Modelling the level of adoption of analytical tools; An implementation of multi-criteria evidential reasoning

TL;DR

This paper introduces a novel multi-criteria decision analysis methodology to aggregate diverse data sources into a unified framework, aiding the assessment of analytical tool adoption levels across different companies.

Contribution

It presents a practical, consistent approach for data aggregation from multiple sources using MCDA, with a detailed six-step process and numerical example for implementation.

Findings

Effective data aggregation from different sources achieved

Methodology successfully applied to assess analytical tool adoption

Guidelines facilitate replication in various contexts

Abstract

In the future, competitive advantages will be given to organisations that can extract valuable information from massive data and make better decisions. In most cases, this data comes from multiple sources. Therefore, the challenge is to aggregate them into a common framework in order to make them meaningful and useful. This paper will first review the most important multi-criteria decision analysis methods (MCDA) existing in current literature. We will offer a novel, practical and consistent methodology based on a type of MCDA, to aggregate data from two different sources into a common framework. Two datasets that are different in nature but related to the same topic are aggregated to a common scale by implementing a set of transformation rules. This allows us to generate appropriate evidence for assessing and finally prioritising the level of adoption of analytical tools in four types of companies. A numerical example is provided to clarify the form for implementing this methodology. A six-step process is offered as a guideline to assist engineers, researchers or practitioners interested in replicating this methodology in any situation where there is a need to aggregate and transform multiple source data.