A Temporal Approach to Retrenchment and Successful Turnaround in Declining Firms

A Temporal Approach to Retrenchment and

Successful Turnaround in Declining Firms

Chanchai Tangpong, Michael Abebe and Zonghui Li

North Dakota State University; The University of Texas–Rio Grande Valley; Mississippi State University

ABSTRACT This study extends current understanding of the retrenchment–turnaround relationship in declining firms by introducing a temporal approach and arguing that the effectiveness of retrenchment as a strategy is contingent on its adoption early in turnaround attempts. Drawing from the two-stage turnaround model and insights from the literature on downward spirals in organizations, we develop and test a theoretical model that explains how temporal considerations in retrenchment influence the likelihood of successful turnaround. Using a matched pair sample of 96 US firms, we find that declining firms that implement retrenchment actions early have a higher likelihood of successful turnaround. The findings also indicate that while two specific retrenchment actions, early divestments and early geographic market exits, significantly contribute to the likelihood of successful turnaround, early layoffs do not. Overall, the findings shed some light on the importance of timing strategic actions in organizational turnarounds. Implications for research and practice are discussed.

Keywords:decline, downward spirals, path dependence, retrenchment, turnaround

INTRODUCTION

Retrenchment refers to efficiency-oriented, short-term turnaround actions, such as downsizing, cost reduction, asset sell-offs, and divestment of businesses, that aim to stem survival-threatening performance decline (Lim et al., 2013; Morrow et al., 2004; O’Neill, 1986). Despite progress in understanding of the important role of ment in successful turnarounds, little is known on the appropriate timing of retrench-ment actions in declining firms (Trahms et al, 2013). Specifically, the implications of early versus late retrenchment actions on successful turnarounds following organiza-tional performance decline are under-explored. Past studies highlight the importance

Address for reprints: Chanchai Tangpong, Department of Management and Marketing, College of Business, North Dakota State University, Dept. # 2420, PO Box 6050, Fargo, ND 58108-6050, USA (Charnchai. Tangpong@ndsu.edu).

VC _{2015 John Wiley & Sons Ltd and Society for the Advancement of Management Studies} Journal of Management Studies52:5 July 2015

of selecting the ‘right’ type of turnaround strategy for the ‘right’ context (e.g., Lim et al, 2013; Morrow et al., 2004); however, they do not explicitly address and empiri-cally investigate the ‘right’ timing for such actions.

Studying the timing of retrenchment actions is also important given its significant managerial implications. Consider the case of Circuit City Stores Inc. The company was a leading electronics retailer in North America with more than 700 stores and annual revenues over $12 billion (Feintzeig, 2012). In the mid-2000s, the company experienced severe decline in same store sales and profitability, as well as a precipi-tous decline in stock price and stakeholder support (Gogoi, 2008; Hudson, 2007). Despite its turnaround efforts, such as massive employee layoffs and store shutdowns nationwide, the company could not reverse this survival-threatening decline (Hudson, 2007; Rosenbloom, 2008). Analysts and key stakeholders questioned the viability and timing of the company’s turnaround plan, noting that it might be ‘too little, too late’ (Gogoi, 2008). The company finally filed for bankruptcy protection in 2008 and for liquidation in 2009 – the outcome that could have been avoided ‘had it woken up sooner’ (Feintzeig, 2012).

This study seeks to advance understanding of the retrenchment–turnaround rela-tionship by incorporating a temporal dimension – earliness versus lateness – into both theoretical development and empirical testing. We aim to address the following research question: When do retrenchment actions need to be taken to increase the likelihood of turn-around success? Specifically, how does the timing of retrenchment actions such as layoffs, divest-ments, and geographic market exits, relate to the likelihood of turnaround success? These questions are quite relevant and critical to corporate turnaround theory and practice, as a growing consensus in the organizational decline literature informs us that the decline of an organization often unfolds in a downward spiral (Hambrick and D’Aveni, 1992; McKinley et al., 2014; Weitzel and Jonsson, 1989), which accelerates the decline tra-jectory and compounds the challenges of turning around the failing organization with passing time. Therefore, the timing of early versus late retrenchment actions should not be overlooked. Nevertheless, the development of retrenchment and turnaround literature has been propelled largely by contextual considerations (e.g., Lim et al., 2013; Morrow et al., 2004) and by the attempt to conceptualize turnaround actions as a staged and sequential process (e.g., Arogyaswamy et al., 1995; Pearce and Rob-bins, 1993). As such, the temporal qualities of theoretical and empirical explanations of retrenchment and turnaround have been largely underdeveloped.

By addressing the above research questions, this study extends the current turn-around literature in three important ways. First, it conceptualizes retrenchment with a temporal consideration and posits that the turnaround effects of retrenchment actions are contingent on the timing of their implementation. Guided by the time-sensitivity insight from the downward-spiral decline literature, this study proposes that the retrenchment–turnaround relationship occurs in a path-dependent manner (Pierson, 2000; Sydow et al., 2009; Vergne and Durand, 2010). This retrenchment–turnaround relationship is captured in a dynamic theoretical model, whereby early retrenchment actions prevent the firm from entering a downward spiral of decline and set off a vir-tuous circle of improving the firm’s internal operating conditions and external support critical to its successful turnaround, whereas late retrenchment actions fail to do so.

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Second, it tests the proposed dynamic theoretical model of retrenchment and turn-around using a longitudinal research design (Bergh, 1993, 1995; Bergh and Holbein, 1997; Mitchell and James, 2001), which can provide more convincing evidence about the retrenchment–turnaround relationship than a commonly used cross-sectional research design with pooled data. Finally, it takes a more nuanced approach and empirically tests specific actions of retrenchment (i.e., layoffs, divestments, and geo-graphic market exits) and their influence on the likelihood of successful turnaround. Given that past operationalization of retrenchment has been mainly at the aggregate level (Trahms et al., 2013), this nuanced operationalization is particularly important as it permits us to further examine whether the retrenchment–turnaround relationship is specific to certain retrenchment actions.

THEORY AND HYPOTHESIS DEVELOPMENT

In this study, organizational decline is defined as ‘a condition in which a substantial, absolute decrease in an organization’s resource base occurs over a specified period of time’ (Cameron et al., 1987, p. 224), while turnaround is defined as restoration of a firm’s performance to the level it had prior to a severe decline (e.g., Barker and Duhaime, 1997; Pearce and Robbins, 1993). In the following sections, we present a path-dependent pattern of the retrenchment–turnaround relationship, specifically emphasizing the impacts of early and late retrenchment actions on turnaround outcomes.

Path-Dependent Pattern of Retrenchment and Turnaround

In this study, we propose that the dynamic relationship between retrenchment and turnaround is characterized by a path-dependent pattern. Path dependence gener-ally involves a set of dynamic processes in which certain actions/events can unin-tendedly trigger a self-reinforcing circle and yield lasting consequences that subsequent actions can modify only to a limited extent due to the largely irreversi-ble and indivisiirreversi-ble nature of the processes (Antonelli, 1997; Garud et al., 2010; Sydow et al., 2009; Vergne and Durand, 2010). Thus, the timing and sequencing of actions and events are important considerations (Pierson, 2000). As illustrated in Figure 1, the path-dependent pattern of retrenchment and turnaround unfolds in four phases: (1) antecedents, (2) actions, (3) results of and dynamics flowing from actions, and (4) outcomes. These phases correspond with the three periods: decline, performance fluctuation, and recovery. In the antecedents phase, the process begins when firms experience performance decline, and then organizational con-tingencies and managerial influences largely shape whether declining firms take early or late retrenchment actions in the actions phase (Garud et al., 2010; Vergne and Durand, 2010). Early or late retrenchment actions in turn shape the subse-quent operating conditions and performance of the firms through a self-reinforcing circle (i.e., a virtuous circle for early retrenchment and a vicious circle for late retrenchment) in the results and subsequent dynamics phase, and eventually lead to a successful turnaround for the path of early retrenchment and an unsuccessful 649 Temporal Approach to Retrenchment

turnaround for the path of late retrenchment in the outcomes phase. As the scope of this study focuses on the turnaround effects of early and late retrenchment actions, our theoretical arguments and hypothesis development efforts do not include organizational contingencies and managerial influences that may determine the timing of retrenchment actions, but rather start from the point at which the declining firms have already taken either early or late retrenchment actions.

