Study of Enterprises'' Antecedents and Optimal Industrial Customers for Cloud Services Adoption in Taiwan

• Main Authors: June-Hong Chen, 陳俊宏
• Other Authors: Shui-Lien Chen
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/2pm2v4

博士 === 淡江大學 === 管理科學學系博士班 === 106 === Cloud computing is not only the next generation of computing but also the next step in the evolution of on-demand information technology services and products. The rapid flourishing of the cloud service market necessitates service providers to identify their optimal potential industry customers using limited firm resources when facing competition, cost pressure, and demand for services and applications for designing customer-oriented and differentiated services, developing precise marketing strategies, reducing redundant investments, and generating the greatest profitability. Many studies have addressed technical and operational concerns related to cloud services. However, only few have focused on the critical topic of identifying determinants and their relationships that affect organizational behavior and its acceptance of cloud services, but these studies have neither confirmed whether the research model is the best-fitting model nor considered the practical application of cloud computing in society. This study aims to build a model development strategy for constructing research competing models (RCMs), discover significant determinants for understanding industrial organization’s acceptance of cloud services, and then apply the findings to explore optimal industrial customers for service providers further. This research integrated the technology acceptance model, diffusion of innovations theory, technology–organization–environment framework, and model parsimony principle to develop four cloud service adoption RCMs with enterprise usage intention as a proxy for actual behavior. A questionnaire-based survey was used to collect data from 227 firms in the manufacturing and services industries in Taiwan. Causal relationships and RCMs comparison were tested through structural equation modeling (SEM), and the ordering of optimal industrial customers was evaluated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The empirical results indicated that although all four RCMs had a high goodness-of-fit in the two-stage comparison procedure (nested and nonnested models), research competing model A (Model A) demonstrated superior performance and was the best-fitting model, accounting for 74.8% of the explanatory power, revealed in organizational behavioral intention to use cloud services. All six constructs—namely perceived information security assurance, service compatibility, entrepreneurship, social influence, perceived cost savings, and top management support—were significant positive factors in the decision to adopt cloud services. Moreover, top management support was the most influential factor, affecting enterprise usage intention, whereas social influence was the most crucial factor affecting top management support. These factors can be used as the criteria in TOPSIS method to analyze optimal industrial customers. The results also revealed that large firms tend to adopt more innovations than do small and medium sized enterprises (SMEs); furthermore, service-type organizations have a higher probability of adoption than manufacturing-type firms, and consequently, large service-type companies are the optimal industrial customer for cloud services adoption. This study not only constructs a model development strategy and clarifies the factors and relationships that considerably affect enterprise intention to use cloud services but also identifies the optimal industrial customers for cloud service providers regarding understanding strategies for the design and promotion of cloud services. Furthermore, this is one of the first studies to combine SEM and TOPSIS method and provide an objective and feasible alternative method for resolving the multiple criteria decision-making problem (independence, incompleteness, and subjectivity of evaluation criteria and weights).