1. technology and computing

Strategic Management Journal
Strat. Mgmt. J., 29: 593–616 (2008)
Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/smj.676
Received 4 June 2006; Final revision received 4 December 2007
LEARNING HOW TO RESTRUCTURE: ABSORPTIVE
CAPACITY AND IMPROVISATIONAL VIEWS OF
RESTRUCTURING ACTIONS AND PERFORMANCE
DONALD D. BERGH1 * and ELIZABETH NGAH-KIING LIM2
1
Daniels College of Business, Department of Management, The University of Denver,
Denver, Colorado, U.S.A.
2
School of Business, Department of Management, The University of Connecticut,
Storrs, Connecticut, U.S.A.
This paper examines the role of learning in corporate restructuring. Drawing from two viewpoints
of organizational learning, absorptive capacity and organizational improvisation, we examine
whether experience with corporate restructuring modes (sell-offs, spin-offs) influences subsequent restructuring and financial performance. Consistent with an absorptive capacity view,
cumulative and repetitive experience with sell-offs was related to the adoption of an ensuing selloff and to higher performance. Conversely, and consistent with an organizational improvisation
view, short-term and contemporaneous experience with spin-offs was related to the subsequent
use of spin-offs and to increases in financial performance. The findings contribute to a dynamic
explanation of corporate restructuring and its influence on financial performance, illustrate differences between learning in a repetitive situation and learning when repetition is rare, and
indicate when absorptive capacity and organizational improvisational views are most profitable.
Overall, these findings show that different kinds of restructuring experiences were associated with
different modes of restructuring and performance records. Considered collectively, the organizational learning perspective offers insights into why some corporate restructuring strategies
appear as intentional and deliberate actions while others resemble more spontaneous and simultaneous responses. Copyright  2008 John Wiley & Sons, Ltd.
INTRODUCTION
Corporate restructuring involves divesting, spinning off assets, and exiting business lines (Bowman and Singh, 1993; Johnson, 1996; Ravenscraft
and Scherer, 1987). These actions are expensive,
visible, and risky (Bergh, 1997; Gaughan, 1999;
Hoskisson and Hitt, 1994). When making decisions
about such events, managers would likely consider their organizations’ restructuring histories, as
Keywords: corporate restructuring; organizational learning; absorptive capacity; organizational improvisation;
spin-off; divestiture
∗
Correspondence to: Donald D. Bergh, Daniels College of Business, Department of Management, The University of Denver,
2101 S. University Boulevard, Denver, CO 80208, U.S.A.
E-mail: [email protected]
Copyright  2008 John Wiley & Sons, Ltd.
prior experiences could be drawn upon to reduce
mistakes, improve decision making, and lower
stakeholder anxieties (e.g., Allen, 1998; Donaldson, 1990). However, we have little knowledge
of whether and how experience might matter
to the restructuring decision; most research on
experience and corporate strategic behaviors has
focused on growth alternatives such as mergers,
acquisitions, and alliances (Amburgey and Miner,
1992; Barkema and Vermeulen, 1998; Chang and
Rosenzweig, 2001; Vermeulen and Barkema, 2001;
Zollo, Reuer, and Singh, 2002). Does experience
apply to restructuring decisions? If so, does it also
influence profitability?
Current understanding of the antecedents to and
implications of restructuring is still in the developmental stages. Most research to date has portrayed
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D. D. Bergh and E. N.-K. Lim
restructuring as a purposeful response to governance, strategy, and industry pressures (Brauer,
2006; Bruner, 2004; Donaldson, 1990). For example, some have argued that firms use restructuring
to improve internal efficiency in response to active
takeover markets (Jensen, 1993; Kaplan and Weisbach, 1992; Shleifer and Vishny, 1991). Others
have posited that shifts from weak to strong internal governance led to the use of restructuring to
refocus corporate strategies (Chatterjee, Harrison,
and Bergh, 2003; Hoskisson, Johnson, and Moesel, 1994; Johnson, Hoskisson, and Hitt, 1993).
Another explanation proposes that restructuring
reverses excessively diversified strategies to more
optimal levels (Bergh and Lawless, 1998; Comment and Jarrell, 1995; Jones and Hill, 1988;
Markides, 1992, 1995), and reduces information
asymmetries between managers and owners
(Bergh, Johnson, and DeWitt, 2008; Krishnaswami
and Subramaniam, 1999). Furthermore, some have
argued that restructuring discards unwanted parts
of acquired asset bundles (Capron, Mitchell, and
Swaminathan, 2001; Chang, 1996; Chang and
Singh, 1999). Finally, theorizing from industrial
organization economics has been used to link
industry characteristics to corporate restructuring
(Ilmakunnas and Topi, 1999; Harrigan, 1982). Few
studies have considered the effects of experience
on restructuring actions (e.g., Allen, 1998; Villalonga and McGahan, 2005), and even fewer
examine how such experience might influence
post-restructuring financial performance. Considered collectively, previous research provides insight into the reasons for and effects of restructuring, yet provides a limited explanatory framework
for understanding how prior restructuring experiences might influence subsequent actions and performances. We currently have incomplete knowledge of whether and how experience matters with
corporate restructuring.
The present study develops and tests a theoretical model that relates experience to restructuring
and to subsequent financial performance. Specifically, the model draws on two viewpoints of organizational learning to link restructuring experience
heterogeneity to the adoption of different forms
or modes of corporate restructuring actions. First,
the model applies absorptive capacity arguments to
relate cumulative and repetitive restructuring experience to the development of explicit knowledge
that serves as the basis for routines and standardized procedures that, in turn, help facilitate
Copyright  2008 John Wiley & Sons, Ltd.
efficient and economically beneficial restructuring
by sell-off. Second, the model uses the organizational improvisation viewpoint to tie short-term,
recent, and real-time experience heterogeneity to
the development of tacit knowledge that leads to
restructuring by spin-off. The different restructuring experiences, development of knowledge
stocks, and subsequent use of sell-off and spin-offs
are then related to post-restructuring financial performance. The study tests these alternative explanations and restructuring behaviors using different
time intervals.
The findings suggest several contributions. They
extend explanations of corporate restructuring by
integrating a dynamic construct—experience—
into the restructuring process and to post-restructuring performance. In addition, by incorporating
absorptive capacity and organizational improvisation into an organizational learning perspective
of how firms restructure, the findings support a
more expansive and comprehensive model of corporate restructuring and how it influences performance. Furthermore, the results integrate different approaches to corporate restructuring; some
actions, particularly sell-offs, appear to reflect
deliberate and intentional responses while others, especially spin-offs, reflect more of a spontaneous and contemporaneous adaptation. The findings contribute to the organizational learning perspective by offering insight into the absorptive
capacity and organizational improvisation perspectives using different methods and conditions, by
illustrating differences between learning in a repetitive situation and in a setting when repetition is
rare, and by indicating a set of conditions when
absorptive capacity and organizational improvisational views are most profitable relative to one
other.
THEORY AND HYPOTHESES
The theoretical model applies arguments from
two viewpoints of organizational learning, absorptive capacity and organizational improvisation,
to explain how restructuring experience could
influence mode adoption and post-restructuring
performance. An organizational learning model is
particularly appropriate because it provides a theoretical rationale for linking experience to actions
and outcomes. More specifically, learning has been
defined as a systematic change in behavior or
Strat. Mgmt. J., 29: 593–616 (2008)
DOI: 10.1002/smj
Learning How to Restructure
595
Absorptive capacity
. Repetition
. Explicit knowledge
. Routines
Restructuring mode
. Sell-off
. Spin-off
Performance
. Accounting performance
. Market performance
Organizational improvisation
. Real-time, novel
. Experience
heterogeneity
. Tacit knowledge
Figure 1.
Theoretical model
knowledge informed by experience (Cyert and
March, 1963; Levitt and March, 1988). Learning is believed to occur when ‘experience generates a systematic change in behavior or knowledge’ (Miner, Bassoff, and Moorman, 2001: 315).
The viewpoints of absorptive capacity and organizational improvisation represent opposite learning explanations (e.g., Levinthal and Rerup, 2006;
Miner et al., 2001; Winter, 2003) and provide a
complementary and integrative basis for hypothesizing how experience may influence restructuring
actions and their effects on financial performance.
We begin by reviewing the two most prevalent
restructuring modes. Then, we present the theoretical logic of the two viewpoints of learning, apply
the arguments to restructuring actions, and ultimately to post-restructuring financial performance.
Figure 1 presents our theoretical model and indicates the hypothesized relationships.
Sell-offs and spin-offs as alternative
restructuring modes
Corporate restructuring is generally used to downscope, downsize, or refocus diversification strategy
(Hoskisson and Hitt, 1994; Johnson, 1996), and
is conducted through a variety of alternatives or
modes including liquidations, sell-offs, spin-offs,
and equity carve-outs (Bruner, 2004; Gaughan,
1999). The most popular modes for implementing
restructuring are the sell-off and spin-off (Khan
and Mehta, 1996; Nixon, Roenfeldt, and Sicherman, 2000). A sell-off, also known as a divestiture, arises when assets are sold from one firm
to another in exchange for cash and/or securities (Hite, Owers, and Rogers, 1987; Jain, 1985;
Rosenfeld, 1984). A spin-off occurs when a firm
‘distributes on a pro rata basis all the shares it owns
Copyright  2008 John Wiley & Sons, Ltd.
in a subsidiary to its own shareholders’ (Weston,
Chung, and Hoag, 1990: 224; Miles and Rosenfeld,
1983; Schipper and Smith, 1983), and in the process creates a separate, publicly traded firm from
the spun-off assets.
Sell-offs and spin-offs represent substantially
different ways to restructure. Sell-offs are used
to transfer assets to other firms that might realize higher value from their acquisition or to rid
the selling firm of assets that interfere with its
operations or strategy (John and Ofek, 1995). In
most cases, sold-off assets were not performing
well, were not creating value that met expectations,
or were used to raise proceeds that could be utilized to pay down debt and/or be reinvested in the
restructuring firm’s strategy (Bergh, 1997; Brauer,
2006; Duhaime and Grant, 1984; Hoskisson et al.,
1994; Taylor, 1988). Sell-offs involve a liquidation process, decisions about how the restructured
assets will be marketed, and how the transaction
will be managed. Investment banks often manage these processes: they locate buyers, arrange
financing, and manage the exchange of the soldoff assets. Sell-offs usually involve assets residing in secondary and unrelated businesses relative
to the primary and core lines of the parent firm
(Bergh, 1995a; Bergh et al., 2008; Comment and
Jarrell, 1995; Ravenscraft and Scherer, 1987). The
sell-off ends with the transfer of asset property
rights (Alexander, Benson, and Kampmeyer, 1984;
Donaldson, 1990).
