Modelling and analysis of big data platform group adoption behaviour based on social network analysis

• Published in: Technology in society 2021-05, Vol.65, p.101570, Article 101570
• Main Authors: Lei, Zhimei, Chen, Yandan, Lim, Ming K.
• Format: Article
• Language: English
• Subjects: Behavior; Big Data; Center and periphery; Cliques; Cognitive-behavioral factors; Corporate group behaviour; Decision analysis; Decision making; Density; Group dynamics; Network analysis; Platforms; Social network analysis; Social networks; Technology; Technology adoption
• ISSN: 0160-791X; 1879-3274
• DOI: 10.1016/j.techsoc.2021.101570

Due to the importance of big data technology in decision-making, production and service provision, enterprises have adopted various big data technologies and platforms to improve their operational efficiency. However, the number of enterprises that have adopted big data is not promising. The purpose of this study is to explore the current status of big data adoption by Chinese enterprises and to reveal the possible factors that hinder big data adoption from the group behaviour network perspective. Based on a real case survey of 54 big data platforms (BDPs), four types of networks—i.e., the enterprise-platform network, enterprise network, platform network and industry similarity and difference (ISD) network—are constructed and analysed on the basis of social network analysis (SNA). This study finds that among Chinese enterprises, the level and scope of big data adoption are generally low and are imbalanced among industries; the cognitive level and adoption behaviour of enterprises on BDPs are inconsistent, the compatibility of BDPs is different, and the density and distance-based cohesion of networks are weak; although the current big data adoption behaviours of Chinese enterprises have formed some structural features, core-periphery structures and maximal complete cliques are found, and the current network structure has little impact on individual enterprises and platforms; enterprises in the same industry prefer to adopt the same kind of big data technology or platform. Based on these findings, several strategies and suggestions to improve big data adoption are provided. •Among Chinese enterprises, the level and scope of big data adoption are generally low and are imbalanced among industries.•The cognitive level and adoption behaviour of enterprises on BDPs are inconsistent, the compatibility of BDPs is different.•The density and distance-based cohesion of four networks are weak.•The current network structure has little impact on individual enterprises and platforms.•Enterprises in the same industry prefer to adopt the same kind of big data technology or platform.