Exploration and Long-Run Organizational Performance: The Moderating Role of Technological Interdependence

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Journal of Management Vol. XX No. X, Month XXXX 1-26

DOI: 10.1177/0149206312466146 The Author(s) 2012

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Exploration and Long-Run Organizational Performance: The Moderating Role of Technological Interdependence

Gianluca VagnaniSapienza University of Rome

This study considers how cross-sectional differences and intertemporal variations in interde-pendencies between productive activities at the industry level moderate the contribution of exploration to long-run organizational performance. We use patent data to measure interdepen-dencies between productive activities at the industry level and computer-assisted content analy-sis to derive firms orientation toward exploration. We also introduce statistical techniques to control bias in estimates induced by potential sources of endogeneity. Our analysis shows that exploration largely contributes positively to long-run organizational performance. This positive effect is stronger in industries with more extensive levels of interdependency or that exhibit more changes in such interdependencies. This study shows the unique and contingent ways in which exploration affects long-run performance. We hope our ideas will influence several areas of future research, not the least of which involves exploration and interdependencies in developing our understanding of organizational success.

Keywords: exploration; technological interdependence; firm long-run performance

How much should an organization focus on the broad exploration of new possibilities? For two decades this has been a primary question in research on organizational learning and

466146JOMXXX10.1177/0149206312466146JOuRNAL OF MANAGEMENT / MONTH XXXXVagnani / Exploration and Long-Run Organizational Performance2012

Acknowledgments: For detailed discussions and suggestions I thank Corrado Gatti, Gaetano Maria Golinelli, Salih Zeki Ozdemir, Peter Murmann, Paola Pisano, Loredana Volpe, and participants in seminars at the Australian School of Business, the University of New South Wales, and the Sapienza University of Rome. I also thank Catherine Maritan, acting editor, and two anonymous referees for insightful suggestions on earlier drafts. I would also like to acknowledge the valuable comments of Peter Moran and Michele Simoni. All errors are the sole responsibility of the author. Support from the Sapienza University of Rome is gratefully acknowledged.

Corresponding author: Gianluca Vagnani, Sapienza University of Rome, 9 Castro Laurenziano Street, Rome, 00161, Italy

E-mail: [email protected]

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strategy (March, 1991), innovation (Abernathy & Clark, 1985), and organizational design (Tushman & OReilly, 1996). Scholars have observed that managers must explore possibili-ties that lie far beyond currently deployed alternatives or else firm long-run performance suffers (Levinthal & March, 1993). At the same time, managers must limit exploration because it competes with exploitationanother crucial organizational activityfor scarce resources (March, 1991). Excessive exploration also exposes a firm to greater risks of failure and increases the costs of integrating the new with the well established (Levinthal & March, 1993; Levitt & March, 1988). How can one determine whether exploration is beneficial to long-run performance?

Studies of complex adaptive systems, originated in the physical and biological disci-plines, have started to address this issue. These studies have focused on exploration and exploitation as distinct search activities and considered their long-run organizational perfor-mance effects (e.g., Rivkin & Siggelkow, 2007). They have argued that the long-run perfor-mance effects of exploration and exploitation are influenced by the interdependencies that characterize the environment in which organizations operate (e.g., Levinthal, 1997). Interdependencies exist whenever the value of conducting a given activity or activities depends on how an organization conducts other activities. Activities that are subject to the effects of interdependencies include aspects of organizational form, features of production processes, and specific product characteristics (Lenox, Rockart, & Lewin, 2010: 121).

using simulation methods, prior studies have illustrated how interdependencies expand the number of suboptimal combinations of activities and increase firms risks of becoming trapped in a web of conflicting constraints (Rivkin & Siggelkow, 2006). In addition, these studies have shown how changes in such interdependencies expose organizations to new constraints, alter the value of currently deployed combinations, and raise the risk of obsolescence (Levinthal, 1997; Siggelkow, 2001). Scholars have acknowledged that if organizations wish to capitalize on inter-dependencies, they must broaden their search and shift from exploitation to the exploration of new possibilities (see, e.g., Lenox, Rockart, & Lewin, 2007; Levinthal, 1997). In particular, a greater focus on exploration allows organizations to extend the number of alternatives and trajec-tories within their reach, limit the risk of becoming prematurely locked into inferior alternatives, mitigate the risk that changes in interdependencies will rapidly render currently deployed combi-nations obsolete, and better capitalize on changing environmental conditions. Both exploration and exploitation are necessary for firm performance, but interdependencies make exploration activities even more essential.

Despite the theoretical clarity and pervasiveness of these arguments, computer-based simulation studies must still test the external validity of their propositions and conclusions. Such testing represents an important task for empirical research (Davis, Eisenhardt, & Bingham, 2007), and, as observed by Lenox et al. (2007), developing good ways to measure interdependencies is one of its big challenges. However, no empirical studies have yet tested the role of interdependencies on the relationship between exploration and long-run organi-zational performance. This study examines how the effects of exploration on long-run organizational performance are moderated by cross-sectional differences and intertemporal variations in interdependencies between productive activities at the industry level. These effects are analyzed in a longitudinal panel research design that covers the years 19892004 for 274 firms included in the 1989 Standard & Poors 500 (S&P 500) index. We find that



Vagnani / Exploration and Long-Run Organizational Performance 3

exploration has a prevalently positive effect on long-run performance. We also observe that this positive effect is stronger in industries characterized by either more extensive levels of technological interdependencies or greater changes in such interdependencies.

Our article contributes to the literature in several ways. First, we provide a test of the long-run performance effects of exploration in a longitudinal research design. The extended time frame of our panel data attenuates the effects on estimates of many idiosyncratic vari-ations in performance and of short-term performance differences between organizations (Kirby, 2005). It also allows us to apply advanced econometric models to mitigate and better control for potential distortions of estimates induced by endogeneity (Hamilton & Nickerson, 2003) and particularly by potential feedback loops between dependent and independent variables (March & Sutton, 1997). This is one of only a few studies incorporating such a control for endogeneity (also see uotila, Maula, Keil, & Zahra, 2009).

Second, we propose an industry-level measure of interdependency (i.e., technological interde-pendence) that encompasses interdependencies in activities related to the introduction of new features in the manufacturing processes and/or new product characteristics. Finally, our tests include both cross-sectional differences and intertemporal variations in technological interde-pendencies. Both of these dimensions contribute to the complexity of organizational activities and increase the long-term benefits of exploratory activities (Levinthal, 1997).1 We know of no other empirical study that tests the moderating impact of interdependencies on the relationship between exploration and long-run organizational performance.