By contrast, spin-offs are typically used to separate assets that have promising and high growth
potential opportunities that cannot be realized
within the parent firms’ structure (Aron, 1991;
Bruner, 2004), oftentimes while maintaining postrestructuring relationships with the parent firm
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D. D. Bergh and E. N.-K. Lim
(Ito, 1995; Kudla and McInish, 1984). They reorganize ownership among existing shareholders,
produce no cash proceeds, and reduce the level
of assets under the control of the parent management. The spun-off assets become independent from the parent and require a new corporate governance system, including leadership and
directory boards (Seward and Walsh, 1996; Walsh
and Seward, 1990). In addition, spin-offs often
involve assets residing in or related to the restructuring firm’s core business lines (Bergh et al.,
2008; Nixon et al., 2000) and employ internal control systems that emphasize interface management,
which coordinate ongoing strategic and organizational relationships after the restructuring has been
completed (cf., Aron, 1991; Ito, 1995). For example, when PepsiCo spun-off its fast food division,
Tricon (consisting of KFC, Pizza Hut, and Taco
Bell), long-term contracts were designed to sustain
continuing value-creating interactions among the
firms. One such contract involved the assignment
of soda fountain property rights and obligations.
In this case, some Tricon firms were required to
agree to continue to exclusively sell PepsiCo soda
fountain products before the spin-off was finalized.
Overall, sell-offs and spin-offs have unique
motives and are used under different circumstances
(Bergh et al., 2008; Bruner, 2004; Nixon et al.,
2000). These dissimilarities provide conditions and
opportunities for managers to develop specific and
unique knowledge about how to formulate and execute each type of restructuring mode. The implications for learning and adoption of sell-offs and
spin-offs are considered next. Two different viewpoints of organizational learning, absorptive capacity, and organizational improvisation are related
individually to the restructuring actions.
Absorptive capacity, sell-offs, and financial
performance
One research stream in the organization learning
literature posits that experience creates knowledge
that can be stored into and retrieved from an
organization’s memory (Huber, 1991; Levitt and
March, 1988; Fiol and Lyles, 1985). Managers and
their firms have an ability to recognize the value
of new knowledge, assimilate it, and apply it to
commercial ends, a learning viewpoint known as
absorptive capacity (Cohen and Levinthal, 1990;
see Lane, Koka, and Pathak, 2006; and Zahra and
Copyright  2008 John Wiley & Sons, Ltd.
George, 2002, for expanded definitions). Absorptive capacity is a function of prior organizational problem solving (Lane, Salk, and Lyles,
2001), and is developed through the accumulation of experiences (Cohen and Levinthal, 1990;
Lane and Lubatkin, 1998; Pennings and Harianto,
1992). Specifically, the underlying premise of
absorptive capacity ‘is that the organization needs
prior related knowledge to assimilate and use
new knowledge. . .accumulated prior knowledge
increases both the ability to put new knowledge
into memory. . .and the ability to recall and use it’
(Cohen and Levinthal, 1990: 129). The absorptive
capacity view assumes that learning is cumulative and learning performance is highest when the
object of learning is related to what is already
known (Cohen and Levinthal, 1990; Lane et al.,
2006; Zahra and George, 2002). Moreover, absorptive capacity also includes an organization’s ability
to exploit information. From this view, a firm’s
absorptive capacity is influenced through its level
of prior related knowledge, repetition, and intensity of its exposures to similar events (Kim, 1998;
Vermeulen and Barkema, 2001; Zahra and George,
2002).
Firms draw from absorptive capacity to create
explicit knowledge that can be developed, codified,
and applied to improve decision making, revamp
knowledge stocks, and overcome traps to knowledge development (Lane et al., 2006; Lane and
Lubatkin, 1998; Zahra and George, 2002). Cumulative experiences would be translated into explicit
knowledge that would guide organizational actions
and behaviors (Amburgey, Kelly, and Barnett,
1993; Haleblian, Kim, and Rajagopalan, 2006;
Shaver, Mitchell, and Yeung, 1997). Once developed, learning is then made explicit in operating procedures, formalized systems, and routines (Haleblian and Finkelstein, 1999; Cyert and
March, 1963; March and Sevon, 1984). In addition, new search rules evolve slowly (Chang and
Rosenzweig, 2001; Cyert and March, 1963; Miller
and Friesen, 1980), so organizations tend to persist
in the same activity over time (Miller and Friesen,
1980, 1982). Overall, the creation and maintenance
of absorptive capacity is an iterative and repetitive process where firms learn from experiences,
make inferences, and store knowledge that can be
codified and applied to future decisions (Cohen
and Levinthal, 1990; Hayward, 2002; Zahra and
George, 2002). Hence, past experience can lead
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Learning How to Restructure
to both greater absorptive capacity and greater
learning.
The absorptive capacity view of organization
learning has been used to help explain growth
and expansionary behaviors such as mergers and
acquisitions (M&As). For example, when a firm
engages in an M&A, it develops absorptive capacity for understanding that action (Barkema and
Vermeulen, 1998; Baum, Li, and Usher, 2000;
Haleblian and Finkelstein, 1999). Additional
M&As of the same type allow ‘competencies to be
refined, which [subsequently] increases the likelihood of even more acquisitions of the same type’
(Amburgey and Miner, 1992: 336). This experience creates absorptive capacity that in turn allows
firms to learn how to become more efficient at
clearly defined problems, such as those involving
corporate strategy behaviors such as acquisitions
and alliances (e.g., Hayward, 2002; Villalonga and
McGahan, 2005). In addition, after accumulating
knowledge with a particular M&A activity, managers tend not to welcome new risks associated
with using a different type (Nelson and Winter, 1982; Pennings, Barkema, and Douma, 1994).
Even negative consequences associated with prior
experiences may not cause managers to change;
indeed, poor performance may not deter the repetitive learning that can be ascribed to experience (Amburgey and Miner, 1992; March, 1991).
Instead, M&A performance problems may be attributed to execution issues rather than evidence of
mistaken actions or problems in learning (Haleblian et al., 2006).
This reasoning can be extended to explaining
exiting behaviors such as corporate restructuring
and how managers might select between restructuring modes (Figure 1). Experience with restructuring could create absorptive capacity that would
facilitate knowledge development that would be
explicitly codified into systems, routines, and procedures that could help guide future behaviors.
Repetition would also create momentum to repeat
the same type of restructurings used in the past
(Amburgey and Miner, 1992; Baum et al., 2000;
Villalonga and McGahan, 2005). Furthermore, corporate restructuring is a vehicle for realizing an
objective (Bowman and Singh, 1993; Johnson,
1996; Markides, 1992, 1995), so managers would
likely focus on the desired outcome and might not
want to reconsider how to restructure each time
such an action was necessary. Managers would
have incentives to exploit organizational memory
Copyright  2008 John Wiley & Sons, Ltd.
597
and absorptive capacity, and to make their decision and move forward as efficiently as possible to
accomplish the objective of the restructuring. Past
experiences lead to greater absorptive capacity and
learning that would likely apply to restructuring
decisions.
The absorptive capacity view of learning pertains more to sell-offs than spin-offs because selloffs provide more of the conditions necessary
for accumulating experience benefits, assimilating
knowledge, and developing the explicit knowledge
that can be codified into routines and standardized
procedures (Figure 1). First, the process for selling
assets involves more stages, parties, and decisions
than spin-offs, thus presenting greater opportunities for identifying and realizing economies from
repetition. For example, sell-offs involve the validation, liquidation, and replacement of assets, marketing, and management of transaction costs associated with searching, negotiating, and exchanging
the assets with an external third-party buyer. Spinoffs do not require asset liquidation, replacement,
or marketing, and involve parties affiliated with
the restructuring firm, where the transaction occurs
through a reorganization plan involving established
relationships (Kudla and McInish, 1984). The selloff process involves more stages and complexities
than the one used for spin-off, creating greater
potential for experience and learning curve benefits
(cf. Lieberman, 1987, 1989).
Second, the control systems used to manage
the assets in sell-offs would facilitate the communication necessary to create absorptive capacity.
Specifically, and as noted above, sell-offs typically
involve assets in unrelated business units and lines
(Bergh, 1995a; Bergh et al., 2008; Nixon et al.,
2000; Ravenscraft and Scherer, 1987). These assets
are typically managed with arms-length financial
controls and profit center accounting techniques
that focus on general and objective measures such
as return on assets, market shares, and profit
margins (Hill, Hitt, and Hoskisson, 1992; Hill
and Hoskisson, 1987; Jones and Hill, 1988). The
use of observable measures create transparencies
that enhance interpretation, communication, and
knowledge transfer (Szulanski, 1996), while also
providing managers with a clear understanding of
the factors that influence the performance of the
restructuring transaction. In addition, with greater
transparency, absorptive capacity is increased and
more learning can occur (Lane et al., 2001). Managers would have a higher quality knowledge
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D. D. Bergh and E. N.-K. Lim
basis for drawing correct inferences about the
restructuring process and better understanding for
future decisions (Hayward, 2002; cf. Haleblian and
Finkelstein, 1999), both of which contribute to
absorptive capacity.
Spin-offs, by contrast, tend to involve idiosyncratic and related business assets and are structured on a case-by-case basis (Bergh et al., 2008;
Ito, 1995; Nixon et al., 2000). The restructuring process for spin-offs would be more variable
than that used by sell-offs, making it difficult to
develop experience and repetition benefits, absorptive capacity, and the explicit knowledge that leads
to routines and systematized procedures that can
be leveraged for learning curve economies. Moreover, spun-off firms are more likely to have close
relationships with their restructuring firms. These
associations are managed using controls that focus
on interface management and sharing and accounting of specialized resources, and ambiguity can
exist about the communication of knowledge sharing and transfer between the two firms (Khan and
Mehta, 1996; Kudla and McInish, 1984). These
characteristics impede the benefits of experience,
reduce the potential for developing absorptive
capacity, lower the development of the procedures
needed for standardization, and constrain the economizing potential associated with repeated actions
(Cohen and Levinthal, 1990; Szulanski, 1996).
