Business growth and management costs as moderators of the inventory-performance link: Evidence from Vietnamese manufacturing firms
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DOIhttp://dx.doi.org/10.21511/imfi.22(3).2025.09
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Article InfoVolume 22 2025, Issue #3, pp. 108-125
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Type of the article: Research Article
Abstract
In emerging markets such as Vietnam, where firms face rapid growth and rising operational complexity, understanding how inventory efficiency interacts with business dynamics is vital. This study aims to examine the relationship between inventory management and firm performance, with a specific focus on how this relationship is moderated by business growth and management costs. Using a balanced panel dataset of 364 manufacturing firms listed on Vietnam’s stock exchanges from 2012 to 2023, this study employs panel data regression methods, including Fixed Effects Model, Random Effects Model, Feasible Generalized Least Squares, and, notably, the System Generalized Method of Moments to address issues of endogeneity and unobserved firm-level heterogeneity. Firm performance is measured by return on assets, and inventory management is proxied by average inventory days. The results show that average inventory days are negatively associated with firm performance, indicating that longer inventory cycles reduce profitability. The interaction term between inventory days and business growth is also negative, suggesting that growth exacerbates the adverse effects of inefficient inventory practices. In contrast, the interaction between inventory days and management costs is positive, implying that effective cost control can mitigate inventory inefficiency. These findings highlight the need for Vietnamese manufacturing firms to align inventory practices with growth strategies and cost management to sustain profitability in dynamic markets.
Acknowledgment
This research was conducted as part of the doctoral dissertation project under Decision No. 5379/QĐ-ĐHDT dated December 31, 2022, issued by Duy Tan University.
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JEL Classification (Paper profile tab)M11, D22, G32
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References37
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Tables9
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Figures0
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- Table 1. Variables measurement
- Table 2. Descriptive analysis
- Table 3. Correlation matrix
- Table 4. VIF for all models
- Table 5. Breusch-Pagan Lagrangian multiplier test for all models
- Table 6. FEM, REM, and FGLS estimates for Model 1
- Table 7. FEM, REM, and FGLS estimates for Model 2
- Table 8. FEM, REM, and FGLS estimates for Model 3
- Table 9. Dynamic panel-data estimation, two-step system GMM results for all models
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