IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING...

40
1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School of Business and Economics Department of Management Email: [email protected] Tel: +351 938797411 Maurizio Zollo Dean’s Professor of Strategy and Corporate Responsibility Management Department Bocconi University [email protected] 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.

Transcript of IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING...

Page 1: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

1

THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD

Francesco Castellaneta Catolica Lisbon School of Business and Economics

Department of Management Email: [email protected]

Tel: +351 938797411

Maurizio Zollo Dean’s Professor of Strategy and Corporate Responsibility

Management Department Bocconi University

[email protected]

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.

Page 2: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

2

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.”

Page 3: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

3

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

Page 4: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

4

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.

Page 5: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

5

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).

Page 6: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

6

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

Page 7: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

7

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).

Page 8: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

8

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

Page 9: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

9

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

Page 10: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

10

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

Page 11: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

11

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

Page 12: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

12

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).

Page 13: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

13

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.

Page 14: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

14

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

Page 15: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

15

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.

Page 16: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

16

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.

Page 17: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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,

Page 18: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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:

Page 19: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 20: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 21: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 22: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 23: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 24: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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).

Page 25: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 26: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 27: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 28: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 29: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 30: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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.

Page 31: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

31

References: Adler, P.S., B. Borys. 1996. Two Types of Bureaucracy: Enabling and Coercive. Administrative Science Quarterly 41(1) 61-89. Aiken, L.S., S.G. West, R.R. Reno. 1991. Multiple Regression: Testing and Interpreting Interactions. Sage Publications, Inc, Newbury Park. Amburgey, T.L., A.S. Miner. 1992. Strategic momentum: the effects of repetitive, positional, and contextual momentum on merger activity. Strategic Management Journal 13(5) 335-348. Atkinson, A.C. 1985. Plots, transformations, and regression: an introduction to graphical methods of diagnostic regression analysis. Clarendon Press Oxford. Barkema, H.G., M. Schijven. 2008. How do firms learn to make acquisitions? A review of past research and an agenda for the future. Journal of Management 34(3) 594-634. Barney, J.B. 1986. Strategic Factor Markets: Expectations, Luck, and Business Strategy. Management Science 32(10) 1231-1241. Baum, J.A., S.X. Li, J.M. Usher. 2000. Making the next move: How experiential and vicarious learning shape the locations of chains' acquisitions. Administrative Science Quarterly 45(4) 766-801. Becker, M., M.C. Becker, N. Lazaric. 2009. Organizational routines: advancing empirical research. Edward Elgar Publishing. Becker, M.C. 2004. Organizational routines: a review of the literature. Industrial and Corporate Change 13(4) 643-678. Beckman, C.M., P.R. Haunschild. 2002. Network Learning: The Effects of Partner Experience Heterogeneity on Corporate Acquisitions. Administrative Science Quarterly 47(1) 92-124. Berg, A., O. Gottschalg. 2005. Understanding value generation in buyouts. Journal of Restructuring Finance 2(01) 9-37. Bingham, C.B., K.M. Eisenhardt. 2011. Rational heuristics: the ‘simple rules’ that strategists learn from process experience. Strategic Management Journal 32(13) 1437-1464. Bingham, C.B., K.M. Eisenhardt, N.R. Furr. 2007. What makes a process a capability? Heuristics, strategy, and effective capture of opportunities. Strategic Entrepreneurship Journal 1(1) 27-47. Birkinshaw, J., J. Cohen. 2013. Make time for the work that matters. Harvard Business Review(September) 2-5. Bresman, H. 2013. Changing routines: A process model of vicarious group learning in pharmaceutical R&D. Academy of Management Journal 56(1) 35-61. Brown, S.L., K.M. Eisenhardt. 1997. The art of continuous change: Linking complexity theory and time-paced evolution in relentlessly shifting organizations. Administrative Science Quarterly 1-34. Cameron, A.C., P.K. Trivedi. 2009. Microeconometrics Using Stata 5. Cohen, W.M., D.A. Levinthal. 1990. Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly 15 128-152. Cohendet, P., P. Llerena. 2008. 12 The role of teams and communities in the emergence of organizational routines. Handbook of organizational routines 256. Cumming, D., N. Dai. 2010. Fund size, limited attention and valuation of venture capital backed firms. Journal of Empirical Finance. Cumming, D., U. Walz. 2009. Private equity returns and disclosure around the world. Journal of International Business Studies 41(4) 727-754. Cyert, R.M., J.G. March. 1963. A Behavioral Theory of the Firm. Prentice Hall, Englewood Cliffs, New York. Daft, R.L., K.E. Weick. 1984. Toward a Model of Organizations as Interpretation Systems. Academy of Management Review 9(2) 284-295. Datta, D.K., G.E. Pinches, V. Narayanan. 1992. Factors influencing wealth creation from mergers and acquisitions: A meta‚Äêanalysis. Strategic Management Journal 13(1) 67-84. Denrell, J. 2003. Vicarious learning, undersampling of failure, and the myths of management. Organization Science 14(3) 227-243. Edmunds, A., A. Morris. 2000. The problem of information overload in business organizations: a review of the literature. International Journal of Information Management 20(1) 17-28. Eppler, M., J. Mengis. 2004. The Concept of Information Overload: A Review of Literature from Organization Science, Accounting, Marketing, MIS, and Related Disciplines. Information Society 20(5) 325-344.

