Analysis of companies' digital maturity by hesitant fuzzy linguistic MCDM methods

BÜYÜKÖZKAN FEYZİOĞLU G., GÜLER KESMEZ M.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, cilt.38, sa.1, ss.1119-1132, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 1
  • Basım Tarihi: 2020
  • Doi Numarası: 10.3233/jifs-179473
  • Dergi Adı: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1119-1132
  • Anahtar Kelimeler: Digital maturity, hesitant fuzzy linguistic term sets, multi criteria decision making, AHP, ARAS, ARAS METHOD, MODEL, SELECTION, VIKOR, EXTENSION
  • Galatasaray Üniversitesi Adresli: Evet

Özet

Digital Transformation (DT) is the journey of using digital technologies to develop new business models and strategies. DT aims to achieve competitive advantage and to realize activities that will create efficiency in the corporate value chain. Digital Maturity Model (DMM) provides a practical approach to DT. There is a need for an analytical tool to analyze the significance of the factors in the DMM and to rank the companies according to their digital maturity. It is a multi-criteria decision-making (MCDM) problem with multiple factors under vagueness and impreciseness. Hesitant fuzzy linguistic term sets (HFLTS) is a technique used to facilitate Decision Makers' (DMs) judgment process in imprecise situations. HFLTS technique gives DMs possibility to use linguistic expressions with comparative judgments. This article introduces a decision framework based on the HFLTS, Hesitant Fuzzy Linguistic (HFL) Analytic Hierarchy Process (AHP) and HFL Additive Ratio ASsessment (ARAS) methods. It is aimed to provide a scientific method that helps to determine the most important criteria for companies' DMM and to rank companies. A case study about the banking sector is presented to verify the usability of this method.