LEARNING HOW TO RESTRUCTURE: ABSORPTIVE CAPACITY …

Strategic Management JournalStrat. 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: ABSORPTIVECAPACITY AND IMPROVISATIONAL VIEWS OFRESTRUCTURING 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 viewpointsof organizational learning, absorptive capacity and organizational improvisation, we examinewhether experience with corporate restructuring modes (sell-offs, spin-offs) influences subse-quent 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 sell-off and to higher performance. Conversely, and consistent with an organizational improvisationview, short-term and contemporaneous experience with spin-offs was related to the subsequentuse of spin-offs and to increases in financial performance. The findings contribute to a dynamicexplanation of corporate restructuring and its influence on financial performance, illustrate dif-ferences between learning in a repetitive situation and learning when repetition is rare, andindicate when absorptive capacity and organizational improvisational views are most profitable.Overall, these findings show that different kinds of restructuring experiences were associated withdifferent modes of restructuring and performance records. Considered collectively, the organi-zational learning perspective offers insights into why some corporate restructuring strategiesappear as intentional and deliberate actions while others resemble more spontaneous and simul-taneous responses. Copyright 2008 John Wiley & Sons, Ltd.

INTRODUCTION

Corporate restructuring involves divesting, spin-ning off assets, and exiting business lines (Bow-man and Singh, 1993; Johnson, 1996; Ravenscraftand Scherer, 1987). These actions are expensive,visible, and risky (Bergh, 1997; Gaughan, 1999;Hoskisson and Hitt, 1994). When making decisionsabout such events, managers would likely con-sider their organizations’ restructuring histories, as

Keywords: corporate restructuring; organizational learn-ing; absorptive capacity; organizational improvisation;spin-off; divestiture∗ Correspondence to: Donald D. Bergh, Daniels College of Busi-ness, Department of Management, The University of Denver,2101 S. University Boulevard, Denver, CO 80208, U.S.A.E-mail: [email protected]

prior experiences could be drawn upon to reducemistakes, improve decision making, and lowerstakeholder anxieties (e.g., Allen, 1998; Donald-son, 1990). However, we have little knowledgeof whether and how experience might matterto the restructuring decision; most research onexperience and corporate strategic behaviors hasfocused on growth alternatives such as mergers,acquisitions, and alliances (Amburgey and Miner,1992; Barkema and Vermeulen, 1998; Chang andRosenzweig, 2001; Vermeulen and Barkema, 2001;Zollo, Reuer, and Singh, 2002). Does experienceapply to restructuring decisions? If so, does it alsoinfluence profitability?

Current understanding of the antecedents to andimplications of restructuring is still in the develop-mental stages. Most research to date has portrayed

Copyright 2008 John Wiley & Sons, Ltd.

594 D. D. Bergh and E. N.-K. Lim

restructuring as a purposeful response to gover-nance, strategy, and industry pressures (Brauer,2006; Bruner, 2004; Donaldson, 1990). For exam-ple, some have argued that firms use restructuringto improve internal efficiency in response to activetakeover markets (Jensen, 1993; Kaplan and Weis-bach, 1992; Shleifer and Vishny, 1991). Othershave posited that shifts from weak to strong inter-nal governance led to the use of restructuring torefocus corporate strategies (Chatterjee, Harrison,and Bergh, 2003; Hoskisson, Johnson, and Moe-sel, 1994; Johnson, Hoskisson, and Hitt, 1993).Another explanation proposes that restructuringreverses excessively diversified strategies to moreoptimal levels (Bergh and Lawless, 1998; Com-ment and Jarrell, 1995; Jones and Hill, 1988;Markides, 1992, 1995), and reduces informationasymmetries between managers and owners(Bergh, Johnson, and DeWitt, 2008; Krishnaswamiand Subramaniam, 1999). Furthermore, some haveargued that restructuring discards unwanted partsof acquired asset bundles (Capron, Mitchell, andSwaminathan, 2001; Chang, 1996; Chang andSingh, 1999). Finally, theorizing from industrialorganization economics has been used to linkindustry characteristics to corporate restructuring(Ilmakunnas and Topi, 1999; Harrigan, 1982). Fewstudies have considered the effects of experienceon restructuring actions (e.g., Allen, 1998; Vil-lalonga and McGahan, 2005), and even fewerexamine how such experience might influencepost-restructuring financial performance. Consid-ered collectively, previous research provides in-sight into the reasons for and effects of restructur-ing, yet provides a limited explanatory frameworkfor understanding how prior restructuring experi-ences might influence subsequent actions and per-formances. We currently have incomplete knowl-edge of whether and how experience matters withcorporate restructuring.

The present study develops and tests a theoret-ical model that relates experience to restructuringand to subsequent financial performance. Specifi-cally, the model draws on two viewpoints of orga-nizational learning to link restructuring experienceheterogeneity to the adoption of different formsor modes of corporate restructuring actions. First,the model applies absorptive capacity arguments torelate cumulative and repetitive restructuring expe-rience to the development of explicit knowledgethat serves as the basis for routines and stan-dardized procedures that, in turn, help facilitate

efficient and economically beneficial restructuringby sell-off. Second, the model uses the organiza-tional improvisation viewpoint to tie short-term,recent, and real-time experience heterogeneity tothe development of tacit knowledge that leads torestructuring by spin-off. The different restruc-turing experiences, development of knowledgestocks, and subsequent use of sell-off and spin-offsare then related to post-restructuring financial per-formance. The study tests these alternative expla-nations and restructuring behaviors using differenttime intervals.

The findings suggest several contributions. Theyextend explanations of corporate restructuring byintegrating a dynamic construct—experience—into the restructuring process and to post-restruc-turing performance. In addition, by incorporatingabsorptive capacity and organizational improvisa-tion into an organizational learning perspectiveof how firms restructure, the findings support amore expansive and comprehensive model of cor-porate restructuring and how it influences perfor-mance. Furthermore, the results integrate differ-ent approaches to corporate restructuring; someactions, particularly sell-offs, appear to reflectdeliberate and intentional responses while oth-ers, especially spin-offs, reflect more of a sponta-neous and contemporaneous adaptation. The find-ings contribute to the organizational learning per-spective by offering insight into the absorptivecapacity and organizational improvisation perspec-tives using different methods and conditions, byillustrating differences between learning in a repet-itive situation and in a setting when repetition israre, and by indicating a set of conditions whenabsorptive capacity and organizational improvisa-tional views are most profitable relative to oneother.

THEORY AND HYPOTHESES

The theoretical model applies arguments fromtwo viewpoints of organizational learning, absorp-tive capacity and organizational improvisation,to explain how restructuring experience couldinfluence mode adoption and post-restructuringperformance. An organizational learning model isparticularly appropriate because it provides a the-oretical rationale for linking experience to actionsand outcomes. More specifically, learning has beendefined as a systematic change in behavior or

Copyright 2008 John Wiley & Sons, Ltd. Strat. Mgmt. J., 29: 593–616 (2008)DOI: 10.1002/smj

Learning How to Restructure 595

Restructuring mode Performance

Absorptive capacity

Organizational improvisation

Sell-offSpin-off

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. Accounting performanceMarket performance

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RepetitionExplicit knowledgeRoutines

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Real-time, novelExperienceheterogeneityTacit knowledge

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Figure 1. Theoretical model

knowledge informed by experience (Cyert andMarch, 1963; Levitt and March, 1988). Learn-ing is believed to occur when ‘experience gener-ates a systematic change in behavior or knowl-edge’ (Miner, Bassoff, and Moorman, 2001: 315).The viewpoints of absorptive capacity and orga-nizational improvisation represent opposite learn-ing explanations (e.g., Levinthal and Rerup, 2006;Miner et al., 2001; Winter, 2003) and provide acomplementary and integrative basis for hypothe-sizing how experience may influence restructuringactions and their effects on financial performance.

We begin by reviewing the two most prevalentrestructuring modes. Then, we present the theoret-ical logic of the two viewpoints of learning, applythe arguments to restructuring actions, and ulti-mately to post-restructuring financial performance.Figure 1 presents our theoretical model and indi-cates the hypothesized relationships.

Sell-offs and spin-offs as alternativerestructuring modes

Corporate restructuring is generally used to down-scope, downsize, or refocus diversification strategy(Hoskisson and Hitt, 1994; Johnson, 1996), andis conducted through a variety of alternatives ormodes including liquidations, sell-offs, spin-offs,and equity carve-outs (Bruner, 2004; Gaughan,1999). The most popular modes for implementingrestructuring are the sell-off and spin-off (Khanand Mehta, 1996; Nixon, Roenfeldt, and Sicher-man, 2000). A sell-off, also known as a divesti-ture, arises when assets are sold from one firmto another in exchange for cash and/or securi-ties (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

in a subsidiary to its own shareholders’ (Weston,Chung, and Hoag, 1990: 224; Miles and Rosenfeld,1983; Schipper and Smith, 1983), and in the pro-cess creates a separate, publicly traded firm fromthe spun-off assets.

Sell-offs and spin-offs represent substantiallydifferent ways to restructure. Sell-offs are usedto transfer assets to other firms that might real-ize higher value from their acquisition or to ridthe selling firm of assets that interfere with itsoperations or strategy (John and Ofek, 1995). Inmost cases, sold-off assets were not performingwell, were not creating value that met expectations,or were used to raise proceeds that could be uti-lized to pay down debt and/or be reinvested in therestructuring firm’s strategy (Bergh, 1997; Brauer,2006; Duhaime and Grant, 1984; Hoskisson et al.,1994; Taylor, 1988). Sell-offs involve a liquida-tion process, decisions about how the restructuredassets will be marketed, and how the transactionwill be managed. Investment banks often man-age these processes: they locate buyers, arrangefinancing, and manage the exchange of the sold-off assets. Sell-offs usually involve assets resid-ing in secondary and unrelated businesses relativeto the primary and core lines of the parent firm(Bergh, 1995a; Bergh et al., 2008; Comment andJarrell, 1995; Ravenscraft and Scherer, 1987). Thesell-off ends with the transfer of asset propertyrights (Alexander, Benson, and Kampmeyer, 1984;Donaldson, 1990).