Hypothesis Development

Exploration and Long-Run Organizational Performance

March (1991: 71) relates exploration activities to such things as search, variation, risk taking, experimentation, play, flexibility, discovery, innovation. Such a broad concept of exploration has been employed in various contexts, such as technological innovation (He & Wong, 2004), strategic alliances (Lavie & Rosenkopf, 2006), external corporate ventures (Schildt, Maula, & Keil, 2005), and intraorganizational knowledge flows (Schulz, 2001). Across different contexts, scholars have often suggested that exploration-oriented activities are likely to contribute positively to firm long-run performance. Exploration involves a wider time commitment and a broader space horizon (March, 2008), which greatly extends a firms search beyond the neighborhood of current known alternatives (Abernathy & Clark, 1985; Fleming, 2001; Rosenkopf & Nerkar, 2001). In addition, by expanding the set of pos-sible alternatives, exploration enhances recombinatory search, considered a primary source of organizational novelty and innovation (Fleming & Sorenson, 2001). The expanded variety of available nonlocal alternatives and the enhanced recombinatory search offer the organ-ization opportunities to introduce new products and production processes, create or access new markets, and regenerate consumer value. Such outcomes are expected to have a positive impact on the organizations long-run performance (Lewin, Long, & Caroll, 1999).

It has also been observed that excessive exploration can have negative consequences for long-run performance since it increases the risk of failure and magnifies the variance of performance (March, 1991). Moreover, as noted by Levinthal and March (1993), organizational focus on exploration




can lead to a cascade of experiments without the development of sufficient competence to exploit or even assess their merits (i.e., a failure trap). Finally, exploration competes with exploitation for organizational resources, and too much exploration can be considered tanta-mount to insufficient exploitation (March, 1991). Hence, over the long run and given the current level of exploitation, across all possible environments and possible alternatives to explore, a little focus on exploration is more beneficial than none; too much exploration, however, can be as harmful to long-run performance as too little. Therefore, we propose the following hypothesis.

Hypothesis 1: Exploration is curvilinearly related to long-run organizational performance.

The Moderating Role of the Level of Technological Interdependency

March (1991) noted that the effectiveness of exploration activities is a function of eco-logical interactions. We consider interdependencies a key part of such interactions, perhaps best portrayed in computer-based simulation studies where the fitness landscape represents a search space whose peaks and valleys are associated with configurations that give rise to successful and unsuccessful outcomes, respectively. Evolution is thus viewed as the adaptive walk of populations across a fitness landscape (Kauffman, 1993).

Referring to this metaphor, Levinthal (1997) proposed a computer-based simulation model to show how interdependencies between broadly defined firm activities influence the benefits of exploration for organizational survival. In particular, when interdependencies are limited, the fitness landscape is dominated by only a few optimal alternatives. In addition, all combinations of activities can be sequentially changed to globally optimal combinations by exploiting successive modifications of each component, from a less favorable to a more favorable combination. In contrast, more extensive levels of interdependency render the landscape more rugged, with numerous combinations of activities that are locally superior but globally inferior. It implies that fewer improvement alternatives will lie on any path of continuous improvement. Therefore, under such conditions, a firm survival in the face of a rugged landscape becomes more associated to successful exploratory activities.

More recently, Lenox, Rockart, and Lewin (2006) related interdependency to an indus-trys production functionwhich includes such things as just-in-time logistics, piece-rate payments, sampling techniques to control quality, work teams, and stock options as incentivesand assessed its implications for local search, innovation, and imitation (also see Kauffman, Lobo, & Macready, 2000; Sahal, 1985). Greater numbers of interdependen-cies increase trade-offs between firm productive activities, enhance any constraints imposed by the initially developed combination on subsequent improvement (Lenox et al., 2007) and reinforce the dependence of current and future organizational efforts on past choices (also see Levinthal, 1997). In addition, more extensive levels of interdependency expand the num-ber of local inferior optima and potentially unprofitable trajectories (Lenox et al., 2007). Therefore, increased interdependencies raise the risk of an organization becoming prema-turely locked into a neighboring combination of productive activities that has inferior long-run potential.




More extensive levels of interdependency at the industry level increase the positive long-run performance effects of exploration activities at the firm level. Exploratory activities allow organizations to attenuate their dependence on their current deployed combinations of productive activities and related path of improvements. These activities also mitigate the firms risk of becoming prematurely locked into inferior alternatives, extend the number of combinations within the firms reach, and help drive the organization into the basins of attractions of superior combinations of productive activities (Levinthal, 1997). In addition, more extensive interdependencies hamper imitation (Lenox et al., 2007; Rivkin, 2000). A prospective imitator or potential new entrant could understand most of a firms productive activities yet still fail to grasp the whole recipe. Moreover, more extensive levels of interde-pendency dramatically increase the effect of errors in the reproduction of one or more ele-mentary components of the whole combination. In a rugged landscape, imitation of entire configurations of activities is likely to be imperfect, which can have negative consequences for imitators (Rivkin, 2000). Hence, exploration helps organizations mitigate the challenges that interdependencies impose on organizational adaptation. At the same time, interdepend-encies allow a firm that has discovered and selected a new combination of productive activities to more easily retain its value, which further contributes to the benefits of explora-tion for long-run organizational performance.

The discussion then yields the following hypothesis.

Hypothesis 2: Technological interdependency moderates the relationship between exploration and long-run organizational performance such that greater levels of exploration have a stronger positive impact on long-run organizational performance in industries with more extensive levels of interdependency than in those with more limited levels.

The Moderating Role of Changes in Technological Interdependencies

This study considers technological interdependencies an industry-level attribute, yet they have a tendency to change somewhat over time, sometimes quite markedly and within rela-tively brief intervals: New interdependencies between productive activities can appear, old interdependencies can be strengthened, and others can become obsolete or even vanish. Such a tendency reflects the variability of interdependencies and is often referred to in the litera-ture as turbulence (Siggelkow, 2001).

Changes in interdependencies make the value of a given activity or set of activities dependent on other, new activities. Organizations are then exposed to new, different interde-pendencies. When many changes in interdependencies occur, organizations may be required to modify many activities simultaneously (Siggelkow, 2001). Changes in interdependencies also have important implications for the value of currently deployed combinations. Kauffman (1993) pointed out how, given the overall number of interdependencies, the appearance of new epistatic couplings and the disappearance of others in the context of a given genotype can alter that genotypes fitness. As proposed by Siggelkow (2001), environ-mental changes can trigger variations in the internal and external fit between productive activities, which, in turn, can induce higher-valued combinations to lose value and lower-valued combinations to gain value. As further illustrated by Rivkin and Siggelkow (2007)




through a simulation model, holding constant the total number of interdependencies, changes in the distribution of interdependencies between activities can alter the number, position, and value of alternatives available in organizational adaptation. An organization can then experience a drop in the value of the currently deployed combinations or lose opportunities for promising new alternatives.

For example, the introduction of proximity alignment equipment in the photolithographic industry significantly changed the interdependencies between the gap-setting mechanism and the other components of the aligner. As noted by Henderson and Clark (1990), these new interdependencies imposed changes in the gap-setting mechanism. Such changes destroyed the compatibility between the contact aligner technology and the gap-setting mechanism, thus contributing to rapidly rendering the contact aligner obsolete. We therefore observe that challenges to organizations originate from changes in interdependencies, as well as from existing levels of interdependency.

More extensive changes in interdependencies at the industry level increase the positive effects of exploration-oriented activities on long-run organizational performance. Exploration fosters flexibility and experimentation in search activities, which allows organizations to modify their current combinations of productive activities as they become aware of changes in interdependencies. As such, exploration limits firms need to engage in costly and time-consuming information-gathering activities aimed at predicting ex ante and minimizing ex post the consequences of changes in interdependencies. It also makes organizations less dependent on the value of currently deployed combinations, which mitigates the risk that changes in interdependencies can rapidly render currently deployed combinations obsolete. Finally, exploration increases the likelihood that, through different search attempts, a firm can find the optimum in a global landscape and thus align itself with the changing environ-mental conditions (Brown & Eisenhardt, 1997).