We use Weitzel and Jonsson’s (1989) five-stage model of organization decline as our guide in defining early versus late retrenchment actions. In this model’s first stage – blinded – a firm fails to anticipate and detect deficiencies, leading to its performance decline. In the second stage – inaction – the firm fails to decide on corrective actions, resulting in delayed responses, further decline, and higher stress. In the third stage – faulty action – stress is high, and the firm’s dominant coalitions are competing for diminishing resources; the firm thus initiates and implements faulty decisions and/or actions, deepening its decline problems. In the fourth stage – crisis – faulty actions lead to the critical point of either major revitalization or certain failure, and key stakeholders withdraw their support for the firm. In the final stage – dissolution – decline reaches the irreversible point, and the firm collapses and dissolves, either rap-idly or gradually, depending on the external environment. We contend that the sec-ond stage, inaction, is the temporal pivot for defining retrenchment actions as early or late. If retrenchment actions are taken soon after a performance decline pattern takes hold (at the end of the first stage), it can be considered early. If no retrenchment action is taken despite a clear performance decline pattern, the inaction stage begins, and any retrenchment actions taken thereafter are considered late.

Figure 1. Path-dependent pattern of retrenchment and corporate turnaround

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Theoretical Arguments and Hypotheses

The path-dependent pattern of retrenchment and turnaround, described above, rests upon the synthesis of theoretical insights from two literature streams: (1) the two-stage model of corporate turnaround; and (2) the downward spiral of organization decline. The two-stage model of corporate turnaround prescribes that declining firms take two stages of actions in sequence to turn themselves around: first retrenchment and then recovery or reorientation (Arogyaswamy et al., 1995; Pearce and Robbins, 1993; Trahms et al., 2013). The logic behind this two-stage model is that declining firms take efficiency-based retrenchment actions to first stabilize their financial conditions (Hofer, 1980; Lohrke et al., 2004). Once financial stability has been restored, the declining firms are then in a better position to pursue market-based reorientation actions that seek to strengthen their long-term competitiveness (Arogyaswamy et al., 1995; Barker and Duhaime, 1997; Ndofor et al., 2013). The key insight from this lit-erature stream is that retrenchment actions are considered the initial response to survival-threatening decline in a sequential turnaround process and play a critical role in paving the necessary foundation for successful turnarounds (e.g., Pearce and Rob-bins, 1994).

While the two-stage model of corporate turnaround highlights the importance of sequencing turnaround actions (i.e., retrenchment before reorientation), it does not explicitly specify the temporal dimension of organization decline, retrenchment, and turnaround. As the downward-spiral decline literature suggested, when the decline is left unchecked, performance worsens with time, and declining firms drift into a downward-spiral into crises or even death (Hambrick and D’Aveni, 1988; McKinley et al., 2014; Rudolph and Repenning, 2002; Weitzel and Jonsson, 1989). Specifically, once poor performance sets in, it tends to become self-reinforcing, in which poor per-formance depletes firms’ slack resources, which in turn further deteriorate perform-ance, eventually leading to their failure (Hambrick and D’Aveni, 1988). Under such circumstances, declining firms’ investment in new initiatives may further exacerbate the decline problems as the firms’ resources and slacks are rapidly depleted (McKinley et al., 2014). From this standpoint, early retrenchment actions to quickly stem the decline, stabilize financial conditions, and free up organizational slacks are important to prevent the declining firms from entering the downward spiral of decline, within which the firms are far less likely to make successful turnarounds.

On the behavioural and psychological level, Rudolph and Repenning (2002) main-tain that the downward-spiral decline operates under the Yerkes–Dodson law suggest-ing that there is an inverted U-shaped relationship between stress and performance under moderate-to-difficult tasks. They further elaborate that on the left side of the inverted U-shaped curve where the stress level is low to moderate, the relationship between stress and performance is positive; thus, an increase in stress can indeed lead to an increase in performance. However, when the level of stress passes the threshold point, the relationship between stress and performance becomes negative. An increase in stress will then result in a decrease in performance. As the decrease in performance can also increase the stress level, which in turn diminishes performance further, the declining firm thus enters into a vicious circle (Masuch, 1985), once the stress level 651 Temporal Approach to Retrenchment

has passed the threshold point. Building on Rudolph and Repenning’s (2002) standpoint, we argue that performance decline induces stress among managers attempting to turn around failing firms. As Weitzel and Jonsson (1989) noted, the level of stress in declining firms escalates as the decline problems deepen. Kanter (2003) also observed that consider-ably high degrees of stress and anxiety are evident among executives and organizational leaders who face the issue of performance decline and are charged with a challenging turnaround task. Therefore, early retrenchment actions have an important function to curb further decline, stabilize financial conditions, and thus relieve the decline-induced stress before the stress level reaches the threshold point of the downward-spiral decline. As early retrenchment actions contain the stress level below the threshold point, the stress during the turnaround attempts can then be counterintuitively productive in enhancing the performance among managers, following the Yerkes–Dodson inverted U-shaped curve of the stress–performance relationship (Rudolph and Repenning, 2002). In short, early retrenchment actions play an instrumental role in shaping the stress–performance dynamics in favour of successful turnaround attempts while preventing declining firms from entering the downward spiral of decline. The above arguments collectively suggest that declining firms that implement early retrenchment actions are more likely to achieve successful turnarounds; thus, we propose:

Hypothesis 1: In declining firms, early retrenchment actions are positively related to the likelihood of turnaround success.

As retrenchment actions can be broken down into employee layoffs, divestments, and geographic market exits, we further specify Hypothesis 1 into the following three sub-hypotheses:

Hypothesis 1a: In declining firms, the extent of layoffs as early retrenchment actions is positively related to the likelihood of turnaround success.

Hypothesis 1b: In declining firms, the extent of divestments as early retrenchment actions is positively related to the likelihood of turnaround success.

Hypothesis 1c: In declining firms, the extent of geographic market exits as early retrenchment actions is positively related to the likelihood of turnaround success.

At the operational level, we also argue that early retrenchment actions, in the path to a successful turnaround, can set off a virtuous circle of (a) improved operating con-ditions, (b) improved internal firm performance, and (c) enhanced support from exter-nal capital market stakeholders, all of which reinforce one another. Specifically, we argue that the early implementation of retrenchment actions (e.g., layoffs, divestments, and/or geographic market exits) promptly halts the performance deterioration, stabil-izes the financials, and improves operating conditions in declining firms (Bibeault, 1982; Lohrke et al., 2004; Robbins and Pearce, 1992). Improved operating condi-tions, such as higher cash flow/slack resources and lower debt, assist the firms to recapture productivity, profitability, and growth (Morrow et al., 2004; Pearce and Robbins, 1993). Improved internal performance can then restore the confidence of

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key investors and other external capital market stakeholders, thereby enhancing the firms’ ability to obtain external financial resources via the capital market (Arogyasw-amy et al., 1995; Pajunen, 2006). Such resource support from key external stakehold-ers can further improve the operating conditions and internal performance of the firms during their turnaround. Therefore, we expect early retrenchment actions to yield three specific and interrelated outcomes – operating condition improvement, internal performance improvement, and increased external capital market support, as illustrated in Figure 1, and propose the following hypotheses:

Hypothesis 2: In declining firms, early retrenchment actions are positively related to subsequent improvement in operating conditions.

Hypothesis 3: In declining firms, early retrenchment actions are positively related to subsequent improvement in internal firm performance.

Hypothesis 4: In declining firms, early retrenchment actions are positively related to subsequent improvement in external capital market support.

