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View corpus contextTransparent ethical governance—not just usefulness or ease—most strongly predicts Thai HR professionals' willingness to adopt AI, and greater intention to use AI is linked to higher perceived HR effectiveness; firm size and HR experience shape which adoption drivers matter most.
The primacy of ethical governance: Unraveling the AI-HRM adoption paradox in an emerging economy
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In a national survey of 284 Thai HR practitioners, a context-specific 'Ethical Governance' construct was the strongest predictor of intention to use AI, which in turn was associated with higher perceived HRM effectiveness, with facilitating conditions more important in large firms and governance particularly influential for experienced HR professionals.
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The integration of Artificial Intelligence (AI) is reshaping the global landscape of Human Resource Management (HRM), presenting unique opportunities and challenges, particularly for emerging economies. This study examines the intricate dynamics of AI adoption within Thailand's HRM function, a significant emerging market characterized by a paradox: high public optimism about AI and low corporate adoption of HRM. Employing a sequential exploratory mixed-methods design, the research first utilizes thematic analysis of semi-structured interviews with senior HR professionals to unearth the nuanced socio-technical barriers to AI integration. Subsequently, a second phase develops, validates, and tests an extended technology acceptance model using Partial Least Squares Structural Equation Modeling (PLS-SEM) on data collected from a nationwide survey of 284 HR practitioners. The qualitative results reveal three core themes: ‘Strategic Imperative vs. Operational Paralysis,’ ‘The Trust Deficit,’ and ‘The Human-AI Competency Chasm.’ The quantitative findings from the main effects model validate the proposed framework, demonstrating that while traditional technology adoption drivers are significant, ‘Ethical Governance'—a novel construct rigorously developed and validated in this study—emerges as the most potent predictor of HR professionals' intention to use AI. This intention, in turn, positively influences Perceived HRM Effectiveness. Furthermore, a post-hoc Multi-Group Analysis (MGA) reveals critical contextual moderators: Facilitating Conditions are a more potent driver of adoption intention in large organizations, whereas Ethical Governance is more salient for highly experienced HR professionals. This study contributes to theory by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) with a contextually grounded and methodologically robust construct and by empirically identifying key boundary conditions that highlight the contextual nuances and specific boundary conditions of standard technology acceptance models. It also offers critical practical recommendations for executives and policymakers, underscoring that building trust through transparent governance is a necessary precondition for unlocking AI's transformative potential and that strategies must be tailored to organizational size and practitioner expertise.
Summary
Main Finding
Ethical Governance — a rigorously developed, context-specific construct extending UTAUT — is the strongest predictor of HR professionals’ intention to use AI in Thailand. Intention to use AI then increases perceived HRM effectiveness. Traditional adoption drivers remain significant, but adoption is shaped by contextual boundary conditions: facilitating conditions matter more in large firms, while ethical governance is especially influential for highly experienced HR professionals.
Key Points
- Context: Thailand, an emerging economy with high public optimism about AI but low corporate uptake in HRM.
- Research design: Sequential exploratory mixed-methods (qual → quan).
- Qualitative themes from senior HR professionals:
- Strategic Imperative vs. Operational Paralysis (desire to adopt but implementation blockers)
- The Trust Deficit (concerns about fairness, transparency, accountability)
- The Human–AI Competency Chasm (skills and role-change gaps)
- Quantitative results (n = 284 HR practitioners; PLS-SEM):
- Extended UTAUT model validated; traditional drivers (e.g., performance/effort expectancy, social influence, facilitating conditions) significant.
- Ethical Governance is the most potent predictor of intention to use AI.
- Intention to use AI positively influences perceived HRM effectiveness.
- Post-hoc MGA: Facilitating conditions stronger in large organizations; Ethical Governance more salient for highly experienced HR professionals.
- Theoretical contribution: introduces and validates Ethical Governance as a contextually grounded extension to UTAUT and documents boundary conditions of standard technology-acceptance models.
- Practical recommendation: prioritize transparent governance and trust-building; tailor adoption strategies by firm size and HR experience level.
Data & Methods
- Design: Sequential exploratory mixed-methods.
- Qualitative phase: Semi-structured interviews with senior HR professionals; thematic analysis yielding three core themes (see Key Points).
- Quantitative phase: Nationwide survey of 284 HR practitioners; development and validation of an Ethical Governance construct; hypothesis testing via Partial Least Squares Structural Equation Modeling (PLS-SEM).
- Model elements:
- Extended UTAUT constructs (performance expectancy, effort expectancy, social influence, facilitating conditions) plus Ethical Governance → Intention to Use AI → Perceived HRM Effectiveness.
- Construct validation procedures applied (reliability and validity tests reported); main effects model tested; post-hoc Multi-Group Analysis (MGA) for moderators (organization size, HR experience).
