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Cited by 277

The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.

Understanding the impact of business analytics on innovation

Open access
Duan, Yanqing, Cao, Guangming, Edwards, John S.·2020·European Journal of Operational Research

Abstract

Advances in Business Analytics in the era of Big Data have provided unprecedented opportunities for organizations to innovate. With insights gained from Business Analytics, companies are able to develop new or improved products/services. However, few studies have investigated the mechanism through which Business Analytics contributes to a firm's innovation success. This research aims to address this gap by theoretically and empirically investigating the relationship between Business Analytics and innovation. To achieve this aim, absorptive capacity theory is used as a theoretical lens to inform the development of a research model. Absorptive capacity theory refers to a firm's ability to recognize the value of new, external information, assimilate it and apply it to commercial ends. The research model covers the use of Business Analytics, environmental scanning, data-driven culture, innovation (new product newness and meaningfulness), and competitive advantage. The research model is tested through a questionnaire survey of 218 UK businesses. The results suggest that Business Analytics directly improves environmental scanning which in turn helps to enhance a company's innovation. Business Analytics also directly enhances data-driven culture that in turn impacts on environmental scanning. Data-driven culture plays another important role by moderating the effect of environmental scanning on new product meaningfulness. The findings demonstrate the positive impact of business analytics on innovation and the pivotal roles of environmental scanning and data-driven culture. Organizations wishing to realize the potential of Business Analytics thus need changes in both their external and internal focus.

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Cites

Show all 108 references

Research works printed but not linked: 11

  1. Davenport, 2013, Analytics 3.0, Harvard Business Review, 91, 64
  2. Goes, 2014, Big data and IS research, MIS Quarterly, 38, 3
  3. Kiron, 2013, From value to vision: Reimagining the possible with data analytics, MIT Sloan Management Review, 54, 1
  4. Kiron, 2012, Innovating with analytics, MIT Sloan Management Review, 54, 47
  5. Kiron, 2011, Creating business value with analytics, MIT Sloan Management Review, 53, 57
  6. Lavalle, 2011, Special Report: Analytics and the new path to value, MIT Sloan Management Review, 52, 22
  7. Malhotra, 2007, BIAS Breakdown, Marketing Research, 19, 24
  8. Robinson, 2010, INFORMS to officially join analytics movement, OR/MS Today, 37, 59
  9. Schein, 1990, Vol. 45
  10. Shearer, 2000, The CRISP-DM model: The new blueprint for data mining, Journal of data warehousing, 5, 13
  11. Wyld, 2009, Keys to Innovation: The right measures and the right culture, The Academy of Management Perspectives, 23, 96, 10.5465/amp.2009.39985544 · doi:10.5465/amp.2009.39985544

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Metadata

Authors
Duan, Y., Cao, G., & Edwards, J. S.
Venue
European Journal of Operational Research
Publisher
Elsevier BV
Published
2020-03
Volume
281
Issue
3
Pages
673-686
ISSN
0377-2217 (ISSN-L)
OpenAlex
W929847186
Cited by
277
References
108
FWCI
26.43
Type
journal article
Language
en
Open access
green
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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CoChat. (2026). Citation check: Understanding the impact of business analytics on innovation. https://app.cochat.ai/papers/10.1016/j.ejor.2018.06.021
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Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2018.06.021

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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