Modern firms increasingly rely on external digital ecosystems to access infrastructure, artificial intelligence, data, and analytical capabilities that are difficult to build entirely in house. Cloud platforms, AI vendors, and data intermediaries now support core business operations rather than peripheral technical functions. This shift has expanded firm capabilities, but it has also created new forms of dependence. The central problem addressed in this article is that digital ecosystem dependence is often governed through fragmented IT, procurement, compliance, and legal processes. These arrangements can manage service delivery and contractual performance, but they are less suited to strategic vulnerabilities such as lock-in, opacity, bargaining asymmetry, data control loss, and exit difficulty. As a result, firms may become operationally efficient while becoming strategically constrained. The objective of this article is to develop a Digital Ecosystem Governance Framework for firms that depend on cloud platforms, AI vendors, and data intermediaries. The framework identifies the distinct dependence risks associated with each ecosystem partner type and integrates them into a unified governance logic. It treats dependence as a strategic management issue rather than a narrow technology sourcing problem. The proposed framework shows that effective governance of digital ecosystem dependence requires three interrelated capabilities: dependency risk assessment, protective governance mechanisms, and strategic governance oversight. Firms need contractual safeguards, technical portability, internal capability building, vendor diversification, data control mechanisms, and board-level visibility over dependence thresholds. The article contributes a governance-oriented perspective on how firms can use external digital ecosystems without becoming strategically captured by them.
Firms increasingly compete through digital services that are embedded in external platforms, infrastructures, and data networks rather than fully owned internal systems. Platform ecosystem research shows that value creation often depends on complementarities among multiple actors, yet these complementarities also reshape firm boundaries and control over strategic resources [1]. Digital platforms allow firms to scale innovation, access external capabilities, and connect with ecosystem partners, but they also expose firms to governance problems that are not captured by conventional internal hierarchy or market contracting models [2].
Digital ecosystem dependence becomes strategically significant when firms cannot easily substitute, exit, or internally reproduce the capabilities supplied by external digital partners. Platform-based ecosystems can invert traditional firm control because external providers may own the interfaces, standards, data flows, and rules through which dependent firms operate [3]. The issue is therefore not only whether firms obtain reliable digital services, but whether they retain sufficient bargaining power, architectural flexibility, and strategic autonomy as their operations become ecosystem-embedded.
Existing IT governance and procurement practices often divide digital dependence into separate categories such as infrastructure contracting, vendor selection, data compliance, and software risk management. However, ecosystem theory suggests that interdependence, role allocation, and value capture are structurally linked across digital networks [4]. When governance remains fragmented, firms may negotiate cloud contracts, AI service agreements, and data partnerships separately while missing the cumulative strategic exposure created by their combined dependence.
This article addresses that gap by proposing a Digital Ecosystem Governance Framework for firms using cloud platforms, AI vendors, and data intermediaries. The framework draws on research showing that ecosystems broaden the locus of value creation beyond the firm while requiring new coordination and governance arrangements [5]. Its aim is to help managers identify dependence risks, compare risks across partner types, and design governance mechanisms that preserve strategic flexibility while still benefiting from external digital capabilities.
Digital ecosystem dependence refers to a strategic condition in which a firm’s critical operations, innovation processes, analytics, customer interactions, or data assets rely on external digital actors whose resources cannot be easily replaced. Dynamic capability research suggests that firms benefit from digital platform ecosystems when they can integrate external resources with internal capabilities, but this integration also increases dependence on ecosystem architectures and partner decisions [6]. Dependence therefore emerges not simply from outsourcing, but from the embedding of firm routines, data, and decision processes within externally governed digital environments.
The strategic risk of dependence is intensified because digital platforms and infrastructures define interfaces, technical standards, access rules, and monetisation mechanisms. Research on digital platforms and infrastructures highlights that firms operate through layered architectures that are simultaneously enabling and constraining [7]. A dependent firm may gain speed and scale from external platforms while losing architectural discretion over migration, interoperability, data portability, and downstream innovation options.