Hence, the benefits and learning from long-term
experience and absorptive capacity are likely to
apply more to sell-offs than to spin-offs.
In sum, greater experience with sell-offs would
lead to higher absorptive capacity and to the development of explicit knowledge that can be codified
into routines and standardized procedures. Managers would exploit absorptive capacity by applying the learning from past experience in similar
choices to reduce risks in subsequent decisions
(Chang and Rosenzweig, 2001). As firms therefore
gain experience with restructuring by sell-offs, we
predict that they will continue to use those methods. Changing to spin-offs poses additional costs.
We hypothesize:
Hypothesis 1: As firms gain experience with selloffs, they will continue to use sell-offs as a form
of corporate restructuring strategy.
The absorptive capacity view of organization
learning may also help explain post-restructuring
financial performance (Figure 1). Experience with
Copyright  2008 John Wiley & Sons, Ltd.
corporate restructuring would increase absorptive
capacity and knowledge (Hayward, 2002; Pennings et al., 1994; Zollo and Singh, 2004), which
would provide a basis for more effective management. With subsequent increases in absorptive
capacity there would likely be fewer errors, the
development of specialized and standardized routines, and increased execution effectiveness (Ahuja
and Katila, 2001; Levinthal and March, 1993). In
addition, accumulating absorptive capacity in one
period will permit its more efficient exploitation in
the next (Cohen and Levinthal, 1990). It follows
that firms with greater absorptive capacity gained
through prior experience will have a better foundation to create knowledge, assimilate and interpret
opportunities, and more effectively develop and
apply explicit knowledge than firms with less experience.
Furthermore, the potential to develop absorptive capacity is critical; firms with higher levels
of experience can better ‘refine, extend, and leverage existing competencies or. . .create new ones by
incorporating acquired and transformed knowledge
into [their] operations’ (Zahra and George, 2002:
190) than firms with lower levels of experience.
Firms having more experience and higher absorptive capacity would be able to use their resources
more effectively and leverage their greater ability
to transform experience benefits than firms with
less. Absorptive capacity has been conceptualized
as a dynamic capability that can lead to competitive advantage and above-normal performance
returns (Narasimhan, Rajiv, and Dutta, 2006; Winter, 2003; Zahra and George, 2002; Zollo et al.,
2002).
Experiences with restructuring would help
reduce process costs and competency traps. Moreover, by reducing costs associated with the processes and activities of assimilating and integrating
newly acquired information, firms having more
experience with restructuring are likely to have
higher post-restructuring performance than those
having less. This logic likely applies more closely
to sell-offs because they have higher potential for
absorptive capacity than spin-offs. Sell-offs have
the potential for standardization of the restructuring process, are managed with financial control systems that present lower barriers to internal knowledge transfer, and offer conditions more
favorable for generalization—none of which can
be as easily realized by spin-offs. Firms having
more experience with sell-offs can draw on their
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Learning How to Restructure
absorptive capacity to develop explicit knowledge
to codify routines and standardized procedures that
can economically and advantageously guide the
deal-making process, terms, and governance process. That experience translates into higher absorptive capacity and knowledge to apply to subsequent
sell-offs.
By contrast, firms with low numbers of prior
sell-offs have lower absorptive capacity, less
knowledge to exploit, and are more prone to procedural errors that can lead to disadvantageous
situations. Firms with less experience are lower
on the learning curve and do not have the explicit
knowledge to develop economically valuable routines and standardized procedures. These firms are
also likely to be less effective at managing the
processes of developing and exploiting new knowledge. They stand to gain less financial benefits. We
predict that those with the most repetition over
time with sell-offs will have standardized procedures and routines for better formulating and executing the sell-offs resulting in higher performance.
Hypothesis 2: Firms that have more experience
with sell-offs will have higher financial performance after a subsequent sell-off than firms that
have less experience with sell-offs.
Improvisational learning, spin-offs, and
financial performance
Another research stream in the organizational
learning literature describes how learning can
occur in short-term, recent, and real-time settings.
Based on observing musicians, actors, firefighters, and new product development teams (Berliner,
1994; Brown and Eisenhardt, 1995; Vera and
Crossan, 2005; Weick, 1993), a much different
conception of organizational learning known as
organizational improvisation is emerging, one that
applies to settings where planning models and
prior and repetitive experiences play a smaller role.
Some actions can occur without advanced planning or long-term experience (Cohen, March, and
Olsen, 1972; Cyert and March, 1963; Moorman
and Miner, 1998b), and the conception and logic of
organizational improvisation has been developed
in an effort to help explain learning in such settings (Crossan et al., 2005; Eisenhardt and Tabrizi,
1995; Hatch, 1997; Moorman and Miner, 1998a,
1998b). Organizational improvisation is a type of
short-term learning, where experience and related
Copyright  2008 John Wiley & Sons, Ltd.
599
change occur at or near the same time (Miner et al.,
2001; Vera and Crossan, 2005). It has been presented as a form of learning on the basis that experience heterogeneity and recombinations of stored
knowledge, routines, and skills can lead to systematic changes in behavior (Hatch, 1997; Miner
et al., 2001; Moorman and Miner, 1998a).
Improvisation has several characteristics that
distinguish it from other learning views (Miner
et al., 2001). First, it has a reduced temporal
gap between the planning and implementation of
unique actions; the more temporally proximate the
design and execution of a behavior, the more likely
the action is improvisational (Crossan et al., 2005,
1996; Moorman and Miner, 1998a, 1998b). Improvisation has been described as spontaneous and
‘in-the-present’ (Crossan et al., 2005: 131), and
has been used to explain how managers resolve
a surprising problem and/or create value from an
unexpected opportunity (Weick, 1996, 2001). It is
not the repeating of a preexisting routine, nor is
it pre-designed or standardized. Second, it applies
to actions and decisions that are novel, or deviations from standard practices, and improvisers
draw from information and resources available to
them at the time the decision is necessary, also
known as bricolage (Levi-Strauss, 1967). Improvisation is tailored to specific contexts and idiosyncratic to a time and place (Baker and Nelson, 2005;
Miner et al., 2001; Vera and Crossan, 2005). Third,
organizational improvisation pertains to fast and
uncertain decisions (Brown and Eisenhardt, 1995;
Moorman and Miner, 1998b; Vera and Crossan,
2005), requiring managers to draw from organizational memory, or ‘stored information from an
organization’s history that can be brought to bear
on present decisions’ (Walsh and Rivera, 1991:
61). Improvisation draws from experience heterogeneity and creatively recombining and applying
learned routines and knowledge (Weick, 1993;
Hatch, 1997; Miner et al., 2001). Greater memory
enhances improvisation because it allows decision
makers to apply retrospectives and real-time information to unanticipated situations (Crossan et al.,
2005; Weick, 1998; Weick and Roberts, 1993).
Organizational memory is a product of two parts:
procedural memory and declarative memory. Procedural memory is ‘how things are done’ (Cohen
and Bacdayan, 1994: 554) and ‘things you can
do’ (Berliner, 1994: 102). It is derived from rich
understanding of skills and routines, and a welldeveloped procedural memory allows managers to
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draw from declarative memory, or the ‘memory
for facts, events, or propositions’ (Cohen, 1991:
137; Tippins and Sohi, 2003). When managers can
combine well-developed procedural memory with
declarative memory, they are likely to have higher
tacit knowledge of how their organizations operate
(Cohen, 1991; Cohen and Bacdayan, 1994; Winter, 1987), and can make improvisational decisions
that are more coherent, novel, and timely (Moorman and Miner, 1998a).
For example, Moorman and Miner (1998a)
invoke the instance of a jazz musician being
able to improvise when he/she has developed
a large repertoire of relevant musical experiences, while Weick (1993) describes a master
bricoleur as requiring preexisting routines to create a new tool to solve a novel problem. Weick
(2001) goes another step by describing improvisation as ‘just-in-time strategy’ that is predicated less by investment in anticipation and more
on the development of general knowledge, large
skill sets, an ability to react quickly, and trust
in intuition (Weick, 2001: 352). Winter (2003)
notes that organizations can be ‘pushed into “firefighting” mode, a high-paced, contingent, opportunistic and perhaps creative search for satisfactory alternative behaviors. . .[where] problem solving is not routine. . .not highly patterned and not
repetitious. . .it typically appears as a response
to novel challenges. . .or other relatively unpredictable events. . .[and] typically arises from
a foundation of patterned and practiced
performance. . .’ (Winter, 2003: 992–993). Hence,
improvisation requires tacit knowledge gained
from having procedural memory of routines and
skills that can access the declarative memory of the
organization’s key knowledge stocks (Moorman
and Miner, 1998a; Miner et al., 2001). Higher levels of tacit knowledge facilitate improvisation by
materially reducing the time between the composition of a solution to a problem and the execution
of the action to affect it.
Organizational improvisation may describe corporate restructuring actions (Figure 1). Most theoretical perspectives depict restructuring as a response behavior (see Brauer, 2006; Haynes, Thompson, and Wright, 2003; Johnson, 1996, for reviews), used for quickly improving the firm’s economic and strategic conditions (Hoskisson and
Hitt, 1994; Hoskisson, Johnson and Moesel, 1994;
Markides, 1992). It is often driven by uncertainty
(Bergh, 1998; Bergh and Lawless, 1998; Leiblein
Copyright  2008 John Wiley & Sons, Ltd.
and Miller, 2003), and can be a punctuated and fast
action rather than a continuous and evolving process (e.g., Donaldson, 1990; Hoskisson and Hitt,
1994; Ravenscraft and Scherer, 1987). Improvisation may apply to such actions, as ‘under conditions of time pressures and/or uncertainty, a planning orientation is insufficient. . .[and] [i]improvisation becomes an alternative or complementary
orientation’ (Crossan et al., 2005: 133).
