Modelling cloud computing adoption barriers for Indian SMEs' supply chain using TISM and MICMAC analysis

Rohit Kumar and Manish Gupta

International Journal of Business Performance and Supply Chain Modelling, 2023, vol. 14, issue 1, 30-55

Abstract: The implementation of CC can have an impact on the performance of small and medium-sized enterprises by changing the process of designing, producing, delivering, and discarding products. CC is advantageous to emerging countries, particularly India, and requires a comprehensive description for proper understanding and application in business. The aim of this paper is to identify the impediments to CC adoption from the perspective of Indian SMEs. The present research work is to identify the barriers to adopting CC in Indian SMEs using extensive literature and the response obtained from experts. The second main objective of this research is to develop an interpretive hierarchy of CC adoption barriers using TISM technique and categorise these identified barriers into different clusters using MICMAC analysis. This paper identifies four driving barriers named: 1) lack of government support; 2) low understanding of CC; 3) limitation of IT system; 4) lack of connectivity.

Keywords: digital technology; supply chain integration; supply chain relationship; inhibiting factor for ICT adoption; cloud computing adoption. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=130466 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbpsc:v:14:y:2023:i:1:p:30-55

Access Statistics for this article

More articles in International Journal of Business Performance and Supply Chain Modelling from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().