Exploring big data-driven innovation in the manufacturing sector: evidence from UK firms

Mujahid Mohiuddin Babu; Mahfuzur  Rahman; Ashraful Alam; Bidit Lal Dey

doi:10.1007/s10479-021-04077-1

Exploring big data-driven innovation in the manufacturing sector: evidence from UK firms

Mujahid Mohiuddin Babu, Mahfuzur Rahman, Ashraful Alam, Bidit Lal Dey

School of Marketing and Management

Research output: Contribution to journal › Article › peer-review

29 Citations (Scopus)

212 Downloads (Pure)

Abstract

Although innovation from analytics is surging in the manufacturing sector, the understanding of the data-driven innovation (DDI) process remains a challenge. Drawing on a systematic literature review, thematic analysis and qualitative interview findings, this study presents a seven-step process to understand DDI in the context of the UK manufacturing sector. The findings discuss the significance of critical seven-step in DDI, ranging from conceptualisation to commercialisation of innovative data products. The results reveal that the steps in DDI are sequential, but they are all interlinked. The proposed seven-step DDI process with solid evidence from the UK manufacturing and research implications based on dynamic capability theory, institutional theory and TOE framework establish the building blocks for future studies and industry practice.

Original language	English
Pages (from-to)	689-716
Number of pages	28
Journal	Annals of Operations Research
Volume	333
Issue number	2-3
Early online date	21 Apr 2021
DOIs	https://doi.org/10.1007/s10479-021-04077-1
Publication status	Published - Feb 2024

Bibliographical note

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords

Big data analytics
Data-driven innovation (DDI)
Data products
Data governance

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10.1007/s10479-021-04077-1Licence: CC BY

PublishedFinal published version, 841 KBLicence: CC BY

https://link.springer.com/article/10.1007/s10479-021-04077-1Licence: CC BY

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abstract = "Although innovation from analytics is surging in the manufacturing sector, the understanding of the data-driven innovation (DDI) process remains a challenge. Drawing on a systematic literature review, thematic analysis and qualitative interview findings, this study presents a seven-step process to understand DDI in the context of the UK manufacturing sector. The findings discuss the significance of critical seven-step in DDI, ranging from conceptualisation to commercialisation of innovative data products. The results reveal that the steps in DDI are sequential, but they are all interlinked. The proposed seven-step DDI process with solid evidence from the UK manufacturing and research implications based on dynamic capability theory, institutional theory and TOE framework establish the building blocks for future studies and industry practice.",

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Exploring big data-driven innovation in the manufacturing sector: evidence from UK firms

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Keywords

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