In addition, restructuring tends to occur when
needed, so the time interval between restructuring actions may be too long or too short to allow
for absorptive capacity to be developed and sustained. Very long intervals make it difficult for
managers to remember or apply the lessons and
routines from prior experience, while short ones
do not provide ample time for repetitive learning to occur (Argote, Beckman, and Epple, 1990;
Baum and Ginsberg, 1997). Moreover, managers
may be reluctant to codify learning and generate
inferences from activities that they do not expect
to repeat (Szulanski, 1996; Winter and Szulanski, 2001). Furthermore, a firm’s post-restructuring
performance can vary (Cusatis, Miles, and Woolridge, 1993; Desai and Jain, 1999; Daley, Mehrotra, and Sivakumar, 1997), which could influence
the intensity with which managers search for inferences from prior experiences (Hayward, 2002;
Levinthal and March, 1993). Finally, restructurings that occurred in recent years are frequently
integrated with ensuing actions that collectively
represent a strategic reaction to resolving the matter that drove the restructuring (Donaldson, 1990;
Hoskisson and Hitt, 1994; Hoskisson et al., 1994).
Restructurings in more distant years may not be as
pertinent or applicable. Even numerous restructurings spread over several prior years could play a
weaker role, because they may not be part of the
response that was created for resolving the motivating factors. Hence, some restructurings may not
offer the conditions for constructing the routines
and repetition necessary for developing absorptive
capacity and may be influenced more by short-term
and real-time experience that draws from stored
knowledge, learned routines, and skills.
The improvisation logic likely applies more to
restructuring through spin-offs rather than sell-offs.
First, spin-offs typically occur infrequently (e.g.,
Donaldson, 1990), and are less numerous than
sell-offs (Gaughan, 1999; Bruner, 2004). Because
of their lower occurrence, spin-offs offer fewer
opportunities for developing repetitive routines and
Strat. Mgmt. J., 29: 593–616 (2008)
DOI: 10.1002/smj
Learning How to Restructure
may be seen as deviations from a firm’s planned
corporate strategy. For example, spin-offs are not
usually motivated by financial proceeds, as their
financial implications are limited to possible tax
savings and expected future gains associated with
adjusting and making clearer the restructuring
firm’s strategy (Allen, 2001; Cusatis et al., 1993;
Daley et al., 1997; Desai and Jain, 1999; Krishnaswami and Subramaniam, 1999; see Bruner,
2004, for a review). Spin-offs would not likely be
preplanned or anticipated as a method of value creation. However, by contrast, firms can buy and sell
off units for profit, a value-creation process known
as ‘arbitrage,’ where gain is realized by selling at
a higher price than the original purchase amount.
The differences in financial proceed potential may
help explain why spin-offs are relatively rare compared to sell-offs.
Second, the principal parties to a spin-off are
directly related to the restructuring firm, while
those associated with a sell-off are at least partly
external (e.g., the buying firm and its stakeholders). When making a spin-off, there is less potential for temporal holdups between the design and
execution of the restructuring action; the need
for temporal delays would be minimized, as the
restructuring firm’s managers would develop and
implement a process that would transfer property
rights to their owners. The separation and postrestructuring governance of the spun-off assets
would all occur internally. By contrast, the selloff process involves several iterative stages that
would serve to lengthen the temporal gap between
design and execution, including finding an acquiring firm, negotiation, due-diligence, the approval
of different sets of stockholders and government
entities, and ultimately a ‘close’ of the deal. The
time interval between the design and execution of
the spin-off would likely be less than that for the
sell-off.
Third, managers can draw from rich organizational memory when making spin-off decisions.
In particular, as noted above, spin-offs usually
involve assets that reside within the core businesses of the restructuring firm (Bergh et al., 2008;
Ito, 1995; Khan and Mehta, 1996; Nixon et al.,
2000). These assets are typically managed with the
face-to-face methods necessary for developing the
memory necessary for improvisation (Eisenhardt,
1989; Moorman and Miner, 1998b; Sproull and
Keisler, 1991). In such settings, managers tend to
use control systems that involve close attention and
Copyright  2008 John Wiley & Sons, Ltd.
601
detailed and subjective judgment, and focus on the
transaction level of analysis where performance
is assessed by factors such as quality, transfer
price, and productivity (Hill, Hitt, and Hoskisson,
1992; Hill and Hoskisson, 1987). This managerial intensity would create high procedural and
declarative memory, as the managers have detailed
knowledge of the operating affairs and control
procedures used for managing the relationships
with core business assets. Similarly, through having interfaced closely over time, managers develop
a refined understanding of the restructured assets
and a set of routines that guide their decision
making. The operational-level detailed knowledge
achieved through close and repeated interactions
could create partner-specific experiences that in
turn would contribute to tacit knowledge (Zollo
et al., 2002).
Finally, the top managers of the spun-off assets
usually come from the restructuring firm (Aron,
1991; Seward and Walsh, 1996). The restructuring
firm’s managers would have developed a rich set of
routines for working with the leaders of the to-be
restructured assets. With a higher degree of familiarity and likely face-to-face knowledge of the
spun-off asset’s managers, the restructuring firm’s
managers would have greater tacit knowledge to
draw upon, enabling faster decision making and
facilitating convergence in the time between planning and execution of a restructuring action (cf.
Moorman and Miner, 1998a).
In sum, the organizational improvisational view
may help describe restructuring by spin-offs. These
actions tend to be rare and novel, have less potential for temporal holdup between design and execution, and involve managers who can leverage
learning from rich organizational memory and
high-tacit knowledge. Short-term and contemporaneous experience would provide the impetus for
generating organizational change and recombining
stored knowledge and skills to act in real-time
manner. We predict that recent spin-offs will have
a more influential effect on subsequent spin-off
adoption than those occurring in temporally distant
years.
Hypothesis 3: Recent experience with spin-offs
is related more positively to the likelihood of
a subsequent spin-off than temporally distant
experience with spin-offs.
Strat. Mgmt. J., 29: 593–616 (2008)
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D. D. Bergh and E. N.-K. Lim
Improvisation has been linked to financial performance, a relationship that is influenced by several factors, including expertise, teamwork skills,
organizational memory, and real-time information
and communication (Crossan et al., 2005; Tippins and Sohi, 2003; Vera and Crossan, 2005).
When any of these factors is high, managers have
more specialized knowledge, increased collaboration, and higher coordination that in turn facilitates adaptation and anticipation (Amabile, 1996;
Weick, 1993; Weick and Roberts, 1993). With a
more richly developed basis from which to draw
upon, managers are better able to apply learning from recent and relevant events to improvisational decisions (Eisenhardt, 1989; Eisenhardt
and Tabrizi, 1995). They have an increased ability to react to real-time information flows and can
make improvisations of higher quality that lead
to higher performance and effectiveness (Crossan
et al., 2005; Vera and Crossan, 2005; Moorman
and Miner, 1998b; Weick, 1993).
This logic applies most closely to spin-offs,
as spun-off assets are typically in business lines
where restructuring managers have high expertise,
have worked closely together and interfaced regularly with other managers, and are more likely to
have developed open information sharing and fast
and accurate communication routines (e.g., Bergh
et al., 2008; Ito, 1995; Khan and Mehta, 1996;
Nixon et al., 2000). The managers in these circumstances could apply their tacit knowledge of
the assets to subsequent actions and make better
informed and more effective decisions (Baker and
Nelson, 2005; Winter, 2003). Their actions could
be made in real time and based upon immediate
feedback from recent information (Crossan et al.,
2005; Moorman and Miner, 1998a). And, by drawing upon high degrees of expertise, teamwork, and
open information-sharing, managers could implement the most appropriate spin-offs that provide
effective and immediate resolution to the problem
driving the restructuring, leading to a faster and
more positive effect (e.g., Brown and Eisenhardt,
1995; Miner et al., 2001).
In addition, since spin-offs tend to occur less
frequently than sell-offs (Bruner, 2004; Gaughan,
1999; Nixon et al, 2000), managers would be less
likely and less able to store information about them
for long periods of time (e.g., Szulanski, 1996).
Consequently, short-term experience from recent
spin-offs and those in consecutive years would
Copyright  2008 John Wiley & Sons, Ltd.
be more relevant and valuable for current decision making. Spin-offs occurring in years more
distant to a focal restructuring event are less applicable because the learning from those events may
not be directly associated with the idiosyncratic
nature and problems of the current restructuring.
When learning is rooted in more recent spinoff experiences, then those particular events will
have the highest potential for influencing learning, tacit knowledge, decision making, and postrestructuring performance. Overall, financial performance would be influenced less by spin-offs
occurring in the distant past and more by those
that resolve the current issues facing the firm (e.g.,
Hamilton and Chow, 1993; Markides, 1992, 1995).
Hypothesis 4: Recent experience with spin-offs
is related more positively to financial performance following a focal spin-off than experience
with spin-offs in temporally distant years.
METHOD
Sample
Consistent with other empirical studies of restructuring (Bethel and Liebeskind, 1993; Markides,
1992, 1995; Hoskisson et al., 1994), the hypotheses were tested by a sample of restructuring firms.
The sample was determined using several steps.
First, we randomly identified 300 firms that made
restructuring announcements between 1 January
1990 and 31 December 1997. This number and
time period were selected to help ensure a large
sample that was diverse enough for testing the
hypotheses. The firms and restructuring announcements were found in the Securities Data Corporation’s Worldwide Merger & Acquisition Database
(SDC), 2000. Second, we examined each restructuring firm to determine whether it was publicly
held, that it resided in a nonregulated industry,
that the restructuring was voluntary and completed,
and that it was based in the United States. These
screens were necessary to ensure data availability
and consistency. Ninety-five firms were removed,
and the final sample consisted of 205 firms that
announced and implemented a restructuring action.
Third, mean comparisons of the 205 retained firms
with the 95 discarded firms indicated no significant
differences in terms of the transaction size (dollar value of the restructuring event), profitability
Strat. Mgmt. J., 29: 593–616 (2008)
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Learning How to Restructure
(return on assets [ROA]), year, debt, and spin-off
and sell-off occurrence.
Dependent variables
Restructuring mode represented how the focal
restructuring event occurred. This variable was
coded as 1 when the focal restructuring event was a
spin-off and as a 0 when it was a sell-off. The classification of spin-off and sell-off was found in the
SDC‘s Worldwide Merger & Acquisition Database,
2000. No restructuring events were a hybrid of the
two alternatives or another type of restructuring.
