Organizational backing and managerial mentoring determine whether AI delivers value: firms that invest in strategic support and developmental mentoring are more likely to realize productivity and innovation gains from human–AI collaboration.

Revolutionizing Human Resource Development: A Theoretical Framework for Enhancing Effective Human–AI Collaboration

Chieh‐Peng Lin · Fetched March 18, 2026 · Human Resource Development Quarterly

semantic_scholar theoretical n/a evidence 7/10 relevance DOI Source

The paper synthesizes Adaptive Structuration Theory and Affordance Actualization Theory into a multi-level framework showing that organizational strategy and mentoring determine whether human–AI collaboration translates into individual, team, and firm-level gains.

Human‐AI collaboration has the potential to revolutionize organizational development amid rapid technological changes. By integrating the theoretical foundations of adaptive structuration theory (AST) and affordance actualization theory (AAT), this study proposes a theoretical framework to facilitate effective human‐AI collaboration for organizations in the AI era. These theories complement each other by linking human and AI elements, synergizing the adaptive process of human‐AI collaboration to yield positive outcomes at the individual, team, and organizational levels. Based on the framework, the roles of strategic innovation backing and mentoring styles that intervene in the process are examined. Finally, the study discusses implications and outlines a future research agenda.

Summary

Main Finding

The paper develops a conceptual framework that integrates adaptive structuration theory (AST) and affordance actualization theory (AAT) to explain how effective human–AI collaboration can be structured within organizations. The combined framework links human and AI elements across adaptive processes and affordance realization, identifying how organizational supports (strategic innovation backing) and managerial behaviors (mentoring styles) intervene to produce positive outcomes at individual, team, and organizational levels. The work is theoretical and sets an agenda for empirical validation.

Key Points

Theories integrated:
- Adaptive Structuration Theory (AST): focuses on how groups adapt technologies and organizational structures through social interaction and emergent use patterns.
- Affordance Actualization Theory (AAT): emphasizes how users perceive and realize the action possibilities (affordances) offered by technologies.
Complementarity: AST explains adaptive processes and social structuring around AI; AAT explains how actors perceive and enact AI’s potential. Together they map how human behavior and AI capabilities co-evolve to produce outcomes.
Multi-level outcomes: framework links micro (individual skills, task performance), meso (team coordination, workflows), and macro (organizational strategy, innovation, productivity) effects.
Intervening factors:
- Strategic innovation backing: organizational investments, resource allocation, governance, and incentive structures that enable experimentation and scaling of human–AI work.
- Mentoring styles: managerial and peer mentoring approaches that influence how affordances are perceived and actualized — e.g., directive vs. developmental mentoring affects learning, trust, and task allocation.
Contributions: provides a theoretical basis for designing interventions to improve human–AI collaboration and proposes research directions to test mechanisms and boundary conditions.

Data & Methods

Type of study: conceptual/theoretical synthesis — no original empirical dataset reported.
Methodological approach: literature integration and theoretical framework development combining AST and AAT literature streams; delineation of mechanisms and moderators; identification of testable propositions and an agenda for future empirical work.
Implied empirical needs: the paper calls for mixed-methods validation (case studies, field experiments, longitudinal studies), measurement development for affordance actualization and adaptive structuration processes, and multi-level data collection (individual, team, firm).

Implications for AI Economics

Productivity and growth: The framework implies that productivity gains from AI depend not only on technology capabilities but on organizational adaptation and successful affordance actualization; investments in supportive strategy and mentoring can increase realized returns to AI.
Complementarity and skill demand: Effective human–AI collaboration will shift task content toward complementary activities (supervision, interpretation, creative/problem-solving), increasing demand for different skills and potentially raising skill premia for workers who can actualize AI affordances.
Diffusion and heterogeneity: Organizational heterogeneity in strategic backing and mentoring explains variation in AI adoption benefits across firms and sectors—this shapes cross-firm productivity dispersion and aggregate productivity dynamics.
Labor market and inequality: If organizational supports are uneven, returns to AI may concentrate in firms and workers that can actualize affordances, potentially widening wage and employment disparities; policy and training investments can mitigate such effects.
Measurement and empirical identification: Economists should measure not just AI adoption but mediating organizational factors (governance, mentoring practices, learning processes). Suggested empirical designs include difference-in-differences exploiting phased rollouts, randomized mentoring or training interventions, matched employer–employee panels, and instrumental-variables exploiting exogenous shocks to innovation backing.
Policy and managerial takeaways: Subsidies or incentives for organizational capacity-building (training, change management, governance) may be as important as subsidies for acquiring AI technologies. Firms should invest in mentoring and managerial practices that foster affordance actualization to capture AI’s economic benefits.
Research agenda for AI economics:
- Quantify the contribution of adaptive structuration and affordance actualization to measured productivity gains from AI.
- Estimate heterogeneous treatment effects of AI across firms with varying strategic backing and mentoring regimes.
- Evaluate cost-effectiveness of interventions (training, mentoring, governance changes) that aim to improve human–AI collaboration.
- Develop and validate survey and administrative measures for affordance actualization and collaborative adaptation to be used in econometric analyses.

