Qualitative Insights into Organizational Value Creation: Decoding Characteristics of Metaverse Platforms

Tingelhoff, Fabian; Schultheiss, Raphael; Schöbel, Sofia Marlena; Leimeister, Jan Marco Article — Published Version Qualitative Insights into Organizational Value Creation: Decoding Characteristics of Metaverse Platforms Information Systems Frontiers Provided in Cooperation with: Springer Nature Suggested Citation: Tingelhoff, Fabian; Schultheiss, Raphael; Schöbel, Sofia Marlena; Leimeister, Jan Marco (2024) : Qualitative Insights into Organizational Value Creation: Decoding Characteristics of Metaverse Platforms, Information Systems Frontiers, ISSN 1572-9419, Springer US, New York, NY, Vol. 27, Iss. 2, pp. 467-486, https://doi.org/10.1007/s10796-024-10494-x This Version is available at: https://hdl.handle.net/10419/323333 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Information Systems Frontiers (2025) 27:467–486 https://doi.org/10.1007/s10796-024-10494-x Qualitative Insights intoOrganizational Value Creation: Decoding Characteristics ofMetaverse Platforms FabianTingelhoff1 · RaphaelSchultheiss1· SofiaMarlenaSchöbel2· JanMarcoLeimeister1 Accepted: 2 May 2024 / Published online: 15 May 2024 © The Author(s) 2024 Abstract The significance of metaverse platforms is growing in both research and practical applications. To utilize the chances and opportunities metaverse platforms offer, research and practice must understand how these platforms create value, which has not been adequately explored. Our research explores the characteristics of metaverse platforms that facilitate value creation for organizations in both B2B and B2C sectors. Employing a qualitative inductive approach, we conducted 15 interviews with decision-makers from international corporations active in the metaverse. We identified 26 metaverse platform characteristics, which we categorized into six dimensions based on the DeLone and McLean Information Systems success model. Subsequently, we provide examples to illustrate the application of these identified characteristics within metaverse platforms. This study contributes to the academic discourse by uncovering the characteristics that shape the competitive landscape of emerging metaverse platforms. Leveraging these characteristics may offer metaverse providers a competitive edge in attracting complementary organizations to their platforms. Keywords Metaverse Platform Ecosystem· Organizational Value Creation· Platform Characteristics· Complementors 1 Introduction Recently, companies have begun to invest in metaverse platforms. McKinsey and J.P. Morgan project the metaverse as a trillion-dollar opportunity (McKinsey, 2022; Moy & Gadgil, 2022), while Gartner predicts 30% of companies will serve customers via metaverse platforms by 2027 (Gartner, 2022). This culminates in JP Morgan’s assessment, emphasizing that “metaverses will likely infiltrate every sector in some way in the coming years” (Moy & Gadgil, 2022). Indeed, with annual spending over $54 billion, expenditure on metaverses nearly doubled that of music purchases in 2021 (Moy & Gadgil, 2022). Researchers attribute this growth to metaverses revolutionizing virtual societal and economic interactions (Di Pietro & Cresci, 2021). Specifically, the metaverse benefit from network effects, becoming more appealing as user and organizational participation increases (Gawer & Cusumano, 2014; Tingelhoff etal., 2024). Accordingly, metaverse users benefit from the presence of more participating organizations, and reciprocally, having many users renders the metaverse more attractive for organizations (Kim etal., 2016). While research deems metaverses only as viable once many users and complementors participate, about 500 companies already joined a metaverse platform, which only amounts to about 0.00015% of all companies (Brimco, 2024; Newzoo, 2022). A major contributing circumstance to the discrepancy of predicted and actual predicted and actual corporate engagement in metaverses is the inconsistencies of how different metaverse platforms influence organizational value creation. While past studies have examined how organizations mitigate challenges when creating value on metaverse platforms (e.g., Schöbel & Tingelhoff, 2023), organizations find it challenging to join metaverses due to the diverse ways in which various metaverses support value * Fabian Tingelhoff f[email protected]h Raphael Schultheiss [email protected]h Sofia Marlena Schöbel [email protected] Jan Marco Leimeister [email protected]h 1 Institute ofInformation Systems andDigital Business, University ofSt.Gallen, St.Gallen, Switzerland 2 Information Systems, University ofOsnabrück, Osnabrück, Germany 468 Information Systems Frontiers (2025) 27:467–486 creation. Ultimately, it has not been fully explored how organizations can utilize characteristics of metaverse platforms (e.g., personalization and immersion) in their offerings to maximize their value proposition. When selecting a metaverse platform, organizations must prioritize its characteristics carefully to guarantee it fits their strategy. This study aims to uncover which metaverse platform features aid or hinder organizational value creation, leading to the following research questions: RQ1: What platform characteristics influence an organization’s ability to create value on a metaverse platform? RQ2: How have these characteristics already been configured in existing metaverse platforms? This study seeks to contribute towards the research questions using qualitative data. We analyzed interviews with 15 decision-makers, all tasked with strategizing for corporations' engagements on metaverse platforms. We built on the DeLone and McLean Information Systems success model (D&M IS success model) (DeLone & McLean, 1992, 2002, 2003) to structure our data collection and analysis. From this, we identified 26 platform characteristics across six dimensions. Furthermore, we discovered these characteristics, such as accessibility and privacy, play different roles in metaverse platforms compared to traditional ones, a topic we expand on in our discussion. To substantiate our