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

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.

Abstract

Modern organizations typically regard information and communication technologies (ICTs) as one of the significant direct or indirect inputs for achieving operational excellence and competitive advantage. Since the concept of competitive advantage involves a relative comparison of the performance of organizational entities, then the concepts of organizational capabilities, context, and benchmarking are relevant. In this paper we present a new multi-method methodology for benchmarking that explicitly takes into consideration context-specific factors impacting the performance of organizational entities. This novel methodology allows for obtaining actionable information, in the form of non-obvious common causal structures, for improving the performance of the less efficient entities vis-à-vis their more efficient counterparts. The new methodology is state-of-the-art and is novel because it explicitly takes into consideration the context within which the organizational entities perform. Such “context awareness” allows for expanding the universe of discourse within which the process improvement initiatives are usually considered, thus allowing to consider the impact of external to the process factors on internal to the process mechanisms. This methodology involves the creative integration of several Information Systems (IS)’ artifacts (i.e. multiple data mining methods) with Data Envelopment Analysis (DEA). We present an illustrative application of this methodology to an IS/ICT & Productivity research problem in the ‘developing’ countries context.

  

  

Introduction

It is commonly acknowledged that operational excellence is one of the sources of competitive advantage of modern enterprises. Fundamentally, the concept refers to achieving a high level of efficiency of conversion of inputs into outputs, where a higher level of efficiency implies, ceteris paribus, a greater degree of excellence. Modern organizations typically regard information and communication technologies (ICTs) as one of the significant direct or indirect inputs for achieving such operational excellence and competitive advantage. Since the concept of competitive advantage involves a relative comparison of the performance of organizational entities then the concepts of organizational capabilities and benchmarking (e.g., Gouveia, Dias, Antunes, Boucinha, & Inácio, 2015) are relevant. Ayabakan, Bardhan, and Zheng (2017) noted that with regards to the investigation of this pair of concepts: “A dominant approach in IS research involves the use of survey instruments designed to elicit user responses on their perceptions about competencies and capabilities … A limitation of such perception-based approaches is that they represent a subjective measure of firm/organizational capabilities”; these researchers therefore proposed an approach that involves the use of non-subjective data and the data envelopment analysis (DEA) method for doing benchmarking (e.g., Adler, Liebert and Yazhemsky, 2013, LaPlante and Paradi, 2015) of the Input-Output conversion process. In this paper we also take a similar approach to benchmarking but are also interested in the context of the Input-Output conversion process, including those that involve ICTs as input(s). The motivating idea for this research project is that the process of benchmarking could possibly be enhanced by the discovery and application of non-obvious common causal structures that differentiate more efficient organizational entities from less efficient ones. This motivating idea triggered our intention to design an appropriate methodology artifact that involves the analysis of non-subjective data. This research project can be considered to fall within the realm of Information Systems (IS) research for at least the following reasons: (1) benchmarking research is an aspect of well-established IS/ICT & Productivity research stream (e.g. Hitt and Brynjolfsson, 1996, Ko and Osei‐Bryson, 2004); (2) benchmarking research has appeared in leading IS journals (e.g. Ayabakan et al., 2017); (3) the proposed solution artifact involves the a creative integration of several IS artifacts (i.e. multiple data mining methods) with DEA; and (4) our illustrative example falls within the well-established IS/ICT & Productivity research stream.

The presentation of the project starts with our analysis of the concept of benchmarking, followed by the presentation of the research problem and associated research questions of the study. Then we offer to our reader an overview on supporting data analytic and data mining methods, with the methodology of the study presented next. The illustrative example demonstrates the application of the methodology and is followed by conclusion of the paper.