Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.770241
Title: The impact of big data analytics maturity on firm performance : evidence from the UK manufacturing sector
Author: Arunachalam, Deepak
ISNI:       0000 0004 7651 7938
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2019
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Abstract:
Big Data Analytics (BDA) is perceived as one of the most prodigious technologies of the 21st century. However, relatively few studies have demonstrated the positive outcome of developing BDA capabilities. Moreover, although BDA is argued to advance innovation (Tan et al. 2015), the influence of BDA on innovation performance is yet to be confirmed empirically. Studies explaining the underlying mechanism through which BDA influences operational and innovation performances are limited, especially from a BDA maturity and organisational learning perspective. Further, the phenomenon of a digital divide, between SMEs and large organisations, instigated by the adoption of BDA remains unexplored. Therefore, the main purpose of this study is to address these gaps and contribute to the literature by empirically investigating the value of Big Data Analytics capabilities from a maturity perspective, and to explore the digital divide in the context of the UK's manufacturing sector. A systematic literature review is conducted to identify potential research gaps and to frame the research questions. Drawing on the Resource-Based View, Dynamic Capabilities, and a hierarchy of capabilities perspective, a conceptual model is developed depicting the relationship between the BDA capabilities, the higher-order capabilities such as absorptive capacity, data and information quality and supply chain analytics, and its effect on innovation and operational performance. A survey-based research method is adopted to investigate the model and test the hypotheses. For data collection, an online questionnaire is distributed via Qualtrics to senior executives in UK manufacturing sector. After a rigorous pre-processing of collected data, a sample of 221 responses is estimated to be appropriate for further analysis. This study used two types of data analysis techniques: Structural equation modelling and Cluster analysis. Structural equation modelling is used to examining the causal relationship between the variables in the conceptual model. Whereas, cluster analysis is used to explore the phenomenon of a digital divide between SMEs and large organisations. The findings of this study indicate that BDA capabilities improves operational and innovation performances. The impact of BDA capabilities on operational performance is partially mediated by absorptive capacity, data and information quality, and supply chain analytics capability. However, the impact of BDA capabilities on innovation performance is only mediated by absorptive capacity. In terms of cluster analysis, the findings indicate the presence of four homogeneous clusters of organisations with varied levels of BDA capabilities maturity, signifying the reality of digital divide. This study makes some significant contributions. First, in terms of the contribution to literature, this study synthesised arguments from the Resource-Based View, the Dynamic Capabilities View and the hierarchy of capabilities view to provide a holistic explanation of the relationship between lower-order capabilities, higherorder capabilities and operational and innovation performances. Second, in terms of contribution to practice, this research will help to improve the practitioners' understanding about how BDA capabilities can improve firm performance. The BDA maturity framework developed in this research can be used by the practitioners to assess the current level of BDA capabilities allowing the organisations to determine the areas of improvement. Third, this research provides implications for policy that could be advantageous to SMEs, who are mainly data and information poor.
Supervisor: Brint, Andrew Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.770241  DOI: Not available
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