The greater benefits of exploration in a changing environment have been illustrated in a simulation study by Levinthal (1997). Levinthal observed that firm long-run performance is more strongly linked to successful exploration when the fitness of currently deployable combinations of activities is subject to a major respecification and the landscape in which organizations search is highly interdependent. The benefits of exploration can also be envis-aged in environments with more limited but highly changeable interdependencies.

Consider the case of the computer industry. The spectacular success of modular design has contributed to resolving many interdependencies (Baldwin & Clark, 2000). In this indus-try, one would then expect exploration to no longer be necessary. However, the development of reduced instruction set computing (RISC) considerably changed some of the linkages between the microprocessor and other PC components. These changes made it difficult for many firms that were producing and refining the old generation of complex instruction set computing machines to adapt to the new architecture promoted by RISC technologies. By contrast, other firms, like Intel, focused on the exploration of the new RISC architecture and were able to further develop it and launch many successful innovative products based on this architecture (also see Burgelman, 1991). Thus, even in industries where the levels of inter-dependency are stable or falling, exploration is highly beneficial when interdependencies exhibit greater variability.




It follows that, controlling for levels of interdependency, changes in interdependencies render exploration more beneficial for long-run organizational performance. Consequently, we propose the following hypothesis.

Hypothesis 3: Changes in technological interdependencies positively moderate the relationship between exploration and long-run organizational performance such that greater levels of explo-ration have a stronger positive impact on long-run performance in industries that exhibit more extensive changes in interdependencies than in those that exhibit more limited changes.

Review of the Literature and Our Contribution

The proposed hypotheses are not new to the literature, but despite their importance and acceptance, they have received little empirical support. Moreover, most studies have not considered the implications of search activities for long-run organizational performance and have not clearly separated the effects of exploration from those of exploitation. In addition, no study has controlled for interdependencies or considered them as a moderating variable.

Many studies have analyzed the relationships between exploration and organiza-tional performance. Some employed such measures of performance as the number of new products, sales from new products, the radicalness of innovations, and the use-fulness of inventions (e.g., Katila & Ahuja, 2002; Nerkar, 2003), while others con-sidered organizational survival (Piao, 2010). Although these measures are linked to the outcome of search activities, they can best be considered antecedent to or a consequence of organizational performance.

Other empirical studies have directly used measures of organizational performance. Most of them employed accounting measures in a cross-sectional design. Since a cross-sectional design uses pooled aggregated data, it is impossible to capture trends in firm performance (Bergh, 1993). In addition, accounting measures are not adjusted for risk and, more rele-vantly, are insensitive to the time lags necessary to realize the potential of investments made by organizations (Richard, Devinney, Yip, & Johnson, 2009). Therefore, considering accounting measures in a cross-sectional design may underestimate the benefits of explora-tion (Bierly & Daly, 2007: 510). Furthermore, the use of a cross-sectional design does not allow one to control for potential feedbacks between dependent and independent variables when testing the relationship between search activities and firm performance. He and Wong (2004: 487) noted that their cross-sectional design did not allow them to rule out the pos-sibility of endogeneity between sales growth and innovation strategy. Studies that referred to measures of organizational performance and used a longitudinal design considered a single measure combining exploration and exploitation (e.g., Lin, Yang, & Demirkan, 2007; uotila et al., 2009; Wang & Li, 2008). However, operationalizing exploration in this way makes it impossible to determine if its effect on firm performance is the result of a greater or lesser emphasis on exploration or exploitation.

While there are studies that have examined interdependencies, as well as the problems they pose for search activities, none has considered in a large-scale empirical analysis how interdependen-cies moderate the relationship between exploration and long-run organizational performance. For




example, individual case studies have analyzed how a greater number of interdependen-cies among broadly defined firm activities makes the exploitation of already deployed combinationsby the organization itself or by other organizationsno longer sufficient to achieve a sustainable competitive advantage (Porter, 1996). Related studies have deep-ened our understanding of how firms developing complex technologies face higher risks of failure (Singh, 1997), how interdependencies between the components of a complex product influence the allocation of inventive efforts (Ethiraj, 2007), and how couplings between technological components determine the value of scientific knowledge in inventive activities (Fleming & Sorenson, 2004).

While such empirical studies indisputably contribute to our understanding of how search activities and interdependencies contribute to organizational adaptation, they cannot speak directly for the effects of interdependencies on the relationship between exploration and long-run organizational performance. Our study deepens the issues implicated in testing hypotheses about interdependencies and proposes an empirical test of the relationship between exploration and long-run organizational performance, controlling for interdepend-ence at the industry level.

Following uotila et al. (2009), we introduce content analysis to directly evaluate a firms broad orientation toward exploration over extended periods. We refer to a financial measure of performance as the dependent variable. Financial measures are of great interest in man-agement (Richard et al., 2009) and are considered a direct representation of the overall qual-ity of the long-term adaptation process (Chakravarthy, 1986; Lubatkin & Shrieves, 1986). Moreover, measures of financial performance are widely used by managers, especially those of large organizations, to describe and implement strategy, guide employee behavior, assess managerial effectiveness, and provide the basis for rewards (Malina & Selto, 2004).

In particular, this study refers to Tobins q (Tobin, 1969, 1978). The q value is related to traditional accounting measures of performance (Devinney, Yip, & Johnson, 2010), but, as other financial market measures, it is independent of accounting practices (Chakravarthy, 1986), which attenuates idiosyncratic and erratic variations. Tobins q also incorporates both an organizations prospects for future earnings and the risks it faces in its current activities (Lubatkin & Shrieves, 1986; Richard et al., 2009). Thus, this single performance variable is likely to account for the long-term benefits of exploration-oriented activities and to discount the greater uncertainty and higher probability of failure associated with such activities (Lavie, Kang, & Rosenkopf, 2011; uotila et al., 2009).

Moreover, being an aggregate evaluation of firm performance, the q value incorporates the broad implications of interdependency and exploration for organizations. In addition, the literature specifies a standard set of controlling variablesfor example, the scale of research and development (R&D) investmentsthat limit the unobserved heterogeneity in testing the relationship between exploration and long-run organizational performance (Griliches, 1981). Note that financial market measures have often been used in empirical studies of the long-run performance implications of search activities (e.g., Hall, 2000; Lavie et al., 2011; Wang & Li, 2008). As a robustness test, we perform regression analyses (not shown here but avail-able on request) with an accounting-based measure of performance. In particular, as He and Wong (2004) did, we refer to the compounded average sales growth rate calculated over 3-year intervals. This test offers consistent results for this study.




To test the relationship between exploration and long-run organizational performance we use a longitudinal panel research design. This design allows us to control for endogeneity in estimates, take into account the time-dependent nature of performance, and limit measure-ment errors in the dependent variable (Richard et al., 2009). We then introduce an industry measure of technological interdependence that allows us to control for cross-sectional dif-ferences and intertemporal variations in interdependencies.