- Sample: Nationally distributed sample of HR practitioners in Thailand (N = 284); qualitative sample comprised senior HR professionals (sample size not specified in the summary).
Implications for AI Economics
- Adoption determinants: Shows governance/trust variables can dominate classic technology-demand drivers in AI diffusion—models of AI adoption should incorporate ethical governance and trust as core explanatory variables, especially in emerging markets.
- Heterogeneous returns: Organizational size and worker experience moderate adoption drivers, implying heterogeneous adoption elasticities and productivity returns across firm types and workforce segments. Economic models should allow for such heterogeneity when estimating aggregate impacts.
- Policy design: To accelerate beneficial AI diffusion, policymakers should prioritize transparent governance frameworks, certification/standards, and accountability mechanisms alongside digital infrastructure and incentives. Targeted support (e.g., subsidies, technical assistance) should be calibrated by firm size and workforce skill.
- Labor-market dynamics: The Human–AI Competency Chasm signals potential for skill-biased technological change; investments in reskilling and HR capability-building are necessary to realize productivity gains and mitigate displacement risks.
- Measurement and empirical work: Future empirical studies and macro models of AI’s economic impact should:
- Include measures of ethical governance/trust and organization-level facilitating conditions.
- Explore causal impacts (longitudinal, experimental designs) to quantify how governance reforms change adoption and productivity.
- Compare across emerging economies to identify generalizable vs. context-specific mechanisms.
- Welfare and externalities: Because governance influences adoption and perceived effectiveness, there are likely social returns to public interventions that reduce information asymmetries and trust deficits, potentially correcting underinvestment in AI where private actors under‑provision governance.
Assessment
Paper Typecorrelational
Evidence Strengthmedium — Sequential exploratory mixed-methods with construct development and PLS-SEM provides credible associative evidence that Ethical Governance predicts intention to use AI, but the cross-sectional, self-reported survey design and lack of experimental/longitudinal identification prevent causal claims and leave potential for common-method bias and endogeneity.
Methods Rigormedium — Appropriate mixed-methods design: qualitative interviews guided construct development, construct validation procedures were reported, and PLS-SEM is suitable for latent-variable modeling and exploratory theory extension; however, sampling details are incomplete, sample is moderate (n=284), measures are self-reported, analysis is cross-sectional, and no strong strategies to address endogeneity or common-method variance are described.
SampleQuantitative: nationwide cross-sectional survey of 284 HR practitioners in Thailand (self-reported measures); Qualitative: semi-structured interviews with senior HR professionals (number not specified) used to derive thematic elements and an Ethical Governance construct.
Themesadoption governance org_design human_ai_collab skills_training
GeneralizabilitySingle-country study (Thailand) — cultural and regulatory context may not generalize to other countries., Sample limited to HR practitioners — findings may not apply to other occupational groups or to firm-level adoption behavior., Self-reported intention and perceived effectiveness, not observed adoption or objective productivity outcomes., Moderate sample size and unclear sampling frame (potential selection bias/non-probability sampling)., Cross-sectional design — temporal dynamics and causality unresolved.
Claims (7)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| Ethical Governance is the strongest predictor of Thai HR professionals’ intention to use AI. Adoption Rate | positive | Intention to use AI |
Reading fidelity
high
Study strength
medium
|
n=284
|
| Performance expectancy, effort expectancy, social influence, and facilitating conditions significantly predict intention to use AI among HR practitioners in Thailand. Adoption Rate | positive | Intention to use AI |
Reading fidelity
high
Study strength
medium
|
n=284
|
| Greater intention to use AI is associated with higher perceived HRM effectiveness. Organizational Efficiency | positive | Perceived HRM effectiveness |
Reading fidelity
high
Study strength
medium
|
n=284
|
| The effect of facilitating conditions on AI adoption is stronger in large organizations than in smaller organizations. Adoption Rate | positive | Intention to use AI |
Reading fidelity
high
Study strength
medium
|
n=284
|
| Ethical Governance is more influential for the AI-use intentions of highly experienced HR professionals. Adoption Rate | positive | Intention to use AI |
Reading fidelity
high
Study strength
medium
|
n=284
|
| Senior HR professionals described a trust deficit involving concerns about fairness, transparency, and accountability in AI adoption. Ai Safety And Ethics | negative | Perceived trustworthiness and acceptability of AI adoption |
Reading fidelity
high
Study strength
low
|
not reported
|
| Senior HR professionals reported a gap between the competencies required for human–AI collaboration and existing HR skills and roles. Skill Obsolescence | negative | HR workforce competencies for AI adoption |
Reading fidelity
high
Study strength
low
|
not reported
|