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Predicting service industry performance using decision tree analysis

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  • Yeo, Benjamin
  • Grant, Delvin

Abstract

Using global industry data on service industries from the World Bank, we investigate the impact of ICTs within the financial context, on service industry performance. This is motivated by the lack of consensus on the impact of ICTs at the industry level of analysis, and insufficient attention given to service industries. Our decision tree analysis is framed by the Technology, Organization, and Environment (TOE) framework. We discover that financial factors are better predictors than ICTs on service industry performance. Access to loans and lines of credit is the strongest predictor. It is often assumed that websites expand markets and increase revenues, but we find that they negatively affect sales revenue growth in Africa and Eastern Europe. These findings help companies and policy makers understand that ICTs alone are insufficient to improve service industry performance. Our findings lead to theoretical contributions in the form of nine hypotheses for future research.

Suggested Citation

  • Yeo, Benjamin & Grant, Delvin, 2018. "Predicting service industry performance using decision tree analysis," International Journal of Information Management, Elsevier, vol. 38(1), pages 288-300.
    • Handle: RePEc:eee:ininma:v:38:y:2018:i:1:p:288-300
    DOI: 10.1016/j.ijinfomgt.2017.10.002
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