Exploring suitability of cloud computing for small and medium-sized enterprises in India

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Abstract
Purpose: The purpose of this paper is to evaluate the suitability of cloud computing for small and medium-sized enterprises (SMEs) in India. Design/methodology/approach: Literature review is used to identify benefits, challenges, and factors concerning usage and adoption of cloud computing by SMEs. A conceptual framework was developed based on the existing theoretical models and factors identified from the literature. A survey based on questionnaire method was followed to collect data from 121 manufacturing SMEs in India. The conceptual framework was refined by using factor analysis, and multiple regression analysis was used to test the hypotheses. Findings: Major benefits of cloud computing for SMEs include cost advantage, easy deployment process, easier access to latest information and communication technologies, automatic updates and upgrades, scalability, flexibility, and improved disaster recovery and back-up capabilities. The concern area includes perceived loss of control, vendor lock-in, security issues, reliability and availability issues, and internet connectivity and speed. The factors that are found significant include perceived benefits, top management support, competitive pressure, and perceived concerns. The model explained 73 percent of cloud computing adoption. Research limitations/implications: The study is expected to make significant contribution toward body of knowledge pertaining to information technology adoption in SMEs. Practical implications: The study is expected to provide SMEs an insight into real benefits and challenges associated with adopting cloud computing. The model can help SMEs in evaluating their readiness for adopting cloud computing and can ensure successful outcome of cloud computing adoption in SMEs. Providers are also going to be benefited through their enhanced understanding of SMEs’ requirements. Originality/value: The proposed framework incorporates all the relevant factors including perceived benefits, perceived concerns, and other organizational and environmental factors to improve model’s predictive power.

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