Cloud computing adoption and its impact on SMEs' performance for cloud supported operations: A dual-stage analytical approach. (February 2020)

• Record Type: Journal Article
• Authors: Khayer, Abul; Talukder, Md. Shamim; Bao, Yukun; Hossain, Md. Nahin
• Is Part Of: Technology in society. Volume 60(2020)
• Publication Date: 2020-02
• Subjects: Cloud computing -- SMEs -- Adoption -- Performance -- SEM-ANN -- IPMA; Technology -- Social aspects -- Periodicals; 303.483
• DOI: 10.1016/j.techsoc.2019.101225
• Languages: English
• ISSNs: 0160-791X

Abstract

Abstract: This paper investigates the key predictors of cloud computing adoption, and further, assesses how cloud computing adoption affects small and medium enterprises' (SMEs') performance. To test the proposed model, we have applied a dual-stage analytical approach by combining structural equation modeling (SEM) and artificial neural network (ANN). SEM results reveal that relative advantage, service quality, perceived risks, top management supports, facilitating conditions, cloud providers influence, server location, computer self-efficacy, and resistance to change have a significant effect on the adoption of cloud computing. Also, this study confirms the positive impact of cloud computing adoption on firm performance. The results of importance-performance map analysis (IPMA) suggest that managerial actions should focus more on improving perceived risk, relative advantage, and top management support. Besides, the results of neural network analysis indicate that the most significant predictor of cloud adoption is server location followed by facilitating conditions, relative advantage, service quality, top management support, computer self-efficacy, perceived risks, cloud provider's influence, and resistance to change. We also discuss the implications of the research that can assist researchers, owners/managers, policymakers, and cloud providers by offering valuable insights regarding cloud computing adoption in SMEs. Highlights: Extending the technology-organization-environment (TOE) framework with individual dimension. Employing a hybrid approach by integrating SEM method and neural network analysis. Performing IPMA in PLS to identify the critical factors that need special managerial consideration. Identifying server location as the most important predictor of cloud computing adoption. Establishing the positive impact of cloud adoption on SMEs' performance for cloud supported operations.