findings, we illustrate how these characteristics are divergently implemented in two existing metaverse platforms: Roblox and Decentraland. Our study deepens the understanding of how organizations adopt metaverse platforms, laying groundwork for future research into corporate activities within these platforms. The study also broadens our knowledge of how emerging technologies' features support value-creation, beyond known platform mechanisms. Our results can help metaverse providers design more effective and appealing platforms. Moreover, by clarifying which platform features affect value creation, our study aids organizations in deciding how to offer their products or services on metaverse platforms. In summary, this study enables informed decisions about engaging with metaverse platforms for organizations. This paper is structured as follows. The next section introduces foundational knowledge on metaverse platform ecosystems and outlines the structuring of their characteristics. Following that, we detail the methodology, then report and analyze the study's results. Subsequently, we exemplify our findings with case studies of two prominent metaverse platforms. In the sixth section, we explore the study's limitations and suggest directions for future research. The paper concludes with a summary of key insights. 2 Theoretical Background 2.1 The Metaverse Platform Ecosystem andHow It Can Create Organizational Value The metaverse is a multi-user virtual platform built on Web3 technology to revolutionize how people interact in any context (Di Pietro & Cresci, 2021). The malleable features of this emerging platform generate an immersive user experience, mimicking the real world sans physical constraints (Jaynes etal., 2003), thus enabling new and richer kinds of social and business interactions (e.g., through immersive 3D communication) (Bourlakis etal., 2009). By combining technologies like self-sovereign identity, virtual reality, and blockchain, these platforms evolve into parallel societies and economies. Though social media platforms (e.g., Facebook) or e-commerce platforms (e.g., Amazon) have already impacted human interactions and purchasing behaviors, this unique technology blend is unprecedented in virtual environments (Wang etal., 2021), enabling users to construct a parallel life with all its facets (i.e., work and leisure) in one platform for the first time. The organizational structures around metaverse platforms classify them as an emergent platform ecosystem type (Schöbel & Leimeister, 2023). An ecosystem can be described as the underlying structure among partners, designed to enhance interactions and deliver a core value proposition (Adner, 2017). Generally, Typically, a platform is any product, service, or technology used by ecosystem actors to innovate and create complementary offerings (Gawer & Cusumano, 2014). It is typically integrated into ecosystems to help actors fulfill their value propositions (Gawer & Cusumano, 2014). For instance, the Apple App Store is integrated into the Apple ecosystem, including hardware and operating systems, enabling partners to create valuable apps and services. Value is defined by an individual’s overall utility assessment based on their perception of input and output (Zeithaml, 1988). Two processual value components exist: use value and exchange value (Bowman & Ambrosini, 2000). Use value describes the recipient’s perception of a product or service based on their needs and values. Thus, use value stems from an organization's products and services, shaped by the recipient's personal and situational evaluation. Conversely, exchange value is what the recipient is willing to give in return for the use value. It can take other forms than monetary compensation, such as brand awareness or customer relationship, and is realized through the reciprocal transfer via the platform (Sanders & Simons, 2009). 469Information Systems Frontiers (2025) 27:467–486 Platform ecosystems comprise three major roles: platform leaders, complementors, and users. Platform leaders (or orchestrators) are the providers of the platform ecosystem (Gawer & Cusumano, 2002; Oliveira etal., 2019). They orchestrate the platform and are responsible for its regulation, maintenance, and adoption of standards within the ecosystem. Complementors provide products and services built for the platform, enhancing the ecosystem's core value. Lastly, users procure the complementors’ products and services via the platform. Usually, they access the platform and its complementary offering directly through a user interface. Consequently, in a metaverse platform ecosystem, value creation occurs through the reciprocal exchange of use and exchange values between complementors and users. Users receive products or services in the metaverse (use value) and are willing to compensate complementors with money and attention (exchange value). Accordingly, complementors are the source, users are the target, and the metaverse platform is the locus of value creation. Some researchers contend that metaverse platform ecosystems challenge traditional role allocations (Schöbel & Leimeister, 2023). Notably, metaverse platforms represent early examples of decentralized governance models (Goldberg & Schär, 2023). Decentralized platforms are collectively owned by stakeholders rather than a single orchestrator. This collective ownership model is known as a Decentralized Autonomous Organization (DAO). On such a platform, users and complementors can impact decisions about platform governance, such as technical standards or the maximum amount of land created for users. This shifts the power balance among ecosystem participants, unlike in traditional platforms. In decentralized platforms, power is distributed; complementors and customers can propose and implement changes, not just the orchestrator (Goldberg & Schär, 2023). reducing the orchestrator's role also affects the direct relationships between customers and complementors (Yoo etal., 2023). While traditionally, both parties only interacted through the platform and, hence, the orchestrator, metaverses enable customers and complementors