The digital economy has fundamentally altered how firms innovate, shifting the emphasis from linear value chains to platform-based architectures that unlock multi-sided interactions and ecosystem-wide value creation. This narrative literature review synthesizes conceptual insights from peer-reviewed studies published in leading management and information systems journals. It examines platform-driven mechanisms that enable novel forms of value co-creation, data monetization, and ecosystem orchestration while navigating inherent tensions between value creation and capture. Core themes include the architecture of multi-sided markets, value co-creation practices, AI and data-enabled transformations, governance and monetization strategies, and evolutionary pathways in digital ecosystems. By comparing theoretical perspectives across these streams, the review reveals how platforms reduce transaction costs, amplify network effects, and integrate artificial intelligence to build dynamic capabilities. It also surfaces unresolved challenges such as platform governance in complex ecosystems, ethical implications of AI-driven revenue models, and the sustainability of value capture amid rapid technological change. The synthesis concludes by identifying promising research directions, including cross-ecosystem interactions and hybrid governance models. This conceptual overview equips scholars and practitioners with an integrated understanding of platform-based value creation as the cornerstone of competitive advantage in the digital era.
In the contemporary digital economy, business model innovation (BMI) has evolved from an incremental process to a core strategic imperative that determines firm survival and growth. Traditional product-centric models, reliant on ownership and linear transactions [1, 2], have been displaced by platform-based configurations that connect multiple stakeholder groups—producers, consumers, complementors, and data providers—within interdependent ecosystems. These platforms leverage network effects, data flows, and modular architectures to generate value at unprecedented scale and speed [3-12].
The platform economy now accounts for a substantial share of global economic activity [13], exemplified by transformative cases in mobility, healthcare, and idle-asset sharing [14-18], where value emerges not from ownership but from orchestrated interactions. Conceptual scholarship underscores that, in this context, BMI extends beyond technological adoption [1]; it requires reconfiguring value propositions, delivery mechanisms, and capture logics to align heterogeneous participants [3]. Early frameworks emphasized value creation through resource integration [1], yet digital platforms introduce triadic or multi-actor dynamics that challenge conventional dyadic assumptions.
A pivotal shift occurred around 2017–2020, coinciding with widespread smartphone penetration, the maturation of cloud computing, and initial AI integration [12]. Researchers documented how firms redesigned one-sided models into multi-sided platforms to internalize externalities and foster positive feedback loops [14]. For instance, transitions from proprietary product offerings to open ecosystems enabled complementors to co-develop offerings [13], thereby expanding total value while distributing capture across participants. This evolution has been further accelerated by data as a strategic asset and AI as an enabler of predictive personalization and automated orchestration [19-26].
Despite these advances, conceptual tensions persist. Platforms simultaneously amplify value creation through co-creation practices [3] and intensify value-capture dilemmas, as platform owners must balance openness for participation with control for monetization [22]. Governance mechanisms—ranging from algorithmic rules to contractual safeguards—emerge as critical yet underexplored levers [9, 10]. Moreover, sustainability considerations have gained prominence [16], with digital platforms increasingly expected to support circular and socially inclusive value propositions [7].
This narrative review addresses these developments by synthesizing conceptual approaches to platform-based value creation. Unlike empirical or meta-analytic works, it focuses exclusively on theoretical and conceptual contributions to map intellectual progress [2], identify integrative patterns [18], and illuminate debates [23]. The review period (2017–2024) captures the post-platform-maturity phase, including the surge in AI-enabled models post-2020 [24, 27]. Leading outlets such as Journal of Business Research, Technological Forecasting and Social Change, and MIS Quarterly provide the foundational corpus.
The central objective is to delineate how platform structures reshape BMI, with particular emphasis on value creation mechanisms that transcend firm boundaries. By integrating overlapping perspectives—multi-sided market design, ecosystem co-creation, data-AI augmentation, governance-monetization, and evolutionary trajectories—the analysis reveals synergies and frictions that define the digital business landscape. Ultimately, the review contributes a structured conceptual synthesis that clarifies pathways for future scholarship and managerial practice amid accelerating digital transformation.
