The maritime shipping industry, responsible for 80% of global trade, faces the critical challenge of achieving net-zero emissions while operating with thin profit margins, aging infrastructure, and legacy systems. This study investigates how AI-driven technologies can enable sustainable business model innovation within the Dutch maritime shipping ecosystem, addressing growing regulatory pressures including and decarbonization targets.

Using a qualitative grounded theory approach, this research conducted 22 semi-structured interviews with decision-makers from 13 organizations across the maritime ecosystem, including shipping operators, technology providers, and more. Data analysis followed the Gioia methodology, allowing themes to emerge organically through iterative coding and theoretical development.

The study reveals that successful transformation requires collaboration among distinct ecosystem roles, with Maritime Service Operators at the center supported by Digital Transformation Partners, Port & Infrastructure Managers, and more roles which were identified in the study.

The research culminates in a three-level transformation approach progressing from business model assessment through collaborative AI-enabled innovations to measurable sustainable benefits. The framework demonstrates that operational efficiency and sustainability are mutually reinforcing, with early adopters achieving benefits, such as fuel consumption reduction, enhanced predictive maintenance, improved regulatory compliance, and access to green financing.

This research provides structured guidance for practitioners seeking AI adoption without compromising operational stability, supports policymakers in accelerating decarbonization goals, and advances digital servitization knowledge in conservative industries.

Sustainable and digital manufacturing is gaining traction. This shift has increased interest in digital servitization, the strategic transformation process of industrial firms and their ecosystems. Digital servitization integrates business model innovation, ecosystem orchestration, and digital technologies. The process enables firms to integrate digital services into their business models, taking advantage of technologies such as the Internet of Things, artificial intelligence, and cloud computing to drive operational efficiency, resource optimization, and new revenue streams.

Although additive manufacturing is widely recognized for its potential to enhance material efficiency, reduce waste, and enable circular economy principles, its integration with digital servitization remains underexplored. This study investigates how digital servitization contributes to sustainable benefits in the manufacturing industry and how firms can structure its adoption within an additive manufacturing ecosystem. Using an exploratory single-case study approach, the research examines a laser powder bed fusion additive manufacturing ecosystem orchestrated by a leading original equipment manufacturer. The study draws on 25 in-depth interviews with key stakeholders, including industry executives, supply chain actors, and independent experts. Thematic analysis, following the Gioia methodology, identifies key enablers, barriers, and pathways to the adoption of digital servitization in an industrial ecosystem.
The findings highlight that digital servitization fosters sustainable benefits by enabling business model transformation, ecosystem-wide coordination, and digital technology integration. However, its successful implementation requires a structured, staged approach in which business model innovation precedes ecosystem orchestration and adoption of digital technology. The study proposes a framework that guides firms through this transformation, emphasizing the need for strategic alignment between these three pillars. In addition, it identified key challenges such as the reluctance to share data, difficulties in demonstrating return on investment, and limitations on interoperability.
This research contributes to the theoretical discourse by extending digital servitization research beyond firm-level implementations and providing empirical validation of its role in enabling sustainability in manufacturing ecosystems. It also offers actionable information for industrial firms, policymakers, and technology providers on how to systematically adopt digital servitization strategies to improve economic, environmental, and social sustainability. By bridging the gap between theoretical frameworks and industrial implementation, this study advances the understanding of how digital servitization can be effectively leveraged to drive sustainable industrial transformation.

Digital platform ecosystems such as online marketplaces or app stores rely on third-party complementors (e.g. sellers, developers) to create and deliver value to end-users. However, much of the existing literature offers a one-sided, static “blueprint” for how a platform orchestrator should manage its ecosystem, which fails to reflect the dynamic, multi-actor evolution observed in practice. Platform orchestrators and their complementors continuously influence each other’s development through iterative changes and feedback. This ongoing process of co-evolution wherein a new platform policy or feature triggers complementors’ responses, prompting further platform adjustments is crucial for sustained ecosystem success. This study addresses the problem by investigating how a platform orchestrator and its diverse partners co-evolve over time, developing a new framework to explain their reciprocal adaptations and the resulting benefits for alignment, innovation, and resilience.

