Hostname: page-component-848d4c4894-xfwgj Total loading time: 0 Render date: 2024-06-16T18:46:11.095Z Has data issue: false hasContentIssue false

It's a matter of time! CEO turnover and corporate turnarounds in Italy

Published online by Cambridge University Press:  28 November 2022

Maurizio Dallocchio
Affiliation:
Università Commerciale L. Bocconi, Via Sarfatti, 25, 20136 Milan, Italy SDA Bocconi School of Management, Via Sarfatti, 25, 20136 Milan, Italy
Andrea Caputo*
Affiliation:
University of Trento, Via Inama, 5, 38122 Trento, Italy University of Lincoln, Lincoln, UK
Alberto Tron
Affiliation:
Università Commerciale L. Bocconi, Via Sarfatti, 25, 20136 Milan, Italy Università di Torino, Torino, Italy
Federico Colantoni
Affiliation:
SDA Bocconi School of Management, Via Sarfatti, 25, 20136 Milan, Italy University of St. Gallen, Dufourstrasse, 50, 9000 St. Gallen, Switzerland
*
Author for correspondence: Andrea Caputo, E-mail: andrea.caputo@unitn.it
Rights & Permissions [Opens in a new window]

Abstract

This paper examines whether CEO turnover affects company performance and the optimal time for CEO renewal during a turnaround process. Results, derived from data collected from Italian companies, highlight the necessity of introducing the new CEO before beginning an insolvency procedure. A later appointment can reduce his/her impact, probably due to the difficulty of managing negotiations with the creditors. Moreover, we show a positive and significant relationship between CEO turnover and the likelihood of a bankrupt firm re-emerging from an insolvency procedure. The analysis was based on the traditional logit model and more modern approaches like the random forest and the AdaBoost models, combined with the SHAP technique. Overall, our findings provide valuable insight for all company stakeholders, whose interests are significantly impacted by its default.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press in association with the Australian and New Zealand Academy of Management

Introduction

The current pandemic crisis has deeply affected companies worldwide, with devastating and long-lasting impacts for several of them and our societies (Ayoko, Caputo, & Mendy, Reference Ayoko, Caputo and Mendy2022). The economic consequences of COVID-19 have led to a significant increase in the indebtedness of firms, often leading to entering a restructuring process (Demmou, Calligaris, Franco, Dlugosch, McGowan, & Sakha, Reference Demmou, Calligaris, Franco, Dlugosch, McGowan and Sakha2021). Furthermore, the consequences of this process will not only impact the defaulted company, but also the local economy and community, especially in countries with an unstable political situation (Cuervo-Cazurrra, Mudambi, & Pedersen, Reference Cuervo-Cazurrra, Mudambi and Pedersen2017).

A similar scenario, which is the consequence of a low-probability but high-impact event (that, therefore, is largely unexpected), can threaten the life of a company if it cannot quickly adapt to the new economic and strategic framework (Haibing, Jinhong, Qi, & Wilbur, Reference Haibing, Jinhong, Qi and Wilbur2015). Research reports that typically large companies have the necessary experience in managing a crisis and a successful turnaround (Parnell & Crandall, Reference Parnell and Crandall2020; Serrasqueiro, Leitão, & Smallbone, Reference Serrasqueiro, Leitão and Smallbone2021), while high firm mortality rates are reported among firms of smaller size (Kücher, Mayr, & Mitter, Reference Kücher, Mayr, Mitter, Duller and Feldbauer-Durstmüller2020). Consequently, it is not a surprise that the percentage of firms that manage to implement an effective turnaround is very low: it has been estimated that two-thirds of companies in difficulty are unable to overcome the crisis (Chowdhury, Reference Chowdhury1996). Understanding which variables can impact the result of a turnaround process, and when they do so, is fundamental, especially for responding effectively to crises such as the COVID-19 pandemic (Wenzel, Stanske, & Lieberman, Reference Wenzel, Stanske and Lieberman2021).

In this field, various scholars have highlighted the role of a company's management team in promoting and introducing strategic changes and new decision-making processes for survival during a crisis (Alipour, Reference Alipour2013). Several studies have shown that the top executives' background and skills (Mazzotta, Reference Mazzotta2018; Paoloni, Mattei, Dello Strologo, & Celli, Reference Paoloni, Mattei, Dello Strologo and Celli2020) and the renewal of the management team, through the introduction of new human capital (Agostini & Nosella, Reference Agostini and Nosella2017), can affect the performance of a firm (Bennedsen, Pérez-González, & Wolfenzon, Reference Bennedsen, Pérez-González and Wolfenzon2020; Gong & Wu, Reference Gong and Wu2011; Hilger, Mankel, & Richter, Reference Hilger, Mankel and Richter2013). However, literature in this field is still inconclusive and contradictory and, thus, more evidence on these issues is needed. This is especially true for smaller companies facing turnarounds because previous studies mostly focused on periods of stability (Domínguez-CC & Barroso-Castro, Reference Domínguez-CC and Barroso-Castro2017). Moreover, prior research had been still focused on large corporations and there tends to be less analysis of small-medium companies which, on the contrary, tend to face more crises because of internal resource shortcomings and fragility in responding to competition and economic slowdowns (Kücher, Mayr, & Mitter, Reference Kücher, Mayr, Mitter, Duller and Feldbauer-Durstmüller2020; Parnell & Crandall, Reference Parnell and Crandall2020).

In the context of turnaround, prior literature focused on the relationship between the renewal of the CEO and the impact on a company's performance (e.g., Dimopoulos & Wagner, Reference Dimopoulos and Wagner2016). Yet, to our knowledge, a gap emerges about studying when a new CEO should join a company during a situation of crisis. Knowing the right time to hire a new CEO can play a crucial role in company survival during a turnaround. Furthermore, the relationship between the role of management in a restructuring process (Elloumi & Gueyiè, Reference Elloumi and Gueyiè2001) and the effect of the turnaround is focused mainly on geographical contexts like the USA (Dardour, Boussada, Yekini, & Makhlouf, Reference Dardour, Boussada, Yekini and Makhlouf2018). An investigation of countries with different legislations and procedures in terms of creditor power and corporate governance practices is still lacking (Manzaneque, Priego, & Merino, Reference Manzaneque, Priego and Merino2016). By analysing a set of Italian companies, the aim of this paper is to study the timing for companies in crisis to hire a new CEO, and to analyse the impact between the renewal of the CEO and company performance, and, thus, its probability of re-emerging from a crisis, during a turnaround process.

As such, the paper brings several contributions to theory and practice. First, the Italian sample has unique features because companies are characterized by: (a) inadequate corporate governance practices; (b) the insolvency procedures managed by the debtor and (c) an economic environment where small-medium enterprises are predominant. Thus, studying the impact of a new CEO in the Italian small and medium enterprises (SMEs) context is important in comparison with the more widely researched US large-companies context. Second, we adopted a different perspective, in comparison with previous research, regarding the definition of a successful turnaround process, which is often subjective. Instead of using, as in past research (Scafarto, Ricci, Della Corte, & De Luca, Reference Scafarto, Ricci, Della Corte and De Luca2017), the Return on Assets (“ROA”), which it is only a profitability ratio, we adopted a more objective and global ratio by examining the increase of the Altman Z-score (Altman, Reference Altman1993), as an indicator of the likelihood of bankruptcy. Furthermore, from a methodological point of view, this paper applied an innovative approach to interpreting the impact of corporate governance variables through the use of novel machine-learning techniques (Althey, Reference Althey2018). Until now, the literature has focused, to the best of our knowledge, only on the ability of machine-learning models to predict bankruptcy situations or the exit from the turnaround process, without analysing and interpreting how these models are impacted by corporate governance variables. Specifically, we applied the SHAP techniques (Lundberg & Lee, Reference Lundberg and Lee2017), which allowed to interpret the relationship between CEO replacement and the likelihood of a company emerging from a crisis, not only in the case of the traditional logit model but also in the case of the random forest and AdaBoost models. Finally, the main contribution of this paper is represented by the identification of the correct timing for changing the CEO during a turnaround. The findings indicate that the renewal of the CEOs is significant only if the new CEO is appointed before the true declaration of the state of crisis. This confirms that a successful turnaround requires a strategy (Parnell & Crandall, Reference Parnell and Crandall2020) and the implementation of new business plans (Cirka & Corrigall, Reference Cirka and Corrigall2010) for managing the crisis before the default event. The results confirm that companies that replace the CEO during, and especially before, a turnaround process are associated with a greater probability of firm survival and performance improvement. From a practical point of view, this paper highlights the fact that a new CEO is a prerequisite for surviving during a crisis since it allows to introduce new human and relational capital. Moreover, the entrance of a new CEO allows also to obtain more bargaining power with creditors, and thus the possibility of obtaining new financing, which is a necessity for overcoming a crisis.