Method

Data and Sample

The original sample comprises 274 large organizations included in the S&P 500 in 1989 whose primary Standard Industrial Classification (SIC) codes range from 2000 to 3999 and from 7370 to 7374. Note that measures of exploration and of interdependency are particu-larly relevant for the manufacturing and software industries (Lenox et al., 2010; uotila et al., 2009). In addition, empirical studies have widely referred to these industries to analyze the effectiveness of organizational strategies (Chen, 2008; Ethiraj, 2007; Miller, 2006). To ensure comparability with other studies (uotila et al., 2009) and to capture sufficient time variations in the variables of interest (Arellano, 2003), the data cover 16 years, from 1989 to 2004. We restrict the records to those for which we can calculate our dependent variables, resulting in an initial data set of 3,867 observations.

Three main data sources were employed. In particular, we referred to Compustat for financial and accounting information. Given its many important features discussed in the literature (Griliches, 1990; Patel & Pavitt, 1997), we use patent information to evaluate technological interdependence at the industry level (e.g., Fleming & Sorenson, 2004; Sorenson, Rivkin, & Fleming, 2006). We collected from the u.S. Patent and Trademark Office all the available information attached to utility patents with application data from 1979 to 2005. In total, we analyzed approximately 3.5 million patents, almost 150,000 tech-nological subclasses, and 12 million co-occurrences of these subclasses. As suggested in the literature (Duriau, Reger, & Pfarrer, 2007), for each organization included in the sample, we collected archival data from annual reports (Courtis, 1982; Previts, Bricker, Robinson, & Young, 1994), including presidents letters to stockholders and managements discussions and analyses of financial results. Such documents have been shown to constitute a forum for managers to discuss themes important to the firm (Osborne, Stubbart, & Ramaprasad, 2001) and its performance (Abrahamson & Amir, 1996; Bowman, 1984; Michalisin, 2001). Annual reports that were not available in text format were transformed into electronic files.

Measures

There are multiple interdependencies spanning across organizational levels. At higher levels, interdependencies exist at a horizontal level between similar organizations and verti-cally between organizations related through the production process or between business




partners (Adner & Kapoor, 2010; Aldrich & Pfeffer, 1976). At the firm level, scholars have considered multiple levels of interdependency that span across decisions (Rivkin & Siggelkow, 2006), organizational units, roles, and individuals (Thompson, 1967). At more specific levels, interdependencies exist within and across products (Devinney & Stewart, 1988), tasks, processes, roles, and knowledge (Pennings, 1975).

Despite the multifaceted nature of interdependencies, Lenox et al. (2010: 124) observed that most measures of interdependency operate at only one or two levels and thus present a lower bound on interdependency as it affects firm and industry performance. Accordingly, instead of considering a variable that encompasses multiple forms and levels of interdepend-ency, we propose a measure that focuses on one kind of interdependency: that between activities related to the introduction of new features in the manufacturing processes and/or new product characteristics.2

As such, our measure does not capture, for example, whether a change in a product process requires changes in the piece-rate payment system or the introduction of a cross-functional team. Although the restrictions introduced to our measure of interdependency induce a measurement error, increased errors in an explanatory variable should only reduce the significance of our estimates, making the empirical test more conservative (Aguinis, 1995; Arellano, 2003). Moreover, by restricting the scope of our measure to technological interdependencies, we share two premises that drive the theoretical results in the literature. The first premise is that interdependencies capture the interaction effects among the elemen-tary components of a given combination of productive activities. By considering interde-pendence an industry-level attribute, we share the second premise, that interdependency is exogenous to firm actions (Lenox et al., 2007, 2010; Levinthal, 1997). Thus, through these common premises, our measure relates to the concept of interdependence, particularly that between productive activities, developed and used in computer-based simulation works (e.g., Kauffman et al., 2000; Lenox et al., 2007; Levinthal, 1997).

Interdependency measures. Following Fleming and Sorenson (2001, 2004), we use sub-classes references, as provided by the u.S. Patent and Trademark Office, to approximate the elementary components of an industrys production function. In many cases, the subclasses correspond quite closely to physical components. For example, subclass 300.1 is associated with machines for making brushes. In other subclasses, however, the equivalence is not always so obvious. For example, subclass 713.300 represents computer power control, which includes the details of the steps or means to modify the power used by a digital data processing system. Such imperfect matching between subclasses and physical components does not affect our measure, however. We require only that a subclass defines a physical component or an elemen-tary piece of knowledge used by an organization to specify the features of manufacturing processes and/or product characteristics (Fleming & Sorenson, 2004).

Thanks to the concordance table between u.S. patent subclasses and the SIC codes, developed early in 1985 and last reviewed in 2007 by the uS Patent and Trademark Office, we approximate the set of technological components available to organizations belonging to a given industry. There are 44 industries (i.e., SIC-based product fields), and they gener-ally correspond to the two-digit SIC code industries (see W. Chung & Yeaple, 2008, for an application).3




Following Engelsman and van Raan (1994), for each possible combination of subclasses in a given industry we measure the number Ri,j,t of raw co-occurrences, that is, the number of times that two subclasses belonging to a given industry have been jointly considered in a patent whose application date is in a given period t.4 Note that we set t = 10, that is, a 10-year moving lag. Fleming and Sorenson (2004) use such a time frame and note that the results are consistent with a broader time frame. To make the results comparable across dif-ferent industries and to control for the effect of using particularly popular combinations of subclasses (Henderson, 1995), following the work of Teece, Rumelt, Dosi, and Winter (1994) on firm diversification, we normalize the raw number of co-occurrences with the expected average number of co-occurrences and its standard deviation (also see Nesta & Saviotti, 2006).5 By averaging the normalized number of co-occurrences over the total num-ber of subclasses of a given industry, we obtain our industry-level measure of technological interdependence.6

For each industry we also calculate intertemporal variations in the normalized number of co-occurrences between technological components as the weighted number of technology subclass pairs exhibiting significant variation in the normalized number of co-occurrences between two periods. Following Yayavaram and Ahuja (2008), we consider that a significant variation in the normalized number of co-occurrences occurs (a) when a subclass pairs co-occurrence changes from the first (fourth) quartile in the period previous to the third (first) or fourth (second) quartile in the next period, (b) when a subclass pairs co-occurrence changes from the second (third) to the fourth (first) quartile, (c) when a new co-occurrence occurs, or (d) when an old one disappears.

The weight is equal to (pi,t + pj,t)/2 + (pi,t+1 + pj,t+1)/2, where pi,t(pi,t+1) and pj,t(pj,t+1) represent the fraction of patents that belong to a subclass i(j) in periods t and t + 1, respec-tively. We normalize the total number of changes in interdependencies by the weighted number of co-occurrences found in a given industry in periods t and t + 1. Our measure then ranges between zero and one. Consistent with our measure of interdependence, we set t [t 10, t], t + 1 [t 9, t + 1]. In our measure an interdependence that existed at time t 10 vanishes if it is no longer present from time t 9 up to time t + 1. Moreover, a new interdependence appears at time t + 1 that was not present from time t 10 up to time t. Hence, consideration of such extended periods makes our measure more conservative, less dependent on fluctuations in the yearly number of patent applications, and, more relevantly, less affected by idiosyncrasies in firms patent strategies (Griliches, 1990).7

Note that our measures of interdependence are generally allowed to vary between indus-tries but not within industries. We thus differ from Fleming and Sorenson (2004), who con-sider interdependency a specific trait of single inventions. Our measure also differs from other product-based measures of interdependencies (Fixson & Park, 2008) that can be affected, at least in the short term, by firm actions, as well as from other specific intraor-ganizational measurements of the interdependencies inherent in the knowledge architecture, the organizational structure, and the production process (Brusoni & Prencipe, 2006; Yayavaram & Ahuja, 2008).

