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THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD
Francesco Castellaneta Catolica Lisbon School of Business and Economics
Department of Management Email: francesco.castellaneta@ucp.pt
Tel: +351 938797411
Maurizio Zollo Dean’s Professor of Strategy and Corporate Responsibility
Management Department Bocconi University
maurizio.zollo@unibocconi.it
Keywords: experiential learning, attention, buyouts, organizational learning, mergers and acquisitions
Acknowledgements: This paper, which is based on the first author dissertation, received the valuable feedback of Pino Audia, Ilidio Barreto, Stefano Brusoni, Eugenia Cacciatori, Gianluca Carnabuci, Vittorio Coda, Raffaele Conti, Erwin Danneels, Alfonso Gambardella, Oliver Gottschalg, Sarah Kaplan, Robert Grant, Tomi Laamanen, Gianvito Lanzolla, Daniella Laureiro, Dan Levinthal, Anita McGahan, Nicola Misani, Elena Novelli, William Ocasio, Martina Pasquini, Samira Reis, Claus Rerup, Martin Schreier, Tom Stein, Giovanni Valentini, Francisco Veloso, Gianmario Verona, Gordon Walker, Filippo Wezel, and Sidney Winter. Thanks to Carlo Salvato in particular for his patience and guidance through the review process. Thanks to participants in AOM meetings (2010 and 2012), Bocconi Study Days (2010), SMS (2010), and participants in seminars at Bocconi University, Catolica Lisbon, ETH Zurich, London Business School, Private Equity Forum in Paris, and USI. Funding was provided by Catolica Lisbon, the CROMA Research Center at Bocconi University, and Fundação para a Ciência e a Tecnologia.
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THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD
ABSTRACT
Drawing on the attention-based view of the firm and the experiential learning literature, this paper
develops and tests a theory on how firms learn to cope with the strains of activity load. We first
empirically test the impact of activity load on the performance of a focal activity. We then study how
this relationship is moderated by four dimensions of experiential learning: stock, homogeneity, pacing,
and past success. We test our hypotheses on a proprietary database of 6,913 investments by 248
private equity firms in 77 countries between 1973 and 2008. We find that heavier activity loads exact
a smaller toll on performance when firms have larger and more homogenous stocks of prior
experience. However, when firms' prior experience is more rapidly paced or successful, the toll of
heavier activity loads on performance grows. Taken together, these four dimensions of experiential
learning provide an initial theoretical basis for the development of a capability that we term “attention
modulation capability.”
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INTRODUCTION
The behavioral theory of the firm posits that organizations’ behavior depends on how firms channel
and modulate their limited stocks of managerial attention (Cyert and March 1963; March and Simon
1958; Simon 1947). The central argument of this perspective is that decision makers’ attention is a
valuable and scarce resource that is selectively distributed among competing organizational activities.
Consequently, when a firm increases the number of independent activities it handles simultaneously—
when it raises its "activity load"—it may inadvertently trigger an information overload, saturating its
limited attention capacity and weakening its decision-making abilities during a focal activity (Ocasio
1997).
Despite an emphasis on the problems generated by information overload, this stream of
literature is characterized by two major limitations. First, Sutcliffe and Weick (2008) note that
information “overload has been subject to much speculation and conceptual attention for decades, yet
empirical research on overload, particularly in organizational theory [...] is surprisingly sparse”
(Sutcliffe and Weick 2008: 60). Second, the received literature has so far produced only a limited
understanding of the factors that would render certain organizations more capable of managing
simultaneous activities, particularly when those activities are strategic in nature (Laamanen and Keil
2008; Ocasio 2011). This is an important issue because organizations managing a portfolio of
simultaneous strategic activities—such as alliances, acquisitions, or divisions in a multi-business
firm—will likely differ in their ability to manage the strains posed by a heavier activity load
(Heimeriks et al. 2007; Martin and Eisenhardt 2010).
Drawing on the attention-based view of the firm (Ocasio 1997), we begin by testing the
theoretical prediction that increasing activity load will have a negative impact on the performance of
the focal activity, an idea that has been discussed in the literature but has rarely been empirically
assessed (Lopez de Silanes et al. forthcoming; Ocasio 2011). Then, drawing on experiential learning
theory (Levitt and March 1988; Nelson and Winter 1982), we proceed to develop and test a theory on
how firms learn to cope with the negative effects of activity load using the routines created through
experiential learning. In this respect, the central role of routines has been highlighted by Shapira
(1994) who noted that “routines… should not be dismissed as insignificant” (Shapira 1994: 123) when
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studying how decision makers learn to cope with their managerial cognition, and therefore with its
limited nature.
One core intuition of the attention-based view of the firm is that “the selective focus of
attention of decision makers is ameliorated, at least in part, in the case of routine…” (Ocasio 1997:
190). By guiding search and reducing the space of behavioral options that managers should scan
(Shapira 1994), routines reduce the amount of attention that must be channeled to each single activity
(Ocasio 1997; Sullivan 2010) and economize on decision makers’ limited attention capacity (Becker
2004). 1 Routines economize attention even when the decisions at hand are complex, like strategic
ones (Grise and Gallupe 1999; Schneider 1987). Consequently, routines should decrease the amount of
cognitive resources necessary to process a given quantity of parallel activities (Eppler and Mengis
2004) and will likely increase decision makers’ attention capacity (i.e. the capacity to process
information) (Kahneman 1973). As such, we can expect routines to mitigate the negative effects
created by activity load, even in the context of strategic activities (Laamanen and Keil 2008).
We focus this paper on four dimensions of experience that are likely to hurt or sustain the
formation of routines: stock, homogeneity, pacing, and success. While these four dimensions cannot
measure routines directly, they are likely to proxy for routinization processes in a large-scale
quantitative study at the organizational level (Becker et al. 2009). More importantly, these four
dimensions allow us to develop a model that reflects both the positive and negative effects of
experiential learning (Kim et al. 2009) on the development of an organizational ability to manage
heavier activity loads.
We propose that larger and more homogeneous stocks of prior experience reduce the negative
impact of activity load on performance, because they are likely to increase the formation of
organizational routines (Zollo and Winter 2002). In this regard, Gavetti and Levinthal (2000) note that
“routines reflect experiential wisdom in that they are the outcome of trial and error learning and the
selection and retention of past behaviors” (Gavetti and Levinthal 2000: 113). Homogeneity is also
1 The role of routines is twofold. First, they can be patterns of action that form repositories for lessons learned from experience. Second, they can be cognitive maps that provide a common structure for a range of similar problems, but supply few details regarding specific solutions to address them. Both roles are important to save organizational attention when handling high levels of activity load in the context of strategic activites.
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likely to be at the origin of routine formation given that experiential learning typically benefits from
repeated experiences of a similar nature (Nelson and Winter 1982, Finkelstein and Haleblian 2002).
On the contrary, we propose that rapid pacing and past success undermine the formation of
routines and therefore reduce an organization’s ability to manage heavier activity loads. A rapid
accumulation of experience would decrease the time available for the articulation of knowledge, and
therefore for the formation of routines (Hayward 2002). A history of success at the firm level would
increases managers’ confidence in the firm’s existing routines, reducing their incentive to look
beyond, adapt, or extend the firm’s existing portfolio of routines (Gavetti et al. 2005; Greve 2003).
Together, rapid pacing and firm success could ultimately encourage firms to form routines that
encapsulate inaccurate or even erroneous lessons (Perlow et al. 2002; Zollo 2009). This would
weaken, rather than increase, the organizational ability to handle a heavier activity load.
We test the proposed model in an empirical context that is both appropriate and of particular
economic relevance: private equity buyouts (Kaplan and Schoar 2005). We draw on a proprietary
database containing 6,913 investments undertaken by 248 private equity firms in 77 countries between
1973 and 2008. The nature of our dataset allows us to overcome several problems traditionally
encountered in research on activity load (Ocasio 2011). First, it provides an objective way to measure
activity load at any point in time—in this case, measuring the average number of companies managed
by a private equity firm during the investment period of the focal buyout—as well as an objective way
to measure the performance outcome of each individual buyout, in the form of its Internal Rate of
Return (IRR). Second, our dataset allows us to disentangle the negative effect of activity load from the
positive effects of business synergies and risk diversification, three forces that are commonly at work
where firms manage simultaneous strategic activities (Goold and Luchs 1993). By measuring IRR at
the buyout level, we can capture the impact of activity load on the performance of each single
investment (i.e. at the activity level) and disentangle it from the confounding effect of risk
diversification, which manifests only at the organizational level. Moreover, we can separate the
activity load effect from business synergies because private equity firms do not realize significant
value through business synergies, as acquired companies operate as stand-alone firms (Landau and
Bock 2013).