to interact directly. This is further emphasized by the fact that a metaverse does not require transaction intermediaries (e.g., banks). Specifically, the metaverse enables the direct exchanges of value items (Tapscott & Tapscott, 2017). This uniqueness to metaverse platforms potentially leads to simpler, more flexible, and efficient business processes by reducing communication complexities. While many researchers contributed conceptualizing the metaverse, Schöbel etal. (2023) made the first effort to distinguish different types of metaverse platforms. Their taxonomy evaluates the technologies in a metaverse platform's infrastructure to highlight differences in value propositions. For example, they argue that a metaverse platform ecosystem like Decentraland focuses more on the creator economy and, hence, the creation and distribution of value items. In contrast, game-based platforms like Roblox focus on entertaining experiences that deepen user-brand relationships, alongside transactional value. This visualizes the paucity of platform types and business foci under the metaverse umbrella. 2.2 The DeLone andMcLean ISSuccess Model Identifying key success factors for organizational value creation is essential for insights that stay relevant in the rapidly changing metaverse platform ecosystems (Schöbel & Leimeister, 2023). In this context, past research employed value creation or value co-creation theory, predominantly focusing on aspects of value creation that are within an organization’s control. For instance, value creation theory typically centers on the consumer's perceived assessment, which organizations can influence by modifying their offerings or engaging in value co-creation with consumers. This aspect of how organizations can steer this process has been explored in existing literature, including in the context of the metaverse (e.g., Schöbel & Tingelhoff, 2023; Tingelhoff etal., 2024). Conversely, our study examines platform characteristics that impact an organization's ability to create value. These external factors, often outside organizations' control, significantly influence value creation dynamics in technologydriven settings like metaverse platforms. In this context, the DeLone and McLean IS success model (D&M IS success model) (DeLone & McLean, 1992, 2002, 2003) has emerged as a cornerstone in the domain of Information Systems (Wang, 2008), enabling analysis of platform characteristics and their effects on organizational value creation. It provides a multifaceted perspective, deconstructing information systems into six key dimensions: information quality, system quality, service quality, usage intentions, user satisfaction, and net benefits. Firstly, metaverse platforms are inherently complex digital ecosystems that thrive on exchanging information (Schöbel & Leimeister, 2023). The dimension of information quality is directly tied to the nature of data presented in metaverses, its accuracy, timeliness, and relevance. High-quality information will invariably influence user and organizational decision-making within a metaverse platform (Balica etal., 2022). System quality mirrors the technical prowess of a metaverse platform. As immersive environments, metaverse platforms demand high system performance, ease of navigation, and reliability. The better the system quality, the more seamless the user experience, thus attracting more complementors (Schöbel & Tingelhoff, 2023). Service quality in the metaverse context indicates the support structures in place. This could involve technical 470 Information Systems Frontiers (2025) 27:467–486 support, user guidelines, and assistance in content creation (Park & Kim, 2022). A high-quality service structure can significantly enhance the desirability of a metaverse platform for users and, in turn, organizations (Jo & Park, 2022). The next dimensions, usage intentions and user satisfaction, arise from the interaction of the prior dimensions. Usage intentions resonate with how frequently users and organizations engage with the metaverse platform. A platform with a higher intent of usage becomes a hotspot for value creation and exchange, which is paramount in a networked environment like the metaverse (Ataman etal., 2023). Conversely, increased user satisfaction levels in a metaverse indicate that the platform effectively meets or surpasses the multifaceted expectations of users and organizations. When users are satisfied with their experiences, they are more likely to invest time, resources, and encourage peer participation, thus amplifying the platform's network effects and cementing its value-creation potential for organizations (Golf-Papez etal., 2022). Lastly, the dimension of net benefits encapsulates the tangible and intangible outcomes that organizations derive from their engagement with an information system. When organizations can measure the positive impact of their involvement in a metaverse platform—whether in terms of revenue, brand recognition, skills acquisition, or social connections— it reinforces their commitment to the platform and ensures sustained engagement (Polyviou & Pappas, 2022). This sustained engagement, powered by recognized net benefits, can indicate the platform's long-term value-creation potential for organizations (Periyasami & Periyasamy, 2022). While the D&M IS success model is tried and tested across various IS contexts, its inherent adaptability makes it particularly apt for metaverse platforms. The model’s validity at both individual and organizational levels (Petter etal., 2008) makes it a versatile tool for understanding an emergent and complex environment like the metaverse. Furthermore, previous validations of this model in diverse IS environments, from enterprise systems to e-commerce platforms (Wang, 2008), have shown its robustness and adaptability (Ahlan, 2014; Al-Kofahi etal., 2020). Applying it to the metaverse, an amalgamation of various IS types, seems like a logical progression. The essence of the D&M IS success model is its capability to elucidate key dimensions that contribute to its capability to support complementors to create and deliver