IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 553 10.51583/IJLTEMAS.2026.151000034 Industrial Marketing Operational and Relational Dimensions of Supplier Readiness in Industrial Spare Parts Procurement S. Guruprasad Doctoral Scholar, Department of Business Administration, Annamalai University, Annamalai Nagar, Tamil Nadu, India India Selvarasu A. Professor, Department of Business Administration, Annamalai University, Annamalai Nagar, Tamil Nadu, India India 15 10 409 425 17 10 2026 22 10 2026 01 11 2026 Supplier readiness; industrial procurement; purchase intention; machinery spare parts; structural equation modelling Abstract

This study examines the associations of inventory readiness, delivery reliability, technical service support, and trust and reputation with industrial buyers’ machinery spare-parts purchase intention. A cross-sectional survey yielded 144 respondents across six industry categories. The achieved sample is treated as nonprobability because probability selection and state allocation cannot be established. A five-construct covariance-based model with 22 previously retained indicators was re-estimated by maximum likelihood. Fit was acceptable rather than uniformly excellent: χ²(199) = 277.401, CFI = .950, TLI = .942, RMSEA = .052 (90% CI .037–.067), and off-diagonal SRMR = .054. Trust and reputation had the largest standardized association with purchase intention (β = .560, p < .001); inventory, delivery and technical support were not significant at .05 in the reduced SEM. Reliability and convergent validity were satisfactory for four constructs, whereas technical support remained weak. HTMT ranged from .320 to .809; the stricter Fornell–Larcker criterion failed for trust versus purchase intention. Full seven-item composite regressions with HC3 errors and bootstrap intervals retained trust’s prominence but supported inventory and technical support, revealing specification-sensitive conclusions. Exploratory industry comparisons showed no significant mean differences. Practical recommendations combine transparent supplier conduct with verified inventory, logistics and service performance. Cross-sectional self-reporting, limited geographical information and measurement weaknesses constrain causal and population-level inference.

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