Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: A dual-stage analytical approach

Abul Khayer, Md. Shamim Talukder, Yukun Bao and Md. Nahin Hossain

Technology in Society, 2020, vol. 60, issue C

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.

Keywords: Cloud computing; SMEs; Adoption; Performance; SEM-ANN; IPMA (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:60:y:2020:i:c:s0160791x19301599

DOI: 10.1016/j.techsoc.2019.101225

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