The IUP Journal of Operations Management
Assessment of Total Quality Management Implementation in Indian Service Industries
Article Details
| Pub. Date | :May, 2020 |
|---|---|
| Product Name | : The IUP Journal of Operations Management |
| Product Type | : Article |
| Product Code | : IJOM10520 |
| Author Name | : Faisal Talib and Mohd. Nishat Faisal |
| Availability | : YES |
| Subject/Domain | : Management |
| Download Format | : PDF Format |
| No. of Pages | : 22 |
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Abstract
The purpose of this paper is to examine the extent to which Total Quality Management (TQM) practices have been implemented in Indian service industries. A self-structured instrument was used to collect data from four different service industries, viz., healthcare, banking, hospitality and Information and Communication Technology (ICT). Using empirical data gathered from a sample of 172 (generated from stratified sampling methodology) top and middle managers in Indian service industries, the findings were reported. The instrument was tested and validated through reliability and validity tests. A one-way Analysis of Variance (ANOVA) was used to examine the extent of TQM implementation in Indian service industries. The results revealed that six TQM practices, i.e., top-management commitment, customer focus, process management, quality systems, teamwork, and communication, are effectively implemented across the Indian service industries, suggesting that no significant difference was found, while the remaining 11 practices are partially implemented as significant difference was observed in their implementation. The proposed framework can help Indian service managers and practitioners to make the process of TQM implementation effective by proper implementation of the identified set of TQM practices to gain maximum benefits and advantages of the TQM program.
Description
In the present era of global and increased competitive environment, industries have been experiencing rapid changes in the business performance propelled by phenomena An earlier version of the paper was presented at the Annual Meeting of the Decision Sciences Institute Conference (DSI-2016) which was held between November 19-22, 2016, Austin, TX, USA.
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