Understanding and predicting the determinants of cloud computing adoption: A two staged hybrid SEM - Neural networks approach

• Published in: Computers in human behavior Vol. 76; pp. 341 - 362
• Main Authors: Priyadarshinee, Pragati, Raut, Rakesh D., Jha, Manoj Kumar, Gardas, Bhaskar B.
• Format: Journal Article
• Language: English
• Published: Elmsford Elsevier Ltd 01-11-2017; Elsevier Science Ltd
• Subjects: ANN; Artificial neural networks; Cloud computing; Cloud computing adoption; Cybersecurity; Indian industries; Innovations; Internet; Mathematical models; Modelling; Private sector; Risk analysis; Risk analysis model; Risk perception; SEM; Technology adoption; Technology assessment; Technology utilization; India; ANN; SEM; Cloud computing adoption; Indian industries; Risk analysis model
• ISSN: 0747-5632; 1873-7692
• DOI: 10.1016/j.chb.2017.07.027

Along with water, gas, electricity, and telephone, cloud computing has been considered as the fifth utility. Like other utility services available in today's social computing services are readily available on demand (Buyya, Yeo, Venugopal, Broberg, & Brandic, 2009). The purpose of the study is to develop a hybrid two-stage, structural equation modeling (SEM) – artificial neural network (ANN) model to predict motivators affecting cloud computing adoption services in the Indian private organizations. This research article proposes a new paradigm by extending the Technology Organization Environment Model (TOE) with external factors, namely, perceived IT security risk and risk analysis for the first time in a technology adoption study. One of the core contributions of the study is the introduction of new factors, perceived IT security risk and risk analysis. Data were collected from 660 professional experts and analyzed using structural equation modeling (SEM) and artificial neural network (ANN) modeling. The SEM results showed that perceived IT security risk (PITR), risk analysis (RA), technology innovation (TI), management style (MS) and trust (T) have a significant influence on cloud computing adoption. The only exceptions were the usage of technology (UT) and industry usage (IU) which witnessed statistically insignificant influence on cloud computing adoption. Furthermore, the results obtained from SEM were employed as input to the artificial neural network (ANN) model and results showed that ‘trust’, ‘perceived IT security risk’, and ‘management style’ as most important predictors in cloud computing adoption.
•To develop a hybrid two- stage model, SEM– ANN model.•Proposes a new model by extending the TOE model with external factors.•Data were collected from 660 professional experts.•Analyzed using SEM and ANN modeling.