Causal Influences of Big Data Analytics Adoption for Small and Medium-Sized Enterprises in the Eastern Economic Corridor (EEC) of Thailand
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Abstract
Background and Aim: This study examines the adoption of Big Data Analytics (BDA) among Small and Medium-Sized Enterprises (SMEs) in the Eastern Economic Corridor (EEC). Big Data is increasingly important for improving strategic decision-making, operational efficiency, and competitive advantage. However, many SMEs still face challenges in adopting BDA due to limitations in technology, organizational readiness, leadership capability, and external support. The study applies the Technology-Organization-Environment (TOE) framework and the Technology Acceptance Model (TAM) to explain key factors influencing BDA adoption. The main objectives are to identify critical factors affecting BDA adoption and to analyze the relationships among technological, organizational, environmental, and user acceptance variables.
Material and Methods: A quantitative research design was employed. Data were collected from 340 SME entrepreneurs operating in the EEC using purposive sampling. The research instrument was a structured questionnaire consisting of six sections, including both open-ended questions and closed-ended items measured on a Likert scale. The questionnaire was validated by experts, with an Index of Item-Objective Congruence (IOC) of 0.874. Reliability testing showed a Cronbach’s alpha coefficient of 0.936, indicating a high level of internal consistency. Data were analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to assess factor validity and examine relationships among the proposed variables.
Results: The findings indicate that organizational leadership capability is the most influential observed variable, with a beta coefficient of 0.915. This highlights the importance of leadership in supporting technology acceptance and successful BDA implementation. The SEM results further reveal a strong and statistically significant relationship between technological factors and perceived usefulness (β = 0.653, p < 0.01). The model demonstrated good fit, with Chi-square = 686, df = 350, p-value = 0.001, and RMSEA = 0.032. These results confirm that technological readiness, organizational capability, and perceived usefulness play essential roles in encouraging SMEs to adopt BDA.
Conclusions: The study concludes that a supportive organizational system significantly enhances employees’ confidence and perceived ease of use when adopting information technology. Strong leadership, flexible internal systems, and a culture that encourages innovation are essential for successful BDA adoption. SMEs in the EEC should prioritize leadership development, continuous training, and knowledge enhancement to strengthen their digital capabilities. In addition, organizations should improve internal processes to respond effectively to technological change and provide employees with practical BDA training. These actions can help SMEs increase competitiveness, achieve sustainable growth, and adapt successfully to the digital economy. For greater clarity and research visibility, keywords should include more specific terms such as “Big Data Analytics Adoption,” “Organizational Readiness,” “Technological Factors,” “SMEs,” and “Eastern Economic Corridor.”
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