Page 32: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

32

Fama, E.F., K.R. French. 1997. Industry costs of equity. Journal of Financial Economics 43(2) 153-193. Feldman, M.S. 2000. Organizational routines as a source of continous change. Organization Science 11(6) 611-629. Feldman, M.S., W.J. Orlikowski. 2011. Theorizing practice and practicing theory. Organization Science 22(5) 1240-1253. Ferris, S.P., M. Jagannathan, A.C. Pritchard. 2003. Too Busy to Mind the Business? Monitoring by Directors with Multiple Board Appointments. Journal of Finance 58(3) 1087-1111. Fich, E.M., A. Shivdasani. 2006. Are Busy Boards Effective Monitors? Journal of Finance 61(2) 689-724. Finkelstein, S., J. Haleblian. 2002. Understanding acquisition performance: The role of transfer effects. Organization Science 13(1) 36-47. Finkelstein, S., J. Whitehead, A. Campbell. 2009. Why good leaders make bad decisions and how to keep it from happening to you. Harvard Business School Publishing. Folta, T.B., J.J. Janney. 2004. Strategic benefits to firms issuing private equity placements. Strategic Management Journal 25(3) 223-242. Galbraith, J. 1974. Organization Design: An Information Processing View. Interfaces 4 28-36. Garicano, L. 2000. Hierarchies and the Organization of Knowledge in Production. Journal of Political Economy 108(5) 874-904. Gavetti, G., H.R. Greve, D.A. Levinthal, W. Ocasio. 2012. The behavioral theory of the firm: Assessment and prospects. The Academy of Management Annals 6(1). Gavetti, G., D. Levinthal. 2000. Looking forward and looking backward: Cognitive and experiential search. Administrative Science Quarterly 45(1) 113-137. Gavetti, G., D. Levinthal, W. Ocasio. 2007. Neo-Carnegie: The Carnegie School's Past, Present, and Reconstructing for the Future. Organization Science 18(3) 523-536. Gavetti, G., D.A. Levinthal, J.W. Rivkin. 2005. Strategy making in novel and complex worlds: the power of analogy. Strategic Management Journal 26(8) 691-712. Gilligan, J., M. Wright. 2012. Private Equity Demystified: An Explanatory Guide, 2012 ed. ICAEW Coporate Finance Faculty, London. Gompers, P., A. Kovner, J. Lerner, D. Scharfstein. 2008. Venture capital investment cycles: The impact of public markets. Journal of Financial Economics 87(1) 1-23. Goold, M., K. Luchs. 1993. Why diversify? Four decades of management thinking. The Academy of Management Executive 7(3) 7-25. Greve, H.R. 1996. Patterns of Competition: The Diffusion of a Market Position in Radio Broadcasting. Administrative Science Quarterly 41(1) 29-60. Greve, H.R. 1999. The Effect of Core Change on Performance: Inertia and Regression toward the Mean. Administrative Science Quarterly 44(3) 590-614. Greve, H.R. 2003. Organizational Learning from Performance Feedback. Cambridge University Press, Cambridge, UK. Grise, M.-L., R.B. Gallupe. 1999. Information Overload: Addressing the Productivity Paradox in Face-to-Face Electronic Meetings. Journal of Management Information Systems 16(3) 157-185. Haleblian, J., S. Finkelstein. 1999. The influence of organizational acquisition experience on acquisition performance: a behavioral learning perspective. Administrative Science Quarterly 44(1) 29-56. Haleblian, J., K.I.M. Ji-Yub, N. Rajagoplan. 2006. The influence of acquisition experience and performance on acquisition behavior: evidence from the U.S. commercial banking industry. Academy of Management Journal 49(2) 357-370. Hamilton, L.C. 2009. Statistics with Stata. Cengage. Haunschild, P.R., B.N. Sullivan. 2002. Learning from complexity: Effects of prior accidents and incidents on airlines' learning. Administrative Science Quarterly 47(4) 609-643. Hausman, D.M. 1989. Economic methodology in a nutshell. Journal of Economic Perspectives 3(2) 115-127. Hayward, M.L.A. 2002. When do firms learn from their acquisition experience? Evidence from 1990-1995. Strategic Management Journal 23(1) 21-39.