Digital ecosystem dependence has multiple dimensions that should be distinguished analytically. The digital platform literature emphasises that platforms differ from traditional information systems because they support distributed innovation, network effects, and evolving ecosystem governance [8]. Table 1 categorises the dimensions and risks of digital ecosystem dependence. This categorisation clarifies why dependence should be assessed across operational continuity, financial exposure, strategic autonomy, data control, regulatory risk, and reputational vulnerability rather than reduced to vendor performance alone.
Table 1. Digital Ecosystem Dependence: Dimensions, Sources, and Strategic Risks for Dependent Firms
Dependence dimension | Main source of dependence | Typical ecosystem partner | Strategic risk for dependent firms | Governance implication |
Operational dependence | Critical business processes run through external digital infrastructure | Cloud platforms, software platforms, analytics platforms | Service disruption, reduced continuity, limited fallback capacity | Map mission-critical workflows and require continuity, redundancy, and recovery provisions |
Architectural dependence | Firm systems are built around proprietary interfaces, APIs, or technical standards | Cloud platforms, AI vendors | Lock-in, switching difficulty, integration rigidity | Require portability standards, modular architecture, and documented exit pathways |
Analytical dependence | Decisions rely on externally supplied models, algorithms, or scoring systems | AI vendors, analytics providers | Algorithmic opacity, model drift, biased outputs, reduced internal judgement | Establish model audit rights, explainability requirements, validation routines, and internal AI literacy |
Data dependence | Data is sourced, enriched, stored, or interpreted by external intermediaries | Data brokers, data aggregators, data marketplaces | Data quality uncertainty, provenance risk, privacy exposure, loss of strategic data control | Require data provenance documentation, quality controls, privacy warranties, and data-use restrictions |
Financial dependence | Pricing, egress costs, subscription structures, and usage fees shape switching choices | Cloud platforms, AI vendors | Escalating costs, bargaining weakness, economically unattractive exit | Monitor total cost of dependence and negotiate price transparency and termination protections |
Strategic dependence | Competitive capabilities become tied to external ecosystem rules and partner priorities | Cloud platforms, AI vendors, data intermediaries | Loss of autonomy, reduced differentiation, vulnerability to partner strategy changes | Set board-level dependence thresholds and preserve internal capability options |
Reputational dependence | External failures or misuse affect the focal firm’s legitimacy | AI vendors, data intermediaries, cloud platforms | Customer distrust, regulatory scrutiny, brand damage | Link vendor governance to enterprise risk, ethics, compliance, and crisis communication processes |
Digital ecosystem dependence is especially important because digital transformation has changed the location of innovation and entrepreneurship from firm-owned assets toward externally connected platforms and services. Research on digital innovation shows that digital technologies make resources more generative and recombinable, but they also create new coordination challenges across organisational boundaries [9]. For managers, this means that dependence should be governed as a portfolio of strategic exposures rather than as isolated technology contracts.
Cloud platform dependence arises when firms rely on hyperscale providers for infrastructure, platform services, software environments, storage, security tools, and advanced analytics capabilities. Global platform research suggests that large digital platforms can become central coordination structures across markets and industries, which increases their influence over dependent firms’ international and strategic choices [10]. In cloud contexts, this influence can appear through proprietary services, pricing models, certification requirements, data location rules, and ecosystem-specific development practices.
Cloud dependence is not inherently negative because cloud platforms provide scalability, resilience, security investment, and access to technical capabilities that many firms cannot efficiently develop alone. However, research on platform-dependent entrepreneurship shows that dependence creates power asymmetries when external platforms control access, visibility, technical rules, and the terms of value capture [11]. For firms using cloud services, similar asymmetries can emerge when migration costs are high, workloads are deeply integrated into proprietary services, and bargaining power is concentrated with a small number of hyperscalers.
Cloud platform dependence is also shaped by the boundaries of digital platforms and the interfaces through which firms connect to them. Platform boundary research shows that firm scope, platform sides, and digital interfaces interact in ways that affect governance and control [12]. Table 2 identifies the specific governance challenges and risk factors of cloud platform dependence. The table highlights that cloud governance must address not only uptime and service-level performance, but also lock-in mechanisms, contractual leverage, data portability, security responsibility, and exit readiness.