The remainder of this paper is structured as follows. The next section presents a review of the literature to build the theoretical framework in support of the hypotheses for the study. The third section describes the methodology employed. The fourth section reports the findings, followed by the fifth section subsequently discussing these results. The paper concludes with a summary of the conclusions and the implications for future research.

Theoretical background and hypothesis development

Corporate crisis and turnaround

The concepts, causes and effects of a crisis have been studied by various scholars with different and heterogenous backgrounds: strategic (Wenzel, Stanske, & Lieberman, Reference Wenzel, Stanske and Lieberman2021), for example, to identify the most adequate strategic responses to a crisis; public relations (Eriksson, Reference Eriksson2018), for example, to find the appropriate tools for managing a social media crisis communication; marketing (Clark, Reference Clark1988), for example, to understand the causes of a marketing crisis and its relationship with a financial crisis; disaster management (Shaluf, Ahmadun, & Mat Said, Reference Shaluf, Ahmadun and Mat Said2003), for example, for understanding the management procedures to put in place during a disaster or a crisis and financial (Li & Faff, Reference Li and Faff2019), for example, to predict bankruptcies, and, thus, the destruction of value.

From a financial point of view, research still debates the definition of corporate crisis: it can be considered as the asset value of the company under its debt (Riccetti, Russo, & Gallegati, Reference Riccetti, Russo and Gallegati2015), or as a synonym of insolvency and, therefore, as the unsustainability of future debt repayment (Dallocchio, Pirrone, & Lucchini, Reference Dallocchio, Pirrone and Lucchini2021) or as a series of continuous negative economic results (Whitaker, Reference Whitaker1999). Despite this fact, predicting corporate survival through bankruptcy is an important area of investigation in corporate finance which has been analysed by several studies over the last few decades (e.g., Lian, Reference Lian2017; Lin, Liu, Tan, & Zhou, Reference Lin, Liu, Tan and Zhou2020).

The increasing research interest in this field is due to the importance and the consequences of corporate defaults on the economic system. A company bankruptcy can impact severely all the stakeholders of a company (Trahms, Ndofor, & Sirmon, Reference Trahms, Ndofor and Sirmon2013): creditors, since the loss of credit can affect their profitability and their financial sustainability (Hansen & Ziebarth, Reference Hansen and Ziebarth2017); suppliers and customers, since the customer–supplier relationship is typically based on long-term contracts (Lian, Reference Lian2017), the cost of finding a new supplier/customer can be significant with negative impacts for the supplier/customer on profitability (Kim, Song, & Zhang, Reference Kim, Song and Zhang2015), leverage (Oliveira, Kadapakkam, & Beyhaghi, Reference Oliveira, Kadapakkam and Beyhaghi2017) and cost of capital (Dhaliwal, Judd, Serfling, & Shaikh, Reference Dhaliwal, Judd, Serfling and Shaikh2016) and employees, since bankruptcy can cause loss of human capital, with relevant consequences on the salaries of employees and on the local labour market (Bae, Kang, & Wang, Reference Bae, Kang and Wang2011; Graham, Kim, Li, & Qiu, Reference Graham, Kim, Li and Qiu2015). Therefore, in order to prevent defaults and, thus, minimize the economic and social impacts of insolvency, a large body of literature seeks to predict corporate bankruptcy, using various methodological approaches based on logit/probit models (among others, Foreman, Reference Foreman2003; Ohlson, Reference Ohlson1980), discriminant analysis models (e.g., Altman, Reference Altman1968; Altman, Danovi, & Falini, Reference Altman, Danovi and Falini2013) and, recently, on machine-learning models (among others, Barboza, Kimura, & Altman, Reference Barboza, Kimura and Altman2017; Jones, Johnstone, & Wilson, Reference Jones, Johnstone and Wilson2017).

Findings indicate that small-medium firms are more vulnerable to the effects of a crisis since they tend to face more financing restrictions than large companies (Denis & Rodgers, Reference Denis and Rodgers2007; Serrasqueiro, Leitão, & Smallbone, Reference Serrasqueiro, Leitão and Smallbone2021). Moreover, the difficulty in accessing credit and the strong dependence of small-medium firms on bank loans can reduce their possibility for future investments, with consequent negative impacts on their growth (Serrasqueiro, Leitão, & Smallbone, Reference Serrasqueiro, Leitão and Smallbone2021). Therefore, a minor growth combined with the unfeasibility of receiving external finance can affect the long-term probability of survival of small-medium firms, especially during a financial crisis (Collignon & Esposito, Reference Collignon and Esposito2013).

The study of turnaround processes and their effects should take into consideration the legal framework within which they take place. Country-specific corporate governance practices can affect the relationship between CEO turnover and firm performance. Scholars, until now, analysed this relationship mainly in the USA (Manzaneque, Priego, & Merino, Reference Manzaneque, Priego and Merino2016), where creditors have a considerable influence over the governance of bankrupt firms (Lin et al., Reference Lin, Liu, Tan and Zhou2020). However, the USA and the UK economic systems share several distinctions from the European one: (i) shareholder rights are stronger in the USA and in the UK, thanks to the common law, than under the civil law in Europe; (ii) the majority of firms in Europe are familiar with a unique dominant shareholder while in the USA and the UK the public company is more common and (iii) in Europe, the dominant shareholder can severely damage corporate governance control systems (Owen, Kirchmaier, & Grant, Reference Owen, Kirchmaier and Grant2006).

Across Europe, it is possible to identify two types of corporate governance and ownership systems: (i) Rhenish type, characterized by the fact that companies tend to be controlled by workers and banks; and (ii) Latin-type, in which the majority shareholder controls the management through the board of directors (Luo, Reference Luo2007). The Latin corporate governance characteristics are shared by several countries, such as Italy, France, Belgium, Spain, Portugal and Greece (Luo, Reference Luo2007), which would benefit from more research, as most studies currently focus only on the USA and UK (Dardour et al., Reference Dardour, Boussada, Yekini and Makhlouf2018). While the governance of the companies in these countries has been profoundly renewed during the last few decades, there still are significant differences with the Anglo-Saxon systems (Dardour et al., Reference Dardour, Boussada, Yekini and Makhlouf2018; Luo, Reference Luo2007). Therefore, as mentioned before, these relevant differences, in terms of control and corporate governance systems, may impact the relationship between CEO turnover and firm performance. Further research on these countries is needed for a better understanding of the impact of CEO turnover on firm performances and if there are differences with the Anglo-Saxon results (Dardour et al., Reference Dardour, Boussada, Yekini and Makhlouf2018). This research gap is also shown by the fact that literature in this field is still contradictory and scholars found that the relationship between CEO renewal and firm performance is both influenced (Burns, Minnick, & Starks, Reference Burns, Minnick and Starks2018) and not influenced (Dimopoulos & Wagner, Reference Dimopoulos and Wagner2016) by country-specific factors.

These differences are even more evident in the Italian context (Cortesi, Tettamanzi, & Corno, Reference Cortesi, Tettamanzi and Corno2009), which is characterized by a lack of separation between ownership and management combined with an inadequate governance system (Tron, Reference Tron2021). In several Italian companies, the controlling shareholder is also the CEO of the company – and often also the unique sole director. In some cases, to replace the CEO a transfer of ownership may need to happen, which can cause a delay in the implementation of the turnaround strategy. Furthermore, according to the Italian bankruptcy law, creditors have only limited power over defaulted firms, and the insolvency procedures are managed by the debtor (Tron, Reference Tron2021). Therefore, the replacement of the CEO in Italy or similar countries, where literature is still limited (Manzaneque, Priego, & Merino, Reference Manzaneque, Priego and Merino2016), can have a different impact, from the US case, on the probability of a company emerging from bankruptcy. The study at hand contributes to such a picture by investigating the Italian context.