Page 33: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

33

Heimeriks, K.H. 2010. Confident or Competent? How to Avoid Superstitious Learning in Alliance Portfolios. Long Range Planning 43(1) 57-84. Heimeriks, K.H., G. Duysters, W. Vanhaverbeke. 2007. Learning mechanisms and differential performance in alliance portfolios. Strategic Organization 5(4) 373-408. Heimeriks, K.H., E. Klijn, J.J. Reuer. 2009. Building capabilities for alliance portfolios. Long Range Planning 42(1) 96-114. Heimeriks, K.H., M. Schijven, S. Gates. 2012. Manifestations of higher-order routines: the underlying mechanisms of deliberate learning in the context of post-acquisition integration. Academy of Management Journal 55(3) 703-726. Kahneman, D. 1973. Attention and effort. Prentice-Hall, Englewood Cliffs, NJ. Kaplan, S.N., A. Schoar. 2005. Private equity performance: Returns, persistence, and capital flows. Journal of Finance 60(4) 1791-1823. Kaplan, S.N., P. Stromberg. 2009. Leveraged buyouts and private equity. Journal of Economic Perspectives 23(1) 121-146. Kim, J.-Y., S. Finkelstein. 2009. The effects of strategic and market complementarity on acquisition performance: Evidence from the U.S. commercial banking industry, 1989-2001. Strategic Management Journal 30(6) 617-646. Kim, J.-Y., J.-Y. Kim, A.S. Miner. 2009. Organizational learning from extreme performance experience: The impact of success and recovery experience. Organization Science 20(6) 958-978. Kim, J.-Y., A.S. Miner. 2007. Vicarious learning from the failures and near-failures of others: evidence from the u.s. commercial banking industry. Academy of Management Journal 50(3) 687-714. King, D.R., D.R. Dalton, C.M. Daily, J.G. Covin. 2004. Meta-analyses of post-acquisition performance: indications of unidentified moderators. Strategic Management Journal 25(2) 187-200. Kosman, J. 2009. The buyout of America: how private equity will cause the next great credit crisis. Portfolio (Hardcover), New York. Laamanen, T., T. Keil. 2008. Performance of serial acquirers: toward an acquisition program perspective. Strategic Management Journal 29(6) 663-672. Landau, C., C. Bock. 2013. Value Creation through Vertical Intervention of Corporate Centres in Single Business Units of Unrelated Diversified Portfolios – The Case of Private Equity Firms. Long Range Planning 46(1-2) 97-124. Lant, T.K. 2002. Organizational Cognition and Interpretation. J.A.C. Baum, ed. Blackwell Companion to Organizations, 344-362. Lant, T.K., Z. Shapira. 2001. Organizational Cognition Computation and Interpretation. Psychology Press, USA Mahwah, NJ. Lavie, D. 2007. Alliance portfolios and firm performance: A study of value creation and appropriation in the US software industry. Strategic Management Journal 28(12) 1187-1212. Leonard-Barton, D. 1992. Core capabilities and core rigidities: a paradox in managing new product development. Strategic Management Journal 13 111-125. Levinthal, D., C. Rerup. 2006. Crossing an Apparent Chasm: Bridging Mindful and Less-Mindful Perspectives on Organizational Learning. Organization Science 17(4) 502-513. Levinthal, D.A., J.G. March. 1993. The myopia of learning. Strategic Management Journal 14 95-112. Levitt, B., J.G. March. 1988. Organizational Learning. Annual Review of Sociology 14 319-340. Lopez de Silanes, F., L. Phalippou, O. Gottschalg. forthcoming. Giants at the Gate: On the Cross-Section of Private Equity Investment Returns. Journal of Financial and Quantitative Analysis. Louis, M.R., R.I. Sutton. 1991. Switching cognitive gears: From habits of mind to active thinking. Human Relations 44(1) 55. March, J.G. 1991. Exploration and exploitation in organizational learning. Organization Science 2(1) 71-87. March, J.G., H.A. Simon. 1958. Organizations. Wiley, New York. Martin, J.A., K.M. Eisenhardt. 2010. Rewiring: Cross-business-unit collaborations in multibusiness organizations. The Academy of Management Journal (AMJ) 53(2) 265-301. Meyer, T., P.Y. Mathonet. 2005. Beyond the J-curve: Managing a portfolio of venture capital and private equity funds. John Wiley & Sons Inc. Miner, A.S., P.R. Haunschild, A. Schwab. 2003. Experience and convergence: Curiosities and speculation. Industrial and Corporate Change 12(4) 789-813.