On the contrary, when declining firms delay their implementation of retrenchment actions, we argue that the firms are at risk of permitting the decline problems to magnify and deepen into a situation of high stress and dwindling resources (Weitzel and Jonsson, 1989). Based on the inverted U-shaped relationship of stress and performance (Rudolph and Repenning, 2002), we argue that under such a high stress level, declining firms oper-ate on the right side of the inverted U-shaped curve of stress–performance relationship where stress is negatively related to performance. The increasingly high degree of stress therefore undermines managers’ ability to perform and to cope with the challenges inher-ent in their turnaround tasks, thus potinher-entially resulting in strategic errors among manag-ers, failed innovative initiatives, poor organizational performance, and further diminishing resources (Hambrick and D’Aveni, 1992; McKinley et al., 2014; Weitzel and Jonsson, 1989). Such a deteriorating situation in turn increases the stress level and provokes vari-ous organizational pathologies, such as secrecy, scapegoating, isolation, avoidance, and passivity, therefore compromising decision-making quality and inducing further manage-rial errors and performance decline (Hambrick and D’Aveni, 1992; Kanter, 2003; Rudolph and Repenning, 2002). In other words, when declining firms implement late retrenchment actions, they fail to contain the decline problems in a timely manner. The performance decline trajectory therefore continues, and the stress associated with per-formance decline escalates, passing the threshold point, leading to the downward spiral of performance decline. As such, the declining firms under such a circumstance are less likely to achieve successful turnarounds. This line of reasoning suggests Hypothesis 5:

Hypothesis 5: In declining firms, late retrenchment actions are negatively related to the likelihood of turnaround success.

As layoffs, divestments, and geographic market exits are three specific retrenchment actions, investigated in this study, we further break Hypothesis 5 down into three sub-hypotheses as follows:

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Hypothesis 5a: In declining firms, the extent of layoffs as late retrenchment actions is negatively related to the likelihood of turnaround success.

Hypothesis 5b: In declining firms, the extent of divestment as late retrenchment actions is negatively related to the likelihood of turnaround success.

Hypothesis 5c: In declining firms, the extent of geographic market exits as late retrenchment actions is negatively related to the likelihood of turnaround success.

Operationally, when declining firms with late retrenchment actions fail to stem the decline in a timely manner, they can also drift into a vicious circle of (a) sustained poor operating conditions, (b) sustained poor internal performance, and (c) decreasing support from external capital market stakeholders, all of which reinforce one another. Specifically, as late retrenchment actions allow the decline problems to persist and escalate, firm resources and slacks are further depleted (Weitzel and Jonsson, 1989). The firms’ operating conditions therefore deteriorate with diminishing cash flow and increasing debt level (Sudarsanam and Lai, 2001), thus limiting the firms’ ability to operate profitably. The sustained poor internal performance (i.e., profitability) in turn erodes the confidence and commitment of key capital market stakeholders, thus con-straining the firms’ ability to access financial resources (Arogyaswamy et al., 1995; Flynn and Staw, 2004; Pajunen, 2006), which further worsens their operating condi-tions and sustains their subpar performance. We thus expect late retrenchment accondi-tions to associate with three specific and interrelated outcomes – deteriorating operating conditions, declining internal performance, and decreasing external capital market support, as illustrated in Figure 1, and propose the following hypotheses:

Hypothesis 6: In declining firms, late retrenchment actions are negatively related to subsequent improvement in operating conditions.

Hypothesis 7: In declining firms, late retrenchment actions are negatively related to subsequent improvement in internal firm performance.

Hypothesis 8: In declining firms, late retrenchment actions are negatively related to subsequent improvement in external capital market support.

RESEARCH METHODS Sample and Research Design

To test our hypotheses, we used a matched-pair sampling technique that is common in turnaround studies (e.g., Clapham et al., 2005; Hambrick and D’Aveni, 1992; Mueller and Barker, 1997). Our sample included 48 case–control matched pairs of firms that either achieved a turnaround or did not (successful turnaround and unsuc-cessful turnaround firms). A matched case–control research design is appropriate for turnaround research, as turnarounds are relatively sparse in the overall population of firms. Thus, random sampling may not have generated an adequate number of suc-cessful turnaround firms for the analysis. Retrospective and observational in nature,

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case–control research (e.g., Schlesselman, 1982) starts with identifying cases (i.e., a group with the outcome of interest; here, successful turnaround) and controls (i.e., a group without the outcome) and then traces back in time to investigate past events, activities, and exposures that differ between the cases and controls (i.e., early and late retrenchment actions in this study). As the cases and controls are matched on certain characteristics, some confounding variability can be reduced (e.g., Schlesselman, 1982).

We screened the Compustat North American Database for turnaround firms between 1993 and 2008, using Barker and Duhaime’s (1997) turnaround selection cri-teria. Accordingly, firms that meet the following criteria were considered turnaround: (1) return on investment (ROI) above the risk-free rate of return for two consecutive years before decline; (2) during decline, ROI below both the risk-free rate of return and industry-average ROI for at least three consecutive years, and a Z-score below 3 for at least one year (indicating bankruptcy risk; Altman, 1983); (3) during recovery, ROI above the risk-free rate of return and industry-average ROI for at least three consecutive years; and (4) performance fluctuation allowed for up to three years between the decline and recovery periods. The screening process resulted in 51 suc-cessful turnaround firms. We then matched the sucsuc-cessful turnaround firms with their unsuccessful counterparts (i.e., those that met Barker and Duhaime’s criteria for pre-decline and pre-decline but not for recovery) based on their: (1) industry sector and (2) firm size, using data from Mergent Online and Compustat.[1] Three of the turn-around firms (i.e., 5.88 per cent of the total) did not have a matched unsuccessful counterpart and were dropped from subsequent analyses. The final sample therefore consisted of 48 matched pairs of successful turnaround firms and unsuccessful turn-around firms. A t-test indicates no significant difference between the successful and unsuccessful subsamples on assets, sales, number of business segments (a proxy for diversification), and return on assets (ROA). The final matched-pair sample consisted of 96 US publicly traded firms in a broad range of industries, including manufactur-ing (58.33 per cent), minmanufactur-ing (2.08 per cent), communications, wholesale, retail, invest-ment/real estate (4.17 per cent each), and other services (22.92 per cent). We considered this sample size to be reasonable when compared to the median sample size of 97 in previous firm-level turnaround research (Boyne, 2006; Pandit, 2000; Trahms et al., 2013).

Since strategic decisions (including retrenchment) typically are not random and are often endogenously linked to other organizational variables (Shaver, 1998), there is a possibility of sample selection bias. To correct for such possible bias, we followed the two-stage procedure outlined by Heckman (1979) and subsequently elaborated by strategy scholars (Bascle, 2008; Hamilton and Nickerson, 2003). In the first stage, we ran a Probit analysis regressing the retrenchment dummy (early retrenchment51,

otherwise 0) on ten organizational and industry predictors (firm size, firm age, level of diversification, capital investment intensity, R&D intensity, past profitability perform-ance, firm slack depletion, firm revenue decline rate, industry sector of the firm (1 for manufacturing, 0 otherwise), and industry median ROA). These variables have been shown in past studies to influence the likelihood of retrenchment actions (Lim et al., 2013; Nixon et al., 2004). In the second stage, we included the results of the Probit 655 Temporal Approach to Retrenchment

analysis as the inverse Mills ratio variable in the main regression analyses. As can be seen in Tables (II–V), the overall results are not significantly altered, suggesting that endogeneity is not a major concern.

Analysis Period, Early Versus Late Retrenchment, and Data Source

The analysis period in this study was a four-year period after two consecutive years of decline, as depicted in Figure 2. We considered retrenchment actions taken in the first two years of the analysis period (i.e., first and second years after two consecutive years of decline) as early, and those taken in the last two years of the analysis period (i.e., Years 3 and 4) as late. We believe that beginning our analysis period after two years of performance decline had been observed was reasonable, because a decline trajec-tory forms more clearly after two or three consecutive years of declining performance (Hambrick and Schecter, 1983; Ndofor et al., 2013; Robbins and Pearce, 1992). While the calendar years comprising the four-year analysis period differed across the sample firms, the analysis period was the same for all the sample firms in relation to their performance decline trajectories, starting from the third year of their decline plus three subsequent years (a total four years). In addition, we obtained data on early and late retrenchment from sample firms’ annual reports issued during the four-year analysis period. While it is possible that the sample firms could have taken other actions not reported in their annual reports, it is reasonable to expect that they report their most significant actions in the reports due to reporting requirements from regu-latory agencies.

