlike other connected products, connected cars have already become a mass IoT case, and data from connected cars is already being shared with thirdparties through APIs. Of course, exchanging connected car data raises a series of privacy-related concerns (Cichy etal., 2021) as both the car users’ informational and physical spaces may be intruded (Koester etal., 2022), which may result in an increased privacy risk. Sensitive information about actual driving behavior or daily routines might be inferred from connected car data (Lechte etal., 2023). To mitigate this privacy risk, in Europe, for example, the data shared by connected products such as connected cars is being regulated by the European Commission, forcing automakers to build data collection and sharing systems that do not compromise individual privacy but also do not prohibit data sharing. Applying a privacy-by-design approach (Schaar, 2010) and implementing user consent procedures are two possible ways to address the privacy challenge. Managerial implications Our research offers managerial implications by providing valuable tools for navigating the vastly uncharted territory of data-driven business models in the automotive industry. Thereby, our taxonomy goes beyond technical or economic considerations, offering a differentiated view of business model design in the connected car space. This enables automotive incumbents, startups, and non-industry players to gain a detailed understanding of the interactions among car data-driven business models and learn about different ways to monetize connected car data. In addition, our research provides a comprehensive market overview Electronic Markets (2024) 34:1313 Page 20 of 24 and analysis of the connected car ecosystem and presents seven representative archetypes that specify the relevant dimensions for business model innovation. Managers can use these archetypes to identify business opportunities and potential market entry points in the automotive ecosystem and assess their implementation in their company’s specific context, as discussed by Kaiser etal. (2021). By employing archetypes, practitioners can gain insights into potential configurations that have been widely applied, serving as a reference point for further exploration and customization, thus helping to develop unique business models tailored to their specific goals and target markets. In doing so, our cross-table (Table5) is a valuable tool that facilitates assessing how market participants typically structure their business models concerning specific archetypes. While our work does not provide a one-size-fits-all prescription, it does offer a prescriptive component in providing actionable insights and guiding principles. Practitioners can use the taxonomy and archetypes as strategic management tools to explain their current business model to stakeholders, focus on improving specific operational aspects, or develop new business models aligned with their corporate strategy (Spieth etal., 2014). They may further use them to systematically analyze competitors and identify unique combinations of features that have not yet been used in the market. By conducting a morphological analysis, our work can help practitioners systematically develop innovative ideas (Geum etal., 2016). The archetypes and associated real-world business models highlight established innovation paths that executives can follow to digitalize their legacy business models and advance car data monetization. Overall, the taxonomy and archetypes provide industry-specific support for business model innovation, enabling practitioners to expand their market offerings and create value throughout the vehicle life cycle. In addition, several policy initiatives are underway, such as the European Data Act (European Commission, 2022b), to protect the privacy of individuals in the case of connected products that will impact the implementation of DDBM. The European Data Act regulates data generated by connected products and grants stakeholders more control over their data through a strengthened right to data portability. The directive is also expected to give users of connected vehicles more control over their data and allow third-parties fair and non-discriminatory access to and use of the data in services. Due to the complexity of the connected car context and the reactions of European automotive organizations, represented by the European Automobile Manufacturers’ Association (ACEA) and the European Association of Automotive Suppliers (CLEPA), we expect sector-specific legislation to be published shortly that will provide more detail on OEMs’ obligations regarding what connected car data can be shared for use in DDBMs and how. Limitations andfuture research Like any study, ours is subject to limitations that also suggest potential avenues for further research. Taxonomy-based research is never complete as it reflects a snapshot in time (Nickerson etal., 2013), which is also true for our taxonomy and archetypes that represent the current state of connected car business models. As the field is still developing, future research could revisit and extend our findings to keep them relevant and applicable. For example, legislation (e.g., European Commission, 2022a) mandating safety-related systems in cars (e.g., to monitor driver attention, distraction, drowsiness, and even health) will likely drive future innovation and potentially lead to further archetypes. However, our findings