Finally, we extract for 19892004 all the primary SIC codes associated with the business segments in which our sample organizations operated. Data were acquired from Compustat industry segment data. We write a computer program to relate each business segment (via its




primary SIC code) to the SIC-based product fields considered.8 We then determine the level of interdependency faced by each organization in a given year as the sales-weighted average of the levels of interdependency associated with each firms SIC-based product field (denoted Interdependency). Similarly, we express the extent of changes in interdependencies (denoted Interdependency). For robustness, we perform additional regressions by using both an asset-weighted level of interdependency and the primary SIC code associated with the segment with the highest sales as a reference. Similarly, we measure the extent of changes in interdependencies. These tests present consistent results.

Exploration measure. Due to the longitudinal nature of our study, obtaining data on intraorganization problem-solving behaviors over a long period is a major challenge. Data to assess search activities over time are usually not public and, if even available, are often extremely time-consuming to gather (Katila, 2002). Therefore, orientation toward explora-tion is measured through computer-assisted content analysis (CATA). Similar to human coding schemes, CATA analyzes content via word usage (Morris, 1994) and assumes that insights about managers orientations can be detected through the occurrence, absence, and frequency of certain concepts in text. In addition, CATA can analyze multiple texts without the errors or bias associated with human coders (Carley, 1997; Short, Broberg, Cogliser, & Brigham, 2010).

We build our dictionary according to the previously quoted definition of exploration, captured by such terms as search, variation, risk taking, experimentation, play, flexibility, discovery, innovation (March, 1991: 71). For each word considered we generate all possi-ble variants (e.g., explore leads to explores and exploring). We then perform a keyword-in-context analysis (Krippendorff, 2004) to identify and eliminate inconsistencies and irrelevant expressions. In addition, future-oriented discourses in our documents are coupled with sec-tions of narrative about current and past actions and results. It has been observed that these two categories of information, when considered together, confounded statistical analysis (Osborne et al., 2001: 443). Therefore, we formulate a list of words (e.g., we hope to, we will, we plan to) whose occurrence signals a potential future-oriented strategic intention. We then build a computer program that marks with a special character all inconsistencies and mentions of future-oriented intention.

We count the words included in the exploration dictionary for all the available documents, excluding those marked with a special character, and assess the convergent validity of our measures. Because some firms have longer documents and therefore the incidence of using key words in the content analysis may be higher, we divide the total number of explorative words by the total number of words included in each document. We then consider the effec-tive implementation of the declared orientation (March & Sutton, 1997).9 Finally, since prior studies have pointed out that search activities tend to be quite stable over a number of years (Bierly & Chakrabarti, 1996; He & Wong, 2004), we assess the temporal stability of our measures by studying their autocorrelation functions. From these analyses, we note that our measures are generally reliable and can be used to assess the amount of explorative orienta-tion in productive activities of a firm.

Long-run organizational performance measure. This study uses K. H. Chung and Pruitts (1994) approximation to calculate the q value. The advantage of this method is that it uses




a simple formula that requires financial and accounting information available from the Compustat database. Moreover, this measure is highly correlated with Tobins q calculated by using the more theoretically correct LindenbergRoss (1981) model. Note that the data limitations associated with the LindenbergRoss model can introduce sample biases and measurement errors, which can affect the estimation and hinder the interpretation of results (K. H. Chung & Pruitt, 1994).

The measure proposed by K. H. Chung and Pruitt (1994) approximates the replacement value of the firms assets through the book value of assets. The measure then considers the historical rather than the current replacement costs. Despite the empirical similarity (Perfect & Wiles, 1994), adopting the book value introduces in the dependent variable the potential for a number of accounting distortions. To control for these potential distortions, we consider the logarithm of firm market value (Log MV) as our dependent variable, calculated as log (price of outstanding common shares number of shares + book value of preferred stock + book value of debt). To limit the effect on estimates of excessive market volatility (Shiller, 1981), the price of common stock is calculated as the average of the 12 closing prices of shares at the end of each month for the financial year (Lavie et al., 2011). As in the work of Hall (1993), the logarithm of total assets (Log A) is considered as a control variable. By doing so, we confine potential measurement errors to the independent variables, which makes, as already observed, our empirical test more conservative (Arellano, 2003). Finally, we exclude from the sample any observations whose firm market value was negative or whose absolute variation from one period to another was greater than four (Griliches, 1981; Lenox et al., 2010). This reduces our data set to 3,731 observations for 274 firms.10

Controls. According to Griliches (1981), we control for the scale of investments in intangi-ble assets. In particular, we assume that the value of intangible assets can be approximated by R&D stock. Such stock is constructed from R&D expenditures under the assumption of a depreciation rate of 15% per annum. Missing observations are derived by computer through software that implements the procedure proposed by Hall (1990, 1993). The time frame ranges from 1967 to 2004. Another important intangible asset is the value of the brand. This asset is considered a product of advertising expenditures (ADV). However, because of the many miss-ing values that make the procedure for transforming a flow of expenses into a stock infeasible and highly biased, we consider the yearly amount of advertising expenditures. As suggested by Lenox et al. (2010), we report regressions assuming the unreported values are zero.

We find our results to be robust to alternative assumptions, as when we omit advertising from the model entirely or assume unreported values are zero and include a dummy variable to mark missing values that have been substituted by zero. We then normalize both the R&D stocks and advertising expenditures over the total value of assets (A). We also control for any market power or long-run profitability of firms that is not specifically related to search activities. Accordingly, we use Compustat data to compute firm cash flow (CF), measured as the 2-year moving average of the ratio, that is, earnings before interest and taxes plus depreciation minus taxes divided by total assets. We consider prospects for future growth by including the contemporaneous logarithm of the growth rate of sales (LogS; see Hall, 1993, for an extensive discussion of the proposed control variables).




Long-run organizational performance is also influenced by exploitation activities. We therefore control for the number of exploitative words reported by managers in the two sec-tions already considered of the annual reports. The exploitation dictionary comes from Marchs (1991: 71) definition of exploitation as such things as refinement, choice, produc-tion, efficiency, selection, implementation, execution. Keyword-in-context analysis, con-vergent validity, and stability analysis were performed on the exploitation dictionary. As for exploration, we divide the total number of exploitative words by the total number of words included in each document.

