FASECO: A Framework for Advanced Support of E-Commerce and digital transformation in SMEs with natural language processing-enhanced analysis

Abstract

Objectives

The primary aim of this study is to develop a framework that identifies the factors, organizational processes, and mechanisms that facilitate the successful integration of e-commerce within the digital transformation journey of Small and medium-sized enterprises (SMEs). The FASECO framework seeks to enhance strategic alignment, IT infrastructure, customer experience, and data analytics in SMEs, and is compared with existing theoretical frameworks to provide a holistic solution.

Methods/Approach

The research employs a mixed-methods approach, starting with an extensive literature review on digital transformation and e-commerce integration in SMEs, followed by qualitative semi-structured interviews with 13 industry professionals. The data is analysed using thematic analysis, significantly enhanced by Natural Language Processing (NLP) tools, which allowed for the extraction of deeper insights into e-commerce adoption challenges and organizational dynamics that traditional methods might overlook.

Results

The study reveals that effective e-commerce integration in SMEs requires robust IT infrastructure, strategic alignment, personalized customer experiences, and data-driven decision-making. The FASECO framework supports SMEs by providing a comprehensive structure that bridges the gap between strategy and technology, enhancing their digital maturity and competitiveness. The framework’s holistic approach and detailed comparison with existing models underscore its effectiveness.

Conclusions

The FASECO framework offers a novel approach tailored to SMEs for digital transformation and e-commerce integration, highlighting the critical role of strategic and technological alignment in achieving digital maturity and competitive advantage. The use of NLP techniques in this study enhanced the analysis of qualitative data, providing deeper insights into organizational processes. Future research should explore the adaptation of this model for micro-SMEs and the integration of emerging technologies.