By contrast, spin-offs are typically used to sepa-rate assets that have promising and high growthpotential opportunities that cannot be realizedwithin the parent firms’ structure (Aron, 1991;Bruner, 2004), oftentimes while maintaining post-restructuring relationships with the parent firm



(Ito, 1995; Kudla and McInish, 1984). They reor-ganize ownership among existing shareholders,produce no cash proceeds, and reduce the levelof assets under the control of the parent man-agement. The spun-off assets become indepen-dent from the parent and require a new corpo-rate governance system, including leadership anddirectory boards (Seward and Walsh, 1996; Walshand Seward, 1990). In addition, spin-offs ofteninvolve assets residing in or related to the restruc-turing firm’s core business lines (Bergh et al.,2008; Nixon et al., 2000) and employ internal con-trol systems that emphasize interface management,which coordinate ongoing strategic and organiza-tional relationships after the restructuring has beencompleted (cf., Aron, 1991; Ito, 1995). For exam-ple, when PepsiCo spun-off its fast food division,Tricon (consisting of KFC, Pizza Hut, and TacoBell), long-term contracts were designed to sustaincontinuing value-creating interactions among thefirms. One such contract involved the assignmentof soda fountain property rights and obligations.In this case, some Tricon firms were required toagree to continue to exclusively sell PepsiCo sodafountain products before the spin-off was finalized.

Overall, sell-offs and spin-offs have uniquemotives and are used under different circumstances(Bergh et al., 2008; Bruner, 2004; Nixon et al.,2000). These dissimilarities provide conditions andopportunities for managers to develop specific andunique knowledge about how to formulate and exe-cute each type of restructuring mode. The impli-cations for learning and adoption of sell-offs andspin-offs are considered next. Two different view-points of organizational learning, absorptive capac-ity, and organizational improvisation are relatedindividually to the restructuring actions.

Absorptive capacity, sell-offs, and financialperformance

One research stream in the organization learningliterature posits that experience creates knowledgethat can be stored into and retrieved from anorganization’s memory (Huber, 1991; Levitt andMarch, 1988; Fiol and Lyles, 1985). Managers andtheir firms have an ability to recognize the valueof new knowledge, assimilate it, and apply it tocommercial ends, a learning viewpoint known asabsorptive capacity (Cohen and Levinthal, 1990;see Lane, Koka, and Pathak, 2006; and Zahra and

George, 2002, for expanded definitions). Absorp-tive capacity is a function of prior organiza-tional problem solving (Lane, Salk, and Lyles,2001), and is developed through the accumula-tion of experiences (Cohen and Levinthal, 1990;Lane and Lubatkin, 1998; Pennings and Harianto,1992). Specifically, the underlying premise ofabsorptive capacity ‘is that the organization needsprior related knowledge to assimilate and usenew knowledge. . .accumulated prior knowledgeincreases both the ability to put new knowledgeinto memory. . .and the ability to recall and use it’(Cohen and Levinthal, 1990: 129). The absorptivecapacity view assumes that learning is cumula-tive and learning performance is highest when theobject of learning is related to what is alreadyknown (Cohen and Levinthal, 1990; Lane et al.,2006; Zahra and George, 2002). Moreover, absorp-tive capacity also includes an organization’s abilityto exploit information. From this view, a firm’sabsorptive capacity is influenced through its levelof prior related knowledge, repetition, and inten-sity of its exposures to similar events (Kim, 1998;Vermeulen and Barkema, 2001; Zahra and George,2002).

Firms draw from absorptive capacity to createexplicit knowledge that can be developed, codified,and applied to improve decision making, revampknowledge stocks, and overcome traps to knowl-edge development (Lane et al., 2006; Lane andLubatkin, 1998; Zahra and George, 2002). Cumu-lative experiences would be translated into explicitknowledge that would guide organizational actionsand behaviors (Amburgey, Kelly, and Barnett,1993; Haleblian, Kim, and Rajagopalan, 2006;Shaver, Mitchell, and Yeung, 1997). Once devel-oped, learning is then made explicit in oper-ating procedures, formalized systems, and rou-tines (Haleblian and Finkelstein, 1999; Cyert andMarch, 1963; March and Sevon, 1984). In addi-tion, new search rules evolve slowly (Chang andRosenzweig, 2001; Cyert and March, 1963; Millerand Friesen, 1980), so organizations tend to persistin the same activity over time (Miller and Friesen,1980, 1982). Overall, the creation and maintenanceof absorptive capacity is an iterative and repeti-tive process where firms learn from experiences,make inferences, and store knowledge that can becodified and applied to future decisions (Cohenand Levinthal, 1990; Hayward, 2002; Zahra andGeorge, 2002). Hence, past experience can lead



to both greater absorptive capacity and greaterlearning.

The absorptive capacity view of organizationlearning has been used to help explain growthand expansionary behaviors such as mergers andacquisitions (M&As). For example, when a firmengages in an M&A, it develops absorptive capac-ity for understanding that action (Barkema andVermeulen, 1998; Baum, Li, and Usher, 2000;Haleblian and Finkelstein, 1999). AdditionalM&As of the same type allow ‘competencies to berefined, which [subsequently] increases the likeli-hood of even more acquisitions of the same type’(Amburgey and Miner, 1992: 336). This experi-ence creates absorptive capacity that in turn allowsfirms to learn how to become more efficient atclearly defined problems, such as those involvingcorporate strategy behaviors such as acquisitionsand alliances (e.g., Hayward, 2002; Villalonga andMcGahan, 2005). In addition, after accumulatingknowledge with a particular M&A activity, man-agers tend not to welcome new risks associatedwith using a different type (Nelson and Win-ter, 1982; Pennings, Barkema, and Douma, 1994).Even negative consequences associated with priorexperiences may not cause managers to change;indeed, poor performance may not deter the repet-itive learning that can be ascribed to experi-ence (Amburgey and Miner, 1992; March, 1991).Instead, M&A performance problems may be attri-buted to execution issues rather than evidence ofmistaken actions or problems in learning (Hale-blian et al., 2006).

This reasoning can be extended to explainingexiting behaviors such as corporate restructuringand how managers might select between restruc-turing modes (Figure 1). Experience with restruc-turing could create absorptive capacity that wouldfacilitate knowledge development that would beexplicitly codified into systems, routines, and pro-cedures that could help guide future behaviors.Repetition would also create momentum to repeatthe same type of restructurings used in the past(Amburgey and Miner, 1992; Baum et al., 2000;Villalonga and McGahan, 2005). Furthermore, cor-porate restructuring is a vehicle for realizing anobjective (Bowman and Singh, 1993; Johnson,1996; Markides, 1992, 1995), so managers wouldlikely focus on the desired outcome and might notwant to reconsider how to restructure each timesuch an action was necessary. Managers wouldhave incentives to exploit organizational memory

and absorptive capacity, and to make their deci-sion and move forward as efficiently as possible toaccomplish the objective of the restructuring. Pastexperiences lead to greater absorptive capacity andlearning that would likely apply to restructuringdecisions.

The absorptive capacity view of learning per-tains more to sell-offs than spin-offs because sell-offs provide more of the conditions necessaryfor accumulating experience benefits, assimilatingknowledge, and developing the explicit knowledgethat can be codified into routines and standardizedprocedures (Figure 1). First, the process for sellingassets involves more stages, parties, and decisionsthan spin-offs, thus presenting greater opportuni-ties for identifying and realizing economies fromrepetition. For example, sell-offs involve the vali-dation, liquidation, and replacement of assets, mar-keting, and management of transaction costs asso-ciated with searching, negotiating, and exchangingthe assets with an external third-party buyer. Spin-offs do not require asset liquidation, replacement,or marketing, and involve parties affiliated withthe restructuring firm, where the transaction occursthrough a reorganization plan involving establishedrelationships (Kudla and McInish, 1984). The sell-off process involves more stages and complexitiesthan the one used for spin-off, creating greaterpotential for experience and learning curve benefits(cf. Lieberman, 1987, 1989).

Second, the control systems used to managethe assets in sell-offs would facilitate the commu-nication necessary to create absorptive capacity.Specifically, and as noted above, sell-offs typicallyinvolve assets in unrelated business units and lines(Bergh, 1995a; Bergh et al., 2008; Nixon et al.,2000; Ravenscraft and Scherer, 1987). These assetsare typically managed with arms-length financialcontrols and profit center accounting techniquesthat focus on general and objective measures suchas return on assets, market shares, and profitmargins (Hill, Hitt, and Hoskisson, 1992; Hilland Hoskisson, 1987; Jones and Hill, 1988). Theuse of observable measures create transparenciesthat enhance interpretation, communication, andknowledge transfer (Szulanski, 1996), while alsoproviding managers with a clear understanding ofthe factors that influence the performance of therestructuring transaction. In addition, with greatertransparency, absorptive capacity is increased andmore learning can occur (Lane et al., 2001). Man-agers would have a higher quality knowledge



basis for drawing correct inferences about therestructuring process and better understanding forfuture decisions (Hayward, 2002; cf. Haleblian andFinkelstein, 1999), both of which contribute toabsorptive capacity.