Financial performance was measured as the
ROA and mean earnings per share (EPS) for each
of the five years after the year of the focal restructuring event. ROA is one of the most common
accounting-based performance measures and correlates highly (r = 0.9 or higher) with other such
proxies, including return on sales (ROS) and return
on equity (ROE). EPS reflects financial performance from the investor’s perspective. Using both
measures reflects the multidimensional aspects of
financial performance. The data were found in
COMPUSTAT.
Independent variable
Experience was measured as the count of prior
restructuring events; it was the number of spinoffs and sell-offs made by each restructuring firm
during the 10 years prior to the year of the
focal restructuring. The SDC Merger & Acquisition Database, 2000, reported the dates of the prior
spin-offs and sell-offs for the 205 firms. These
counts were summed for several different periods,
including the number that occurred one year prior
to the focal event, two years prior, three and four
years prior, five to 10 years prior, and the sum over
the entire 10-year period. These window lengths
were used to separate immediate, short-term, and
long-term counts from one another. The unit of
analysis is the restructuring firm and its specific
experiences over each of the 10 years prior to the
focal restructuring event.
Control variables
We controlled for several explanations that represent motives for corporate restructuring and/or
could influence the restructuring/performance relationship. First, we included control variables to
Copyright  2008 John Wiley & Sons, Ltd.
603
account for a financial distress hypothesis. We
measured the restructuring firm’s financial performance (ROA) and debt (debt/sales) for the year
prior to the focal restructuring year. Second, we
included a control variable for strategy, defined in
terms of the relatedness of the restructured assets.
A dummy variable was coded as 1 if the restructured assets were in the same primary four-digit
SIC as the restructuring firm and as 0 otherwise.
Third, we used two variables to control for a managerial hubris hypothesis. We measured the size
of the restructuring transaction (dollars, logged)
and the size of the restructuring firm (total assets,
logged). Fourth, we controlled for the agency
hypothesis of owner/manager control. A variable
called Blockholdings was measured as the percentage of outstanding common stock held in 5 percent
blocks or larger. Fifth, we accounted for industry effects in three ways; by norming all financial
variables relative to their industry averages (differences from the mean), by including the restructuring firm’s primary two-digit SIC as a dummy
variable in the regression equations, and by subsample analyses that that either included or did not
include the most popular industries (no industry
influences existed, so the variables are not reported
for space purposes).
Sixth, we measured investor assessment of the
quality and expected performance effects of the
focal restructuring with the cumulative abnormal returns (CAR) variable. If the restructuring
involved assets whose disposal are expected to
raise the aggregate value of the restructuring firm,
then this variable would be positive, and negative
otherwise (Krishnaswami and Subramaniam, 1999;
Montgomery, Thomas, and Kamath, 1984). CAR
was operationalized using the standard event study
methodology, whereby CAR was computed for
the days surrounding the restructuring announcement. The standard event study approach estimates a market model for each firm and then
calculates a cumulative abnormal return for the
event. Specifically, the CARs were estimated as
ARit = Rit − (ai + bi Rmt ), where ai and bi are the
ordinary least squares (OLS) parameter estimates
obtained for the regression of Rit on Rmt over an
estimation period (T ) preceding the event; ARit is
daily abnormal returns, Rit is the rate of return on
the share price of firm i on day t and Rmt is the rate
of return on the S&P 500 on day t. The parameter
estimates were based on an estimation period of
200 days (−250 to −50) before the restructuring
Strat. Mgmt. J., 29: 593–616 (2008)
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D. D. Bergh and E. N.-K. Lim
announcement. Abnormal returns were cumulated
over the two-day window (day 0 is the announcement business day, +1 is the next business day)
surrounding the announcement date. Stock market data were found in the Center for Research in
Security Prices’2000 data tapes.
In addition, we accounted for year-specific
effects by recording the year the focal restructuring
event occurred. Finally, continuing restructuring
activity was measured as a dummy variable coded
as 1 if a restructuring firm made additional restructuring actions after a focal restructuring and as 0
if not. Data for the control variables were found
in the SDC database, COMPUSTAT, and proxy
statements for the year of the restructuring.
coefficients and model parameters. The coefficients
are nonstandardized, range from positive to negative infinity, and are distributed as z-scores. The
signs of these coefficients (+, 0, −) can be interpreted like those produced by OLS regression (+
is more, − is less). The model parameters are
reflected in the Cox and Snell R 2 and the Nagelkerke R 2 . Like the R-square measure in OLS, these
measures range from 0 to 1, approaching 0 as
the quality of fit diminishes and 1 as it improves.
Hypotheses 2 and 4 were tested using OLS regression. Standardized coefficients are reported. Multicollinearity was not apparent in the results (variance inflation factors were below 2, below the
value of 10 where multicollinearity becomes an
alternative explanation).
Analyses
The hypotheses were tested with hierarchical
regression analyses. Logistic regression was used
for testing Hypothesis 1 and Hypothesis 3 because
the dependent variable in these hypotheses (restructuring mode) was dichotomous. Similar to ordinary
least squares (OLS) regression analyses, hierarchical logistic regression analyses provide variable
RESULTS
Table 1 reports means, standard deviations, and
intercorrelations for the study variables. Most of
the restructuring events were sell-offs (59%, 123
of 205), the stock market reacted positively to
the restructuring announcement (2.2% increase in
Table 1. Means, standard deviations, and correlations
Variables
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Mean
1
2
3
4
5
6
7
Restructuring mode
0.405 0.492
Focal CAR
0.022 0.047 0.186∗
Pre-restructuring ROA
1.682 10.213 0.125 −0.052
Debt/sales
0.429 0.559 −0.164∗ −0.028 −0.055
0.088 −0.048
Asset relatedness
0.610 0.489 0.191∗ −0.053
0.125
Transaction price (log)
2.504 0.838 0.049
0.074
0.284∗ 0.067
0.543∗
Total assets (log)
3.482 0.847 −0.080 −0.161∗ 0.210∗ 0.026 −0.021
Blockholdings (%)
21.683 20.563 −0.064
0.018 −0.058
0.043
0.055 −0.159∗ −0.273∗
Year
1994.107 2.119 −0.051
0.053
0.014 −0.024 −0.082
0.104
0.006
0.263∗ 0.487∗
Post-EPS 5 year average
1.627 1.985 −0.095 −0.057
0.228∗ −0.176∗ −0.114
Post-ROA 5 year average
4.095 5.570 −0.059
0.125
0.502∗ −0.214∗ −0.082
0.083
0.095
0.010
0.248∗ 0.358∗
Post-Restructuring dummy
0.223 0.278 0.067 −0.037
0.150∗ 0.012
Count of spin-offs
0.380 0.694 0.508∗ 0.039
0.068 −0.071
0.165∗ 0.083
0.011
Count of sell-offs
7.493 9.695 −0.074 −0.073
0.117 −0.084
0.012
0.225∗ 0.419∗
Variables
9
10
11
12
13
14
SD
Year
Post EPS average
Post ROA average
Post Restructuring dummy
Count of spin-offs
Count of sell-offs
8
9
10
11
12
13
0.078
−0.152
−0.076
−0.187∗
−0.057
−0.136
−0.040
−0.005
0.044
0.015
0.101
0.532∗
0.139
−0.143
0.361∗
−0.021
−0.061
0.093
0.050
0.293∗
0.149∗
Note: ∗ (p < 0.05); n = 205, except for n(EPS) = 149, n(ROA) = 118; Restructuring mode is for focal event. It is coded as 1 for
spin-off, 0 for sell-off.
Copyright  2008 John Wiley & Sons, Ltd.
Strat. Mgmt. J., 29: 593–616 (2008)
DOI: 10.1002/smj
Learning How to Restructure
605
Table 2. Number of sell-offs and spin-offs per year
before focal event year
Table 3. Logistic regression analysis: restructuring
mode regressed onto restructuring experience
Year relative to
focal event year
Variables
Model 1
ROA
0.040+
(0.021)
−0.626+
(0.349)
−0.846∗∗
(0.330)
0.111
(0.238)
−0.316
(0.237)
−0.011
(0.008)
−0.063
(0.073)
9.286∗∗
(3.572)
−1
−2
−3
−4
−5
−6
−7
−8
−9
−10
# of
sell-offs
206
141
143
162
140
132
115
105
85
67
# of
spin-offs
8
2
2
1
0
1
1
3
2
5
Debt/sales
Asset relatedness
Transaction price
(log)
Total assets (log)
Blockholdings (%)
Note: These figures represent the summed count of prior restructuring actions for all of the 205 restructuring firms.
Year
CAR), and the five-year post-restructuring mean
return on assets was higher than the one-year
pre-restructuring mean (4.09% versus 1.68%). In
addition, most firms had far fewer pre-restructuring
spin-offs (mean = 0.38) than sell-offs (mean =
7.49) during the 10 years prior to the year of the
focal restructuring event.
Table 2 provides additional insight into the 10year history of spin-offs and sell-offs prior to the
focal restructuring event. Based on the entire sample of 205 firms, the data indicate that sell-offs
far outnumber spin-offs for any of the years. For
example, during the year immediately prior to the
focal event year, the 205 restructuring firms made
206 sell-offs and eight spin-offs. Although the
sample consists of 82 firms that restructured by
spin-offs, this restructuring method was used considerably less often beforehand than sell-offs. The
restructuring firms were not concentrated in any
particular industry (results available upon request).
Table 3 reports the results of the first set of
logistic regression analyses. The values reported
in the table are nonstandardized coefficients, and
standard errors are reported in parentheses. The
dependent variable, restructuring mode (spin-off,
sell-off), is regressed onto the full set of controls
(Model 1), and onto restructuring experience, as
the summed restructuring counts over the entire
10-year period prior to the year of the focal restructuring event (Model 2). The results indicate that
the 10-year count of sell-offs is associated with
a subsequent sell-off (b = −0.066, p < 0.01; the
negative coefficient is due to the coding of the
mode variable as 1 for spin-off, 0 for sell-off).
Count of prior
spinoffs (10y)
Count of prior
selloffs (10y)
Pseudo R 2
% correctly classified
Log likelihood
Log likelihood ratio
test (df)
χ 2 value (df)
p-value
Observations
Copyright  2008 John Wiley & Sons, Ltd.