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THOERETICAL DEVELOPMENT
Activity load: the computation and interpretation perspectives
Foundational studies of the attention-based view of the firm (Cyert and March 1963; March and
Simon 1958; Simon 1947) propose that decision makers’ attention is a valuable and scarce resource.
As a result of bounded rationality, decision makers can pay only limited attention to the various
consequences of their actions, to the objective valuation of those consequences, and to the scope of
available decision alternatives (Simon 1947). Due to the limited supply of attention and to the quantity
of data inputs firms generally have to process in their daily activites, information overload is a
common organizational problem (Edmunds and Morris 2000). Information overload “is also more
likely if managers face an ever greater number of parallel projects or tasks that they have to manage
(i.e. quality management projects, Intranet initiatives, knowledge management issues, customer focus
programs…)” (Eppler and Mengis 2004). That is, information overload problems are particularly
important when firms manage higher levels of simultaneous activity.
Managing parallel projects is common in the context of strategic activities, such as when firms
pursue an acquisitions-based growth strategy. Consider, for example, a serial acquirer like Cisco,
which made its first acquisition in 1993, nine years after it was founded, and went on to acquire
another 159 firms before May 2013—an average of 7.6 acquisition a year.2 Moreover, Cisco’s
acquisition activity was particularly intense in certain years (e.g. 19 acquisitions in 1999; 23 in 2000;
12 each in 2004, 2005, and 2007). It follows that Cisco, like many serial acquirers, has been frequently
called on to manage high levels of simultaneous activity, a state we refer to as managing a heavier
activity load.
Managing a heavier activity load will be problematic because it saturates decision makers’
limited attention capacity, becoming a source of information overload (Ocasio 1997; Ocasio 2011).
Information overload caused by a heavier activity load can be defined as a state in which information
processing requirements (i.e. the overall amount of information received due to the number of
activities simultaneously managed) exceed information processing capacity (i.e. the quantity of
2 These statistics have been elaborated based on the full list of Cisco acquisitions available at the following link: http://www.cisco.com/web/about/doing_business/corporate_development/acquisitions/ac_year/about_cisco_acquisition_years_list.html
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information decision makers can process within a specific time period) (Galbraith 1974; Tushman and
Nadler 1978). This view centers on a computational perspective, according to which the problem
decision makers face is “one of searching for and processing relevant information when such searches
are costly and decision makers are boundedly rational” (Lant and Shapira 2001: 2).
This computational perspective offers only a limited understanding of the problems generated
by activity load in the context of strategic activities (Sutcliffe and Weick 2008). Decision makers’
attention capacity is likely to become saturated in the context of strategic activities not only because of
the amount of relevant information processed, but also because of the challenges decision makers face
when trying to interpret the information generated by simultaneous strategic activities (Daft and
Weick 1984). According to this view, overload is not simply a case of too much data, but of difficulty
in creating “meaning around information in a social context” (Lant 2002: 345), that is, in interpreting
the information received.
Interpretation processes aimed at making sense of new information (i.e. sense-making) when
handling high levels of activity load may become particularly complex in the context of strategic
activities. Schneider (1987) stresses that it is not only the amount of information that determines
information overload, but also the specific characteristics of that information. Such characteristics are
the level of uncertainty, ambiguity, novelty, complexity, and intensity associated with the information
received. Each of these characteristics are commonly associated with strategic activities, making
information overload more likely.
Therefore, information overload due to heavier activity loads is likely a problem of
interpretation as well as a problem of computation. Organizations are subject to both causes of
information overload when they manage a portfolio of activities, such as an array of alliances
(Heimeriks et al. 2009), a portfolio of investments (Meyer and Mathonet 2005), or a company of
multiple businesses (Martin and Eisenhardt 2010). In these cases, as the number of activities
simultaneously managed increases (e.g. with the addition of a new alliance, investment, or business),
the quantity of firm attention available for each single activity decreases, ultimately causing a decline
in performance (Ocasio 1997).
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One could argue that the negative effect of activity load is likely to be less strong when firms
have more organizational resources to manage their strains (see Eppler and Mengis (2004) for a
review). However, previous research has shown that increasing levels of activity load are likely to
generate diseconomies of scale and communication costs that can be only partially counterbalanced by
increasing the level of organizational resources (Williamson 1975). For instance, Garicano (2000)
shows that as a firm scales up—thereby increasing its activity load—it faces greater communication
costs, which can only be partially resolved by improvements in its information technology systems.
Another stream of research emphasizes that an increase in certain organizational resources can even be
harmful. For instance, the introduction of push-technologies (e.g. emails) reduces information retrieval
time, but also makes it necessary to review large quantities of potentially useless information
(Edmunds and Morris 2000), increases the frequency of job interruptions (Speier et al. 1999), and
encourages a focus on low-value tasks (Birkinshaw and Cohen 2013).
Based on this understanding of activity load, we begin by testing the theoretical prediction that
the absolute level of activity load—rather than its relative amount with respect to organizational
resources—will have a negative impact on the performance of the focal activity. This idea has been
discussed extensively in the literature but has rarely been tested empirically (Ocasio 2011; Sutcliffe
and Weick 2008). This gap in the literature is likely because it is difficult to separate the positive
effects of managing a portfolio of activities (i.e. risk diversification and business synergies) on
organizational performance from the negative effect of activity load on the performance of each single
activity. This study achieves that critical separation, and also develops a theoretical model of the
dimensions of experiential learning that may explain why organizations vary in their ability to manage
the strains of heavier activity loads.
Experience stock
The first dimension we take into consideration to explain how organizations cope with the negative
effects of activity load is the stock of prior experience. We build on the received theories of
experiential learning (March 1991; Nelson and Winter 1982; Simon 1947) to argue that the
accumulation of experience in the execution of prior activities might nurture a firm’s ability to handle
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simultaneous activities. The reason is that experience accumulation leads firms to form routines,
which in turn free decision makers’ attention for other activities. Therefore, routines reduce the strain
of activity load on decision makers’ attention capacity, decreasing its negative effects.
The effect of routines on the economization of decision makers’ limited attention capacity can
be understood by analyzing the two fundamental modes of attention processing: automatic and
controlled (Ocasio 1997). Automatic processing—as compared to controlled processing—requires
lower attention capacity. Because routines function as repositories of organizational memory,
knowledge, and learning (Levitt and March 1988), they increase automatic processing. In turn,
routines reduce the amount of attention that must be channeled to each single activity and allow
decision makers to allocate less attention cumulatively (Ocasio 1997; Sullivan 2010). The attention
saved through automatic processing can then be channeled to controlled processing, that is, to
organizational activites that require higher levels of attention. Thus, by increasing automatic
processing and, consequently, the amount of attention that can be channeled to controlled processing,
routines are likely to mitigate the negative effects of activity load.
Experience could contribute not only to the formation of routines but also to the training—and
therefore the better use—of limited organizational attention capacity (Levinthal and Rerup 2006;
Louis and Sutton 1991; Rerup 2009; Rerup and Feldman 2011; Weick and Sutcliffe 2006). For
example, a serial acquirer that is repeatedly exposed to multiple, simultaneous due diligence and
integration activities would hone its ability to distribute the responsibilities of handling those activities
among the limited number of expert personnel it has at disposal (Heimeriks et al. 2009). This would
happen because, in addition to forming or refining routines based on past experiences, the firm would
learn to recognize idiosyncrasies in the focal activity and correctly use routines deriving from past
experience to tackle relevant issues (Finkelstein and Haleblian 2002; Gavetti et al. 2005).
In sum, routines alone may not be capable of countering the attention demands of activity
load. Attention freed up by routines must also be correctly allocated to activities that require higher
levels of attention (Levinthal and Rerup 2006; Weick and Sutcliffe 2006). To achieve this, decision
makers must first detect those activites in need of special attention and then correctly allocate them to
the individuals or groups most capable of tackling them (Rerup 2009). This ability to detect and
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allocate priority activities—which will be enhanced by experience accumulation—likely plays a
crucial role in managing multiple simultaneous activities. We therefore expect firms with a larger
stock of accumulated experience in similar activities to suffer a smaller decline in performance when
activity load rises. Stated formally:
H1: The larger the stock of accumulated experience in similar activities, the weaker the
negative impact of a heavier activity load on the performance of a focal activity.
