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View corpus contextA practical threefold framework — combining adaptive, value-added and extension pathways with strategic/operational layers and an embedded ethical charter — aims to accelerate and de-risk the translation of AI research into economic value; expert Delphi validation (n=20) found strong consensus, but real-world impact on adoption and productivity remains untested.
Development of an Integrated Threefold Framework for Knowledge Application Based on the Connection of Theory, Practice, and Ethics: A Model Design and Expert Validation
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Presents and expert-validates a threefold model (adaptive, value-added, extension) with strategic and operational layers and an embedded ethical charter to improve translation of AI research into practice, supported by practical tools and a Delphi (n=20) showing >85% consensus.
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ABSTRACT The persistent gap between the production of scientific evidence and its practical application remains a significant challenge in the social sciences and management fields. This study aimed to present an innovative model to bridge this gap. First, a systematic literature review was conducted to identify the shortcomings of existing frameworks. Subsequently, drawing on the Delphi method and expert opinion, a threefold model for knowledge application was developed. The model comprises three complementary approaches—adaptive, value‐added, and extension—distinctively delineated at strategic and operational levels. To guide the development and validation of the model, two research questions were formulated: (i) how to design a threefold model incorporating the three approaches at both levels, and (ii) to what extent experts validate its components, prerequisites, and ethical charter. The findings provide explicit answers to these questions through a dedicated section in the Results and a summary table. A systematic literature review was undertaken to identify theoretical and practical gaps in existing frameworks. Based on this analysis, the proposed threefold model was developed. A three‐round Delphi process was then conducted with 20 academic and executive experts to evaluate and refine the model. Content validity and expert consensus were assessed using the Content Validity Index (CVI), Content Validity Ratio (CVR), and Interquartile Range (IQR). To operationalize the model, an application scenario of the value‐added approach, a responsibility matrix (RACI), and a 20‐item implementation checklist were designed. The model was also systematically compared with the widely cited CFIR, EPIS, and NASSS frameworks. The expert panel achieved a consensus level exceeding 85% for all model components, confirming the framework's robustness and scientific adequacy. The value‐added scenario demonstrated that the model can successfully guide interdisciplinary knowledge transfer (from artificial intelligence to medicine) through the six defined stages. The implementation checklist provided a tangible tool for monitoring application progress in real‐world projects. The structured comparison revealed that the threefold model fills three principal gaps in existing frameworks: the distinction between strategic and operational levels, the coverage of multiple knowledge application pathways, and the structural embedding of an operational ethical charter. The threefold model can serve as a practical tool for policymakers, managers, and practitioners to facilitate the transfer of knowledge into action. The study's innovation lies in integrating three complementary approaches, distinguishing between strategic and operational layers, embedding an ethical charter within the framework, and providing operational tools (scenario, RACI matrix, checklist). Future research should empirically test the model in real‐world contexts and develop quantitative indicators to assess its effectiveness.
Summary
Main Finding
The paper develops and validates a novel "threefold model" to bridge the gap between scientific knowledge production and practical application. The model integrates three complementary approaches—adaptive, value-added, and extension—each specified at strategic and operational levels, and embeds an operational ethical charter. Expert validation (three-round Delphi; n=20) produced >85% consensus on components, prerequisites, and ethics, and the authors supply operational tools (application scenario, RACI responsibility matrix, 20‑item implementation checklist). Compared with CFIR, EPIS, and NASSS, the model fills gaps around (1) strategic vs operational distinction, (2) multiple knowledge-application pathways, and (3) structural embedding of ethics.
Key Points
- Three complementary approaches:
- Adaptive: tailor research outputs to changing contexts and feedback.
- Value‑added: actively transform research outputs into usable products/services (demonstrated via an AI→medicine scenario with six stages).
- Extension: disseminate and scale knowledge through outreach and networks.
- Two-layer structure: each approach is articulated at both strategic and operational levels to guide policy and implementation actions separately.
- Embedded ethical charter: operationalized ethics included structurally (not just as principles) to guide responsible application.
- Practical tools provided:
- Value‑added application scenario (AI to medicine interdisciplinary transfer).
- RACI responsibility matrix to assign roles.
- 20‑item implementation checklist to monitor progress.
- Validation: Three Delphi rounds with 20 academic and executive experts; content validity assessed using CVI, CVR, and IQR; consensus >85% across items.
- Comparative analysis: Model addresses three principal gaps relative to CFIR, EPIS, NASSS.
- Limitations and next steps: Authors call for empirical, real‑world testing and the development of quantitative effectiveness indicators.
Data & Methods
- Systematic literature review to identify limitations in existing knowledge-application frameworks.
- Model development grounded in literature synthesis and expert input.
- Delphi study:
- Three rounds with 20 mixed academic and practitioner experts.
- Evaluation metrics: Content Validity Index (CVI), Content Validity Ratio (CVR), and Interquartile Range (IQR).
- Consensus threshold: achieved >85% agreement on model components, prerequisites, and ethical charter.
- Operationalization:
- Constructed a value‑added application scenario (AI → medicine) illustrating six implementation stages.
- Designed RACI responsibility matrix and a 20‑item implementation checklist for monitoring.
