Artificial Intelligence-Enabled Decision Making in Social Media Adoption for Sustainable Digital Business in Thai SMEs

Saranee Visuthiphol; Sumaman Pankham

doi:10.31181/dmame8120251366

Authors

Saranee Visuthiphol College of Digital Innovation Technology, Rangsit University, Thailand. https://orcid.org/0009-0006-1431-4643
Sumaman Pankham College of Digital Innovation Technology, Rangsit University, Thailand. https://orcid.org/0009-0006-1431-4643

DOI:

https://doi.org/10.31181/dmame8120251366

Keywords:

Social Media, Artificial Intelligence, Entrepreneurial Orientation, Sustainability, Competitive Advantage, SMEs, Rough Set e-Delphi, SEM

Abstract

This study develops and validates a structural equation modeling (SEM) to examine social media adoption by SMEs and its impact on sustainability. It aims to identify key factors influencing SMEs sustainability in the digital context and explore their interrelationships. This study employed a mixed-methods approach. In the qualitative phase, the Rough Set e-Delphi technique was utilised to collect insights from experts and refine the research constructs through three rounds of online questionnaires, involving 20 experts. The quantitative phase involved a survey of 776 executives from Thai SMEs, which served as the primary data collection method. The study results indicate that social media adoption is positively influenced by four factors: perceived usefulness, perceived ease of use, compatibility, and business orientation. The implementation of social media networks has a positive impact, enhancing both performance metrics and competitive advantages for SMEs. This research confirms that the sustainable performance of SMEs significantly contributes to sustainability across their business processes. The findings demonstrate that social media serves as a powerful tool for improving SMEs operations, while fostering competitive advantages that support sustainability. This study demonstrates that entrepreneurial orientation, in conjunction with technology acceptance, facilitates social media adoption. It offers practical implications for artificial intelligence engineering by exploring how AI systems, with digital analytical capabilities, automated functions, and predictive modelling, can improve operational decision-making, supply chain management, and overall performance in SMEs. The insights gained can be utilised by SMEs owners, policymakers, and researchers to enhance the use of social media through AI-driven strategies, promoting sustainable SME growth in emerging economies.

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