Experience homogeneity
The study of the relationship between experience homogeneity and performance has received
significant attention. Some scholars propose that experience homogeneity facilitates learning by
increasing decision makers’ specialization and focalization, allowing routines to be refined gradually
(Haleblian and Finkelstein 1999; Levitt and March 1988; Zollo et al. 2002). Consequently, experience
homogeneity should result in steeper learning curves (Von Hippel 1998). On the contrary, other
scholars argue that experience homogeneity harms the learning process by reducing variance, making
it harder for firms to uncover causal relationships (Beckman and Haunschild 2002; Greve 1996;
Haunschild and Sullivan 2002; Hayward 2002; Kim and Miner 2007; Miner et al. 2003). In this view,
experience homogeneity makes it more likely that a firm will develop core rigidities (Leonard-Barton
1992), under-invest in exploration (March 1991), and fail to recognize fresh opportunities for growth
and profit (Schilling et al. 2003).
These mixed findings suggest that experience homogeneity might have an important impact
on firm performance in conjunction with other variables. Thus, rather than following the many
previous works examining the direct effect of experience homogeneity on performance, we use this
paper to explore the joint effect of experience homogeneity and activity load. Approached from this
vantage point, we propose that experience homogeneity should exert a positive, moderating influence
on the link between activity load and performance, for the following three reasons.
First, experience homogeneity is likely to facilitate the formation of routines by increasing
specialization and focalization, making it more likely that similar experiences will be repeated
frequently (Haleblian and Finkelstein 1999; Levitt and March 1988; Zollo et al. 2002). Repetition will
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make it easier to identify common traits among past experiences, which may be candidates for
routinization (Nelson and Winter 1982). Moreover, experience homogeneity allows decision makers
“to focus their time and effort, elaborate on existing knowledge, and develop deeper causal
understandings for how to accomplish tasks” (Bingham et al. 2007: 30). As such, increasing the
frequency with which experiences of a similar nature are repeated is likely to aide the formation of
routines.
Second, a firm with a more homogenous portfolio of experiences may be better able not only
to form routines, but also to change them (Bingham and Eisenhardt 2011; Bresman 2013). In this
respect, the research focused on the study of “how” routines change—the so-called practice
perspective in the organizational routines literature (Feldman and Orlikowski 2011; Parmigiani and
Howard-Grenville 2011)—has shown that experiential learning plays a critical role (Cohendet and
Llerena 2008). Based on this understanding, the process of learning through trial and error (Rerup and
Feldman 2011) during repeated experiences of the same nature will increase the accumulation of
knowledge and insights and thereby encourage firms to refine their routines. Moreover, experience
homogeneity may promote the refinement of routines by encouraging collective reflection—what
Feldman called “people doing things, reflecting on what they are doing, and doing different things (or
doing the same things differently)” (Feldman 2000: 625). This collective reflection process is likely
more effective if based on more homogeneous experiences. Overall, this suggests that the increased
repetition created by experience homogeneity will make it easier for firms to change their existing
routines.
Third, experience homogeneity is likely to make organizations more aware of the potential
benefits of routinization when handling high levels of activity load, even in the context of strategic
activities. Returning to our Cisco case, for example, that firm handles an average of 7.6 acquisitions
per year in the computer networking industry. The challenges inherent to handling parallel due
diligence processes, together with a relatively homogeneous industry experience, have allowed Cisco
to partially routinize this crucial process by creating a due diligence checklist (Paulson 2001). As a
result, Cisco “does the standard due diligence checks to verify all of the things that must be verified.
But underlying the due diligence process is the search for the answer to an overriding question: Will
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these people, their products, and their culture merge well with Cisco?” (Paulson 2001: 166). The Cisco
due diligence checklist reduces the amount of decision makers’ attention necessary for each due
diligence process and allows decision makers to reserve their limited attention for issues that are
relatively more important to the ultimate success of an acquisition.
In sum, firms whose past experiences include a more homogenous array of strategic activities
should be more capable of forming effective routines, freeing up an organization’s limited attention
capacity, improving efficiency in the allocation of managerial attention (by reserving it for those
elements of simultaneous activities that are novel or uncommon), and thereby reducing the potential
hazards inherent to managing simultaneous activities. Therefore, we would expect firms with more
homogenous experience to suffer a smaller decline in performance when activity loads rise. Stated
formally:
H2: The higher the homogeneity of accumulated experience in similar activities, the weaker
the negative impact of a heavier activity load on the performance of a focal activity.
Experience Pacing
Another dimension of experiential learning likely to affect a firm’s ability to handle activity load is the
pace at which experience is accumulated (Hayward 2002). For the purpose of this paper, we define
“experience pacing” as the mean temporal interval between one decision and the following decision in
the process of accumulating experience. Pacing is a well-established construct in the literature (Brown
and Eisenhardt 1997; Perlow et al. 2002; Turner et al. 2010) and it is important for a comprehensive
theory of experiential learning for two reasons. First, it adds a critical time dimension, including the
dynamic flow of activities, to the analysis of experiential learning, which has been hitherto understood
in a rather static way (Barkema and Schijven 2008). Second, the amount of time available between
experiences can influence the quality of the learning process in the context of strategic activities by
allowing firms to allocate managerial time and attention to a deliberate analysis of its prior
experiences, their outcomes, and of the internal and external factors potentially responsible for those
outcomes (Zollo and Winter 2002).
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Concerning the general effect of pacing on an organization’s ability to handle a heavier
activity load, two contrasting arguments apply. On the one hand, rapid pacing may favor the formation
of routines. Routines are a result of several iterative and dynamic processes: the drawing of inferences
from accumulated experience, the storing of inferred lessons in routines, and the application of those
routines to specific activities triggered by internal or external stimuli (Nelson and Winter 1982). Based
on this understanding of routine formation, high pacing—by sustaining the frequent repetition of
similar activites over time (Haleblian and Finkelstein 1999; Levitt and March 1988; Zollo et al.
2002)—may facilitate the identification of common traits among past experiences, therefore sustaining
the formation of routines.
However, if we focus specifically on the impact of pacing in the context of strategic activities,
a second stream of research would argue that fast pacing might be harmful to both the quantity and
quality of routines. By decreasing the time available for articulating and codifying the lessons learned
during prior experiences, faster pacing could harm routinization for two reasons. First, the
mechanisms at the origin of routinization—the “myriad intentional microactivities performed daily by
organizational agents” (Salvato 2009: 384)—require time to unfold. Second, short time intervals
between past experiences might lead firms to incorrectly specify the connections between actions and
outcomes, which might in turn increase the risk of forming vicious routines through superstitious
learning processes (Levitt and March 1988; Zollo 2009). Short time intervals should be particularly
problematic in the context of strategic activities, the complexity of which will make it more difficult to
accurately specify cause–effect linkages (Levinthal and March 1993).
In sum, fast pacing might not only reduce the formation of routines but also their quality. This
last effect will encourage the development of “vicious” routines based on misspecified cause–effect
linkages, which might magnify, rather than relieve, the negative impact of activity load. Applying
vicious routines to manage an increasing activity load would then decrease the performance of the
focal activity. Therefore, a faster pace during experience accumulation will magnify the negative
impact of a heavier activity load on performance. Stated formally:
H3: The faster the pacing of accumulated experience in similar activities, the stronger the
negative performance impact of a heavier activity load during the focal activity.
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Past success
The role of past success on organizational learning has received considerably less attention in the
literature than other dimensions of experience (Haleblian et al. 2006; Hayward 2002; Kim et al. 2009).
This may be because the general link between past success and learning is not straightforward (see
Kim et al. 2009, for a review). On the one hand, past performance provides the resources, the
knowledge base, and the emotional commitment to invest in the development of future capabilities.
Established theories of absorptive capacity (Cohen and Levinthal 1990) and of the strategic value of
resources and competencies (Barney 1986) highlight that the value of knowledge and resource bases
persists over time, creating a positive feedback loop between existing sources of competitive
advantage and the development of future sources. In fact, the mere notion of a “sustainable”
competitive advantage implies that the positive value of resources and capabilities that create an
advantage can be maintained over time (Amburgey and Miner 1992; Haleblian et al. 2006).