Focal CAR
0.103
66.8%
−124.067
—
28.590 (8)
0.000
205
Model 2
0.045+
(0.026)
−0.856+
(0.499)
−0.520
(0.390)
0.106
(0.285)
−0.075
(0.294)
−0.009
(0.009)
−0.098
(0.087)
11.611∗∗
(4.299)
2.475∗∗
(0.432)
−0.066∗∗
(0.025)
0.325
78.0%
−93.373
61.389 (2)
(p < 0.000)
89.980 (10)
0.000
205
Note: Dependent variable is restructuring mode (1 = spin-off,
0 = sell-off). Standard errors in parentheses.
∗∗
p < 0.01, ∗ p < 0.05, + p < 0.10
The sign and significance of the coefficient supports the absorptive capacity argument represented
by Hypothesis 1, that as firms gain experience
with sell-offs, they will continue to use sell-offs in
the long term as a form of corporate restructuring
strategy.
The results reported in Table 3 also indicate
that the 10-year count of spin-offs is associated
with a subsequent spin-off (b = 2.475, p < 0.01).
Although this relationship appears to support a
long-term effect of spin-off count on the likelihood of subsequent spin-off adoption, a finding
that appears consistent with an absorptive capacity
argument, the results in Table 4 provide additional
insights into the meaning of that association. In
Table 4, we separated the 10-year summed counts
of spin-offs and sell-offs into different temporal
window intervals. We computed the numbers of
spin-offs and sell-offs five to 10 years prior to the
Strat. Mgmt. J., 29: 593–616 (2008)
DOI: 10.1002/smj
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D. D. Bergh and E. N.-K. Lim
Table 4. Logistic regression analysis: restructuring
mode regressed onto experience at different time intervals
(n = 205)
Variables
Model 1
ROA
0.040+
(0.021)
−0.626+
(0.349)
−0.846∗∗
(0.330)
0.111
(0.238)
−0.316
(0.237)
−0.011
(0.008)
−0.063
(0.073)
9.286∗∗
(3.572)
Debt/sales
Asset relatedness
Transaction price (log)
Total assets (log)
Blockholdings (%)
Year
Focal CAR
Count of spin-offs, prior
years 5 to 10
Count of sell-offs, prior
years 5 to 10
Count of spin-offs, prior
years 3 to 4
Count of sell-offs, prior
years 3 to 4
Count of spin-offs, prior
year 2
Count of sell-offs, prior
year 2
Count of spin-offs, prior
year
Count of sell-offs, prior
year
0.103
Pseudo R 2
% correctly classified
66.8%
Log likelihood
−124.067
Log likelihood ratio test
—
(df)
28.590
χ 2 value (df)
p-value
0.000
Observations
205
Model 2
0.050∗
(0.023)
−0.806∗
(0.397)
−0.877∗
(0.346)
0.097
(0.248)
−0.316
(0.261)
−0.013
(0.009)
−0.064
(0.079)
9.689∗
(3.782)
0.024
(0.593)
−0.030
(0.045)
−0.614
(1.522)
−0.177
(0.111)
1.204
(1.558)
0.222
(0.157)
2.129∗
(0.918)
0.046
(0.123)
0.153
69.1%
−117.183
13.768 (8)
(p < 0.10)
(8) 40.543 (16)
0.001
204
Note: Standard errors in parentheses. Initial −2 Log Likelihood
= 276.725. ∗∗ p < 0.01, ∗ p < 0.05, + p < 0.10.
focal restructuring event, those during three and
four years, two years and one year prior. Tests
reported under Model 2 of Table 4 indicate that
only the count of spin-offs during the year immediately prior to the focal restructuring event is
associated with a subsequent spin-off (b = 2.129,
p < 0.05). The counts of spin-offs (and sell-offs)
during the other time intervals are not significant
predictors. The closer examination offered by the
Copyright  2008 John Wiley & Sons, Ltd.
tests of the alternative time windows reveals that
more recent experience with spin-offs is associated with subsequent spin-offs. Collectively, these
results lend support for the organizational improvisation argument represented in Hypothesis 3, that
recent experience is related more positively to the
likelihood of a subsequent spin-off than temporally
distant experience.
In addition, none of the sell-off counts for any
of the windows within the 10-year period is associated with restructuring mode. Apparently, the
effects of sell-off experience on focal restructuring
sell-offs do not depend on any particular temporal
window. This evidence lends additional support for
the absorptive capacity hypothesis (Hypothesis 1)
for sell-offs.
Tables 5 and 6 report the results of regressing performance after the focal restructuring event
onto restructuring experience levels. The coefficients in those tables are standardized. The two
performance variables, ROA and mean EPS, were
recorded at each of the five years after the restructuring event, and an average of each was calculated
for that five-year period. The 10-year sum of prior
sell-offs was related positively to ROA (b = 0.283,
p < 0.01) and to EPS (b = 0.337, p < 0.01) at one
year after the restructuring event (Models 2 in both
tables). In addition, that long-term sell-off count
variable was related positively to the EPS fiveyear average (b = 0.227, p < 0.01). These results
are consistent with the second absorptive capacity hypothesis, Hypothesis 2, firms that have more
experience with sell-offs will have higher financial
performance after a subsequent sell-off than firms
having less experience with sell-offs. (The negative relationship between long-term spin-offs and
EPS is discussed below.)
Tables 7 and 8 report the results of regressing financial performance onto the restructuring
counts during the different time intervals. Standardized coefficients are again reported. Firms
making higher numbers of spin-offs during a threeto four-year period prior to a focal restructuring event tended to have higher post-restructuring
ROA (b = 0.333, p < 0.01 for one year after;
b = 0.197, p < 0.05 for two years after, and b =
0.178, p < 0.05 for the five-year average). However, firms that made higher numbers of spinoffs in the period five to 10 years before a focal
restructuring tended to have lower ROA for the
year after the focal restructuring (b = −0.167,
p < 0.05), but higher four years later (b = 0.290,
Strat. Mgmt. J., 29: 593–616 (2008)
DOI: 10.1002/smj
Learning How to Restructure
Table 5.
607
Ordinary least squares regression analysis: post-restructuring ROA regressed onto restructuring experience
Variables
Pre-restructuring ROA
Debt/sales
Asset relatedness
Transaction price (log)
Total assets (log)
Blockholdings (%)
Year
Restructuring dummy
Focal CAR
Count of spin-offs
Count of sell-offs
R2
Adjusted R 2
F
Observations
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
0.529∗∗
−0.112
−0.059
0.005
0.062
0.060
−0.173∗
−0.068
0.193∗
−0.092
0.134
0.340
0.272
4.971∗∗
118
0.473∗∗
−0.167+
0.065
−0.103
−0.027
0.036
−0.085
−0.010
0.236∗∗
−0.019
0.283∗∗
0.345
0.277
5.066∗∗
118
0.438∗∗
0.016
0.009
−0.026
0.190
0.116
−0.043
−0.104
−0.040
−0.068
0.118
0.245
0.162
2.954∗∗
112
0.191+
−0.118
−0.110
0.160
−0.187
0.009
−0.037
−0.014
0.039
−0.008
0.041
0.100
−0.005
0.949
106
0.336∗∗
−0.210∗
−0.182+
0.039
0.129
0.030
−0.180
0.087
0.058+
0.168+
−0.018
0.264
0.170
2.805∗∗
98
0.377∗∗
−0.110
0.012
0.038
0.021
0.017
−0.257∗
−0.032
−0.121
0.026
−0.041
0.212
0.095
1.815+
86
Note: Model 1 dependent variable is five-year ROA average; Model 2 dependent variable is the ROA at one year after focal
restructuring year, Model 3 dependent variable is the ROA at two years after, up to Model 6, where the dependent variable is the
ROA at five years after the focal restructuring year.
∗∗
p < 0.01, ∗ p < 0.05, + p < 0.10.
Table 6. Ordinary least squares regression analysis: post-restructuring earnings per share regressed onto restructuring
experience
Variables
Pre-restructuring ROA
Debt/sales
Asset relatedness
Transaction price (log)
Total assets (log)
Blockholdings (%)
Year
Restructuring dummy
Focal CAR
Count of spin-offs
Count of sell-offs
R2
Adjusted R 2
F
Observations
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
0.109
−0.192∗∗
−0.110
0.034
0.376∗∗
0.026
−0.124+
−0.076
−0.007
−0.209∗∗
0.227∗∗
0.379
0.329
7.588∗∗
149
0.159+
−0.137+
0.000
−0.012
0.048
−0.019
0.076
−0.015
−0.021
−0.098
0.337∗∗
0.211
0.148
3.339∗∗
149
0.060
0.085
−0.063
−0.088
0.457∗∗
0.079
0.009
−0.076
−0.006
−0.139+
0.150+
0.264
0.201
4.148∗∗
139
0.089
−0.253∗∗
−0.090
0.158
0.194+
0.059
−0.197∗
−0.022
−0.043
−0.087
0.107
0.226
0.153
3.107∗∗
129
0.028
−0.288∗∗
−0.241∗∗
0.015
0.350∗∗
−0.016
−0.237∗∗
0.054
−0.036
0.002
0.044
0.294
0.221
4.044∗∗
119
−0.020
−0.315∗∗
−0.114
0.099
0.223
0.045
−0.212∗
−0.037
−0.049
−0.129
−0.008
0.197
0.103
2.096∗
106
Note: Model 1 dependent variable is the five-year EPS average; Model 2 dependent variable is the EPS at one year after the focal
restructuring year, up to Model 6 where the dependent variable is the EPS at five years after the focal restructuring year.
∗∗
p < 0.01, ∗ p < 0.05, + p < 0.10.
p < 0.01). None of the restructuring count intervals were related to EPS at any of the years after
the restructuring. These results provide partial support for Hypothesis 4, that recent experience with
spin-offs is related more positively to financial
performance following a focal spin-off than experience with spin-offs in temporally distant years.