Spin-offs, by contrast, tend to involve idiosyn-cratic and related business assets and are struc-tured on a case-by-case basis (Bergh et al., 2008;Ito, 1995; Nixon et al., 2000). The restructur-ing process for spin-offs would be more variablethan that used by sell-offs, making it difficult todevelop experience and repetition benefits, absorp-tive capacity, and the explicit knowledge that leadsto routines and systematized procedures that canbe leveraged for learning curve economies. More-over, spun-off firms are more likely to have closerelationships with their restructuring firms. Theseassociations are managed using controls that focuson interface management and sharing and account-ing of specialized resources, and ambiguity canexist about the communication of knowledge shar-ing and transfer between the two firms (Khan andMehta, 1996; Kudla and McInish, 1984). Thesecharacteristics impede the benefits of experience,reduce the potential for developing absorptivecapacity, lower the development of the proceduresneeded for standardization, and constrain the econ-omizing potential associated with repeated actions(Cohen and Levinthal, 1990; Szulanski, 1996).Hence, the benefits and learning from long-termexperience and absorptive capacity are likely toapply more to sell-offs than to spin-offs.

In sum, greater experience with sell-offs wouldlead to higher absorptive capacity and to the devel-opment of explicit knowledge that can be codifiedinto routines and standardized procedures. Man-agers would exploit absorptive capacity by apply-ing the learning from past experience in similarchoices to reduce risks in subsequent decisions(Chang and Rosenzweig, 2001). As firms thereforegain experience with restructuring by sell-offs, wepredict that they will continue to use those meth-ods. Changing to spin-offs poses additional costs.We hypothesize:

Hypothesis 1: As firms gain experience with sell-offs, they will continue to use sell-offs as a formof corporate restructuring strategy.

The absorptive capacity view of organizationlearning may also help explain post-restructuringfinancial performance (Figure 1). Experience with

corporate restructuring would increase absorptivecapacity and knowledge (Hayward, 2002; Pen-nings et al., 1994; Zollo and Singh, 2004), whichwould provide a basis for more effective man-agement. With subsequent increases in absorptivecapacity there would likely be fewer errors, thedevelopment of specialized and standardized rou-tines, and increased execution effectiveness (Ahujaand Katila, 2001; Levinthal and March, 1993). Inaddition, accumulating absorptive capacity in oneperiod will permit its more efficient exploitation inthe next (Cohen and Levinthal, 1990). It followsthat firms with greater absorptive capacity gainedthrough prior experience will have a better founda-tion to create knowledge, assimilate and interpretopportunities, and more effectively develop andapply explicit knowledge than firms with less expe-rience.

Furthermore, the potential to develop absorp-tive capacity is critical; firms with higher levelsof experience can better ‘refine, extend, and lever-age existing competencies or. . .create new ones byincorporating acquired and transformed knowledgeinto [their] operations’ (Zahra and George, 2002:190) than firms with lower levels of experience.Firms having more experience and higher absorp-tive capacity would be able to use their resourcesmore effectively and leverage their greater abilityto transform experience benefits than firms withless. Absorptive capacity has been conceptualizedas a dynamic capability that can lead to com-petitive advantage and above-normal performancereturns (Narasimhan, Rajiv, and Dutta, 2006; Win-ter, 2003; Zahra and George, 2002; Zollo et al.,2002).

Experiences with restructuring would helpreduce process costs and competency traps. More-over, by reducing costs associated with the pro-cesses and activities of assimilating and integratingnewly acquired information, firms having moreexperience with restructuring are likely to havehigher post-restructuring performance than thosehaving less. This logic likely applies more closelyto sell-offs because they have higher potential forabsorptive capacity than spin-offs. Sell-offs havethe potential for standardization of the restruc-turing process, are managed with financial con-trol systems that present lower barriers to inter-nal knowledge transfer, and offer conditions morefavorable for generalization—none of which canbe as easily realized by spin-offs. Firms havingmore experience with sell-offs can draw on their



absorptive capacity to develop explicit knowledgeto codify routines and standardized procedures thatcan economically and advantageously guide thedeal-making process, terms, and governance pro-cess. That experience translates into higher absorp-tive capacity and knowledge to apply to subsequentsell-offs.

By contrast, firms with low numbers of priorsell-offs have lower absorptive capacity, lessknowledge to exploit, and are more prone to pro-cedural errors that can lead to disadvantageoussituations. Firms with less experience are loweron the learning curve and do not have the explicitknowledge to develop economically valuable rou-tines and standardized procedures. These firms arealso likely to be less effective at managing theprocesses of developing and exploiting new knowl-edge. They stand to gain less financial benefits. Wepredict that those with the most repetition overtime with sell-offs will have standardized proce-dures and routines for better formulating and exe-cuting the sell-offs resulting in higher performance.

Hypothesis 2: Firms that have more experiencewith sell-offs will have higher financial perfor-mance after a subsequent sell-off than firms thathave less experience with sell-offs.

Improvisational learning, spin-offs, andfinancial performance

Another research stream in the organizationallearning literature describes how learning canoccur in short-term, recent, and real-time settings.Based on observing musicians, actors, firefight-ers, and new product development teams (Berliner,1994; Brown and Eisenhardt, 1995; Vera andCrossan, 2005; Weick, 1993), a much differentconception of organizational learning known asorganizational improvisation is emerging, one thatapplies to settings where planning models andprior and repetitive experiences play a smaller role.Some actions can occur without advanced plan-ning or long-term experience (Cohen, March, andOlsen, 1972; Cyert and March, 1963; Moormanand Miner, 1998b), and the conception and logic oforganizational improvisation has been developedin an effort to help explain learning in such set-tings (Crossan et al., 2005; Eisenhardt and Tabrizi,1995; Hatch, 1997; Moorman and Miner, 1998a,1998b). Organizational improvisation is a type ofshort-term learning, where experience and related

change occur at or near the same time (Miner et al.,2001; Vera and Crossan, 2005). It has been pre-sented as a form of learning on the basis that expe-rience heterogeneity and recombinations of storedknowledge, routines, and skills can lead to sys-tematic changes in behavior (Hatch, 1997; Mineret al., 2001; Moorman and Miner, 1998a).

Improvisation has several characteristics thatdistinguish it from other learning views (Mineret al., 2001). First, it has a reduced temporalgap between the planning and implementation ofunique actions; the more temporally proximate thedesign and execution of a behavior, the more likelythe action is improvisational (Crossan et al., 2005,1996; Moorman and Miner, 1998a, 1998b). Impro-visation has been described as spontaneous and‘in-the-present’ (Crossan et al., 2005: 131), andhas been used to explain how managers resolvea surprising problem and/or create value from anunexpected opportunity (Weick, 1996, 2001). It isnot the repeating of a preexisting routine, nor isit pre-designed or standardized. Second, it appliesto actions and decisions that are novel, or devi-ations from standard practices, and improvisersdraw from information and resources available tothem at the time the decision is necessary, alsoknown as bricolage (Levi-Strauss, 1967). Improvi-sation is tailored to specific contexts and idiosyn-cratic to a time and place (Baker and Nelson, 2005;Miner et al., 2001; Vera and Crossan, 2005). Third,organizational improvisation pertains to fast anduncertain decisions (Brown and Eisenhardt, 1995;Moorman and Miner, 1998b; Vera and Crossan,2005), requiring managers to draw from organi-zational memory, or ‘stored information from anorganization’s history that can be brought to bearon present decisions’ (Walsh and Rivera, 1991:61). Improvisation draws from experience hetero-geneity and creatively recombining and applyinglearned routines and knowledge (Weick, 1993;Hatch, 1997; Miner et al., 2001). Greater memoryenhances improvisation because it allows decisionmakers to apply retrospectives and real-time infor-mation 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. Pro-cedural memory is ‘how things are done’ (Cohenand Bacdayan, 1994: 554) and ‘things you cando’ (Berliner, 1994: 102). It is derived from richunderstanding of skills and routines, and a well-developed procedural memory allows managers to



draw from declarative memory, or the ‘memoryfor facts, events, or propositions’ (Cohen, 1991:137; Tippins and Sohi, 2003). When managers cancombine well-developed procedural memory withdeclarative memory, they are likely to have highertacit knowledge of how their organizations operate(Cohen, 1991; Cohen and Bacdayan, 1994; Win-ter, 1987), and can make improvisational decisionsthat are more coherent, novel, and timely (Moor-man and Miner, 1998a).

For example, Moorman and Miner (1998a)invoke the instance of a jazz musician beingable to improvise when he/she has developeda large repertoire of relevant musical experi-ences, while Weick (1993) describes a masterbricoleur as requiring preexisting routines to cre-ate a new tool to solve a novel problem. Weick(2001) goes another step by describing impro-visation as ‘just-in-time strategy’ that is predi-cated less by investment in anticipation and moreon the development of general knowledge, largeskill sets, an ability to react quickly, and trustin intuition (Weick, 2001: 352). Winter (2003)notes that organizations can be ‘pushed into “fire-fighting” mode, a high-paced, contingent, oppor-tunistic and perhaps creative search for satisfac-tory alternative behaviors. . .[where] problem solv-ing is not routine. . .not highly patterned and notrepetitious. . .it typically appears as a responseto novel challenges. . .or other relatively unpre-dictable events. . .[and] typically arises froma foundation of patterned and practicedperformance. . .’ (Winter, 2003: 992–993). Hence,improvisation requires tacit knowledge gainedfrom having procedural memory of routines andskills that can access the declarative memory of theorganization’s key knowledge stocks (Moormanand Miner, 1998a; Miner et al., 2001). Higher lev-els of tacit knowledge facilitate improvisation bymaterially reducing the time between the compo-sition of a solution to a problem and the executionof the action to affect it.