Measurement

Dependent variables. The first dependent variable was the likelihood of turnaround success, binary coded ‘1’ for successful turnaround and ‘0’ otherwise as in previous turnaround research (e.g., Abebe et al., 2012; Hambrick and D’Aveni, 1992; Mueller and Barker, 1997; Ndofor et al., 2013). The other dependent variables were three specific outcomes – changes in firm operating conditions,internal firm performance, and external capital market support– that directly followed early and late retrenchment actions, thus allowing finer-grained longitudinal analyses. Change in firm operating conditions was measured as debt ratio change (log-transformed to correct for skewness) and cash flow to asset ratio change (cash flow change; ratios in years after retrenchment actions minus those in the years of the actions). Levels of debt and cash flow are critical operating conditions for firms attempting turnaround (e.g., Hofer, 1980; Robbins and Pearce, 1992), as these indicators reflect the constraints on and availability of firm resources. We measured change in internal firm performance using ROA change (i.e., ROA in the years after retrenchment actions minus ROA in the years of the actions). The final outcome variable is change in external capital market support, and was measured using stock price change(i.e., the difference between year-end stock prices in the years after retrenchment actions and those prices in the years of the actions, log-transformed to correct for skewness).

In the case of early retrenchment actions, we measured these outcome variables at two different times: first during Years 1 and 2 of the analysis period, when early retrenchment actions were taken (see Figure 2), and second during the two subsequent

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Table I. Means, standard deviations, and correlations

Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13

1. Turnaround success 0.50 0.50 1.00

2. Log debt ratio change after early actions 20.24 0.1820.41*** 1.00

3. Log debt ratio change after late actions 20.23 0.1820.37*** 0.10 1.00

4.Cash flow change after early actions 0.07 0.22 0.45***₂0.49***20.25* 1.00

5. Cash flow change after late actions 20.02 0.24 0.13 0.31** 20.49***20.03 1.00

6. ROA change after early actions 0.10 0.24 0.45***20.54***20.24* 0.94***20.08 1.00

7. ROA change after late actions 20.04 0.33 0.13 0.30** 20.48*** 0.01 0.90*** 0.01 1.00

8. Log stock price change after early actions 0.05 0.42 0.53***20.40***20.33** 0.55***20.04 0.59*** 0.07 1.00

9. Log stock price change after late actions 0.02 0.37 0.33***20.05 20.38*** 0.15 0.45*** 0.17† 0.51*** 0.05 1.00 10. Log firm size 2.21 0.8420.04 20.13 20.06 0.14 0.04 0.06 0.04 20.11 0.03 1.00

Table I.Continued

Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13

30. Extended reorientation 0.34 0.48 0.02 0.09 0.02 20.10 20.02 20.06 0.04 20.07 20.01 0.08 0.07 20.04 0.08 31. Early retrenchment 2.35 2.18 0.18† ₂0.17 ₂0.21* 0.30** 0.14 0.30** 0.11 0.11 0.03 0.46*** 0.27**₂0.15 ₂0.07 32. Late retrenchment 1.78 2.3720.28*** 0.15 0.02 20.08 0.03 20.11 0.01 20.33*** 0.05 0.30** 0.01 20.03 20.03 33. Early layoffs 1.02 1.65₂0.24* 0.10 0.01 0.08 0.09 0.04 0.10 ₂0.06 ₂0.05 0.34*** 0.13 0.04 ₂0.14 34. Late layoffs 0.85 1.6420.24* 0.09 0.06 20.05 0.07 20.08 0.09 20.20* 0.05 0.29** 20.01 0.05 0.07 35. Early divestments 1.19 1.54 0.44***₂0.30** ₂0.29** 0.27** 0.10 0.32** 0.06 0.20† 0.07 0.26* 0.23* ₂0.24* 0.04 36. Late divestments 0.84 1.4620.16 0.13 20.03 20.11 20.03 20.11 20.09 20.30** 0.01 0.13 0.03 20.15 20.06 37. Early geographic market exits 0.15 0.38 0.33***₂0.16 ₂0.06 0.26* 0.00 0.22* ₂0.04 0.08 0.12 0.11 0.04 ₂0.03 0.05 38. Late geographic market exits 0.08 0.3120.07 0.06 20.02 0.17 0.05 0.11 0.06 20.06 0.03 0.13 0.05 0.20*20.28** 39. Extended retrenchment 0.26 0.44₂0.02 0.01 ₂0.02 0.17† 0.00 0.12 ₂0.01 ₂0.01 ₂0.03 0.37*** 0.15 ₂0.09 ₂0.14

Mean SD 14 15 16 17 18 19 20 21 22 23 24 25 26

14. Firm profitability decline rate 0.10 0.34 1.00

15. Firm revenue decline rate 20.32 1.91 0.17† 1.00

16. Industry median profitability 0.05 0.02 20.04 20.16 1.00 17. Industry revenue decline rate 20.14 0.25 20.03 0.12 20.03 1.00

18. Industry revenue recovery rate 0.31 1.12 0.08 0.02 0.02 0.00 1.00 19. CEO replacement 0.67 0.85 0.03 20.16 0.11 0.02 20.08 1.00 20. Largest shareholder concentration 0.26 0.33 20.08 0.05 0.01 20.11 20.04 20.03 1.00

21. Type of largest shareholder 0.70 0.46 20.21* 20.04 20.26* 20.06 0.03 0.01 0.04 1.00 22. Early reorientation 1.34 2.27 20.20† 20.18† 20.01 0.04 20.06 0.04 0.11 0.08 1.00

23. Late reorientation 1.35 1.97 ₂0.08 ₂0.03 0.11 0.00 ₂0.08 0.03 0.12 ₂0.12 0.35*** 1.00

24. Early acquisitions 1.12 2.21 20.21* 20.10 20.04 0.07 20.06 0.04 0.04 0.09 0.96*** 0.31** 1.00

25. Late acquisitions 1.15 1.84 ₂0.10 ₂0.04 0.12 0.05 ₂0.08 0.07 0.03 ₂0.15 0.35** 0.96*** 0.31** 1.00

Table I.Continued

Mean SD 14 15 16 17 18 19 20 21 22 23 24 25 26

30. Extended reorientation 0.34 0.48 20.11 20.06 20.06 20.03 20.06 0.05 0.14 20.14 0.54*** 0.64*** 0.46*** 0.60*** 0.15 31. Early retrenchment 2.35 2.18 0.20† ₂0.07 ₂0.20† 0.10 0.08 0.14 ₂0.15 ₂0.01 ₂0.03 ₂0.06 ₂0.04 ₂0.03 ₂0.14 32. Late retrenchment 1.78 2.37 20.02 20.08 0.12 0.11 0.02 20.01 20.14 20.16 0.05 0.13 0.09 0.13 20.07 33. Early layoffs 1.02 1.65 0.00 ₂0.14 ₂0.22* 0.19† 0.18 0.01 ₂0.12 0.02 0.04 0.01 0.07 0.08 ₂0.03 34. Late layoffs 0.85 1.64 0.02 0.02 0.05 0.11 0.09 0.04 20.15 20.13 0.03 0.10 0.07 0.11 20.07 35. Early divestments 1.19 1.54 0.26** 0.03 ₂0.06 ₂0.07 ₂0.08 0.15 ₂0.05 0.01 ₂0.09 ₂0.08 ₂0.13 ₂0.12 ₂0.15 36. Late divestments 0.84 1.46 20.05 20.09 0.10 0.05 20.07 20.05 20.05 20.09 0.05 0.07 0.07 0.07 20.05 37. Early geographic market exits 0.15 0.38 0.06 0.09 0.06 0.05 ₂0.01 0.12 ₂0.11 ₂0.16 0.01 ₂0.04 0.00 0.00 ₂0.08 38. Late geographic market exits 0.08 0.31 20.05 20.29** 0.14 0.04 20.02 20.05 20.03 20.12 20.04 0.10 20.03 0.11 0.06 39. Extended retrenchment 0.26 0.44 0.16 ₂0.06 ₂0.09 0.27** ₂0.06 0.07 ₂0.15 ₂0.07 0.08 ₂0.01 0.12 ₂0.01 ₂0.05