However, a number of theoretical arguments would question this positive-feedback loop. To
start with, the satisficing principle, one of the principal tenets of the behavioral theory of the firm
(March and Simon 1958; Simon 1947) and of evolutionary economics (Nelson and Winter 1982;
Winter 2000), suggests that the higher the level of past success, the lower a firm’s willingness to
engage in search processes aimed at learning from its own errors and mistakes (Finkelstein et al.
2009). Relatedly, Greve argued that past success should increase a firm’s willingness to reuse
established routines, rather than engage in organizational changes that are particularly difficult and
risky (Greve 2003). Therefore, successful organizations should be more likely to satisfice—i.e. to
apply lessons learned in the past without engaging in a search for alternative solutions. This problem
may be exacerbated when decision makers manage heaveir activity loads and, as a result, do not have
a sufficient excess of cognitive resources to look beyond the handling of simultaneous activities and
consider the lessons of past errors and mistakes.
Relatedly, success may reduce not only learning from a firm’s own experience (i.e. its
experiential learning), but also vicarious learning from other organizations’ experience (Baum et al.
2000). Previous studies have shown that firms tend to focus on successful organizations and to
undersample unsuccessful ones (Denrell 2003). In other words, decision makers tend to observe and
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analyze the practices of successful firms, but they may not observe and analyze the practices of firms
that have failed. This undersampling of failure could be worsened by success, which may make firms
overestimate their own capabilities and underestimate others’ capabilities (Moore and Cain 2007). The
reason is that more-successful firms are likely to consider failed organizations ‘not similar enough’ to
themselves, making successful firms less likely to examine, and therefore to learn vicariously, from
their failed counterparts’ experiences (Kim and Miner 2007).
A second argument against a positive-feedback loop relates to the influence of prior success
on the probability of negative transfer, that is, the likelihood of transferring established behaviors to a
new setting in which the transferred behaviors are inappropriate (Finkelstein and Haleblian 2002;
Haleblian and Finkelstein 1999). A negative-transfer problem could amplify the negative influence of
a heavier activity load by lowering an organization’s motivation to search for better ways to handle the
focal activity, and by enhancing the probability that applying consolidated organizational approaches
will harm the performance of future activities.
Lastly, firms that achieved success in the past may become overconfident in their ability to
handle a heavier activity load (Heimeriks 2010; Moore and Cain 2007; Zollo 2009). Overconfident
decision makers might tend to believe that they have developed the ‘right’ competences to manage a
higher quantity of simultaneous activities than is actually feasible, given their real managerial
capabilities. They will therefore misallocate their attention, reducing the quality and quantity of
attention devoted to identifying the idiosyncrasies of the focal activity and to searching for novel ways
to handle those idiosyncrasies.
In sum, we expect that firms that achieved a higher level of success during prior activities will
suffer greater declines in performance when their activity loads rise. Stated formally:
H4: The greater an organization’s past success, the stronger the negative impact of a heavier
activity load on the performance of a focal activity.
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RESEARCH DESIGN
Research Setting and sample
The proposed hypotheses are tested using data from private equity investments. Private equity firms
acquire equity securities in non-listed companies with the aim of reselling their ownership shares at a
profit. Each investment, called a buyout, remains in the portfolio of the private equity firm for a
limited time and is managed independently from other buyouts in the portfolio (Gilligan and Wright
2012). In fact, companies acquired during buyouts remain totally separate legal and financial entities,
operating as stand-alone firms with no cross subsidies or forced inter-firm sales. In this respect,
Landau and Bock (2013) have shown that PE firms realize value through corporate parenting (i.e. by
sharing the PE firm’s strategic resources with each single business in its portfolio), and do not realize
significant value through horizontal synergies (Landau and Bock 2013). For this reason, the private
equity setting offers a good laboratory to disentangle the negative effects of heavier activity loads
from the positive effects of synergies common to strategic settings in which managers handle
simultaneous activities, such as portfolios of alliances (Heimeriks et al. 2009) or multi-business firms
(Martin and Eisenhardt 2010).
In addition, the private equity industry offers a way to solve the measurement challenges faced
by studies on the attention-based view of the firm (Ocasio 2011). In fact, the specificity of this
industry allows us to directly measure activity load, examine its boundary conditions, and apply
appropriate control variables at each point in time and throughout a firm’s history. Moreover, the
private equity setting offers a suitable empirical context in which to test our theory in the context of
strategic activities. Buyouts, as strategic activites, tend to be complex and characterized by the
difficulty of correctly specifying cause–effect linkages (Berg and Gottschalg 2005; Zollo 2009).
We base our analysis on a database of 6,913 buyouts realized by 248 private equity firms in 77
countries between 1973 and 2008. The data were assembled by collecting fundraising prospectuses—a
document usually referred to as a “Private Placement Memorandum” (PPM)—from various
investment firms operating in Europe, the United States, and emerging markets. PPMs contain the
performance and key characteristics of all prior investments a PE firm has made.
17
Our dataset is an improvement on other academic data-collection efforts for several reasons.
First, it allows us to go beyond fund-level performance (Kaplan and Schoar 2005) to provide results
for individual investments, using an array of relevant control variables. Second, unlike other
investment-level datasets (Cumming and Walz 2009), our dataset contains the full track record of each
PE firm, allowing us to compute the number of simultaneous investments a firm held at any point in
time. This is essential for a precise calculation of activity load. Third, unlike other databases, ours is
more likely to represent the universe of PE investments because it comes from different investors and
it includes PE firms these investors chose not to invest in. Finally, to the best of our knowledge, our
dataset is the largest existing panel of worldwide PE investment performance (Wood and Wright
2009).
Measures
Dependent variables
In a typical buyout, a private equity firm invests a certain amount of money to acquire a company and,
after a certain period of time, sells it. The performance of each investment can be measured using the
internal rate of return (IRR) (Kaplan and Schoar 2005), which measures the gross return earned by
investors from the acquisition of the company until it is sold. Mathematically, the IRR number
corresponds to the annually compounded discount rate that would make the Net Present Value (NPV)
of all cash flows related to a given investment equal to zero. The IRR is calculated using monthly cash
flows for each company. The most intuitive way of understanding the meaning of the IRR is to think
of it as the equivalent constant interest rate during the life of the investment “at which a given series of
capital drawdowns must be invested in order for the private equity investor to earn a given series of
cash distributions as income” (Talmor and Vasvari 2011: 43). The IRR is a commonly used measure
of performance in the private equity industry because it takes into account the timing of cash flows
realized at different points in time during the investment life.
As the data analyzed in this paper includes significant outliers—e.g. one valuation in our
sample is 154,900% the median—we Winsorized the dependent variable (IRR) at the 95th percentile
(i.e. 191%). The Winsorized IRR is still 860% the median and, as shown by the descriptive statistics,
18
has a mean of 0.25 and a standard deviation of 0.67. There are two reasons for this choice. First,
outliers could significantly change regression results, affecting the sign and the significance of the
slope (Hamilton 2009). Second, we use an independent variable, mean IRR, to measure past success.
Phalippou (2009) demonstrated that an average of simple IRRs is significantly positively biased,
which may in turn cause a problem of regression to the mean (see robustness checks for further
details). Using Winsorized IRRs reduces this problem. Our robustness checks also applied other
transformations of the dependent variable, obtaining equal results.
Independent variables
Activity load. This measure captures the number of investments the private equity firm handled
concurrently with the focal investment (Ferris et al. 2003; Fich and Shivdasani 2006; Lopez de Silanes
et al. forthcoming). This variable was constructed in two steps. For each month in the life of the focal
investment, we tallied the number of ongoing investments. Next, we computed the average of these
variables across all months of the focal investment’s life. This measure captures the activity load faced
by the private equity firm during the management of the focal investment, and also indicates the
number of similar projects carried out in parallel. The management of parallel investments represents a
challenge for private equity firms, which must divide their limited managerial attention among several
simultaneous investments.
Experience stock. The stock of experience is measured as the number of investments
completely sold by the private equity firm prior to the starting date of the focal investment (Reagans et
al. 2005). The reasons to include only the exited investments in this measure are twofold. First, the
measure takes into account only those deals for which the PE firm observed the entire buyout process
from investment to exit. Second, this operationalization of experience stock allows us to disentangle
experience stock from activity load, which includes ongoing investments.
