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Building Big Data Analytics as a Strategic Capability in Industrial Firms: Firm Level Capabilities and Project Level Practices

Alexander, Dijo T
http://orcid.org/0000-0002-8791-941X
http://rave.ohiolink.edu/etdc/view?acc_num=case1544006213410606

Abstract Details

2019, Doctor of Philosophy, Case Western Reserve University, Management.
Big data analytics is a new and emerging business opportunity and soon a strategic necessity that established industrial firms have embraced with mixed success. While venturing into the uncharted and shifting territory of analytics, most firms encounter a steep learning curve. At the same time those who have succeeded in developing the organizational knowledge and skills to leverage big data analytics enjoy disproportionate gains in market share and profit. We apply dynamic capabilities theory to understand how firms successfully develop big data analytics capabilities at the organizational level, and how such capabilities manifest at the operational level as micro-foundations in specific practices. We conduct an exploratory mixed method study to establish a tentative theory of big data capabilities. The overall research program consists of three studies that (1) explore and validate the effect of firm level capabilities on big data success, and (2) identify as micro-foundation a set of project level practices that underlie successful big data projects in configurations of routines. The first qualitative study explores and identifies higher order firm level capabilities such as continuous learning, cross functional collaboration, experimental validation and market orientation that firms garner to succeed with big data analytics. The second study includes a quantitative structural equation modeling analysis of 224 industrial firms and what explains their success in big data analytics. We find broad support for the positive effect of firm level organizational capabilities such as learning and experimentation on successful analytics outcomes. Surprisingly, collaboration and market orientation are not found to have a significant effect. The study indicates the significant role of project level operational practices of firms in influencing big data analytics success. Drawing on this insight, we conduct an exploratory third study into the emergence, stabilization and diffusion of project level practices that form micro-level foundations associated with big data capabilities. The study is a multi-level repeated case study of 12 projects in three industrial firms covering mixed outcomes. We find low routinization of operational practices of sensing for potential projects, seizing on high value high potential opportunities, and reconfiguring to deliver is yet to stabilize as big data related practices are still fast evolving. Overall, the results show how organizational capabilities of experimentation and learning play a significant role in establishing and stabilizing the operational routines. The results also show the significance of organizational capabilities to discover, share and distribute emerging operational practices and analytics knowledge between projects across the firm. Overall, our findings build up a better understanding of dynamic capabilities as knowledge reconfiguration capabilities that underlie big data analytics. Big data competencies manifest and exhibit unique practices at operating environment with firm level commonalities. Therefore, managers should proactively search to build and develop distinct and contextual operational knowledge of big data analytics and facilitate their routinization at the firm level.
Kalle Lyytinen (Committee Chair)
Rakesh Niraj (Committee Member)
Nicholas Berente (Committee Member)
Varun Grover (Committee Member)
189 p.
Business Administration; Information Systems; Management
Big data; data analytics; dynamic capabilities; organizational capabilities; operational practices; routinization

Recommended Citations

Citations

  • Alexander, D. T. (2019). Building Big Data Analytics as a Strategic Capability in Industrial Firms: Firm Level Capabilities and Project Level Practices [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1544006213410606

    APA Style (7th edition)

  • Alexander, Dijo. Building Big Data Analytics as a Strategic Capability in Industrial Firms: Firm Level Capabilities and Project Level Practices. 2019. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1544006213410606.

    MLA Style (8th edition)

  • Alexander, Dijo. "Building Big Data Analytics as a Strategic Capability in Industrial Firms: Firm Level Capabilities and Project Level Practices." Doctoral dissertation, Case Western Reserve University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1544006213410606

    Chicago Manual of Style (17th edition)

case1544006213410606
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This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.