Organizational improvisation may describe cor-porate restructuring actions (Figure 1). Most the-oretical perspectives depict restructuring as a res-ponse behavior (see Brauer, 2006; Haynes, Thomp-son, and Wright, 2003; Johnson, 1996, for re-views), used for quickly improving the firm’s eco-nomic and strategic conditions (Hoskisson andHitt, 1994; Hoskisson, Johnson and Moesel, 1994;Markides, 1992). It is often driven by uncertainty(Bergh, 1998; Bergh and Lawless, 1998; Leiblein

and Miller, 2003), and can be a punctuated and fastaction rather than a continuous and evolving pro-cess (e.g., Donaldson, 1990; Hoskisson and Hitt,1994; Ravenscraft and Scherer, 1987). Improvisa-tion may apply to such actions, as ‘under condi-tions of time pressures and/or uncertainty, a plan-ning orientation is insufficient. . .[and] [i]improv-isation becomes an alternative or complementaryorientation’ (Crossan et al., 2005: 133).

In addition, restructuring tends to occur whenneeded, so the time interval between restructur-ing actions may be too long or too short to allowfor absorptive capacity to be developed and sus-tained. Very long intervals make it difficult formanagers to remember or apply the lessons androutines from prior experience, while short onesdo not provide ample time for repetitive learn-ing to occur (Argote, Beckman, and Epple, 1990;Baum and Ginsberg, 1997). Moreover, managersmay be reluctant to codify learning and generateinferences from activities that they do not expectto repeat (Szulanski, 1996; Winter and Szulan-ski, 2001). Furthermore, a firm’s post-restructuringperformance can vary (Cusatis, Miles, and Wool-ridge, 1993; Desai and Jain, 1999; Daley, Mehro-tra, and Sivakumar, 1997), which could influencethe intensity with which managers search for infer-ences from prior experiences (Hayward, 2002;Levinthal and March, 1993). Finally, restructur-ings that occurred in recent years are frequentlyintegrated with ensuing actions that collectivelyrepresent a strategic reaction to resolving the mat-ter that drove the restructuring (Donaldson, 1990;Hoskisson and Hitt, 1994; Hoskisson et al., 1994).Restructurings in more distant years may not be aspertinent or applicable. Even numerous restructur-ings spread over several prior years could play aweaker role, because they may not be part of theresponse that was created for resolving the moti-vating factors. Hence, some restructurings may notoffer the conditions for constructing the routinesand repetition necessary for developing absorptivecapacity and may be influenced more by short-termand real-time experience that draws from storedknowledge, learned routines, and skills.

The improvisation logic likely applies more torestructuring through spin-offs rather than sell-offs.First, spin-offs typically occur infrequently (e.g.,Donaldson, 1990), and are less numerous thansell-offs (Gaughan, 1999; Bruner, 2004). Becauseof their lower occurrence, spin-offs offer feweropportunities for developing repetitive routines and



may be seen as deviations from a firm’s plannedcorporate strategy. For example, spin-offs are notusually motivated by financial proceeds, as theirfinancial implications are limited to possible taxsavings and expected future gains associated withadjusting and making clearer the restructuringfirm’s strategy (Allen, 2001; Cusatis et al., 1993;Daley et al., 1997; Desai and Jain, 1999; Krish-naswami and Subramaniam, 1999; see Bruner,2004, for a review). Spin-offs would not likely bepreplanned or anticipated as a method of value cre-ation. However, by contrast, firms can buy and selloff units for profit, a value-creation process knownas ‘arbitrage,’ where gain is realized by selling ata higher price than the original purchase amount.The differences in financial proceed potential mayhelp explain why spin-offs are relatively rare com-pared to sell-offs.

Second, the principal parties to a spin-off aredirectly related to the restructuring firm, whilethose associated with a sell-off are at least partlyexternal (e.g., the buying firm and its stakehold-ers). When making a spin-off, there is less poten-tial for temporal holdups between the design andexecution of the restructuring action; the needfor temporal delays would be minimized, as therestructuring firm’s managers would develop andimplement a process that would transfer propertyrights to their owners. The separation and post-restructuring governance of the spun-off assetswould all occur internally. By contrast, the sell-off process involves several iterative stages thatwould serve to lengthen the temporal gap betweendesign and execution, including finding an acquir-ing firm, negotiation, due-diligence, the approvalof different sets of stockholders and governmententities, and ultimately a ‘close’ of the deal. Thetime interval between the design and execution ofthe spin-off would likely be less than that for thesell-off.

Third, managers can draw from rich organiza-tional memory when making spin-off decisions.In particular, as noted above, spin-offs usuallyinvolve assets that reside within the core busi-nesses of the restructuring firm (Bergh et al., 2008;Ito, 1995; Khan and Mehta, 1996; Nixon et al.,2000). These assets are typically managed with theface-to-face methods necessary for developing thememory necessary for improvisation (Eisenhardt,1989; Moorman and Miner, 1998b; Sproull andKeisler, 1991). In such settings, managers tend touse control systems that involve close attention and

detailed and subjective judgment, and focus on thetransaction level of analysis where performanceis assessed by factors such as quality, transferprice, and productivity (Hill, Hitt, and Hoskisson,1992; Hill and Hoskisson, 1987). This manage-rial intensity would create high procedural anddeclarative memory, as the managers have detailedknowledge of the operating affairs and controlprocedures used for managing the relationshipswith core business assets. Similarly, through hav-ing interfaced closely over time, managers developa refined understanding of the restructured assetsand a set of routines that guide their decisionmaking. The operational-level detailed knowledgeachieved through close and repeated interactionscould create partner-specific experiences that inturn would contribute to tacit knowledge (Zolloet al., 2002).

Finally, the top managers of the spun-off assetsusually come from the restructuring firm (Aron,1991; Seward and Walsh, 1996). The restructuringfirm’s managers would have developed a rich set ofroutines for working with the leaders of the to-berestructured assets. With a higher degree of famil-iarity and likely face-to-face knowledge of thespun-off asset’s managers, the restructuring firm’smanagers would have greater tacit knowledge todraw upon, enabling faster decision making andfacilitating convergence in the time between plan-ning and execution of a restructuring action (cf.Moorman and Miner, 1998a).

In sum, the organizational improvisational viewmay help describe restructuring by spin-offs. Theseactions tend to be rare and novel, have less poten-tial for temporal holdup between design and exe-cution, and involve managers who can leveragelearning from rich organizational memory andhigh-tacit knowledge. Short-term and contempo-raneous experience would provide the impetus forgenerating organizational change and recombiningstored knowledge and skills to act in real-timemanner. We predict that recent spin-offs will havea more influential effect on subsequent spin-offadoption than those occurring in temporally distantyears.

Hypothesis 3: Recent experience with spin-offsis related more positively to the likelihood ofa subsequent spin-off than temporally distantexperience with spin-offs.



Improvisation has been linked to financial per-formance, a relationship that is influenced by sev-eral factors, including expertise, teamwork skills,organizational memory, and real-time informationand communication (Crossan et al., 2005; Tip-pins and Sohi, 2003; Vera and Crossan, 2005).When any of these factors is high, managers havemore specialized knowledge, increased collabora-tion, and higher coordination that in turn facili-tates adaptation and anticipation (Amabile, 1996;Weick, 1993; Weick and Roberts, 1993). With amore richly developed basis from which to drawupon, managers are better able to apply learn-ing from recent and relevant events to improvi-sational decisions (Eisenhardt, 1989; Eisenhardtand Tabrizi, 1995). They have an increased abil-ity to react to real-time information flows and canmake improvisations of higher quality that leadto higher performance and effectiveness (Crossanet al., 2005; Vera and Crossan, 2005; Moormanand Miner, 1998b; Weick, 1993).

This logic applies most closely to spin-offs,as spun-off assets are typically in business lineswhere restructuring managers have high expertise,have worked closely together and interfaced regu-larly with other managers, and are more likely tohave developed open information sharing and fastand accurate communication routines (e.g., Berghet al., 2008; Ito, 1995; Khan and Mehta, 1996;Nixon et al., 2000). The managers in these cir-cumstances could apply their tacit knowledge ofthe assets to subsequent actions and make betterinformed and more effective decisions (Baker andNelson, 2005; Winter, 2003). Their actions couldbe made in real time and based upon immediatefeedback from recent information (Crossan et al.,2005; Moorman and Miner, 1998a). And, by draw-ing upon high degrees of expertise, teamwork, andopen information-sharing, managers could imple-ment the most appropriate spin-offs that provideeffective and immediate resolution to the problemdriving the restructuring, leading to a faster andmore positive effect (e.g., Brown and Eisenhardt,1995; Miner et al., 2001).

In addition, since spin-offs tend to occur lessfrequently than sell-offs (Bruner, 2004; Gaughan,1999; Nixon et al, 2000), managers would be lesslikely and less able to store information about themfor long periods of time (e.g., Szulanski, 1996).Consequently, short-term experience from recentspin-offs and those in consecutive years would

be more relevant and valuable for current deci-sion making. Spin-offs occurring in years moredistant to a focal restructuring event are less appli-cable because the learning from those events maynot be directly associated with the idiosyncraticnature and problems of the current restructuring.When learning is rooted in more recent spin-off experiences, then those particular events willhave the highest potential for influencing learn-ing, tacit knowledge, decision making, and post-restructuring performance. Overall, financial per-formance would be influenced less by spin-offsoccurring in the distant past and more by thosethat resolve the current issues facing the firm (e.g.,Hamilton and Chow, 1993; Markides, 1992, 1995).

Hypothesis 4: Recent experience with spin-offsis related more positively to financial perfor-mance following a focal spin-off than experiencewith spin-offs in temporally distant years.