value through an information system (Wang, 2008). Yet, the interplay of these dimensions over time and the progressive stages of adoption need to be considered for a more comprehensive understanding. This aspect is vital as it bridges the D&M IS success model with real-world technological adoption behaviors and patterns, making it more contextually relevant, especially for evolving digital realms like the metaverse. By integrating process steps, one can understand not just what supports value creation (as indicated by the D&M IS success model) but also how and when these characteristics manifest and influence organizational value creation over the adoption lifecycle. To expand theories for a temporal interplay of characteristics, Ahlan (2014) proposed three process steps: system creation, system use, and system impact. This hierarchical order of influence characteristics can be applied to the D&M IS success model, where information, system, and service quality correspond to the system creation, usage intention and user satisfaction to system usage, and net benefits to system impact. The combined model is depicted in Fig.1. The initial phase of any technological endeavor involves its conceptualization, development, and implementation (Weber etal., 2023). This phase is particularly crucial within the metaverse context as it lays the foundation for the user experience. Information, system, and service quality become essential metrics at this juncture. The quality and relevance of information guide the design and functionalities of the metaverse platform (Bayraktar etal., 2023). System quality ensures the Fig. 1 Research Model, based on DeLone and McLean (1992, 2002, 2003) and Ahlan (2014) 471Information Systems Frontiers (2025) 27:467–486 platform’s technical soundness and scalability, which is vital to handling the dynamic nature of the metaverse’s ever-evolving virtual landscapes and foundational technologies (Peukert etal., 2022). Service quality, conversely, pertains to the support mechanisms, ensuring that complementors have a smooth onboarding process and immediate resolution to any technical hitches (Xi etal., 2023). A robust system creation phase, bolstered by these quality metrics, sets the tone for subsequent adoption and usage. Once a system has been created and launched, its success is predominantly gauged by its acceptance and the extent of its use. Here, usage intention is a precursor, indicating initial interest and potential adoption rates (Jeong & Kim, 2023). However, for the system to embed itself into the daily routines of users and organizations, satisfaction becomes pivotal (Xi etal., 2023). As users engage with the metaverse platform, their experiences, which are shaped by immersive interactions, realistic representations, and the fulfillment of intended purposes, dictate their satisfaction levels. Satisfied users not only continue their engagement but also promote organic growth through positive word-of-mouth and peer recommendations (Mladenović etal., 2023). For metaverse platforms, the system impact, as denoted by net benefits, encapsulates how effectively platform characteristics support organizational value creation (Polyviou & Pappas, 2022). This could manifest in diverse ways, from driving innovation in product or service offerings, facilitating unique customer engagement models, to fostering new revenue streams or enhancing brand visibility within the virtual realms (Hadi etal., 2023). Moreover, it might also encompass intangible benefits such as enhanced collaborative potentials, access to new market segments, or the ability to test and iterate offerings in risk-mitigated virtual scenarios (Yoo etal., 2023). Thus, system impact, in this context, underscores whether the metaverse platform meets the technical and experiential needs of its complementors and provides a conducive environment for organizations to harness its potential and realize tangible value. In summary, the adapted D&M IS success model provides an exhaustive framework to unpack, understand, and measure how metaverse platform characteristics influence organizational value creation. By mapping its six dimensions to the specific characteristics of metaverse platforms along Ahlan (2014)’s process steps, this study aims to clarify the characteristics that influence organizational value creation in this burgeoning digital frontier. 3 Methodology 3.1 Ensuring theQuality ofQualitative Data This study investigates how metaverse platform characteristics impact organizational value creation. The phenomenon of the metaverse is relatively new, and less is known about how organizations create value on metaverse platforms. To gain exploratory insights into organizational value creation, we adopted a qualitative research method (Draper, 2004). To ensure our qualitative data's validity and reliability, we adhered to Lincoln and Guba (1985)’s criteria: credibility, transferability, dependability, and confirmability. Credibility refers to the data's internal validity and its alignment with reality (Merriam & Grenier, 2019). We selected our interviewees based on three criteria. First, we considered whether interviewees had a holistic overview of organizational value creation. This includes individuals who are not only involved in strategic decision-making but also with a broad understanding of how various organizational units contribute to value creation. Second, as we explored metaverse platforms, our participants needed substantial experience in the metaverse sector. This ensures that our data comes from individuals who are not only familiar with the concept but are actively engaged in its application and development within their organizations. Third, we focused on participants whose organizations actively offer products or services on metaverse platforms (e.g., Roblox or Decentraland). This practical involvement ensures our insights are grounded in real-world experiences and challenges. To guarantee a similar level of abstraction and comparability of our findings, we selected interview partners based on similar levels of responsibility regarding the metaverse. Furthermore, to