In our findings, recent spin-offs (three to four
Copyright  2008 John Wiley & Sons, Ltd.
years in this case) are related more positively to
financial performance (ROA) following a subsequent spin-off than the count of spin-offs occurring
in the years that do not immediately precede the
focal event. Furthermore, none of the counts of
sell-offs for the temporal windows are associated
with either performance measure, lending additional support for the absorptive capacity effect
Strat. Mgmt. J., 29: 593–616 (2008)
DOI: 10.1002/smj
608
D. D. Bergh and E. N.-K. Lim
Table 7. Post-restructuring ROA regressed onto experience at different time intervals
Variables
Pre-restructuring ROA
Debt/sales
Asset relatedness
Transaction price (log)
Total assets (log)
Blockholdings (%)
Year
Restructuring dummy
Focal CAR
Count of spin-offs 5–10 years prior
Count of sell-offs 5–10 years prior
Count of spin-offs 3–4 years prior
Count of sell-offs 3–4 years prior
Count of spin-offs 2 years prior
Count of sell-offs 2 years prior
Count of spin-offs 1 year prior
Count of sell-offs 1 year prior
R2
Adjusted R 2
F
Observations
Model 1
Model 2
Model 3
0.556∗∗
−0.104
−0.072
−0.007
0.102
0.040
−0.151+
−0.051
0.190∗
−0.131
0.089
0.178∗
−0.055
−0.003
0.021
−0.083
0.013
0.380
0.275
3.607∗∗
118
0.538∗∗
−0.148+
0.062
−0.155
0.028
−0.014
−0.027
−0.008
0.215∗∗
−0.167∗
0.159
0.333∗∗
−0.169
0.051
0.132
0.039
0.087
0.462
0.370
5.047∗∗
118
0.483∗∗
0.032
0.005
−0.065
0.232+
0.081
−0.006
−0.097
−0.046
−0.142
0.049
0.197∗
−0.103
0.034
0.096
−0.024
0.009
0.294
0.166
2.303∗∗
112
Model 4
0.188
−0.124
−0.119
0.167
−0.180
0.023
−0.040
−0.004
0.054
0.021
−0.001
0.041
0.041
−0.044
−0.020
−0.057
0.026
0.107
−0.066
0.619
106
Model 5
Model 6
0.289∗∗
−0.236∗
−0.218∗
0.085
0.144
0.080
−0.208+
0.139
0.109
0.290∗∗
−0.071
0.098
0.152
−0.031
−0.085
−0.138
−0.013
0.346
0.207
2.493∗∗
98
0.310∗
−0.182
−0.048
0.089
0.011
0.058
−0.297∗
0.033
−0.082
0.178
−0.009
0.031
0.046
−0.102
−0.188
−0.234+
0.132
0.297
0.122
1.692+
86
Note: Model 1 dependent variable is the five-year ROA average; Model 2 dependent variable is the ROA at one year after the focal
restructuring year, up to Model 6 where the dependent variable is the ROA at five years after the focal restructuring year.
∗∗
p < 0.01, ∗ p < 0.05, + p < 0.10.
Table 8. Post-restructuring EPS regressed onto experience at different time intervals
Variables
Pre-restructuring ROA
Debt/sales
Asset relatedness
Transaction price (log)
Total assets (log)
Blockholdings (%)
Year
Restructuring dummy
Focal CAR
Count of spin-offs 5–10 years prior
Count of sell-offs 5–10 years prior
Count of spin-offs 3–4 years prior
Count of sell-offs 3–4 years prior
Count of spin-offs 2 years prior
Count of sell-offs 2 years prior
Count of spin-offs 1 year prior
Count of sell-offs 1 year prior
R2
Adjusted R 2
F
Observations
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
0.111
−0.183∗
−0.109
0.028
0.380∗∗
0.022
−0.112
−0.056
−0.008
−0.184∗
0.098
−0.006
0.129
−0.057
0.048
−0.074
−0.041
0.389
0.310
4.908∗∗
149
0.175∗
−0.140+
−0.001
−0.027
0.050
−0.038
0.105
−0.012
−0.030
−0.138+
0.162
0.082
0.049
−0.005
0.049
−0.006
0.109
0.228
0.127
2.270∗∗
149
0.068
0.092
−0.063
−0.096
0.466∗∗
0.075
0.021
−0.070
−0.005
−0.112
0.041
0.009
0.030
−0.035
0.106
−0.073
−0.011
0.272
0.170
2.661∗∗
139
0.069
−0.251∗∗
−0.091
0.170
0.204+
0.084
−0.221∗
0.001
−0.029
−0.041
0.120
−0.023
0.145
−0.098
−0.096
−0.043
−0.071
0.244
0.128
2.109∗
129
0.010
−0.274∗∗
−0.247∗∗
0.020
0.354∗∗
0.012
−0.242∗∗
0.092
−0.008
0.105
−0.084
−0.037
0.225
−0.031
0.025
−0.083
−0.103
0.331
0.219
2.944∗∗
119
−0.043
−0.310∗∗
−0.114
0.119
0.226
0.087
−0.243∗
−0.003
−0.020
−0.002
−0.034
−0.094
0.134
−0.139
0.015
−0.106
−0.106
0.225
0.075
1.500
106
Note: Model 1 dependent variable is the five-year EPS average; Model 2 dependent variable is the EPS at one year after the focal
restructuring year, up to Model 6 where the dependent variable is the EPS at five years after the focal restructuring year. ∗∗ p < 0.01,
∗
p < 0.05, + p < 0.10.
Copyright  2008 John Wiley & Sons, Ltd.
Strat. Mgmt. J., 29: 593–616 (2008)
DOI: 10.1002/smj
Learning How to Restructure
of sell-offs on post-restructuring financial performance.
DISCUSSION
During the 1980s and 1990s, thousands of companies restructured their portfolios of business lines,
spinning and selling off assets worth hundreds of
billions of dollars (Bruner, 2004; Gaughan, 1999;
Hoskisson and Hitt, 1994). To date, most knowledge on restructuring has focused on how restructuring is used as a mechanism to reduce overdiversification, reallocate resources, or improve internal efficiency in order to improve financial performance (see Bauer, 2006; Haynes et al., 2003;
Johnson, 1996 for reviews of the restructuring
literature). Although that research has provided
important insights, we still have an incomplete
understanding of the drivers and implications of
corporate restructuring. In particular, we know little about the dynamic aspects of restructuring.
Although managers would likely consider temporal factors such as prior history and experience
in their restructuring decisions, previous research
has not fully considered how such dimensions
might influence restructuring and its implications
for financial performance. The current study was
designed to address these gaps by testing two questions: (1) Does experience apply to restructuring
decisions? (2) If so, does it also influence profitability?
The study finds that experience matters, but
in different ways. First, the use of a sell-off is
consistent with an absorptive capacity viewpoint
of organizational learning. Having more experience with sell-offs over time makes them more
likely to be used in subsequent events. By contrast,
the use of spin-offs is consistent with an organizational improvisation view. The results indicate that a focal spin-off is related to the incidence of other spin-offs in immediately preceding
years only; no other prior time intervals mattered
for spin-offs. The adoption of a spin-off appears
to be used more often as a short-term response
than part of a long-term activity spread over multiple years, as spin-offs frequently occur close
together in time. Second, restructuring experience
has implications for financial performance following a focal restructuring event. Firms with more
sell-off experience realized higher financial performance than firms having less. By contrast, those
Copyright  2008 John Wiley & Sons, Ltd.
609
having more recent spin-off experience tended
to realize an increase in post-restructuring financial performance while those with older experience tended to achieve performance decreases.
Overall, these findings suggest that different kinds
of experience are associated with the adoption
of different restructuring actions and their subsequent financial performance records. The results
of testing the control variables further support the
theoretical explanations; most spin-offs involved
core business lines and sell-offs tended to occur
when the restructuring firm had financial pressures
due to lower financial performance and higher
debt.
Implications for corporate restructuring
The study results suggest several potential contributions to explanations of corporate restructuring. First, current understanding of the temporal qualities of restructuring is mostly based on
evolutionary models, where firms are depicted as
using divestitures to move out of business lines
as part of a search and selection sequential process (see Chang, 1996), or transaction cost explanations, where restructuring balances the costs
and benefits of managing portfolios of business
lines (Bergh and Lawless, 1998; Jones and Hill,
1988; Markides, 1992, 1995). Our study adds
to the understanding of the dynamic properties
of restructuring in several ways. First, a historical effect—prior restructuring experience heterogeneity—appears to influence the use of different restructuring modes. Second, the relationship
between prior restructuring experience and subsequent restructuring appears to have different
forms. Third, theoretical explanations of restructuring can be revised to include different time intervals; the absorptive capacity viewpoint of learning
applies to sell-offs, while improvisation appears to
describe spin-offs. Fourth, experience has a shortlived relationship with post-restructuring financial
performance. Collectively, these implications suggest new theoretical insights because most current explanations do not account for prior historical relationships, how they might vary over
time, or the amount and rate of temporal lag. Our
study suggests that addressing temporal relationships, together with experience, restructuring alternatives, and performance, will add significantly
to the restructuring literature. The findings offer
Strat. Mgmt. J., 29: 593–616 (2008)
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D. D. Bergh and E. N.-K. Lim
new empirical evidence and explanations, suggesting possible contributions to a dynamic view of
restructuring.
More specifically, the results support an integrative explanation of corporate restructuring that
is more expansive than prevailing perspectives.
To date, most explanations imply that a planned,
deliberate and intentional execution of restructuring actions is called for in order to meet a predetermined objective. Hence, a temporal delay is
embedded between the composition and implementation of the restructuring action. However, the
findings imply that this dominant explanation of
restructuring may be incomplete. It appears that
some restructurings may occur in a more simultaneous and contemporaneous manner where cumulative and repetitive prior experience plays a much
smaller role. Other factors such as expertise and
tacit knowledge may develop and shape corporate strategy as a continuous and adaptive process.
The support for the organizational improvisational
depiction of strategy may open new and different
avenues of inquiry into the formulation and implementation of strategic actions and their effects on
performance.
Moreover, most prior research has portrayed
restructuring as a method for improving internal
control and efficiency (Hoskisson and Hitt,
1994; Hoskisson and Turk, 1990; Shleifer
and Vishny, 1991), refocusing diversification
strategies (Comment and Jarrell, 1995; Hoskisson
et al., 1994; Markides, 1992, 1995), reducing
information asymmetries (Bergh et al., 2008;
Krishnaswami and Subramaniam, 1999), and
generating internal liquidity (John and Ofek, 1995;
Lang, Poulsen, and Stulz, 1995; Ofek, 1993).