METHOD

Sample

Consistent with other empirical studies of restruc-turing (Bethel and Liebeskind, 1993; Markides,1992, 1995; Hoskisson et al., 1994), the hypothe-ses were tested by a sample of restructuring firms.The sample was determined using several steps.First, we randomly identified 300 firms that maderestructuring announcements between 1 January1990 and 31 December 1997. This number andtime period were selected to help ensure a largesample that was diverse enough for testing thehypotheses. The firms and restructuring announce-ments were found in the Securities Data Corpora-tion’s Worldwide Merger & Acquisition Database(SDC), 2000. Second, we examined each restruc-turing firm to determine whether it was publiclyheld, that it resided in a nonregulated industry,that the restructuring was voluntary and completed,and that it was based in the United States. Thesescreens were necessary to ensure data availabilityand consistency. Ninety-five firms were removed,and the final sample consisted of 205 firms thatannounced and implemented a restructuring action.Third, mean comparisons of the 205 retained firmswith the 95 discarded firms indicated no significantdifferences in terms of the transaction size (dol-lar value of the restructuring event), profitability



(return on assets [ROA]), year, debt, and spin-offand sell-off occurrence.

Dependent variables

Restructuring mode represented how the focalrestructuring event occurred. This variable wascoded as 1 when the focal restructuring event was aspin-off and as a 0 when it was a sell-off. The clas-sification of spin-off and sell-off was found in theSDC‘s Worldwide Merger & Acquisition Database,2000. No restructuring events were a hybrid of thetwo alternatives or another type of restructuring.

Financial performance was measured as theROA and mean earnings per share (EPS) for eachof the five years after the year of the focal restruc-turing event. ROA is one of the most commonaccounting-based performance measures and cor-relates highly (r = 0.9 or higher) with other suchproxies, including return on sales (ROS) and returnon equity (ROE). EPS reflects financial perfor-mance from the investor’s perspective. Using bothmeasures reflects the multidimensional aspects offinancial performance. The data were found inCOMPUSTAT.

Independent variable

Experience was measured as the count of priorrestructuring events; it was the number of spin-offs and sell-offs made by each restructuring firmduring the 10 years prior to the year of thefocal restructuring. The SDC Merger & Acquisi-tion Database, 2000, reported the dates of the priorspin-offs and sell-offs for the 205 firms. Thesecounts were summed for several different periods,including the number that occurred one year priorto the focal event, two years prior, three and fouryears prior, five to 10 years prior, and the sum overthe entire 10-year period. These window lengthswere used to separate immediate, short-term, andlong-term counts from one another. The unit ofanalysis is the restructuring firm and its specificexperiences over each of the 10 years prior to thefocal restructuring event.

Control variables

We controlled for several explanations that rep-resent motives for corporate restructuring and/orcould influence the restructuring/performance rela-tionship. First, we included control variables to

account for a financial distress hypothesis. Wemeasured the restructuring firm’s financial perfor-mance (ROA) and debt (debt/sales) for the yearprior to the focal restructuring year. Second, weincluded a control variable for strategy, defined interms of the relatedness of the restructured assets.A dummy variable was coded as 1 if the restruc-tured assets were in the same primary four-digitSIC as the restructuring firm and as 0 otherwise.Third, we used two variables to control for a man-agerial hubris hypothesis. We measured the sizeof the restructuring transaction (dollars, logged)and the size of the restructuring firm (total assets,logged). Fourth, we controlled for the agencyhypothesis of owner/manager control. A variablecalled Blockholdings was measured as the percent-age of outstanding common stock held in 5 percentblocks or larger. Fifth, we accounted for indus-try effects in three ways; by norming all financialvariables relative to their industry averages (dif-ferences from the mean), by including the restruc-turing firm’s primary two-digit SIC as a dummyvariable in the regression equations, and by sub-sample analyses that that either included or did notinclude the most popular industries (no industryinfluences existed, so the variables are not reportedfor space purposes).

Sixth, we measured investor assessment of thequality and expected performance effects of thefocal restructuring with the cumulative abnor-mal returns (CAR) variable. If the restructuringinvolved assets whose disposal are expected toraise the aggregate value of the restructuring firm,then this variable would be positive, and negativeotherwise (Krishnaswami and Subramaniam, 1999;Montgomery, Thomas, and Kamath, 1984). CARwas operationalized using the standard event studymethodology, whereby CAR was computed forthe days surrounding the restructuring announce-ment. The standard event study approach esti-mates a market model for each firm and thencalculates a cumulative abnormal return for theevent. Specifically, the CARs were estimated asARit = Rit − (ai + biRmt), where ai and bi are theordinary least squares (OLS) parameter estimatesobtained for the regression of Rit on Rmt over anestimation period (T ) preceding the event; ARit isdaily abnormal returns, Rit is the rate of return onthe share price of firm i on day t and Rmt is the rateof return on the S&P 500 on day t . The parameterestimates were based on an estimation period of200 days (−250 to −50) before the restructuring



announcement. Abnormal returns were cumulatedover the two-day window (day 0 is the announce-ment business day, +1 is the next business day)surrounding the announcement date. Stock mar-ket data were found in the Center for Research inSecurity Prices’2000 data tapes.

In addition, we accounted for year-specificeffects by recording the year the focal restructuringevent occurred. Finally, continuing restructuringactivity was measured as a dummy variable codedas 1 if a restructuring firm made additional restruc-turing actions after a focal restructuring and as 0if not. Data for the control variables were foundin the SDC database, COMPUSTAT, and proxystatements for the year of the restructuring.

Analyses

The hypotheses were tested with hierarchicalregression analyses. Logistic regression was usedfor testing Hypothesis 1 and Hypothesis 3 becausethe dependent variable in these hypotheses (restruc-turing mode) was dichotomous. Similar to ordinaryleast squares (OLS) regression analyses, hierarchi-cal logistic regression analyses provide variable

coefficients and model parameters. The coefficientsare nonstandardized, range from positive to nega-tive infinity, and are distributed as z-scores. Thesigns of these coefficients (+, 0, −) can be inter-preted like those produced by OLS regression (+is more, − is less). The model parameters arereflected in the Cox and Snell R2 and the Nagelk-erke R2. Like the R-square measure in OLS, thesemeasures range from 0 to 1, approaching 0 asthe quality of fit diminishes and 1 as it improves.Hypotheses 2 and 4 were tested using OLS regres-sion. Standardized coefficients are reported. Mul-ticollinearity was not apparent in the results (vari-ance inflation factors were below 2, below thevalue of 10 where multicollinearity becomes analternative explanation).

RESULTS

Table 1 reports means, standard deviations, andintercorrelations for the study variables. Most ofthe restructuring events were sell-offs (59%, 123of 205), the stock market reacted positively tothe restructuring announcement (2.2% increase in

Table 1. Means, standard deviations, and correlations

Variables Mean SD 1 2 3 4 5 6 7

1 Restructuring mode 0.405 0.4922 Focal CAR 0.022 0.047 0.186∗

3 Pre-restructuring ROA 1.682 10.213 0.125 −0.0524 Debt/sales 0.429 0.559 −0.164∗ −0.028 −0.0555 Asset relatedness 0.610 0.489 0.191∗ −0.053 0.088 −0.0486 Transaction price (log) 2.504 0.838 0.049 0.074 0.284∗ 0.067 0.1257 Total assets (log) 3.482 0.847 −0.080 −0.161∗ 0.210∗ 0.026 −0.021 0.543∗

8 Blockholdings (%) 21.683 20.563 −0.064 0.018 −0.058 0.043 0.055 −0.159∗ −0.273∗

9 Year 1994.107 2.119 −0.051 0.053 0.014 −0.024 −0.082 0.104 0.00610 Post-EPS 5 year average 1.627 1.985 −0.095 −0.057 0.228∗ −0.176∗ −0.114 0.263∗ 0.487∗

11 Post-ROA 5 year average 4.095 5.570 −0.059 0.125 0.502∗ −0.214∗ −0.082 0.083 0.09512 Post-Restructuring dummy 0.223 0.278 0.067 −0.037 0.150∗ 0.012 0.010 0.248∗ 0.358∗

13 Count of spin-offs 0.380 0.694 0.508∗ 0.039 0.068 −0.071 0.165∗ 0.083 0.01114 Count of sell-offs 7.493 9.695 −0.074 −0.073 0.117 −0.084 0.012 0.225∗ 0.419∗

Variables 8 9 10 11 12 13

9 Year 0.07810 Post EPS average −0.152 −0.04011 Post ROA average −0.076 −0.005 0.532∗

12 Post Restructuring dummy −0.187∗ 0.044 0.139 −0.02113 Count of spin-offs −0.057 0.015 −0.143 −0.061 0.05014 Count of sell-offs −0.136 0.101 0.361∗ 0.093 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 forspin-off, 0 for sell-off.



Table 2. Number of sell-offs and spin-offs per yearbefore focal event year

Year relative tofocal event year

# ofsell-offs

# ofspin-offs

−1 206 8−2 141 2−3 143 2−4 162 1−5 140 0−6 132 1−7 115 1−8 105 3−9 85 2

−10 67 5

Note: These figures represent the summed count of prior restruc-turing actions for all of the 205 restructuring firms.

CAR), and the five-year post-restructuring meanreturn on assets was higher than the one-yearpre-restructuring mean (4.09% versus 1.68%). Inaddition, most firms had far fewer pre-restructuringspin-offs (mean = 0.38) than sell-offs (mean =7.49) during the 10 years prior to the year of thefocal restructuring event.

Table 2 provides additional insight into the 10-year history of spin-offs and sell-offs prior to thefocal restructuring event. Based on the entire sam-ple of 205 firms, the data indicate that sell-offsfar outnumber spin-offs for any of the years. Forexample, during the year immediately prior to thefocal event year, the 205 restructuring firms made206 sell-offs and eight spin-offs. Although thesample consists of 82 firms that restructured byspin-offs, this restructuring method was used con-siderably less often beforehand than sell-offs. Therestructuring firms were not concentrated in anyparticular industry (results available upon request).

