Advance Selling to Ease Financial Distress

Author:

Papanastasiou Yiangos1ORCID,Xiao Shuang2ORCID,Yang S. Alex3ORCID

Affiliation:

1. London Business School, Rice University, Houston, Texas 77005;

2. School of Finance, Zhongnan University of Economics and Law, Wuhan 430073, China;

3. London School of Business, London NW1 4SA, United Kingdom

Abstract

Left unable to provide service during the COVID-19 pandemic, many small businesses have experimented with alternative ways of generating income. One approach that has gained traction is the use of advance selling, whereby the firm asks consumers in its local community to support the business by paying in advance for consumption at a future date. In this paper, we develop a game theoretic model to investigate whether and how advance selling schemes can be successfully implemented by firms facing financial distress. In cases of high distress (i.e., where obtaining bank financing is infeasible given the firm’s financial need), we show that advance selling in its classic implementation can help the firm secure its survival in some scenarios but may suffer from significant inefficiencies associated with strategic consumer behavior and firm moral hazard. We demonstrate that two modifications of the classic scheme currently observed in practice—namely, (i) the introduction of an “all-or-nothing” clause and (ii) selling future discount coupons as opposed to the full service—can expand the set of scenarios in which survival is ensured while also allowing the firm to extract higher profit. In cases of moderate financial distress (i.e., where bank financing is a feasible but inefficient option), we find that simple advance selling schemes typically fail to make an impact. However, we show that a more complex scheme, combining both of the aforementioned modifications simultaneously, can be used in conjunction with bank financing to generate a substantial improvement in firm profit. This paper was accepted by Jeannette Song, operations management. Funding: The work of author S. Xiao was supported by the National Natural Science Foundation of China [Grant 71901221].

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

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