Model Specification

We test our hypotheses under the assumption that endogeneity characterizes the way exploration activities are determined under firm- and industry-specific factors. The first source of endogeneity is the possibility of omitted variables that are relevant to the choice of a firm orientation toward exploration. Second, the presence of endogeneity is related to measurement errors and, more relevantly, to some forms of potential feedback (or simultane-ity) from the dependent variable to the independent variables (March & Sutton, 1997). We therefore introduce a system generalized methods of moments (GMM) estimator (Blundell & Bond, 1998b) to control for such distortions, as has been used in strategic management (e.g., Cassar, 2010; Keil, Maula, Schildt, & Zahra, 2008; uotila et al., 2009). Employing multiple instrumental variables, the system GMM estimator is able to not only control for firm- and industry-specific effects, but also accounts for possible sources of simultaneity between dependent and independent variables without biasing the estimates. In addition, the system GMM estimator is considered particularly suitable for dealing with persistent panel data (Arellano, 2003) and including lagged values of the dependent variable (Jacobson, 1990).

Following conventional GMM estimation, we instrumentalize our variables by considering a three-period lag.11 Standard errors are heteroscedastic-consistent estimates. Moreover, as Blundell and Bond (1998b) suggested, system GMM estimates are not biased when the dependent variable follows a stationary process and, more relevant, deviations from the value of the dependent variable are distributed randomly and are not correlated with any independent variables. We test for these conditions using the Hansen J test for overidentifying restrictions, as suggested in models that include standard errors that are robust to heteroscedasticity (Hansen, 1982). We also report second order autocorrelation coefficients in first differences (z2). As observed by Arellano (2003: 121), If the errors in levels are serially independent, those in first differences will exhibit first- but not second-order serial correlation.

This study also controls for multicollinearity. In particular, we introduce estimates in which variables are progressively introduced. In addition, for each model we report the mean variance inflation factor (VIF), an indicator of the severity of multicollinearity among inde-pendent variables. Finally, as suggested by Cohen (1978), in testing our hypotheses we fol-low a hierarchical step-up approach; that is, the main effect is tested in an equation containing only that effect, and interactions are then tested for their contribution over and above main effect. We can then report the Wald chi-square test of the marginal contribution of the added variables to model fit.




Estimations and Results

The final sample comprises 3,731 observations from 1989 to 2004. Table 1 reports the descriptive statistics. It is important to note that some of our variables are highly time per-sistent, particularly those whose correlation coefficients between times t and t 1 are greater than 0.90. The strong intertemporal correlation between these variables thus supports our choice of a system GMM as a preferred estimator. In addition, the limited magnitude of the correlation coefficient between Interdependency and Interdependency indicates that these variables are distinct industry attributes. The positive and significant correlation between exploration and exploitation also supports our choice to introduce exploration as distinct from exploitation instead of considering these two search activities on a unidimensional scale (Bierly & Daly, 2007). Finally, as suggested by Aiken and West (1991), the variables Exploration, Interdependency, and Interdependency are mean centered to minimize the potential for multicollinearity in equations for interaction terms. To make our estimates of exploration and exploitation comparable, Exploitation is also mean centered. Finally, the log of total assets is also mean centered to minimize the multicollinearity effects among control variables. Note that for all models the VIF values are far below the rule-of-thumb cutoff of 10 (Neter, Wasserman, & Kutner, 1985).

Table 2 shows the panel regression results for the logarithm of market value. First, Model 1 provides a basic estimate using only control variables. Hypothesis 1 considers exploration to be curvilinearly related to long-run organizational performance. Model 2 signals that the explo-ration and exploration-squared terms are significant. For such variables, the marginal Wald chi-square statistic with two degrees of freedom (df) equals 8.87 (p < .01). Hence, adding exploration and exploration squared significantly improves the model fit over the model with control variables only (Model 1).

Hypothesis 2 proposes that the levels of technological interdependency positively moder-ate the effect of exploration on long-run organizational performance. Model 3 suggests that the more firms focus on exploration in industries with more extensive levels of interdepend-ency, the more likely they are to increase their long-run performance. The interaction term is positive and significant ( = 42.79, p < .01). For such a model, the marginal Wald chi-square statistic is equal to 13.61 (p < .01, df = 2), which signals that the inclusion of inter-dependency and its interaction with exploration significantly improve model fit over the model with exploration and control variables only (see Model 2 of Table 1). We then intro-duce exploration squared in Model 4 and observe consistent results.

Hypothesis 3 proposes that changes in technological interdependencies positively moder-ate the effect of exploration on long-run organizational performance. Model 5 provides consistent evidence that in industries that exhibit more extensive changes in interdependen-cies, the positive effect of greater levels of exploration on long-run performance should be increased. For such a model, the marginal Wald chi-square statistic is equal to 10.88 (p < .01, df = 3), which again confirms that the addition of moderating variables significantly increases model fit over the model with main effects and control only (Model 2). The inter-action term is positive and significant ( = 334.2, p < .01). We also observe consistent results in models where exploration squared is considered (see Model 6 of Table 2).




When interaction effects are graphed, exploration and interdependence take the values of one standard deviation above (i.e., high level) and one standard deviation below (i.e., low level) the mean, respectively. All other variables are assumed at their mean value. Figure 1 shows that exploratory activities offer a positive contribution to long-run performance that becomes stronger in industries with either more extensive levels of interdependency (i.e., Figure 1a) or broad changes in such interdependencies (i.e., Figure 1b).

The composition of the panel changes over time due to the failure of some organizations and the merger and acquisition of others. These changes represent another bias that can affect estimates (Baltagi, 2001). using Compustat Note 35 (i.e., reason for deletion), we include a dummy variable as an instrument to control for possible firm exits during the period. This analysis yields consistent results. We also test the potential effects on estimates

Table 1 Descriptive Statistics and Pairwise Correlations for Sampled Firms

Variable N Mean SD 1 2 3 4 5 6 7 8 9

1. Log MVt 3,731 3.63 0.71 12. Explorationt 3,729 0.71 0.82 .28* 13. Exploitationt 3,729 2.49 2.07 .05 .47* 14. Interdependencyt 3,731 0.29 0.37 .36* .13* .08* 15. Interdependencyt 3,731 0.20 0.03 .01 .10* .20* .04 16. Log At 3,730 3.60 0.59 .89* .26* .14* .21* .04 17. R&D/At 3,731 0.17 0.20 .03 .01 .21* .02 .33* .08* 18. ADV/At 3,730 0.02 0.04 .06* .02 .14* .37* .01 .07* .08* 19. CF/At 3,731 0.12 0.05 .27* .07* .09* .24* .07* .01 .01 .28* 110. LogSt 3,728 0.01 0.09 .12* .04 .01 .02 .01 .07* .17* .03 .14*

11. Log MVt-1 3,457 3.62 0.70 .97* .28* .05 .36* .01 .89* .02 .06 .26*

12. Explorationt-1 3,455 0.71 0.83 .28* .76* .46* .13* .11* .26* .01 .02 .08*

13. Exploitationt-1 3,455 2.51 2.09 .04 .44* .86* .08* .20* .13* .21* .14* .10*

14. Interdependencyt-1 3,457 0.29 0.37 .36* .14* .08* .99* .03 .21* .02 .37* .24*

15. Interdependencyt-1 3,457 0.20 0.03 .01 .12* .24* .05 .81* .03 .35* .01 .06*

16. Log At-1 3,456 3.59 0.58 .87* .26* .14* .20* .03 .99* .06* .07* .0217. R&D/At-1 3,457 0.17 0.20 .02 .01 .21* .02 .32* .07* .92* .09* .0118. ADV/At-1 3,456 0.02 0.04 .06* .02 .14* .38* .01 .06* .08* .95* .28*