CEO renewal in corporate turnarounds

Previous studies have also examined if managers can be considered a key factor for a company to emerge from bankruptcy (Goyal & Wang, Reference Goyal and Wang2017; Lin et al., Reference Lin, Liu, Tan and Zhou2020). Since a crisis typically stimulates innovation (Wenzel, Stanske, & Lieberman, Reference Wenzel, Stanske and Lieberman2021). According to Hothckiss (Reference Hothckiss1995), the sustained involvement of pre-bankruptcy management in the restructuring process is strongly connected with poor performances after the bankruptcy. Therefore, management replacement represents a significant point of discontinuity for the company in relation to its past, a discontinuity that may be necessary when the company is in an irreversible state of crisis. This is also shown by the fact that most business failures are attributable to managers (Ahn, Cho, & Kim, Reference Ahn, Cho and Kim2000) and their poor managerial choices, such as: failure to respond to corporate decline (Balgobin & Pandit, Reference Balgobin and Pandit2001), incompetence (Altman, Reference Altman1983), failure to recognize early warning signs or changes in the target market (Dunbar & Goldberg, Reference Dunbar and Goldberg1978) and poor performance measurement systems (Schendel, Patton, & Riggs, Reference Schendel, Patton and Riggs1976). Therefore, during a crisis, which typically requires making decisions in an unpredictable contest (Parnell & Crandall, Reference Parnell and Crandall2020), the most dangerous strategy is passivity, and thus, the renewal of the management is a necessity (Domínguez-CC & Barroso-Castro, Reference Domínguez-CC and Barroso-Castro2017).

A key role, especially in SMEs, is played by the CEO, who has a more direct imprint and impact on the firm's strategic directions and actions. Being characterized by smaller sizes, SMEs usually employ a limited number of hierarchical level that reduces the distance between the top decision-maker and the implementation of the decision itself. Thus, the studies on the relationship between CEO turnover and firm performance are important to our investigation. According to the agency theory, this relationship is explained by the fact that owners and managers tend to have conflicting interests since CEOs tend to pursue their own interests and not the corporate ones unless proper corporate governance policies are applied by the company to protect the interests of shareholders (Donaldson & Davis, Reference Donaldson and Davis1991). Specifically, agency theory suggests that the interests of firms' owners and CEO could diverge, especially in the long term when the different structure of their compensation becomes more evident (Nyberg, Fulmer, Gehart, & Carpenter, Reference Nyberg, Fulmer, Gehart and Carpenter2010), thus a CEO renewal can become a necessity for the re-alignment of the interests between firms' owners and managers. Therefore, current literature has further analysed the consequences of CEO renewal as it is an extraordinary event that affects all company processes, from strategy to performance (Schepker, Kim, Patel, Thatcher, & Campion, Reference Schepker, Kim, Patel, Thatcher and Campion2017). The CEO can be considered the key internal figure in determining the strategy of a company and, thus, can facilitate the application of innovative processes and products (Hilger, Mankel, & Richter, Reference Hilger, Mankel and Richter2013).

However, the choice to change the CEO of a company is far from obvious and is the result of a cost–benefit analysis. CEO turnover can also negatively affect firm performance. Prior literature has suggested that the recruitment of a new CEO can lead to unexpected events. These incidents can decrease financial performance since strategic plans applied by new CEOs can be incoherent with company structure and relationship systems (Schepker et al., Reference Schepker, Kim, Patel, Thatcher and Campion2017). The effects on a company's managerial performance depend largely on the decisions of the new CEO and on his/her integration within the organization (Finkelstein, Hambrick, & Cannella, Reference Finkelstein, Hambrick and Cannella2009). During a crisis, the failure to replace the CEO can be caused by the fact that a crisis requires prompt intervention, which can only be guaranteed by an internal figure who comprehensively understands the company (O'Kane & Cunningham, Reference O'Kane and Cunningham2012). This view seems to be confirmed by PWC (2019) data which show that, in the years following the great financial crisis of 2008, the forced turnover rate of CEOs reached an all-time low, signalling the necessity of stability in an uncertain environment. In such a context, replacement of the CEO could create further uncertainty and aggravate the crisis. Moreover, when the economic situation and the general framework are stable, the recruitment of a new CEO can have a negative impact on the profitability of the company (Domínguez-CC & Barroso-Castro, Reference Domínguez-CC and Barroso-Castro2017).

Scholars have found both positive (Bennedsen, Pérez-González, & Wolfenzon, Reference Bennedsen, Pérez-González and Wolfenzon2020; Gong & Wu, Reference Gong and Wu2011) and negative relationships (Dardour et al., Reference Dardour, Boussada, Yekini and Makhlouf2018; Hilger, Mankel, & Richter, Reference Hilger, Mankel and Richter2013) between CEO renewal and a firm's performance (Bennedsen, Pérez-González, & Wolfenzon, Reference Bennedsen, Pérez-González and Wolfenzon2020; Gong & Wu, Reference Gong and Wu2011), also during a crisis (Chen & Hambrick, Reference Chen and Hambrick2011; Hothckiss, Reference Hothckiss1995). Consequently, literature in this field is still inconclusive and contradictory and further studies are needed, especially for small-medium companies (Parnell & Crandall, Reference Parnell and Crandall2020) facing a financial crisis (Domínguez-CC & Barroso-Castro, Reference Domínguez-CC and Barroso-Castro2017). Moreover, in the literature, the in-depth relationship between CEO replacement and the likelihood for a company to emerge from a crisis has not yet been analysed (Lin et al., Reference Lin, Liu, Tan and Zhou2020).

Such contradictory results in literature can be explained by the fact that CEOs have various profiles, backgrounds and behaviours which can affect the probability of success for a CEO turnover. Prior literature has suggested various reasons for this incoherence in results. New CEOs have been related to increasing the likelihood of strategic changes (Hutzschenreuter, Kleindienst, & Greger, Reference Hutzschenreuter, Kleindienst and Greger2012) as they would bring new ideas and culture based on their previous experience. For example, the previous experience has been linked to many aspects of behaviour, like attention creativity, allocation, alertness and perception (Hutzschenreuter, Kleindienst, & Greger, Reference Hutzschenreuter, Kleindienst and Greger2012). Similarly, the type of turnover can have an impact, since forced turnovers tend to be fewer and to affect company performance less than unforced turnovers (Jenter & Lewellen, Reference Jenter and Lewellen2021). Also, the frequency of turnovers is a fundamental factor because firms with more frequent CEO turnovers have a lower firm performance as they are not able to build long-term relationships (Kim, Jeong, Yiu, & Moon, Reference Kim, Jeong, Yiu and Moon2021). Moreover, a CEO's education is an important factor for the selection of a new CEO, however, it does not seem to impact the long-term performance of a company (Bhagat, Bolton, & Subramanian, Reference Bhagat, Bolton and Subramanian2010). Finally, the network and the long-term relationships of the CEO are crucial factors since CEOs with a more robust network, especially with political connections, tend to be replaced less and to affect company performances less often (Cao, Pan, Qian, & Tian, Reference Cao, Pan, Qian and Tian2017).

Therefore, we expect that the CEO turnover should positively impact the financial performance of a company in crisis because, especially in Latin corporate governance systems, the renewal of the CEO allows the introduction of new human capital and relational capital, it brings discontinuity from previous strategic actions, and provide a cleaner negotiation position with the creditors (e.g., Tron, Valenza, & Caputo, Reference Tron, Valenza and Caputo2018). A new CEO should positively enhance the company's organizational and financial performances (Fee, Hadlock, Huang, & Pierce, Reference Fee, Hadlock, Huang and Pierce2018; Kim et al., Reference Kim, Jeong, Yiu and Moon2021). Thus, we propose the following hypothesis:

Hypothesis 1: The renewal of the CEO positively affects the financial performance of a company in crisis.