Mean SD 27 28 29 30 31 32 303 34 35 36 37 38 39

27. Late geographic market expansions 0.07 0.26 1.00

28. Early other growth initiatives 0.17 0.57 20.01 1.00

29. Late other growth initiatives 0.14 0.49 20.08 0.07 1.00

30. Extended reorientation 0.34 0.48 0.13 0.29** 0.25* 1.00 31. Early retrenchment 2.35 2.18 0.01 0.010 20.12 0.01 1.00

32. Late retrenchment 1.78 2.37 20.01 20.11 0.03 0.14 0.18† 1.00

33. Early layoffs 1.02 1.65 20.13 20.10 20.16 0.06 0.68*** 0.21* 1.00

34. Late layoffs 0.85 1.64 20.10 20.12 0.04 0.12 0.19† 0.77*** 0.32** 1.00

35. Early divestments 1.19 1.54 0.18† 0.24* 0.02 20.03 0.65*** 0.01 20.10 20.09 1.00 36. Late divestments 0.84 1.46 0.06 ₂0.03 0.02 0.08 0.04 0.71*** ₂0.07 0.11 0.12 1.00

37. Early geographic market exits 0.15 0.38 20.11 0.08 20.11 20.05 0.18† 0.09 20.07 0.07 0.08 0.08 1.00 38. Late geographic market exits 0.08 0.31 0.18† ₂0.08 ₂0.07 0.09 0.16 0.24* 0.18† 0.07 0.03 0.10 ₂0.01 1.00 39. Extended retrenchment 0.26 0.44 0.02 20.13 20.02 0.12 0.54*** 0.56*** 0.38** 0.45*** 0.32** 0.34*** 0.15 0.30** 1.00

years, the years after the early retrenchment actions (i.e., Years 3 and 4 of the analy-sis period). Our use of a two-year lag for longitudinally investigating outcomes of retrenchment actions is consistent with the time duration used in previous lag-effect

Table II. Results of early and late retrenchment and turnaround analysis

DV: Turnaround success Firm profitability decline rate 0.01 0.00

(0.01) (0.01) Firm revenue decline rate 0.83† 0.75† (0.46) (0.45) Industry median profitability 0.14 0.30† (0.13) (0.16) Industry revenue decline rate –1.67 –2.10† (1.08) (1.18) Industry revenue recovery rate –0.37 –0.49

(0.56) (0.70)

CEO replacement 0.46 0.33

(0.30) (0.33) Largest shareholder concentration –0.90 –1.77

(1.06) (1.73) Type of largest shareholder –0.25 –0.37

(0.62) (0.66)

Early reorientation –0.12 –0.10

(0.13) (0.15)

Late reorientation –0.28 –0.27

(0.19) (0.20) Extended reorientation 1.47† 1.75† (0.85) (0.94)

studies of organizational actions (e.g., Branch, 1974; Hoffman et al., 1994). We then calculated the changes of the average values of these variables between the two time periods (i.e., the average values of these variables in Years 3 and 4 minus those in Years 1 and 2). Similarly, for late retrenchment actions, we measured these outcome variables at two different times: first during Years 3 and 4 of the analysis period, when late retrenchment actions were taken; and second during the two subsequent years, the two years after the late retrenchment actions (i.e., Years 5 and 6). Changes in the average values of these variables between these two time periods were calcu-lated as above.

Independent variables.The independent variables in this study are early and late retrenchment actions, which were further classified into early and late layoffs, early and late divestments, and early and late geographic market exits. Guided by Barker and Duhaime’s (1997) action-based strategic change measurement, we examined employee layoffs, divestments, and geographic market exits taking place in each year of each analysis period, using the criteria described next to quantify and measure them.[2]

The total number of employee layoffs in each year of the analysis period was coded (05no layoffs; 15up to 5% of total employees; 256–10%; 3511–25%; 4526–

50%; and 55more than 50%). The number of layoffs was the reported number of

headcount reductions independent of those due to divestments or geographic market exits; this definition allowed us to examine separate effects of these three retrench-ment actions. The layoff scores in Years 1 and 2 of the analysis period were summed into a proxy for early layoffs, and the layoff scores in Years 3 and 4 were summed into a proxy for late layoffs. After coding each of the divestments (05no divestment;

15divestment value up to 5% of total assets; 256–10%; 3511–25%; 45more

than 25%), we then summed divestments taken in Years 1 and 2 to form a proxy for early divestments and summed Years 3 and 4 for late divestments. Geographic market exit was binary-coded (15firm engaged in exits; 05otherwise), given that firms typically do

not report the potential magnitudes of geographic market exits relative to total sales, Table II.Continued

DV: Turnaround success

Model 1 (Bb)

Model 2 (Bb) Model summary

Chi-square 25.11† 40.23**

Pseudo R-squared (Cox and Snell) 0.23 0.34 Correct classification 65.60% 77.10%

22 log likelihood (Log L) 107.98 92.85

Da:22log Lreduced model2(22Log Lfull model) 15.13***

(N₅96)

a

Improvement of goodness-of-fit. b_{Logistic regression coefficients.} Comparing with Control Model.

†_{p<0.10; * p<0.05; ** p<0.01; *** p<0.001.}

661 Temporal Approach to Retrenchment

Table III. Results of early and late retrenchment and turnaround decomposition analysis

Log firm size –0.04 0.59 0.10 –0.32 –1.02 (0.41) (0.50) (0.51) (0.45) (0.76) Diversification 0.14 0.12 –0.09 0.08 –0.61

(0.22) (0.23) (0.27) (0.23) (0.50) Liquidity –0.07 –0.05 –0.14 –0.04 0.10

(0.14) (0.13) (0.16) (0.15) (0.22) Operating margin 0.00 0.00 0.00 0.00 0.00

(0.01) (0.00) (0.00) (0.01) (0.01) Firm profitability decline rate 0.01 0.00 0.00 0.01 0.01

(0.01) (0.01) (0.01) (0.01) (0.02) Firm revenue decline rate 0.88† 0.73 0.73† 0.51 0.55

(0.49) (0.45) (0.41) (0.49) (0.51) Industry median profitability 0.12 0.20 0.34† 0.06 0.36

(0.14) (0.16) (0.19) (0.16) (0.26) Industry revenue decline rate –1.77 –1.84 –2.03 –1.96 –3.83

(1.14) (1.18) (1.32) (1.24) (2.33) Industry revenue recovery rate –0.30 –0.08 –0.86 –0.72 –2.30

(0.45) (0.53) (1.31) (1.23) (3.03) CEO replacement 0.58† 0.67† 0.22 0.60† –0.48

(0.31) (0.34) (0.37) (0.34) (0.62) Largest shareholder concentration –1.46 –2.43 –2.92 –0.85 0.11

(1.38) (1.83) (2.27) (1.20) (2.55) Type of largest shareholder –0.47 –0.58 –0.41 0.11 –0.34

(0.65) (0.68) (0.78) (0.73) (1.22) Early acquisition –0.05 –0.13 –0.04 –0.21 –0.17

(0.14) (0.15) (0.16) (0.17) (0.60) Late acquisition –0.36 –0.43† –0.38 –0.30 –1.15† (0.23) (0.24) (0.28) (0.24) (0.59) Early geographic market expansion –1.06 –1.49 –0.81 –1.21 –3.49

(1.10) (1.18) (1.32) (1.21) (2.93) Late geographic market expansion 2.39† 2.18 2.00 2.78† 5.56† (1.45) (1.44) (1.91) (1.55) (2.91) Early other growth initiative 0.13 –0.50 –1.66* –0.44 –4.60* (0.56) (0.63) (0.84) (0.86) (1.82) Late other growth initiative –0.12 –0.44 –0.60 –0.05 –0.33

(0.54) (0.60) (0.71) (0.55) (0.91) Extended reorientation 1.46 2.55* 2.54* 2.05* 6.18* (0.95) (1.22) (1.21) (1.01) (2.44) Extended retrenchment –0.57 0.40 –1.87† –0.68 –2.59

