Designing incentive systems for participation in digital ecosystems—An integrated framework

Mihale-Wilson, Cristina; Carl, K. Valerie Article — Published Version Designing incentive systems for participation in digital ecosystems—An integrated framework Electronic Markets Provided in Cooperation with: Springer Nature Suggested Citation: Mihale-Wilson, Cristina; Carl, K. Valerie (2024) : Designing incentive systems for participation in digital ecosystems—An integrated framework, Electronic Markets, ISSN 1422-8890, Springer, Berlin, Heidelberg, Vol. 34, Iss. 1, https://doi.org/10.1007/s12525-024-00703-5 This Version is available at: https://hdl.handle.net/10419/315793 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. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. 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) 34:16 https://doi.org/10.1007/s12525-024-00703-5 RESEARCH PAPER Designing incentive systems forparticipation indigital ecosystems—An integrated framework CristinaMihale‑Wilson1 · K.ValerieCarl1 Received: 15 February 2023 / Accepted: 12 February 2024 / Published online: 28 February 2024 © The Author(s) 2024 Abstract Digital ecosystems are a highly relevant phenomenon in contemporary practice, offering unprecedented value creation opportunities for both companies and consumers. However, the success of these ecosystems hinges on their ability to establish the appropriate incentive systems that attract and engage diverse actors. Following the notion that setting “the right” incentives is essential for forming and growing digital ecosystems, this article presents an integrated framework that supports scholars and practitioners in identifying and orchestrating incentives into powerful incentive systems that encourage active participation and engagement. This framework emphasizes the importance of understanding how individuals and groups are motivated to engage in the ecosystem to incentivize them effectively. To demonstrate its applicability and value, we show its application in the context of an emergent digital ecosystem within the Smart Living domain. Keywords Incentive system design· Digital ecosystem participation· Company incentives· Consumer incentives· Incentive orchestration JEL classification 03/039 Introduction In today’s highly competitive business environment, digital ecosystems (DEs) are a pathway to success and growth (Subramaniam etal., 2019). As digital counterparts of natural ecosystems, digital ecosystems are self-organizing, robust, and scalable environments where various species (i.e., hardware, software, platforms, consumers, and companies) interact with each other to solve complex problems (Hein etal., 2020; Teece, 2018). DEs can also be described as dynamic multi-player environments where value co-creation relies heavily on exchanging data and services between different actors (Hein etal., 2020; Wang, 2021). Such actors might include technology and platform providers, operators of digital artifacts, vendors of technical devices, and consumers (Bonina etal., 2021; B. Tan etal., 2015; F. Tan etal., 2016). In this paper, we introduce a framework designed to facilitate the successful formation of DEs. After all, DEs do not just emerge. Instead, they are the results of one or various key actors’ efforts (usually one or more companies) that team up to create more value than they could on their own (Jacobides etal., 2018). As practice shows, efforts and success to develop digital ecosystems vary significantly. Within this literature, scholars document that one crucial success factor for DEs lies in finding the suitable set of incentives— i.e., incentives and incentives systems—to ensure that ecosystem participation and user enrollment are self-perpetuating (Jacobides, 2019; Lettner etal., 2022; Valdez-De-Leon, 2019). Incentives are the motivation or reason for someone to take a particular action. Incentive systems, however, are a structured and coordinated set of incentives that work together to drive a specific behavior or set of behaviors over time (Deci etal., 1999). In the context of DEs, incentive systems encompass various stimuli, mechanisms, and rewards designed to motivate a target group to join and actively use Responsible Editor: Ulrike E. Lechner. * Cristina Mihale-Wilson [email protected]t.de K. Valerie Carl [email protected] 1 Information Systems andInformation Management, Goethe University Frankfurt, Theodor-W.-Adorno-Platz 4, 60323FrankfurtAmMain, Germany Electronic Markets (2024) 34:1616 Page 2 of 32 and engage in activities within the ecosystem. Although prior literature emphasizes the importance of setting the “right” incentives for ensuring participation and engagement within the ecosystem (e.g., Adner, 2017; L. Chen etal., 2022; Valdez-De-Leon, 2019), it remains silent on how to identify what really incentivizes various actor groups and how to orchestrate potentially conflicting or complementary incentives into a set of stimuli able to maximally scale DE participation (Ojala & Lyytinen, 2022; Parker etal., 2017; Pellizzoni etal., 2019). Surprisingly, although both activities are non-trivial and critical for achieving effective participation and engagement within digital ecosystems, they remain chronically under-researched. Our work builds on prior related literature (e.g., L. Chen etal., 2022; Kretschmer etal., 2022; Ojala & Lyytinen, 2022) and introduces a framework demonstrating the process of identifying and integrating incentives into a cohesive system designed to attract both organizations and consumers to an emergent DE. Consequently, our work makes two valuable contributions to the scholarly literature on the topic. Firstly, our work thoroughly compiles goals and needs crucial for identifying matching incentives to encourage targeted groups to join a DE. This compilation serves as a foundation for discerning DE incentives. Current literature tends to emphasize various incentives like financial rewards, recognition, status, and access to resources (L. Chen etal., 2022; Jacobides etal., 2018), without consistently highlighting the importance of aligning these incentives to goals for optimal motivation. While prior literature offers some examples of stimuli, there is room in the current research landscape for a more structured and comprehensive compilation of needs and goals that can enhance the effectiveness of incentives on their recipients. Our work offers insights into orchestrating incentives for various actor groups, ensuring a cohesive incentive system. While certain incentives might resonate with specific groups, integrating them into a larger DE framework can sometimes dilute their efficacy. By strategically coordinating these stimuli and considering their potential interactions, we aim to optimize the desired DE participation outcomes, focusing on the collective impact of combined stimuli on their target audience. To date, most studies treat incentives as isolated entities, overlooking potential conflicts or synergies between them. In our work, we account for the fact that stimuli might influence each other’s effects (i.e., can be complementary, conflicting, or unrelated) and thus lead to less optimal outcomes in terms of participation. In the subsequent sections of this article, we introduce an integrated framework designed to help orchestrate incentives for enhancing participation in DEs. We begin with a discussion on the theoretical underpinnings behind our proposed framework, followed by an overview of our methodology and the key elements of the framework. After presenting the main concepts and elements of the framework, we illustrate its application in the context of an emergent DE in the Smart Living domain. Finally, we conclude with a discussion of the advantages and limitations of the proposed framework, as well as potential paths for future research. Theoretical background This work relates to various literature streams but particularly to digital ecosystems, incentive system design, organizational strategic management, and consumer technology adoption. In this section, we discuss theories and prior work from the relevant individual streams of literature. We start by elucidating on the digital ecosystems and ecosystem design. Digital ecosystems andecosystem design The information systems (IS) and organization studies (OS) disciplines present various definitions and types of “digital ecosystems” (Bonina etal., 2021; Hein etal., 2020; Isckia etal., 2018; Nambisan etal., 2019; Wang, 2021). Digital innovation ecosystems (Wang, 2021), Internet of things (IoT) ecosystems (Leminen etal., 2012), and platform ecosystems (Parker etal., 2017; Schreieck etal., 2016) are only a few exemplary types of ecosystems mentioned by prior research. These ecosystems, while similar in that ecosystems, represent a community collaborating toward a shared objective (Hein etal., 2020) and exhibit structural and operational differences. Innovation ecosystems, for instance, refer to a community that fosters and facilitates new and disruptive technologies (Wang, 2021). In comparison, the Internet of things (IoT) ecosystems revolve around smart sensors and devices that share data to perform a wide range of (automated) tasks (Mihale-Wilson etal.,2019). Another prominent type of ecosystem mentioned in the literature— platform ecosystems—refers to a community of participants who form around a platform (Parker etal., 2017; Schreieck etal., 2016). In contrast to these ecosystem examples, in this work, we understand DEs in more broad terms—i.e., as dynamic multi-agent environments where agnostic but interconnected species (i.e., technology, digital services, products and platforms, organizations, individual consumers) work loosely together to achieve individual and shared goals (Barykin etal., 2020; Jacobides etal., 2018; ValdezDe-Leon, 2019). This distinction between the various types of ecosystems is particularly important since it reveals differences in scope, emergence, and the set of potentially suitable incentives (Rochet & Tirole, 2003). For a better understanding, we elaborate on the structural differences between platform ecosystems and DEs. Electronic Markets (2024) 34:16 Page 3 of 32 16 In the context of platform ecosystems, the cornerstone of the ecosystem is the platform itself or a few interconnected platforms. However, the platform(s) is (are) a crucial piece for the value creation process, and the community builds around the platform owner(s) (Hein etal., 2020). This(these) platform(s) facilitate interactions between various participants, often bridging providers and consumers. The platform owner has the power to set the rules of governance for all interactions between actors and benefits from the transactions linked to their platform (Hein etal., 2020). The platform owner drives the platform ecosystem’s inception and keeps a relatively high degree of control as the platform ecosystem evolves and matures. In fact, the platform owner plays a central role in shaping the evolution of the platform ecosystem by curating the product and service assortment as well as the participating providers (Gawer & Cusumano, 2014). By contrast, in the context of a broader DE concept, the foundational elements of the ecosystem encompass multiple species (e.g., digital tools, platforms, technologies, services, organizations, and consumers) that coexist and benefit from one another (Jacobides etal., 2018). The community builds modularly around delivering novel value or creating new opportunities that can be platformor technology-agnostic. Modularity refers to the fact that once the ecosystem has formed, no single dominant or governing entity regulates collaborations and ties between actors (Jacobides etal., 2018). Instead, within DEs, participants operate independently and interact with each other based on their own goals and objectives. Although collaboration between actors in DEs is more loose, dynamic, and uncontrollable than in platform ecosystems, the formation of these collaborations and by extension the formation of a DE can be purposefully initiated (Barykin etal., 2020). In this work, we refer to the strategic establishment of the necessary cornerstones to form an ecosystem as DE