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AI uptake among entrepreneurial firms is driven more by outside forces — infrastructure, peer influence and competition — than by internal expectations, and adoption is linked to strategic renewal and stronger triple‑bottom‑line performance; paradoxically, higher initial performance expectations reduced adoption, reflecting implementation frictions.

Drivers and Sustainable Performance Outcomes of AI Adoption Intention: A Multi-Theoretical Analysis in the Entrepreneurial Ecosystem
Mahdi Ashkani, Léo‐Paul Dana, A. Rashidi, Fatemeh Shafaei, Aidin Salamzadeh · Fetched March 15, 2026 · Sustainability
semantic_scholar correlational medium evidence 7/10 relevance DOI Source
In a survey of 207 entrepreneurial firms, external/contextual factors (facilitating conditions, social influence, competitive pressure) predict AI adoption, which is associated with strategic renewal and improved economic, environmental, and social performance, while performance expectancy unexpectedly relates negatively to adoption.

Artificial Intelligence (AI) will drastically change the way entrepreneurs operate within their respective fields toward sustainable performance. However, although we have some data about how companies will adopt AI and how it is implemented, it is still an under-studied area of research. The goal of this study was to examine the antecedents and consequences of AI Adoption using the Technology–Organization–Environment (TOE) model and Unified Theory of Acceptance and Use of Technology (UTAUT). The researchers collected data from 207 entrepreneurial businesses (including SMEs, startups, and knowledge-based businesses) using a structured questionnaire and analyzed the data using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 3. The study’s findings suggest that facilitating conditions, social influences, and competitive pressures are all important positive factors contributing to the firm’s decision on AI Adoption. On the other hand, the data indicate that performance expectancy is a negative factor related to the company’s decision to adopt because of the “reality check” influence of the initial implementation challenges diminishing ease of use. It is also important to mention that several internal factors including effort expectancy and top management support do not have a direct influence. Most importantly, however, the results show that AI Adoption provides companies with an opportunity for strategic renewal (opportunities) and sustainable business models (holistic sustainability). Also, this research provides insight into the Resource-Based View (RBV) and Dynamic Capabilities (DC) theory by showing that AI Adoption creates a significant competitive advantage for companies, making them more successful at creating entrepreneurial and technology-based firms, while providing them increased economic, environmental, and social performance. In conclusion, AI Adoption is a major game-changer for entrepreneurs interested in sustainable practices and the ability to achieve successful, holistic, and sustainable business performance.

Summary

Main Finding

AI adoption among entrepreneurial firms (SMEs, startups, knowledge-based businesses) is driven mainly by external and contextual factors — facilitating conditions, social influence, and competitive pressure — and, once adopted, delivers strategic renewal and holistic sustainable performance (economic, environmental, social), creating competitive advantage consistent with Resource‑Based View and Dynamic Capabilities arguments. Unexpectedly, performance expectancy was negatively associated with adoption, and some internal factors (effort expectancy, top management support) showed no direct effect.

Key Points

  • Sample: 207 entrepreneurial firms (SMEs, startups, knowledge‑based businesses).
  • Theoretical framing: Technology–Organization–Environment (TOE) model and Unified Theory of Acceptance and Use of Technology (UTAUT).
  • Positive antecedents of AI adoption: facilitating conditions, social influence, competitive pressure.
  • Negative antecedent: performance expectancy — initial implementation challenges produced a “reality check” that reduced perceived ease or payoff, lowering adoption propensity.
  • No direct effects found for some internal factors: effort expectancy and top management support.
  • Consequences of AI adoption: strategic renewal, sustainable business models, and improvements in economic, environmental, and social performance.
  • Contribution to theory: empirical support that AI functions as a valuable, hard‑to‑replicate resource and capability, strengthening RBV and Dynamic Capabilities narratives about competitive advantage in entrepreneurial/technology‑based firms.

Data & Methods

  • Data collection: structured questionnaire administered to 207 firms categorized as entrepreneurial (SMEs, startups, knowledge‑based).
  • Analytical approach: Partial Least Squares Structural Equation Modeling (PLS‑SEM).
  • Software: SmartPLS 3.
  • Variables and constructs: TOE and UTAUT constructs (e.g., facilitating conditions, social influence, competitive pressure, performance expectancy, effort expectancy, top management support) and firm outcomes (strategic renewal, sustainability/performance).
  • Note: geographic/sampling frame details were not provided in the summary.