To ensure a focused yet comprehensive synthesis, this narrative review adhered to a targeted yet transparent literature identification protocol grounded in the conceptual scope of platform-based value creation within the digital economy. Searches were conducted across major academic databases, including Scopus, Web of Science, and Google Scholar, supplemented by targeted journal scans in Strategic Management Journal, Journal of Business Research, MIS Quarterly, Information & Management, Organization Science, Long Range Planning, Journal of Strategic Information Systems, Technovation, and Technological Forecasting & Social Change.
The search string combined core terms (“business model innovation” OR “BMI”) with platform-specific descriptors (“platform” OR “multi-sided” OR “ecosystem” OR “value co-creation” OR “data-driven” OR “AI-enabled” OR “digital transformation”), restricted to English-language peer-reviewed articles published inclusively between 2017 and 2024. Preprints were considered only if subsequently published in refereed outlets. Initial retrieval yielded over 450 documents; iterative screening applied three inclusion criteria: (1) explicit conceptual or theoretical engagement with platform-based BMI and value creation, (2) relevance to digital economy contexts (multi-sided markets, ecosystems, data/AI integration, or governance), and (3) contribution to conceptual advancement rather than purely empirical testing or sector-specific case reporting. Exclusion removed purely technical, non-management, or pre-2017 works.
The final corpus comprises exactly 29 publications that collectively represent the most influential conceptual strands. These include seminal works on multi-sided platform redesign [12, 14], value alignment processes [1, 3], AI augmentation of servitization models [23, 26, 27], data-driven innovation logics [28], and ecosystem evolution [5, 17]. Journal diversity ensures breadth: Journal of Business Research (multiple entries), Technological Forecasting and Social Change, Journal of Product Innovation Management, Electronic Markets, and Sustainability, among others.
This selection process prioritized conceptual depth over quantity, enabling synthesis of theoretical perspectives without introducing new frameworks. Citations throughout the manuscript refer exclusively to this curated list, preserving traceability. The resulting review therefore offers a coherent, non-redundant mapping of the intellectual terrain while highlighting conceptual overlaps—such as the interplay between network effects and data monetization—and persistent debates around the sustainability of value capture.
Five interrelated research streams emerge from the synthesized literature, each illuminating distinct yet interdependent facets of platform-based BMI. These streams collectively trace the progression from structural design to technological enablement and ecosystem evolution.
The first stream addresses the architecture of multi-sided platforms and the formation of ecosystems. Early conceptualizations emphasized deliberate redesign from one-sided to multi-sided configurations to internalize network externalities and lower transaction costs [12, 13]. Subsequent work advanced this by detailing mechanisms for scaling participation through modular interfaces and complementor onboarding [2, 14]. Studies highlight how platform owners orchestrate indirect network effects, transforming passive users into active value contributors and creating self-reinforcing ecosystems [13].
The second stream centers on mechanisms of value creation and co-creation in platform ecosystems. Researchers underscore triadic or multi-actor interactions where value emerges collaboratively rather than unilaterally [1, 3]. Empirical-conceptual cases illustrate how platforms facilitate resource integration across stakeholders, yielding mutual benefits while surfacing tensions in value capture alignment [19, 20, 22]. Key insights reveal that effective co-creation requires dynamic feedback loops between platform owners, users, and complementors, shifting emphasis from firm-centric to ecosystem-centric value propositions [21, 22].
The third stream examines data-driven and artificial intelligence-enabled business model transformations. Conceptual contributions frame AI and big data as pivotal enablers that augment traditional BMI with predictive, adaptive, and circular capabilities [23, 26, 27]. Scholars conceptualize AI as a dynamic capability that scales innovation through co-evolutionary processes, enabling new revenue logics such as outcome-based or usage-based models [24, 25, 28]. This stream stresses the fusion of data assets with platform architectures to personalize offerings and optimize ecosystem interactions, yet warns of governance challenges arising from algorithmic opacity [23].
The fourth stream explores governance, monetization strategies, and revenue model innovation. Literature delineates platform governance as a hybrid of algorithmic control, contractual rules, and community norms that balance openness with value appropriation [9, 10]. Monetization evolves beyond simple fees toward layered mechanisms—freemium, subscription, data licensing, and ecosystem royalties—supported by platform canvas tools for visualization [4, 6]. Studies emphasize the need to align governance with monetization to sustain complementor participation without stifling innovation [10].
