An integrated Grey-DEMATEL approach for evaluating ICT adoption barriers in manufacturing SMEs: Analysing Indian MSMEs. Issue 6 (26th November 2019)

• Record Type: Journal Article
• Authors: Singh, Amit; Thakkar, Jitesh; Jenamani, Mamata
• Is Part Of: Journal of enterprise information management. Volume 35:Issue 6(2022)
• Page Start: 1427
• Page End: 1455
• Publication Date: 2019-11-26
• Subjects: Information and Communication Technology -- MSME; Management information systems -- Periodicals; Business logistics -- Periodicals; Business -- Data processing -- Periodicals; Management -- Data processing -- Periodicals; 658.05
• DOI: 10.1108/JEIM-09-2018-0211
• Languages: English
• ISSNs: 1741-0398

Abstract

Abstract : Purpose: The purpose of this paper is to propose an integrated gray-decision making trial and evaluation laboratory (Grey-DEMATEL) framework to evaluate the ICT adoption barriers in manufacturing small and medium enterprises (MSMEs) running in developing countries. The proposed model is also tested for Indian MSMEs. Design/methodology/approach: The model consists of 16 potential ICT adoption barriers selected from existing literature and refined based on discussion with domain experts. While DEMATEL analyses causal relationship and prioritizes them, Grey approach tackles individual biases and data insufficiency. Findings: The authors conclude that lack of awareness about benchmarking and lack of management vision are the most critical ICT adoption barriers in the case of Indian MSMEs. Research limitations/implications: Outcomes of the present research are based on the experts' inputs which are subject to the biases related to their experience and exposure. In India practices adopted by SMEs have geographical and political influence that is also neglected. Originality/value: This study provides a model consisting of 16 ICT adoption barriers for MSMEs in developing country and a framework to analyze causal relationships among the barriers with the flexibility of data input from their own domain experts. The framework is also capable of dealing with expert biases and data insufficiency.