A Temporal Approach to Retrenchment and Successful Turnaround in Declining Firms

Journal of Management Studies 52:5 July 2015
doi: 10.1111/joms.12131

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
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.

ABSTRACT

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 retrenchment in successful turnarounds, little is known on the appropriate timing of retrenchment actions in declining firms (Trahms et al, 2013). Specifically, the implications of
early versus late retrenchment actions on successful turnarounds following organizational 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).
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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 empirically 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 precipitous 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 relationship 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 turnaround success? Specifically, how does the timing of retrenchment actions such as layoffs, divestments, 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 trajectory 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 Robbins, 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 turnaround 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 virtuous 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 turnaround 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 geographic 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 generally involves a set of dynamic processes in which certain actions/events can unintendedly trigger a self-reinforcing circle and yield lasting consequences that
subsequent actions can modify only to a limited extent due to the largely irreversible and indivisible 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 contingencies 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 subsequent 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
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Figure 1. Path-dependent pattern of retrenchment and corporate turnaround

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 rapidly or gradually, depending on the external environment. We contend that the second 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.
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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 literature 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 Robbins, 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 performance depletes firms’ slack resources, which in turn further deteriorate performance, 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) maintain that the downward-spiral decline operates under the Yerkes–Dodson law suggesting 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
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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 considerably 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 conditions, (b) improved internal firm performance, and (c) enhanced support from external 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, stabilizes the financials, and improves operating conditions in declining firms (Bibeault,
1982; Lohrke et al., 2004; Robbins and Pearce, 1992). Improved operating conditions, 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 (Arogyaswamy et al., 1995; Pajunen, 2006). Such resource support from key external stakeholders 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
subsequent improvement
Hypothesis 3: In declining
subsequent improvement
Hypothesis 4: In declining
subsequent improvement

firms, early retrenchment actions are positively related to
in operating conditions.
firms, early retrenchment actions are positively related to
in internal firm performance.
firms, early retrenchment actions are positively related to
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 operate 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 inherent in their turnaround tasks, thus potentially resulting in strategic errors among managers, 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 various organizational pathologies, such as secrecy, scapegoating, isolation, avoidance, and
passivity, therefore compromising decision-making quality and inducing further managerial 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 performance 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 subhypotheses 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 constraining the firms’ ability to access financial resources (Arogyaswamy et al., 1995;
Flynn and Staw, 2004; Pajunen, 2006), which further worsens their operating conditions and sustains their subpar performance. We thus expect late retrenchment actions
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 unsuccessful 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 successful 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 criteria. 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 successful turnaround firms. We then matched the successful turnaround firms with their
unsuccessful counterparts (i.e., those that met Barker and Duhaime’s criteria for predecline and 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 turnaround 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 turnaround 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 manufacturing (58.33 per cent), mining (2.08 per cent), communications, wholesale, retail, investment/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 retrenchment 5 1,
otherwise 0) on ten organizational and industry predictors (firm size, firm age, level of
diversification, capital investment intensity, R&D intensity, past profitability performance, 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
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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 trajectory 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 regulatory 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, logtransformed 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