design. The DE designers are the companies striving for the formation of the DE. Hence, the formation of an ecosystem starts with the DE designers’ vision and ambition to push for establishing not only a technical infrastructure on which the DE can form but also suitable rules for governing the interactions between the actors in the DE (Floetgen etal., 2022; Hein etal., 2020). The technical infrastructure and suitable governance are equally important for DE’s success (L. Chen etal., 2022; Teece, 2017). Because DEs rely heavily on autonomous agents that contribute to the ecosystems’ value propositions, it is crucial to implement governance mechanisms that enable and coordinate the interactions between actors (e.g., the flow of resources) without losing the advantages of decentralized decisions (L. Chen etal., 2022; Teece, 2017). From an organizational perspective, governance mechanisms can be classified into incentive and control mechanisms (L. Chen etal., 2022). Control mechanisms rely on coercion to ensure that the actors in DEs behave in ways that align with the goals of the DE (e.g., monitoring, sanctions, and penalties for non-compliance). In contrast, incentives rely on motivation and refer to stimuli or benefits offered to actor groups to encourage them to participate in and contribute to the ecosystem voluntarily (L. Chen etal., 2022). Incentives andtheir recipients By definition, an incentive refers to the stimuli or benefit that motivates individuals or entities to take specific actions or behave in a certain way. For incentives to effectively influence their targets, they must resonate with the target’s motivations and self-interest (Adner, 2017; Weber, 2006). Such stimuli spark action by catering to a particular need or objective of the targeted group, whether a consumer or a company. If these groups discern that the incentive aligns with their objectives—essentially, that it resonates with their core goals—they will respond positively (Weber, 2006). On the flip side, a misaligned incentive will not produce the desired outcome. This underscores the idea that incentives are designed to sway entities with agency and defined aspirations. Put simply, the beneficiaries of incentives must have the capacity for intent, ambition, and awareness to identify and pursue specific goals. In terms of agency, we note that entities like organizations, consumers, and regulatory bodies possess agency in a digital ecosystem, making choices based on their objectives. In contrast, species of a more technological nature (e.g., technology infrastructure, digital services, platforms) lack agency, meaning they operate without conscious and intentional decision-making capacity. Acknowledging this distinction, we deduce that only those with agency within the digital ecosystem (i.e., organizations, consumers, and regulatory bodies) can indeed be influenced by incentives. While incentives must be strategically aligned with these agents’ goals and behaviors, they must be tailored to the distinct nature of the ecosystem in question. As we will briefly discuss in the following, structural differences between various types of ecosystems (e.g., platform ecosystems versus digital ecosystems in the broader sense) require broadly different incentives. In essence, platform ecosystems promote a degree of centralization (because they revolve around one (or a few) primary platform(s)) (Gawer & Cusumano, 2014), whereas digital ecosystems emphasize decentralization, modularity, and broad interconnectivity (Jacobides etal., 2018). Consequently, the incentives for participation in these two environments will be tailored to these unique ecosystem characteristics and will differ in scale and focus, nature of engagement, or potential benefits. In terms of scale and focus, platform ecosystem incentives are designed to Electronic Markets (2024) 34:1616 Page 4 of 32 encourage the development of products and services for the focal platform(s) (e.g., through platform-specific developer tools and sharing models) (Gawer & Cusumano, 2014). In contrast, digital ecosystem incentives aim to grow the entire ecosystem (e.g., by educating developers about multiple tools and technologies in the ecosystem). Regarding the nature of engagement, in platform ecosystems, incentives primarily focus on facilitating transactions and direct interactions with the platform (e.g., by providing sellers with analytics tools or discounted transaction fees) (Rietveld etal., 2019). On the contrary, in digital ecosystems, incentives focus more on collaboration (Camarinha-Matos & Abreu, 2007), knowledge sharing (Cresswell etal., 2021), and developing complementary products and services that are agnostic to one technology or platform (Briscoe etal., 2011). Accordingly, in digital ecosystems, the incentives seek to form and establish communities (Immonen etal., 2014), promote interoperability among different platforms, or establish standards that help different ecosystem components work together seamlessly (Hodapp & Hanelt, 2022). Regarding potential benefits and monetization strategies, incentives in platform ecosystems are transactional and will include reduced fees, access to premium features, or specific revenue-sharing agreements. Platform ecosystems also often have a built-in monetization model (e.g., commission-based, subscription fees) with the platform ecosystem provider being a central beneficiary of the platforms’ transactions (Rochet & Tirole, 2003). In contrast, since digital ecosystems are more modular, with no entity exerting too much control (Jacobides etal., 2018), DEs exhibit multiple monetization tactics that are likely to vary across different tools and services. Additionally, incentives in the digital ecosystem are more geared toward long-term objectives and encompass strategic initiatives, partnerships, or investments that enhance the ecosystem’s overall infrastructure, knowledge base, or collaborative potential. Recognizing these distinctions is crucial when determining the optimal incentives to encourage participation in either platform ecosystems or DEs. Furthermore, it is essential to appreciate that individual incentives are components of broader incentive systems which combine and reconcile various incentives into a structure that aligns the interests of various DE groups (Davis, 1993; Kretschmer etal., 2022). Incentive systems The design of incentive systems involves considering factors such as the target audience, desired outcomes, and the overall objectives of the system (Kopalle etal., 2020; Kretschmer etal., 2022; Y. Sun etal., 2022; Valdez-De-Leon, 2019). This is necessary for mainly two reasons: Firstly, incentives are not isolated entities that never influence each other. Secondly, incentive systems are not static, one-size-fits-all solutions. Incentives are notisolated entities Depending on the target audience, incentives might be independent of each other, complementary, or even contradictory (Adner, 2017; Kretschmer etal., 2022). Complementary incentives are those that align and reinforce each other, while contradictory incentives represent conflicting or opposing ones that can lead to conflicting behaviors between actor groups. In the digital economy, a classic example of conflicting incentives can be observed between tech companies and consumers around data privacy. On the one hand, consumers desire and often demand products and services that prioritize their privacy, wishing to safeguard their personal information and limit data collection (Carl etal., 2023;Mihale-Wilson etal.,2021). This incentive is especially strong due to increasing awareness about data breaches and misuse. On the other hand, many tech companies are incentivized to collect as much user data as possible. This data not only informs their product development and enhances user experience but also becomes a significant revenue source when monetized, either through targeted advertising or by selling to third parties (Mihale-Wilson etal.,2021). Such conflicting incentives can pose challenges in achieving a harmonious digital ecosystem, as they push the entities involved in different directions—consumers toward heightened data protection and businesses toward expansive data usage. Viewing an incentive system as the aggregate of all the incentives intentionally put forth to influence the behavior of various groups and prompt a specific desired action, it is essential to distinguish between conflicting goals and conflicting incentives. While divergent goals between actor groups can foster innovation and yield new value propositions, conflicting incentives—those that induce behaviors that neutralize each other or collectively lead to undesired outcomes for the ecosystem’s overall participation—should be approached with caution. Incentive systems are notstatic The dynamic nature of DEs (Adner, 2017) implies that incentive systems are not static, one-size-fits-all solutions. Rather, incentive systems must be flexible and able to evolve with the DE to fit the ecosystems’ current life cycle phase (Panico & Cennamo, 2022). Under the premise that DEs do not just “appear” but develop and evolve over time, literature on DEs distinguishes four life cycle phases: inception, growth, maturity, and renewal (Isckia etal., 2018; Teece, 2018). Each life cycle phase is linked Electronic Markets (2024) 34:16 Page 5 of 32 16 to slightly different challenges, the incentive system needs to be aligned with (Panico & Cennamo, 2022). During inception, for instance, participants must imagine and understand the new opportunities that the DE affords and view the new ecosystem as appealing (Isckia etal., 2018). Hence, at this initial stage, DE designers might want to focus on attracting industry leaders and early adopters (Khanagha etal., 2022) who can then serve as advocates, demonstrating the DE’s innovativeness and potential to other organizations. During growth, attracting outsiders and broadening the user base are vital to achieving a critical mass of active participants (Isckia etal., 2018; Teece, 2017). Hence, during the growth stage, DE designers’ focus might be on the exponential growth of the DE’s participant base (Sebastian etal., 2020). Once the ecosystem possesses the critical mass to unlock its full potential, the DE reaches maturity, and participants are now starting to explore business opportunities within other ecosystems (Isckia etal., 2018; Teece, 2017). If DE designers do not counter the transition of ecosystem partners and value to competing ecosystems, the DE will shrink and eventually disappear. Hence, during this post-maturity phase, DE designers might seek ways to rejuvenate the ecosystem (Isckia etal., 2018; Teece, 2017). Therefore, at this juncture, DE designers might seek to attract new and highly innovative actors that can help the ecosystem penetrate other industries or find new and innovative ways for value creation. Given the varying strategic emphases that accompany each stage of an ecosystem’s life cycle, it becomes imperative to re-align incentives within the system when deemed necessary (X. Sun & Zhang, 2021). To sum up, designing incentive systems for DE participation requires designers to comprehensively understand the expectations, goals, and needs of the target audience (actor groups) when joining and participating in the DE. Furthermore, designers must have access to suitable strategies and mechanisms that allow them to orchestrate incentives into an incentive system—i.e., one able to attract companies and consumers alike to join and participate in the ecosystem. It is important to note that companies join and participate in DEs primarily by playing an active role on the supply side of the ecosystem (e.g., by co-developing products and services). In contrast, consumers are typically on the demand side of the ecosystem (e.g., by adopting and using the products and services provided in the ecosystem) (Hein etal., 2020). Thus, we can draw on the literature stream on organizational strategic management to structure and explore companies’ expectations and goals when deciding to join DE. To understand consumers’ needs and goals when adopting and using the products and services provided