19. CF/At-1 3,457 0.12 0.05 .28* .07* .10* .24* .06* .01 .01 .27* .91*

20. LogSt-1 3,455 0.01 0.09 .13* .03 .01 .02 .05 .09* .03 .03 .22*

10 11 12 13 14 15 16 17 18 19 20

10. LogSt 111. Log MVt-1 .06 112. Explorationt-1 .03 .28* 113. Exploitationt-1 .01 .04 .47* 114. Interdependencyt-1 .02 .36* .13* .08* 115. Interdependencyt-1 .01 .01 .11* .23* .03 116. Log At-1 .01 .89* .26* .13* .21* .03 117. R&D/At-1 .07* .03 .01 .21* .02 .33* .08* 118. ADV/At-1 .02 .05 .02 .14* .37* .01 .07* .08* 119. CF/At-1 .03 .27* .07* .10* .24* .06* .01 .01 .28* 120. LogSt-1 .11* .12* .04 .01 .02 .03 .06* .18* .03 .16* 1

Note: Robust standard errors. Mean and SD for exploration and exploitation are in the form of value 103. The sample period is 19892004. Log MV = logarithm of firm market value; Log A = logarithm of total assets; R&D = research and development; ADV = advertising expenditures; CF = cash flow; LogS = logarithm of the growth rate of sales.*p < .05.




related to instrument proliferation. Following Roodman (2009), we propose a model with a minimal number of instruments by limiting lag depths to one. Again, the signs and signifi-cance of our coefficients hold and the value of the Hansen J test remains not significant. In addition, there are cases in our sample in which an increase in the level of technological interdependency is associated to an increase in the extent of changes in such interdependen-cies. Accordingly, we select from these cases and run a further regression with the specifica-tion of Model 6 of Table 2. The analysis does not show significant differences from reported results. Results from these tests are not shown here but available on request.

All models include periodic effects that are omitted from Table 2 but that are generally significant. Variables representing the stock of intangible assets, particularly those related to R&D activities, are significant and contribute positively to firm market value, as already observed in the literature (Griliches, 1981). Our estimates also reveal the presence of het-eroscedasticity. We run a simple pooled ordinary least squares regression with the specifica-tion of Model 1 of Table 2 and observe that the WhiteKoenker test statistic has a chi-square

Table 2Results of System Generalized Methods of Moments Estimator for Long-Run

Organizational Performance

Dependent variable: Log MVt Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Intercept 0.95*** (0.09) 0.97*** (0.09) 0.99*** (0.10) 0.99*** (0.09) 0.94*** (0.09) 0.92*** (0.10)Log A 0.36*** (0.03) 0.34*** (0.03) 0.34*** (0.04) 0.34*** (0.03) 0.32*** (0.03) 0.31*** (0.04)R&D/A 0.15*** (0.04) 0.14*** (0.04) 0.12** (0.14) 0.12** (0.04) 0.13** (0.04) 0.12** (0.04)ADV/A 0.44** (0.13) 0.36** (0.13) 0.19 (0.15) 0.21 (0.14) 0.21 (0.17) 0.13 (0.13)CF/A 0.99*** (0.11) 0.96*** (0.11) 0.97*** (0.11) 0.95*** (0.11) 0.92*** (0.13) 0.95*** (0.13)LogS 0.49*** (0.14) 0.48*** (0.14) 0.48*** (0.14) 0.48** (0.14) 0.48** (0.14) 0.49** (0.14)Exploitation 5.82 (4.01) 6.63 (3.70) 4.52 (3.67) 7.62* (3.46) 7.69* (3.30)Exploration 37.82**(12.74) 20.23*(10.05) 36.88**(12.02) 22.83**(8.39) 15.10* (7.59)Exploration2 (10-3) 5.15** (2.58) 5.28** (2.68) 2.18 (1.34)Interdependency 0.04**(0.01) 0.04**(0.01) 0.04**(0.01) 0.04** (0.01)Interdependency 0.26 (0.16) 0.25 (0.15)Exploration x

Interdependency42.79** (17.12) 38.67* (19.91)

Exploration x Interdependency

334.2** (121.5) 272.6*(137.1)

Log MVt-1 0.70*** (0.03) 0.70*** (0.02) 0.69***(0.03) 0.69***(0.03) 0.71***(0.02) 0.71***(0.02)

z2ObservationsWald 2(df)No. of instrumentsHansen J test Mean variance

inflation factor

0.543,453

26,385 (20)***

339246.83.67

0.353,451

33,417(23)***

498248.1 3.52

0.423,451

26,623 (24)***

554251.53.34

0.393,451

33,011 (25)***

607249.03.36

0.373,451

35,816 (25)***

610254.93.53

0.383,451

42,153 (26)***

663251.43.60

Note: All independent variables are measured at time t (end of the year). All estimates include year dummies. All variables are considered endogenous but CF/A, LogS, Interdependency, and Interdependency are predetermined. Year dummies are exoge-nous. For the equation in levels, Xt-1,..., Xt-l and year dummies are used as instruments. For the equation in first differences, Xt-1,...,Xt-l and year dummies are used as instruments. Heteroskedasticity robust standard errors are in parentheses. The z2 values are for the Arellano-Bond test of AR(2) in first differences. The panel is unbalanced. Log MV = logarithm of firm market value; Log A = logarithm of total assets; R&D = research and development; ADV = advertising expenditures; CF = cash flow; LogS = logarithm of the growth rate of sales. p < .1. * p < .05. ** p < .01. *** p < .001




of 322.9 (p < .001, df = 20). This result supports our choice to consider all estimates standard errors that are robust to heteroscedasticity. In addition, potential sources of endogeneity and their effects on estimates are well controlled by our GMM system. The second-order auto-correlation coefficients z2 are not significant in all models. Hypotheses of the exogeneity of instruments are not rejected by the Hansen J test. Finally, we inspect the cross-time and cross-industry dimensions of our sample. Again, these analyses do not suggest any evidence of systematic deviation from our general results.

Discussion and Conclusions

Exploration has emerged as a key concept underpinning organizational learning, innova-tion, and strategic management research (Gupta, Smith, & Shalley, 2006; Lavie, Stettner, & Tushman, 2010). The literature has emphasized that exploration-oriented activities tend to produce wild ideas and actions that lead to returns with large variances and relatively low means, particularly in the short run. Attractive returns from such ideas and actions are real-ized only at greater temporal distance from the point of action (Levinthal & March, 1993; March, 2006). Computer-based simulation studies also suggest that the long-run benefits of exploration are greater in the presence of interdependencies (e.g., Levinthal, 1997; Rivkin & Siggelkow, 2007). However, there is scant empirical evidence to support these compelling theoretical arguments.

Figure 1Exploration, Technological Interdependence, and Long-Run Organizational

Performance

Note: Curve a is generated from Model 3 in Table 2 by applying the coefficient estimates on Exploration ( = 20.23), Interdependency ( = 0.04), and its interaction with Exploration ( = 42.79). Curve b is generated from Model 5 in Table 2 by applying the coefficient estimates on Exploration ( = 22.83) and the interaction between Exploration and Interdependency ( = 334.2). Coefficients that are not significant are omitted.