1. Turnaround success
0.500.50 1.00
2. Log debt ratio change after early actions 20.240.18 20.41*** 1.00
3. Log debt ratio change after late actions 20.230.18 20.37*** 0.10
1.00
4.Cash flow change after early actions
0.070.22 0.45*** 20.49*** 20.25*
1.00
5. Cash flow change after late actions
20.020.24 0.13
0.31** 20.49*** 20.03
1.00
6. ROA change after early actions
0.100.24 0.45*** 20.54*** 20.24*
0.94*** 20.08
1.00
7. ROA change after late actions
20.040.33 0.13
0.30** 20.48*** 0.01
0.90*** 0.01
1.00
8. Log stock price change after early actions 0.050.42 0.53*** 20.40*** 20.33** 0.55*** 20.04
0.59*** 0.07
1.00
9. Log stock price change after late actions 0.020.37 0.33*** 20.05 20.38*** 0.15
0.45*** 0.17†
0.51*** 0.05
10. Log firm size
2.210.84 20.04 20.13 20.06
0.14
0.04
0.06
0.04 20.11
11. Diversification
2.001.31 0.06 20.12 20.03
0.06
0.13
0.03
0.15
0.09
12. Liquidity
2.482.60 20.15
0.29** 0.27** 20.07
0.14 20.12
0.11 20.11
13. Operating margin
20.784.12 0.13 20.05
0.00 20.25*
0.00 20.23* 20.04 20.08
14. Firm profitability decline rate
0.100.34 0.15 20.41*** 20.06
0.60*** 20.06
0.61*** 20.01
0.16
15. Firm revenue decline rate
20.321.91 0.21* 20.09
0.02 20.08 20.06 20.15 20.14
0.00
16. Industry median profitability
0.050.02 0.04 20.01 20.16
0.14
0.04
0.17†
0.04
0.17
†
17. Industry revenue decline rate
20.140.25 20.13
0.10 20.17
0.05
0.19 20.01
0.16 20.05
18. Industry revenue recovery rate
0.311.12 20.10 20.04 20.02
0.03
0.02
0.02
0.03
0.12
19. CEO replacement
0.670.85 0.17† 20.17† 20.09
0.10
0.02
0.15 20.02
0.09
20. Largest shareholder concentration
0.260.33 20.08
0.07 20.08 20.09
0.03 20.11
0.03 20.08
21. Type of largest shareholder
0.700.46 20.07 20.10
0.04 20.24* 20.13 20.23* 20.09 20.03
22. Early reorientation
1.342.27 20.12
0.06
0.10 20.09
0.01 20.09
0.02 20.24*
23. Late reorientation
1.351.97 20.12 20.02
0.06 20.05
0.00 20.01
0.03 20.03
24. Early acquisitions
1.122.21 20.10
0.07
0.09 20.11
0.01 20.12
0.01 20.25*
25. Late acquisitions
1.151.84 20.14
0.01
0.11 20.04
0.01 20.01
0.03 20.04
26. Early geographic market expansions
0.060.28 20.07
0.05
0.11
0.02 20.03 20.01 20.02
0.06
27. Late geographic market expansions
0.070.26 0.04 20.06 20.28** 0.06
0.06
0.08
0.08
0.19†
28. Early other growth initiatives
0.170.57 20.04 20.05 20.01
0.04
0.04
0.11
0.04 20.03
29. Late other growth initiatives
0.140.49 20.06 20.10
0.01 20.05 20.05 20.04 20.04 20.04

9

10

11

1.00
0.03 1.00
0.02 0.33*** 1.00
20.13 20.19† 20.19†
0.03 0.07
0.03
0.08 0.16
0.00
20.05 0.08
0.02
0.20* 20.32** 20.14
0.19† 0.09 20.04
20.05 20.06 20.08
0.07 0.10 20.12
20.06 0.09 20.04
0.00 0.24*
0.07
0.08 0.09
0.18†
20.11 0.11
0.02
0.10 0.07
0.21*
20.12 0.08
0.02
20.07 20.04 20.11
0.12 20.02
0.03
20.02 0.09 20.04
20.03 0.18† 20.02

12

13

1.00
20.14 1.00
0.06 0.05
20.08 0.86***
0.08 20.11
20.01 0.01
20.02 0.03
20.12 20.16
20.06 0.08
20.13 20.08
20.01 20.11
0.12 0.05
20.04 20.07
0.15 0.04
0.19† 20.33**
0.02 0.04
0.00 20.02
20.12 0.04

Table I. Continued
Mean SD
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.