The fifth stream adopts an evolutionary lens on business models in digital environments. Researchers trace transitions from product-centric to service, platform, and ultimately ecosystem logics, incorporating sustainability and circular principles [5, 7, 16, 17]. This perspective highlights path dependencies and adaptive processes that allow firms to navigate technological disruption while maintaining value coherence across generations of innovation [17].
These streams are not isolated; overlaps abound—AI capabilities enhance multi-sided governance [10, 23], while co-creation practices underpin evolutionary sustainability [16, 21]. Tensions persist, notably between expansive value creation (favoring openness) and protective value capture (favoring control), as well as scalability limits in data-intensive ecosystems.
Figure 1 illustrates the integrated architecture through which platform-based business model innovation links multi-sided design, co-creation, data-AI augmentation, governance, and monetization mechanisms, as well as ecosystem evolution, to produce scalable value creation in the digital economy.
Figure 1. Integrated architecture of platform-based business model innovation in the digital economy. The figure visualizes the manuscript’s central synthesis by positioning platform-based business model innovation as the organizing hub through which five interdependent conceptual streams interact: multi-sided market architectures, value co-creation mechanisms, data and AI-driven innovations, governance and monetization strategies, and evolutionary ecosystem dynamics. The left-to-right pathway shows the transition from traditional linear business models to platform-orchestrated ecosystems, while cross-links among the five streams depict complementarities and tensions. The outer boundary highlights the persistent strategic balance between openness for participation and control for value capture, with sustained competitive advantage emerging as the cumulative system-level outcome.
Table 1 compares the five research streams across unit of analysis, value logic, strategic mechanism, and value-capture tension to clarify their distinct but complementary contributions to platform-based business model innovation.
Table 1. Conceptual comparison of the five research streams in platform-based business model innovation
Research stream | Primary unit of analysis | Core value creation logic | Dominant strategic mechanism | Principal value-capture tension | Main theoretical contribution |
Multi-sided market architectures | Platform structure and market sides | Value emerges by connecting distinct participant groups and activating indirect network effects [12, 14] | Interface design, modularity, side activation, complementor access [2, 13] | Growth through openness may weaken control over standards and appropriation [9, 10] | Reframes BMI from firm-level redesign to market-mediating architecture |
Value co-creation mechanisms | Interactions among platform owners, users, and complementors | Value is jointly produced through resource integration and coordinated participation [1, 3, 21] | Incentive alignment, feedback loops, stakeholder integration, participation quality [19, 22] | Collaborative value expansion may not translate into equitable capture across actors [3, 22] | Moves BMI beyond dyadic exchange toward triadic and ecosystem-centric value logic |
Data and AI-driven innovations | Data assets, algorithmic systems, adaptive platform capabilities | Value is enhanced through prediction, personalization, matching, and continuous optimization [23, 24, 26] | Analytics, machine learning, automated orchestration, adaptive capability building [27, 28] | Data extraction and algorithmic opacity may undermine legitimacy, trust, and long-term participation [10, 23] | Positions AI and data as dynamic capability enablers within BMI rather than auxiliary technologies |
Governance and monetization strategies | Rule systems and appropriation design | Value is sustained when participation rules and revenue models remain mutually reinforcing [4, 6, 9] | Algorithmic governance, contractual safeguards, pricing logic, layered monetization [10] | Strong appropriation can suppress innovation and complementor commitment, while weak control can erode capture [3, 9] | Clarifies that governance and monetization are constitutive elements of BMI, not downstream add-ons |
Evolutionary ecosystem dynamics | Longitudinal business model transformation | Value creation changes over time as firms shift from product to service, platform, and ecosystem logics [5, 7, 16, 17] | Path adaptation, hybridization, sustainability integration, ecosystem repositioning [13, 14] | Business model extension may increase complexity faster than governance and coherence mechanisms can adapt [16, 17] | Introduces a temporal and developmental lens showing BMI as cumulative, path-dependent, and reconfigurable |
A defining transformation in business model innovation (BMI) within the digital economy is the shift from linear value chains toward platform-orchestrated ecosystems. Traditional firms created value through sequential processes—production, distribution, and consumption—whereas platform firms facilitate interactions among multiple actors simultaneously [12, 13]. This transition reflects a deeper reconfiguration of economic logic: value is no longer embedded solely in products or services but emerges dynamically through interactions among ecosystem participants.
