Influencing models and determinants in big data analytics research: A bibliometric analysis

作者：

Highlights：

• The article scrutinizes influencing models and determinants in 229 big data studies.

• Big data adoption research (BDAD) is broadly dispersed across different domains.

• The models emphasize mainly on variance-based relationships and snapshot prediction with little consensus.

• The analysis highlights vibrant determinants of BDAD within each model and level of analysis.

• Insights of this bibliometric study could guide rigorous big data research and practice in various contexts.

摘要

•The article scrutinizes influencing models and determinants in 229 big data studies.•Big data adoption research (BDAD) is broadly dispersed across different domains.•The models emphasize mainly on variance-based relationships and snapshot prediction with little consensus.•The analysis highlights vibrant determinants of BDAD within each model and level of analysis.•Insights of this bibliometric study could guide rigorous big data research and practice in various contexts.

论文关键词：Big data analytics,Technology adoption,Literature review,Bibliometric analysis,Theoretical models,Adoption frameworks

论文评审过程：Received 17 November 2019, Revised 12 February 2020, Accepted 3 March 2020, Available online 10 April 2020, Version of Record 10 April 2020.

论文官网地址：https://doi.org/10.1016/j.ipm.2020.102234