Table 2. Cloud Platform Dependence: Governance Challenges, Lock-In Mechanisms, and Mitigation Strategies
Cloud dependence issue | Lock-in or risk mechanism | Governance challenge | Mitigation strategy | Managerial ownership |
Proprietary cloud services | Applications depend on provider-specific databases, analytics tools, or serverless functions | Switching becomes technically complex and costly | Use modular design, containerisation, open standards, and portability reviews | Chief information officer, enterprise architecture team |
Data egress and migration costs | Large data volumes become expensive to move out of the platform | Exit is economically unattractive even when technically feasible | Negotiate egress terms, classify portable data, and model exit costs in advance | Procurement, finance, legal, data governance office |
Integrated security tooling | Security monitoring and identity management are tied to one provider’s ecosystem | Firm may lose independent security visibility | Maintain independent security logs, third-party monitoring, and shared-responsibility audits | Chief information security officer |
Service concentration | Core systems depend on one cloud provider or region | Outage or provider failure affects business continuity | Build resilience architecture, backup environments, and recovery playbooks | Operations, risk management, business continuity team |
Contractual asymmetry | Standardised cloud terms limit negotiation leverage | Firm accepts terms that weaken audit, liability, or termination rights | Prioritise negotiable clauses for critical workloads and create escalation routes | Legal, procurement, executive sponsor |
Skills dependence | Internal teams specialise in one provider’s tools and certifications | Capability becomes provider-specific rather than transferable | Invest in multi-cloud literacy, architecture documentation, and cross-provider training | Human resources, IT leadership |
Strategic roadmap dependence | Firm innovation follows the provider’s service roadmap | Digital strategy becomes indirectly shaped by cloud vendor priorities | Review cloud roadmap dependence during strategy cycles and maintain alternative technology options | Executive committee, digital strategy office |
Effective cloud governance therefore requires firms to move beyond narrow vendor administration and toward managed ecosystem participation. Research on managed ecosystems emphasises that value creation and capture require deliberate coordination among firms, communities, and ecosystem actors rather than passive reliance on external platforms [13]. For cloud-dependent firms, this means treating cloud architecture, contract design, capability development, and exit planning as integrated governance concerns rather than separate IT tasks.
AI vendor dependence arises when firms rely on external providers for machine learning models, automated decision systems, generative AI services, model hosting, training pipelines, or vendor-specific APIs. Cloud provider management research shows that digital service dependence requires contingency-sensitive governance processes rather than uniform outsourcing routines [14]. This logic is even more important for AI vendors because dependence may involve not only technical service delivery, but also model behaviour, data access, intellectual property, and decision accountability.
AI vendor dependence differs from traditional software outsourcing because the purchased capability may be probabilistic, adaptive, and difficult to inspect directly. Research on artificial intelligence as a service shows that organisations increasingly acquire AI capabilities through external service models, creating dependence on vendor infrastructure, model updates, embedded analytics, and platform-specific development environments [15]. The dependent firm may gain rapid AI functionality but lose visibility into how models are trained, validated, updated, and aligned with organisational goals.
The governance challenge is intensified by algorithmic opacity and the managerial difficulty of evaluating AI systems without internal expertise. Research on managing artificial intelligence emphasises that AI changes organisational decision-making because it requires new forms of accountability, learning, and human oversight [16]. When vendors control model architecture, training data, performance monitoring, and update cycles, firms may struggle to detect drift, bias, declining accuracy, or misalignment between vendor optimisation and firm-specific risk tolerance.
AI vendor dependence should therefore be governed through a combination of contractual, technical, and organisational mechanisms. Legal and policy research on AI as a service highlights the importance of clarifying responsibilities, liabilities, and accountability when AI capabilities are supplied by external providers [17]. Firms should negotiate model audit rights, data ownership clauses, explanation requirements, update notification obligations, intellectual property protections, human review procedures, and internal AI literacy programs so that vendor dependence does not become organisational ignorance.