in the ecosystem, we can draw on the technology adoption literature. Below, we discuss both streams of literature in more detail. Organizational strategic management literature In our case, the organizational strategic management literature provides a framework to analyze how companies plan and make strategic decisions, such as the decision to join a DE. Companies often join DEs to achieve specific business goals, such as expanding market reach or leveraging new technologies for innovation. The organizational strategic management literature provides the necessary insights and tools to identify these goals and how they align with the broader strategic objectives of the company. As previously noted, the decision to participate in DEs, akin to other strategic company choices, depends on the anticipated value from the ecosystem. However, just as quantifying the value and impact of IT in organizations is complex, so is assessing the precise benefits of DE participation. Delving deeper into this argument, existing literature indicates that technology and IS investments can yield tangible and intangible returns, which might only manifest in the midto long-term. Directly correlating these investments with organizational profits remains difficult, both in retrospective and, even more so, in predictive evaluations (Rosati etal., 2017; Tallon & Kraemer, 2007; Tallon etal., 2020). Committing to a digital ecosystem can parallel IT investment decisions, for instance, in terms of risks, long-term commitment, and potential need for alignment with the organization’s broader strategic goals. Also, similar to IT investment decisions, the choice to enter a DE potentially yields tangible and intangible results, whose realization may vary over time, making their upfront quantification notably challenging. Motivated by the challenge of capturing less tangible benefits such as improved customer service (Volberda etal., 2021) or new collaborations and complementarities that would not form outside the DE (Jacobides, 2019), scholars (e.g., Martinsons etal., 1999; Milis & Mercken, 2004; Shen etal., 2022) suggest using the well-established balanced scorecard (BSC). Originally developed by Kaplan and Norton (1992), the BSC aims to complement the financial perspective on business performance with the non-financial perspective. Applied to technology projects and decisions, the BSC is also useful for developing metrics reflecting the tangible and intangible benefits of technology implementations (Martinsons etal., 1999; Shen etal., 2022). Because “the metrics used in a balanced scorecard framework are aligned to the company’s strategy and business aims”(Milis & Mercken, 2004, p. 94), the BSC model allows managers to adopt a comprehensive view on technology investments while also serving as a map for navigating the strategic goals of the company (Milis & Mercken, 2004). In particular, the BSC allows organizations to measure their intangible assets, such as customer relationships, innovative products, services, technology, knowledge, and the organizational structures that provide a company with a competitive advantage Electronic Markets (2024) 34:1616 Page 6 of 32 (R. S. Kaplan & Norton, 1992, 2001). Accordingly, the BSC has practical relevance for strategy and focuses on financial and non-financial aspects, short-term and long-term strategy, and internal and external business measures (Wu, 2012). Specifically, the BSC takes on four perspectives: a financial perspective, a customer (or market) perspective, an internal process perspective, and a learning and growth perspective. Following Kaplan and Norton (1992, 2001), from a financial perspective, companies focus on their economic and financial health (e.g., profitability and value creation of the organization) (Fischer & Himme, 2017; Kliestik etal. 2020). From a customer perspective, companies seek to understand their market performance regarding their customer relationships (e.g., customer satisfaction, retention, churn) and market share (Kamalaldin etal., 2020; Krizanova etal., 2019). From an internal process perspective, companies seek to understand the efficiency and effectiveness of their operations and processes (H. Chen etal., 2021). Ultimately, the learning and growth perspective encompasses factors crucial for fostering continuous learning, improvement, and adaptability within the company—e.g., employee training and development, knowledge management, innovativeness, and organizational culture (Kimiloglu etal., 2017). We use the BSC model as a structured blueprint for exploring companies’ goals and expectations when deciding to join DE. To now turn to the consumers’ side and delve into the goals of this group when deciding to join a DE (specifically, to use the offerings of the DE rather than alternative options), we draw on the technology adoption literature. Consulting the technology adoption literature, particularly its theories, is fitting, as these theories have traditionally examined the factors influencing individuals’ decisions to accept or reject new technologies and products. Technology adoption literature Research on technology adoption is one of the most mature streams in IS literature (Ho etal., 2020). It entails theories concerning individuals’ preand post-adoption behaviors (Mishra etal., 2023). While pre-adoption theories focus on explaining individuals’ intentions to adopt, post-adoption behaviors focus on what drives usage continuance. Given that we seek to explore and structure both—consumers’ needs and goals when initially joining the DE but also their needs and goals in relation to continuous participation in DE (i.e., continuous use of the ecosystem products and services)—our work relates to both streams of literature within this corpus of research. Within the technology pre-adoption literature, we mainly refer to two of the wellestablished adoption models: Davis’ (1989) technology adoption model (TAM) and Venkatesh etal.’s (2003) unified theory of acceptance and use of technology (UTAUT). According to TAM, technology acceptance is mainly driven by individuals’ attitudes toward the technology, which in turn is shaped by the individuals’ perception of the technology’s usefulness (PU) and ease of use (PEoU) (Davis, 1989; Venkatesh etal., 2003). PU describes to which degree individuals think a particular technology can fulfill predefined goals. PEoU reflects individuals’ perception of how effortless a technology’s usage might be (Davis, 1989; Venkatesh etal., 2003). TAM has served as a foundation for various other research models for technology adoption. The UTAUT, for instance, posits that technology acceptance and use are determined by four constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh etal., 2003). While performance expectancy refers to individuals’ beliefs that using the target technology will advance their goals (i.e., it is equivalent to PU), effort expectancy represents the same as PEoU. However, social influences (also referred to as “subjective norms” (Brown etal., 2010)) relate to individuals’ beliefs that adopting technology will enhance their status within a relevant peer group (Maruping etal., 2017). Extant literature corroborates the link between important actors and individuals’ technology adoption intention (Brown etal., 2010). This link is compelling for novices (i.e., individuals with no prior experience with the target technology) and within the work-related context when important external others (e.g., supervisors, colleagues) can exert some sort of pressure on the potential adoption candidate (Maruping etal., 2017). Finally, facilitating conditions refer to objective factors that make technology use possible. Such factors include technical and organizational support (Venkatesh etal., 2003). Within the technology post-adoption literature, our work relates to Bhattacherjee’s (2001) expectation-confirmation model (ECM) and Liao etal.’s technology continuance theory (TCT). In both models, individuals’ continued technology use is strongly driven by consumers’ satisfaction with the technology, which again depends on factors such as PU and PEoU (Liao etal., 2009). With PU and PEoU influencing both the technology preand post-adoption, while other factors might influence only consumers’ satisfaction with the technology (post-adoption), we suggest distinguishing between “first-tier” drivers of adoption and use (e.g., PU, PEoU) and additional “second-tier” drivers of continuous use (i.e., any factors that can increase satisfaction in the postadoption phase). We combine the previously discussed streams of literature to develop a framework for designing incentive systems for DE participation. Based on the literature on digital ecosystems and ecosystem design, please remember that DEs are dynamic multi-agent environments where diverse but interconnected species—ranging from technology, digital services, products, and platforms to organizations and individual consumers—operate in a loosely coupled manner. Their interaction dynamics aim to realize unique and collective Electronic Markets (2024) 34:16 Page 7 of 32 16 goals, reflecting the intricate and often symbiotic relationships within these digital realms. The literature on incentives provides the foundational rationale for our framework. Incentives are stimuli crafted to motivate specific actions or behaviors. These incentives work only with entities that possess the necessary agency and conscious decision-making ability to be swayed toward a particular behavior. In the context of DEs, not all entities have the necessary agency; our study recognizes that besides regulatory or governmental bodies, only the consumer and organizational species possess agency. Building on this, we delve into incentive systems literature, emphasizing that incentives do not operate in isolation. They are components of intricate systems where individual incentives interact and potentially influence each other. An effective incentive system, therefore, necessitates a careful orchestration of these incentives, ensuring that their collective influence yields the most desirable outcomes in terms of ecosystem participation. Yet it is important to acknowledge that organizations and consumers are two different species that are driven by a different set of goals. From the realm of organizational strategic management, we adopt insights from the BSC model. Given its structured approach to exploring and articulating corporate objectives and aspirations, the BSC model serves as our guiding blueprint for exploring companies’ goals and expectations when deciding to join DE. Lastly, our framework is also informed by the technology adoption literature, which is pivotal because technology acceptance theories shed light on the nuances that drive individuals’ decisions around embracing the offerings of the DE over alternative offerings. By blending the insights from all these research dimensions, our framework aims to offer a comprehensive, nuanced, and actionable guide for devising effective incentive systems tailored for the DE landscape. Having laid out this foundation, let us transition into the structured process through which we developed the framework. Methodology fordeveloping theframework For the development of the framework, we follow various scholars’ insights on theorizing and developing (integrated) IS frameworks (e.g., Baird & Maruping, 2021; Burton-Jones & Volkoff, 2017; Hassan etal., 2022; Maxwell, 2012; Miles & Huberman, 1994). Specifically, we design our framework in a multi-step approach (see Fig.1). First, we conducted comprehensive literature reviews (step 1, Fig.1): Initially, we performed a systematic literature review approach employing a keyworddriven search (consumers: “digital AND (preferenc* OR nee*)”; companies: “compan* AND “strategic goals””). For consumers, we focused on the best-ranked publications in IS research (VHB A + , A, and B), and for companies, we conducted a broader search, capturing the online library EBSCO. The search led to 1032 (consumers) and 1336 (companies) results. In the first step of assessing the title and abstract, we retained 112 (consumers) and 386 (companies) publications. Facing high exclusion