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AI capabilities boost project outcomes mainly by improving knowledge sharing and making teams more agile; agility delivers the largest direct performance gain, and results hold under SEM and 2SLS but are based on self-reported, short-panel data from Turkish project firms.

Artificial intelligence capability and project performance: integrating dynamic capabilities theory and the knowledge-based view

Rıza Banavand, Çağdaş Tunca, Mustafa Rimaz · September 07, 2026 · Business Technology & Innovation Studies Journal

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AI capability raises project performance directly and indirectly by improving knowledge integration and project agility, with a sequential mediation path (AI → knowledge integration → agility → performance).

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Artificial intelligence (AI) is transforming project management by enhancing organizational decision-making and operational efficiency. However, the organizational mechanisms through which AI capability improves project performance remain insufficiently understood. Drawing upon dynamic capabilities theory and the knowledge-based view, this study examines the direct effect of AI capability on project performance and investigates the mediating roles of knowledge integration and project agility. Data were collected from 267 project-based organizations operating in Turkey using a two-wave survey with a four-month interval between data collection waves. Confirmatory factor analysis (CFA) was employed to assess the reliability and validity of the measurement model. The proposed hypotheses were tested using hierarchical regression analysis, bootstrapping procedures, structural equation modeling (SEM) for robustness analysis, and two-stage least squares (2SLS) estimation to address potential endogeneity. The findings indicate that AI capability positively influences knowledge integration, project agility, and project performance. Knowledge integration significantly enhances both project agility and project performance, while project agility has the strongest direct effect on project performance. The bootstrapping analysis further demonstrates that knowledge integration and project agility partially mediate the relationship between AI capability and project performance. Moreover, the sequential mediation results reveal that AI capability improves project performance by strengthening knowledge integration, which subsequently enhances project agility. This study contributes to the project management and AI literature by integrating dynamic capabilities theory and the knowledge-based view within a unified framework to explain how AI capability is transformed into superior project performance. By identifying knowledge integration and project agility as complementary organizational mechanisms, the study offers a more comprehensive explanation of AI-enabled project success and provides valuable theoretical and managerial insights for organizations seeking to improve project outcomes through AI capability.

Summary

Main Finding

AI capability in project-based organizations improves project performance both directly and indirectly. The primary mechanisms are enhanced knowledge integration and increased project agility; knowledge integration also operates upstream to boost agility, producing a sequential mediation path (AI capability → knowledge integration → project agility → project performance).

Key Points

Theoretical framing: integrates dynamic capabilities theory and the knowledge-based view to explain how AI capability translates into superior project outcomes.
Direct effects: AI capability positively affects knowledge integration, project agility, and project performance.
Mediators:
- Knowledge integration significantly improves both project agility and project performance.
- Project agility has the strongest direct effect on project performance.
- Knowledge integration and project agility partially mediate the AI capability → project performance link.
- Sequential mediation confirmed: AI capability → knowledge integration → project agility → project performance.
Robustness and endogeneity: results hold under hierarchical regression, bootstrapping, SEM, and 2SLS estimation.
Sample/context: 267 project-based organizations in Turkey; two-wave survey with a four-month interval.

Data & Methods

Sample: 267 firms operating on projects in Turkey; two-wave survey design (four months apart) to mitigate common method bias and support temporal ordering.
Measurement validation: Confirmatory factor analysis (CFA) to assess reliability and construct validity.
Hypothesis testing and robustness:
- Hierarchical regression analysis for main effects.
- Bootstrapping procedures to test mediation effects.
- Structural equation modeling (SEM) as robustness analysis.
- Two-stage least squares (2SLS) estimation to address potential endogeneity concerns.
Limitations of data: survey-based and self-reported measures; geographically confined to Turkey; relatively short inter-wave interval (4 months).

