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Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach

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  • Yadegaridehkordi, Elaheh
  • Hourmand, Mehdi
  • Nilashi, Mehrbakhsh
  • Shuib, Liyana
  • Ahani, Ali
  • Ibrahim, Othman

Abstract

Big Data is one of the recent technological advances with the strong applicability in almost every industry, including manufacturing. However, despite business opportunities offered by this technology, its adoption is still in early stage in many industries. Thus, this study aimed to identify and rank the significant factors influencing adoption of big data and in turn to predict the influence of big data adoption on manufacturing companies' performance using a hybrid approach of decision-making trial and evaluation laboratory (DEMATEL)- adaptive neuro-fuzzy inference systems (ANFIS). This study identified the critical adoption factors from literature review and categorized them into technological, organizational and environmental dimensions. Data was collected from 234 industrial managers who were involved in the decision-making process regarding IT procurement in Malaysian manufacturing companies. Research results showed that technological factors (perceived benefits, complexity, technology resources, big data quality and integration) have the highest influence on the big data adoption and firms' performance. This study is one of the pioneers in using DEMATEL-ANFIS approach in the big data adoption context. In addition to the academic contribution, findings of this study can hopefully assist manufacturing industries, big data service providers, and governments to precisely focus on vital factors found in this study in order to improve firm performance by adopting big data.

Suggested Citation

  • Yadegaridehkordi, Elaheh & Hourmand, Mehdi & Nilashi, Mehrbakhsh & Shuib, Liyana & Ahani, Ali & Ibrahim, Othman, 2018. "Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 199-210.
    • Handle: RePEc:eee:tefoso:v:137:y:2018:i:c:p:199-210
    DOI: 10.1016/j.techfore.2018.07.043
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