(0.67) (0.81) (0.10) (0.76) (2.43) Inverse Mills ratio –0.19 –0.06 –0.56 0.05 0.84

(0.58) (0.63) (0.66) (0.63) (1.26) Constant –0.23 –1.49 –0.89 –0.34 –1.93

(1.44) (1.66) (1.83) (1.58) (2.44)

662 C. Tangpong et al.

assets, or markets. This binary measure was also used in Barker and Duhaime’s (1997) study. Accordingly, we coded 1 forearly geographic market exitfor exits in Years 1 and 2 of the analysis period and 0 otherwise. Similarly, we coded 1 for late geographic market exit for exits in Years 3 and 4 of the analysis period and coded 0 otherwise. Finally, early and late retrenchment actions were measured using the respective summed scores of early and late layoffs, divestments, and geographic market exits. In addition, the coding was performed by two independent coders, and resulted in initial agreement of 95.83 per cent (i.e., 92 of 96 firms yielding inter-coder agreement). The coding results from the two coders were highly correlated (r50.92–0.99, p<_{0.001). The coders’ disagreements} were then resolved through further discussion and recoding efforts.

Control variables. We controlled for several managerial-, organizational-, and industry-level factors that have been suggested by previous research to potentially affect the likelihood of successful turnaround and yield alternative explanations of the findings. These included: (1) firm size (i.e., total assets in the first year of decline, then log-transformed for potential non-linear effects); (2) diversification (i.e., firm’s number of business segments in the first year of decline); (3) liquidity(i.e., quick ratio in the first year of decline); (4) three aspects of decline severity: operating margin in the first year Table III.Continued

Early layoff (Hypothesis 1a) –0.40 –0.21 (0.25) (0.49) Late layoff (Hypothesis 5a) –0.56* –0.80

(0.24) (0.55) Early divestment (Hypothesis 1b) 1.46*** 2.66**

(0.42) (0.99) Late divestment (Hypothesis 5b) –0.46* –1.13**

(0.23) (0.42) Early geographic market exit (Hypothesis 1c) 3.63** 8.84*

(1.39) (3.55) Late geographic market exit (Hypothesis 5c) –0.85 –7.05† (1.41) (3.72)

Model summary

Chi-square 30.05† 40.17* 56.82*** 43.08** 85.42*** Pseudo R-squared (Cox and Snell) 0.27 0.34 0.45 0.36 0.59 Correct classification 66.70% 72.90% 82.30% 79.20% 89.60%

22 log likelihood (Log L) 103.04 92.92 76.27 90.00 47.66

Da:₂2log Lreduced model2(22Log Lfull model) 10.12 c,f

26.77c,g 13.04c,f 55.38c,g (N596)

Table IV. Longitudinal analysis results of early retrenchment and subsequent outcomes

Log firm size 0.04 0.06 0.11 0.10 –0.01 –0.02 –0.15 –0.15 Diversification –0.04 –0.03 0.02 0.01 0.00 –0.01 0.22† 0.22† Liquidity 0.27** 0.25* –0.20* –0.18* –0.23** –0.20* –0.16 –0.15 Operating margin –0.17 –0.25 –0.87*** –0.79** –0.47† –0.40 –0.72* –0.67† Firm profitability decline rate –0.47*** –0.46*** 0.52*** 0.53*** 0.60*** 0.60*** 0.12 0.11 Firm revenue decline rate –0.07 –0.03 0.41* 0.37* 0.05 0.01 0.32 0.23 Industry median profitability –0.09 –0.06 0.21* 0.19* 0.20* 0.18* 0.23* 0.22* Industry revenue decline rate 0.05 0.03 0.02 0.04 0.01 0.04 0.00 0.01 Industry revenue recovery rate 0.02 0.00 –0.02 –0.02 –0.05 –0.04 0.08 0.09 CEO replacement –0.11 –0.09 –0.03 –0.05 0.01 –0.01 0.08 0.04 Largest shareholder concentration 0.08 0.07 –0.02 –0.01 –0.04 –0.04 –0.02 0.02 Type of largest shareholder –0.18† –0.22† –0.19* –0.16† –0.12 –0.09 0.05 0.09

Early reorientation –0.05 0.04 0.01 –0.31*

Early acquisition –0.02 0.02 0.00 –0.33**

Early geographic market expansion –0.05 –0.03 –0.06 0.10 Early other growth initiatives –0.01 0.00 0.01 –0.20 Extended reorientation 0.17 0.00 0.00 0.03 0.04 0.07 0.21 0.24† Extended retrenchment –0.22 0.17 –0.16† _–0.17† _–0.19† _–0.20† _–0.24† _–0.26†

Inverse Mills ratio –0.12 -0.27 –0.22 –0.14 –0.16 –0.08 –0.34 –0.40

Table IV.Continued

Early layoff –0.02 0.15 0.16 0.12

Early divestment –0.14 0.16† 0.20* 0.29*

Early geographic market exit –0.17† 0.20* 0.18* 0.13

Model summary

Table V. Longitudinal analysis results of late retrenchment and subsequent outcomes

Log firm size –0.10 –0.10 0.06 0.06 0.04 0.02 0.14 0.12 Diversification 0.02 0.01 0.19 0.19 0.22† 0.23† –0.01 0.00 Liquidity 0.25* 0.24* 0.21† 0.21† 0.15 0.13 –0.11 –0.14 Operating margin –0.14 –0.14 0.31 0.30 0.30 0.27 0.07 0.06 Firm profitability decline rate –0.09 –0.04 –0.07 –0.09 0.04 0.02 0.11 0.10 Firm revenue decline rate 0.09 0.14 –0.31 –0.34 –0.52* –0.54* –0.16 –0.17 Industry median profitability –0.21† –0.19 –0.01 –0.01 0.02 0.01 0.29* 0.27* Industry revenue decline rate –0.21† –0.23* 0.24* 0.25* 0.25* 0.26* –0.17 –0.17 Industry revenue recovery rate –0.01 –0.03 0.05 0.05 0.05 0.04 –0.08 –0.09 CEO replacement –0.02 –0.07 0.07 0.08 0.00 0.01 0.01 0.03 Largest shareholder concentration –0.07 –0.05 0.07 0.06 0.07 0.07 –0.10 –0.10 Type of largest shareholder 0.03 0.04 –0.14 –0.15 –0.08 –0.08 0.04 0.04

Late reorientation 0.11 –0.04 –0.04 –0.22

Late acquisition 0.11 –0.05 –0.04 –0.20

Late geographic market expansion –0.29* 0.11 0.12 0.08 Late other growth initiatives –0.03 0.04 0.02 –0.05 Extended reorientation –0.05 0.01 –0.07 –0.09 0.00 –0.04 0.14 0.11 Extended retrenchment 0.05 0.05 –0.06 –0.05 –0.07 –0.08 –0.07 –0.09 Inverse Mills ratio –0.12 –0.07 0.04 0.03 –0.10 –0.12 –0.12 –0.14

Table V.Continued

Late layoff 0.05 0.04 0.10 0.07

Late divestment 0.04 –0.03 –0.10 –0.02

Late geographic market exit 0.01 –0.05 –0.03 0.04

Model summary

R-squared 0.17 0.25 0.15 0.16 0.16 0.19 0.17 0.19 Adjusted R-squared 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00

F value 0.94 1.19 0.79 0.68 0.86 0.80 0.94 0.80

of a decline and firm profitability decline rate and firm revenue decline rate (inflation-adjusted) during the decline; and (5) three aspects of industry conditions: industry median profitability (two-digit SIC industry median ROA in the first year of decline), industry revenue decline rate (inflation-adjusted) during the decline period, and industry revenue recovery (growth) rate (inflation-adjusted) during the recovery period. We also controlled for three governance-related variables (e.g., Cumming et al., 2007): (1) CEO replacement (coded 0, 1, or 2 for no CEO succession, internal CEO succession, and external CEO succession prior to the recovery period, respectively); (2) largest shareholder concentration (i.e., the average largest percentage of ownership by a single shareholder during the analysis period); and (3) type of largest shareholder (institutional51; individual50). In addition, we controlled for early and late

reorien-tation actions (i.e., acquisitions, geographic market expansions, and other growth ini-tiatives in forms of internal development/new ventures and joint ventures/alliances), which were measured through Barker and Duhaime’s (1997) action-based strategic change measurement approach.[3]