Data intermediary dependence emerges when firms rely on brokers, aggregators, marketplaces, data-sharing platforms, or analytics intermediaries to access, enrich, verify, or interpret data. Data governance research defines data governance as a system of decision rights and accountabilities for data-related processes, which makes it directly relevant when critical data assets are partially controlled outside the firm [18]. In this setting, dependence is not only about obtaining data, but about trusting external actors to shape the quality, legality, provenance, and strategic meaning of that data.
The dependence risk is particularly acute because data intermediaries can sit between firms and the data sources that inform customers, markets, supply chains, or risk assessments. Research on data intermediaries shows that these actors perform brokerage and facilitation roles in data ecosystems, but their position can also influence access, interpretation, and control [19]. When firms depend on intermediaries for data inputs, they may lose direct visibility into collection conditions, consent arrangements, transformation methods, and potential biases embedded in supplied datasets.
Data broker research further indicates that intermediaries can operate in ways that are difficult for users, regulators, and dependent firms to observe fully. Studies of data brokers in surveillance capitalism show that data aggregation can generate opacity, privacy risks, and transnational governance challenges [20]. Table 3 summarises the dependence risks and governance needs related to data intermediaries. The table clarifies why firms must govern provenance, quality, privacy compliance, contractual reuse rights, and strategic data control rather than simply buying data as a commodity.
Table 3. Data Intermediary Dependence: Risk Dimensions, Control Loss Mechanisms, and Governance Requirements
Risk dimension | Control loss mechanism | Consequence for dependent firms | Governance requirement | Practical control mechanism |
Data provenance risk | Firm cannot fully verify how data was collected, transformed, or licensed | Legal exposure, weak auditability, poor trust in downstream analytics | Provenance transparency and documentation | Require source documentation, collection method disclosure, and audit trails |
Data quality risk | Intermediary supplies incomplete, outdated, duplicated, or biased data | Poor decisions, model error, reputational harm | Quality assurance and validation | Use sampling checks, quality thresholds, error reporting, and periodic validation |
Privacy compliance risk | Consent, purpose limitation, or cross-border transfer conditions are unclear | Regulatory penalties and customer trust loss | Privacy warranties and compliance verification | Include privacy clauses, compliance certifications, and data protection impact reviews |
Strategic data control risk | Firm becomes dependent on external data assets it cannot reproduce internally | Loss of differentiation and bargaining leverage | Internal data capability development | Build first-party data strategies and reduce reliance on irreplaceable third-party sources |
Reuse and resale risk | Intermediary retains broad rights over data use or onward sharing | Competitive leakage and unclear ownership boundaries | Contractual restrictions on reuse and onward transfer | Define permitted use, resale limits, deletion duties, and exclusivity where appropriate |
Analytical interpretation risk | Intermediary controls classifications, scores, enrichment logic, or segmentation | Hidden assumptions shape firm decisions | Methodological transparency | Require explanation of enrichment methods, scoring logic, and known limitations |
Ecosystem concentration risk | Few intermediaries control access to important data categories | Reduced bargaining power and higher switching costs | Supplier diversification and substitution planning | Maintain alternative data sources and assess replaceability of critical datasets |
Data intermediary dependence also has a regulatory and market-order dimension because intermediaries increasingly mediate structured data sharing across sectors. Research on the Data Governance Act and data intermediation shows that new legal frameworks may both enable and constrain data intermediary roles in data markets [21]. For managers, this means that governance must combine contractual safeguards with regulatory monitoring and internal data strategy, especially where intermediaries influence data access and competitive positioning.
The proposed Digital Ecosystem Governance Framework is built around three pillars: dependency risk assessment, protective governance mechanisms, and strategic governance oversight. Research on formal and relational governance in AI outsourcing shows that firms need both contractual controls and trust-based coordination when external providers supply complex digital capabilities [22]. This article extends that logic across cloud platforms, AI vendors, and data intermediaries by treating dependence as a cross-domain governance problem rather than a set of unrelated sourcing decisions.
