Effect of customer concentration on firms’ operating performance during the COVID-19 pandemic

Abstract

This study investigated the effect of a supplier’s customer concentration level on its operating performance during the COVID-19 pandemic. Using a large sample of publicly listed U.S. manufacturing firms, we measured abnormal changes in operating performance of the sample firms across two distinct stages of the pandemic: the disruption phase ($N=2,557$) and the recovery phase ($N=2,454$). Our regression results show that the effect of customer concentration varied depending on the phase of the pandemic. Suppliers with a concentrated customer base performed worse during the first year of the pandemic when the global supply chain experienced significant disruptions. During this disruption, firms with lower degrees of customer concentration managed their supply chain risks more effectively, resulting in higher operating performance than their benchmark firms. However, when firms entered the recovery phase, we found that a firm’s concentration level had the opposite effect on its operating performance. During the recovery phase, firms with concentrated customer bases could coordinate and collaborate more effectively with major customers, leading to improved operating performance. Based on our findings, we discuss the theoretical and managerial implications of managing the supply chain structure during its disruption and recovery.

KEYWORDS:

• Customer concentration
• supply chain structure
• COVID-19
• supply chain resilience
• abnormal operating performance

Data availability statement

The data that support the findings of this study are available from the corresponding author upon request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Refer to the WHO Coronavirus (COVID-19) Dashboard: https://covid19.who.int

2 According to the U.S. Census Bureau’s Small Business Pulse Survey (SBPS), the supply chain disruption was particularly concentrated in the manufacturing, construction, and trade sectors (Callen 2021).

3 Since 1975, all publicly traded companies have been required to report their major customers who contribute 10% or more to their total revenue as directed in the Statement of Financial Accounting Standard No. 14 (SFAS 14).

4 The Compustat Customer Segment database provides seven types of customers. ‘COMPANY’ for firms; ‘GOVDUM’ for domestic government customers; ‘GOVSTATE’ for state governments; ‘GOVLOC’ for local governments; ‘GEOREG’ for geographic region; ‘MARKET’ for market; and ‘GOVFRN’ for a foreign government.

5 In additional analysis, we reestimated Equation (2(2) $\begin{array}{rl}{\mathit{\text{AOP}}}_i& =({\mathit{\text{ROA}}}_{i,t+j}-{\mathit{\text{ROA}}}_{i,t})-({\mathit{\text{Benchmark ROA}}}_{i,t+j}\\ & -{\mathit{\text{Benchmark ROA}}}_{i,t})\end{array}$(2) ) using EBIT (earnings before interest and taxes) instead of the income before extraordinary items, and the results were qualitatively the same.

6 We also examined our hypotheses by applying another event study method proposed by Swift, Guide, and Muthulingam (2019). This method matches high customer concentration firms with a set of low customer concentration firms and compare their performance. It leads to results that are qualitatively similar to main results reported in the manuscript.

7 All the correlation coefficients among the independent variables are less than 0.7, and variance inflation factors (VIF) ranged from 1.01 to 2.26, suggesting that multicollinearity is not a concern in our analysis.

8 U.S. Bureau of Economic Analysis, Real Gross Domestic Product [GDPC1], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/GDPC1, June 29, 2022. We refer to the real GDP instead of the nominal GDP to eliminate the distortion from inflation or deflation.

Additional information

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00248403).

Notes on contributors

Young Soo Park

Young Soo Park is an Assistant Professor at the College of Business Administration, Kookmin University, Republic of Korea. He received his Ph.D. in management engineering from Korea Advanced Institute of Science and Technology (KAIST). His research interests include supply chain management, online retail strategy, and behavioural operations management.

Jaeseog Na

Jaeseog Na is an Assistant Professor of Business Administration at Duksung Women’s University. He received his Ph.D. in management engineering from Korea Advanced Institute of Science and Technology (KAIST). His research interests include empirical operations management, supply chain management, and supply chain sustainability.

Yun Shin Lee

Yun Shin Lee is an Associate Professor of Operations Strategy and Management Science at the KAIST Business School at Korea Advanced Institute of Science and Technology. She received her Ph.D. in Management Science from the Judge Business School at the University of Cambridge. Her research interests include empirical operations management, behavioural operations management, and judgmental forecasting. Her work has appeared in academic journals including Management Science, Production and Operations Management, and Journal of Operations Management.