Factors Affecting the Success of Smart Logistics Implementation in Thai Logistics Companies

Abstract

Background and Aims: Thailand's logistics sector is undergoing a critical transformation toward smart-logistics adoption under the Thailand 4.0 policy and the 13th National Economic and Social Development Plan (2023-2027). Smart logistics is operationally defined here as the organizational application of IoT devices, AI/big-data analytics, and automated warehouse and routing systems; more advanced applications (blockchain, digital twins, autonomous vehicles) remain at an early adoption stage in Thailand and fall outside this scope. Although prior studies have separately examined technological, organizational, and human antecedents of logistics digitalization, few have integrated technology readiness, managerial support, and human-resource capability within one framework grounded in complementary theories; this study addresses that gap. Implementation success is defined as employees' perceived improvement in operational efficiency, cost reduction, service quality, and competitiveness following adoption. Three hypotheses proposed that technology (H1), management (H2), and human-resource (H3) factors each positively influence implementation success.

Methodology: A quantitative, cross-sectional survey was conducted with 200 logistics managers, technology executives, and operational staff at logistics companies in the Bangkok metropolitan area, using convenience combined with purposive (non-probability) sampling; findings should therefore be read as indicative of relationships within technologically engaged Bangkok-area firms rather than statistically generalizable nationwide. A G*Power 3.1 analysis (3 predictors, α = .05, power = .95, f² = .15) indicated a minimum sample of 119, confirming the achieved sample of 200 was adequate. The instrument was a five-part, five-point Likert questionnaire (eight items per part) with content validity (IOC ≥ .67), reliability (α ≥ .70), and convergent/discriminant validity

(AVE ≥ .50; square-root-of-AVE exceeding inter-construct correlations) verified beforehand; common method bias was assessed via Harman's single-factor test. Data were analyzed with descriptive statistics and multiple regression, after checking linearity, normality, multicollinearity, independence of errors, and influential outliers.

Results: The regression model explained 67.4 percent of the variance in implementation success. All three factor groups demonstrated statistically significant positive relationships with implementation success: technology factors, management factors, and human-resource factors. Hypotheses H1, H2, and H3 were therefore all supported, with technology factors carrying the strongest influence (β = .382, p < .001), followed by management factors (β = .341, p < .001) and human-resource factors (β = .298, p < .001) respectively. Among sub-dimensions, infrastructure readiness, top management involvement, and employee skills and knowledge emerged as the most influential components within each respective factor group.

Conclusion: Smart-logistics success among Thai firms rests on the joint strength of technological readiness, managerial commitment, and workforce development, rather than any single dimension. Findings offer practical direction for operators, policymakers, and practitioners, emphasizing infrastructure investment, leadership engagement, and continuous skill development. The study is limited by its cross-sectional design and non-probability, Bangkok-concentrated sample, discussed together with directions for future research.