Having argued for the benefits of changing the CEO in crisis situations, a further practically led question to which scholars are yet to find an answer is about when the change is best to happen during a crisis situation. While the literature has studied in-depth the relationship between CEO tenure and firm performance (e.g., Im & Cao, Reference Im and Cao2015), the investigation of the timing for CEO replacement has not yet been fully investigated (Schweizer & Nienhaus, Reference Schweizer and Nienhaus2017). The analysis of the timing is a fundamental aspect since it allows us to properly study when the strategic change should occur in the context of turbulent periods, enhancing our knowledge regarding the relationship between CEO turnover and firm performance (Hutzschenreuter, Kleindienst, & Greger, Reference Hutzschenreuter, Kleindienst and Greger2012). Theoretical studies argue that the effects of a turnaround process depend on proper timing within the organizational life cycle (Agarwal & Gort, Reference Agarwal and Gort2002; Amburgey, Kelly, & Barnett, Reference Amburgey, Kelly and Barnett1993). Yet empirical proof of the procedural aspects of turnarounds are still lacking (Schweizer & Nienhaus, 2017).

In the context of SMEs, understanding when a new CEO should be hired during a crisis has a central role because it can affect deeply the final outcome (Ciampa, Reference Ciampa2020). During a crisis, typically the new CEO is not recruited until the true declaration of the state of crisis. However, if the turnover is anticipated, this allows the new CEO to gain a better knowledge of the company and, thus, to apply a more efficient strategic change (Ciampa, Reference Ciampa2020). Recognizing the best timing for CEO turnover and anticipating the state of crisis allows a company to reduce the loss of intellectual capital, since top employees tend to leave a company in crisis. It also allows a business to better prepare for CEO succession, which, if not properly managed, tends to destroy value (Rivolta, Reference Rivolta2018). However, research has not yet studied this topic in-depth and, thus, more research on the correct timing of the CEO turnover is needed (Hutzschenreuter, Kleindienst, & Greger, Reference Hutzschenreuter, Kleindienst and Greger2012).

We expect that the CEO turnover should occur before the true declaration of the state of crisis. Recognizing the appropriate timing for CEO turnover can also impact the effects of their renewal on the company's performance. Since a crisis requires prompt solutions, a CEO should have the necessary time for understanding a company, like its market, structure and network, in order to be able to apply an efficient strategic change (Schweizer & Nienhaus, 2017; Tron, Valenza, & Caputo, Reference Tron, Valenza and Caputo2018). Acting promptly may allow reducing the loss of intellectual capital by keeping key employees and may allow to raise new financing before it is too late (Tron, Reference Tron2021). Thus, we propose the following hypothesis:

Hypothesis 2: The timing of a CEO turnover is important for the resolution of the crisis.

Methods

Research design

As for the methodology, a logistic regression, a random forest model and an AdaBoost model were carried out.

The logit model allows one to predict the probability of a certain class or event by using a set of independent variables (Hilbe, Reference Hilbe2015). The logit model is useful for economic-finance studies because it does not require the independent variables to be normally distributed or to have equal variance in each group (Hilbe, Reference Hilbe2015). Moreover, a logit model is particularly suitable for determining the probability of binary events such as pass/fail, win/lose, alive/dead (Omondi-Ochieng, Reference Omondi-Ochieng2020).

Regarding the two machine-learning techniques, we selected the random forest and the AdaBoost techniques since several authors (i.e., Barboza, Kimura, & Altman, Reference Barboza, Kimura and Altman2017) have shown they perform better than the logit model in predictions, especially for defaulted companies (Jones, Johnstone, & Wilson, Reference Jones, Johnstone and Wilson2017).

The random forest, created by Breiman (Reference Breiman2001), is an algorithm which can randomly select a series of characteristics from each node of the tree, following a bagging technique. The AdaBoost model derives from the boosting techniques, which allows one to identify the best model according to a sample (Friedman, Reference Friedman2001), thanks to its ability to create various training sets and to identify the one with the lowest error rate (Hastie, Tibshirani, & Friedman, Reference Hastie, Tibshirani and Friedman2009). Different from the AdaBoost, which is based on the concept of charting decision rules, the random forest method uses a tree structure. Both the random forest and the AdaBoost, differently from the logit model, are robust to overfitting and outliers and can manage data of mixed type (Jones, Johnstone, & Wilson, Reference Jones, Johnstone and Wilson2017). To reinforce our results and as a robustness check, we employed 10 times a 10th K-fold cross-validation approach (Hastie, Tibshirani, & Friedman, Reference Hastie, Tibshirani and Friedman2009) to accurately select and test the parameters of both models, such as the number of trees and the number of variables randomly selected in the case of the random forest.

However, one of the main problems of machine-learning techniques is their lack of transparency and interpretability (Lantz, Reference Lantz2019), which prevents one from capturing the importance of each variable used in the model. To solve this issue, we used the SHAP technique as an innovative approach (Lundberg & Lee, Reference Lundberg and Lee2017). The SHAP method, through the Shapley values (a technique borrowed from the game theory), is used to calculate the influence of each variable on the model. Thanks to the use of the SHAP technique, we were able to easily interpret the results in the case of the random forest and AdaBoost models. To our knowledge, scholars have not yet examined, using new machine-learning models, the impact of corporate governance variables on the probability of emerging from a crisis.

Until recently (Barboza, Kimura, & Altman, Reference Barboza, Kimura and Altman2017), prior literature has focused on analysing only the impact that financial variables have on these models. However, the literature has not yet studied how corporate governance variables may affect the outcomes of these models. As a consequence, these models, given their characteristics and advantages, allow us to test the relationship between different variables with greater precision than other models. This allows us to confirm or disprove the effect of CEO turnover on performance improvement. In the second place, given that these models are increasingly used (Althey, Reference Althey2018), it is necessary to verify whether the variables have the same effect, or rather, the effect we expect, even using these new-age models. Indeed, unexpected relationships may emerge from these analyses. Finally, machine-learning techniques can be an innovative tool in studying the relationship between CEO turnover and firm performance, especially for companies in crisis and for which, many times, data are unavailable or anomalous. In fact, these models are immune to these problems (Jones, Johnstone, & Wilson, Reference Jones, Johnstone and Wilson2017) and, therefore, allow the use of a larger and more varied sample that can certainly contribute to the development of research in this field.

Sample and data collection procedure

The sample contains 90 Italian companies that were identified using the following criteria:

  • adopted an Italian insolvency procedure during the period 2007–2016;

  • the insolvency procedure did not have the objective of liquidating the company.

Out of the initial sample of the 90 companies, 45 changed their CEO during the 2 years before the beginning of the insolvency procedure, while the remaining 45 decided to maintain the CEO for the entire duration of the insolvency procedure. In order to compare two homogeneous groups, the companies were selected to be comparable in terms of sector, size, ownership, time and the crisis resolution tool used.

Therefore, an overview of the selected companies can be found in Tables 1 and 2.

Table 1. Overview of the characteristics of the sample

Table 2. Overview of the characteristics of the sample

To classify the companies within one of the two groups (‘Change of the management’ and ‘NOT Change of the management’), it was necessary to define a time horizon of analysis. The chosen time included 6 years, starting from the 2 years before the entrance in the insolvency procedure (T − 2) and ending 3 years after the entrance in the insolvency procedure (T + 3). T0 represents the year in which the insolvency procedure started from a legal point of view (e.g., judge authorization). The choice of our analysis covering 6 years was because companies tend to show the first signs of a crisis 2 years before the default (Altman, Danovi, & Falini, Reference Altman, Danovi and Falini2013), and the recovery plan in order to be efficient should be completed 3 years after the beginning of the crisis (Tron, Reference Tron2021).

After establishing the time horizon, we adopted the following criteria in order to classify the companies within one of the two groups, the managerial discontinuity (‘Change of the management’) or managerial continuity group (‘NOT Change of the management’):

  1. (1) Companies that never changed their CEO in the analysis period (from T − 2 to T + 3) are considered in the managerial continuity group.

  2. (2) Companies that changed the CEO after T0 have been categorized in the managerial continuity group. We decided to apply this criterion since the CEO was present in T − 2 and, therefore, they prepared the turnaround and execution plan, which was approved by creditors in T − 0. Therefore, in judging the likelihood of insolvency and granting new finance, creditors assessed the capabilities of the ‘historical’ CEO without considering discontinuity factors at the CEO level. Consequently, the historical CEO significantly influenced the financial results in T + 3.