- Comparative framework analysis against CFIR, EPIS, and NASSS to demonstrate novelty and coverage.
Implications for AI Economics
- Faster, more accountable translation of AI research into economic value:
- The value‑added approach plus RACI/checklist can guide commercialization, diffusion, and firm-level adoption decisions—helpful for measuring and accelerating returns to AI R&D investments.
- Better policy design and governance:
- The strategic/operational split helps policymakers distinguish high-level incentives and regulation (strategic) from operational procurement, standards, and deployment practices (operational).
- An embedded ethical charter supports regulation-sensitive deployment (privacy, fairness, accountability) reducing socially costly harms that can stall adoption.
- Improved evaluation and measurement opportunities:
- The framework’s operational checklist and suggested future quantitative indicators can be adapted to track economic metrics (adoption rates, productivity gains, cost-benefit, diffusion speed, social welfare impacts).
- Cross-sector diffusion and interdisciplinary value capture:
- The AI→medicine scenario illustrates how the model supports sectoral transfer of AI capabilities, informing models of cross-sector productivity spillovers and human-capital complementarities.
- Research agenda for AI economics:
- Empirical validation of the model via field experiments, quasi-experimental evaluations, or panel data on adoption and outcomes.
- Development of standardized quantitative indicators (e.g., time-to-market, adoption elasticity, ROI, distributional impacts) to assess effectiveness and inform cost‑benefit analyses.
- Study of governance structures (RACI-like arrangements) on organizational performance and externalities from AI deployment.
- Practical takeaway for practitioners and economists:
- Use the threefold model to design interventions that explicitly separate strategy from operations, integrate ethical safeguards, and monitor implementation—thereby improving the likelihood that AI-generated knowledge yields measurable economic benefits.
Assessment
Paper Typedescriptive
Evidence Strengthlow — The paper provides conceptual development and content validation via a three‑round Delphi (n=20) with CVI/CVR/IQR metrics, which establishes expert consensus on the framework's components and ethics but contains no empirical tests linking the model to economic outcomes, adoption rates, productivity gains, or causal impacts.
Methods Rigormedium — Methods combine a systematic literature review, structured model development, and a formal Delphi with content-validity metrics — appropriate for framework validation — but the Delphi sample is small and potentially non-representative, selection/response details are limited, and there is no field testing or quantitative evaluation of effectiveness.
SampleSystematic literature review of existing knowledge-application frameworks; three-round Delphi study with 20 mixed academic and executive/practitioner experts who evaluated model components, prerequisites, and an operational ethical charter (metrics: CVI, CVR, IQR; consensus >85%); model illustrated with an AI→medicine value-added scenario, plus RACI matrix and 20-item implementation checklist.
Themesadoption governance org_design productivity human_ai_collab
GeneralizabilityDelphi sample small (n=20) and likely selected purposively; findings reflect expert opinion rather than observed outcomes, No empirical, real-world testing — tools (checklist, RACI, scenario) unvalidated across sectors, firm sizes, or geographies, AI→medicine scenario is illustrative and may not generalize to other domains with different regulatory, technical, or market dynamics, Cultural, institutional, and regulatory context of experts and literature unclear, limiting transferability across countries
Claims (7)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| The paper proposes a threefold knowledge-application model comprising adaptive, value-added, and extension approaches, each articulated at strategic and operational levels. Organizational Efficiency | positive | Structured translation of scientific knowledge into practical application |
Reading fidelity
high
Study strength
medium
|
not reported
|
| Expert validation found more than 85% consensus on the model components, prerequisites, and embedded ethical charter. Governance And Regulation | positive | Expert consensus and content validity of the proposed framework |
Reading fidelity
high
Study strength
medium
|
n=20
>85% consensus
|
| The value-added approach is operationalized through a six-stage scenario illustrating the transfer of AI knowledge into medicine. Task Allocation | positive | Cross-sector transfer and application of AI-generated knowledge |
Reading fidelity
high
Study strength
low
|
not reported
|
| The model provides operational implementation tools consisting of an application scenario, a RACI responsibility matrix, and a 20-item implementation checklist. Organizational Efficiency | positive | Implementation monitoring and assignment of organizational responsibilities |
Reading fidelity
high
Study strength
low
|
20-item checklist
|
| The model embeds an operational ethical charter as a structural component of knowledge application rather than treating ethics only as a set of general principles. Ai Safety And Ethics | positive | Integration of ethical safeguards into knowledge-application processes |
Reading fidelity
high
Study strength
medium
|
n=20
|
| Compared with CFIR, EPIS, and NASSS, the proposed model addresses gaps involving the distinction between strategic and operational levels, the availability of multiple knowledge-application pathways, and the structural embedding of ethics. Governance And Regulation | positive | Framework coverage of knowledge-application and implementation dimensions |
Reading fidelity
high
Study strength
low
|
not reported
|
| The paper does not establish real-world effectiveness of the model and calls for empirical testing and quantitative effectiveness indicators in future research. Organizational Efficiency | null_result | Empirical effectiveness of the proposed knowledge-application model |
Reading fidelity
high
Study strength
high
|
not reported
|