Extended reorientation
Early retrenchment
Late retrenchment
Early layoffs
Late layoffs
Early divestments
Late divestments
Early geographic market exits
Late geographic market exits
Extended retrenchment

2

3

0.34 0.48 0.02
0.09
2.35 2.18 0.18† 20.17
1.78 2.3720.28*** 0.15
1.02 1.6520.24*
0.10
0.85 1.6420.24*
0.09
1.19 1.54 0.44***20.30**
0.84 1.4620.16
0.13
0.15 0.38 0.33*** 20.16
0.08 0.3120.07
0.06
0.26 0.4420.02
0.01

Mean
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.

1

SD

14

Firm profitability decline rate
0.10 0.34 1.00
Firm revenue decline rate
20.32 1.91 0.17†
Industry median profitability
0.05 0.02 20.04
Industry revenue decline rate
20.14 0.25 20.03
Industry revenue recovery rate
0.31 1.12 0.08
CEO replacement
0.67 0.85 0.03
Largest shareholder concentration
0.26 0.33 20.08
Type of largest shareholder
0.70 0.46 20.21*
Early reorientation
1.34 2.27 20.20†
Late reorientation
1.35 1.97 20.08
Early acquisitions
1.12 2.21 20.21*
Late acquisitions
1.15 1.84 20.10
Early geographic market expansions 0.06 0.28 20.02
Late geographic market expansions
0.07 0.26 0.13
Early other growth initiatives
0.17 0.57 0.01
Late other growth initiatives
0.14 0.49 0.00

4

5

6

0.02
20.21*
0.02
0.01
0.06
20.29**
20.03
20.06
20.02
20.02

20.10 20.02
0.30** 0.14
20.08
0.03
0.08
0.09
20.05
0.07
0.27** 0.10
20.11 20.03
0.26*
0.00
0.17
0.05
0.17†
0.00

15

16

17

18

19

1.00
20.16
0.12
0.02
20.16
0.05
20.04
20.18†
20.03
20.10
20.04
20.29**
0.07
20.19†
0.01

1.00
20.03
0.02
0.11
0.01
20.26*
20.01
0.11
20.04
0.12
0.07
0.05
0.07
20.06

1.00
0.00
0.02
20.11
20.06
0.04
0.00
0.07
0.05
0.07
20.04
20.14
20.18†

1.00
20.08
20.04
0.03
20.06
20.08
20.06
20.08
0.00
20.05
20.02
20.1

1.00
20.03
0.01
0.04
0.03
0.04
0.07
0.09
20.13
20.01
20.09

7

8

20.06
0.04
0.30** 0.11
20.11
0.01
0.04
0.10
20.08
0.09
0.32** 0.06
20.11 20.09
0.22* 20.04
0.11
0.06
0.12 20.01

20

1.00
0.04
0.11
0.12
0.04
0.03
0.00
0.03
0.29**
0.35***

21

9

10

1.00
0.08 1.00
20.12 0.35***
0.09 0.96***
20.15 0.35**
20.10 0.23*
20.08 20.10
0.03 0.14
0.09 0.15

12

13

0.08
0.07 20.04 0.08
0.46*** 0.27**20.15 20.07
0.30** 0.01 20.03 20.03
0.34*** 0.13
0.04 20.14
0.29** 20.01
0.05 0.07
0.26*
0.23* 20.24* 0.04
0.13
0.03 20.15 20.06
0.11
0.04 20.03 0.05
0.13
0.05
0.20*20.28**
0.37*** 0.15 20.09 20.14

20.07 20.01
0.11
0.03
20.33*** 0.05
20.06 20.05
20.20*
0.05
0.20†
0.07
20.30** 0.01
0.08
0.12
20.06
0.03
20.01 20.03

22

11

23

24

25

1.00
0.31**
1.00
0.96*** 0.31**
1.00
0.09
0.12
0.06
0.09
20.12
20.02
0.14
20.11
0.14
0.36*** 0.13
0.13

26

1.00
0.22*
20.06
0.01

Table I. Continued
Mean
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.

