BEST-WORST ANALYSIS METHOD OF BUSINESS MATURITY THROUGH DIGITAL TRANSFORMATION PROCESSES: AN EXAMPLE FOR IT SECTOR

Abstract

Purpose- It is aimed to evaluate the maturity of the business through the transition to digital transformation with Best-Worst Method. Methodology- In this maturity assesstment the weights of the criteria and sub-criteria will be modelled in accordance with the Multi-Criteria-Decision-Making methodology. In this study, digital tansformation process dimensions have been clustered by the experts. In this clustering, the weights of the criteria have been determined by using BWM method. It has been used this method. Because, BWM, in the not-fully-consistent cases with more than three criteria (or alternatives) might bring about multiple optimal solutions. Afterwards, a survey of company employees has been conducted to evaluate the maturity for business. In the model digital transformation criteria were defined for business according to expert opinions. Finally, the maturity of digital transformation of the enterprise has been determined. Findings- The results of the solution shows that the most important criteria is competence of automation and the least important one is inflexible company structure criterion. Furthermore, the sub-criteria that belong to each main criterion have been listed in themselves. Conclusion- The study provides a maturity assessment methodology which is an important part of digital transformation process. This is the first maturity assesstment study under the BWM in the literature.

Keywords

• Maturity assesstment
• multicriteria decision making
• technology firms
• BWM

References

1. Boakai, S. (2016, 6 10). https://tez.yok.gov.tr/UlusalTezMerkezi/. https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp received from..
2. Çakır, E., & Can, M. (2019). Best-Worst Yöntemine Dayalı ARAS Yöntemi ile Dış Kaynak Kullanım Tercihinin Belirlenmesi: Turizm Sektöründe Bir Uygulama. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1273-1300. https://dergipark.org.tr/en/download/article-file/835917
3. Donnelly, C., & Morrison, J. (2018). Implementing a Digital Transformation. RockStep, 1-4.
4. Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 23-31. https://doi.org/10.1016/j.knosys.2017.01.010
5. Morakanyane, R., O’Reilly, Philip, & McAvoy, J. (2020). Determining Digital Transformation Success Factors. 53rd Hawaii International Conference on System Sciences (s. 4357-4365). Hawaii, Manoa: Proceedings of the 53rd Hawaii International Conference on System Sciences.
6. Moslem, S., Farooq, D., Ghorbanzadeh, O., & Blaschke, T. (2020). Application of the AHP‐BWM Model for Evaluating Driver Behavior Factors Related to Road Safety: A Case Study for Budapest. Symmetry, 1-11. https://doi:10.3390/sym12020243
7. Parusheva, S. (2019). Digitalization and Digital Transformation in Construction - Benefits and Challenges. Information and Communication Technologies in Business and Education, (s. 126-134). Varna. ISBN: 978-954-21-1004-0
8. Rezaei, J. (2015). Best-worstmulti-criteriadecision-makingmethod. Omega, 49-57. https://doi.org/10.1016/j.omega.2015.12.001

9. Rezaei, J. (2016). Best-worstmulti-criteriadecision-makingmethod: Some properties and a linear model. Omega, 126-130. https://doi.org/10.1016/j.omega.2015.12.001
10. Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 577-578. https://doi.org/10.1016/j.jclepro.2016.06.125
11. Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems With Applications, 9152-9164. https://doi.org/10.1016/j.eswa.2015.07.073
12. Sayabek, Z., Suieubayeva, S., & Utegenova, A. (2019). Digital Transformation in Business. S. I. Ashmarina, M. Vochozka, & V. V. Mantulenko içinde, Digital Age: Chances, Challenges and Future (s. 408-415). Switzerland: Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-27015-5_49