Experience homogeneity measures the extent to which a private equity firm’s accumulated
experience (before the focal investment) was concentrated in specific industries. Buyout industries are
classified following the 48-industry Fama and French classification (Fama and French 1997). To
measure experience heterogeneity, we used the Herfindhal index (HI) that is computed as:
19
where si is the percentage of buyouts done by the private equity firm in the i industry, for a total of N
industries (i.e. 48 industries). High values indicate high experience homogeneity.
Pacing. Pacing is measured as the mean time interval between acquisition of one buyout and
acquisition of the subsequent buyout, taking into account the entire investment history of the private
equity firm before the focal buyout (Hayward 2002). This measure is constructed by dividing the
number of months between the focal buyout and the first acquisition in the history of the firm by the
total number of investments acquired by the private equity firm. Therefore, high values of this variable
(i.e. long time intervals between one acquisition and the subsequent acquisition) indicate a slow pace,
while low values indicate a fast pace.
Past success. Prior success is calculated by averaging the IRR of all investments done by the
private equity firm prior to the focal investment (Kaplan and Schoar 2005). To compute prior success,
we averaged IRRs Winsorized at the 95th percentile (i.e. 191%) to avoid the possibility that outliers
would lead to an overestimation of mean past performance (Phalippou 2009; Phalippou and
Gottschalg 2009). Winsorized success avoids problems of regression to the mean due to the presence
of significant outliers when computing mean values (Greve 1999).
Control variables
In addition to the proposed variables, other factors may affect the performance of the focal investment.
Based on a systematic review of prior empirical studies on buyouts (Kaplan and Schoar 2005; Kaplan
and Stromberg 2009; Phalippou and Gottschalg 2009) and corporate acquisitions (Kim and Finkelstein
2009), we have employed an extensive set of control variables to rule out potentially confounding
factors that might influence buyout performance and the ability of the PE firm to handle activity load
(i.e. variation in the attention capacity of the PE firm). In this regard, we include a number of control
variables that capture and proxy changes in the attention capacity of the firm.
The first set of controls accounts for various characteristics of the acquiring private equity
firm. Older and larger firms often have more resources, management skills, reputation, and legitimacy,
which are helpful in executing a successful buyout (Folta and Janney 2004). For this reason, we
20
included two variables in the model: private equity firm age, measured as the number of years since
the foundation of the private equity firm; and private equity fund size (expressed in 2006 USD
millions), measured as the equity raised by the fund that acquired the focal company. Bigger funds
have more resources and, therefore, find it easier to manage more parallel investments.
We also control for the average size of the portfolio value managed by the private equity firm
during all the months of the focal investment (i.e. portfolio value). This proxies the importance of
other investments held in the portfolio during the focal investment: the more important they were with
respect to the focal investment, the more likely it is that these other investments received more
attention than the focal investment. Moreover, we control for past activity load, that is, the average
number of parallel investments a private equity firm has managed on a yearly basis from its inception
to the year before entering the focal investment. PE firms that have been repeatedly exposed to high
levels of activity load in the past may develop an ability to better handle activity load in the present;
these PE firms would be less harmed by heavier activity loads (Heimeriks et al. 2009). In addition, we
added private equity firm fixed effects to capture a number of unobservable characteristics that might
be related to our independent variables.
The second set of controls accounts for various characteristics of the deal that could influence
activity load. First, activity load can be influenced by the holding period, that is by the duration of the
focal investment. Second, the model includes a control for investment size (total equity paid for the
investment expressed in 2006 USD millions). Third, the variable IPO controls for whether the exit
from the company was realized through a public offer to the stock market. The IPO route of exit may
absorb more private equity firm attention. Fourth, the activity load of the private equity firm during
the focal investment might be influenced by the quality of other investments in portfolio. Private
equity firms tend to postpone exits from investments that are performing poorly. Because they keep
these investments longer, they will have more investments running in parallel. Since this problem
might increase activity load, we controlled for the average duration of other investments held.
The third set of control variables accounts for market conditions that might influence the
performance of the focal investment. First, we controlled for change in stock market valuations
between the starting date and the exit date of the focal investment. We define market return as the
21
average return of the S&P 500 index during the investment holding period. Second, we included time
fixed effects at the time of entry (i.e., acquisition year fixed effects) and exit (i.e., exit year fixed
effects) from the focal investment to capture a number of important unobservable drivers of
performance (e.g. the supply of debt financing). Third, to capture competition among private equity
firms, we used investment category fixed effects of the focal investment (i.e. top-, mid-, or small-
market) relative to other investments realized in that year. This measure is computed by building
entry-year terciles. Fourth, we used country and industry fixed effects to control for country and
industry unobserved heterogeneity, respectively. Finally, we added a control for the general economic
conditions faced by the private equity firm during the year in which the focal fund was raised (i.e.
vintage-year fixed effects).
Analysis
As previously specified, our data include 6,913 buyouts realized by 248 private equity firms. Pooling
repeated observations on the same private equity deal violates the assumption of independence of
residuals within each firm required for ordinary least square (OLS) regressions. We addressed this
issue using a within-group fixed-effects model that, according to a Hausman test, was preferable to a
random-effects model (Cameron and Trivedi 2009; Hausman 1989). This model also allows us to
control for any time-invariant heterogeneity across private equity firms that might be correlated with
our independent variables.
We used mean-centered values of the predictors that enter the models multiple times (as both
direct and interaction effects) to minimize multicollinearity problems (Aiken et al. 1991). Moreover,
activity load, experience, past success, investment size, holding period, fund size, and age were log-
transformed to deal with their extremely positively skewed distribution (Cameron and Trivedi 2009).
RESULTS
Insert Tables 1, 2, and 3 about here
Model 1 shows the baseline specification consisting of the control variables plus the fixed effects.
Notably, IRR is positively and significantly related to market return, suggesting that companies owned
by private equity firms perform in line with the stock market. Interestingly, investment size is
22
negatively related to IRR, suggesting that big investments perform worse than small investments. To
identify the impact of activity load, we first plot the median IRR across activity load deciles (see
Figure 1). The graph shows that there is a downward slope that is not simply determined by
differences between the lowest and the highest deciles. The nature of the relationship is confirmed by
Model 2, which shows the negative impact of a heavier activity load on the performance of the focal
activity. Model 3 shows, by introducing the squared term of activity load, that there is no optimal level
of activity load and that the relationship is linear.
Model 5 shows that the interaction effect between activity load and accumulated experience is
positive and significant, which supports H1. Model 7 shows that the interaction effect between
activity load and homogeneity is positive and significant, which supports H2. Therefore, private
equity firms that are more focused (in terms of industries) may be more effective at handling the
problems deriving from a heavier activity load. Interestingly, we also find that the direct impact of
homogeneity is positive and significant.
Model 9 shows that the interaction effect between activity load and pacing is positive and not
significant. However, this interaction becomes marginally significant (i.e. p value equal to 0.06) in
Model 11 when controlling for past performance and its interaction effect with activity load. This
finding only marginally supports H3 and shows that long time intervals between one acquisition and
the following one (corresponding to high levels of the pacing variable) may strengthen a firm’s ability
to handle activity load, i.e. fast pacing worsens the negative impact of a heavier activity load. Model
11 offers evidence of a negative relationship between activity load and past success, indicating that
past success reduces a firm’s ability to handle a heavier activity load. This finding supports H4.
Robustness checks Insert Table 4 about here
An issue that deserves further investigation is whether the negative impact of activity load is
dependent on the number of managers at the firm at the time of the focal investment. We conducted
two different analyses to explore this issue. We re-ran the baseline model by substituting the fixed
effects at the firm level with fixed effects at the fund level. We conducted this analysis because the
23
number of decision makers (i.e. members of the investment committee) composing the private equity
firm remains relatively stable during the fund life because of clauses (e.g. a key-man clause) and/or
fund conditions (e.g. incentive structures) that limit the replacement of key professional figures during
the fund life (Gilligan and Wright 2012). Fixed effects at the fund level, therefore, capture the
unobserved number of key employees at a private equity firm during the focal investment
(Wooldridge 2009). As shown in Model 12, the impact of activity load remains linear and negative.