27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
†

Extended reorientation
Early retrenchment
Late retrenchment
Early layoffs
Late layoffs
Early divestments
Late divestments
Early geographic market exits
Late geographic market exits
Extended retrenchment

Late geographic market expansions
Early other growth initiatives
Late other growth initiatives
Extended reorientation
Early retrenchment
Late retrenchment
Early layoffs
Late layoffs
Early divestments
Late divestments
Early geographic market exits
Late geographic market exits
Extended retrenchment

SD

14

15

0.48
2.18
2.37
1.65
1.64
1.54
1.46
0.38
0.31
0.44

20.11
0.20†
20.02
0.00
0.02
0.26**
20.05
0.06
20.05
0.16

20.06
20.07
20.08
20.14
0.02
0.03
20.09
0.09
20.29**
20.06

Mean

SD

27

28

29

30

31

0.07
0.17
0.14
0.34
2.35
1.78
1.02
0.85
1.19
0.84
0.15
0.08
0.26

0.26
0.57
0.49
0.48
2.18
2.37
1.65
1.64
1.54
1.46
0.38
0.31
0.44

1.00
20.01
20.08
0.13
0.01
20.01
20.13
20.10
0.18†
0.06
20.11
0.18†
0.02

1.00
0.07
0.29**
0.010
20.11
20.10
20.12
0.24*
20.03
0.08
20.08
20.13

1.00
0.25*
20.12
0.03
20.16
0.04
0.02
0.02
20.11
20.07
20.02

1.00
0.01
0.14
0.06
0.12
20.03
0.08
20.05
0.09
0.12

1.00
0.18†
0.68***
0.19†
0.65***
0.04
0.18†
0.16
0.54***

0.34
2.35
1.78
1.02
0.85
1.19
0.84
0.15
0.08
0.26

p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001.

16

17

20.06 20.03
20.20† 0.10
0.12
0.11
20.22* 0.19†
0.05
0.11
20.06 20.07
0.10
0.05
0.06
0.05
0.14
0.04
20.09
0.27**

18

19

20

20.06 0.05 0.14
0.08 0.14 20.15
0.02 20.01 20.14
0.18 0.01 20.12
0.09 0.04 20.15
20.08 0.15 20.05
20.07 20.05 20.05
20.01 0.12 20.11
20.02 20.05 20.03
20.06 0.07 20.15

32

21

22

20.14 0.54***
20.01 20.03
20.16 0.05
0.02 0.04
20.13 0.03
0.01 20.09
20.09 0.05
20.16 0.01
20.12 20.04
20.07 0.08

303

34

1.00
0.21*
1.00
0.77***
0.32**
1.00
0.01
20.10
20.09
0.71*** 20.07
0.11
0.09
20.07
0.07
0.24*
0.18†
0.07
0.56***
0.38**
0.45***

23

24

25

26

0.64*** 0.46*** 0.60***
20.06
20.04
20.03
0.13
0.09
0.13
0.01
0.07
0.08
0.10
0.07
0.11
20.08
20.13
20.12
0.07
0.07
0.07
20.04
0.00
0.00
0.10
20.03
0.11
20.01
0.12
20.01

35

1.00
0.12
0.08
0.03
0.32**

36

37

38

0.15
20.14
20.07
20.03
20.07
20.15
20.05
20.08
0.06
20.05

39

1.00
0.08
1.00
0.10
20.01 1.00
0.34***
0.15 0.30** 1.00

660

C. Tangpong et al.

years, the years after the early retrenchment actions (i.e., Years 3 and 4 of the analysis 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
Control variables
Log firm size
Diversification
Liquidity
Operating margin
Firm profitability decline rate
Firm revenue decline rate
Industry median profitability
Industry revenue decline rate
Industry revenue recovery rate
CEO replacement
Largest shareholder concentration
Type of largest shareholder
Early reorientation
Late reorientation
Extended reorientation
Extended retrenchment
Inverse Mills ratio
Constant

Model 1
(Bb)

Model 2
(Bb)