The first pillar, dependency risk assessment, requires firms to map critical dependencies, classify partner-specific risks, measure substitutability, and monitor cumulative exposure across cloud, AI, and data relationships. Research on AI governance identifies best practices and barriers related to accountability, transparency, oversight, and organisational readiness [23]. Applied more broadly, these governance concerns imply that firms should maintain dependency registers, assess switching difficulty, monitor model and data risks, and connect vendor exposure to enterprise risk management.
The second pillar, protective governance mechanisms, focuses on contractual protections, technical portability, multi-vendor options, data control, audit rights, and internal capability building. Research on artificial intelligence capability shows that firms benefit when AI resources are combined with organisational capabilities rather than merely acquired as external tools [24]. Table 4 presents the proposed Digital Ecosystem Governance Framework. The framework shows how general governance principles can be translated into dependence-specific mechanisms for cloud platforms, AI vendors, and data intermediaries.
Table 4. Digital Ecosystem Governance Framework: Principles, Components, and Dependence-Specific Governance Mechanisms
Framework pillar | Governance principle | Cloud platform mechanisms | AI vendor mechanisms | Data intermediary mechanisms | Strategic outcome |
Dependency risk assessment | Make dependence visible before it becomes irreversible | Map critical workloads, proprietary services, regions, egress exposure, and recovery gaps | Map vendor models, APIs, training data access, update cycles, and decision use cases | Map data sources, provenance chains, privacy obligations, and strategic data gaps | Clear view of where the firm is exposed |
Protective governance mechanisms | Reduce lock-in and preserve control | Use modular architecture, portability standards, multi-cloud options, and exit-tested contracts | Negotiate audit rights, explainability, model validation, data ownership, and update controls | Require provenance documentation, quality testing, privacy warranties, and reuse restrictions | Lower switching barriers and stronger accountability |
Strategic governance oversight | Treat dependence as an enterprise-level strategic risk | Set cloud concentration limits and review platform roadmap exposure | Define AI risk appetite and require human accountability for high-impact uses | Decide which data assets must be internally controlled or directly sourced | Board and executive visibility over dependence thresholds |
Internal capability building | Avoid capability hollowing caused by excessive external reliance | Develop architecture literacy and cloud-independent operational knowledge | Build internal AI literacy, validation skills, and model risk management capacity | Build first-party data strategy and data governance competence | Stronger absorptive capacity and better vendor discipline |
Exit and continuity readiness | Prepare for disruption, regulatory change, or partner failure | Maintain backup environments, migration documentation, and recovery playbooks | Maintain fallback decision processes and alternative model options | Maintain substitute data sources and deletion or transition procedures | Improved resilience under provider failure or strategic conflict |
Relational governance | Coordinate with partners while preserving firm interests | Establish executive cloud governance reviews and escalation paths | Create model performance review routines and joint accountability forums | Conduct periodic intermediary reviews and data quality negotiations | Balanced collaboration and control |
Governance learning | Use incidents and reviews to update dependence controls | Review outages, cost escalation, and migration constraints | Review model drift, bias incidents, and vendor update effects | Review data defects, compliance issues, and provenance gaps | Continuous improvement of dependence governance |
The third pillar, strategic governance oversight, places digital ecosystem dependence within board-level and executive risk management rather than leaving it solely to technology teams. Recent research on data intermediaries and data intermediation services shows that intermediary roles are evolving and becoming more specialised, which increases the need for managerial understanding of ecosystem positions and control points [25]. The framework therefore requires senior managers to define acceptable dependence thresholds, identify non-substitutable partners, and decide which digital capabilities must remain internally governable.
Figure 1 presents the proposed Digital Ecosystem Governance Framework for managing firm dependence on cloud platforms, AI vendors, and data intermediaries.
Figure 1. Digital Ecosystem Governance Framework for Managing Cloud Platform, AI Vendor, and Data Intermediary Dependence
Managers can apply the framework by beginning with a dependency audit that identifies which operations, data flows, models, interfaces, and customer-facing services depend on external digital actors. Open data intermediary research shows that intermediaries can contribute to data ecosystems, but their value depends on the roles they perform and the governance conditions surrounding those roles [26]. A dependency audit should therefore evaluate not only the presence of vendors, but also the degree to which each vendor controls continuity, data access, model interpretation, or strategic differentiation.