  3. (3) Companies that changed CEO before T0 were classified in the managerial discontinuity group.

  4. (4) Companies that changed the CEO in T0 were classified in the managerial discontinuity group. In this case, the negotiations with the creditors were mainly conducted by the previous CEO, however, the new CEO had the role to execute the turnaround and execution plan.

Data analysis

Since in the literature the moment of exit from a crisis is still not clearly defined, and since insolvency procedures can last for several years and defaulted companies tend to re-enter a crisis after the first period of recovery (Tron, Reference Tron2021), we preferred to adopt a more objective approach, using the Altman Z-score as an indicator of the improvement of a company's economic-financial performance. This ratio, created by Altman (Reference Altman1993), proved to be reliable in the Italian context (Altman, Danovi, & Falini, Reference Altman, Danovi and Falini2013; Dallocchio, Ferri, Tron, & Vizzaccaro, Reference Dallocchio, Ferri, Tron and Vizzaccaro2020) and more precise than the Z-score (Altman, Danovi, & Falini, Reference Altman, Danovi and Falini2013). Moreover, it does not consider only the profitability of a company, like the ROA typically used in past research (Scafarto et al., Reference Scafarto, Ricci, Della Corte and De Luca2017), but also other several factors, like the leverage of a company, a key indicator during a turnaround.

Therefore, to objectively measure the impact of the change of CEO on the performance of a company, and on its probability of emerging from a crisis, we used as the dependent variable the increase in the Z-score between T − 2 and T + 3 (case 1) and between T0 and T + 3 (case 2). Every model was run using firstly, as the dependent variable, the dummy variable Z-score increase between T − 2 and T + 3, and secondly, using as the dependent variable the dummy variable Z-score increase between T0 and T + 3.

The reliability of the Z-score was also confirmed in the case of our sample (Table 3). Table 3 also shows that, between the period T − 2 and T + 3, the Change of the management group is characterized by a greater number of companies transiting outside the distressed area.

Table 3. Z″-score analysis

Note: Companies with a Z″-score under 1.1 were classified in the distressed zone, companies with a Z″-score between 1.1 and 2.6 were classified in the grey zone, companies with a Z″-score over 2.6 were classified in the safe zone.

An overview of the variables that we used in this paper and their source can be found in Table 4. Their values were obtained relying on ORBIS, one of the largest financial databases, as in Succurro (Reference Succurro2017).

Table 4. Variable description

In Table 5, descriptive statistics of the variables are reported.

Table 5. Descriptive statistics by company status

We proceeded to the analysis of the correlation among the variables, which showed a positive relationship between the increase in the Z-score and the change of management, both at time T − 2 and at time T0. The results are shown in Table 6.

Table 6. Correlation analysis

For the logit model, to test for multicollinearity, we performed the Variance Inflation Test (“VIF”) test. In any case, the variance inflation factor was not higher than two, therefore, we did not have any signs of serious multicollinearity requiring correction. We also conducted the Shapiro and the Breusch Pagan test without any sign of non-normality or heteroskedasticity.

Results

Firstly, we ran a T-test on the average increase of the Z-score between T0 and T + 3 for the two groups (Change of the management and NOT Change of the management). The results are shown in Table 7.

Table 7. T-test results

The T-test was estimated using the sample variance.

The results of the T-test suggest companies that change CEOs during a turnaround procedure can reach higher performances and, therefore, tend to have more probability of exiting from a crisis.

Secondly, we ran the logit model using the Z-score increase between T − 2 and T + 3 (model 1) and the Z-score increase between T0 and T + 3 (model 2). The results are shown in Table 8.

Table 8. Logit results

***, ** and * indicate statistically significant levels at 1, 5 and 10%, respectively.

In model 1, the variable change of management is statically significant, with a positive impact on the increase of the Z-score between T − 2 and T + 3, thus confirming the previous results of the T-test. However, in model 2, the increase of the Z-score between T0 and T + 3 is not statistically significant.

Thirdly, we ran the random forest model using the Z-score increase between T − 2 and T + 3 (model 3) and the Z-score increase between T0 and T + 3 (model 4). The SHAP values are shown in Figure 1 (model 3) and Figure 2 (model 4).

Fig. 1. SHAP values – random forest model 3.

Fig. 2. SHAP values – random forest model 4.

Results of the random forest model confirm the previous results of the logit model. In model 3, the Change of Management is the second most important variable and, mainly, it has a significant positive impact on the increase of the Z-score between T − 2 and T + 3. These results confirmed our hypothesis 1. On the contrary, the Change of Management does not have a considerable impact on the increase of the Z-score between T0 and T + 3, thus confirming our hypothesis 2.

Finally, we ran the AdaBoost model using the Z-score increase between T − 2 and T + 3 (model 5) and the Z-score increase between T0 and T + 3 (model 6). The SHAP values are shown in Figure 3 (model 5) and Figure 4 (model 6).

Fig. 3. SHAP values – AdaBoost model 5.

Fig. 4. SHAP values – AdaBoost model 6.

Also, in the case of the AdaBoost model, previous results and our hypothesis are confirmed. The Change of Management has a positive impact on the increased performance of the company between T − 2 and T + 3 (model 5), while it seems to have less influence on the Z-score between T0 and T + 3 (model 6).

Overall, all our analysis demonstrates: (i) a positive and significant relationship between CEO turnover and the likelihood for a bankrupt firm to re-emerge from an insolvency procedure; and (ii) the CEO turnover should occur before the true declaration of the state of the crisis.

Discussion

In this study, using a set of defaulted Italian companies combined with an innovative approach, we extend the literature on predicting a bankrupt firm's likelihood of emerging from insolvency procedures by documenting a significant and positive relationship between CEO turnover and firm Z-score increase. Indeed, results indicate that the entrance of a new CEO in a company during a turnaround process can raise its performance, and thus, its probability of survival, in line with past research (Berezinets, Garanina, & Ilina, Reference Berezinets, Garanina and Ilina2016; Gong & Wu, Reference Gong and Wu2011; Lin et al., Reference Lin, Liu, Tan and Zhou2020), confirming our first hypothesis. In addition, the results confirm that positive linkage between CEO renewal and firm performance also exists in the Italian context, despite its lack of corporate governance control system. The results are robust and confirmed by all the models applied in this paper (T-test, logit, random forest and AdaBoost). These findings seem to support the connection between CEO turnover and the renewal of strategy and therefore, the possible introduction of products/processes in line with previous scholars (Domínguez-CC & Barroso-Castro, Reference Domínguez-CC and Barroso-Castro2017; Parnell & Crandall, Reference Parnell and Crandall2020), confirming the necessity of the renewal of firm strategy for a successful turnaround.

Moreover, in all the models, as expected, the Change of Management does not have a considerable impact on the increase of the Z-score between T0 and T + 3. Therefore, the analysis indicates that the impact of the new CEO is significant only if the new CEO is appointed before the true declaration of the state of crisis (T0), which confirms our second hypothesis. This could be a consequence of the fact that the appointment of a new CEO before the default announcement serves as a signal about the firm's viability and quality and prospects to a potential capital-provider. Therefore, the later appointment of a new CEO can suggest the true unwillingness of shareholders in supporting the company during the turnaround. Furthermore, during the period T − 2 and T0, a CEO can participate in the negotiations with the creditors and can further extend their knowledge of the company, and thus, they can have a greater impact during the turnaround procedure. As a consequence, the lack of significance of the Change of Management on the increase of the Z-score between T0 and T + 3 confirms the fact that a crisis requires prompt intervention, as suggested by various scholars (e.g., Domínguez-CC & Barroso-Castro, Reference Domínguez-CC and Barroso-Castro2017; Parnell & Crandall, Reference Parnell and Crandall2020; Tron, Reference Tron2021).

Furthermore, this research highlights the fact that time is a crucial variable during a period of crisis: the ability to anticipate the beginning of a crisis raises the possibility of survival for a company.