A clear gap in the literature motivated this research, prior studies have lacked a process-oriented, multi-actor view of platform ecosystem evolution, overlooking the reciprocal adaptations between the orchestrator and complementors over time. The objective of this thesis was to fill that gap by providing a dynamic account of how a platform and its complementors evolve together, rather than viewing ecosystem strategy as a static plan. Conceptually, the study builds on the established literature about digital platform ecosystems and governance, and it adopts co-evolutionary theory as the core explanatory framework. Co-evolutionary theory explicitly focuses on reciprocal, interdependent change – in this context, how one party’s strategic change (for instance, a new API or policy by the platform) triggers adaptive responses in others, resulting in an ongoing cycle of mutual adaptation. By applying this lens, the research links traditional platform strategy concepts with the dynamic feedback loops characteristic of co-evolution.

Accordingly, the study asks a broad research question: How do the platform orchestrator and its ecosystem complementors co-evolve within a digital platform ecosystem? This main question is explored through three sub-questions that break down the co-evolutionary dynamics. (1) How do the orchestrator’s strategic initiatives and governance decisions influence the evolutionary trajectories of its complementors? (2) How do the complementors’ adaptations, innovations, and strategic responses feed back to shape the orchestrator’s evolution and platform strategy? (3) What outcomes emerge from the ongoing, multi-actor interactions between the orchestrator and complementors in terms of ecosystem alignment, innovation, and resilience? Answering these questions allows for a comprehensive understanding of co-evolution in the platform context, covering the platform-to-partner influence, the partner-to-platform feedback, and the emergent results of their continuous interaction.

To investigate these questions, the research employed a qualitative single-case study design. The focal case, termed “AlphaPlatform,” is a pseudonymous European B2C digital marketplace that orchestrates an ecosystem of roughly 150 complementor firms in various domains to support the sellers on the platform. Data were collected through 23 semi-structured interviews with both the platform’s management (orchestrator perspective) and multiple types of complementors (including software integrators, data/analytics providers, digital agencies, and logistics partners). These interviews were supplemented by internal documents and archival records, providing rich, triangulated evidence of interactions. The analysis followed an inductive coding approach (Gioia methodology) and utilized temporal bracketing and process tracing to reconstruct how platform–complementor interactions unfolded over time. This multi-actor, process-based approach captured the fine-grained “action–reaction” sequences that are rarely visible in single-firm studies, enabling the research to trace the back-and-forth evolutionary changes in the ecosystem and to identify key patterns and mechanisms of co-evolution.

Empirically, the case study revealed a complex co-evolutionary process structured around four interrelated dimensions. These dimensions represent the main areas in which the platform and its partners must continuously adapt: (1) the platform’s technological infrastructure and data exchange mechanisms, (2) the organizational capabilities and routines of both the platform firm and complementors, (3) the platform’s ecosystem governance and power dynamics in managing partners, and (4) the broader regulatory and societal context that can enable or constrain ecosystem changes. Based on these findings, the study developed a co-evolutionary framework conceptualized as a “double-loop” (infinity loop) model that links the platform orchestrator’s actions on one side with the complementors’ responses on the other. On the orchestrator side, AlphaPlatform continually adjusts its architecture, interfaces (APIs), and rules of engagement, the so-called boundary resources of the ecosystem, to support value creation and respond to emerging needs. On the complementor side, partners invest in new capabilities and adapt their offerings and behaviours to align with the platform’s changes, while also feeding back requirements or innovations (especially those partners who multi-home across platforms and bring external insights). This ongoing interplay is driven by three key interaction mechanisms identified in the case: boundary resource tuning (the platform and complementors co-developing and refining APIs, tools, and standards to better integrate their services), strategic recalibration (the orchestrator adjusting policies or strategic directions in response to partner behaviour and performance, and partners in turn altering their strategies to align with platform goals), and joint experimentation (collaborating on pilot projects, beta releases, and other experiments to learn and innovate together). Through iterative cycles of action, feedback, and readjustment, these mechanisms link the platform’s evolution with that of its complementors in a continuous co-evolutionary loop. Notably, the study found that this reciprocal process produces three important outcomes for the ecosystem as a whole: alignment (partners remain in sync with the platform’s goals and standards through ongoing fine-tuning of rules and interfaces, which also helps lock-in key contributors), innovation (the back-and-forth interaction spurs new features and services co-created by the platform and its complementors), and resilience (the ecosystem as a collective becomes more capable of withstanding external shocks, such as market disruptions or regulatory changes – by virtue of its adaptive, co-operative evolution). These findings culminate in a holistic framework illustrating co-evolution in digital platform ecosystems as an ongoing, double-loop cycle of mutual adaptation that drives beneficial outcomes.