rates in both steps of the paper analysis due to the required transferability of results to the context of DEs, we received 10 relevant publications for consumers and 9 for companies. Thus, we performed more explorative further searches employing multiple backward and forward search steps as well as more explorative search on received goals and needs and regularly updated the search, leading to a total of 34 publications regarding consumers’ needs and 32 covering companies’ goals. To sum up, the aim of this literature review was to capture the current state of related work on (i) companies’ expectations and goals concerning DE participation, (ii) consumers’ expectations and needs concerning DE participation, and (iii) potential set of strategies and mechanisms to orchestrate individual incentives into incentive systems. The literature reviews capture the current state of related work and allow us, in a second step, to aggregate knowledge from prior research efforts into one comprehensive framework (Baird & Maruping, 2021; Okoli & Schabram, 2010). We follow the established propositions by Kitchenham etal. (2009), which ensure that synthesizing the extracted research findings informs and guides practitioners in a structured and comprehensive manner (Kitchenham etal., 2009; Snyder, 2019). Besides, the literature reviews also serve as a basis to further aggregate and cluster previous research based on predefined criteria, informing the conceptualization of a (new) theory construct (Paré etal., 2015; Snyder, 2019). In a second step, the insights from prior literature were aggregated and synthesized through a DE-specific lens (step 2, Fig.1), however interacting with the first step (literature review) to adapt the process accordingly. The Fig. 1 Methodological approach to the framework development Electronic Markets (2024) 34:1616 Page 8 of 32 DE-specific lens enables us to account for idiosyncratic characteristics of DEs, such as the collaborative value creation in a coopetitive environment1 (Lettner etal., 2022), particularly for DEs. For instance, in DEs, value creation occurs through collaboration (i.e., via common business practices, interoperability between products and services, shared data spaces, and knowledge transfer). Hence, the organizational goals concerning DE participation might not include only goals such as improving the company cost structure (R. S. Kaplan etal. 2004; Wu, 2012) but also goals such as creating new value (i.e., products and services) that otherwise would not be possible to develop. At the same time, organizational goals that might be important in other contexts (e.g., financial transparency (S. Lee etal., 2021)) might play no significant role in the context of DE participation. We compiled a preliminary framework based on the aggregated insights from prior literature (step 3, Fig.1). To this end, we use the BSC to structure and document companies’ expectations and goals concerning DE participation. Analogously, we structure and document consumers’ expectations and goals concerning DE participation by distinguishing between first-tier (must-have expectations and goals to join and continuously participate in the DE) and second-tier factors (optional factors that can increase consumers’ satisfaction with the offerings of the DE and thus support continuous participation). The preliminary version of the framework was validated in two workshops with domain experts working on a joint research project (step 4, Fig.1). The research project aims to design and develop the necessary components for a DE in the Smart Living domain. The workshops were conducted with six domain experts with different backgrounds and research foci: Three participants represented the R&D departments of leading global suppliers of smart home, mobility, and consumer goods technology. One participant represented the association of electric and consumer goods. Another participant represented an SME supplying smart home solutions. Finally, two participants work for research entities researching digital (services and consumption) ecosystems. In the first workshop, the experts discussed and chose the most relevant companies’ organizational goals in relation to DE participation. In the second workshop, the discussion revolved around the most critical consumer needs concerning DE participation. Both workshops resulted in a curated list of company goals and consumer needs most relevant concerning DE participation. Finally, we combined all findings in one framework to design DE participation incentive systems. Framework fordesigning incentive systems forDEs Figure2 visualizes the proposed design framework. It consists of three building blocks (i.e., identify incentives, combine incentives into a system, and incentive system realignment) and three key elements (i.e., (i) company goals, (ii) consumer needs, and (iii) orchestration mechanisms). Subsequently, we discuss each building block individually, as they indicate how to use our framework. First building block: Identify incentives The first building block of the framework suggests identifying the incentives for each of the targeted actor groups— i.e., in our context, companies and consumers—by analyzing these actors’ expectations, goals, and needs when joining and participating in DEs. Only once designers document companies’ and consumers’ needs and expectations in relation to DE participation can they derive incentives that will be effective for each of the individual target groups. For these activities, designers can use a range of methods: Expert interviews and Delphi studies, for instance, are suitable for documenting companies’ goals and potentially deriving applicable incentives for companies. Analogously, expert interviews, focus groups, or consumer surveys are helpful to gather consumers’ needs and derive suitable incentives for this group. To support these activities, our framework offers concrete support by providing a comprehensive set of goals and needs that companies and consumers have concerning their DE participation decisions. As mentioned in the previous section (the “Methodology” section for developing the framework), these company and consumer goals were derived from prior literature. We first present the (i) company goals along the four perspectives proposed by the BSC. From a financial perspective, companies focus on economic and financial status. There are various ways to increase the value of a company through strategic actions. Common measures are, for example, the development of a new business field, the acquisition of a company, or a strategic realignment. Short-term profits should be subordinated to long-term successes (Rappaport, 2006). Thereby, investors closely monitor revenue, profitability, and expected cash flows (R. S. Kaplan & Norton, 1992). Additionally, investors also monitor the decisions about adopting new technological developments. In general, DE participation can directly or indirectly improve various key financial performance indicators. For instance, DEs require a certain degree of homogeneity 1 Coopetition is a business strategy in which companies work together to achieve a common goal while still competing against each other. This approach combines the benefits of cooperation and competition, allowing companies to share resources and knowledge while striving to be the best in their industry. Electronic Markets (2024) 34:16 Page 15 of 32 16 2006). Ultimately, the monitoring and re-tuning mechanism ensures that the incentives system continues to be effective over time. Since DEs are dynamic environments that evolve, incentive systems must be monitored and re-tuned whenever necessary (Panico & Cennamo, 2022). Table1 provides an overview of the discussed orchestration mechanisms and names exemplary methods that can be used to leverage each respective mechanism. For instance, to prioritize incentives for companies (i.e., identify top priority incentives), designers can conduct interviews with companies appertaining to the targeted companies group. Then, if the top priority company and consumer incentives are contradictory, designers can broker between these incentives based on the input from expert workshops and expert interviews. Third building block: Incentive system realignment Following the arguments presented earlier, incentive systems cannot be static and should evolve with the changing conditions of each DE life cycle. As discussed previously, depending on whether the DE is in its inception, growth, maturity, or renewal phase (Isckia etal., 2018), the incentive system must address the respective life cycle challenges. During the inception phase, for instance, DE designers might want to attract industry leaders and early adopters. At later stages, such as the growth phase, DE designers’ focus might be on the exponential growth of DE’s participant base. Similarly, once the first-tier consumer needs are satisfied, incentive systems should consider the second-tier needs most important for the biggest group of consumers that the DE intends to appeal to. Accordingly, it is essential to monitor the goals of the incentive system and, if necessary, re-tune the system to be effective and continuously attract the DE actors it seeks to attract (Panico & Cennamo, 2022). To this end, designers can employ the previously stated orchestration mechanisms to both monitor and re-tune existing incentive systems. We detail the process of this “realignment” within the system in the Use Case section of this study, where we provide a comprehensive guide on the practical application and expected outcomes of such strategic adjustments. Case study: Applying theframework toaDE inSmart Living To highlight the practical usefulness of our proposed framework, we present its application in a real-world scenario: an emerging digital ecosystem in the Smart Living space. This illustration is underpinned by expert interviews and a survey conducted to assess the robustness and relevance of the framework. Before delving further into the case study, it is pivotal to elaborate on the concept of Smart Living. Advancements in fundamental technologies, such as cloud computing, artificial intelligence, or the Internet of things, gain ever-increasing traction and abet a new generation of digital products and services (Hosseinian-Far etal., 2018; Mihale-Wilson etal., 2022). Along with these advancements, scholars and practitioners expect a significantly growing importance of the Smart Living domain in the upcoming years (Makkonen etal., 2022; Murthy & Madhok, 2021). In essence, the Smart Living concept refers to weaving technology into our daily lives to improve convenience, efficiency, sustainability, and the overall quality of life (Hosseinian-Far etal., 2018; Jiménez etal., 2014). Among others, Smart Living envisions more convenience and higher quality of life by streamlining and automating various tasks to make routine activities more efficient (Bauer etal., 2020). With various daily activities being automated, consumers might have less stress and more free time to do whatever they love (Mihale-Wilson etal., 2017). Besides automation of tasks, Smart Living also envisions that smart services and systems can support consumers to lead healthier (e.g., through monitoring and recommending dietary and sports activities) and more sustainable lifestyles (e.g., through optimized energy consumption and waste reduction) (Bauer etal., 2020; Cimmino etal., 2014; Liu etal., 2019). Although desirable from a welfare and well-being point of view, the materialization of the Smart Living promise requires a high level of interoperability and cooperation between actors (Jiménez etal., 2014). To achieve the cooperation and interoperability needed to materialize the Smart Living concept, European governments have started various initiatives (e.g., the German program SmartLivingNext4) that aim to create DEs that can merge the currently fragmented market and its respective actors. Because Smart Living DEs are only starting to form, such ecosystems are in their inception phase. Thus, their main focus is to attract as many actors (on the supply and demand side) as possible. We can use the proposed framework