The effects of big data analytics capability on supply chain resilience and financial performance: An empirical study of China’s manufacturing industry

Sun, Xiaoyi (2020) The effects of big data analytics capability on supply chain resilience and financial performance: An empirical study of China’s manufacturing industry. [Dissertation (University of Nottingham only)]

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Abstract

Due to the significant uncertainties and disruptions caused by the COVID-19 pandemic, it has been become crucial for firms to develop supply chain resilience (SCRes). Both researchers and practitioners have recognised the importance of big data analytics capability (BDAC) in helping firms deal with the uncertainties caused by the COVID-19 pandemic. However, to date there has been limited research that has examined the relationships among data-driven culture, BDAC, SCRes, and financial performance, especially in the Chinese manufacturing industry. Therefore, drawing upon resource-based view (RBV), this research develops and empirically test a conceptual framework that investigate the relationships among data-driven culture, BDAC, SCRes, and financial performance. To test the hypothesised relationships, structural equation modelling is used to analyse the survey data collected from 113 manufacturing firms in China. The results show that there is a significant positive effect of data-driven culture on BDAC and SCRes. The results also reveal that BDAC has a significant positive effect on SCRes, and that SCRes is significantly and positively associated with financial performance. The findings provide useful and timely guidance for managers on how to develop resilience supply chains through the development of data analytics capability for financial performance improvement during and after the COVID-19 pandemic.

Item Type: Dissertation (University of Nottingham only)
Keywords: Data-driven culture; Big data analytics capability; Supply chain resilience; Financial performance; Resource-based view
Depositing User: Sun, Xiaoyi
Date Deposited: 18 Apr 2023 10:11
Last Modified: 18 Apr 2023 10:11
URI: https://eprints.nottingham.ac.uk/id/eprint/62805

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