Entrepreneurial orientation, dynamic capabilities, and startup performance: The amplifying role of AI

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Type of the article: Research Article

Abstract
Startups in emerging economies face pressures to translate strategic orientations and organizational capabilities into competitive performance amid resource constraints and institutional uncertainty. Despite interest in entrepreneurial orientation and dynamic capabilities as performance drivers, how they jointly influence startup performance through capability-conversion mechanisms – and how artificial intelligence (AI) amplifies these effects – remains underexplored. This study examines the mediating role of innovation capability in the entrepreneurial orientation–performance and dynamic capabilities–performance relationships, and the moderating role of AI adoption intensity, within Vietnamese startups. Data were collected from 315 founders, chief executive officers, and senior managers from September to December 2025 and analyzed using PLS-SEM. Results reveal that entrepreneurial orientation exerts a weak direct effect on performance (β = 0.135, p < 0.01) and a significant indirect effect through innovation capability (β = 0.098, p < 0.001), suggesting entrepreneurial orientation operates as a strategic catalyst rather than an immediate performance driver. Dynamic capabilities demonstrate both direct (β = 0.176, p < 0.001) and indirect effects via innovation capability (β = 0.044, p = 0.004), positioning it as a core value-creation engine. AI adoption intensity significantly moderates the entrepreneurial orientation–performance (β = 0.232), innovation capability–performance (β = 0.202), and dynamic capabilities–performance (β = 0.282) relationships, with the strongest amplification observed for dynamic capabilities. The model explains 66.5% of performance variance (R2 = 0.665). These findings advance understanding of capability-conversion logic in emerging-economy startups and offer practical guidance for managers seeking to build competitive advantage through capability development and technology adoption.

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    • Figure 1. Research model
    • Figure 2. Final structural model with standardized path coefficients and R2 values
    • Figure 3. Moderating effect of AI adoption intensity on the entrepreneurial orientation–startup performance relationship
    • Figure 4. Moderating effect of AI adoption intensity on the innovation capability–startup performance relationship
    • Figure 5. Moderating effect of AI adoption intensity on the dynamic capabilities–startup performance relationship
    • Table 1. Sample characteristics
    • Table 2. Reliability and validity assessment
    • Table 3. Discriminant validity assessment (HTMT)
    • Table 4. Structural path analysis: VIF and hypothesis testing
    • Table 5. Indirect effects and mediation analysis
    • Table 6. Explanatory and predictive power
    • Table A1. Measurement scales
    • Data curation
      Nguyen Kien Quoc
    • Formal Analysis
      Nguyen Kien Quoc
    • Investigation
      Nguyen Kien Quoc
    • Resources
      Nguyen Kien Quoc
    • Software
      Nguyen Kien Quoc
    • Visualization
      Nguyen Kien Quoc
    • Writing – review & editing
      Nguyen Kien Quoc, Nguyen Ngoc-Long
    • Conceptualization
      Nguyen Ngoc-Long
    • Funding acquisition
      Nguyen Ngoc-Long
    • Methodology
      Nguyen Ngoc-Long
    • Project administration
      Nguyen Ngoc-Long
    • Supervision
      Nguyen Ngoc-Long
    • Validation
      Nguyen Ngoc-Long
    • Writing – original draft
      Nguyen Ngoc-Long