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The nexus of knowledge management processes and innovation performance: the moderates of big data analytical

Shadi Abualoush (Department of Business Administration/MIS, Irbid National University, Irbid, Jordan) (Department of Business Administration, Faculty of Economics and Administrative Science, Irbid National University, Irbid, Jordan)

Kybernetes

ISSN: 0368-492X

Article publication date: 20 December 2023

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Abstract

Purpose

The purpose of the study is to identify how knowledge management processes impact innovation performance in the Jordanian medical sector (private hospitals) as well as identify how big data analytics moderates this performance.

Design/methodology/approach

Two hundred ninety-one questionnaires were analyzed for the purpose of this study. A structural equation model (SEM) was used to test convergence validity, discriminant validity and reliability. In order to analyze the data, bootstrapping was used.

Findings

The empirical results showed that all knowledge management processes are statistically significant in influencing innovation performance. Furthermore, big data analytics moderates the relationship between knowledge management processes and innovation performance.

Research limitations/implications

The results of this cross-sectional study are limited to one country and one industry due to methodological limitations, and the results represent a snapshot at a particular point in time.

Originality/value

Jordan's medical leaders will benefit from this study, since it emphasizes the importance of knowledge management processes to enhance innovation performance, especially given the importance of big data analytics in the field, increasing innovation capabilities in the medical field, thereby increasing innovation levels.

Keywords

Acknowledgements

The author would like to express deep gratitude to the editor and editorial assistant and the reviewers of the manuscript for their time and effort. Thank you all so much.

Citation

Abualoush, S. (2023), "The nexus of knowledge management processes and innovation performance: the moderates of big data analytical", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-01-2023-0056

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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