Implications for AI Economics

  • For entrepreneurs and investors: external market and institutional forces (facilitating infrastructure, peer pressures, competition) matter more than some internal expectations when deciding to adopt AI; thus ecosystem investments (digital infrastructure, training networks, demonstration projects) can accelerate uptake.
  • For firms implementing AI: address early implementation frictions to prevent negative performance expectancy — realistic pilots, change management, and incremental rollouts can mitigate the “reality check.”
  • For policy: enabling conditions and visible success stories amplify adoption; policy that reduces adoption costs/risks and creates competitive incentives can foster widespread AI diffusion with sustainable outcomes.
  • For theory and research: supports RBV/DC hypotheses that AI can be a source of sustained competitive advantage and improved triple‑bottom‑line performance; future work should probe why internal factors lacked direct effects (e.g., mediation/moderation, contextual moderators, or measurement issues) and examine longitudinal outcomes of adoption.

Assessment

Paper Typecorrelational Evidence Strengthmedium — Findings are based on a reasonably sized firm-level survey (n=207) and an established SEM approach, which supports detection of plausible associations, but the cross-sectional, self-reported design, potential common-method bias, endogeneity, and lack of exogenous variation prevent strong causal inference. Methods Rigormedium — Use of validated theoretical frameworks (TOE, UTAUT) and PLS-SEM is appropriate for exploratory model testing; however, modest sample size, unclear sampling frame/geography, reliance on single-respondent questionnaires, and no robustness checks (e.g., alternative estimators, longitudinal or instrumental approaches) limit methodological rigor. SampleStructured questionnaire responses from 207 entrepreneurial firms (SMEs, startups, and knowledge-based businesses); sample demographics (country/region, sector breakdown, sampling/response rate) not provided in the summary. Themesadoption innovation productivity IdentificationCross-sectional survey of firms analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) to estimate associations among TOE/UTAUT constructs and firm outcomes; no experimental or quasi-experimental source of exogenous variation, instrument, or temporal ordering to support causal identification. GeneralizabilityUnclear geographic and sampling frame — may not generalize beyond the (unspecified) region or country sampled, Restricted to entrepreneurial/knowledge-based small firms — findings may not apply to large firms or manufacturing/non-knowledge sectors, Self-selected survey respondents may introduce selection bias (early adopters or digitally engaged firms overrepresented), Cross-sectional snapshot — cannot generalize to long-run adoption dynamics or causal effects over time, Self-reported measures of adoption and performance raise concerns about measurement bias

Claims (13)

ClaimDirectionConfidenceOutcomeDetails
The study collected data from 207 entrepreneurial businesses (including SMEs, startups, and knowledge-based businesses) using a structured questionnaire and analyzed the data using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 3. Other null_result high N/A (methodological/sample description)
n=207
0.3
Facilitating conditions are a significant positive factor contributing to a firm's decision to adopt AI. Adoption Rate positive medium AI Adoption
n=207
0.18
Social influences are a significant positive factor contributing to a firm's decision to adopt AI. Adoption Rate positive medium AI Adoption
n=207
0.18
Competitive pressures are a significant positive factor contributing to a firm's decision to adopt AI. Adoption Rate positive medium AI Adoption
n=207
0.18
Performance expectancy is a negative factor related to the company's decision to adopt AI (attributed to initial implementation challenges reducing perceived ease of use). Adoption Rate negative medium AI Adoption
n=207
0.18
Effort expectancy does not have a direct influence on AI Adoption in the sampled firms. Adoption Rate null_result medium AI Adoption
n=207
0.18
Top management support does not have a direct influence on AI Adoption in the sampled firms. Adoption Rate null_result medium AI Adoption
n=207
0.18
AI Adoption provides companies with opportunities for strategic renewal. Innovation Output positive medium Strategic renewal (opportunities)
n=207
0.18
AI Adoption enables sustainable business models (holistic sustainability) and is associated with increased economic, environmental, and social performance. Firm Productivity positive medium Sustainable business models (holistic sustainability); economic performance; environmental performance; social performance
n=207
0.18
AI Adoption creates a significant competitive advantage for companies, improving their success in creating entrepreneurial and technology-based firms. Firm Productivity positive medium Competitive advantage; success of entrepreneurial/technology-based firms
n=207
0.18
The research provides insight into Resource-Based View (RBV) and Dynamic Capabilities (DC) theory by showing that AI Adoption contributes to competitive advantage and sustainability-related firm performance. Firm Productivity positive medium Competitive advantage; sustainability-related firm performance
n=207
0.18
AI Adoption is a major game-changer for entrepreneurs interested in sustainable practices and the ability to achieve successful, holistic, and sustainable business performance. Firm Productivity positive low Holistic/sustainable business performance (composite interpretation)
n=207
0.09
The adoption and implementation of AI in entrepreneurial firms is an under-studied area of research. Other null_result high N/A (research gap statement)
0.3

Notes