The Influence of Digital Technology on the Speed and Accuracy of Air Cargo Handling Operations in Thailand

Abstract

Background and Aims: Air cargo underpins Thailand's international logistics network, and the growing pressure of a digital economy has made speed and reliability non-negotiable expectations rather than competitive extras. Yet import–export air cargo handling in Thailand still struggles with delays and errors across the handling chain. Grounded in the Technology Acceptance Model and the Resource-Based View, this study set out to (1) assess how far four categories of digital technology Auto-ID/IoT, AI/Data Analytics, Blockchain/Digital Platform, and Automation/Robotics have been adopted in Thai air cargo handling; (2) analyze how each category influences handling speed and accuracy; and (3) test whether the pattern of influence differs between large and medium-sized operators.

Methodology: A quantitative survey was administered to executives and managers overseeing logistics, warehouse, or IT operations at Ground Handling Agents and cargo airlines based mainly at Suvarnabhumi and Don Mueang airports. A purposive (non-probability) sample of 150 organizations was drawn from the membership records of Thailand's air cargo service-provider associations. A structured online questionnaire covered four areas organizational profile, level of digital technology adoption, measures of operational speed and accuracy, and open-ended comments and the resulting data were examined with descriptive statistics (frequency, percentage, mean, standard deviation) alongside inferential statistics (Cronbach's Alpha, Pearson correlation, multiple regression, and independent-samples t-test).

Results: Digital adoption was positively associated with both speed (β = 0.345, p < 0.001 for AI/Data Analytics) and accuracy (β = 0.385, p < 0.001 for Auto-ID/IoT), while Blockchain/Digital Platform showed a positive but statistically non-significant relationship with either outcome (p = 0.239 and p = 0.164, respectively). Auto-ID and IoT contributed more to accuracy than to speed, while AI and Data Analytics contributed more to speed than to accuracy. Large operators consistently showed higher adoption levels and a stronger technology-performance link than medium-sized ones. The regression models accounted for 45.2% of the variance in speed (R² = .452) and 48.5% of the variance in accuracy (R² = .485).

Conclusion: These results give policymakers, investors, and operators concrete, technology-specific evidence to guide investment sequencing and digital-transformation planning in Thai air cargo, and they speak to Sustainable Development Goals 8 (Decent Work and Economic Growth), 9 (Industry, Innovation and Infrastructure), 12 (Responsible Consumption and Production), and 17 (Partnerships for the Goals).