A data analytic benchmarking methodology for discovering common causal structures that describe context-diverse heterogeneous groups

作者：

Highlights：

• Process improvement via benchmarking requires addressing context-related factors.

• Non-obvious common causal structures can describe a context of benchmarking.

• Association Rules Mining can be used to discover context-related factors.

摘要

•Process improvement via benchmarking requires addressing context-related factors.•Non-obvious common causal structures can describe a context of benchmarking.•Association Rules Mining can be used to discover context-related factors.

论文关键词：Benchmarking methodology,Information and communication technology capabilities,Causal structures,Data envelopment analysis,Market basket analysis,Decision tree induction,Cluster analysis,Association rules mining,Sub-Saharan Economies

论文评审过程：Received 3 March 2018, Revised 26 September 2018, Accepted 27 September 2018, Available online 27 September 2018, Version of Record 4 October 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.09.054