Data-driven innovation development: an empirical analysis of the antecedents using PLS-SEM and fsQCA

Mohamamd Alamgir Hossain (), Mohammed Quaddus (), Md Moazzem Hossain () and Gopika Gopakumar ()
Additional contact information
Mohamamd Alamgir Hossain: RMIT University
Mohammed Quaddus: Curtin University
Md Moazzem Hossain: Murdoch University
Gopika Gopakumar: PricewaterhouseCoopers

Annals of Operations Research, 2024, vol. 333, issue 2, No 16, 895-937

Abstract: Abstract Data-driven innovation (DDI) is a primary source of competitive advantage for firms and is a contemporary research priority. However, what facilitates the development of DDI has largely been understudied in literature. Through a systematic literature review, this study finds technological, organizational, and environmental variables under the TOE framework, which would drive effective DDI development. We thus develop a research model, which is tested using survey data from 264 Australian firms engaged in DDI development. The data have been analysed using both symmetric (partial least squares based structural equation modelling (PLS-SEM)) and asymmetric (fuzzy-set qualitative comparative analysis (fsQCA)) methods. The mixed method enhances the confidence in our empirical analyses of the antecedent variables of DDI development. PLS-SEM has revealed that technological readiness (i.e., data quality and metadata quality), and organizational absorptive capacity and readiness (i.e., technology-oriented leadership and availability of IT skilled professionals) affect DDI development. Our fsQCA results complement and extend the findings of PSL-SEM analysis. It reveals that quality of data and metadata, technology-oriented leadership, and exploitation capacity individually are necessary—but are not sufficient—conditions for high DDI development. Further, it identifies three different solutions each for small, medium, and large firms by combining the TOE factors. Additionally, this study suggests that the TOE framework is more applicable to small firms, on DDI context. Findings of our study have been related with theoretical and practical implications.

Keywords: Data-driven innovation (DDI); Open data; TOE framework; SEM-PLS; fsQCA (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-022-04873-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:333:y:2024:i:2:d:10.1007_s10479-022-04873-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-022-04873-3

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().