Analyzing the factors influencing cloud computing adoption using three stage hybrid SEM-ANN-ISM (SEANIS) approach

Abstract

This investigation aims to propose a hybrid three-stage Structural Equation Modeling (SEM) - Artificial Neural Network (ANN) - Interpretive Structural Modeling (ISM) approach, together abbreviated as the SEANIS, for analyzing the factors influencing cloud computing adoption (CCA) services in the context of Indian private organizations. This study proposed new determinants, namely risk analysis and perceived IT security risk as an extension of the Technology Organization Environment (TOE) model. The data collected from the industry experts were analyzed using SEM and ANN approaches. The results of SEM revealed that trust (T), management style (MS), technology innovation (TI), risk analysis (RA), and perceived IT security risk (PITR) exercised a significant influence on CCA. The SEM results were taken as inputs for the ANN approach and ISM methodology. The results of ANN highlighted that perceived IT security risk, trust, and management style were the most important determinants for CCA. On the other hand, the ISM tool identified five factors, namely, decrease of internal systems availability (F1) (PITR cluster), utilization of internal resources (F14) (MS cluster), assurance of data privacy increases adoption rate (F16) (T cluster), innovativeness (F21), and previous experience (F22) (both from the TI cluster) as the top five significant variables with high driving power, among the 43 factors. The outcome of the hybrid approach is intended to guide the decision and policy-makers for easy evaluation of their organizational goals for choosing the most suitable computing environment for improving the efficiency and effectiveness of their business performance.