increase credibility and mitigate possible biases in the data collection and analysis (Valenzuela & Shrivastava, 2002), the first three authors of the paper coded the data independently. Specifically, we employed the qualitative inductive approach described by Gioia etal. (2013), which is designed to enhance qualitative rigor in conducting and presenting inductive research. This approach is particularly suitable for inductively developing grounded theory, offering rich and detailed theoretical descriptions, which, in our case, pertains to the value creation on metaverse platforms. In line with the method proposed by Gioia etal. (2013), we constructed first-order concepts, second-order themes, and aggregate dimensions, with the latter reflecting the theoretical constructs. To ensure intercoder reliability, the coders critically discussed their initial coding results until the first-order codes were sufficiently reviewed, and a second iteration could start that led to a detailed description of our second-order constructs. During this process, the coders aimed for the codes to be on a comparable level of abstraction and still reflect the individual experiences of each informant. Our analysis was rooted in a hermeneutic approach, emphasizing the shared understanding of the nature of metaverse platforms. Following the insights of Paterson and Higgs (2005), we embraced hermeneutics for its dialogical nature, which in 472 Information Systems Frontiers (2025) 27:467–486 our research manifested through the interviews conducted between experts and the interviewer. To improve the transferability—whether the results of one study can be transferred to different settings with different participants—of our results, we have provided sufficient contextual information about each informant and their interview in Appendix1. Dependability describes the extent to which the findings of a study can be replicated (Merriam & Grenier, 2019). We noticed a high degree of concept/coding saturation (95% of codes surfaced) after the 11th interview, which aligns with previous studies on the method (Guest etal., 2006) and the topic at hand (Schöbel & Tingelhoff, 2023). Finally, confirmability mainly concerns objectivity and addressing potential research biases that can result, for instance, from inherent values and beliefs but also the mere presence or timing of follow-up questions (Valenzuela & Shrivastava, 2002). We addressed confirmability by independently executing tasks simultaneously and comparing results (e.g., for data coding and interpretation). Further, all the researchers were at least aware of their potential biases and influences, which we critically, openly, and proactively addressed during the planning and execution of this research. 3.2 Data Collection In our qualitative study, we interviewed 15 metaverse decision-makers from multilateral organizations in both B2B and B2C markets. These participants represent the mission and vision of an organization; they have the highest responsibility for their organization’s metaverse strategy, were familiar with key metaverse platforms, and understood how different organizational units contribute to value creation. In other words, we interviewed CEOs and senior leaders who are deeply familiar with their organizations and can insightfully discuss how metaverse platforms align with their business models to create value. Interviewees explicitly consented to their interviews being recorded and their personal data being published, both before and after the interviews. This procedure adhered to ethical research standards, ensuring transparency and reliability in data collection. We refer to individual participants using the abbreviation IP (interview partner) and their number (1–15). We used a structured interview guide to ensure interviewees fully understood the platform characteristics being studied. The guide featured open-ended, concise questions, supplemented with follow-up probes to resolve ambiguities. Additionally, we provided contextual information and examples where necessary to aid comprehension. We began by asking participants about their demographics. This was followed by general questions about the metaverse (how they defined it, for instance). We then focused on which platform characteristics they deemed crucial for strategic decisions regarding their metaverse offerings. We explicitly referenced the three process steps of system creation, system use, and system impact. In addition to discussing the impact on their organizations, we invited opinions on future metaverse platform developments and their relevance to the interviewee’s organization. Interviews were held online (Lo Iacono etal., 2016) and conducted in the interviewees' native languages when possible (Harzing & Maznevski, 2002). To address the potential for translation-induced alterations, we employed a rigorous process. First, we conducted 66% of the interviews in the native languages of both interviewees and interviewers, reducing the necessity for translation. The remaining interviews were conducted in English. For interviews needing translation, a bilingual, topic-knowledgeable co-author handled the task. A second bilingual co-author reviewed and verified the transcripts against the audio recordings for translation accuracy. The interviews lasted between 26 and 56 min (mean: 34). In one interview (IP 5), technical difficulties obstructed the recording. The interviewer—in this case, the second author— immediately after the interview reconstructed the interview details from notes and memory. We based our coding, for which we used the Atlas.ti22 software, on the transcripts (Gioia etal., 2013). The first three authors coded simultaneously and independently. According to Gioia etal. (2013), the first-order coding was an open coding mechanism that remained faithful to the words of each interviewee. Ending up with a high number of codes prompted us to summarize those codes where the informants addressed the same topic but with different terminology. Since the initial open coding was executed independently, ongoing researcher discussions shaped the final constructs. After identifying platform characteristics, we assessed their relative importance. To this end, we presented the ordered variables to our interview partners for an ordinal rating of relative strategic importance. To substantiate our findings, we showcased the highest-rated characteristics using two practical examples. Roblox and Decentraland serve as leading examples of early metaverse platforms (Schöbel etal., 2023). As current recipients of immense funding and publicity from practitioners (Kamin, 2021; Wang & Ho, 2022), they are ideal subjects for further investigation. 