This empirical research analyses the influence of HSN Codes (Harmonised System of Nomenclature) on the efficiency of operations (OE) and performance of logistics outcomes (LPO) in the import and export industries. Primary data used in the analysis consisted of responses to a structured questionnaire by fifty logistics professionals, IMPEX executives, customs house agents (CBHC) and brokers. The techniques used to analyse the primary data were: Percentage analysis, Descriptive statistics, Pearson Correlation, Multiple Regression, Sobel Test, Mediation Analysis, and Structural Equation Modelling (using SPSS/AMOS). Results indicate that HSN Code classification practices are the strongest predictor of Trade Efficiency (r = .831, p < .01). HSN Awareness, Knowledge Level, and Training Requirements do not predict improvements either in Customs Clearance or reductions in cost on their own; however, the effects of these three variables are indirect through a mediator variable, Ease of Classification, which plays a significant role in classifying goods for entering a country. The Structural Equation Model (SEM) has acceptable fit indices across each of the eight fit indices. Brand Trust, analogous to Supply Chain Efficiency within the conceptual framework developed for this research, maintains structural independence; therefore, Performance Logistics operates primarily from operational accuracy rather than based on perceptual constructs. The Study recommends that logistics companies develop a structured training programme for HSN classification, clear the use of Artificial Intelligence to enhance and support the overall HSN classification processes, and create an integrated HSN compliance process in their digital Customs process in order to be competitive at sustainable levels.
KEYWORDS: HSN Code Classification, Import–Export Operations, Logistics Performance, Trade Compliance, Trade Efficiency, Supply Chain, Customs Clearance, B2B Logistics
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