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An Econometric Analysis of Inventory Turnover Performance in Retail Services
Abstract
Inventory turnover varies widely across retailers and over time. This variation undermines the usefulness of inventory turnover in performance analysis, benchmarking, and working capital management. We develop an empirical model using financial data for 311 publicly listed retail firms for the years 1987–2000 to investigate the correlation of inventory turnover with gross margin, capital intensity, and sales surprise (the ratio of actual sales to expected sales for the year). The model explains 66.7% of the within-firm variation and 97.2% of the total variation (across and within firms) in inventory turnover. It yields an alternative metric of inventory productivity, adjusted inventory turnover, which empirically adjusts inventory turnover for changes in gross margin, capital intensity, and sales surprise, and can be applied in performance analysis and managerial decision making. We also compute time trends in inventory turnover and adjusted inventory turnover, and find that both have declined in retailing during the 1987–2000 period.
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“Overstocking can increase storage, obsolescence, and opportunity expenses, which can hurt a company's profitability (Gaur et al., 2005).”
From Inventory Management and Financial Performance of Listed Manufacturing Firms in Nigeria (2024) · cites this paper as Gaur et al., 2005
In the citing paper
“A lengthy inventory conversion period may imply ineffective inventory management, which keeps items idle, wasting money and perhaps causing obsolescence or spoilage.ROA might decline due to asset utilisation and profitability issues (Koumanakos, 2008;Gaur et al., 2005).”
From Inventory Management and Financial Performance of Listed Manufacturing Firms in Nigeria (2024) · cites this paper as Gaur et al., 2005
In the citing paper
“retail firms can benefit from high inventory turnover”
From Working capital management, COVID-19, and profitability of UK retail firms (2025) · cites this paper as Gaur et al. (2005)
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“Using retailer data from 1985 to 2003, Gaur and Kesavan (2009) confirm the results of Gaur et al. (2005) and find evidence for diminishing returns to scale.”
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Background
Metadata
- Authors
- Gaur, V., Fisher, M. L., & Raman, A.
- Affiliations
- New York University (US)
- University of Pennsylvania (US)
- Harvard University (US)
- Venue
- Management Science
- Published
- 2005-02
- Volume
- 51
- Issue
- 2
- Pages
- 181-194
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2130158558
- Cited by
- 469
- References
- 0
- Citations by year
- 2026: 33 · 2025: 39 · 2024: 32 · 2023: 35 · 2022: 19 · 2021: 19 · 2020: 28 · 2019: 24 · 2018: 15 · 2017: 17 · 2016: 24 · 2015: 28 · 2014: 22 · 2013: 31 · 2012: 25
- FWCI
- 48.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Management Science (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Working Capital and Financial Performance
- Social Sciences › Decision Sciences › Management Science and Operations Research › Supply Chain and Inventory Management
- Social Sciences › Business, Management and Accounting › Accounting › Financial Analysis and Corporate Governance
- Keywords
- inventory turnover, retail trade, gross margin, capital intensity, working capital management, performance benchmarking
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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