4 Results In line with our research goals, we identified 26 platform characteristics and categorized them according to the six dimensions of the D&M IS success model. Additionally, our metaverse experts ranked these characteristics based on their importance and relevance to organizational value creation in metaverse environments. Figure2 reports the characteristics per dimension ranked by their average importance 473Information Systems Frontiers (2025) 27:467–486 (with 1 indicating the highest importance). However, the rankings reflect the experts' average views. Though indicative of relative importance, they are not meant as precise statistical measures. Given this, we advise that small differences in rankings, particularly those within 0.1 or 0.2, may be negligible. In subsequent sections, we detail our empirical findings and analyze each of the six success dimensions. 4.1 System Creation: System Quality, Information Quality, andService Quality The system quality dimension encompassed the most platform characteristics. In this dimension, interview partners (IPs 1, 2, 4, 8, 9, 12, 13, 14, and 15) ranked accessibility— ease of access for complementors and users—as the most crucial. The interviewees specifically mentioned that platforms must be accessible through various devices, including tablets, smartphones, and desktop PCs (IPs 1, 2, and 9). IP 9 elaborates: It is all about being available across the devices. The more devices you have available for the platform, the easier it is to enter. I mean, as we all know, traffic is coming from mobile and desktop. […] So, you know, if your platform is only compatible with PCs, you’re missing out on a big portion of the market.1 However, accessibility encompasses not only device compatibility but also the ease of navigating the platform's functions. “One should be able to move around and execute actions without having to read a one-hour manual,” argued IP8 when asked about the necessary functionalities regarding the system quality of a metaverse platform. Similarly, IP 14 emphasized that operability is currently more important than functionality. Interviewees (IPs 4, 12, 14, and 15) also deemed an integrated economic system essential. The interviewees highlighted the necessity of integrated payment systems (IP 12) and their ability to create new user experiences (IP 15). This includes a currency that offers voting rights on decentralized platforms. Further, economic systema enable new functionalities, such as play-to-earn, where users are rewarded with items or tokens of monetary value. In this way, a platform can ensure its most active users receive a higher voting share. Additionally, new business models—for instance, transferring goods from the virtual to the physical world and vice versa—require digital ownership structures that metaverse platforms must provide (IP 14). Across all dimensions, platform stability emerged as the most frequently mentioned characteristic (IPs 2, 3, 4, 6, 7, 9, 11, 12, 14, and 15). Interviewees expressed concerns that platform bugs, like a faulty login page, could harm their brand image (IPs 4, 6, and 9). While some worried that these “bugs lead to users exiting the metaverse altogether” (IP11), others cautioned against entry barriers: IP12 warned that “[t]he metaverse-guest’s fear about crossing the threshold Fig. 2 Adapted Research Model (including the average ranks by IPs) 1 All quotes were translated to English for this paper. 474 Information Systems Frontiers (2025) 27:467–486 into the virtual immersion must be addressed for them to use it at all.” Information quality represents the second dimension in the D&M IS success model. Content quality emerged as the primary concern in the interviews (IPs 3, 4, 6, 7, 8, 10, 12, 13, 14, and 15). Organizations worry about losing potential customers if the platform's overall user experience and other complementors' offerings are unsatisfactory. “So, you know, if the content is not attractive,” IP 14 explained, “people will just hop onto it, take a look, and probably never come back. If you don’t engage your participants, that becomes an issue.” Specifically, IP14 worries that a bad experience on one platform could deter customers from using any metaverse platform. Compared to other platforms, this concern is particularly pertinent for metaverse platforms given their current stage of development. Additionally, companies consider the connotation of the content produced on a platform. For instance, companies may hesitate to join gamebased metaverses due to concerns that users could associate the game's characteristics with the company. IP12 specified this concern: “We try hard to avoid becoming a Disneyland. This is not just a game and fun. It is totally okay if it is around fun; that is not wrong. […] Even though a platform might have entertainment aspects, we also want to be able to educate our customers in a serious manner.” Personalization (IPs 1, 8, 11, and 14) is another integral metaverse platform characteristic. This encompasses two facets of customization. First, organizations aim to align their virtual presence with their brand identity: “Nobody wants to access a generic platform with this great templateworld. No, everybody wants their own logos, their own trees, their own information channels” (IP 11). In addition to platform design flexibility, organizations also aim to offer personalized products to their customers. This introduces several technological challenges, as discussed by Duan etal. (2021). To provide personalized features