Table 3 reports the results of the first set oflogistic regression analyses. The values reportedin the table are nonstandardized coefficients, andstandard errors are reported in parentheses. Thedependent variable, restructuring mode (spin-off,sell-off), is regressed onto the full set of controls(Model 1), and onto restructuring experience, asthe summed restructuring counts over the entire10-year period prior to the year of the focal restruc-turing event (Model 2). The results indicate thatthe 10-year count of sell-offs is associated witha subsequent sell-off (b = −0.066, p < 0.01; thenegative coefficient is due to the coding of themode variable as 1 for spin-off, 0 for sell-off).

Table 3. Logistic regression analysis: restructuringmode regressed onto restructuring experience

Variables Model 1 Model 2

ROA 0.040+ 0.045+

(0.021) (0.026)Debt/sales −0.626+ −0.856+

(0.349) (0.499)Asset relatedness −0.846∗∗ −0.520

(0.330) (0.390)Transaction price 0.111 0.106

(log) (0.238) (0.285)Total assets (log) −0.316 −0.075

(0.237) (0.294)Blockholdings (%) −0.011 −0.009

(0.008) (0.009)Year −0.063 −0.098

(0.073) (0.087)Focal CAR 9.286∗∗ 11.611∗∗

(3.572) (4.299)Count of prior 2.475∗∗

spinoffs (10y) (0.432)Count of prior −0.066∗∗

selloffs (10y) (0.025)Pseudo R2 0.103 0.325% correctly classified 66.8% 78.0%Log likelihood −124.067 −93.373Log likelihood ratio — 61.389 (2)

test (df) (p < 0.000)χ 2 value (df) 28.590 (8) 89.980 (10)p-value 0.000 0.000Observations 205 205

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 sup-ports the absorptive capacity argument representedby Hypothesis 1, that as firms gain experiencewith sell-offs, they will continue to use sell-offs inthe long term as a form of corporate restructuringstrategy.

The results reported in Table 3 also indicatethat the 10-year count of spin-offs is associatedwith a subsequent spin-off (b = 2.475, p < 0.01).Although this relationship appears to support along-term effect of spin-off count on the likeli-hood of subsequent spin-off adoption, a findingthat appears consistent with an absorptive capacityargument, the results in Table 4 provide additionalinsights into the meaning of that association. InTable 4, we separated the 10-year summed countsof spin-offs and sell-offs into different temporalwindow intervals. We computed the numbers ofspin-offs and sell-offs five to 10 years prior to the



Table 4. Logistic regression analysis: restructuringmode regressed onto experience at different time intervals(n = 205)

Variables Model 1 Model 2

ROA 0.040+ 0.050∗

(0.021) (0.023)Debt/sales −0.626+ −0.806∗

(0.349) (0.397)Asset relatedness −0.846∗∗ −0.877∗

(0.330) (0.346)Transaction price (log) 0.111 0.097

(0.238) (0.248)Total assets (log) −0.316 −0.316

(0.237) (0.261)Blockholdings (%) −0.011 −0.013

(0.008) (0.009)Year −0.063 −0.064

(0.073) (0.079)Focal CAR 9.286∗∗ 9.689∗

(3.572) (3.782)Count of spin-offs, prior 0.024

years 5 to 10 (0.593)Count of sell-offs, prior −0.030

years 5 to 10 (0.045)Count of spin-offs, prior −0.614

years 3 to 4 (1.522)Count of sell-offs, prior −0.177

years 3 to 4 (0.111)Count of spin-offs, prior 1.204

year 2 (1.558)Count of sell-offs, prior 0.222

year 2 (0.157)Count of spin-offs, prior 2.129∗

year (0.918)Count of sell-offs, prior 0.046

year (0.123)Pseudo R2 0.103 0.153% correctly classified 66.8% 69.1%Log likelihood −124.067 −117.183Log likelihood ratio test — 13.768 (8)

(df) (p < 0.10)χ 2 value (df) 28.590 (8) 40.543 (16)p-value 0.000 0.001Observations 205 204

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 andfour years, two years and one year prior. Testsreported under Model 2 of Table 4 indicate thatonly the count of spin-offs during the year imme-diately prior to the focal restructuring event isassociated 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 significantpredictors. The closer examination offered by the

tests of the alternative time windows reveals thatmore recent experience with spin-offs is associ-ated with subsequent spin-offs. Collectively, theseresults lend support for the organizational improvi-sation argument represented in Hypothesis 3, thatrecent experience is related more positively to thelikelihood of a subsequent spin-off than temporallydistant experience.

In addition, none of the sell-off counts for anyof the windows within the 10-year period is asso-ciated with restructuring mode. Apparently, theeffects of sell-off experience on focal restructuringsell-offs do not depend on any particular temporalwindow. This evidence lends additional support forthe absorptive capacity hypothesis (Hypothesis 1)for sell-offs.

Tables 5 and 6 report the results of regress-ing performance after the focal restructuring eventonto restructuring experience levels. The coeffi-cients in those tables are standardized. The twoperformance variables, ROA and mean EPS, wererecorded at each of the five years after the restruc-turing event, and an average of each was calculatedfor that five-year period. The 10-year sum of priorsell-offs was related positively to ROA (b = 0.283,p < 0.01) and to EPS (b = 0.337, p < 0.01) at oneyear after the restructuring event (Models 2 in bothtables). In addition, that long-term sell-off countvariable was related positively to the EPS five-year average (b = 0.227, p < 0.01). These resultsare consistent with the second absorptive capac-ity hypothesis, Hypothesis 2, firms that have moreexperience with sell-offs will have higher financialperformance after a subsequent sell-off than firmshaving less experience with sell-offs. (The nega-tive relationship between long-term spin-offs andEPS is discussed below.)

Tables 7 and 8 report the results of regress-ing financial performance onto the restructuringcounts during the different time intervals. Stan-dardized coefficients are again reported. Firmsmaking higher numbers of spin-offs during a three-to four-year period prior to a focal restructur-ing event tended to have higher post-restructuringROA (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). How-ever, firms that made higher numbers of spin-offs in the period five to 10 years before a focalrestructuring tended to have lower ROA for theyear after the focal restructuring (b = −0.167,p < 0.05), but higher four years later (b = 0.290,



Table 5. Ordinary least squares regression analysis: post-restructuring ROA regressed onto restructuring experience

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Pre-restructuring ROA 0.529∗∗ 0.473∗∗ 0.438∗∗ 0.191+ 0.336∗∗ 0.377∗∗

Debt/sales −0.112 −0.167+ 0.016 −0.118 −0.210∗ −0.110Asset relatedness −0.059 0.065 0.009 −0.110 −0.182+ 0.012Transaction price (log) 0.005 −0.103 −0.026 0.160 0.039 0.038Total assets (log) 0.062 −0.027 0.190 −0.187 0.129 0.021Blockholdings (%) 0.060 0.036 0.116 0.009 0.030 0.017Year −0.173∗ −0.085 −0.043 −0.037 −0.180 −0.257∗

Restructuring dummy −0.068 −0.010 −0.104 −0.014 0.087 −0.032Focal CAR 0.193∗ 0.236∗∗ −0.040 0.039 0.058+ −0.121Count of spin-offs −0.092 −0.019 −0.068 −0.008 0.168+ 0.026Count of sell-offs 0.134 0.283∗∗ 0.118 0.041 −0.018 −0.041

R2 0.340 0.345 0.245 0.100 0.264 0.212Adjusted R2 0.272 0.277 0.162 −0.005 0.170 0.095F 4.971∗∗ 5.066∗∗ 2.954∗∗ 0.949 2.805∗∗ 1.815+

Observations 118 118 112 106 98 86

Note: Model 1 dependent variable is five-year ROA average; Model 2 dependent variable is the ROA at one year after focalrestructuring year, Model 3 dependent variable is the ROA at two years after, up to Model 6, where the dependent variable is theROA 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 restructuringexperience


Pre-restructuring ROA 0.109 0.159+ 0.060 0.089 0.028 −0.020Debt/sales −0.192∗∗ −0.137+ 0.085 −0.253∗∗ −0.288∗∗ −0.315∗∗

Asset relatedness −0.110 0.000 −0.063 −0.090 −0.241∗∗ −0.114Transaction price (log) 0.034 −0.012 −0.088 0.158 0.015 0.099Total assets (log) 0.376∗∗ 0.048 0.457∗∗ 0.194+ 0.350∗∗ 0.223Blockholdings (%) 0.026 −0.019 0.079 0.059 −0.016 0.045Year −0.124+ 0.076 0.009 −0.197∗ −0.237∗∗ −0.212∗

Restructuring dummy −0.076 −0.015 −0.076 −0.022 0.054 −0.037Focal CAR −0.007 −0.021 −0.006 −0.043 −0.036 −0.049Count of spin-offs −0.209∗∗ −0.098 −0.139+ −0.087 0.002 −0.129Count of sell-offs 0.227∗∗ 0.337∗∗ 0.150+ 0.107 0.044 −0.008

R2 0.379 0.211 0.264 0.226 0.294 0.197Adjusted R2 0.329 0.148 0.201 0.153 0.221 0.103F 7.588∗∗ 3.339∗∗ 4.148∗∗ 3.107∗∗ 4.044∗∗ 2.096∗

Observations 149 149 139 129 119 106

Note: Model 1 dependent variable is the five-year EPS average; Model 2 dependent variable is the EPS at one year after the focalrestructuring 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 inter-vals were related to EPS at any of the years afterthe restructuring. These results provide partial sup-port for Hypothesis 4, that recent experience withspin-offs is related more positively to financialperformance following a focal spin-off than expe-rience with spin-offs in temporally distant years.In our findings, recent spin-offs (three to four

years in this case) are related more positively tofinancial performance (ROA) following a subse-quent spin-off than the count of spin-offs occurringin the years that do not immediately precede thefocal event. Furthermore, none of the counts ofsell-offs for the temporal windows are associatedwith either performance measure, lending addi-tional support for the absorptive capacity effect



