Algorithms have shifted from tools to the governance hub of innovation ecosystems, driving a three-stage process — data sensing and resource structuring, algorithmic empowerment and resource bundling, and scenario emergence with dynamic value capture — and requiring a mix of technical algorithmic governance and relational contracts to manage co-creation.

Main Finding

The paper argues that digital-intelligent innovation ecosystems differ qualitatively from earlier digital platforms: algorithms become the governance hub, architectures are granularly decoupled, and systems exhibit adaptive self-emergence. Value co-creation follows a dynamic three-stage micro-evolutionary chain—(1) data sensing & resource structuring, (2) algorithmic empowerment & resource bundling, (3) scenario emergence & resource leveraging—and effective multi-actor co-creation requires coordination of algorithmic governance and ecological (relational) contracts. The study extends Resource Orchestration Theory (ROT) to the ecosystem level and offers a governance framework and strategy guidance, while calling for empirical testing of derived hypotheses.

Key Points

• Conceptual shift: from “connection-driven” digital ecosystems to “computation- and adaptive intelligence-driven” digital-intelligent ecosystems where data + algorithms are dynamic production factors.
• Three defining characteristics:
  • Algorithmic dominance: algorithms act as real-time governance hubs for allocation, matching, evaluation.
  • Architectural decoupling: fine-grained modularization (APIs, models, data components) enabling rapid reconfiguration.
  • Adaptive emergence: nonlinear, self-organizing creation of new scenarios and products beyond single actors’ intent.
• Three-stage micro-evolutionary logic of value co-creation:
• Data sensing & resource structuring — distributed sensing, decoupling, consolidation of heterogeneous data/assets.
• Algorithm empowerment & resource bundling — algorithms integrate decoupled resources into modular capabilities.
• Scenario emergence & resource leveraging — intelligent capabilities are deployed to spawn scenario-based value and capture it via dynamic mechanisms.
• Governance requirements: balance “hard” technical rules (algorithmic governance, programmable contracts) and “soft” relational/ecological contracts to handle issues like data rights, algorithmic black boxes, trust, and cross-sector collaboration.
• Practical levers suggested: open interfaces, algorithmic orchestration by leading firms, dynamic revenue-sharing (e.g., blockchain & smart contracts), embedding/rapid response by complementors, and policy measures on data/property rights and algorithm regulation.
• Research contribution: applies ROT beyond firm boundaries, introduces “algorithm-empowerment” as a micro-mechanism, identifies governance trade-offs, and proposes testable hypotheses for future empirical work.

Data & Methods

• Research approach: qualitative, theory-building study based on systematic literature comparison, conceptual clarification, and logical/mechanism deconstruction.
• Theoretical lenses: Resource Orchestration Theory (structuring, bundling, leveraging) combined with an algorithm-empowerment perspective and service-dominant (S-D) logic for value co-creation.
• Methodology: deductive framework construction that (a) defines the digital-intelligent ecosystem, (b) decomposes micro-mechanisms across three dynamic phases, and (c) derives governance strategies for actors (leading firms, complementors, policymakers).
• Empirical content: illustrative examples and case references (e.g., NIO) are used for plausibility; no original quantitative data or formal empirical tests are presented.
• Limitations noted by author: conceptual/qualitative focus — findings need empirical validation and operationalization; governance prescriptions require context-specific refinement.

Implications for AI Economics

• New unit of production: treat data-algorithm composites as dynamic, endogenous production factors—models of firm behavior and production functions should incorporate algorithmic capital alongside data and compute.
• Allocation and surplus capture:
  • Algorithmic governance can shift bargaining power toward actors who control orchestration rules (platforms/algorithm owners), affecting distribution of rents across ecosystem participants.
  • Decoupling modularizes contributions, potentially lowering entry costs for component providers but concentrating value capture where orchestration/algorithms sit.
• Market structure & competition policy:
  • Algorithmic hubs can generate strong, non-linear network effects; antitrust and regulation should consider algorithmic gatekeeping, access to training data, and model interoperability.
  • Policy levers (data portability, access mandates, algorithmic transparency) will influence competitive dynamics and welfare.
• Incentive and contract design:
  • Dynamic revenue-sharing (smart contracts, on-chain settlement) changes incentive alignment and reduces trust frictions; economic models should allow for programmatic, contribution-based payoff mechanisms.
  • Need for economic theory on multi-party relational contracts in environments where algorithmic matching replaces many traditional contracting frictions.
• Investment and firm strategy:
  • Returns to investing in algorithmic capabilities, labeling/curation of training data, and compute infrastructure are likely to increase; firms should evaluate orchestration value versus owning components.
  • Complementors face two strategic paths: deepen embedding into orchestration layers (to capture spillovers) or specialize in modular capabilities with wide composability.
• Empirical research agenda (suggestions):
  • Measure stages: operationalize indicators for resource structuring, bundling, and leveraging (e.g., API modularity, model reuse rates, scenario-driven revenues).
  • Quantify value capture: estimate how value is divided between algorithm owners, data providers, and complementors across ecosystems.
  • Test hypotheses on governance mechanisms: compare outcomes under different mixes of algorithmic rules vs relational contracts; evaluate smart contract settlements’ effects on participation and innovation.
  • Market concentration & welfare: empirically study whether algorithmic orchestration increases concentration and how regulation moderates welfare effects.

Suggested keywords for follow-up empirical work: algorithmic governance, ecosystem orchestration, data-as-dynamic-factor, resource bundling, smart-contract revenue-sharing, modularity and entry, platform power.

If you’d like, I can (a) convert the three-stage framework into measurable indicators for empirical testing, or (b) draft a short research design to test one of the paper’s hypotheses (e.g., effect of algorithmic openness on complementor innovation).