effectively, orchestrators must balance individual customization with maintaining a cohesive design across the platform. This creates tension among stakeholders, as personalization challenges the standardization and regulations needed for a unified look. IP11 cited Meta’s AI builder as a notable example of addressing these challenges: The amount that individualization and customization are wanted by the customers is extremely effortful. […] There is now this AI builder by Meta. It’s a prototype based on voice recognition. So, Mark Zuckerberg is standing there [on the metaverse platform] as an avatar and says: ‘Hey, I need an island,’ and then an island appears. And then he says, ‘I want trees,’ and a palm tree appears. […] And this is how you could build customized yet standardized objects for your customers in a few minutes. Service quality marks the last dimension within the system creation phase. Here, aspects of privacy and security (IPs 1, 3, 6, 11, 12, 13, and 14) emerged as the focal platform characteristic. This focus stems from the unique types of data collected by metaverse platforms. With advancements enabling the collection of micro-movements and facetracking data, organizations can now access unprecedented types of personal data. Aware of these risks, interviewees unanimously agreed that “if it [a metaverse platform] is not built safely, it won’t be successful” (IP 6). In other words, our interviewees require security and privacy concepts to develop a metaverse platform “into a safe space” (IP 13). Nevertheless, organizations often require sensitive data to effectively serve their customers. For instance, a hotel company testing its designs in the metaverse necessitates personal data on user movements within the platform. Consequently, organizations are concerned that customer reluctance to share data on metaverse platforms, coupled with their cautious approach to data collection, may restrict product sales (IPs 1 and 14). This creates tension between the need for privacy and safety versus the drive for product offerings and sales, often prioritizing the latter at the expense of the former. Therefore, striking the correct balance becomes crucial for complementors, given its significant influence on a platform's operational capabilities. 4.2 System Use: Usage Intention andUser Satisfaction Usage intention determines the target users of a platform and their manner of engagement. Naturally, organizations aim to reach their target audience upon entering a metaverse platform (IPs 1, 6, 10, 11, 12, and 14). IP 10 explains that large corporations might easily financially engage with any major platform, whereas small and medium enterprises (SMEs) must carefully choose platforms where their selected target audience is most active. For instance, while Roblox’s age demographic is age 13 to 25, the users of Meta’s Horizon are potentially older (IP 6). Additionally, complementors seek platforms with an active user base (IPs 3, 4, 6, 7, 8, 9, and 15). Platforms are appealing if “used by active users on a daily basis” (IP 4), as complementors aim to “engage and interact with users to enter a common dialogue and exchange” (IP 7). In other words, complementors view the metaverse as a venue for initiating interactions with current and potential customers. In this context, the metaverse acts as an additional channel for organizations to actively engage with their target audience (Hadi etal., 2023). Some companies aim to educate customers and create leads in the metaverse (IP 12), others focus on product-centric community creation to foster customer loyalty (IPs 3 and 6). This is consistent with the metaverse's focus on social interaction and immersion (Schöbel etal., 2023). 481Information Systems Frontiers (2025) 27:467–486 Nike's values and goals, educating customers. Experts (Hazan etal., 2022) and researchers (Dwivedi etal., 2022) have underscored the metaverse's unique ability to blend gaming with e-commerce in that manner. Figure7 depicts a screenshot of Nikeland with the previously discussed elements highlighted. We caution against assuming that the net benefits for complementors can be definitively assessed from an external perspective. Nevertheless, Decentraland and Roblox demonstrate consideration for several identified characteristics conducive to complementors' value creation. Both platforms also exhibit limitations within certain dimensions, which, from our observation, detract from their overall performance. This may stem from the platforms' evolving nature, underscoring the imperative for ongoing enhancement. Moreover, the presence of the variables from our analysis in both platforms is irrespective of their governance structure. This reinforces the view that the organizational structure of a metaverse platform is not the primary distinguishing factor for complementors. Our interview findings, illustrated in Fig.2, support this observation. 6 Contributions andImplications Our study offers several contributions to researchers and practitioners. Theoretically, our study enriches understanding of ecosystem dynamics in metaverse platforms. Specifically, we explored the role and relationships of complementors within metaverse platforms. We applied the D&M IS success model, a well-established framework, to examine the platform-business model fit across different contexts. From its six dimensions, we pinpointed 26 characteristics specific to metaverse platforms. These characteristics significantly impact complementors' value creation in metaverse environments. They progress the conceptualization of the of metaverse’s capabilities and the tensions with which complementors must deal within the metaverse ecosystem. Consequently, our study encourages researchers to explore the metaverse's nature, meaning, and ecosystem players further. From a practitioner’s perspective, our study guides both orchestrators and complementors. For metaverse decisionmakers, our findings highlight essential platform characteristics vital for effective value creation. This understanding can lead to complementors aligning with platforms that resonate with their business models and, conversely, enable orchestrators to refine their platforms to support organizational value creation better. The implications are dual: improving the alignment between business models and platforms, and fostering platform evolution to better facilitate value creation. Ultimately, these insights could promote stronger business cases in metaverse platforms, accelerating adoption among businesses and consumers. 