If you want, I can draft potential empirical specifications, measurement instruments, or experiment designs to test the framework’s central propositions in firm-level or worker-level data.

Assessment

Paper Typetheoretical Evidence Strengthn/a — The paper is a conceptual/theoretical synthesis and presents no original empirical data or causal estimates; it offers propositions and an agenda for future empirical testing but no direct evidence. Methods Rigormedium — Theoretical integration is structured and draws on two well-established literatures (AST and AAT), lays out clear mechanisms, multi-level linkages, moderators, and testable propositions; however, rigor is limited by the absence of empirical operationalization, measurement validation, or formal modeling that would strengthen internal coherence and falsifiability. SampleNo empirical sample — the paper is a literature-based conceptual synthesis combining Adaptive Structuration Theory and Affordance Actualization Theory and deriving multilevel propositions about human–AI collaboration. Themeshuman_ai_collab productivity org_design skills_training adoption innovation inequality GeneralizabilityNo empirical validation — applicability to real-world firms, sectors, or countries is untested., Conceptual level: does not specify boundary conditions across firm size, industry, or institutional contexts., Operational measures for key constructs (affordance actualization, adaptive structuration) are not developed, limiting immediate empirical application., Does not quantify effect sizes or provide causal identification, so implications for aggregate productivity or labor markets are qualitative.

Claims (10)

Claim	Direction	Confidence	Outcome	Details
The paper develops a conceptual framework that integrates Adaptive Structuration Theory (AST) and Affordance Actualization Theory (AAT) to explain how effective human–AI collaboration can be structured within organizations. Organizational Efficiency	positive	high	explanatory power / conceptual framework for human–AI collaboration	0.02
Strategic innovation backing (organizational investments, resource allocation, governance, and incentives) enables experimentation and scaling of human–AI work and thereby increases realized returns to AI investments. Firm Productivity	positive	speculative	realized returns to AI (e.g., productivity gains, ROI on AI adoption, scaling of AI-enabled processes)	0.0
Managerial and peer mentoring styles (e.g., directive vs. developmental mentoring) influence how affordances are perceived and actualized, affecting learning, trust, and task allocation in human–AI collaboration. Skill Acquisition	mixed	speculative	learning outcomes, trust in AI/human–AI teams, task allocation decisions	0.0
The integrated framework links multi-level outcomes: micro (individual skills, task performance), meso (team coordination, workflows), and macro (organizational strategy, innovation, productivity) effects to adaptive structuration processes and affordance actualization. Organizational Efficiency	positive	high	individual skills and performance; team coordination and workflow quality; organizational strategy outcomes, innovation metrics, productivity	0.02
Productivity gains from AI depend not only on the technology's capabilities but on organizational adaptation and successful affordance actualization; therefore investments in supportive strategy and mentoring can increase the fraction of potential AI productivity realized. Firm Productivity	positive	speculative	productivity gains attributable to AI; share of theoretical AI productivity potential actually realized	0.0
Effective human–AI collaboration will shift task content toward complementary activities (supervision, interpretation, creative/problem-solving), increasing demand for these complementary skills and potentially raising skill premia for workers who actualize AI affordances. Wages	positive	speculative	task composition changes, demand for supervisory/interpretive/creative skills, wage premia for complementary skill workers	0.0
Organizational heterogeneity in strategic backing and mentoring explains variation in benefits from AI adoption across firms and sectors, contributing to cross-firm productivity dispersion. Firm Productivity	mixed	speculative	heterogeneity in firm-level AI productivity gains; cross-firm productivity dispersion	0.0
Uneven organizational supports can concentrate returns to AI in firms and workers that successfully actualize affordances, potentially widening wage and employment disparities; targeted policy and training investments can mitigate these effects. Inequality	negative	speculative	wage inequality, employment disparities, concentration of AI returns across firms/workers	0.0
This work is conceptual/theoretical and reports no original empirical dataset; it explicitly calls for mixed-methods empirical validation (case studies, field experiments, longitudinal studies), measurement development, and multi-level data collection. Other	null_result	high	presence/absence of original empirical data in the paper (none)	0.02
Economists and researchers should measure organizational mediators (governance, mentoring practices, learning processes) alongside AI adoption and use empirical designs such as difference-in-differences with phased rollouts, randomized mentoring/training interventions, matched employer–employee panels, and IV exploiting exogenous shocks to innovation backing to identify causal effects. Research Productivity	positive	high	feasibility and validity of empirical identification strategies for causal effects of AI mediated by organizational factors	0.02