The second application step is vendor diversification, but diversification should be selective rather than symbolic. Cloud, AI, and data partners differ in substitutability, integration complexity, and risk profile, so managers should prioritise diversification where single-partner exposure threatens mission-critical continuity or strategic autonomy [27]. In practice, this means distinguishing between relationships that require full redundancy, relationships that require negotiated exit rights, and relationships where internal capability development is the more realistic mitigation strategy.
The third application step is in-house capability investment, because governance is weak when firms lack the knowledge to evaluate vendor claims. Research on cloud vendor lock-in prediction and multi-cloud environments shows that lock-in can be assessed and mitigated through more systematic evaluation of technical dependence and placement choices [28, 29]. Similarly, firms should maintain internal expertise in cloud architecture, AI validation, data governance, privacy compliance, and contract interpretation so that external partners remain governed suppliers rather than invisible decision-makers.
The fourth application step is the creation of governance scorecards that translate dependence risk into recurring managerial review. Research on data market ordering argues that data intermediaries must be integrated into governance arrangements that shape data sharing and market accountability [30]. A practical scorecard should track concentration, portability, auditability, data provenance, privacy exposure, model transparency, exit feasibility, and internal capability depth, allowing executives to review digital dependence as part of strategic risk governance.
The first limitation is that the framework is conceptual and has not yet been empirically tested across firms, industries, or regulatory environments. Ecosystem theory provides a strong foundation for understanding interdependence and role structure, but empirical research is needed to examine how firms actually measure and govern dependence in cloud, AI, and data intermediary relationships [4]. Future studies could test whether firms that apply dependency assessment, protective governance, and strategic oversight experience lower switching costs, fewer governance failures, or stronger digital resilience.
The second limitation is that the framework may simplify the heterogeneity of cloud platforms, AI vendors, and data intermediaries. Platform and infrastructure research shows that digital systems vary in architecture, governance, modularity, and ecosystem dynamics, which means that dependence mechanisms may differ substantially across provider types and technical layers [7]. For example, dependence on a cloud storage provider is not identical to dependence on a foundation model API, and dependence on a regulated data-sharing intermediary is not identical to dependence on an opaque commercial data broker.
The third limitation is that firm size, industry context, regulation, and internal digital maturity may moderate the feasibility of the proposed governance mechanisms. Research on digital transformation indicates that firms differ in their ability to integrate digital resources, build capabilities, and respond to changing innovation conditions [9]. Small firms may lack bargaining power or technical expertise, while highly regulated firms may require stronger audit rights, data controls, and accountability structures than the general framework can specify in detail.
Digital ecosystem dependence is becoming a central strategic vulnerability for firms that rely on external cloud platforms, AI vendors, and data intermediaries. These relationships can generate scale, flexibility, analytical power, and innovation speed, but they can also produce lock-in, opacity, bargaining weakness, data control loss, and exit difficulty. The central argument of this article is that these risks cannot be governed adequately through fragmented IT, procurement, or legal routines alone.
The Digital Ecosystem Governance Framework developed in this article offers a structured way to identify, categorise, and mitigate dependence risks across three critical ecosystem partner types. It proposes that firms should combine dependency risk assessment, protective governance mechanisms, and strategic governance oversight. This integrated approach helps managers preserve the benefits of external digital ecosystems while reducing the likelihood that operational reliance becomes strategic capture.
Future research should empirically validate the framework, examine how dependence evolves over time, and assess which governance mechanisms are most effective in different industries and firm sizes. Managers should treat digital ecosystem dependence as a recurring strategic governance issue rather than a one-time sourcing decision. Firms that build internal capability, contractual protection, portability, and exit readiness will be better positioned to use external digital ecosystems without surrendering strategic control.
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Institute for Management, Business, and Accounting Studies