–0.06
(0.39)
0.18
(0.21)
–0.08
(0.12)
0.00
(0.00)
0.01
(0.01)
0.83†
(0.46)
0.14
(0.13)
–1.67
(1.08)
–0.37
(0.56)
0.46
(0.30)
–0.90
(1.06)
–0.25
(0.62)
–0.12
(0.13)
–0.28
(0.19)
1.47†
(0.85)
–0.43
(0.63)
–0.21
(0.54)
–0.43
(1.34)

–0.10
(0.46)
–0.01
(0.23)
–0.10
(0.14)
0.00
(0.01)
0.00
(0.01)
0.75†
(0.45)
0.30†
(0.16)
–2.10†
(1.18)
–0.49
(0.70)
0.33
(0.33)
–1.77
(1.73)
–0.37
(0.66)
–0.10
(0.15)
–0.27
(0.20)
1.75†
(0.94)
–0.23
(1.02)
–0.27
(0.61)
–0.69
(1.49)

Independent variables
Early retrenchment (Hypothesis 1)
Late retrenchment (Hypothesis 5)

C 2015 John Wiley & Sons Ltd and Society for the Advancement of Management Studies
V

0.40*
(0.19)
–0.39*
(0.16)

Temporal Approach to Retrenchment

661

Table II. Continued

DV: Turnaround success
Model summary
Chi-square
Pseudo R-squared (Cox and Snell)
Correct classification
22 log likelihood (Log L)
Da: 22log Lreduced model 2 (22Log Lfull
(N 5 96)

model)

Model 1
(Bb)

Model 2
(Bb)

25.11†
0.23
65.60%
107.98

40.23**
0.34
77.10%
92.85
15.13***

a

Improvement of goodness-of-fit.
Logistic regression coefficients.
Comparing with Control Model.
†
p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001.
b

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 calculated 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) actionbased 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
(0 5 no layoffs; 1 5 up to 5% of total employees; 2 5 6–10%; 3 5 11–25%; 4 5 26–
50%; and 5 5 more 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 retrenchment 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 (0 5 no divestment;
1 5 divestment value up to 5% of total assets; 2 5 6–10%; 3 5 11–25%; 4 5 more
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 (1 5 firm engaged in exits; 0 5 otherwise), given that firms typically do
not report the potential magnitudes of geographic market exits relative to total sales,
C 2015 John Wiley & Sons Ltd and Society for the Advancement of Management Studies
V

662

C. Tangpong et al.

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

DV: Turnaround success
Control variables
Log firm size
Diversification
Liquidity
Operating margin
Firm profitability decline rate
Firm revenue decline rate
Industry median profitability
Industry revenue decline rate
Industry revenue recovery rate
CEO replacement
Largest shareholder concentration
Type of largest shareholder
Early acquisition
Late acquisition
Early geographic market expansion
Late geographic market expansion
Early other growth initiative
Late other growth initiative
Extended reorientation
Extended retrenchment
Inverse Mills ratio
Constant

Model 1
(Bb)

Model 2
(Bb)

Model 3
(Bb)

Model 4
(Bb)

Model 5
(Bb)

–0.04
(0.41)
0.14
(0.22)
–0.07
(0.14)
0.00
(0.01)
0.01
(0.01)
0.88†
(0.49)
0.12
(0.14)
–1.77
(1.14)
–0.30
(0.45)
0.58†
(0.31)
–1.46
(1.38)
–0.47
(0.65)
–0.05
(0.14)
–0.36
(0.23)
–1.06
(1.10)
2.39†
(1.45)
0.13
(0.56)
–0.12
(0.54)
1.46
(0.95)
–0.57
(0.67)
–0.19
(0.58)
–0.23
(1.44)

0.59
(0.50)
0.12
(0.23)
–0.05
(0.13)
0.00
(0.00)
0.00
(0.01)
0.73
(0.45)
0.20
(0.16)
–1.84
(1.18)
–0.08
(0.53)
0.67†
(0.34)
–2.43
(1.83)
–0.58
(0.68)
–0.13
(0.15)
–0.43†
(0.24)
–1.49
(1.18)
2.18
(1.44)
–0.50
(0.63)
–0.44
(0.60)
2.55*
(1.22)
0.40
(0.81)
–0.06
(0.63)
–1.49
(1.66)