Page 34: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

34

Moore, D.A., D.M. Cain. 2007. Overconfidence and underconfidence: When and why people underestimate (and overestimate) the competition. Organizational Behavior & Human Decision Processes 103(2) 197-213. Nelson, R., S. Winter. 1982. An evolutionary theory of economic change. Harvard University Press, Cambridge, M.A. Ocasio, W. 1997. Towards an attention-based view of the firm. Strategic Management Journal 18 187-206. Ocasio, W. 2011. Attention to Attention. Organization Science orsc. 1100.0602 v1101. Palich, L.E., L.B. Cardinal, C.C. Miller. 2000. Curvilinearity in the diversification–performance linkage: an examination of over three decades of research. Strategic Management Journal 21(2) 155-174. Parmigiani, A., J. Howard-Grenville. 2011. Routines revisited: Exploring the capabilities and practice perspectives. The academy of management annals 5(1) 413-453. Paulson, E. 2001. Insice Cisco. The real story of sustained M&A growth. John Wiley & Sons, New York. Perlow, L.A., G.A. Okhuysen, N.P. Repenning. 2002. The speed trap: Exploring the relationship between decision making and temporal context. Academy of Management Journal 931-955. Phalippou, L. 2009. The hazards of using IRR to measure performance: the case of private equity. CFA Digest 39(2) 64-65. Phalippou, L., O. Gottschalg. 2009. The performance of private equity funds. Review of Financial Studies 22(4) 1747-1776. Posner, M.I., D.E. Presti. 1987. Selective attention and cognitive control. Trends in Neurosciences; Trends in Neurosciences. Reagans, R., L. Argote, D. Brooks. 2005. Individual experience and experience working together: Predicting learning rates from knowing who knows what and knowing how to work together. Management Science 51(6) 869-881. Rerup, C. 2009. Attentional Triangulation: Learning from Unexpected Rare Crises. Organization Science 20(5) 876-893. Rerup, C., M.S. Feldman. 2011. Routines as a Source of Change in Organizational Schemata: The Role of Trial-And-Error Learning. Academy of Management Journal 54(3) 577-610. Salvato, C. 2009. Capabilities Unveiled: The Role of Ordinary Activities in the Evolution of Product Development Processes. Organization Science 20(2) 384-409. Schilling, M.A., P. Vidal, R.E. Ployhart, A. Marangoni. 2003. Learning by doing something else: Variation, relatedness, and the learning curve. Management science 49(1) 39-56. Schneider, S.C. 1987. Information overload: causes and consequences. Human Systems Management 7 143-153. Shapira, Z. 1994. Evolution, externalities and managerial action. Evolutionary Dynamics of Organizations. Oxford University Press: Oxford 117-126. Simon, H.A. 1947. Administrative Behavior: A Study of Decision-Making Processes in Administrative Organizations. MacMillan, Chicago, IL. Speier, C., J. S. Valacich, I. Vessey. 1999. The Influence of Task Interruption on Individual Decision Making: An Information Overload Perspective, 337-360. Sullivan, B.N. 2010. Competition and Beyond: Problems and Attention Allocation in the Organizational Rulemaking Process, 432-450. Sutcliffe, K.M., K.E. Weick. 