Parmentier and Gandia [12] describe this transition as a redesign from one-sided to multi-sided models, where firms internalize externalities by connecting previously disconnected market sides. This structural shift enables platforms to scale rapidly through indirect network effects, in which the platform’s value increases as more users join [8, 14]. Gatautis [8] further emphasizes that platforms operate as socio-technical systems, combining technological infrastructure with institutional arrangements that govern interactions.
Importantly, this transformation is not merely technological but strategic. Firms must redefine their roles—from producers of value to orchestrators of interactions. This orchestration role involves curating participation, managing complementarities, and ensuring that ecosystem interactions remain productive and mutually beneficial [10, 13]. Consequently, competitive advantage increasingly depends on ecosystem positioning rather than firm-level efficiency.
The literature highlights a fundamental shift from dyadic (firm–customer) relationships to triadic and multi-actor value creation structures. Andreassen et al. [1] introduce the “triadic way,” emphasizing that value emerges through interactions among firms, customers, and partners. This perspective challenges traditional assumptions that firms unilaterally create and deliver value.
Subsequent research extends this logic to complex ecosystems involving multiple stakeholders, including complementors, developers, data providers, and even competitors [21, 22]. Hein et al. [22] conceptualize value co-creation practices as structured interactions where actors integrate resources, share knowledge, and jointly produce outcomes. Similarly, Li et al. [19] demonstrate how platform ecosystems enable distributed innovation, where participants contribute complementary assets to enhance overall system value.
However, multi-actor systems introduce coordination challenges. Aligning incentives across heterogeneous participants becomes critical to sustaining engagement. Sjödin et al. [3] emphasize the importance of aligning value-creation and value-capture mechanisms, noting that misalignment can lead to ecosystem instability or participant withdrawal. Thus, successful platforms must balance inclusivity with strategic control, ensuring that all actors perceive sufficient value from participation.
Network effects are central to platform-based BMI, driving scalability and competitive advantage. Direct network effects arise when the value of the platform increases with the number of users on the same side, while indirect effects occur across different sides of the market [2, 14]. These effects create self-reinforcing feedback loops that can lead to rapid market dominance.
Modularity further enhances scalability by allowing independent development of complementary components. Platforms provide standardized interfaces (e.g., APIs) that enable third-party developers to build on core infrastructure [2, 6]. This modular architecture reduces coordination costs and accelerates innovation by decentralizing development activities.
Markfort et al. [6] identify recurring patterns in IoT platform innovation, highlighting how modularity enables flexible recombination of services and rapid adaptation to changing market conditions. Similarly, Sorri et al. [4] propose the platform canvas as a tool for visualizing and designing these complex interactions, emphasizing the importance of aligning technical architecture with value logic.
Despite these advantages, network effects can also create barriers to entry and lead to monopolistic tendencies. This raises important questions about competition, regulation, and long-term sustainability—issues that remain underexplored in the conceptual literature.
Data has emerged as a central resource in platform-based business models, fundamentally altering how value is created and captured. Sorescu [28] conceptualizes data-driven BMI as a process where firms leverage data to enhance decision-making, personalize offerings, and optimize operations.
In platform ecosystems, data flows continuously across interactions, generating insights that can be monetized directly or indirectly. For example, platforms may use data to improve matching algorithms, enhance user experience, or develop new revenue streams such as targeted advertising or data licensing [25, 29].
However, the strategic use of data introduces new challenges. Issues related to data ownership, privacy, and governance become increasingly critical as platforms accumulate vast amounts of user data. Fürstenau et al. [10] highlight the importance of data governance in healthcare platforms, where sensitive information must be managed carefully to ensure trust and compliance.
Thus, while data enhances value creation, it also necessitates robust governance frameworks to mitigate risks and maintain legitimacy.
Artificial intelligence (AI) represents a transformative force in platform-based BMI, enabling firms to develop dynamic capabilities that enhance adaptability and innovation. Sjödin et al. [27] conceptualize AI as a capability that evolves through feedback loops, allowing firms to continuously refine their business models based on real-time data.
