Causal Influences of Big Data Analytics Adoption for Small and Medium-Sized Enterprises in the Eastern Economic Corridor (EEC) of Thailand

Abstract

Background and Aim: The author provides a foundational understanding of Big Data, describing its characteristics and the inadequacy of traditional management methods. This serves as a general introduction to the topic. However, this section could more explicitly articulate the specific research problem or gap concerning Big Data Analytics (BDA) adoption within Small and Medium-Sized Enterprises (SMEs) in the Eastern Economic Corridor (EEC). While the importance of BDA for strategic decision-making and competitive advantage is mentioned, a more direct linkage to the challenges or unique circumstances of SMEs in this particular region would strengthen the problem statement. The research aims to investigate BDA adoption among SMEs in the EEC, as clearly stated. This section effectively introduces the theoretical underpinnings by referencing the Technology-Organization-Environment (TOE) framework and the Technology Acceptance Model (TAM). The author correctly identifies key components of these frameworks—technological attributes, organizational conditions, external environmental factors from TOE, and perceived usefulness and perceived ease of use from TAM. To further enhance this, a concise justification for selecting these specific frameworks, perhaps highlighting their relevance to understanding technology adoption in an SME context or their complementary nature, would add depth. The objectives are well-defined, aiming to identify critical influencing components and analyze relationships among variables, which guide the study.

Material and Methods: The author clearly outlines the methodological approach. The sample group of 340 SME entrepreneurs in the EEC, selected via purposive sampling, is appropriately described. This section precisely details the primary data collection instrument as a questionnaire, structured into six sections, differentiating between open-ended and closed-ended Likert scale questions. The inclusion of specific validation metrics - a content validity index (IOC) of 0.874 and an overall reliability (Cronbach’s alpha) of 0.936 - is commendable. These values indicate a robust instrument, which is essential for the credibility of quantitative research findings. This level of detail in the abstract is suitable, providing confidence in the data collection process.

Results: The author presents key findings from the statistical analyses with precision. The result from Confirmatory Factor Analysis (CFA) highlights organizational leadership capability as the most influential observed variable, with a beta coefficient of 0.915. This emphasizes its importance in fostering technology acceptance and implementation, especially for SMEs facing competitive pressures and rapid technological changes in the EEC. Furthermore, the Structural Equation Modeling (SEM) analysis is reported to show a strong and statistically significant relationship between technological factors and perceived usefulness (β = 0.653, p < 0.01). The inclusion of specific fit indices (Chi-square = 686, df = 350, p-value = 0.001, and RMSEA = 0.032) confirms a good model fit. These findings provide concrete evidence of the variables influencing BDA adoption, aligning well with the study's objectives.

Conclusions: The analysis concludes that a supportive organizational system significantly influences employees’ confidence and perceived ease of use with information technology. This directly connects to the findings regarding organizational leadership and perceived ease of use. The author emphasizes that when organizational structures align with technological goals, employees are more likely to integrate technology into their work. Based on these insights, the author provides actionable recommendations for SMEs in the EEC. These include prioritizing leadership development through continuous training and knowledge enhancement, enabling leaders to drive technology adoption. Additionally, the author suggests improving internal systems for flexibility and responsiveness to technological changes, fostering an innovation-friendly culture, and providing staff training in BDA. This section effectively summarizes the study's implications, highlighting how these efforts can lead to sustainable growth, market competitiveness, and effective adaptation in the digital era. However, the terms "Organization" and "Technology" are very broad. To enhance specificity and improve the discoverability of this research, it is recommended to use more precise terms. For instance, "Organizational Factors," "Organizational Readiness," or "Organizational Culture" would better capture the nuanced aspects of the organization's role in BDA adoption as discussed in the abstract, particularly the emphasis on leadership and internal systems. Similarly, "Technological Factors," "Technological Readiness," or "Technology Adoption" would be more specific than simply "Technology," aligning more closely with the study's focus on adoption. Including "Small and Medium-Sized Enterprises" (SMEs) and "Eastern Economic Corridor (EEC)" as keywords would also be beneficial, as these are critical contextual elements of the research, ensuring the study is easily found by those interested in this specific sector and geographic region.