2008. Information Overload Revisited. G.P. Hodgkinson, W.H. Starbuck, eds. The Oxford Handbook of Organizational Decision Making. Oxford University Press, Oxford. Talmor, E., F. Vasvari. 2011. International private equity. Wiley, New York. Turner, S.F., W. Mitchell, R.A. Bettis. 2010. Responding to Rivals and Complements: How Market Concentration Shapes Generational Product Innovation Strategy. Organization Science 21(4) 854-872. Tushman, M.L., D.A. Nadler. 1978. Information processing as an integrating concept in organizational design. Academy of Management Review 3 613-625. Von Hippel, E. 1998. Economics of product development by users: The impact of 'sticky' local information. Management Science 44(5) 629-644. Weick, K.E., K.M. Sutcliffe. 2006. Mindfulness and the Quality of Organizational Attention. Organization Science 17(4) 514-524.

Page 35: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

35

Whittaker, J., C. Whitehead, M. Somers. 2005. The neglog transformation and quantile regression for the analysis of a large credit scoring database. Journal of the Royal Statistical Society: Series C (Applied Statistics) 54(5) 863-878. Williamson, O. 1975. Markets and hierarchies, analysis and antitrust implications: A study in the economics of internal organization. Free Press, New York. Winter, S.G. 2000. The satisficing principle in capability learning. Strategic Management Journal 21(10/11) 981. Wood, G., M. Wright. 2009. Private equity: A review and synthesis. International Journal of Management Reviews 11(4) 361-380. Wooldridge, J.M. 2009. Introductory Econometrics: A Modern Approach. South-Western Pub. Wright, M., R.E. Hoskisson, L.W. Busenitz. 2001. Firm rebirth: Buyouts as facilitators of strategic growth and entrepreneurship. The Academy of Management Executive (1993-2005) 111-125. Yeo, I.K., R.A. Johnson. 2000. A new family of power transformations to improve normality or symmetry. Biometrika 87(4) 954-959. Zollo, M. 2009. Superstitious learning with rare strategic decisions: theory and evidence from corporate acquisitions. Organization Science 20(5) 894-908. Zollo, M., J.J. Reuer, H. Singh. 2002. Interorganizational routines and performance in strategic alliances. Organization Science 13(6) 701-713. Zollo, M., S.G. Winter. 2002. Deliberate Learning and the Evolution of Dynamic Capabilities. Organization Science 13(3) 339-351.

Page 36: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 37: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 38: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 39: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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

Page 40: IN THE MANAGEMENT OF ACTIVITY LOAD - Semantic Scholar file1 THE DIMENSIONS OF EXPERIENTIAL LEARNING IN THE MANAGEMENT OF ACTIVITY LOAD Francesco Castellaneta Catolica Lisbon School

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