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Marketing capability and the turnaround of financially distressed firms

  • Original Empirical Research
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Abstract

Financial distress befalls even well-managed firms, many of which find ways to turn around. Hence, it is pertinent to explore how distressed firms recover. Unfortunately, extant research sheds little light on the role of marketing in enabling distressed firms’ turnaround. Using a longitudinal dataset of U.S. firms, we empirically show that when the source of distress is firm-specific, it is marketing capability (as opposed to R&D and operations capabilities) that enables a turnaround. However, when distress is industry-driven, R&D capability is also beneficial. Further, although operations capability and cost-reduction actions do help distressed firms survive, they do not help firms regain financial well-being. Overall, these results highlight the importance of capabilities in the context of distressed firms and have implications for both firm managers and shareholders.

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Notes

  1. We consider distressed firms to have recovered when they enter a state of financial health and solvency following a period of financial distress and their future risk of insolvency is minimal.

  2. Bankruptcy is a dichotomous event that occurs when a firm cannot repay its debtors and is either dissolved or reorganized. Distress refers to the poor financial health of a firm. A firm in distress need not file for bankruptcy and many firms fall into distress without ever experiencing bankruptcy.

  3. We use the term “turnaround” to mean “financial recovery,” as the terms are used interchangeably in past literature (e.g., Pearce II & Robbins, 1993).

  4. Hereafter, “capabilities” refers to dynamic capabilities.

  5. We also test for the impact of cost-reduction strategies such as asset reductions and dividend reductions. We do not explicitly hypothesize for them to maintain the scope of comparison among various capabilities.

  6. Because the LoPucki database records only firms having a book value of assets that exceeded $185 million in 1995 dollars, we used the same criteria for the inclusion of other distressed firms.

  7. On average, firms remained in distress for 7.5 years. The average continuous period over which firms remained in distress was six years.

  8. We do not use a long(er) time frame to avoid additional confounds with longer time frames such as board composition changes and partial takeovers by private equity firms.

  9. We only consider Chapter 11 bankruptcy in this study. In Chapter 11 bankruptcy, the debtor negotiates with creditors to alter the terms of the loan without having to liquidate assets. All other cases of bankruptcy are eliminated, partly because the vast majority of bankruptcy cases in the database (> 95%) are (at least initially) Chapter 11 bankruptcies.

  10. It is beyond the scope of this article to completely delineate how this task was performed. For comprehensive details on the process, please refer to Lim et al. (2020). If marketing expenses are not denoted separately in an annual report (as was the case for a moderate number of firms in our sample), we subtract any expense denoted separately as part of SG&A that does not constitute a marketing expense (e.g., engineering expense or bad debt expense).

  11. To create the frontier to measure capabilities, we consider all firms in the Compustat universe within a particular industry. We define industries in line with Hoberg and Phillips (2018), who measure product market similarity using annual reports to define industry boundaries. However, we conduct the core analyses on distressed firms only.

  12. Some operationalizations of MC use patents as input. We do not do so because patents are typically a result of R&D efforts and we want to avoid confounding MC and RDC.

  13. We provide the derivation in “RDC and distressed firm recovery” section, which discusses the measure of RDC.

  14. If the residual is positive, it indicates that there is some firm-level factor that is buffering the firm from the industry-led distress conditions. Hence, we only use negative residuals (under-explained variance in distress) to proxy firm-specific causes of distress.

  15. We use SIC and not Hoberg–Philips-based segments here as the corresponding database (Compustat Segments) for obtaining segment-level sales.

  16. Most studies concerning distressed firms are in finance, where it is typical to use only financial ratios to predict distress and the probability of recovery. However, such ratios are only symptoms of malaise and do not give any indication as to what a firm should do to recover.

  17. This is empirically shown in our analyses. The length of time the firm stays in distress reduces the likelihood of recovery (β =  − 0.069, p < 0.05) and strongly increases the likelihood of bankruptcy (β =  − 0.406, p < 0.001). Thus, while returns to R&D may be higher as time accumulates, the gains are washed out since a financially distressed firm will find itself in a more precarious position.

  18. A probit or logit model provides only pseudo R2s; thus, it is not easy to distinguish between-, within-, and across-firm variance. However, replacing recovery with a continuous variable such as Z-scores or S&P ratings shows that the across-firm variance explained (approximately 19%) is less than the within-firm variance explained (29%).

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Authors and Affiliations

  1. Culverhouse College of Business, University of Alabama, Tuscaloosa, AL, USA

    Abhi Bhattacharya

  2. University of Miami, Coral Gables, FL, USA

    Joseph Johnson

  3. UCLy, ESDES, Lyon, France

    Ashkan Faramarzi

  4. Trulaske College of Business, University of Missouri, Columbia, MO, USA

    Niket Jindal

  5. G. Brint Ryan College of Business, University of North Texas, Denton, TX, USA

    Ross W. Johnson

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Table 6

Table 6 The effect of capabilities on distressed firms’ survival
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Bhattacharya, A., Johnson, J., Faramarzi, A. et al. Marketing capability and the turnaround of financially distressed firms. J. of the Acad. Mark. Sci. (2023). https://doi.org/10.1007/s11747-023-00985-9

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