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Roles of big data analytics and organizational culture in developing innovation capabilities: a hybrid PLS-fsQCA approach

journal contribution
posted on 2025-03-13, 10:52 authored by Behzad Foroughi, Mohammad Iranmanesh, Nick HajliNick Hajli, Lee Shih Ling, Morteza Ghobakhloo, Davoud Nikbin
Big data analytics creates and consolidates competitive advantage by providing insights on data with enormous variety, velocity, and volume to firms. However, many companies' investments in big data analytics were unsuccessful, and they could not gain full advantage of these technologies. This study investigates the impacts of big data analytics capabilities on innovation quality and speed by considering organizational learning culture as a moderator. The study's data are obtained from a survey of 221 managers in the manufacturing industry. We integrate the Partial Least Squares (PLS) technique and fuzzy-set Qualitative Comparative Analysis (fsQCA) to perform the analysis. The findings of PLS indicated that big data analytics capabilities positively influence both innovation quality and speed. However, innovation quality influences both market performance and financial performance, and innovation speed only affects market performance. Organizational learning culture negatively moderates the impacts of big data analytics on innovation speed and quality. fsQCA uncovered four solutions with varied combinations of factors that predict the high market and financial performance. The theoretical and practical implications are explained at the end of the paper.

History

School

  • Loughborough Business School

Published in

R&D Management

Publisher

RADMA and John Wiley & Sons Ltd

Version

  • AM (Accepted Manuscript)

Rights holder

© RADMA and John Wiley & Sons Ltd

Publisher statement

This is the peer reviewed version of the article, which has been published in final form at https://doi.org/10.1111/radm.12719. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

Publication date

2024-09-16

Copyright date

2024

ISSN

0033-6807

eISSN

1467-9310

Language

  • en

Depositor

Prof Nick Hajli. Deposit date: 28 February 2025

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