cannot represent such future trends because they are empirically informed only by existing real-world business models. Because our research aimed to develop a taxonomy and associated archetypes for the manifold connected car domain, our findings are still broad in scope. For instance, our study covers business models with very different foci, including end-user applications for navigation or driver assistance, technology provision in the area of safety and security, or platform-based business models as enablers for novel services. Future studies should explore specific archetypes in more depth by developing more specific taxonomies and sub-archetypes for these business models, similar to the study on vehicle data marketplaces by Bergman etal. (2022). We built the taxonomy and performed the coding process based on publicly available information, triangulating data from company websites, Crunchbase, and reports to maximize the validity of our dataset. However, information on companies’ revenue models was often limited, so we excluded this dimension from the cluster analysis. Future research should fill these data gaps by contacting companies directly to complete data sets and verify or extend our cluster analysis with new insights. Furthermore, there was a notable lack of comprehensive information regarding the techniques employed for data analysis or the sensors and additional systems utilized to access in-vehicle data. We evaluated the taxonomy both quantitatively and qualitatively but primarily evaluated the archetypes from a quantitative perspective by calculating silhouette width as a measure of cluster validity (Rousseeuw, 1987). Future research endeavors could complement our work by qualitatively evaluating the archetypes through expert interviews. This could reveal dependencies between different business model archetypes and important strategic decision factors for how companies consider the different archetypes in their business model innovation processes. Finally, while our work is focused on connected cars, the next revolutionary leap in the automotive industry is already on the horizon, known as the “software-defined vehicle,” in which vehicle software takes precedence over mechanical hardware and primarily controls and executes vehicle functions (cf., Electronic Markets (2024) 34:13 Page 21 of 24 13 Ohlsen, 2022; Windpassinger, 2022). The increasing reliance on large technology companies such as Amazon, Google, or Microsoft for vehicle operating systems and cloud environments highlights the need for research into collaborative strategies and alliances (Sterk etal., 2023b). However, entirely new business models are also emerging around the softwaredefined vehicle, thanks to the unified software architecture that can be addressed fully remotely, going far beyond the current limited remote capability of connected cars. This creates a need for future research in taxonomy development and archetype identification dealing with the successor of the connected car, for which our work can serve as a starting point. Conclusion Our study explores the potential of connected cars in the area of business model innovation using vehicle data. We contribute two artifacts to the field: a taxonomy and seven archetypes for connected car business models. This research extends the existing body of knowledge on data-driven business models and connected cars by providing a comprehensive examination of such connected car business models. These artifacts can serve as a common language for scholars to analyze, classify, and configure connected car business models, and as a basis for understanding higher-level business model configurations. For decision-makers, these tools can facilitate a smoother transition and enable companies, especially startups, to quickly adapt and excel in this fast-moving industry. In addition, our research benefits established companies by providing clear guidance on how to overcome challenges and update their strategic approaches. Ultimately, this work provides a foundation for future research using the extensible taxonomy and archetypes as constructs to shed more light on the proliferation of connected cars and related business models. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1252502400692-5. Acknowledgements Parts of this publication were written at Virtual Vehicle Research GmbH in Graz, Austria. The authors would like to thank the Austrian Federal Ministry for Climate Action (BMK), the Austrian Federal Ministry for Labour and Economy (BMAW), the Province of Styria (Department 12) and the Styrian Business Promotion Agency (SFG) for their financial support within the framework of the COMET K2 Competence Centers for Excellent Technologies. The Austrian Research Promotion Agency (FFG) was authorized to manage the program. In addition, this work was partially supported by the German Federal Ministry of Education and Research through the research project “bi.smart” (grant no. 02J19B041), which was awarded to the Karlsruhe Institute of Technology. The authors would like to thank all parties involved for their generous support. They would also like to thank their supporting industrial partners, Robert Bosch GmbH and Caruso GmbH. Funding Open Access funding enabled and organized by Projekt DEAL.Funding Open Access funding enabled and organized by Projekt DEAL. 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. 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