to identify and orchestrate the most promising incentives for attracting and engaging companies and consumers into a Smart Living DE. The first step in applying the framework is identifying incentives for companies and consumers by analyzing their expectations and goals when joining a Smart Living DE. Identify incentives forcompanies andconsumers We use the set of company goals and consumer needs provided by the framework to identify incentives for companies and consumers. These need to be first prioritized according 4 https:// www. bmwk. de/ Redak tion/ EN/ Press emitt eilun gen/ 2023/ 01/ 20230 103smart livin gnextcallforfundi ngpubli shed. html Electronic Markets (2024) 34:1616 Page 16 of 32 to their importance for the consumers and companies’ target groups. In our example, to reduce complexity and showcase the framework’s application, we first seek to find the most important goals of the first and second most important dimensions of the BSC and the top 3 consumer needs, thus incentives. Due to the broad nature of the assessed company goals and consumer needs, such a prioritization of goals is pivotal for applying the framework. The plethora of goals and needs will be challenging to satisfy simultaneously, indicating the suitability of an initial focus on the most critical needs and goals while possibly being broadened over time. To get a feeling on (i) companies’ rating of the various company goals proposed by the framework, we conducted structured interviews with 27 companies related to the housing or home automation industry. Table2 indicates the industry of the interviewed companies. Furthermore, we note that 15 interview partners represented large companies, 10 represented SMEs, 1 represented a start-up, and another a public entity related to the housing industry. We constructed the sample to capture a wide variety of companies regarding company size, life cycle, and ownership structure to capture a comprehensive assessment of companies’ goals independent of company types. The interviews were conducted online and lasted 40min on average. The interview guide comprised the companies’ goals compiled in Fig.5 (left side). Specifically, the interviewees were asked to rate (1) the importance of the four BSC perspectives (finance, customer, internal process, and learning and growth) and (2) the respective goals by their importance when deciding to participate in a Smart Living DE. The conducted interviews with companies reveal that when deciding on participation in a Smart Living DE, companies are most interested in the customer perspective, followed by the learning and growth perspective. For instance, the Senior Manager for Strategic Innovation at a home automation company, responsible for strategic partnerships and ecosystems, states, “Yes, I think I would first rank that we already take the customer perspective in the first place, because that should always be the starting point, i.e., also the starting point for action. Learning and growth is then perhaps already two that we also want to grow in the market.” The financial perspective ranks third, revealing a key insight about the Smart Living market: Although customer and growth-related goals might, in the end, also reflect positively in financial key performance indicators, companies seek to participate in a Smart Living DE first and foremost to improve customerand Table 2 Overview of interviewed companies concerning their goals with DE participation Industry Home automation 8 Consulting 4 Mechanical engineering 3 Real estate/housing 1 Solution providers (software) 4 Insurance 2 Electrical engineering 2 Other 3 Table 3 Ranking companies’ goals concerning the participation in a Smart Living DE Rank 1st2nd3rd4th Customer perspective Learning and growth perspective Financial perspectiveInternal process perspective 1Value proposition Collaborations Turnover increase Technological innovation 2 Customer loyalty Culture of innovation Market value increase Performance optimization 3Brand reputationDiscovering improvement potential Cost optimization Decision-making 4Customer insights Information management Profit margin Legal compliance 5New customer acquisitionProfessional training Risk management Productivity 6Market shareRisk minimization Return on investment (ROI)Distribution 7AgilityEmployee satisfaction Asset utilizationSocial compliance Note: Rank 1indicates that the item is most important, and rank 7indicates that the item is least important Electronic Markets (2024) 34:16 Page 17 of 32 16 growth-related goals. In this context, the Managing Director of Technology overseeing the development and production of intercom systems and building communication company explains, “we would like the financial perspective to be at one, but that will then come downstream, and we are working to keep it that way.” From a customer perspective, companies seek to join the Smart Living ecosystem to improve their value proposition, customer loyalty, or brand reputation. From a learning and growth perspective, the interviewed companies value the new collaboration opportunities such a DE brings. Companies also seek to establish a strong culture of innovation and learn from others to discover their potential for improvement. Table3 shows the rank of the respective perspectives and the goal importance within those perspectives. Rank 1 shows that the respective goal is, on average, voted to be the most essential and rank 7 the least important when deciding to join a Smart Living DE. Importantly, the ranks do not reflect the topic’s overall importance in other managerial contexts. Case in point, “employee satisfaction” ranks seven, while “collaboration” ranks first within the learning and growth perspective. This indicates that although improving employee satisfaction and new collaboration are essential goals in the overall context of any company, the management does not expect that joining a Smart Living DE will considerably improve its employees’ satisfaction. Instead, it expects that joining a Smart Living DE will enable numerous opportunities for collaborations that otherwise would not have been possible. To maintain a manageable level of complexity, we will concentrate on the highest-ranked company goals from both the consumers’ and learning and growth perspectives (as highlighted with a grey background in Table3). From these company goals, corresponding incentives can be derived. From the consumer perspective, companies seek to improve their value proposition, customer loyalty, and brand reputation. In discussions with domain experts, we determined that matching incentives to address companies’ goal of improving their value proposition within the DE are setting interoperability standards between the components (tools, services, and other products within the DE); defining common data exchange protocols to effortlessly share and evaluate consumer data across different digital touchpoints in the DE; unified user profiles that allow organizations to have a unified view of a customer's interactions across the digital ecosystem can help in tailoring their offerings more effectively; establish a community for open source collaboration, research and development collaborations, and best practice sharing. To address companies’ goal for improved customer loyalty, potential incentives are again data exchange protocols to improve holistic data-driven personalization of products and services; establishing a customer community where they can provide feedback and experiences; ensure interoperability and seamless integration between the products or services from different entities in the ecosystem; promoting a research and development community where shared value propositions are encouraged and materialized jointly; set joint standards for quality assurance and testing, to make sure that the user experience across different products and services are seamless and of high quality. To target companies’ goal to improve their brand image, potential incentives encompass establishing joint Corporate Digital Responsibility standards—i.e., a set of best practices and guidelines about the responsibilities of the organizations when developing digital products and acting in the DE; providing a customer dialogue platform that bundles consumer concerns and feedback; establishing a provider community to share best practices related to brand image, sustainability commitments, and customer education initiatives; providing a conflict resolution mechanism that demonstrates a commitment to fairness and thus can elevate a brand's image; issue transparency reports standards that highlight the brand’s commitments, achievements, challenges, and plans within the ecosystem. From a learning and growth perspective, companies seek to improve collaborations, establish a culture of innovation, and discover improvement potential. Incentives that could target this company goal encompass establishing a community with shared research and development initiatives, best practices, open innovation challenges, and joint venture initiatives; providing collaborative digital tools between the entities on the supply side of the ecosystem; shared prototyping labs for collaborative idea prototyping and testing; shared knowledge management and learning platforms for idea and value proposition documentation, best practices and prototyping. At this stage, we note that although the listed examples of enabling collaborations might not be exhaustive, they depict stimuli aligned with companies’ goals to achieve collaborations and hone their culture of innovation. For completeness and better understanding, we note that a non-aligned incentive would be one that does not speak to the respective goal of improving collaborations. More specifically, an example of a non-aligned incentive would be establishing a B2C marketplace where users can book smart services the Smart Living DE provides. Because a B2C marketplace serves as a distribution channel for ecosystem services, setting up such a marketplace does not generate better and more diverse collaborations between the companies in the ecosystem, nor does it help to develop companies’ innovation culture. Keeping in mind that alignment of the incentives with the respective entity’s needs and goals is essential, we now turn to the investigation of (ii) consumer needs. To this end, we first prioritize the second-tier goals listed in Fig.4. The rationale for focusing only on second-tier needs is that firsttier consumer needs (such as good functionality, easy to use, Electronic Markets (2024) 34:1616 Page 18 of 32 reliability, and compliance with the current data security and privacy regulations) represent so-called “deal breakers” that are non-negotiable. Accordingly, it makes more sense to focus on those second-tier optional consumer needs, which can make a difference and sway consumers toward purchasing DE offerings instead of alternative ones. To identify the top 3s-tier consumer needs, we conducted a best–worst scaling (BWS) study with 663 German individuals between 17 and 87years old (Table4). Best–worst scaling is an established method to elicit individuals’ preferences for various attributes of products and services (Hinz etal. 2015). However, the method can also be applied to elicit consumer preferences and needs in various contexts. In BWS, participants are asked to choose their most and least preferred attribute from a varying set of attributes (Hinz etal. 2015). In the end, the results of the BWS represent the importance of the attributes queried. Because the topic of DE might be abstract and unknown to individuals, we designed and implemented a BWS study to create a ranking of the consumers’ secondary needs concerning ubiquitous and interoperable Smart Living solutions in the form of a virtual digital assistant that assists their user in all kinds of daily tasks. Table5 illustrates the importance of second-tier consumer needs when individuals decide to consume and engage with a Smart Living DE. First, it shows that data safety and security exceeding legal requirements are the most important factors when choosing to consume products and services in a Smart Living DE. Second, individuals attach great