TITLE:
A Bayesian Inference Model for Sustainable Crowd Source Logistics for Small and Medium Scale Enterprises (SME) in Africa
AUTHORS:
Kesewa Opoku Agyemang
KEYWORDS:
Crowd-logistics, Markov Chain Monte Carlo, Bayesian Statistics, Extended Technology Acceptance Model, Probabilistic Programming, Python
JOURNAL NAME:
American Journal of Industrial and Business Management,
Vol.12 No.4,
April
29,
2022
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.