AI Adoption Pathways for Hong Kong SMEs: A Readi-ness-Based Model for Achieving Expected Outcomes in Business Development
TL;DR
The research indicates that SMEs in Hong Kong are recommended to adopt a stepwise approach that involves first enhancing talent and change management capabilities, followed by securing funding, and finally implementing advanced AI.
Abstract
Small and Medium-sized Enterprises (SMEs) in Hong Kong face intense pressure to adopt Artificial Intelligence (AI) for business development, yet many fail to achieve anticipated outcomes. This study proposes and theoretically validates a readiness-based model explaining AI adoption pathways. Grounded in the Resource-Based View (RBV), the model posits that three independent variables—Talent Readiness, Financial Access, and Change Management—influence the dependent variable of Expected Outcomes in Business Development. Using an explanatory-correlational quantitative design with a target sample of 150 Hong Kong SMEs, this study details how Pearson and partial correlation analyses can be used to test the proposed relationships. Power analysis confirms that N=150 is sufficient to detect SMEs at α = 0.05. The study delineates distinct adoption pathways (transformational, incremental, futile, and stagnant) and assesses the SME's effectiveness in market expansion, revenue growth, and process innovation. The research indicates that SMEs in Hong Kong are recommended to adopt a stepwise approach. This approach involves first enhancing talent and change management capabilities, followed by securing funding, and finally implementing advanced AI. This readiness-first strategy is intended to position AI as a catalyst for business growth rather than a financial liability.