importance to product safety and liability, followed by transparency and technological literacy (needs with a grey background in Table5). Again, based on domain experts’ opinions, we can derive incentives matching the top-ranked consumer needs. For instance, to cater to consumers’ desire for data privacy beyond the legal requirements and information transparency following incentives might apply, establish clear data governance—i.e., explicit policies about how data is stored, used, shared, and eventually deleted, providing users clarity on their data lifecycle; end-to-end encryption when data is transferred; establish the data minimization principle where products and services and gather only the vital data; data anonymization when storing and processing data; usage of open source security standards that are tested by the open community for vulnerabilities; regular 3rd party security audits and certifications that testify that the ecosystem’s data privacy and security measures are up-to-date; transparent data breaches and usage reports. Ultimately, it is pivotal to contribute to consumers’ education regarding DE’s privacy and security measures and how to optimally use the Table 4 Demographic characteristics of the study participants surveyed with regard to their needs concerning DE participation Demographics Gender Male 55.51% Female 44.49% Age < 18 0.15% 18–24 3.32% 25–34 14.03% 35–44 21.42% 45–54 22.17% 55–64 15.08% 65–74 19.16% > 75 4.68% Education Less than secondary school certificate 14.48% Secondary school certificate 34.69% High school diploma 20.51% Bachelor 8.60% Master/diploma or higher 21.72% Table 5 Ranking of second-tier individual needs in a Smart Living DE Rank Optional conditions (second-tier consumer needs) 1Data privacy and security exceeding legal requirements 2Product safety and liability 3Information and transparency 4Education/technological literacy 5Access 6Prioritizing consumers’ economic interest 7Accountability (dispute resolution) N ote: Rank 1indicates that the item is most important, rank 7indicates that the item is least important Electronic Markets (2024) 34:16 Page 19 of 32 16 ecosystem tools and mechanisms to ensure their preferred data privacy and security level. Regarding consumers’ need for product safety and liability, it is essential to erase confusion on who is accountable for any harm caused by using ecosystem offerings. As mentioned previously, the opaque and interdependent nature of DEs makes it challenging to attribute harm to a particular component. This, in turn, can lead to disputes about who should be held accountable and discourage innovation in the digital space. Additionally, due to the dynamic nature of the DE, offerings might be developed and deployed without being able to conduct long-term studies on potentially adverse side effects. To cater to consumers’ need for clear and comprehensive product safety and liability while encouraging innovation within the ecosystem, DE designers might want to assess joint liability via an ecosystemwide entity. This could be operationalized in the form of an insurance mechanism that takes effect in case some damage happens. Besides the joint liability, other suitable incentives to address consumers’ need for safety and liability include building a community for collaborative security measures (where the community shares information about potential threats and collaborates on solutions); clear liability agreements among participants; (i.e., each entity’s liability is clearly defined); 3rd party certifications and audits; distributed trust mechanisms that ensure the traceability and accountability when components from different providers are jointly providing a service. Again, it is ultimately also essential to ensure the education of the consumer in terms of safety. In this vein, it is pivotal to disseminate information and educational resources on safety practices within the ecosystem communities, ensuring that all entities are aware of best practices and potential threats. The identified incentives are now combinable into a system that synergistically amplifies their individual effects, fostering a collaborative, innovative, and continuously improving environment within the digital ecosystem. Combine incentives intoasystem To find the set of incentives that is, on aggregate, most effective (i.e., it has the maximal desired effect on the target audience), we analyze the relationships between the various incentives. To this end, we employ different orchestration mechanisms used in Table1 and assess whether incentives are independent, competing, and complementary to each other. Figure5 visualizes these relationships. For instance, the incentives clear data governance, end-to-end encryption, and data exchange protocols are complementary. They all address different facets of data management and security, especially in the context of data privacy. While clear data governance sets the “rules” for managing data, end-toend encryption provides the “tools” to ensure data remains confidential. On the other hand, data exchange protocols ensure smooth and standardized data transitions across systems. Together, they provide a comprehensive approach to data privacy. This way, the three incentives complement each other and cater to both (i) consumers’ need for data privacy beyond the legal requirements and (ii) organizations’ need to effortlessly share and evaluate consumer data across different digital touchpoints in the DE. In stark contrast, the incentives data minimization principle and data anonymization and unified user profiles are conflicting incentives. Data minimization and anonymization again cater to consumers’ need for privacy-friendly offerings. The unified user profiles target organizations’ wish to improve their value proposition through richer data and the knowledge it holds about the customers. These incentives are fundamentally conflicting since data minimization encourages collecting the least amount of data necessary, while unified user profiles require a comprehensive collection of data for a complete view of the user. Similarly, data anonymization and unified user profiles are totally opposite. While anonymization seeks to obscure consumers’ identities, the purpose of unified profiles is to provide individualized insights on the user. Anonymizing a unified profile significantly limits its usefulness. Based on the relationships between the incentives mapped out in Fig.5, we can design an incentive system that harmonizes conflicting incentives and accommodates both independent and complementary ones. Figure6 depicts the incentive system derived for our specific example. In our case, the incentive system encompasses four DE components: providing a toolbox, establishing a community, ensuring a joint DE-wide liability and dispute resolution and a Personal Data Space. The ecosystem-wide toolbox should offer services and tools (such as knowledge-sharing platforms and prototyping labs) but also standards for the DE collaboration on all levels (such as interoperability standards on a technology level and transparency report standards on a managerial level). Further, DE designers need to invest efforts to form a vivid community that actively communicates and collaborates (for instance, within the framework of visionary forums, regular community events on cutting-edge technologies, best practices for research and development, or matchmaking events to facilitate collaborations between companies with different skill sets and assets). The third element in the incentive system is a DE-wide joint liability and dispute resolution. Knowing that there is a joint liability structure assures consumers that they have avenues for redress if things go wrong. The mere existence of such a system signals that organizations in the ecosystem are confident enough in their offerings to share the risk. Additionally, when every player in the ecosystem shares responsibility, it fosters a culture of accountability and quality assurance, thereby boosting the overall credibility of the ecosystem. Ultimately, the last element of the incentive Electronic Markets (2024) 34:1616 Page 20 of 32 system is establishing a Personal Data Space (PDS)—a digital environment (e.g., a platform) that enables individuals to view, manage, buy, sell, and trade personal data. The concept behind a Personal Data Space is to empower consumers to check and monitor but also monetize their data if they choose to do so. Personal data space can also be essential in making data flows transparent and fair. Incentive system realignment Once in place, the effectiveness of the incentive system needs to be monitored and, if necessary, re-tuned to attract the groups of companies and consumers it was set up for. In particular, as the DE evolves and participants interact, DE designers might seek to appeal to companies and individuals who still need to join the ecosystem. As mentioned previously, incentive systems are not intended to be static approaches but recursively developed and adapted over time. Only this way we can ensure that the proposed incentive system (in its current version) will match the development of the DE itself. Accordingly, monitoring loops and refinement cycles should be implemented in the DE. To this end, the incentive system needs to be expanded with additional stimuli that address company goals and consumer needs that have not been considered yet. Continuing with our example, when the initial version of the incentive system (see Fig.6) accomplishes its objectives and secures the participation of the intended user groups and companies within the ecosystem, its ability to attract further companies and users to the DE will gradually diminish. Fig. 5 Exemplary identification of independent, competing, and complementary incentives for top three company goals (see Table3) and consumer needs (see Table5) Electronic Markets (2024) 34:16 Page 21 of 32 16 Then, it is necessary to expand and readjust the initial version of the incentive system to accommodate the requirements of additional companies and user groups and try to bind these additional entities to the DE. From a consumer perspective, our research revealed that beyond the top three consumer needs data privacy, product safety, and information transparency, consumers value technology education to improve their technology literacy (see Table5). Recognizing the significance of this finding and seeking to attract further consumers to the ecosystem, it is appropriate to expand upon the original version of our incentive system by integrating incentives specifically tailored to address consumers’ wish for enhanced technology literacy. These consumer-directed incentives would play a pivotal role in not only meeting consumer demands but also in fostering a more informed and empowered consumer base. Such incentives encompass the provision of ecosystem-based online learning courses, an expansion of the ecosystem community to accommodate user forums. Other incentives might be the provision of user-focused tech support services or the implementation of credits for user engagement in the ecosystem’s community. On the company side, our interviews revealed that another crucial strategic corporate objective in digital ecosystem participation (see Table3) is gaining access to consumer insights. Consumer insights empower businesses with invaluable information about consumer behavior, preferences, and trends. Armed with this knowledge, companies can make informed decisions, refine their product offerings, and tailor their marketing strategies to better resonate with their target audience. While such insights remain indispensable for being able to compete within a market, in reality, due to limited access to the necessary data, companies cannot always independently generate the key consumer insights they need. Given the strategic importance of consumer insights, enhancing the original version of the incentive system should logically prioritize addressing this need. To address companies’ desire for (better) consumer insights, ecosystem designers can consider implementing a range of incentives: For one, there is the provision of advanced analytics tools