Table 7. Post-restructuring ROA regressed onto experience at different time intervals


Pre-restructuring ROA 0.556∗∗ 0.538∗∗ 0.483∗∗ 0.188 0.289∗∗ 0.310∗

Debt/sales −0.104 −0.148+ 0.032 −0.124 −0.236∗ −0.182Asset relatedness −0.072 0.062 0.005 −0.119 −0.218∗ −0.048Transaction price (log) −0.007 −0.155 −0.065 0.167 0.085 0.089Total assets (log) 0.102 0.028 0.232+ −0.180 0.144 0.011Blockholdings (%) 0.040 −0.014 0.081 0.023 0.080 0.058Year −0.151+ −0.027 −0.006 −0.040 −0.208+ −0.297∗

Restructuring dummy −0.051 −0.008 −0.097 −0.004 0.139 0.033Focal CAR 0.190∗ 0.215∗∗ −0.046 0.054 0.109 −0.082Count of spin-offs 5–10 years prior −0.131 −0.167∗ −0.142 0.021 0.290∗∗ 0.178Count of sell-offs 5–10 years prior 0.089 0.159 0.049 −0.001 −0.071 −0.009Count of spin-offs 3–4 years prior 0.178∗ 0.333∗∗ 0.197∗ 0.041 0.098 0.031Count of sell-offs 3–4 years prior −0.055 −0.169 −0.103 0.041 0.152 0.046Count of spin-offs 2 years prior −0.003 0.051 0.034 −0.044 −0.031 −0.102Count of sell-offs 2 years prior 0.021 0.132 0.096 −0.020 −0.085 −0.188Count of spin-offs 1 year prior −0.083 0.039 −0.024 −0.057 −0.138 −0.234+

Count of sell-offs 1 year prior 0.013 0.087 0.009 0.026 −0.013 0.132

R2 0.380 0.462 0.294 0.107 0.346 0.297Adjusted R2 0.275 0.370 0.166 −0.066 0.207 0.122F 3.607∗∗ 5.047∗∗ 2.303∗∗ 0.619 2.493∗∗ 1.692+

Observations 118 118 112 106 98 86

Note: Model 1 dependent variable is the five-year ROA average; Model 2 dependent variable is the ROA at one year after the focalrestructuring 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


Pre-restructuring ROA 0.111 0.175∗ 0.068 0.069 0.010 −0.043Debt/sales −0.183∗ −0.140+ 0.092 −0.251∗∗ −0.274∗∗ −0.310∗∗

Asset relatedness −0.109 −0.001 −0.063 −0.091 −0.247∗∗ −0.114Transaction price (log) 0.028 −0.027 −0.096 0.170 0.020 0.119Total assets (log) 0.380∗∗ 0.050 0.466∗∗ 0.204+ 0.354∗∗ 0.226Blockholdings (%) 0.022 −0.038 0.075 0.084 0.012 0.087Year −0.112 0.105 0.021 −0.221∗ −0.242∗∗ −0.243∗

Restructuring dummy −0.056 −0.012 −0.070 0.001 0.092 −0.003Focal CAR −0.008 −0.030 −0.005 −0.029 −0.008 −0.020Count of spin-offs 5–10 years prior −0.184∗ −0.138+ −0.112 −0.041 0.105 −0.002Count of sell-offs 5–10 years prior 0.098 0.162 0.041 0.120 −0.084 −0.034Count of spin-offs 3–4 years prior −0.006 0.082 0.009 −0.023 −0.037 −0.094Count of sell-offs 3–4 years prior 0.129 0.049 0.030 0.145 0.225 0.134Count of spin-offs 2 years prior −0.057 −0.005 −0.035 −0.098 −0.031 −0.139Count of sell-offs 2 years prior 0.048 0.049 0.106 −0.096 0.025 0.015Count of spin-offs 1 year prior −0.074 −0.006 −0.073 −0.043 −0.083 −0.106Count of sell-offs 1 year prior −0.041 0.109 −0.011 −0.071 −0.103 −0.106R2 0.389 0.228 0.272 0.244 0.331 0.225Adjusted R2 0.310 0.127 0.170 0.128 0.219 0.075F 4.908∗∗ 2.270∗∗ 2.661∗∗ 2.109∗ 2.944∗∗ 1.500Observations 149 149 139 129 119 106

Note: Model 1 dependent variable is the five-year EPS average; Model 2 dependent variable is the EPS at one year after the focalrestructuring 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.



of sell-offs on post-restructuring financial perfor-mance.

DISCUSSION

During the 1980s and 1990s, thousands of compa-nies restructured their portfolios of business lines,spinning and selling off assets worth hundreds ofbillions of dollars (Bruner, 2004; Gaughan, 1999;Hoskisson and Hitt, 1994). To date, most knowl-edge on restructuring has focused on how restruc-turing is used as a mechanism to reduce overdiver-sification, reallocate resources, or improve inter-nal efficiency in order to improve financial per-formance (see Bauer, 2006; Haynes et al., 2003;Johnson, 1996 for reviews of the restructuringliterature). Although that research has providedimportant insights, we still have an incompleteunderstanding of the drivers and implications ofcorporate restructuring. In particular, we know lit-tle about the dynamic aspects of restructuring.Although managers would likely consider tempo-ral factors such as prior history and experiencein their restructuring decisions, previous researchhas not fully considered how such dimensionsmight influence restructuring and its implicationsfor financial performance. The current study wasdesigned to address these gaps by testing two ques-tions: (1) Does experience apply to restructuringdecisions? (2) If so, does it also influence prof-itability?

The study finds that experience matters, butin different ways. First, the use of a sell-off isconsistent with an absorptive capacity viewpointof organizational learning. Having more experi-ence with sell-offs over time makes them morelikely to be used in subsequent events. By contrast,the use of spin-offs is consistent with an orga-nizational improvisation view. The results indi-cate that a focal spin-off is related to the inci-dence of other spin-offs in immediately precedingyears only; no other prior time intervals matteredfor spin-offs. The adoption of a spin-off appearsto be used more often as a short-term responsethan part of a long-term activity spread over mul-tiple years, as spin-offs frequently occur closetogether in time. Second, restructuring experiencehas implications for financial performance follow-ing a focal restructuring event. Firms with moresell-off experience realized higher financial perfor-mance than firms having less. By contrast, those

having more recent spin-off experience tendedto realize an increase in post-restructuring finan-cial performance while those with older expe-rience tended to achieve performance decreases.Overall, these findings suggest that different kindsof experience are associated with the adoptionof different restructuring actions and their subse-quent financial performance records. The resultsof testing the control variables further support thetheoretical explanations; most spin-offs involvedcore business lines and sell-offs tended to occurwhen the restructuring firm had financial pressuresdue to lower financial performance and higherdebt.

Implications for corporate restructuring

The study results suggest several potential con-tributions to explanations of corporate restructur-ing. First, current understanding of the tempo-ral qualities of restructuring is mostly based onevolutionary models, where firms are depicted asusing divestitures to move out of business linesas part of a search and selection sequential pro-cess (see Chang, 1996), or transaction cost expla-nations, where restructuring balances the costsand benefits of managing portfolios of businesslines (Bergh and Lawless, 1998; Jones and Hill,1988; Markides, 1992, 1995). Our study addsto the understanding of the dynamic propertiesof restructuring in several ways. First, a histori-cal effect—prior restructuring experience hetero-geneity—appears to influence the use of differ-ent restructuring modes. Second, the relationshipbetween prior restructuring experience and sub-sequent restructuring appears to have differentforms. Third, theoretical explanations of restructur-ing can be revised to include different time inter-vals; the absorptive capacity viewpoint of learningapplies to sell-offs, while improvisation appears todescribe spin-offs. Fourth, experience has a short-lived relationship with post-restructuring financialperformance. Collectively, these implications sug-gest new theoretical insights because most cur-rent explanations do not account for prior his-torical relationships, how they might vary overtime, or the amount and rate of temporal lag. Ourstudy suggests that addressing temporal relation-ships, together with experience, restructuring alter-natives, and performance, will add significantlyto the restructuring literature. The findings offer



new empirical evidence and explanations, suggest-ing possible contributions to a dynamic view ofrestructuring.

More specifically, the results support an inte-grative explanation of corporate restructuring thatis more expansive than prevailing perspectives.To date, most explanations imply that a planned,deliberate and intentional execution of restructur-ing actions is called for in order to meet a pre-determined objective. Hence, a temporal delay isembedded between the composition and imple-mentation of the restructuring action. However, thefindings imply that this dominant explanation ofrestructuring may be incomplete. It appears thatsome restructurings may occur in a more simulta-neous and contemporaneous manner where cumu-lative and repetitive prior experience plays a muchsmaller role. Other factors such as expertise andtacit knowledge may develop and shape corpo-rate strategy as a continuous and adaptive process.The support for the organizational improvisationaldepiction of strategy may open new and differentavenues of inquiry into the formulation and imple-mentation of strategic actions and their effects onperformance.

Moreover, most prior research has portrayedrestructuring as a method for improving internalcontrol and efficiency (Hoskisson and Hitt,1994; Hoskisson and Turk, 1990; Shleiferand Vishny, 1991), refocusing diversificationstrategies (Comment and Jarrell, 1995; Hoskissonet al., 1994; Markides, 1992, 1995), reducinginformation asymmetries (Bergh et al., 2008;Krishnaswami and Subramaniam, 1999), andgenerating internal liquidity (John and Ofek, 1995;Lang, Poulsen, and Stulz, 1995; Ofek, 1993).Our study adds to the literature by suggestingthat different types of learning, which draw fromcognitive and behavioral concepts, may provideinsights into what has been primarily an economic-based perspective of the restructuring decision andoutcome.

