TY - JOUR
T1 - A model to evaluate the organizational readiness for big data adoption
AU - Nasrollahi, Mahdi
AU - Ramezani, Javaneh
N1 - UID/EEA/00066/2019
PY - 2020
Y1 - 2020
N2 - Evaluating 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.
AB - Evaluating 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.
KW - Big data adoption
KW - Fuzzy best-worst method
KW - Industry 4.0
KW - Organizational readiness
KW - Principal component analysis
UR - http://www.scopus.com/inward/record.url?scp=85087044184&partnerID=8YFLogxK
U2 - 10.15837/IJCCC.2020.3.3874
DO - 10.15837/IJCCC.2020.3.3874
M3 - Article
AN - SCOPUS:85087044184
SN - 1841-9836
VL - 15
JO - International Journal of Computers, Communications and Control
JF - International Journal of Computers, Communications and Control
IS - 3
M1 - 3874
ER -