0.10
(0.51)
–0.09
(0.27)
–0.14
(0.16)
0.00
(0.00)
0.00
(0.01)
0.73†
(0.41)
0.34†
(0.19)
–2.03
(1.32)
–0.86
(1.31)
0.22
(0.37)
–2.92
(2.27)
–0.41
(0.78)
–0.04
(0.16)
–0.38
(0.28)
–0.81
(1.32)
2.00
(1.91)
–1.66*
(0.84)
–0.60
(0.71)
2.54*
(1.21)
–1.87†
(0.10)
–0.56
(0.66)
–0.89
(1.83)

–0.32
(0.45)
0.08
(0.23)
–0.04
(0.15)
0.00
(0.01)
0.01
(0.01)
0.51
(0.49)
0.06
(0.16)
–1.96
(1.24)
–0.72
(1.23)
0.60†
(0.34)
–0.85
(1.20)
0.11
(0.73)
–0.21
(0.17)
–0.30
(0.24)
–1.21
(1.21)
2.78†
(1.55)
–0.44
(0.86)
–0.05
(0.55)
2.05*
(1.01)
–0.68
(0.76)
0.05
(0.63)
–0.34
(1.58)

–1.02
(0.76)
–0.61
(0.50)
0.10
(0.22)
0.00
(0.01)
0.01
(0.02)
0.55
(0.51)
0.36
(0.26)
–3.83
(2.33)
–2.30
(3.03)
–0.48
(0.62)
0.11
(2.55)
–0.34
(1.22)
–0.17
(0.60)
–1.15†
(0.59)
–3.49
(2.93)
5.56†
(2.91)
–4.60*
(1.82)
–0.33
(0.91)
6.18*
(2.44)
–2.59
(2.43)
0.84
(1.26)
–1.93
(2.44)

C 2015 John Wiley & Sons Ltd and Society for the Advancement of Management Studies
V

Temporal Approach to Retrenchment

663

Table III. Continued
Model 1
(Bb)

DV: Turnaround success
Independent variables
Early layoff (Hypothesis 1a)

Model 2
(Bb)

Model 3
(Bb)

Early divestment (Hypothesis 1b)

Early geographic market exit (Hypothesis 1c)
Late geographic market exit (Hypothesis 5c)

b
c
†

3.63**
(1.39)
–0.85
(1.41)

–0.21
(0.49)
–0.80
(0.55)
2.66**
(0.99)
–1.13**
(0.42)
8.84*
(3.55)
–7.05†
(3.72)

43.08**
0.36
79.20%
90.00
13.04c,f

85.42***
0.59
89.60%
47.66
55.38c,g

1.46***
(0.42)
–0.46*
(0.23)

Late divestment (Hypothesis 5b)

a

Model 5
(Bb)

–0.40
(0.25)
–0.56*
(0.24)

Late layoff (Hypothesis 5a)

Model summary
Chi-square
Pseudo R-squared (Cox and Snell)
Correct classification
22 log likelihood (Log L)
Da: 22log Lreduced model 2 (22Log Lfull
(N 5 96)

Model 4
(Bb)

30.05†
0.27
66.70%
103.04
model)

40.17*
0.34
72.90%
92.92
10.12c,f

56.82***
0.45
82.30%
76.27
26.77c,g

Improvement of goodness-of-fit.
Logistic regression coefficients.
Comparing with Control Model.
p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001.

assets, or markets. This binary measure was also used in Barker and Duhaime’s (1997)
study. Accordingly, we coded 1 for early geographic market exit for 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 (r 5 0.92–0.99, p < 0.001). The coders’ disagreements
were then resolved through further discussion and recoding efforts.
Control variables. We controlled for several managerial-, organizational-, and industrylevel 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 logtransformed for poten