Furthermore, the study indicates that an expla-nation of restructuring that appears to be valid forone time interval may not be valid for another. Forexample, the results initially showed that the adop-tion of a spin-off was related to a summed count ofspin-offs over a 10-year window (Table 3), a find-ing consistent with the absorptive capacity learningargument. However, disaggregating the count ofspin-offs into temporal windows revealed that the

count of spin-offs occurring in the year immedi-ately prior to the focal spin-off was more influen-tial than any other time period within the 10-yearwindow (Table 4). Simultaneously, a much differ-ent relationship existed for sell-offs; the 10-yearcount of sell-off was a significant predictor of sell-off adoption while none of the windows withinthat period mattered. These findings indicate thatthe development of dynamic explanations needs toaccount for the length of time intervals. Becausethe length of the temporal intervals was varied,the explanations for one temporal concept, experi-ence, also varied. The specified time interval maychange the meaning of concepts, relationships, andinterpretations and serve as a baseline condition forexplaining restructuring actions.

The use of temporal frames helps us build uponan earlier study of experience and restructuring.Villalonga and McGahan (2005) recently reporteda consistent positive relationship between experi-ence and the subsequent use of sell-offs, spin-offs,and equity carve-outs. Our findings are similar totheirs with respect to prior experience and sell-offs,but the results differ when it comes to spin-offs.These discrepancies appear due to how experiencewas measured; Villalonga and McGahan representexperience as the average number of events priorto a focal event, while we use temporal windows tobreak down experience into specific intervals. Hadwe summed prior spin-offs over a 10-year periodonly, our conclusions would have again been sim-ilar to those reported by Villalonga and McGahan(2005). However, by recognizing that different per-spectives of learning can be represented with theuse of the temporal windows, we find that spin-offsoccur more spontaneously and in a shorter timeframe, allowing us to posit a finer-grained con-struction of the experience concept. Hence, knowl-edge of the dynamic qualities of restructuring istherefore enhanced by theoretically and method-ologically indicating the time intervals in which theevents 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 understandingof the dynamic quality of restructuring by reveal-ing that the effects of restructuring experience onfinancial performance appear to erode followingthe restructuring. This relationship was consistentacross the different time intervals. Apparently, thebenefits of experience are not only variable overtime, but they do not last long.



Implications for organizational learning

In general terms, the findings suggest a morediverse and integrative approach to viewing andtesting learning constructs; by clarifying and in-cluding different learning perspectives in the samemodel, theory development can be expandeddynamically and comprehensively. The organiza-tional learning literature has received consider-able attention and is extensive and broad, andthe absorptive capacity and organizational impro-visational views are but two of several differ-ent perspectives within a voluminous researchstream. However, most applications of organiza-tional learning tend to focus on one learning viewor process at a time and theoretical models typi-cally do not sufficiently differentiate between alter-native or complementary learning perspectives.Our findings imply that if one disaggregates tem-poral windows into different length periods, thenmore clearly defined learning views and argu-ments can be developed and tested. The choiceof time scales could therefore influence the theo-retical relationships and insights one will obtain(George and Jones, 2000; Zaheer, Albert, andZaheer, 1999), and it is possible that reduced win-dow lengths within longer intervals might yieldsupport for more nuanced learning explanations.Hence, theoretical explanations of learning mightbe further developed, revised, and extended by rec-ognizing complementary views of organizationallearning and developing and testing them usingdifferent time window lengths.

More specifically, the study findings suggestseveral possible implications for theory develop-ment on organizational learning. First, to the bestof our knowledge, our study is the first to com-pare simultaneously the financial implications ofabsorptive capacity and organizational improvisa-tion viewpoints of learning. To date, most concep-tual developments and empirical tests have focusedon one or the other separately, and the researchstreams on each appear to have mostly developedindependently of one another. We have little expla-nation and evidence about when either is likelyto better explain performance. Our study resultsmay help to partly address this gap; by linkingexperience types to adoption of alternative actionsand then to performance, our model providesan initial framework that differentiates these twolearning viewpoints. Overall, the findings suggestthat meaningful boundaries exist between different

viewpoints of learning and that there is theoreticalvalue by considering separate views simultane-ously.

Furthermore, distinguishing between absorptivecapacity and improvisation may have importanttheoretical implications. Most learning explana-tions imply that corporate behaviors are purpose-ful, deliberate, and planned. However, by differen-tiating between both views of learning, actions thatmay be more spontaneous are not inadvertentlyembedded within the absorptive capacity reasoningand method, allowing for a more refined, devel-oped, and transparent explanation to be offered.We call for more research to further examine thepossible linkages and distinctions between absorp-tive capacity and organizational improvisation. Forinstance, for those studies that link absorptivecapacity to alliances and acquisitions and to per-formance (see Lane et al., 2006; Zollo et al., 2002,for recent reviews), we now wonder when impro-visation might also apply. Are the different typesof acquisitions and alliances associated with dif-ferent learning perspectives? Since the theoreticalmodels and empirical tests tend not to disaggre-gate long time periods into shorter ones, questionsarise about whether the alliance and acquisitionstudies that support an absorptive capacity viewmight be obscuring shorter-term improvisationalactions. These issues suggest that we may need torevisit the developments and applications of learn-ing perspectives, especially when the need existsfor managers to reduce the temporal proximity ofdesign and action.

Second, the study findings offer insights intothe absorptive capacity and improvisational view-points using different methods and conditions thanprior studies. The results can be interpreted asfurther evidence of the reach of both views, aseach appears to now explain an exiting actionthat has a performance implication. In addition,the findings refine absorptive capacity to showthat it may not explain settings where the needexists 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 studiesconsider absorptive capacity, but fewer empiricallyexamine its properties (Lane et al., 2006). As such,this study contributes to knowledge of absorptivecapacity by explicating its temporal characteristics.To date, most interpretations of absorptive capac-ity do not account for temporal window lengths,



implying that its properties and effects are con-stant or equal over time. Future research mayprovide new insights into absorptive capacity bytesting its temporal conditions and assumptions.Such study may offer new insights into learningif it considers the relationships between the view-points of absorptive capacity and improvisation, orother viewpoints within the organizational learningliterature.

Third, the study represents one of the initialformulations and applications of organizationalimprovisation to a corporate strategy action and itspossible performance effects. The findings extendthis viewpoint’s logic to a much different settingthan previous studies of musicians, actors, andnew product developers, and imply that impro-visation may have potential for offering insightsinto a variety of corporate behaviors. It appearsthat when managers need to make decisions basedon real-time and short-term information, impro-visation may help explain their actions. Interest-ingly, the results also indicate that the link betweenimprovisation and performance is not as clear asexpected. Although improvisation may apply tohow managers select restructuring modes, it doesnot apply as robustly to financial performance,and the effects appear to be temporally delayed.This finding is consistent with other views of anequivocal improvisation/performance linkage (seeCrossan et al., 2005). More study is needed tounderstand the financial implications of improvi-sation.

The implications of this study should be con-sidered in light of several limitations. First, thefindings are based on large restructuring events. Itis unknown how the results apply to smaller-sizedrestructuring firms. The proposed explanation ofrestructuring and its effects would seem to applyto smaller organizations, but no direct inferencecan be made given that the sample consisted oflarge firms. Second, the study examined volun-tary restructuring efforts only, and it is possiblethat the proposed explanation might not apply toinvoluntary spin-offs and sell-offs. For example,a court-ordered restructuring might lead a firm tospin-off when a sell-off might be the best option.Third, the study does not identify how the restruc-tured assets were originally created, either throughinternal growth or by acquisition. No inference cantherefore be made that links entry and exit behav-iors. A more complete explanation of restructuring

behavior would include growth and exit alterna-tives. We hope that future research will test sucha model.

CONCLUSION

During the 1980s and 1990s, firms restructuredtens of thousands of business lines. The presentstudy considers whether restructuring experiencemight have influenced restructuring actions andpost-restructuring financial performance. The studytests a model that relates different viewpoints oforganizational learning to help explain the adop-tion of different restructuring alternatives and theirinfluence on profitability. The findings show thatthe absorptive capacity view is most pertinent tothe use of sell-offs, while the organizational impro-visation view better explains the selection of spin-offs. These explanations and findings add to thecorporate restructuring literature and may furtherenhance understanding of organizational learning.In addition, our study probes unexplored aspectsof the temporal dimensions of corporate strategy,such as time intervals, time lags, and the formsof longitudinal relationships. Of specific interestis the finding that the learning explanations forrestructuring actions and performance vary overtime. These findings contribute some new buildingblocks toward a dynamic explanation of corporaterestructuring. Restructuring is a longitudinal phe-nomenon and, hopefully, the study will providefor improved understanding of how these actionsare managed while encouraging additional inquiryinto temporal dimensions. Most generally, the the-oretical model helps explain why some corporaterestructuring strategies appear as intentional anddeliberate actions while others resemble sponta-neous and simultaneous responses. Future researchthat integrates alternative learning views may pro-duce a more dynamic and comprehensive under-standing of strategy and its implications for firmperformance.

ACKNOWLEDGEMENTS

We are grateful for the helpful comments fromEditor Richard Bettis and the referees. We alsothank Professor Richard Johnson for his contribu-tions to the dataset and Professors Parthiban Davidand Ravi Madhavan for comments on an earlier



draft of this article. We thank the Krannert Grad-uate School of Management at Purdue Universityfor its support of this research.

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