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Articles

An Empirical Study of Some Critical Adoption Factors of ERP Software

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ABSTRACT

This article complements and/or extends inquiries on information systems (IS) adoption; it develops an eight-factor framework within the T-O-E taxonomy and empirically tests it to explain the adoption of the enterprise resource planning (ERP) software amongst small and medium enterprises (SMEs). Survey data were collected from executives of service SMEs in Nigeria. Purposive and snow ball sampling techniques were used and analysis involved partial least square (PLS). The T-O-E factors had a statistically significant effect on ERP adoption; factors within technology and organization had positive coefficients, and those within an environment had negative coefficients. On the average, unit increase(s) in the complexity of the pressures results in less likelihood to adopt ERP or vice versa. Thus, the observed T-O-E antecedent factors critically explain ERP adoption. Managers will make informed investment decisions on how integrated systems systematically improve competitive advantage.

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Notes on contributors

Hart O. Awa

Hart O. Awa, Ph.D., is an Emerald-Literati-Network member and a marketing/management scholar/consultant in the University of Port-Harcourt, Nigeria. He publishes in top-of-the-class indexed journals and presents leading conference papers across countries. His paper won the best paper award (2015) at the Imperial College conference organized by WBI, Australia and LARAP, UK.

John. P. Uko

John. P. Uko, Ph.D., is a seasoned professor of Consumer Behaviour. He has held lecturing positions in Universities in Nigeria, the United States, and Tanzania and has published extensively in referred journals. Currently, he is the teaching and research point-man in the Departments of Marketing and Management, Veritas University, Abuja.

Ojiabo Ukoha

Ojiabo Ukoha is a professor of Mathematics and Computer Science at the University of Maryland, USA. His research interests include the infusion of technology into mathematics instruction, factor analysis, and recruiting and retaining low-income students in computer science and other STEM disciplines.

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