Nasrollahi, MahdiRamezani, Javaneh2021-07-082021-07-0820201841-9836PURE: 32425127PURE UUID: 31ada9a7-9b7b-47e8-bacb-587b84348563Scopus: 85087044184WOS: 000528258600009http://hdl.handle.net/10362/120724UID/EEA/00066/2019Evaluating organizational readiness for adopting new technologies always was an important issue for managers. This issue for complicated subjects such as Big Data is undeniable. Managers tend to adopt Big Data, with the best readiness. But this is not possible unless they can assess their readiness. In the present paper, we propose a model to evaluate the organizational readiness for Big Data adoption. To accomplish this objective, firstly, we identified the criteria that impact organizational readiness based on a comprehensive literature review. In the next step using Principal Component Analysis (PCA) for criterion reduction and integration, twelve main criteria were identified. Then the hierarchical structure of criteria was developed. Further, Fuzzy Best-Worst Method (FBWM) has been used to identify the weight of the criteria. The finding enables decision-makers to appropriately choose the more important criteria and drop unimportant criteria in strengthening organizational readiness for Big Data adoption. Statistics-based hierarchical model and MCDM based criteria weighting have been proposed, which is a new effort in evaluating organizational readiness for Big Data adoption.992936engBig data adoptionFuzzy best-worst methodIndustry 4.0Organizational readinessPrincipal component analysisComputer Science ApplicationsComputer Networks and CommunicationsComputational Theory and Mathematicsjournal article10.15837/IJCCC.2020.3.3874https://www.scopus.com/pages/publications/85087044184