Generative AI for business sustainability: Examining usability, usefulness, and triple bottom line impacts in small and medium enterprises

Abstract

Generative AI has emerged as a game-changing technology with great potential to enhance business sustainability. This study explores the adoption and application of generative AI among small and medium-sized enterprises (SMEs) in a small island developing state. The study utilizes the Technology Acceptance Model (TAM) and the Triple Bottom Line (TBL) framework. It integrates quantitative and qualitative methods to comprehensively understand generative AI's role in fostering sustainable business practices. Quantitative findings reveal that perceived ease of use and usefulness significantly influence SMEs' intentions to adopt generative AI, ultimately predicting its actual usage. Qualitative insights complement these findings by identifying four key applications: operational efficiency, data-driven decision-making, sustainable product and service innovation, and building a sustainable brand identity. Despite its potential, the study acknowledges limitations, including focusing on a single SIDS and relying on self-reported data, which constrain generalizability. However, these limitations do not diminish the study's importance, as it highlights practical pathways for SMEs to overcome resource constraints and achieve sustainability goals. The findings highlight the transformative role of generative AI in equipping SMEs with innovative tools to balance profitability with environmental and social responsibility. Policymakers are urged to support this transition through education and outreach, making generative AI accessible and practical for SMEs.