AI enables predictive analytics, automated decision-making, and personalized user experiences, thereby enhancing the efficiency and effectiveness of platform operations [24, 26]. For instance, AI-driven recommendation systems can improve matching between supply and demand, while machine learning algorithms can optimize pricing strategies and resource allocation.
Moreover, AI facilitates the transition to outcome-based business models, in which value is delivered based on performance rather than ownership [3, 23]. This shift aligns with broader trends in servitization and circular-economy models, in which firms focus on delivering outcomes rather than products.
Nevertheless, integrating AI raises ethical and governance concerns, particularly regarding algorithmic bias, transparency, and accountability. These issues highlight the need for interdisciplinary approaches that integrate technological innovation with ethical considerations.
A central tension in platform-based BMI lies in balancing value creation and value capture. Platforms must create sufficient value to attract participants while capturing enough value to sustain operations and generate profit [3]. This balance is particularly challenging in multi-sided markets, where pricing and monetization strategies must account for cross-side network effects.
For example, platforms may subsidize one side of the market to attract users while monetizing another side through fees or data-driven services [4, 9]. However, excessive value capture can discourage participation, undermining network effects and ecosystem growth.
This tension is further complicated by powerful complementors, who may capture a significant share of the value created within the ecosystem. As a result, platform owners must carefully design governance mechanisms that ensure equitable value distribution while maintaining control over critical resources.
Governance plays a critical role in ensuring the stability and sustainability of platform ecosystems. Hoch [9] and Fürstenau et al. [10] emphasize that governance mechanisms must balance openness with control, enabling innovation while preventing opportunistic behavior.
Governance mechanisms can be categorized into three types:
Algorithmic governance, where rules are embedded in platform algorithms
Contractual governance, involving formal agreements with participants
Relational governance, based on trust and community norms
Effective governance requires integrating these mechanisms to address the diverse needs of ecosystem participants. For instance, algorithmic governance can enhance efficiency and scalability, while relational governance fosters trust and long-term collaboration.
Table 2 maps governance and monetization configurations across stages of ecosystem maturity, showing how different openness-control combinations generate distinct strategic benefits, capture distinct logics, and entail distinct failure risks.
Table 2. Governance–monetization configurations in platform business model innovation: strategic trade-offs across ecosystem maturity
Platform configuration | Typical ecosystem maturity | Governance orientation | Predominant monetization logic | Strategic advantage | Main risk or failure mode | Best-fit conceptual condition |
Open participation / low appropriation | Early emergence | High openness, low entry barriers, lightweight rule enforcement | Subsidized access, freemium, demand-side growth incentives [4, 9] | Accelerates user adoption and network formation | Weak capture, quality inconsistency, opportunistic participation | When rapid ecosystem seeding is more important than immediate profitability |
Open participation/selective appropriation | Early growth to scaling | Open complementary access with selective data, interface, or transaction control | Transaction fees, premium tools, selective commissions, API monetization [6, 10] | Supports scale while preserving some capture points | Conflict with complementors over dependence and margin extraction | When the platform seeks rapid scaling but also needs early revenue discipline |
Balanced openness/balanced appropriation | Scaling and consolidation | Hybrid governance using algorithmic rules, contracts, and relational norms [9, 10] | Subscription, tiered access, revenue sharing, ecosystem royalties [3, 4] | Improves ecosystem stability and multi-actor alignment | Governance complexity and rising coordination costs | When platform legitimacy and durable complementor participation are both strategic priorities |
Controlled participation/high appropriation | Mature dominant platform | Strong gatekeeping, high standardization, centralized decision rights | Commission extraction, data licensing, advertising leverage, bundled services [25, 29] | Maximizes appropriation efficiency and platform control | Complementor resistance, innovation suppression, and regulatory scrutiny | When market power is high, but ecosystem dependency creates political and strategic backlash |
Sustainability-integrated hybrid model | Mature or transforming ecosystem | Governance includes economic, social, and environmental criteria [7, 16] | Mixed monetization combining transaction, subscription, reuse, and circular value mechanisms | Extends legitimacy and ecosystem resilience beyond short-term capture | Measurement difficulty, stakeholder misalignment, implementation complexity | When long-term viability depends on circularity, inclusion, or trust-sensitive value propositions |
AI-amplified adaptive platform model | Dynamic cross-ecosystem competition | Real-time algorithmic governance with continuous rule adjustment [23, 24, 27] | Personalized pricing, recommendation-driven conversion, usage-based or outcome-based revenues [26, 28] | Enables high responsiveness, personalization, and dynamic optimization | Bias, opacity, privacy concerns, and legitimacy erosion | When data richness and interaction intensity justify adaptive orchestration as a core BMI capability |
However, governance remains an underdeveloped area in the literature, particularly regarding how different mechanisms interact and evolve. Future research should explore hybrid governance models that combine multiple approaches to address the complexity of digital ecosystems.