7 Limitations andFuture Research Our study’s limitations provide grounds for future research. First, a consensus on defining the metaverse remains elusive. While conceptual papers discuss the metaverse's nature (e.g., Hadi etal., 2023), others like Peukert etal. (2022) highlight its ongoing evolution, complicating its definition. While this study aims to contribute to the technological design choices during its development, we also want to highlight the need to replicate our findings in the future to determine their validity over time. Second, our sample spanned experts from the B2B and B2C contexts and several industries, resulting in a reasonably general framework. Future research could replicate this study with industry-specific experts to explore how metaverse applications can be customized to meet distinct industry needs. Third, most of our interviewees were from Europe, with only one from Hong Kong and another from the USA. Therefore, cultural differences might yield varied findings. Given the concentration of metaverse developments in North America and Asia, conducting studies in these regions could uncover additional insights. Finally, our interviewees worked at companies already involved in metaverses or actively considering them, which could have biased our results. Future research should engage experts holding critical views on the metaverse to contrast their perspectives with our findings. 8 Conclusion This study aimed to highlight key characteristics of metaverse platforms crucial for complementors seeking to maximize their value creation. Our research draws on platform ecosystem and value creation theories to provide a foundational understanding of how complementors generate value on metaverse platforms. We interviewed 15 metaverse decision-makers across various organizations, identifying 26 characteristics of metaverse platforms that impact complementors' ability to create value. We structured these characteristics according to the six dimensions of the DeLone and McLean IS success model and exemplified them through Decentraland and Roblox. The journey to fully comprehend metaverse platforms is ongoing. This study clarifies the design characteristics of metaverse platforms and introduces a framework to aid complementors in choosing suitable platforms. Additionally, it empowers researchers and practitioners to design new metaverse platforms purposefully. Ultimately, our research seeks to lay the groundwork for future inquiries in this domain. 482 Information Systems Frontiers (2025) 27:467–486 Appendix Acknowledgements For this research, Fabian Tingelhoff received funding from the Konrad-Adenauer-Foundation (KAS). Funding Open access funding provided by University of St.Gallen. Data Availability All data supporting the findings of this study are available within the paper and its Appendix. Any further data are available from the corresponding author upon reasonable request. Declarations Conflict of Interest The authors declare that they had no conflict of interest when constructing this manuscript. 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Proceedings of the 2017 ACM/IEEE international conference on human-robot interaction. Yoo, K., Welden, R., Hewett, K., & Haenlein, M. (2023). The merchants of meta: A research agenda to understand the future of retailing in the metaverse. Journal of Retailing, 99(2), 173–192. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Fabian Tingelhoff is a PhD candidate and research associate at the University of St. Gallen. In his PhD, he investigates platform ecosystems in the context of virtual environments. His research focuses on organizational venturing in immersive platforms, value creation potentials in the metaverse, and organizational co-evolution in platform ecosystems. Throughout his PhD, he received the merit-based doctoral 486 Information Systems Frontiers (2025) 27:467–486 scholarship of the renowned German-based Konrad-Adenauer-Stiftung (KAS) Raphael Schultheiss holds a Master’s degree in General Management from the University of St. Gallen. During his studies he worked for multiple real estate companies and examined the possibilities and risks of tokenization in the real estate industry. For his master thesis he conducted research in the field of organizational value creation in the context of the metaverse Sofia Schöbel (Ph.D.) is an assistant professor in information systems at the University of Osnabrück. She has written her PhD about gamification in digital learning from the University of Kassel. Her research focuses on persuasive system design, designing smart personal assistants, digital transformation of services, and the design of interactive processes in digital learning. Her research has been published in different outlets such as the European Journal of Information Systems, Communications of the AIS and in leading information systems conferences such as ICIS or ECIS Jan Marco Leimeister (Ph.D.) is Full Professor and Director of the Research Center for Information System Design (ITeG) at the University of Kassel, Germany. He is furthermore Full Professor and Director at the Institute of Information Systems and Digital Business, University of St. Gallen, Switzerland. His research covers Digital Business, Digital Transformation, Service Engineering and Service Management, Crowdsourcing, Digital Work, Collaboration Engineering and IT Innovation Management. He runs research groups and projects that are funded by the European Union, German Ministries, DFG, various foundations, or industry partners. Prof. Leimeister teaches in several Executive Programs. He is a co-founder of several companies and serves as board member, consultant, coach and speaker for numerous companies