A Bayesian Inference Model for Sustainable Crowd Source Logistics for Small and Medium Scale Enterprises (SME) in Africa

Abstract

Trade in Africa is likely to increase and alter significantly during the next decade. Intra-African trade has shown significant potential to reinvigorate African commerce. Logistics and distribution are critical, acting as a catalyst for private sector development and growth. However, little attention has been given to the association crowd logistis platforms and SME’s in Africa. This study applies an Extended Technology Acceptance Model (ETAM) to explore the implications of crowd-sourcing logistics for small and medium-sized enterprises (SMEs) in the African market. A survey was conducted to obtain the necessary primary data from 130 SME owners across Africa. To provide further insight, this study adopts a Bayesian inference model to analyze the data obtained. This research also considers perceived risk as an additional external factor of the TAM as a vehicle to test the hypotheses and relationships and explain users’ willingness to adopt a web-based logistical platform. Empirical results show that in the adoption of new Technology; it is worth noting that for SME owners external factors (i.e. subjective norms, perceived risk, perceived experience) have more effect on the perceived usefulness of crowd logistics platforms than intrinsic factors (i.e. perceived enjoyment, computer anxiety and self efficacy).The analysis also showed that crowd logistics platforms provide a competitive advantage for SMEs but the perceived risks associated with crowd logistics platforms should be regulated.