As a consequence, a manager should use all the financial instruments and tools available, like a cash budget with a horizon of 12 months, for predicting a crisis in order to be able to have the proper time for finding the most suitable solution. If detecting a crisis soon enough to make changes in the top management is vital for the solution of the crisis, also governance bodies and shareholders should deploy resources to effectively prepare for a crisis by monitoring the environment and detecting early signals. Moreover, this paper suggests that banks and creditors should play an active role in the governance of firms during turnaround processes by requiring the hiring of a new management team inside the company.

A further insight of this paper is the measurement of the use of Z-score as an indicator of a company's economic-financial performance improvement, and thus, of its probability of re-emerging from a crisis. Given the typical, unpredictable length of insolvency procedure, this measurement allowed us to use a reliable and objective ratio to measure both the increase of a company's performance and its likelihood of defaulting. Finally, to the best of our knowledge, this is the first study that has applied the SHAP values for measuring the impact of corporate governance variables on the likelihood of re-emerging from bankruptcy. Despite the lack of transparency and interpretability of machine-learning techniques, we showed, thanks to the use of the SHAP value, that these models can be utilized for other purposes, rather than just as prediction tools, as suggested by Lantz (Reference Lantz2019). Given the increasing application of machine-learning techniques by banks and investors (Althey, Reference Althey2018), our results indicate that these techniques have wider applications and additional usefulness for future research.

Conclusion

The research aimed to demonstrate the effects of changing a CEO on company performance and, especially, the correct timing of this renewal during a turnaround process. Results produced evidence regarding the existence of a significant link between CEO turnover and a successful corporate turnaround. However, the main impact of our paper is its contribution to the literature related to the relationship between CEO tenure and firm performance. Our results identified the optimal timing of CEO turnover, which should anticipate 2 years (at least) the declaration of the state of crisis. Delayed CEO renewal does not significantly affect company performance, probably because the turnover takes place when the situation is already compromised.

The complexity of the decision situation framework during a turnaround process – which includes time pressure and short-term targets – leads to an increase in the risk of irrational biases in decision-making, as compared to regular business situations. Therefore, during a crisis, the recruitment of a new CEO can be considered one of the most effective actions for a company to implement, since it introduces new human capital (knowledge, skills and experience) and relational capital (social ties and networks). At the same time, it is important to remark that the entrance of a new CEO can have positive effects also during the negotiations with the creditors of the company. Allowing the company to be managed, during the turnaround process, by the same people who caused the situation and who probably did not recognize the symptoms of the crisis, is one of the main reasons creditors do not agree to grant new financing (Tron, Reference Tron2021). Therefore, a new CEO, besides introducing new competencies, can increase the likelihood of obtaining new financing, which is a necessity and a prerequisite for the recovery of a defaulted firm (Domínguez-CC & Barroso-Castro, Reference Domínguez-CC and Barroso-Castro2017; Parnell & Crandall, Reference Parnell and Crandall2020).

However, our work could be limited by the choice of the sample, which only includes Italian companies. Nevertheless, we consider this feature an additional contribution of our research since Italy has unique conditions compared to the US context (analysed in-depth in previous literature), in terms of credit power during an insolvency procedure and corporate governance characteristics. However, in that sense, our results seem to corroborate the theory of Dimopoulos and Wagner (Reference Dimopoulos and Wagner2016) which showed in the UK and Germany that CEO turnover is followed by significant performance improvements despite their significant differences in terms of corporate governance. Therefore, our findings seem to confirm that, also in the Latin-type corporate governance and ownership systems, the CEO renewal has a positive effect on a company's performance, like in the US and UK case (Dimopoulos & Wagner, Reference Dimopoulos and Wagner2016).

This study could be expanded upon by further widening the interpretation of and the use of modern approaches, such as machine-learning techniques. Furthermore, future research could continue to analyse the impact of corporate governance, or, in general, the impact of environmental, social and corporate governance indicators, on the probability of a successful turnaround process.

Conflict of interest

The authors declare none.

