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Analyzing and Forecasting E‐Commerce Adoption Drivers Among SMEs: A Machine Learning Approach

Year: 2024

Type: article

Abstract: This paper investigated the factors in the technology–organization–environment (TOE) framework that affect the decision of whether to adopt electronic commerce (EC) or not within small‐ and medium‐sized enterprises (SMEs). To this end, a questionnaire‐based survey was conducted to collect data from 60 managers or owners of manufacturing SMEs in Tunisia. Unlike the traditional regression approaches, we referred to novel machine learning (ML) techniques and reveal that ML techniques reach a higher level of performance in forecasting driving factors to EC adoption compared to the traditional logistic regression approach. The achieved results also indicate that EC adoption within SMEs is significantly affected by eight factors, namely, IT vendors’ support, the adopted technology complexity degree, chief executive officer (CEO) innovativeness, technology readiness, customers’ pressure, firm size, infrastructure compatibility, and the innovative technology‐perceived relative advantage.

Source: Human Behavior and Emerging Technologies

DOI: 10.1155/2024/7747136

Authors: Yomna Daoud, Aida Kammoun

Institutions: University of Sfax, Faculty of Economics and Management of Sfax

Language: English

Topic: Technology Adoption and User Behaviour

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Decent work and economic growth

Open Access status: gold

Estimated APC paid: $800