Second, we ran an additional analysis by collecting information about the number of managers
at the time of each focal investment. In fact, the ability of the management team of the private equity
firm to manage activity load depends on the number of key decision makers, that is, on the number of
partners. This information was only available from 1995 to the present because its source—the
Galante Private Equity Directory—was first published in 1996. This information made it possible to
control for the number of managers in 3,112 focal investments, representing 45% of the overall
dataset. The analysis found that the number of key employees (i.e. the number of managers composing
the investment committee of the private equity firm) was not significantly related to performance, and,
more importantly, that the sign and the significance of activity load did not change when we entered
this control variable into Model 13. This finding, while initially counter-intuitive, confirms the finding
of a recent study by Cumming and Dai (2010) in the venture capital industry, which found that
individual attention loads do not have a significant impact on the performance of a focal investment
(Cumming and Dai 2010).
Third, the level of activity load and its impact could be influenced by the market cycle
(Gompers et al. 2008). Indeed, it is likely that PE firms will have a heavier activity load with
investments purchased before the beginning of a financial crisis, because it is more difficult to sell
portfolio investments during a financial crisis (inflating the level of activity load in that period). These
investments are also likely to have a lower performance because the PE firm likely overpaid for them
at the time of acquisition. In such cases, the negative performance impact of activity load could be a
spurious effect driven by market cyclicality. To examine this possibility, we re-ran our baseline
regression with activity load on a subsample from which we excluded investments bought before the
24
beginning of a financial crisis (i.e. in 1999–2001 and in 2005–2008). Model 14 confirms the negative
impact of activity load on performance.
Fourth, the negative moderating influence of past success (on the negative link between
activity load and performance) might be affected by the problem of regression to the mean (Greve
1999). The use of a censored measure of past performance should reduce this problem (Phalippou
2009; Phalippou and Gottschalg 2009), but we conducted an additional analysis to corroborate our
result. A stronger test to check whether regression to the mean distorts the results is to exclude all
observations that had a limited number of prior investments. As past success will regress to the mean
after a number of observations (e.g. the second investment will be closer to the mean than the first),
excluding observations that only had a limited number of prior investments will mitigate the
regression-toward-the-mean problem (Greve 1999). A test using only observations with more than five
previous investments, shown in Model 15, confirmed the negative moderating effect of past success.
Finally, the problem generated by an extremely skewed dependent variable can be solved
either with the already-applied truncation or with a transformation of the dependent variable (Cameron
and Trivedi 2009). Our dependent variable is continuous and takes both positive and negative values,
has extremely positive values (i.e. it is highly positively skewed), and it has a substantial proportion of
both small and large values. In such a case, it might not be appropriate to use a standard log
transformation and add a shift parameter that makes all values positive, because the asymptotic results
of maximum likelihood theory may not apply (Atkinson 1985; Yeo and Johnson 2000). The use of
neglog transformation in such a case would be more appropriate because it has the same advantages of
the log-function and also appropriately extends monotonicity to negative values. This property is
particularly important for financial variables where the sign of the variable corresponds to profit and
loss (Whittaker et al. 2005). As shown in Model 16, our results do not change when the neglog
transformation is applied. In addition, given the distribution of our dependent variable, we applied the
arsinh transformation, which has the same properties of neglog (Yeo and Johnson 2000). Again, the
results (not reported) did not change. Taken together, these findings show that our results are robust to
different transformations of the dependent variable (i.e. truncation, neglog, and arsinh).
25
CONCLUSIONS
This paper examines how firms learn to cope with the negative effects of activity load in the context of
strategic activities. We developed and tested a theory of how experiential learning factors explain
variation in the capacity of organizations to handle a heavier activity load (Ocasio 1997; Ocasio 2011).
Empirically, we explored in the private equity industry the negative effect of a high activity load on
the performance of the focal investment and argued that this relationship is moderated by experience
stock, homogeneity, pacing, and past success. Our results are insightful for the study of the attention-
based view of the firm and for the experiential learning literature in several ways.
By conceptualizing experiential learning as a multidimensional construct consisting of four
dimensions, we have improved upon the treatment of experience in prior work (Barkema and Schijven
2008), which has typically focused on a subset of the dimensions covered in this study. More
importantly, our findings show that, depending on the dimension of experiential learning taken into
consideration, its impact on the management of activity load varies from positive to negative: the
stock and homogeneity of experiential learning can play a positive role, while its pacing and a history
of success can play a negative one. This suggests that experiential learning acts as a double-edged
sword, with some dimensions likely generating positive routines while others generate vicious routines
(Levitt and March 1988; Zollo 2009). Identifying the conditions under which experiential learning
generates positive routines, as opposed to vicious routines, represents a promising area of scientific
exploration.
In addition, this paper enriches our understanding of how routines can economize on decision
makers’ limited attention capacity (Becker 2004)—by reducing the amount of attention channeled to
each single activity (Ocasio 1997; Sullivan 2010). This finding has important implications for the
attention-based view of the firm (Ocasio 1997), in that it offers some first—albeit still rather
speculative—empirical evidence on how firms can use routine formation to reduce the harm caused by
a rising activity load. This finding—which suggests that attention capacity is not fixed and can be
expanded—is consistent with Rerup (2009), who showed that enacting specific organizational
mechanisms can expand a firm’s attention capacity. Taken together, these findings suggest that the
26
study of the link between activity load and attention capacity represents a promising—and
underdeveloped—area of inquiry for the attention-based view of the firm.
Moreover, this theory contributes to the behavioral theory of the firm and Neo-Carnegie
School in three ways. First, the Neo-Carnegie School claims that the focus should be “not on routines
per se, but on the standardized practices, programs, and operating procedures that serve to economize
on bounded rationality” (Gavetti et al. 2007: 527). By showing that experiential learning moderates
the link between activity load and performance, presumably through the formation of routines, we
shed new light on our understanding of the factors that economize bounded rationality. Second,
Gavetti et al. (2007) note that “the organizational level of analysis, although frequently invoked, has
generally been supplanted by either a more micro or a more macro focus” in the behavioral theory of
the firm (Gavetti et al. 2007: 524). We contribute to fill this gap by studying activity load and its
consequences at the organizational level. Third, our study contributes to studies about the link between
attention and performance feedback by showing that past success worsens the harm caused by activity
load (Gavetti et al. 2012). This finding suggests that positive performance feedback—whose impact on
the quality of future decisions has often been found to be negative (see Kim et al. 2009 for a review)—
also reduces the organizational ability to correctly allocate its limited attention capacity to the most
important organizational activites (Gavetti et al. 2007; Ocasio 1997; Rerup 2009).
Our findings also offer an opportunity to develop new theory about the organizational
capabilities related to the management of attention (Ocasio 1997; Rerup 2009). Taken together, the
four dimensions of experiential learning we identify—stock, homogeneity, pacing, and past success—
provide an initial theoretical basis for the development of an organizational capability that might be
termed attention modulation capability. Building on related notions developed in cognitive
psychology (Posner and Presti 1987), this construct could be defined as the ability of the group of
decision makers to selectively shift attention through various simultaneous activites, screening out
those with lower priority and modulating the magnitude, timing, and form of attention to channel to
each selected activity. While our efforts here offer a compelling first representation of the factors
underlying a firm’s attention modulation capability, our four dimensions of experiential learning are
27
certainly not exhaustive. Future research will be necessary to explore the wealth of factors potentially
strengthening or weakening the formation of this capability.
Finally, we shed new light on our understanding of the problems generated by the limitedness
of organizational attention capacity in the context of strategic activities. Our basic result shows that
activity load has a linear and negative impact on performance, indicating that increasing levels of
simultaneous strategic activities saturate the limited attention capacity of the firm and undermine
performance during multiple, simultaneous strategic activities (Cyert and March 1963; March and
Simon 1958; Simon 1947). This finding might initially appear to conflict with findings in the
corporate strategy literature on multi-business firms, which—despite some inconsistent findings—tend
to predict the existence of an optimal level in the portfolio of activities due to risk diversification and
business synergies (Goold and Luchs 1993). On closer inspection it becomes obvious that these two
streams of literature complement each other, rather than being in contrast.