Our study adds to the literature by suggesting
that different types of learning, which draw from
cognitive and behavioral concepts, may provide
insights into what has been primarily an economicbased perspective of the restructuring decision and
outcome.
Furthermore, the study indicates that an explanation of restructuring that appears to be valid for
one time interval may not be valid for another. For
example, the results initially showed that the adoption of a spin-off was related to a summed count of
spin-offs over a 10-year window (Table 3), a finding consistent with the absorptive capacity learning
argument. However, disaggregating the count of
spin-offs into temporal windows revealed that the
Copyright  2008 John Wiley & Sons, Ltd.
count of spin-offs occurring in the year immediately prior to the focal spin-off was more influential than any other time period within the 10-year
window (Table 4). Simultaneously, a much different relationship existed for sell-offs; the 10-year
count of sell-off was a significant predictor of selloff adoption while none of the windows within
that period mattered. These findings indicate that
the development of dynamic explanations needs to
account for the length of time intervals. Because
the length of the temporal intervals was varied,
the explanations for one temporal concept, experience, also varied. The specified time interval may
change the meaning of concepts, relationships, and
interpretations and serve as a baseline condition for
explaining restructuring actions.
The use of temporal frames helps us build upon
an earlier study of experience and restructuring.
Villalonga and McGahan (2005) recently reported
a consistent positive relationship between experience and the subsequent use of sell-offs, spin-offs,
and equity carve-outs. Our findings are similar to
theirs with respect to prior experience and sell-offs,
but the results differ when it comes to spin-offs.
These discrepancies appear due to how experience
was measured; Villalonga and McGahan represent
experience as the average number of events prior
to a focal event, while we use temporal windows to
break down experience into specific intervals. Had
we summed prior spin-offs over a 10-year period
only, our conclusions would have again been similar to those reported by Villalonga and McGahan
(2005). However, by recognizing that different perspectives of learning can be represented with the
use of the temporal windows, we find that spin-offs
occur more spontaneously and in a shorter time
frame, allowing us to posit a finer-grained construction of the experience concept. Hence, knowledge of the dynamic qualities of restructuring is
therefore enhanced by theoretically and methodologically indicating the time intervals in which the
events are most likely to exist (cf. Bergh, 1995b;
Bergh and Holbein, 1997; Mitchell and James,
2001; Zaheer, Albert, and Zaheer, 1999).
The findings also contribute to the understanding
of the dynamic quality of restructuring by revealing that the effects of restructuring experience on
financial performance appear to erode following
the restructuring. This relationship was consistent
across the different time intervals. Apparently, the
benefits of experience are not only variable over
time, but they do not last long.
Strat. Mgmt. J., 29: 593–616 (2008)
DOI: 10.1002/smj
Learning How to Restructure
Implications for organizational learning
In general terms, the findings suggest a more
diverse and integrative approach to viewing and
testing learning constructs; by clarifying and including different learning perspectives in the same
model, theory development can be expanded
dynamically and comprehensively. The organizational learning literature has received considerable attention and is extensive and broad, and
the absorptive capacity and organizational improvisational views are but two of several different perspectives within a voluminous research
stream. However, most applications of organizational learning tend to focus on one learning view
or process at a time and theoretical models typically do not sufficiently differentiate between alternative or complementary learning perspectives.
Our findings imply that if one disaggregates temporal windows into different length periods, then
more clearly defined learning views and arguments can be developed and tested. The choice
of time scales could therefore influence the theoretical relationships and insights one will obtain
(George and Jones, 2000; Zaheer, Albert, and
Zaheer, 1999), and it is possible that reduced window lengths within longer intervals might yield
support for more nuanced learning explanations.
Hence, theoretical explanations of learning might
be further developed, revised, and extended by recognizing complementary views of organizational
learning and developing and testing them using
different time window lengths.
More specifically, the study findings suggest
several possible implications for theory development on organizational learning. First, to the best
of our knowledge, our study is the first to compare simultaneously the financial implications of
absorptive capacity and organizational improvisation viewpoints of learning. To date, most conceptual developments and empirical tests have focused
on one or the other separately, and the research
streams on each appear to have mostly developed
independently of one another. We have little explanation and evidence about when either is likely
to better explain performance. Our study results
may help to partly address this gap; by linking
experience types to adoption of alternative actions
and then to performance, our model provides
an initial framework that differentiates these two
learning viewpoints. Overall, the findings suggest
that meaningful boundaries exist between different
Copyright  2008 John Wiley & Sons, Ltd.
611
viewpoints of learning and that there is theoretical
value by considering separate views simultaneously.
Furthermore, distinguishing between absorptive
capacity and improvisation may have important
theoretical implications. Most learning explanations imply that corporate behaviors are purposeful, deliberate, and planned. However, by differentiating between both views of learning, actions that
may be more spontaneous are not inadvertently
embedded within the absorptive capacity reasoning
and method, allowing for a more refined, developed, and transparent explanation to be offered.
We call for more research to further examine the
possible linkages and distinctions between absorptive capacity and organizational improvisation. For
instance, for those studies that link absorptive
capacity to alliances and acquisitions and to performance (see Lane et al., 2006; Zollo et al., 2002,
for recent reviews), we now wonder when improvisation might also apply. Are the different types
of acquisitions and alliances associated with different learning perspectives? Since the theoretical
models and empirical tests tend not to disaggregate long time periods into shorter ones, questions
arise about whether the alliance and acquisition
studies that support an absorptive capacity view
might be obscuring shorter-term improvisational
actions. These issues suggest that we may need to
revisit the developments and applications of learning perspectives, especially when the need exists
for managers to reduce the temporal proximity of
design and action.
Second, the study findings offer insights into
the absorptive capacity and improvisational viewpoints using different methods and conditions than
prior studies. The results can be interpreted as
further evidence of the reach of both views, as
each appears to now explain an exiting action
that has a performance implication. In addition,
the findings refine absorptive capacity to show
that it may not explain settings where the need
exists to reduce the time between design and action
(Crossan et al., 2005; Levinthan and Rerup, 2006;
Miner et al., 2001; Moorman and Miner, 1998a,
1998b; Winter, 2003). Moreover, many studies
consider absorptive capacity, but fewer empirically
examine its properties (Lane et al., 2006). As such,
this study contributes to knowledge of absorptive
capacity by explicating its temporal characteristics.
To date, most interpretations of absorptive capacity do not account for temporal window lengths,
Strat. Mgmt. J., 29: 593–616 (2008)
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D. D. Bergh and E. N.-K. Lim
implying that its properties and effects are constant or equal over time. Future research may
provide new insights into absorptive capacity by
testing its temporal conditions and assumptions.
Such study may offer new insights into learning
if it considers the relationships between the viewpoints of absorptive capacity and improvisation, or
other viewpoints within the organizational learning
literature.
Third, the study represents one of the initial
formulations and applications of organizational
improvisation to a corporate strategy action and its
possible performance effects. The findings extend
this viewpoint’s logic to a much different setting
than previous studies of musicians, actors, and
new product developers, and imply that improvisation may have potential for offering insights
into a variety of corporate behaviors. It appears
that when managers need to make decisions based
on real-time and short-term information, improvisation may help explain their actions. Interestingly, the results also indicate that the link between
improvisation and performance is not as clear as
expected. Although improvisation may apply to
how managers select restructuring modes, it does
not apply as robustly to financial performance,
and the effects appear to be temporally delayed.
This finding is consistent with other views of an
equivocal improvisation/performance linkage (see
Crossan et al., 2005). More study is needed to
understand the financial implications of improvisation.
The implications of this study should be considered in light of several limitations. First, the
findings are based on large restructuring events. It
is unknown how the results apply to smaller-sized
restructuring firms. The proposed explanation of
restructuring and its effects would seem to apply
to smaller organizations, but no direct inference
can be made given that the sample consisted of
large firms. Second, the study examined voluntary restructuring efforts only, and it is possible
that the proposed explanation might not apply to
involuntary spin-offs and sell-offs. For example,
a court-ordered restructuring might lead a firm to
spin-off when a sell-off might be the best option.
Third, the study does not identify how the restructured assets were originally created, either through
internal growth or by acquisition. No inference can
therefore be made that links entry and exit behaviors. A more complete explanation of restructuring
Copyright  2008 John Wiley & Sons, Ltd.
behavior would include growth and exit alternatives. We hope that future research will test such
a model.
CONCLUSION
During the 1980s and 1990s, firms restructured
tens of thousands of business lines. The present
study considers whether restructuring experience
might have influenced restructuring actions and
post-restructuring financial performance. The study
tests a model that relates different viewpoints of
organizational learning to help explain the adoption of different restructuring alternatives and their
influence on profitability. The findings show that
the absorptive capacity view is most pertinent to
the use of sell-offs, while the organizational improvisation view better explains the selection of spinoffs. These explanations and findings add to the
corporate restructuring literature and may further
enhance understanding of organizational learning.
In addition, our study probes unexplored aspects
of the temporal dimensions of corporate strategy,
such as time intervals, time lags, and the forms
of longitudinal relationships. Of specific interest
is the finding that the learning explanations for
restructuring actions and performance vary over
time. These findings contribute some new building
blocks toward a dynamic explanation of corporate
restructuring. Restructuring is a longitudinal phenomenon and, hopefully, the study will provide
for improved understanding of how these actions
are managed while encouraging additional inquiry
into temporal dimensions. Most generally, the theoretical model helps explain why some corporate
restructuring strategies appear as intentional and
deliberate actions while others resemble spontaneous and simultaneous responses. Future research
that integrates alternative learning views may produce a more dynamic and comprehensive understanding of strategy and its implications for firm
performance.
ACKNOWLEDGEMENTS
We are grateful for the helpful comments from
Editor Richard Bettis and the referees. We also
thank Professor Richard Johnson for his contributions to the dataset and Professors Parthiban David
and Ravi Madhavan for comments on an earlier
Strat. Mgmt. J., 29: 593–616 (2008)
DOI: 10.1002/smj
Learning How to Restructure
draft of this article. We thank the Krannert Graduate School of Management at Purdue University
for its support of this research.
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