Finally, it is noted that declining firms might unfold retrenchment actions at a dif-ferent pace (DeWitt, 1998). Some firms had implemented retrenchment actions in an extended manner while others had done so in a more compressed manner. Therefore, we also controlled for the manner in which the firms’ retrenchment actions unfolded, binary-coded as 1 if firms engaged in extended retrenchment implementation (extended retrenchment hereafter) and 0 otherwise. Similarly, we controlled for extended reorienta-tion implementareorienta-tion (extended reorientareorienta-tion hereafter; coded 1 if extended and 0 other-wise). A firm’s implementation of retrenchment and reorientation actions is considered ‘extended’ if the duration of its implementation is greater than the median Figure 2. Analysis period of the study

668 C. Tangpong et al.

duration of the sample firms. Including these two additional control variables into our data analyses helps tease out the potential confounding effects of the pace of retrench-ment and reorientation impleretrench-mentations on turnaround outcomes, thus allowing us to observe the turnaround effects of early and late retrenchments more clearly.

DATA ANALYSES AND RESULTS

Table I presents the descriptive statistics and correlations for the variables used in the subsequent regression analyses. We used two sets of regressions analyses to test the proposed hypotheses. First, we used binary logistic regression analyses with maximum likelihood estimation (Bowen and Wiersema, 2004; Hoetker, 2007) to examine the overall effects of early and late retrenchment actions on the likelihood of turnaround success (Hypothesis 1 and Hypothesis 5 at both aggregate and specific-action levels of retrenchment), the results of which are presented in Tables II and III. We then per-formed OLS regression analyses for a fine-grained longitudinal investigation of changes in internal operating conditions (i.e., Debt Ratio and Cash Flow Changes; Hypothesis 2 and Hypothesis 6), internal performance (i.e., ROA Change; Hypothesis 3 and Hypothesis 7), and external capital market support (i.e., Stock Price Change; Hypothesis 4 and Hypothesis 8) as the consequences of early and late retrenchment actions, the results of which are presented in Tables IV and V.

As Table II shows, the timing of early and late retrenchment actions at the aggre-gate level matters to the successful turnaround likelihood. Model 1 is the baseline specification consisting of the control variables with a 65.60 per cent correct classifica-tion rate (chi-square525.11, p<_{0.10). Model 2 (full model) introduces Early and} Late Retrenchments and has a significant improvement in goodness of fit (_D -2loglikelihood515.13, p<_{0.001) with a 77.10 per cent correct classification rate} (chi-square540.23, p<0.01). Early Retrenchment is positively related to Turn-around Success (p<_{0.05), but Late Retrenchment is negatively related to it} (p<0.05). In addition, we have calculated the ‘marginal effect at the mean’ in inter-preting results from limited dependent variable techniques such as logistic regression (e.g., Bowen and Wiersema, 2004; Hoetker, 2007; Wiersema and Bowen, 2009). The marginal effect of Early Retrenchment is 0.54 (i.e., a one percentage point increase in Early Retrenchment above its mean improves the likelihood of successful turnaround by 54 per cent), while that of Late Retrenchment is 20.43 (i.e., a one percentage

point increase in this predictor above its mean results in a 43 per cent decline in the likelihood of successful turnaround). Overall, the results yield strong support for Hypothesis 1 and Hypothesis 5. Interestingly, the results in Table II also indicate that Extended Reorientation, although not the focus of this study, is positively related to Turnaround Success (p<0.10) in both control and full models, whereas Extended Retrenchment is not significant. These results may suggest that the slower pace of implementation may be more important to a successful execution of reorientation strategy than that of retrenchment strategy during the turnaround process.

Table III presents the logistic regression results that test Hypothesis 1 and Hypothe-sis 5 concerning specific retrenchment actions. As Table III shows, the relationship between the successful turnaround likelihood and the early versus late retrenchment 669 Temporal Approach to Retrenchment

seems to be specific to certain retrenchment actions. Model 1 is the control model with the baseline specification and has a 66.70 per cent correct classification rate (chi-square530.05, p<_{0.10). Model 2 introduces Early and Late Layoffs and attains a} 72.90 per cent correct classification rate (chi-square540.17, p<_{0.05) with a} signifi-cant improvement in goodness of fit over the control model (_D-2loglikelihood510.12,

p<_{0.01). Early Layoff is not significantly related to Turnaround Success, while Late} Layoff is significantly and negatively related to Turnaround Success (p<_{0.05) but} with a relatively small marginal effect of 20.02. These results thus support Hypothesis

5a but not Hypothesis 1a. Model 3 introduces Early and Late Divestments and yields an 82.30 per cent correct classification rate (chi-square556.82, p<_{0.001) with a} sig-nificant improvement in goodness of fit over the control model (_D -2loglikelihood526.77, p<_{0.001). Early Divestment is positively related to} Turn-around Success (p<_{0.001) with a marginal effect of 0.53, whereas Late Divestment is} negatively related to Turnaround Success (p<_{0.05) with a marginal effect of 20.10.} The results thus support both Hypothesis 1b and Hypothesis 5b. Model 4 introduces Early and Late Geographic Market Exits, and has a 79.20 per cent correct classifica-tion rate (chi-square543.08, p<_{0.01) with a significant improvement in goodness of} fit over the control model (D-2loglikelihood513.04, p<_{0.01). Early Geographic} Market Exit is positively related to Turnaround Success (p<_{0.01) with a marginal} effect of 0.57 (i.e., engaging in early geographic market exits improves the likelihood of successful turnaround by 57 per cent), whereas Late Geographic Market Exit is not significantly related to Turnaround Success. Therefore, these results lend support to Hypothesis 1c but not to Hypothesis 5c.

Model 5 (full model) with all the variables included is the most conservative test of our hypotheses, and has an 89.60 per cent correct classification rate (chi-square585.42, p<_{0.001) with a significant improvement in goodness of fit over the} control model (_D-2loglikelihood555.38, p<_{0.001). In this model, both Early and} Late Layoffs are not significantly related to Turnaround Success, partially consistent with the results in Model 2 that support Hypothesis 5a but not Hypothesis 1a. Both Early and Late Divestments, however, remain significant predictors of Turnaround Success (both at p<_{0.01) as indicated in Model 3, thus strongly supporting} Hypothe-sis 1b and HypotheHypothe-sis 5b. Early Geographic Market Exit also remains positively related to Turnaround Success (p<_{0.05) as in Model 4, which is consistent with} Hypothesis 1c. While not significant in Model 4, Late Geographic Market Exit is sig-nificantly and negatively related to Turnaround Success (p<_{0.10) in Model 5,} pro-viding partial support for Hypothesis 5c. Finally, the results in Table III also indicate that Extended Reorientation is positively related to Turnaround Success (p<_{0.05) in} all the extended and full models (Models 2–5), while Extended Retrenchment is nega-tively related to Turnaround Success (p<_{0.10) only in Model 3. These results are} largely consistent with those in Table II, highlighting that the pace of implementation may be an important consideration for reorientation to a greater degree than for retrenchment as a turnaround strategy.