References

Agarwal, R., & Gort, M. (2002). Firm and product life cycles and firm survival. American Economic Review, 92, 184190.CrossRefGoogle Scholar
Agostini, L., & Nosella, A. (2017). Enhancing radical innovation performance through intellectual capital components. Journal of Intellectual Capital, 18(4), 789806. https://doi.org/10.1108/JIC-10-2016-0103.CrossRefGoogle Scholar
Ahn, B. S., Cho, S. S., & Kim, C. Y. (2000). The integrated methodology of rough set theory and artificial network for business failure prediction. Expert Systems with Applications, 18(2), 6574. https://doi.org/10.1016/S0957-4174(99)00053-6.CrossRefGoogle Scholar
Alipour, M. (2013). An investigation of the association between ownership structure and corporate performance: Empirical evidence from Tehran stock exchange (TSE). Management Research Review, 36(11), 11371166. https://doi.org/10.1108/MRR-08-2012-0188.CrossRefGoogle Scholar
Althey, S. (2018). The economics of artificial intelligence: An agenda. Chicago: University of Chicago Press.Google Scholar
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x.CrossRefGoogle Scholar
Altman, E. (1983). Corporate financial distress: A complete guide to predicting. New York: John Wiley and Sons.Google Scholar
Altman, E. I. (1993). Corporate financial distress and bankruptcy (2nd ed.). New York, NY: Wiley & Sons.Google Scholar
Altman, E. I., Danovi, A., & Falini, A. (2013). Z-score model's application to Italian companies subject to extraordinary administration. Bancaria, 4, 2437.Google Scholar
Amburgey, T. L., Kelly, D., & Barnett, W. P. (1993). Resetting the clock: The dynamics of organizational change and failure. Administrative Science Quarterly, 38, 5173.CrossRefGoogle Scholar
Ayoko, O. B., Caputo, A., & Mendy, J. (2022). Management research contributions to the COVID-19: A bibliometric literature review and analysis of the contributions from the Journal of Management & Organization. Journal of Management & Organization, 27(6), 127.CrossRefGoogle Scholar
Bae, K.-H., Kang, J.-K., & Wang, J. (2011). Employee treatment and firm leverage: A test of the stakeholder theory of capital structure. Journal of Financial Economics, 100(1), 130153. https://doi.org/10.1016/j.jfineco.2010.10.019.CrossRefGoogle Scholar
Balgobin, R., & Pandit, N. (2001). Stages in the turnaround process: The case of IBM. European Management Journal, 19(3), 301316. https://doi.org/10.1016/S0263-2373%2801%2900027-5.CrossRefGoogle Scholar
Barboza, F., Kimura, H., & Altman, E. I. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83(C), 405417. https://doi.org/10.1016/j.eswa.2017.04.006.CrossRefGoogle Scholar
Bennedsen, M., Pérez-González, F., & Wolfenzon, D. (2020). Do CEOs matter? Evidence from hospitalization events. The Journal of Finance, 75(4), 18771911. https://doi.org/10.1111/jofi.12897.CrossRefGoogle Scholar
Berezinets, I., Garanina, T., & Ilina, Y. (2016). Intellectual capital of a board of directors and its elements: Introduction to the concepts. Journal of Intellectual Capital, 17(4), 632653. http://dx.doi.org/10.1108/JIC-01-2016-0003.CrossRefGoogle Scholar
Bhagat, S., Bolton, B., & Subramanian, A. (2010). CEO education, CEO turnover, and firm performance. Available at SSRN: https://ssrn.com/abstract=1670219.Google Scholar
Breiman, L. (2001). Random forests. Machine Learning, 45, 532. https://doi.org/10.1023/A:1010933404324.CrossRefGoogle Scholar
Burns, N., Minnick, K., & Starks, L. (2018). CEO turnover: Cross-country effects. Conference Paper, European Financial Management Association 2019.Google Scholar
Cao, X., Pan, X., Qian, M., & Tian, G. G. (2017). Political capital and CEO entrenchment: Evidence from CEO turnover in Chinese non-SOEs. Journal of Corporate Finance, 42, 114.CrossRefGoogle Scholar
Chen, G., & Hambrick, D. (2011). CEO replacement in turnaround situations: Executive (Mis)fit and its performance implications. Organization Science, 23(1), 225243. https://doi.org/10.1287/orsc.1100.0629.CrossRefGoogle Scholar
Chowdhury, S. D. (1996). Business turnaround in small firms: An assessment of efficiency strategies. Journal of Business Research, 36(2), 169178. https://doi.org/10.1016/0148-2963(95)00119-0.CrossRefGoogle Scholar
Ciampa, D. (2020). Should a crisis change your CEO succession plan? Harvard Business Review, published online at https://hbr.org/2020/04/should-a-crisis-change-your-ceo-succession-plan.Google Scholar
Cirka, C. C., & Corrigall, E. A. (2010). Expanding possibilities through metaphor: Breaking biases to improve crisis management. Journal of Management Education, 34, 303323. https://doi.org/10.1177/1052562909336912.Google Scholar
Clark, T. (1988). The concept of a marketing crisis. Journal of the Academy of Marketing Science, 16(2), 4348. https://doi.org/10.1177/009207038801600207.CrossRefGoogle Scholar
Collignon, S., & Esposito, P. (2013). ‘Finance of small business firms in times of austerity’, Directorate General for internal policies – finance access for SME. European Parliament, 4989.Google Scholar
Cortesi, A., Tettamanzi, P., & Corno, F. (2009). Empirical evidence on internal control systems and corporate governance in Italy. Journal of Management & Governance, 13, 75100. https://doi.org/10.1007/s10997-008-9071-8.CrossRefGoogle Scholar
Cuervo-Cazurrra, A., Mudambi, R., & Pedersen, T. (2017), Globalization: Rising skepticism. Global Strategy Journal, 7 (2), 155158. https://doi.org/10.1002/gsj.1156.CrossRefGoogle Scholar
Dallocchio, M., Ferri, S., Tron, A., & Vizzaccaro, M. (2020). The use of Z-score to predict UTP loans. Corporate Ownership and Control, 18(1), 163178. doi: 10.22495/cocv18i1art13CrossRefGoogle Scholar
Dallocchio, M., Pirrone, C., & Lucchini, G. (2021). Merger and acquisition. Milan: EGEA.Google Scholar
Dardour, A., Boussada, R., Yekini, S., & Makhlouf, M. (2018). The determinants of CEO turnover: Evidence from France. Recherches en Sciences de Gestion, 129, 2955.Google Scholar
Demmou, L., Calligaris, S., Franco, F., Dlugosch, D., McGowan, M., & Sakha, S. (2021). Insolvency and debt overhang following the COVID-19 outbreak: Assessment of risks and policy responses, OECD Working Papers, 1651. https://doi.org/10.1787/747a8226-en.CrossRefGoogle Scholar
Denis, D. K., & Rodgers, K. J. (2007). Chapter 11: Duration, outcome, and post-reorganization performance. Journal of Financial & Quantitative Analysis, 42(1), 101118. https://doi.org/10.1017/s0022109000002209.CrossRefGoogle Scholar
Dhaliwal, D., Judd, J. S., Serfling, M., & Shaikh, S. (2016). Customer concentration risk and the cost of equity capital. Journal of Accounting and Economics, 61(1), 2348. https://doi.org/10.1016/j.jacceco.2015.03.005.CrossRefGoogle Scholar
Dimopoulos, T., & Wagner, H. F. (2016). Corporate governance and CEO turnover decisions. Swiss Finance Institute Research, Paper No. 12-16, http://dx.doi.org/10.2139/ssrn.2040690.CrossRefGoogle Scholar
Domínguez-CC, M., & Barroso-Castro, C. (2017). Managerial change and strategic change: The temporal sequence. Journal of Management & Organization, 23(1), 4673. doi:10.1017/jmo.2016.49CrossRefGoogle Scholar
Donaldson, L., & Davis, J. H. (1991). Stewardship theory or agency theory: CEO governance and shareholder returns. Australian Journal of Management, 16(1), 4964. https://doi.org/10.1177/031289629101600103.CrossRefGoogle Scholar
Dunbar, R., & Goldberg, W. (1978). Crisis development and strategic response in European corporations. Journal of Business Administration, 9(2), 139149.Google Scholar
Elloumi, F., & Gueyiè, J. P. (2001). Financial distress and corporate governance: An empirical analysis. Corporate Governance, 1(1), 1523. https://doi.org/10.1108/14720700110389548.CrossRefGoogle Scholar
Eriksson, M. (2018). Lessons for crisis communication on social media: A systematic review of what research tells the practice. International Journal of Strategic Communication, 12(5), 526551. https://doi.org/10.1080/1553118X.2018.1510405.CrossRefGoogle Scholar
Fee, C., Hadlock, C., Huang, J., & Pierce, J. (2018). Robust models of CEO turnover: New evidence on relative performance evaluation. The Review of Corporate Finance Studies, 7(1), 70100. https://doi.org/10.1093/rcfs/cfx018.CrossRefGoogle Scholar
Finkelstein, S., Hambrick, D., & Cannella, A. Jr. (2009). Strategic leadership: Theory and research on executives, top management teams, and boards. New York. NY: Oxford University Press.Google Scholar
Foreman, R. D. (2003). A logistic analysis of bankruptcy within the US local telecommunications industry. Journal of Economics and Business, 55(2), 135166. https://doi.org/10.1016/S0148-6195(02)00133-9.CrossRefGoogle Scholar
Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 9(5), 11891232. doi: 10.1214/aos/1013203451Google Scholar
Gong, J. J., & Wu, S. Y. (2011). CEO turnover in private equity sponsored leveraged buyouts. Corporate Governance: An International Review, 19(3), 195209. https://doi.org/10.1111/j.1467-8683.2010.00834.x.CrossRefGoogle Scholar
Goyal, V. K., & Wang, W. (2017). Provision of management incentives in bankrupt firms. Journal of Law, Finance, and Accounting, 2(1), 87123. http://dx.doi.org/10.1561/108.00000012.CrossRefGoogle Scholar
Graham, J. R., Kim, H., Li, S., & Qiu, J. (2015). The labor impact of corporate bankruptcy. Working Paper, Fuqua School of Business, Duke University.Google Scholar