Our result on activity load complements previous findings by encouraging strategy scholars to
reflect on the double-edged nature of a multi-business structure: on the one hand, it creates positive
effects due to risk diversification and business synergies; on the other hand, it create negative effects
due to activity load. Therefore, the level of activity that is optimal to mange at the organizational level
rests in part on the relative strengths of the negative effect of activity load and the positive effects of
risk diversification and business synergies. This insight may help to explain the mixed empirical
results in research on the performance impact of a multi-business structure (Palich et al. 2000). Future
studies should attempt to capture the relative strengths of the negative and positive effects of a
portfolio of activites to ascertain the optimal number of divisions in a multi-business structure. More
generally, our finding may help explain why different corporate growth strategies based on managing
a portfolio of activities—alliances or acquisitions programs, for example—often fail to deliver the
expected benefits (Datta et al. 1992; King et al. 2004; Lavie 2007).
Managerial Implications
These findings could be of interest to practicing managers in a variety of ways, but we believe three
deserve particular attention. First, and probably most importantly, the negative and linear impact of
28
activity load on performance is a source of concern for managers involved in making acquisition
decisions with low or minimal synergistic potential, based only on stand-alone optimization of the
acquired company’s capacity to generate rents (Barkema and Schijven 2008). This is certainly the case
in the private equity industry, which tends to be dominated by the logic of stand-alone value creation.
More generally, the absence of an optimal level of activity load might have implications for the
management of portfolios of activities and initiatives, including acquisitions and alliances. It might
also apply to any major organic growth initiative that lacks overwhelming evidence of synergistic
benefits, which would be necessary to counteract the negative effects of a heavier activity load.
Moreover, the magnitude of the negative impact of activity load—an increase of one standard
deviation decreases the IRR of the focal buyout by around 10%—suggests the importance of studying
the remedies to this problem.
Second, the negative impact of the number of investments simultaneously managed during the
focal investment suggests that post-acquisition management capabilities (i.e. value addition) are
strategically important in the private equity sector. This is not trivial given the history of both the
academic (Wright et al. 2001) and the practitioner debates (Kosman 2009), which tend to emphasize
the fact that buyouts create value through selection and deal-making (e.g. the forms of payment,
negotiation, tax benefits, etc.). The economic magnitude of activity load points to the importance of
value addition on buyouts’ performance. The dynamics of collective attention in the management of
acquired companies adds an important element to the performance equation, which should receive
more attention than it currently attracts—not only at the time of the investment decision but, crucially,
in the post-acquisition phase.
Third, managers ought to recognize that experiential learning does not necessarily generate
competence in handling multiple, simultaneous activities: while along some dimensions it exerts a
positive effect, along others it exerts a negative one. Identifying the mechanisms through which it is
possible to reduce the negative effects of experiential learning—while benefiting from the positive
ones—is a work-in-progress for academics as well as for practitioners, who continuously struggle to
overcome the impact of inertia and erroneous decision making deriving from experience (Finkelstein
et al. 2009; Heimeriks et al. 2012). In the meantime, some of the results of this study—the benefits of
29
longer time intervals between subsequent activities, for example, or the potential pitfalls created by a
history of positive performance—might serve as a guide for improved decision making in strategic
decisions.
Limitations and Suggestions for Future Research
As with any empirical study, this one has limitations that suggest interesting avenues for future
research. First, because of the large-scale archival nature of our data, this work is limited in its ability
to observe activity load directly, both at the organizational and individual levels of analysis. Future
works might improve on our measure of activity load by, for instance, taking into account both the
organizational resources (i.e. organizational level) and the decision makers’ attention capacity (i.e.
individual level) that each single activity absorbs. Second, and again because of the large-scale
archival nature of this study, we treat routines as a “black box,” and therefore we cannot explain “how
routines are enacted in the day-to-day and with what consequences” (Parmigiani and Howard-
Grenville 2011: 417). Future works should improve on our understanding of how organizations form
and change the routines necessary to alleviate the problems caused by activity load. Third, our
measures of learning processes rely primarily on experience-related constructs. This doesn’t do justice
to the potential role of other learning mechanisms, such as vicarious and deliberate learning processes,
which might influence (for better or worse) the managerial ability to handle heavier activity loads.
Fourth, our study lacks direct measures of post-acquisition management interventions by private
equity firms (Wright et al. 2001), which could substantiate the implications drawn from some of the
results related to the “weight” of activity load and the related need for an attention modulation
capacity. Fifth, this work is limited in its ability to capture the dynamics that link activity load at the
individual and organizational levels, mainly due to its large-scale empirical nature. Given the
importance of this issue, future research could examine the micro-mechanisms at the origin of the
activity load problem at the organizational level.
Finally, future research will be needed not only to address the limitation listed above, but also
to explore alternative explanations for the magnitude of the negative impacts of activity load. In
addition to the elements considered in our model, the effect could be influenced by a host of
30
organizational characteristics, such as knowledge-management systems and processes, the
centralization of decision making, or the presence of (coercive or enabling) bureaucracy in decision-
making processes (Adler and Borys 1996). It might also be influenced by characteristics of investment
decision processes, such as the degree of decisional autonomy for investment and management
decisions. Several characteristics of the institutional and cultural context could also modify the
negative influence of activity load on focal task performance.
Despite its limitations, we trust this work will provide support to future scholars in their
efforts to study the effects of organizational activity load and the role of the various dimensions of
learning in shaping the managerial capacity to handle it effectively.
31
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Figure 1: Histogram for the median IRR and activity load deciles
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
1 2
Median IRR
Figure 1: Histogram for the median IRR and activity load deciles
2 3 4 5 6 7 8 9 10
Activity Load (deciles)
36
37
Table 1 - Descriptive Statistics and Correlation Matrix
Mean SD
Correlation