Table IV overall shows that early retrenchment actions lead to subsequent improvements in declining firms’ operating conditions, internal performance, and external capital market support. All models in Table IV are statistically significant

670 C. Tangpong et al.

(p<_{0.01 for Models 1–2; p}<_{0.001 for Models 3–6; p}<_{0.10 for Models 7–8).} Mod-els 1, 3, 5, and 7 indicate that at the aggregate level, Early Retrenchment is positively related to Cash Flow Change (p<_{0.05), ROA Change (p}<_{0.01), and Stock Price} Change (p<_{0.10), but is not significantly related to Debt Ratio Change. At the} com-ponent level of early retrenchment, Models 2, 4, 6, and 8 indicate that Early Divest-ment is positively related to Cash Flow Change (p<_{0.10), ROA Change (p}<_0.05), and Stock Price Change (p<_{0.05) while Early Geographic Market Exit is negatively} related to Debt Ratio Change (i.e., improvement in debt ratio; p<_{0.10) and} posi-tively related to Cash Flow and ROA Changes (both at p<_{0.05). However, Early} Layoff is not significant in these models. The overall results thus support Hypothesis 2, Hypothesis 3, and Hypothesis 4. In addition, Models 3–8 in Table IV indicate that Extended Retrenchment is negatively related to Cash Flow, ROA, and Stock Price Changes (all at p<_{0.10), suggesting that these specific outcomes of early} retrench-ment actions may be somewhat undermined by the slow pace of retrenchretrench-ment imple-mentation. Models 7–8 also indicate that Early Reorientation and Early Acquisition are negatively related to Stock Price Change (p<_{0.05 and p}<_{0.01, respectively),} whereas Extended Reorientation is positively related to Stock Price Change (p<_{0.10). These results suggest that early reorientation efforts through acquisitions} tend to lead to a subsequent decrease in the external capital market support, while a more gradual slow-pace approach to reorientation seems to be more supported by the external capital market.

Finally, as Table V suggests, late retrenchment seems to be ineffective in improving declining firms’ overall operations during the turnaround process, as it has no signifi-cant effects on the subsequent changes in the firms’ operating conditions, internal per-formance, and external capital market support. None of the models and the late retrenchment variables in Table V are statistically significant, thus yielding no empiri-cal support for Hypothesis 6, Hypothesis 7, and Hypothesis 8.

DISCUSSION AND CONCLUSION

In this study, we have argued that the retrenchment–turnaround relationship unfolds in a path-dependent manner whereby the timing of early versus late retrenchment matters to the likelihood of turnaround success. Specifically, we inquired: (1) When do retrenchment actions need to be taken to increase the likelihood of turnaround success? and (2) How does the timing of retrenchment actions such as layoffs, divestments, and geographic market exits, relate to the likelihood of turnaround success? The results of our empirical analyses have provided clear answers for these research questions. First, declining firms that implemented retrenchment actions early (i.e., within two years after the decline begins) have a sig-nificantly higher likelihood of turnaround success. Second, specific retrenchment actions including early divestments and geographic market exits are positively related to the likelihood of successful turnaround, whereas early layoffs are not. Third, the late implementation of these retrenchment actions has adverse effects on the likeli-hood of successful turnaround. Finally, early divestments and geographic market exits, not early layoffs, yield significant improvements in declining firms’ operating 671 Temporal Approach to Retrenchment

conditions, internal performance, and external capital market support, thereby con-tributing to the higher likelihood of successful turnaround.

Contributions to Theory and Practice

This study has expanded and revised the extant theoretical perspective on retrenchment and turnaround in four ways. First, it has introduced temporal considerations into the current theoretical perspective on retrenchment and turnaround, and shows that ‘when’ retrenchment actions are taken (early versus late) are as important as ‘what’ type of actions that need to be taken in declining firms. Previous retrenchment research seems to rest on an implicit yet fundamental assumption that the retrenchment–turnaround relationship is stable over time. Our longitudinal empirical evidence clearly challenges such an assumption, given that early and late retrenchment actions do not have homo-geneous effects on turnaround outcomes. As such, this study informs turnaround researchers about the time-contingent nature of the retrenchment–turnaround relation-ship. Without such temporal considerations, the extant theoretical perspective on retrenchment and turnaround is incomplete. Thus, it is probably not surprising that previous retrenchment research has yielded contradicting results (e.g., Trahms et al., 2013), given its primary focus on the ‘what’ aspect of turnaround strategies. The tempo-ral nature of the retrenchment–turnaround relationship unveiled in this study also expands the two-stage model of corporate turnaround, which largely emphasizes the important sequence of implementing retrenchment prior to reorientation (e.g., Aro-gyaswamy et al., 1995; Pearce and Robbins, 1993). Taking both timing and sequencing of actions into consideration, we can then prescribe that retrenchment actions should not only be taken before reorientation actions but also be taken as early as the decline trajectory is formed.

Second, this study highlights the path-dependent pattern of the retrenchment–turn-around relationship, in which the early timing of retrenchment is critical to reverse the decline problems before they reach the threshold point of downward-spiral and potentially irreversible decline. This view on the timing of retrenchment and the potential irreversibility of decline (if retrenchment actions are taken late) is quite new to the corporate turnaround literature, which has long been propelled by the attempts to configure turnaround strategies and align them with organizational or industry contexts (Morrow et al., 2004; Ndofor et al., 2013). Revealing the late retrenchment– unsuccessful turnaround relationship, this study discouragingly implies that the config-urative and aligning efforts to turn around declining firms may be futile if retrench-ment actions are taken late when the threshold point of irreversible decline has been crossed.

Third, this study further clarifies the boundary conditions of the retrenchment– turnaround relationship by articulating that the turnaround effects of retrenchment are not only time-contingent but also action-specific. In other words, different forms of retrenchment actions (i.e., layoffs, divestments, and geographic market exits) have varying effects on turnaround outcomes. As turnaround attempts, early divestments and geographic market exits appear more effective than early layoffs. While the majority of previous research has taken a more piecemeal approach to retrenchment

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and turnaround (e.g., Lim et al., 2013; Morrow et al., 2004), this study has elabo-rated the efficacy of both timing and specific actions of retrenchment, which is a more comprehensive approach to this research inquiry. Finally, it outlines intermedi-ate organizational outcomes (i.e., operating conditions, internal firm performance, and external capital market support) that link retrenchment actions to successful turn-arounds in the theoretical model. Past studies have either largely overlooked or par-tially incorporated these intermediary organizational variables without a direct empirical test (Morrow et al., 2004; Sudarsanam and Lai, 2001). We believe that such theoretical and empirical elaboration is particularly important as it clarifies the mechanisms and the dynamics that operate within the retrenchment–turnaround rela-tionship, thus deepening our understanding of this important relationship.

From the practical utility standpoint, this study offers two important managerial insights applicable to turnaround situations. First, the path-dependent pattern of retrenchment and turnaround makes the timely implementation of retrenchment critical to turnaround success. Specifically, our findings suggest that the two years after a seri-ous performance decline begins seems to be an urgent time window in which retrench-ment actions remain reasonably effective in leading to successful turnarounds. To turn failing firms around successfully, top managers thus need to detect decline early and then decisively stem it through early retrenchment actions. This will prevent the decline problems from developing into a crisis – the stage at which it becomes substantially dif-ficult, if not impossible, to reverse the performance decline. Accordingly, early retrench-ment actions can serve as a crisis-prevention tool in the turnaround process.

Another managerial insight here is that not all retrenchment actions are equally effective in reversing organization decline and achieving successful turnaround. For instance, this study suggests that early divestments and early geographic market exits help to substantially improve the likelihood of successful turnaround whereas early layoffs do not. This is contrary to the widely observed managerial practice of employee layoffs following performance decline among many businesses. Accordingly, our findings urge managers to carefully re-examine the use of layoffs as a turnaround attempt. Overall, the findings suggest that head-count reductions independent of reshaping product and market portfolios are indeed not an advisable retrenchment practice. It seems more advisable to strategically divest poorly per-forming business units and/or exit unprofitable product markets in a timely manner.

Limitations and Future Research Directions

Despite its contributions, this study is not without limitations. First, as our sample firms represent those facing survival-threatening decline problems, the findings in this study may not be applicable to firms with mild performance downturns. Future research can re-examine the effects of early versus late retrenchment actions in less severe performance decline conditions. Second, this study focuses primarily on the turnaround effects of early and late retrenchment actions without examining the driv-ers of such early or late actions. Future studies addressing why declining firms adopt early or late retrenchment actions could further enrich our understanding of success-ful turnarounds. This line of inquiry might be guided by institutional, socio-cognitive, and psychological perspectives (e.g., D’Aveni and MacMillan, 1990; Kanter, 2003; 673 Temporal Approach to Retrenchment