for mining, analyzing, and interpreting consumer data more effectively. Another incentive that targets the goal of improved consumer insights could be the provision of data-sharing agreements (for non-sensitive data) between companies participating in the DE. These agreements should promote mutually beneficial data sharing that profits all involved partners. Further incentives involve the provision of consumer-feedback mechanisms (e.g., customer-feedback platforms, online surveys), improved access to third-party data brokers through DE participation, and interoperability standards for data exchanges within the ecosystem. Also possible is the provision of generally valid customer insights that could serve a wide range of ecosystem participants or establishing a community for collaborative research initiatives that enable the participating entities to extract the key Fig. 6 Exemplary developed incentive system that addresses top three company goals (see Table3) and consumer needs (see Table5) Electronic Markets (2024) 34:1616 Page 22 of 32 consumer insights they need. Ultimately, a further viable alternative to serve companies’ need for (better) customer insights is by providing an ecosystem-wide customer insights intelligence service through an entity owned by the ecosystem itself. In contrast to providing only generally valid consumer insights, this intelligence entity would provide ecosystem participants with customer insights tailored to their specific industry or business area (i.e., insights on customer segments within their business area, aggregatelevel customer profiling relevant to the specific business a company is active in). However, a pivotal element of this approach would involve establishing an intelligence entity with the responsibility of centralizing and overseeing all ecosystem data. This centralization is crucial to guarantee that individual ecosystem participants are granted access only to their own data and not that of other participants, thus safeguarding data privacy and security. In light of all these considerations, we can proceed to expand the original incentive system (see Fig.6) to accommodate the consumer need for improved technology literacy and companies’ wish for better consumer insights and thus ultimately increase ecosystem participation. However, to do so, we first need to align and assess the compatibility of the above-discussed incentives with each other with the incentives that form the original version of the incentive system. The idea is to achieve an expansion of the original incentive system to attract new players into the ecosystem without losing those who are already part of the ecosystem. In practice, aligning and assessing the compatibility of the new incentives require a thorough analysis of conflicts or synergies among consumer and company goals and legacy and new incentives. In our specific case, for instance, the analysis reveals that providing advanced analytics tools contradicts the primary goal of data minimization. After all, advanced analytics tools often require and thrive on having a wealth of data to analyze and derive meaningful insights from. Without an extensive database to perform their functions effectively, analytics tools cannot provide the intelligence they are supposed to offer. Thus, when ecosystem participants are provided with advanced analytics tools, they might be inclined to request and access more consumer data than is strictly required for their immediate needs. Accordingly, the provision of ecosystem-wide advanced analytics tools can create a tension between the desire for enhanced data-driven insights and the principle of data minimization. As more data is gathered and processed than may be strictly necessary, individual privacy and data security are potentially compromised. To reconcile these conflicting goals, we need to combine only incentives that can strike a balance between data utility and minimization. One fruitful avenue to address this challenge is to strengthen the Personal Data Space envisioned in the original incentive system (incentive system stage 1). Within the Personal Data Space, consumers can control the access to their data and thus decide deliberately and—if they wish—on a case-by-case basis whether they sell their data, make it available free of charge, or not at all. In combination with a well-designed Personal Data Space, the provision of advanced analytics tools is, in this case, a feasible option to support companies in their endeavor toward (better) consumer insights. Thereby, the design of the Personal Data Space plays a pivotal role in encouraging users to willingly contribute their data to the ecosystem. Without a substantial amount of consumer data, the utility of the ecosystem’s data analysis tools is highly constrained. This scenario bears resemblance to the concept of datasharing agreements within the diverse companies comprising the DE. Here, the idea is to ensure the exchange of consumer data between the DE companies, enabling DE participants to extract the consumer insights they require. However, even with the implementation of data minimization principles and the Personal Data Space, the mere existence of data-sharing agreements or interoperability standards for data exchanges may not necessarily result in an abundance of improved consumer insights. The crux lies in whether consumers are willing to share or sell their data for analytical purposes. Another potentially fruitful avenue to reconcile consumers’ and companies’ interests concerning data collection is the provision of the consumer insights ecosystem-wide intelligence service that offers companies the necessary insights without divulging individual consumer data to individual companies. It presents a harmonious solution without conflicting with other incentives within the system, as companies within the ecosystem are supposed to gather only the minimal necessary data required to provide their respective products and services. When aggregated, the minimalistic data sources collected by each ecosystem participant through their products and services can evolve into a valuable asset for extracting consumer insights. In this manner, implementing the consumer insights intelligence service aligns with the interests of both companies and consumers. It can offer companies the desired customer insights while upholding the critical principle of data minimization and preserving consumer privacy. Given the importance of consumers’ willingness to share or sell their data for better insights, both of the aforementioned incentives (i.e., the Personal Data Space and consumer insights intelligence service) should be implemented alongside one or more consumer technology literacy campaigns. Such campaigns enable ecosystem designers to address consumers’ need for enhanced technology literacy while fostering greater acceptance and willingness among individuals to share their data for ecosystem-related purposes. In essence, the technology literacy campaigns should offer educational materials explaining the ecosystems’ Electronic Markets (2024) 34:16 Page 23 of 32 16 products and services’ functionality, the underlying technologies, and the role of data for the personalization and utility of the ecosystems’ products and services. In this sense, such campaigns should present detailed information about how the customer insights services are compiled, the specific data sources they utilize, and the potential benefits of these insights for the customers themselves, along with explanations of the measures in place to safeguard consumers’ data safety and privacy. As discussed previously, besides the Personal Data Space and the provision of consumer insights intelligence service, ecosystem designers can facilitate the generation of relevant customer insights by implementing consumer-feedback mechanisms or a community for collaborative research initiatives among companies. The customer-feedback mechanism will most likely have two beneficial outcomes. For one, it will increase consumers’ involvement in the ecosystem and thus their loyalty to the ecosystems’ products and services. At the same time, it will also provide insights into the focal topics that should be addressed during the technology literacy campaign(s). Such a customer-feedback mechanism will also work well with the customer dialogue platforms in the initial version of the incentive system. Likewise, establishing a community for collaborative research initiatives is complementary to the already envisioned efforts of the community in the first version of the incentive system. Thus, these two incentives could extend the initial version of the incentive system without any expected complications. Finally, access to third-party data brokers stands out as another promising avenue for enabling companies to gain the consumer insights they require. This incentive is relatively autonomous from the initial version of the incentive system. Thus, it could expand the initial version of the incentive system through implementing a range of approaches. One viable option is establishing framework contracts or other agreements that grant ecosystem members preferential and costeffective access to third-party brokers. Another option is that the ecosystem itself buys access rights to third-party brokers and distributes these access rights based on a points-based system. In this scenario, every ecosystem member accrues Fig. 7 Realigned (extended) incentive system (based on the incentive system stage 1) Electronic Markets (2024) 34:1616 Page 24 of 32 points based on their level of participation, contribution, or engagement within the ecosystem. These points could then be redeemed for access to third-party data brokers, which ensures that access is tied to active involvement within the ecosystem. This approach maintains fairness within the ecosystem, as it is not favoring any specific member over others. To sum up, a suitable extension of the original version of the incentive system (see Fig.7) should encompass the following additional incentives: a consumer insights intelligence service entity, access to third-party data brokers, and a community expansion. The community expansion should include a customer-feedback mechanism, a collaborative research environment for consumer insights, and educational materials for the technology literacy campaign. Discussion This article draws upon existing literature on digital platforms and platform ecosystems (e.g., L. Chen etal., 2022; Kretschmer etal., 2022; Kuang etal., 2019; Ojala & Lyytinen, 2022; X. Sun & Zhang, 2021) to propose an integrated framework for identifying and orchestrating incentives into incentive systems that attract and engage two species of the DE: consumers and companies. The framework outlines the key components for designing these systems, emphasizing interconnections like the alignment of company and consumer needs. It proposes methods to harmonize intertwined incentives, ensuring cohesive incentives across ecosystem actors. We understand incentive systems as a set of incentives that have a maximum effect on the target audience and lead them toward the desired behavior. Against this background, the complexity of designing incentive systems stems from two factors: First, incentives in the context of DEs are much different from “organizational incentives” (L. Chen etal., 2022). Whereas organizational incentives are typically regarded as a structural attribute of an organization, in the context of DEs, they are key governance mechanisms to ensure ecosystems’ success (L. Chen etal., 2022; X. Sun & Zhang, 2021). They also differ in terms of their operational focus, goals, and the nature of stakeholder engagement. Organizational incentives are primarily designed to enhance employee performance, ensure that employee actions align with the company’s strategic objectives, foster employee loyalty, and increase retention (Saleem, 2011). On the contrary, DE incentives have a broader reach as they foster collaboration and symbiosis among various loosely connected stakeholders with independent and sometimes competing interests. Furthermore, DE incentives are crafted to encourage active