Haibing, G., Jinhong, X., Qi, W., & Wilbur, K. C. (2015). Should ad spending increase or decrease before a recall announcement? The marketing–finance interface in product-harm crisis management. Journal of Marketing, 79(5), 8099. https://doi.org/10.1509/jm.14.0273.Google Scholar
Hansen, M., & Ziebarth, N. (2017). Credit relationships and business bankruptcy during the great depression. American Economic Journal: Macroeconomics, 9(2), 228255. Doi: 10.1257/mac.20150218Google Scholar
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining. Inference and prediction. New York: Springer.CrossRefGoogle Scholar
Hilbe, J. (2015). Practical guide to logistic regression. New York: CRC Press.Google Scholar
Hilger, S., Mankel, S., & Richter, A. (2013). The use and effectiveness of top executive dismissal. The Leadership Quarterly, 24(1), 928. https://doi.org/10.1016/j.leaqua.2012.07.001.CrossRefGoogle Scholar
Hothckiss, E. S. (1995). Postbankruptcy performance and management turnover. The Journal of Finance, 50(1), 321.Google Scholar
Hutzschenreuter, T., Kleindienst, I., & Greger, C. (2012). How new leaders affect strategic change following a succession event: A critical review of the literature. The Leadership Quarterly, 23(5), 729755.CrossRefGoogle Scholar
Im, J., & Cao, X. (2015). CEO tenure and financial performance: A meta-analysis. Academy of Management, 1. https://doi.org/10.5465/ambpp.2015.14970abstract.Google Scholar
Jenter, D., & Lewellen, K. (2021). Performance-Induced CEO turnover. The Review of Financial Studies, 34(2), 569617. https://doi.org/10.1093/rfs/hhaa069.CrossRefGoogle Scholar
Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting corporate bankruptcy: An evaluation of alternative statistical frameworks. Journal of Business Finance & Accounting, 44(1) & (2), 334. doi: 10.1111/jbfa.12218CrossRefGoogle Scholar
Kim, Y., Jeong, S., Yiu, D., & Moon, J. (2021). Frequent CEO turnover and firm performance: The resilience effect of workforce diversity. Journal of Business Ethics, 173(2021), 185203. https://doi.org/10.1007/s10551-020-04534-.CrossRefGoogle Scholar
Kim, J.-B., Song, B. Y., & Zhang, Y. (2015). Earnings performance of major customers and bank loan contracting. Journal of Banking & Finance, 59, 384398. doi: 10.1016/j.jbankfin.2015.06.020CrossRefGoogle Scholar
Kücher, A., Mayr, S., Mitter, C., Duller, C., & Feldbauer-Durstmüller, B. (2020). Firm age dynamics and causes of corporate bankruptcy: Age dependent explanations for business failure. Review of Managerial Science, 14, 633661. https://doi.org/10.1007/s11846-018-0303-2.CrossRefGoogle Scholar
Lantz, B. (2019). Machine learning with R. Birmingham: Packt.Google Scholar
Li, L., & Faff, R. (2019). Predicting corporate bankruptcy: What matters? International Review of Economics & Finance, 62, 119. https://doi.org/10.1016/j.iref.2019.02.016.CrossRefGoogle Scholar
Lian, Y. (2017). Financial distress and customer–supplier relationships. Journal of Corporate Finance, 43, 397406. https://doi.org/10.1016/j.jcorpfin.2017.02.006.CrossRefGoogle Scholar
Lin, B., Liu, C., Tan, K., & Zhou, Q. (2020). CEO turnover and bankrupt firms’ emergence. Journal of Business Finance and Accounting, 47(9–10), 12381267. doi: 10.1111/jbfa.12482CrossRefGoogle Scholar
Lundberg, S., & Lee, S. (2017). A unified approach to interpreting model predictions, NIPS Proceedings 2017, https://arxiv.org/abs/1705.07874.Google Scholar
Luo, Y. (2007). Global dimensions of corporate governance. Hoboken: Blackwell Publishing.Google Scholar
Manzaneque, M., Priego, A., & Merino, E. (2016). Corporate governance effect on financial distress likelihood: Evidence from Spain. Spanish Accounting Review, 19(1), 111121. https://doi.org/10.1016/j.rcsar.2015.04.001.Google Scholar
Mazzotta, R. (2018). The communication of intellectual capital in healthcare organisations: What is disclosed and how? International Journal of Knowledge-Based Development, 9(1), 2348. https://doi.org/10.1504/IJKBD.2018.090500.CrossRefGoogle Scholar
Nyberg, A., Fulmer, I., Gehart, B., & Carpenter, M. (2010). Agency theory revisited: CEO return and shareholder interest alignment. The Academy of Management Journal, 53(5), 10291049. https://www.jstor.org/stable/20788807.Google Scholar
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109131. https://doi.org/10.2307/2490395.CrossRefGoogle Scholar
O'Kane, C., & Cunningham, J. (2012). Leadership changes and approaches during company turnaround. International Studies of Management and Organization, 42(4), 5285. https://doi.org/10.2753/IMO0020-8825420403.CrossRefGoogle Scholar
Oliveira, M., Kadapakkam, P., & Beyhaghi, M. (2017). Effects of customer financial distress on supplier capital structure. Journal of Corporate Finance, 42(C), 131149. https://doi.org/10.1016/j.jcorpfin.2016.11.009.CrossRefGoogle Scholar
Omondi-Ochieng, P. (2020). Success or failure? Predicting the financial performance of United States national nonprofit sports organisations using binary logistic regressions. Managing Sport and Leisure, 26(6), 466483. https://doi.org/10.1080/23750472.2020.1771196.CrossRefGoogle Scholar
Owen, G., Kirchmaier, T., Grant, J. (2006) Corporate governance in the US and Europe: Where are we now?. In Palgrave Macmillan (Ed.), Corporate governance in the US and Europe (pp. 121). London: Palgrave Macmillan. https://doi.org/10.1057/9780230512450_1.CrossRefGoogle Scholar
Paoloni, N., Mattei, G., Dello Strologo, A., & Celli, M. (2020). The present and future of intellectual capital in the healthcare sector: A systematic literature review. Journal of Intellectual Capital, 21(3), 357379. https://doi.org/10.1108/JIC-10-2019-0237.CrossRefGoogle Scholar
Parnell, J., & Crandall, W. (2020). The contribution of behavioral economics to crisis management decision-making. Journal of Management & Organization, 26(4), 585600. doi: 10.1017/jmo.2017.60CrossRefGoogle Scholar
Riccetti, L., Russo, A., & Gallegati, M. (2015). An agent based decentralized matching macroeconomic model. Journal of Economic Interaction and Coordination, 10, 305332. https://doi.org/10.1007/s11403-014-0130-8.CrossRefGoogle Scholar
Rivolta, M. (2018). Does lead time in CEO succession matter? Evidence from planned versus unexpected CEO departures. International Journal of Financial Research, 9(3), 119. doi: 10.5430/ijfr.v9n3p1CrossRefGoogle Scholar
Scafarto, V., Ricci, F., Della Corte, G., & De Luca, P. (2017). Board structure, ownership concentration and corporate performance: Italian evidence. Corporate Ownership & Control, 15(1-2), 347359. http://doi.org/10.22495/cocv15i1c2p4.CrossRefGoogle Scholar
Schendel, D., Patton, G., & Riggs, J. (1976). Corporate turnaround strategies: A study of profit decline and recovery. Journal of General Management, 3(3), 311.CrossRefGoogle Scholar
Schepker, D. J., Kim, Y., Patel, P. C., Thatcher, M. B. T., & Campion, M. C. (2017). CEO succession, strategic change, and post-succession performance: A meta-analysis. The Leadership Quarterly, 28(6), 701720.CrossRefGoogle Scholar
Schweizer, L., & Nienhaus, A. (2017). Corporate distress and turnaround: integrating the literature and directing future research. Business Research, 10(1), 347. https://doi.org/10.1007/s40685-016-0041-8.CrossRefGoogle Scholar
Serrasqueiro, Z., Leitão, J., & Smallbone, D. (2021). Small- and medium-sized enterprises (SME) growth and financing sources: Before and after the financial crisis. Journal of Management & Organization, 27(1), 621. https://doi.org/10.1017/jmo.2018.14.CrossRefGoogle Scholar
Shaluf, I. M., Ahmadun, F., & Mat Said, A. (2003). A review of disaster and crisis. Disaster Prevention and Management, 12(1), 2432. https://doi.org/10.1108/09653560310463829.CrossRefGoogle Scholar
Succurro, M. (2017). Financial bankruptcy across European countries. International Journal of Economics and Finance, 9(7), 132146. https://doi.org/10.5539/ijef.v9n7p132.CrossRefGoogle Scholar
Trahms, C. A., Ndofor, H. A., & Sirmon, D. G. (2013). Organizational decline and turnaround: A review and agenda for future research. Journal of Management, 39(5), 12771307. https://doi.org/10.1177%2F0149206312471390.CrossRefGoogle Scholar
Tron, A. (2021). Corporate financial distress: Restructuring and turnaround. Bingley: Emerald Publishing Limited.CrossRefGoogle Scholar
Tron, A., Valenza, G., & Caputo, A. (2018). Corporate crisis management in Italy: Execution, monitoring and performance analysis of recovery business and financial plans. International Journal of Foresight and Innovation Policy, 13(1/2), 114135.CrossRefGoogle Scholar
Wenzel, M., Stanske, S., & Lieberman, M. (2021). Strategic responses to crisis. Strategic Management Journal, 42(2), O16O27. https://doi.org/10.1002/smj.3161.Google Scholar
Whitaker, R. B. (1999). The early stages of financial distress. Journal of Economics and Finance, 23(2), 123133. https://doi.org/10.1007/BF02745946.CrossRefGoogle Scholar
Figure 0

Table 1. Overview of the characteristics of the sample

Figure 1

Table 2. Overview of the characteristics of the sample

Figure 2

Table 3. Z″-score analysis

Figure 3

Table 4. Variable description

Figure 4

Table 5. Descriptive statistics by company status

Figure 5

Table 6. Correlation analysis

Figure 6

Table 7. T-test results

Figure 7

Table 8. Logit results

Figure 8

Fig. 1. SHAP values – random forest model 3.

Figure 9

Fig. 2. SHAP values – random forest model 4.

Figure 10

Fig. 3. SHAP values – AdaBoost model 5.

Figure 11

Fig. 4. SHAP values – AdaBoost model 6.