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Cited by 260Big Data and Business IntelligenceInformation Technology Governance and StrategyInnovation and Knowledge Management

The DOI matches its Crossref record; 106 of the 106 references it cites were found in a registry or an index.

The role of information governance in big data analytics driven innovation

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Mikalef, Patrick, Boura, Maria, Lekakos, George, Krogstie, John·2020·Information & Management
DOI 10.1016/j.im.2020.103361
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Abstract

The age of big data analytics is now here, with companies increasingly investing in big data initiatives to foster innovation and outperform competition. Nevertheless, while researchers and practitioners started to examine the shifts that these technologies entail and their overall business value, it is still unclear whether and under what conditions they drive innovation. To address this gap, this study draws on the resource-based view (RBV) of the firm and information governance theory to explore the interplay between a firm’s big data analytics capabilities (BDACs) and their information governance practices in shaping innovation capabilities. We argue that a firm’s BDAC helps enhance two distinct types of innovative capabilities, incremental and radical capabilities, and that information governance positively moderates this relationship. To examine our research model, we analyzed survey data collected from 175 IT and business managers. Results from partial least squares structural equation modelling analysis reveal that BDACs have a positive and significant effect on both incremental and radical innovative capabilities. Our analysis also highlights the important role of information governance, as it positively moderates the relationship between BDAC’s and a firm’s radical innovative capability, while there is a nonsignificant moderating effect for incremental innovation capabilities. Finally, we examine the effect of environmental uncertainty conditions in our model and find that information governance and BDACs have amplified effects under conditions of high environmental dynamism.

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Fig. 1 Conceptual research model with hypothesized relationships.
Fig. 1 Conceptual research model with hypothesized relationships.CC-BY · From the PDF
Fig. 2 Estimated causal relationships of structural model.
Fig. 2 Estimated causal relationships of structural model.CC-BY · From the PDF

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Cites

Research works printed but not linked: 2

  1. Kiron, 2017, Lessons from becoming a data-driven organization, MIT sloan management rev., 58
  2. Mikalef, 2017, Big data analytics capabilities: a systematic literature review and research agenda, Inf. Syst. E-business Manag., 1

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Metadata

Authors
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J.
Affiliations
  • Norwegian University of Science and Technology (NO)
  • Athens University of Economics and Business (GR)
Corresponding author
Mikalef, Patrick
ORCID
Venue
Information & Management
Publisher
Elsevier BV
Published
2020-11
Volume
57
Issue
7
Pages
103361
ISSN
0378-7206 (ISSN-L)
DOI
10.1016/j.im.2020.103361
OpenAlex
W3049164745
Cited by
260
References
106
Citations by year
2026: 30 · 2025: 51 · 2024: 63 · 2023: 44 · 2022: 44 · 2021: 26 · 2020: 2
FWCI
32.94
Type
journal article
Language
en
Open access
Licence
Locations
  • Information & Management (publishedVersion, cc-by)
  • NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
Funders
Athens University of Economics and Business
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Innovation and Knowledge Management
Keywords
information governance, big data analytics capability, innovation ability, incremental innovation, radical innovation, resource-based view, environmental uncertainty, environmental dynamism, moderating effect, big data analytics

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