Sustainability has emerged as a critical dimension of platform-based BMI, reflecting growing societal and regulatory pressures. Treptow et al. [7] and Principato et al. [16] highlight how platforms can enable circular business models by facilitating resource sharing, reuse, and redistribution.
For example, sharing economy platforms allow users to monetize underutilized assets, reducing waste and promoting efficient resource use [5]. Similarly, digital platforms in food redistribution help minimize food waste by connecting surplus supply with demand [16].
These models demonstrate that platforms can create both economic and environmental value. However, achieving sustainability requires aligning incentives across stakeholders and integrating sustainability considerations into business model design.
The increasing integration of AI into platform ecosystems raises important ethical and societal questions. While AI enhances efficiency and innovation, it also introduces risks related to bias, privacy, and accountability [23, 24].
For instance, algorithmic decision-making can inadvertently reinforce existing inequalities if not properly designed and monitored. Similarly, the extensive use of personal data raises concerns about privacy and consent.
Addressing these challenges requires developing ethical frameworks and regulatory mechanisms that ensure responsible AI use. This includes enhancing transparency, promoting fairness, and ensuring accountability in algorithmic systems.
An emerging trend in the literature is the convergence of diverse platform models into hybrid configurations that combine multiple value-creation logics. Mancha and Gordon [13] and Trabucchi and Buganza [14] highlight how platforms evolve from simple two-sided models to complex multi-sided ecosystems.
These hybrid models integrate elements of product, service, and platform-based business models, enabling firms to capture value across multiple dimensions. For example, firms may combine subscription-based services with data-driven monetization and ecosystem partnerships.
This convergence reflects the increasing complexity of digital ecosystems, where firms must continuously adapt their business models to remain competitive. As such, BMI is no longer a one-time process but an ongoing, dynamic capability.
This narrative review has synthesized conceptual perspectives on platform-based business model innovation, highlighting the transformative impact of digital technologies on value creation in the modern economy. By integrating insights from 29 peer-reviewed studies, the analysis reveals that platform-based BMI represents a fundamental shift from firm-centric to ecosystem-centric value creation.
Five key insights emerge from the synthesis. First, platform architectures redefine how value is created by enabling multi-sided interactions and leveraging network effects. Second, value co-creation in platform ecosystems involves complex multi-actor dynamics that require careful coordination and alignment. Third, data and AI serve as critical enablers of innovation, enhancing adaptability and enabling new business models. Fourth, governance and monetization strategies play a central role in balancing value creation and capture. Finally, platform ecosystems evolve dynamically, incorporating sustainability and hybrid business model configurations.
Despite these advances, several challenges remain. The tension between openness and control, the ethical implications of AI, and the sustainability of value capture in rapidly evolving ecosystems represent critical areas for future research. Moreover, the increasing complexity of digital ecosystems calls for integrative frameworks that bridge multiple theoretical perspectives.
For practitioners, the findings underscore the importance of adopting an ecosystem mindset, leveraging data and AI capabilities, and designing governance mechanisms that foster trust and collaboration. Firms that successfully navigate these challenges will be better positioned to harness the full potential of platform-based value creation.
In conclusion, platform-based business model innovation is not merely a technological phenomenon but a strategic transformation that reshapes the foundations of value creation in the digital economy. As digital technologies continue to evolve, understanding and leveraging these dynamics will remain essential for achieving sustainable competitive advantage.
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Institute for Management, Business, and Accounting Studies
