An alternative approach for calculating Turkey's digital transformation index: Bayesian BWM

Abstract

The digital transformation process caused by Industry 4.0 requires the restructuring of countries. Digital transformation integrates the knowledge and technology. It aims at sustainable profitability with products with added value by increasing efficiency. Countries that are aware of the opportunities and threats created by digital transformation can find the opportunity to increase their market share by gaining a competitive advantage. For this reason, analyses and evaluations are made by various organizations in order to determine the level of adoption and implementation of digital transformation by countries. In this framework, Turkey Digital Transformation Index was prepared by Informatics Industry Association (TuBISAD) and the first one was published in 2020. It is a useful tool that allows observing the change in Turkey's digital transformation performance over time and making comparisons with other countries. The Turkey Digital Transformation Index includes 4 main components (main criteria) and 10 sub-components (sub-criteria) taking into account various indicators (criteria). It is assumed that the importance of each criterion is equal when calculating this index value. However, it is thought that the main and sub-criteria used in the index value calculation are actually different in importance. Therefore, it will be more meaningful to calculate the index by considering the criteria weights. For this reason, they are calculated in this study with Bayesian Best-Worst Method (BBWM), which is one of the Multi-Criteria Decision Making (MCDM) methods, taking into account the opinions of experts in digital transformation. The digital transformation index value is recalculated with these criteria weights. Thanks to this method, aggregated weights are obtained by measuring the degree to which an expert group prefers one criterion to another. This study provides tips on how to increase Turkey's digital transformation performance. It is thought that the findings will affect the ranking and comments.