The research offers several theoretical contributions to our understanding of platform ecosystems and co-evolution. First, it bridges the platform ecosystem strategy literature with co-evolutionary theory to explain how orchestrators and complementors adapt reciprocally over time, moving beyond static models of platform management. In doing so, the study proposes a process-based model of continuous mutual adaptation that integrates traditional platform governance levers (e.g. setting APIs, standards, incentives) with iterative feedback and learning cycles from the ecosystem. Second, the study identifies specific mechanisms of interaction that underlie this co-evolution: it details how particular governance actions by the platform (for example, changing access policies or interface features) lead to predictable types of complementor responses, and vice versa. By pinpointing these mechanisms (such as boundary resource adjustments and policy enforcement loops), the research adds micro-level clarity on how platform moves translate into complementor behaviours in the ecosystem. Third, it highlights the contextual conditions that moderate the platform’s balance between control and openness in governance. Factors like the platform’s maturity, the heterogeneity of complementor types, or external events (e.g. new regulations or competitor moves) can influence whether the orchestrator tightens control or grants more autonomy, thus shaping the co-evolutionary trajectory. Recognizing these contingencies addresses the classic governance dilemma (control vs. flexibility) in a dynamic way. Fourth, the study connects the iterative orchestrator–complementor interactions to broader system-level outcomes, namely, ecosystem alignment, innovation, and resilience, thereby enriching co-evolutionary theory with an understanding of how these dynamics ultimately shape the long-term evolution and health of a platform ecosystem. In sum, the thesis contributes a nuanced theoretical perspective that synthesizes process dynamics, mechanisms, and outcomes of co-evolution in digital platforms.

Beyond theory, the findings carry practical implications for managers of platforms and their partners. For platform orchestrators, the key lesson is to embrace an adaptive orchestration approach: keep feedback channels open with complementors, maintain flexible governance (continually update APIs, policies, and partnership programs based on input), and coordinate internally across product, policy, and partner management teams to respond swiftly to ecosystem needs. Changes to the platform, whether a new feature rollout or a rule change, should be introduced with careful pacing and sequencing to avoid surprising partners, and orchestrators should monitor the ecosystem for early warning signals of misalignment (such as waves of partner complaints, defections, or increased multi-homing to rival platforms) in order to intervene proactively. For complementor firms, the study underlines the importance of investing in dynamic capabilities so they can quickly adapt to platform changes (for example, upgrading technical skills or processes in response to new APIs). Complementors are encouraged to engage in joint pilots and beta programs early, as these collaborations not only help shape platform decisions but also give them a head start in leveraging new features. At the same time, complementors should manage their dependence on any single platform by maintaining some autonomy and alternative channels, ensuring they are not overly vulnerable to the platform’s strategic shifts. Both sides, platform leaders and complementors, benefit from approaching their relationship as a continuous “sense–experiment–realign” cycle. By regularly sensing changes in the environment or feedback from partners, experimenting with adjustments or innovations, and then realigning their strategies, ecosystem participants can co-navigate change without undermining the overall coherence of the ecosystem. This adaptive co-evolutionary approach enables sustained innovation and a robust, mutually beneficial growth for both the platform and its partners over time.

However, it should be noted that this study’s insights are bounded by its scope as a single-case analysis of one successful platform ecosystem (introducing potential recall bias and limiting generalizability); thus, future research should examine co-evolution in other contexts, including less successful or emerging platform ecosystems and those in different regions, and should employ longitudinal or quantitative designs to validate and extend the framework presented here.