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How to improve firm performance using big data analytics capability and business strategy alignment?

Journal Article


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Abstract


  • The recent interest in big data has led many companies to develop big data analytics capability (BDAC) in order to enhance firm performance (FPER). However, BDAC pays off for some companies but not for others. It appears that very few have achieved a big impact through big data. To address this challenge, this study proposes a BDAC model drawing on the resource-based theory (RBT) and the entanglement view of sociomaterialism. The findings show BDAC as a hierarchical model, which consists of three primary dimensions (i.e., management, technology, and talent capability) and 11 subdimensions (i.e., planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relational knowledge). The findings from two Delphi studies and 152 online surveys of business analysts in the U.S. confirm the value of the entanglement conceptualization of the higher-order BDAC model and its impact on FPER. The results also illuminate the significant moderating impact of analytics capability–business strategy alignment on the BDAC–FPER relationship.

Authors


  •   Akter, Shahriar
  •   Fosso Wamba, Samuel (external author)
  •   Gunasekaran, Angappa (external author)
  •   Dubey, Rameshwar (external author)
  •   Childe, Stephen J. (external author)

Publication Date


  • 2016

Citation


  • Akter, S., Fosso Wamba, S., Gunasekaran, A., Dubey, R. & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment?. International Journal of Production Economics, 182 113-131.

Scopus Eid


  • 2-s2.0-84983595576

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2021&context=buspapers

Ro Metadata Url


  • http://ro.uow.edu.au/buspapers/1015

Has Global Citation Frequency


Number Of Pages


  • 18

Start Page


  • 113

End Page


  • 131

Volume


  • 182

Place Of Publication


  • Netherlands

Abstract


  • The recent interest in big data has led many companies to develop big data analytics capability (BDAC) in order to enhance firm performance (FPER). However, BDAC pays off for some companies but not for others. It appears that very few have achieved a big impact through big data. To address this challenge, this study proposes a BDAC model drawing on the resource-based theory (RBT) and the entanglement view of sociomaterialism. The findings show BDAC as a hierarchical model, which consists of three primary dimensions (i.e., management, technology, and talent capability) and 11 subdimensions (i.e., planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relational knowledge). The findings from two Delphi studies and 152 online surveys of business analysts in the U.S. confirm the value of the entanglement conceptualization of the higher-order BDAC model and its impact on FPER. The results also illuminate the significant moderating impact of analytics capability–business strategy alignment on the BDAC–FPER relationship.

Authors


  •   Akter, Shahriar
  •   Fosso Wamba, Samuel (external author)
  •   Gunasekaran, Angappa (external author)
  •   Dubey, Rameshwar (external author)
  •   Childe, Stephen J. (external author)

Publication Date


  • 2016

Citation


  • Akter, S., Fosso Wamba, S., Gunasekaran, A., Dubey, R. & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment?. International Journal of Production Economics, 182 113-131.

Scopus Eid


  • 2-s2.0-84983595576

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2021&context=buspapers

Ro Metadata Url


  • http://ro.uow.edu.au/buspapers/1015

Has Global Citation Frequency


Number Of Pages


  • 18

Start Page


  • 113

End Page


  • 131

Volume


  • 182

Place Of Publication


  • Netherlands