participation, facilitate cooperation, and drive collective value creation within the ecosystem (Adner, 2017; L. Chen etal., 2022; Valdez-De-Leon, 2019). DE incentives are pivotal governance mechanisms because they orchestrate the complex interplay between autonomous yet interdependent actors. The primary focus of these incentive systems extends beyond merely attracting and maintaining participants; it is about strategically guiding the ecosystem toward sustainable growth. These incentives are meticulously designed not just for resolving conflicts or aligning diverse, often competing interests within the ecosystem. Rather, their pivotal role lies in effectively integrating valuable actors into the ecosystem. By harmonizing these varied interests, the incentives facilitate cooperative and mutually beneficial interactions among all players in the ecosystem. In DEs, participants jointly contribute to the ecosystem’s success by creating technologies, services, or products that other ecosystem participants can recombine to generate new interconnected and complementary products and services. While these interconnected products and services offer their user superior value than traditional products, they typically do not materialize in coopetitive environments, where companies operate independently. Effective harmonization of interests within the DE promotes collaboration and cooperation among the participants. This, in turn, leads to the co-creation of value and an expanded market presence, driving sustainable growth through two main channels: On the consumer side, enabling co-created value enhances user satisfaction and fosters high customer loyalty. This, in turn, activates a second growth channel—the continued engagement of companies within the ecosystem. From a company perspective, in a stable environment with promising collaboration opportunities, businesses are more likely to remain engaged and committed for the long haul, which is vital for the perpetual growth of the ecosystem. In contrast to organizational incentives, DE incentives are structured to ensure the cohesive functioning and strategic progression of the broader digital ecosystem. This way, incentives in DEs are instrumental in the governance of the ecosystem—i.e., creating conditions that enable and coordinate the interactions between actors (e.g., the flow of resources) without losing the advantages of decentralized decisions (L. Chen etal., 2022; Teece, 2017). Due to the complexity of developing incentive systems for DE participation, we draw on various models and insights from the organizational strategic management literature and technology adoption literature to comprehensively explore companies’ and consumers’ needs. For instance, we adapted the BSC, which is typically a general management tool, to the context of DEs to specify and elaborate appropriate business goals and incentives from various strategic perspectives within a company. In addition, we also synthesized and applied several technology adoption models Electronic Markets (2024) 34:16 Page 31 of 32 16 Rosati, P., Fox, G., Kenny, D., & Lynn, T. (2017). Quantifying the financial value of cloud investments: A systematic literature review. 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 194–201). IEEE. https:// doi. org/ 10. 1109/ Cloud Com. 2017. 28 Royakkers, L., Timmer, J., Kool, L., & van Est, R. (2018). Societal and ethical issues of digitization. Ethics and Information Technology, 20(2), 127–142. https:// doi. org/ 10. 1007/ s106760189452-x Saleem, S. (2011). The impact of financial incentives on employees commitment. European Journal of Business and Management, 3(4), 258–266. https:// www. seman ticsc holar. org/ paper/ TheImpactofFinan cialIncen tivesonEmplo yeesSaleem/ ca612 feaec 0c2d7 32579 38c7e 3357a 0c109 5aa28 Schneider, S., & Kokshagina, O. (2021). Digital transformation: What we have learned (thus far) and what is next. Creativity and Innovation Management, 30(2), 384–411. https:// doi. org/ 10. 1111/ caim. 12414 Schreieck, M., Wiesche, M., & Krcmar, H. (2016). Design and governance of platform ecosystems–Key concepts and issues for future research. Proceedings of the 24th European Conference on Information Systems (ECIS). https:// aisel. aisnet. org/ ecis2 016_ rp/ 76/ Sebastian, I. M., Weill, P., & Woerner, S. L. (2020). Driving growth in digital ecosystems. MIT Sloan Management Review 62(1), 58–62. https:// sloan review. mit. edu/ artic le/ drivi ngg rowthindigit alecosy stems/ Shah, D., Rust, R. T., Parasuraman, A., Staelin, R., & Day, G. S. (2006). The path to customer centricity. Journal of Service Research, 9(2), 113–124. https:// doi. org/ 10. 1177/ 10946 70506 294666 Shen, L., Zhang, X., & Liu, H. (2022). Digital technology adoption, digital dynamic capability, and digital transformation performance of textile industry: Moderating role of digital innovation orientation. Managerial and Decision Economics, 43(6), 2038–2054. https:// doi. org/ 10. 1002/ mde. 3507 Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https:// doi. org/ 10. 1016/j. jbusr es. 2019. 07. 039 Statista. (2023). Smart home worldwide market forecast. Statista. Retrieved September 1, 2023, from https:// www. stati sta. com/ outlo ok/ dmo/ smarthome/ world wide. Subramaniam, M. (2020). Digital ecosystems and their implications for competitive strategy. Journal of Organization Design, 9, 1–10. https:// doi. org/ 10. 1186/ s4146902000073-0 Subramaniam, M., Iyer, B., & Venkatraman, V. (2019). Competing in digital ecosystems. Business Horizons, 62(1), 83–94. https:// doi. org/ 10. 1016/j. bushor. 2018. 08. 013 Sun, H., Rabbani, M. R., Ahmad, N., Sial, M. S., Cheng, G., Zia-UdDin, M., & Fu, Q. (2020). CSR, co-creation and green consumer loyalty: Are green banking initiatives important? A moderated mediation approach from an emerging economy. Sustainability, 12(24), 10688. https:// doi. org/ 10. 3390/ su122 410688 Sun, X., & Zhang, Q. (2021). Building digital incentives for digital customer orientation in platform ecosystems. Journal of Business Research, 137, 555–566. https:// doi. org/ 10. 1016/j. jbusr es. 2021. 08. 068 Sun, Y., Li, S., & Yu, L. (2022). The dark sides of AI personal assistant: Effects of service failure on user continuance intention. Electronic Markets, 32(1), 17–39. https:// doi. org/ 10. 1007/ s1252502100483-2 Sussan, F., & Acs, Z. J. (2017). The digital entrepreneurial ecosystem. Small Business Economics, 49, 55–73. https:// doi. org/ 10. 1007/ s111870179867-5 Tallon, P. P., & Kraemer, K. L. (2007). Fact or fiction? A sensemaking perspective on the reality behind executives’ perceptions of IT business value. Journal of Management Information Systems, 24(1), 13–54. https:// doi. org/ 10. 2753/ MIS07 4212222 40101 Tallon, P. P., Mooney, J. G., & Duddek, M. (2020). Measuring the business value of IT. Measuring the Business Value of Cloud Computing, 1–17. https:// doi. org/ 10. 1007/ 978-303043198-3_1 Tan, B., Pan, S., Lu, X., & Huang, L. (2015). The role of IS capabilities in the development of multi-sided platforms: The digital ecosystem strategy of Alibaba.com. Journal of the Association for Information Systems, 16(4), 248–280. https:// doi. org/ 10. 17705/ 1jais. 00393 Tan, F., Tan, B., & Pan, S. (2016). Developing a leading digital multisided platform: Examining IT affordances and competitive actions in Alibaba.com. Communications of the Association for Information Systems, 38(1), 738–760. https:// doi. org/ 10. 17705/ 1CAIS. 03836 Teece, D.J. (2017). Dynamic capabilities and (digital) platform lifecycles. In Entrepreneurship, Innovation, and Platforms 37, Emerald Publishing Limited, Leeds, pp. 211–225. https:// doi. org/ 10. 1108/ S074233222 01700 00037 008 Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https:// doi. org/ 10. 1016/j. lr p. 2017. 06. 007 Treiber, M., Theunissen, T., Grebner, S., Witting, J., & Bernhardt, H. (2023). How to successfully orchestrate content for digital agriecosystems. Agriculture, 13(5), 1003. https:// doi. org/ 10. 3390/ agric ultur e1305 1003 Turilli, M., & Floridi, L. (2009). The ethics of information transparency. Ethics and Information Technology, 11(2), 105–112. https:// doi. org/ 10. 1007/ s106760099187-9 Valdez-De-Leon, O. (2019). How to develop a digital ecosystem: A practical framework. Technology Innovation Management Review, 9(8), 43–54. https:// doi. org/ 10. 22215/ timre view/ 1260 de Vaz, T. N., & Nijkamp, P. (2009). Knowledge and innovation: The strings between global and local dimensions of sustainable growth. Entrepreneurship and Regional Development, 21(4), 441–455. https:// doi. org/ 10. 1080/ 08985 62090 30200 94 Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https:// doi. org/ 10. 2307/ 30036 540 Verma, P., Kumar, V., Daim, T., Sharma, N. K., & Mittal, A. (2022). Identifying and prioritizing impediments of Industry 4.0 to sustainable digital manufacturing: A mixed method approach. Journal of Cleaner Production, 356, 131639. https:// doi. org/ 10. 1016/j. jclep ro. 2022. 131639 Volberda, H. W., Khanagha, S., Baden-Fuller, C., Mihalache, O. R., & Birkinshaw, J. (2021). Strategizing in a digital world: Overcoming cognitive barriers, reconfiguring routines and introducing new organizational forms. Long Range Planning, 54(5), 102110. https:// doi. org/ 10. 1016/j. lrp. 2021. 102110 Wang, P. (2021). Connecting the parts with the whole: Toward an information ecology theory of digital innovation ecosystems. MIS Quarterly, 45(1), 397–422. https:// doi. org/ 10. 25300/ MISQ/ 2021/ 15864 Wareham, J., Fox, P. B., & Cano Giner, J. L. (2014). Technology ecosystem governance. Organization Science, 25(4), 1195–1215. https:// doi. org/ 10. 2139/ ssrn. 22016 88 Wasiuzzaman, S. (2019). Resource sharing in interfirm alliances between SMEs and large firms and SME access to finance: A study of Malaysian SMEs. Management Research Review, 42(12), 1375–1399. https:// doi. org/ 10. 1108/ MRR1020180369 Weber, T. (2006). Anreizsysteme für die betriebliche Forschung und Entwicklung. Deutscher Universitätsverlag. https:// doi. org/ 10. 1007/ 978-383509121-4 Weissenberger-Eibl, M. A., & Hampel, T. (2021). Bridging the gap: Integrating external knowledge from open innovation platforms. SN Business & Economics, 1, 1–32. https:// doi. org/ 10. 1007/ s4354602100101-5 Electronic Markets (2024) 34:1616 Page 32 of 32 Weisstein, F. L., Monroe, K. B., & Kukar-Kinney, M. (2013). Effects of price framing on consumers’ perceptions of online dynamic pricing practices. Journal of the Academy of Marketing Science, 41(5), 501–514. https:// doi. org/ 10. 1007/ s117470130330-0 Wu, H.-Y. (2012). Constructing a strategy map for banking institutions with key performance indicators of the balanced scorecard. Evaluation and Program Planning, 35(3), 303–320. https:// doi. org/ 10. 1016/j. evalp rogpl an. 2011. 11. 009 Xiao, B., & Benbasat, I. (2011). Product-related deception in e-commerce: A theoretical perspective. MIS Quarterly, 35(1), 169–196. https:// doi. org/ 10. 2307/ 23043 494 Xu, J., & Cornelissen, J. (2023). Disequilibrium and complexity across scales: A patch-dynamics framework for organizational ecology. Humanities and Social Sciences Communications, 10(1), 1–13. https:// doi. org/ 10. 1057/ s4159902301730-x Xu, Y., Sun, H., & Lyu, X. (2023). Analysis of decision‐making for value co‐creation in digital innovation systems: An evolutionary game model of complex networks. Managerial and Decision Economics 44(5). https:// doi. org/ 10. 1002/ mde. 3852 Yu, C., & Wong, T. (2014). A supplier pre-selection model for multiple products with synergy effect. International Journal of Production Research, 52(17), 5206–5222. https:// doi. org/ 10. 1080/ 00207 543. 2014. 900199 Zhang, Q., & Sun, X. (2023). How incentive synergy and organizational structures shape innovation ambidexterity. Journal of Knowledge Management, 27(1), 156–177. https:// doi. org/ 10. 1108/ JKM1120210847 Zhang, S., Pauwels, K., & Peng, C. (2019). The impact of adding online-to-offline service platform channels on firms’ offline and total sales and profits. Journal of Interactive Marketing, 47(1), 115–128. https:// doi. org/ 10. 1016/j. intmar. 2019. 03. 001 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.