Implications for AI Economics

Mechanisms of AI productivity: The study identifies concrete organizational channels (knowledge integration and project agility) that convert AI capability (capital/skill) into performance gains, clarifying micro-level mechanisms behind AI-driven productivity improvements in project-based sectors.
Complementarity and complementary investments: Returns to AI capability depend on complementary organizational capabilities (knowledge processes, agile project practices). Economic models of AI adoption should incorporate complementarities and organizational frictions when forecasting firm-level productivity or sectoral gains.
Policy and investment signals: Policy or firm-level incentives to adopt AI should be paired with support for knowledge-sharing, training, and agile governance to realize full productivity benefits; subsidies or programs that only reduce the cost of AI tools may yield limited returns without these complements.
Measurement and evaluation: For empirical studies of AI’s economic impact, include intermediate organizational variables (knowledge integration, agility) and use strategies (panel/two-wave designs, IV/2SLS) to address endogeneity when estimating causal effects.
Research directions: Extend to other countries/industries, use objective performance and longitudinal data for stronger causal inference, quantify welfare and distributional effects of AI-enabled improvements in project productivity, and model the cost-benefit trade-offs of complementary organizational investments.

Assessment

Paper Typequasi_experimental Evidence Strengthmedium — Multiple complementary methods (temporal design, CFA, SEM, bootstrapped mediation, and 2SLS) support the causal pathway, but evidence rests on self-reported survey data from a single country, a relatively short inter-wave interval, and limited detail on instrument validity for 2SLS, which weakens causal claims. Methods Rigormedium — The study uses a reasonable suite of econometric and SEM techniques and attempts to address endogeneity with 2SLS, but relies on cross-sectional/self-reported measures, a short time gap (4 months) between waves, and the supplied text does not report instrument strength or robustness to alternative identification checks. SampleSurvey of 267 project-based organizations operating in Turkey; two-wave design with a four-month interval; measures are self-reported organizational measures of AI capability, knowledge integration, project agility, and project performance. Themesproductivity org_design human_ai_collab IdentificationTwo-wave survey (four-month lag) to establish temporal ordering; construct validation via CFA; main tests using hierarchical regression and SEM; mediation tested with bootstrapping; endogeneity addressed with 2SLS instrumental-variable estimation (instruments not described in supplied text). GeneralizabilitySingle-country (Turkey) sample limits external validity to other institutional/economic contexts, Only project-based organizations studied; findings may not generalize to routine manufacturing or service firms, Self-reported performance measures risk bias compared with objective productivity metrics, Short inter-wave interval (4 months) may not capture longer-term performance effects

Claims (8)

Claim	Direction	Outcome	Confidence & Evidence	Details
AI capability positively affects project performance in project-based organizations. Organizational Efficiency	positive	Project performance	Reading fidelity high Study strength medium	n=267 0.48
AI capability positively affects knowledge integration. Organizational Efficiency	positive	Knowledge integration	Reading fidelity high Study strength medium	n=267 0.48
AI capability positively affects project agility. Organizational Efficiency	positive	Project agility	Reading fidelity high Study strength medium	n=267 0.48
Knowledge integration positively affects both project agility and project performance. Organizational Efficiency	positive	Project agility and project performance	Reading fidelity high Study strength medium	n=267 0.48
Project agility has the strongest direct effect on project performance among the examined direct predictors. Organizational Efficiency	positive	Project performance	Reading fidelity high Study strength medium	n=267 0.48
Knowledge integration and project agility partially mediate the relationship between AI capability and project performance. Organizational Efficiency	positive	Project performance	Reading fidelity high Study strength medium	n=267 0.48
The study supports a sequential mediation pathway in which AI capability improves knowledge integration, which improves project agility, which in turn improves project performance. Organizational Efficiency	positive	Project performance	Reading fidelity high Study strength medium	n=267 0.48
The reported relationships remain supported across hierarchical regression, bootstrapping, structural equation modeling, and two-stage least squares estimation. Organizational Efficiency	positive	Project performance and associated mediation relationships	Reading fidelity high Study strength medium	n=267 0.48