1 2 3 4 5 6 7 8 9 10 11 12 13 14
1. IRR censored 0.25 0.67
2. Activity load 26.26 24.84 -0.0990*
3. Stock 18.42 30.58 -0.0135 0.4664*
4. Homogeneity 0.16 0.15 0.0755* -0.3826* -0.3079*
5. Pacing (months) 3.76 2.95 0.0794* -0.5701* -0.3328* 0.3562*
6. Past Success 0.29 0.29 0.1361* -0.1752* -0.0986* 0.0928* 0.2510*
7. Market Return 0.11 0.10 0.1917* 0.0402* -0.1007* 0.0033 0.0235 0.0827*
8. Investment Size (ml) 42.19 109.59 -0.0203 -0.0425* 0.0974* -0.0536* -0.0055 0.0091 -0.0459*
9. Holding Period (years) 5.29 3.55 -0.1688* -0.0519* -0.1394* -0.0075 0.0523* 0.0302 0.0625* 0.0504*
10. PE Fund Size (ml) 1311.67 1698.63 -0.0415* 0.4267* 0.3299* -0.2344* -0.2783* -0.0522* -0.0231 0.3945* -0.0346*
11. PF Firm Age (years) 7.1 5.21 -0.0363* 0.1449* 0.5756* -0.3759* 0.0633* -0.0021 -0.1449* 0.1976* -0.1237* 0.3250*
12. Duration Other Inv. (years) 4.69 0.96 -0.1108* 0.1087* -0.009 -0.1569* -0.0500* -0.0389* -0.0124 0.016 0.3058* -0.0031 0.0514*
13. IPO 0.12 0.33 0.1633* -0.0299 -0.0583* 0.0002 -0.003 -0.007 0.0582* 0.0461* 0.0358* -0.0002 -0.0254 -0.0022
14. Past Activity load 10.60 13.32 -0.0271 0.5926* 0.8256* -0.3563* -0.4646* -0.1775* -0.0647* 0.0621* -0.0686* 0.2998* 0.4151* 0.1215* -0.0307
15. Portfolio value (ml) 1082.64 1490.14 -0.0804* 0.3763* 0.4214* -0.2589* -0.3064* -0.1277* -0.1073* 0.3787* 0.0206 0.7297* 0.4358* 0.0258 0.0126 0.4059* * p<0.05
38
Table 2 - Results of the fixed-effects estimation
IRR Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Hypotheses
Activity load -0.1426*** -0.1401*** -0.1428*** -0.1373*** -0.1318*** -0.1237*** (0.028) (0.028) (0.028) (0.028) (0.028) (0.028) Activity load ^ 2 0.0072 (0.013) Stock -0.0017 -0.0277 -0.0320 -0.0286 (0.022) (0.023) (0.024) (0.024) Act. Load * Stock H1 0.0534*** 0.0508*** 0.0605*** (0.014) (0.014) (0.015) Homogeneity 0.0327 0.0763* (0.027) (0.033) Act. Load * Homo. H2 0.0449*
(0.020) Controls
Market Return 0.9091*** 0.8769*** 0.8734*** 0.8771*** 0.8669*** 0.8634*** 0.8714*** (0.148) (0.148) (0.148) (0.148) (0.148) (0.148) (0.148)
Past Activity Load 0.0294 0.0232 0.0186 0.0239 -0.0196 -0.0232 -0.0166 (0.023) (0.023) (0.024) (0.025) (0.027) (0.028) (0.028)
Investment Size -0.0723*** -0.0743*** -0.0743*** -0.0742*** -0.0756*** -0.0755*** -0.0753*** (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) Holding Period -0.4061*** -0.3892*** -0.3880*** -0.3892*** -0.3741*** -0.3749*** -0.3735*** (0.029) (0.029) (0.030) (0.029) (0.030) (0.030) (0.030) PE Fund Size 0.0028 0.0150 0.0154 0.0149 0.0228 0.0225 0.0254+ (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) PE Firm Age -0.0256 0.0415 0.0489 0.0423 0.1623** 0.1970** 0.1939** (0.050) (0.052) (0.053) (0.053) (0.061) (0.068) (0.068) Duration Other Inv. -0.0429** -0.0395* -0.0377* -0.0392* -0.0343* -0.0318+ -0.0366*
(0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) Portfolio value -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000+ -0.0000+
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) IPO 0.3950*** 0.3923*** 0.3922*** 0.3923*** 0.3933*** 0.3933*** 0.3942***
(0.024) (0.024) (0.024) (0.024) (0.024) (0.024) (0.024) Constant 2.5222* 2.2356+ 2.1699+ 2.2336+ 2.0019 2.0256 2.1554+
(1.269) (1.267) (1.273) (1.268) (1.268) (1.268) (1.269) Fixed Effects
PE Firm FE YES YES YES YES YES YES YES Acquisition Year FE YES YES YES YES YES YES YES Exit Year FE YES YES YES YES YES YES YES Country FE YES YES YES YES YES YES YES Vintage FE YES YES YES YES YES YES YES Investment Category FE YES YES YES YES YES YES YES
Observations 6,913 6,913 6,913 6,913 6,913 6,913 6,913 R-squared 0.2379 0.2410 0.2411 0.2410 0.2427 0.2429 0.2435 Model F 8.89*** 9.01*** 8.97*** 8.97*** 9.01*** 8.9 8*** 8.96***
Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.1
39
Table 3 - Results of the fixed-effects estimation
IRR Model 8 Model 9 Model 10 Model 11 Hypotheses
Activity load -0.1316*** -0.1331*** -0.1476*** -0.1379*** (0.030) (0.030) (0.031) (0.031)
Stock -0.0347 -0.0347 -0.0323 -0.0251 (0.025) (0.025) (0.025) (0.025) Act. Load * Stock H1 0.0612*** 0.0632*** 0.0596*** 0.0616*** (0.015) (0.016) (0.016) (0.016) Homogeneity 0.0849* 0.0839* 0.0875* 0.1059** (0.035) (0.035) (0.035) (0.036) Act. Load * Homo. H2 0.0468* 0.0443* 0.0384+ 0.0608**
(0.020) (0.021) (0.021) (0.022) Pacing -0.0227 -0.0180 -0.0096 0.0129
(0.031) (0.033) (0.033) (0.033) Act. Load * Pacing H3 0.0083 0.0129 0.0352+
(0.018) (0.018) (0.019) Past Success -0.3608*** -0.5877***
(0.062) (0.083) Act. Load * Past Success H4 -0.0567***
(0.014) Controls
Market Return 0.8689*** 0.8712*** 0.8716*** 0.8850*** (0.148) (0.148) (0.148) (0.147)
Past Activity Load -0.0191 -0.0197 -0.0209 -0.0261 (0.028) (0.028) (0.028) (0.028)
Investment Size -0.0754*** -0.0755*** -0.0752*** -0.0741*** (0.011) (0.011) (0.011) (0.011) Holding Period -0.3717*** -0.3716*** -0.3633*** -0.3643*** (0.030) (0.030) (0.030) (0.030) PE Fund Size 0.0255+ 0.0253+ 0.0249+ 0.0293+ (0.015) (0.015) (0.015) (0.015) PE Firm Age 0.2230** 0.2284** 0.2503** 0.2441** (0.079) (0.080) (0.079) (0.079) Duration Other Inv. -0.0370* -0.0374* -0.0388* -0.0326+
(0.017) (0.017) (0.017) (0.017) Portfolio value -0.0000+ -0.0000+ -0.0000* -0.0000*
(0.000) (0.000) (0.000) (0.000) IPO 0.3939*** 0.3940*** 0.3928*** 0.3911***
(0.024) (0.024) (0.024) (0.024) Constant 2.2835+ 2.2950+ 2.3518+ 2.2055+
(1.281) (1.281) (1.278) (1.277) Fixed Effects
PE Firm FE YES YES YES YES Acquisition Year FE YES YES YES YES Exit Year FE YES YES YES YES Country FE YES YES YES YES Vintage FE YES YES YES YES Investment Category FE YES YES YES YES
Observations 6,913 6,913 6,913 6,913 R-squared 0.2435 0.2436 0.2476 0.2495 Model F 8.93*** 8.89*** 9.04*** 9.10***
Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.1
40
Table 4 - Results of the fixed-effects estimation: robustness checks
Model 12 Model 13 Model 14 Model 15 Model 16 Hypotheses
Activity load -0.1218** -0.1410* -0.1598*** -0.1387* -0.1035*** (0.039) (0.064) (0.045) (0.056) (0.025)
Stock -0.0004 -0.0114 (0.062) (0.020) Act. Load * Stock H1 0.0531*** (0.013) Homogeneity 0.1425 0.0608* (0.109) (0.029) Act. Load * Homo. H2 0.0319+
(0.018) Pacing 0.1753+ 0.0240
(0.099) (0.027) Act. Load * Pacing H3 0.0370*
(0.016) Past Success -1.4217*** -0.5620***
(0.212) (0.067) Act. Load * Past Success H4 -0.1100** -0.0614***
(0.038) (0.011) Controls
Market Return 0.8629*** 0.9480*** 1.1742*** 1.1708*** 0.3725** (0.155) (0.213) (0.203) (0.198) (0.120)
Past Activity Load 0.0426 0.0799 0.0290 0.0092 -0.0170 (0.027) (0.062) (0.030) (0.065) (0.023)
Investment Size -0.0709*** -0.0976*** -0.0891*** -0.0950*** -0.0737*** (0.012) (0.018) (0.014) (0.015) (0.009) Holding Period -0.3886*** -0.3619*** -0.3497*** -0.4104*** -0.3273*** (0.033) (0.055) (0.039) (0.043) (0.024) PE Fund Size 0.2633* 0.0137 0.2762* 0.0285 0.0193 (0.110) (0.030) (0.122) (0.022) (0.012) PE Firm Age 0.0116 -0.1145 0.0806 0.3827+ 0.1579* (0.064) (0.133) (0.074) (0.210) (0.064) Duration Other Inv. -0.0626** -0.0212 -0.0542* 0.0310 -0.0248+
(0.024) (0.036) (0.027) (0.028) (0.014) Portfolio value -0.0001*** -0.0000* -0.0000+ -0.0000 -0.0000*
(0.000) (0.000) (0.000) (0.000) (0.000) IPO 0.4039*** 0.3886*** 0.4355*** 0.4149*** 0.2933***
(0.025) (0.037) (0.028) (0.032) (0.019) Key Employees -0.0011
(0.002) Constant 1.2680 1.2148 0.0000 2.2682 1.6995
(1.171) (1.138) (0.000) (1.395) (1.037) Fixed Effects
PE Firm FE NO YES YES YES YES PE Fund FE YES NO NO NO NO Acquisition Year FE YES YES YES YES YES Exit Year FE YES YES YES YES YES Country FE YES YES YES YES YES Vintage FE YES YES YES YES YES Investment Category FE YES YES YES YES YES
Observations 6,913 3,112 5,072 4,034 6,913 R-squared 0.2936 0.2552 0.3039 0.2649 0.2409 Model F . 6.37*** . 6.61*** 8.69***
Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.1
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