Editorial summary. This is our text summary of an article published by sajhrm. Charts, figures, and the author’s full voice are at the original — read it there .
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Editorial verdict
Methodologically competent regional study with a notable discriminant validity concern between key constructs — the core finding that organisational culture and technology acceptance outweigh technology readiness is credible, but the very high R² values and marginal HTMT breach warrant cautious interpretation.
Executive summary
This study examines the determinants of artificial intelligence (AI) adoption in human resource management (HRM) within Thailand's Eastern Economic Corridor (EEC), a strategically designated industrial zone. The authors argue that organisational culture (OC) and technology acceptance (TA) are stronger predictors of AI use in HRM than technology readiness (TR), and that AI use in turn drives HRM effectiveness. Drawing on the Technology Acceptance Model and related socio-technical frameworks, the study hypothesises that OC and TA positively associate with AI adoption, which mediates their relationship with HRM effectiveness, while TR plays an enabling but insufficient role. Using structural equation modelling (PLS-SEM) with SmartPLS on a cross-sectional survey of 500 HR professionals across three EEC provinces, the study finds that OC (β=0.549) and TA (β=0.410) significantly predict AI use, whereas TR shows no significant effect (β=0.001). AI use strongly predicts HRM effectiveness (β=0.910), and fully mediates the OC–effectiveness and TA–effectiveness relationships. The findings suggest that cultural alignment and employee behavioural readiness are more consequential than infrastructure availability for AI-HRM adoption in emerging-economy industrial contexts, challenging infrastructure-first assumptions in digital transformation policy.
Key insights
- 1Organisational culture was the strongest predictor of AI use in HRM (β=0.549, f²=0.531), indicating that innovation-supportive norms and leadership endorsement matter more than technical infrastructure in driving AI adoption.
- 2Technology readiness had no statistically significant association with AI use in HRM (β=0.001, p=0.984), suggesting that infrastructure availability alone does not translate into effective AI utilisation without complementary cultural and behavioural conditions.
- 3AI use in HRM acted as a full mediator between both organisational culture and technology acceptance on HRM effectiveness, implying that the pathway to improved HR outcomes runs through actual technology adoption rather than readiness or acceptance alone.
Practical takeaways
- Organisations seeking to improve AI-enabled HRM outcomes may find investment in culture change — fostering innovation, psychological safety, and leadership visibility on AI — more consequential than additional infrastructure spend.
- Initiatives to build employees' perceived usefulness of and trust in AI tools (digital literacy, transparent communication, participatory implementation) appear more strongly linked to adoption outcomes than upgrading technological infrastructure.
Frameworks mentioned
Technology Acceptance Model (TAM)
A framework positing that perceived usefulness and perceived ease of use are the primary determinants of individuals' intention to adopt new technologies.
Unified Theory of Acceptance and Use of Technology (UTAUT)
An integrated model explaining technology adoption through performance expectancy, effort expectancy, social influence, and facilitating conditions.
Technology–Organisation–Environment (TOE) Framework
A framework explaining technology adoption as a function of the interplay between an organisation's internal technological and organisational characteristics and its external environment.
Resource-Based View (RBV)
A strategic management framework arguing that sustained competitive advantage derives from an organisation's unique, valuable, and inimitable internal resources and capabilities.
References
- Venkatesh & Davis (2000).The technology acceptance model: A meta-analysis of empirical findings.
- Parasuraman & Colby (2015).Technology Readiness Index (TRI).
- Cameron & Quinn (2011).Competing values framework.
- Hair et al. (2019).A primer on partial least squares structural equation modeling.
- Fornell & Larcker (1981).Evaluating structural equation models with unobservable variables and measurement error.
- Henseler et al. (2015).Common method biases in behavioral research.
- Davenport & Ronanki (2018).Artificial intelligence: The next digital frontier.
- Tambe et al. (2019).Talent and technology.
- Uren & Edwards (2023).Developing AI readiness.
- Krejcie & Morgan (1970).Determining sample size for research activities.
Source & Provenance
sajhrm
Kantima Pongnairat
October 15, 2026
Research Study
Asia-Pacific
Original source metadata is preserved. AI analysis is generated separately.
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