The source of SMEs’ competitive performance in COVID-19: Matching big data analytics capability to business models

Jianmin Song, Senmao Xia, Demetris Vrontis, Arun Sukumar, Bing Liao, Qi Li, Kun Tian, Nengzhi Yao

Research output: Contribution to journalArticlepeer-review

Abstract

Literature notes that firms are keen to develop big data analytics capability (BDAC, e.g. big data analytics (BDA) management and technology capability) to improve their competitive performance (e.g. financial performance and growth performance). Unfortunately, the extant literature has limited understanding of the mechanisms by which firms’ BDAC affects their competitive performance, especially in the context of small and medium-sized enterprises (SMEs). Using resource capability as the theoretical lens, this paper specifically examines how BDAC influences SMEs’ competitive performance via the mediating role of business models (BMs). Also, this study explores the moderating effect of COVID-19 on the relationship between BDAC and BMs. Supported by Partial Least Squares-Structural Equation Modelling (PLS-SEM) and data from 242 SMEs in China, this study finds the mediating roles of infrastructure and value attributes of BMs in enhancing the relationship of BDAC on competitive performance. Furthermore, the improvement of financial performance comes from the matching of BDA management capability with infrastructure attributes of BMs, while the improvements in growth come from the matching of BDA management capability and BDA technology capability with value attributes of BMs. The result also confirms the positive moderating effects of COVID-19 on the relationship of BDA management capability and value attributes of BMs. This study enriches the integration of BDAC and BMs literature by showing that the match between BDAC and BMs is vital to achieve competitive performance, and it is helpful for managers to adopt an informed BDA strategy to promote widespread use of BDAs and BMs.
Original languageEnglish
JournalInformation Systems Frontiers
Early online date19 May 2022
DOIs
Publication statusE-pub ahead of print - 19 May 2022

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