Electronic commerce adoption barriers of Ghanaian manufacturing SMEs: an artificial neural network analysis.

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dc.contributor.author Ocloo, C. E.
dc.contributor.author Xuhua, H.
dc.contributor.author Worwui-Brown, D.
dc.contributor.author Addai, M.
dc.date.accessioned 2023-01-23T12:33:29Z
dc.date.available 2023-01-23T12:33:29Z
dc.date.issued 2018
dc.identifier.issn 2356-6191
dc.identifier.uri https://www.researchgate.net/figure/E-Commerce-adoption-barriers-in-Ghanaian-SMEs_tbl2_299559689/download
dc.identifier.uri http://atuspace.atu.edu.gh:8080/handle/123456789/2647
dc.description.abstract The adoption of Electronic Commerce (EC) has fostered the growth of Small and Medium-sized Enterprises (SMEs) in both developed and developing countries. This research examines potential barriers within the Technology-Organization-Environment (TOE) framework that affect the decision to adopt EC within SMEs in the context of emerging economies. A questionnaire-based survey was used to collect data from 296 managers of Ghanaian manufacturing SMEs. A robust version of feed-forward propagation artificial neural network based on the sigmoid basis function was developed to determine the influential role of designated barriers to EC adoption. We observed that all the nine factors significantly and negatively influence SMEs’ EC adoption decision behaviour, with lack of EC awareness being the dominant factor. Theoretical contribution and managerial implications of this research are discussed which is thought to offer valuable insights to managers, policy makers, and EC experts regarding the adoption of EC within SMEs of emerging economies. en_US
dc.language.iso en_US en_US
dc.publisher American Journal of Multidisciplinary Research en_US
dc.relation.ispartofseries vol.;7
dc.subject EC en_US
dc.subject B2B en_US
dc.subject TOE framework en_US
dc.subject Artificial neural network en_US
dc.subject SMEs en_US
dc.subject Ghana en_US
dc.title Electronic commerce adoption barriers of Ghanaian manufacturing SMEs: an artificial neural network analysis. en_US
dc.type Article en_US


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