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Research Article | volume 2 Issue 3 (Jul-Sep, 2026) | Pages 1 - 13
Supply Chain Management Practices and Operational Performance in Indian Hospitality: A Robust Quantitative Assessment
 ,
1
Research scholar Faculty of Management Studies Banasthali Vidyapith, Rajasthan
2
Assistant Registrar Academic Section Banasthali Vidyapith, Rajasthan
Under a Creative Commons license
Open Access
Received
July 29, 2026
Revised
Aug. 1, 2026
Accepted
Aug. 10, 2026
Published
Aug. 17, 2026
Abstract

Supply chain management is increasingly central to service reliability in hospitality because operational performance depends on the timely availability, quality, visibility and movement of critical inputs. This study develops and tests a parsimonious model linking five supply chain management practices—supplier relationship management, information technology integration, inventory management, quality management, and logistics and distribution management—to operational performance in an Indian hospitality context. A constructed survey-style dataset comprising 120 respondent profiles across hotels, restaurants, serviced apartments, catering firms and cafés was analysed. The measurement structure was examined using reliability analysis and exploratory factor analysis. Hypotheses were tested using multiple regression with HC3 heteroscedasticity-consistent standard errors. Predictor importance was then decomposed using dominance/relative-importance analysis, while quantile regression examined whether the effects were stable at the 25th, 50th and 75th percentiles of operational performance. The six-construct measurement model exhibited strong reliability (Cronbach’s alpha = 0.877–0.914), a Kaiser–Meyer–Olkin statistic of 0.879, and a significant Bartlett test (χ²(406) = 2242.39, p < 0.001). The robust regression model explained 55.4% of the variance in operational performance (adjusted R² = 0.534). All five practices had positive mean effects: supplier relationship management (B = 0.303, p < 0.001), information technology integration (B = 0.155, p = 0.034), inventory management (B = 0.201, p = 0.002), quality management (B = 0.353, p < 0.001), and logistics and distribution management (B = 0.253, p = 0.003). Relative-importance analysis showed that supplier relationship management and quality management together accounted for more than half of the model’s explained variance. Quantile regression further suggested that supplier relationships and quality management were the most stable contributors across the performance distribution, whereas the contribution of inventory and logistics varied by performance level. The study demonstrates how a focused SCM model can be evaluated using complementary robustness techniques and provides a transparent analytical template for subsequent validation with primary hospitality data.

Keywords
Supply chain management; operational performance; hospitality; supplier relationship management; inventory management; quality management; logistics; robust regression; dominance analysis; quantile regression; India
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