Purpose – Designing knowledge management systems capable of transforming big data into information characterised by strategic value is a major challenge faced nowadays by firms in almost all industries. However, in the managerial field, big data is now mainly used to support operational activities, while its strategic potential is still largely unexploited. Based on these considerations, this study proposes an overview of the literature regarding the relationship between big data and business strategy. Design/methodology/approach – A bibliographic coupling method is applied over a dataset of 128 peer-reviewed articles, published from 2013 (first year when articles regarding the big data-business strategy relationship were published) to 2019. Thereafter, a systematic literature review is presented on 116 papers, which were found to be interconnected based on the VOSviewer algorithm. Findings – This study discovers the existence of four thematic clusters. Three of the clusters relate to the following topics: big data and supply chain strategy; big data, personalisation and co-creation strategies; big data, strategic planning and strategic value creation. The fourth cluster concerns the relationship between big data and knowledge management and represents a ‘bridge’ between the other three clusters. Research implications – Based on the bibliometric analysis and the systematic literature review, this study identifies relevant understudied topics and research gaps, which are suggested as future research directions. Originality/value – This is the first study to systematise and discuss the literature concerning the relationship between big data and firm strategy.

The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives / Ciampi Francesco, Marzi Giacomo, Demi Stefano, Faraoni Monica. - In: JOURNAL OF KNOWLEDGE MANAGEMENT. - ISSN 1367-3270. - ELETTRONICO. - 24:(2020), pp. 1157-1176. [10.1108/JKM-02-2020-0156]

The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives.

Ciampi Francesco;Faraoni Monica
2020

Abstract

Purpose – Designing knowledge management systems capable of transforming big data into information characterised by strategic value is a major challenge faced nowadays by firms in almost all industries. However, in the managerial field, big data is now mainly used to support operational activities, while its strategic potential is still largely unexploited. Based on these considerations, this study proposes an overview of the literature regarding the relationship between big data and business strategy. Design/methodology/approach – A bibliographic coupling method is applied over a dataset of 128 peer-reviewed articles, published from 2013 (first year when articles regarding the big data-business strategy relationship were published) to 2019. Thereafter, a systematic literature review is presented on 116 papers, which were found to be interconnected based on the VOSviewer algorithm. Findings – This study discovers the existence of four thematic clusters. Three of the clusters relate to the following topics: big data and supply chain strategy; big data, personalisation and co-creation strategies; big data, strategic planning and strategic value creation. The fourth cluster concerns the relationship between big data and knowledge management and represents a ‘bridge’ between the other three clusters. Research implications – Based on the bibliometric analysis and the systematic literature review, this study identifies relevant understudied topics and research gaps, which are suggested as future research directions. Originality/value – This is the first study to systematise and discuss the literature concerning the relationship between big data and firm strategy.
2020
24
1157
1176
Goal 9: Industry, Innovation, and Infrastructure
Ciampi Francesco, Marzi Giacomo, Demi Stefano, Faraoni Monica
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1194593
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