Among the few works to offer empirical evidence of the long-term performance effects of exploration, this article shows that exploration matters over the long run. Furthermore, we find that the positive effect of exploration on long-run performance is greater in industries with either greater levels of technological interdependency or more extensive changes in such interdependencies. This study helps direct academic attention to the importance of interde-pendencies in the long-run performance effect of exploration activities. Interdependencies are an industry-level/period attribute. Their effects are not only identifiable in practice but also analogous in magnitude to the effects of other environmental aspectssuch as dynamism, exogenous shocks, munificence, and competitive intensity (for a review, see Lavie et al., 2010)that have been given noticeably more emphasis in empirical studies.

We also distinguish between cross-sectional differences and intertemporal variations in interdependencies. These dimensions are distinct industry attributes and contribute to the positive effect of exploration on long-run performance. Moreover, our data suggest that the long-run performance effects of exploration are greater in industries that exhibit greater changes in interdependencies than in industries that exhibit more limited changes. This effect holds true even when such changes in interdependencies lead to a reduction in the overall level of interdependency. Note that in computer-based simulation studies a reduction in the level of interdependency over time would make exploration less essential for long-run organizational performance (e.g., Levinthal, 1997; Rivkin & Siggelkow, 2007). One may ask if this is the case here as well. More to the point, our data show that exploration is important even when the levels of interdependency are declining over time, as long as the decline is accompanied by greater changes in such interdependencies. Perhaps, even when the levels of interdependency are relatively low, changes in interdependencies are another manifesta-tion of greater and not lesser complexity, which still makes exploration necessary for firm long-run performance.

Finally, our results are robust across a number of specifications. Our study uses a longi-tudinal panel research design that makes it possible to examine and better evaluate the per-formance implications of exploration over the long run. It also proposes reliable measures of interdependencies and addresses the problem of endogeneity in the estimates.

Limitations and Future Research

This study has many limitations, which can open up opportunities for future inquiries. It is restricted to only one kind of interdependency, namely, technological interdependence. But many interdependencies can exist and can originate, for example, from institutional, competitive, and social interactions. Future studies can account for multiple interdependen-cies and assess their implications for the performance effects of exploration. In this vein, an intriguing research question is whether interdependencies that arise from different sources add linearly in determining the value of exploration or whether the effect of multiple inter-dependencies is both qualitatively and quantitatively different from that observed from higher levels of interdependency of just one kind.

This study concentrates on exploration in the context of a firms general productive activi-ties. Future studies can examine the performance effects of exploration in more specific areas (e.g., within a business unit, geographical area, or product line). In addition, this study refers




to measures of long-run organizational performance. Future studies can introduce various operational outcomes (e.g., a number of new products, revenues generated from new lines of activity, or the radicalness of firm innovations) and analyze how much of the long-run benefits of exploration activities are mediated by such intermediate, more proximate varia-bles. However, because of temporal delays between exploration activities and consequent performance effects (Levinthal & March, 1993), scholars should account for the possibility that the proportion of the total effect mediated may be limited. In particular, it has been observed in a computer-based simulation study that when there are long delays between actions and results, exploration may often appear to be unsustainable in terms of more imme-diate outcomes. However, keeping such apparently low-performing exploration activities alive benefits the organization over the long term (Rahmandad, 2008).

Furthermore, scholars can consider combining a mediation model with a moderation model (see Baron & Kenny, 1986). In such a model, operational outcomes have a mediator status and interdependencies can be conceived of as moderating the relationships both between exploration and firm operational outcome and between these intermediate outcomes and firm long-run organizational performance. Finally, while our results are limited to explo-ration activities, we suggest that this studys methodological approach be adapted to also test our hypotheses on other organizational phenomena. For example, prior studies have illus-trated that interdependencies have implications on the long-run effect of exploitation activi-ties (Levinthal, 1997), on balancing exploration and exploitation, and on achieving greater levels of both (Beinhocker, 1999). The implications of interdependencies also extend to other aspects, such as imitation of the success of others (Rivkin, 2000) and replication of a firms own successes (Rivkin, 2001). Furthermore, since our sample is composed of large u.S.-based companies, future works could consider whether our results extend to small and medium firms or to firms operating in different contexts.

Implications for Practice

This study also has implications for managerial practice. Early theoretical studies have observed that the current level of exploration is less than optimal (March, 2006). Our data confirm this prediction and show that the level of exploration at which performance is maximized (i.e., 0.03) is far greater than one standard deviation above its mean level in the sample (i.e., 0.0007). This result suggests that most of the firms in our sample tend to engage in less than optimum levels of exploration. Evidently, a great majority of our organizations would benefit largely from greater attention toward exploration, an important activity that can be expected to yield new wealth creation gains and above-average returns in the long run (Lewin et al., 1999: 538).

This study further suggests that exploration is most important in industries with either greater levels of technological interdependency or more extensive changes in such interde-pendencies, such as agricultural chemicals, drugs and medicine, electronic components and accessories, industrial inorganic chemistry, and plastic materials and synthetic resins. Our data suggest that in these industries too firms are engaging in limited levels of exploration. Yet, most of these firms have been experiencing declining long-run performance. Therefore,




their senior managers are advised to either find sufficient resources to devote to exploration activities or evaluate the opportunity of divesting from such industries and selling related activities to firms that could benefit from them. Our data also show that high-exploration firms exist in industries with greater and changing interdependencies. Most of these firms have been experiencing increasing long-run performance. As a result, we advise their man-agers to set forth routines, incentives, and structures to maintain greater focus on exploration despite natural tendencies to limit exploration activities.

In conclusion, our study addresses a fundamental question in organizational learning and strategy: How much should an organization focus on the broad exploration of new possi-bilities? This study suggests that the answer partly resides in considering the long run and accounting for the role of technological interdependencies. By testing the long-run perfor-mance effects of exploration activities and controlling for cross-sectional differences in interdependencies and for intertemporal variations in such interdependencies, this study advances our understanding of exploration in organizational adaptation.

Notes

1. There are various academic definitions of the term complexity (see, e.g., Anderson, 1999; Cannon & John, 2007; Castrogiovanni, 2002; Sharfman & Dean, 1991). They generally refer either to the difficulty of describing or duplicating an object or to its degree of organization (Lloyd, 2001). In all these definitions, complexity corresponds closely to certain manifestations of interdependency (see Boyd & Fulk, 1996: 4). This correspondence becomes even stronger if one refers to the Latin word complexus, which signifies entwined or twisted together. A complex object is then made up of a large number of parts that interact in a nonsimple way (Simon, 1962: 468). Note that the definition of complexity as involving many interacting parts relates directly to the mathematical relationship hypothesized in studies of complex adaptive systems, particularly those that refer to the NK model with N elements and with K interactions (Lenox, Rockart, & Lewin, 2010: 125; see also Moran, Simoni, & Vagnani, 2011).

2. Our measure of interdependence includes, for example, the possibility that changing the technology for a mask will improve or worsen the performance of technologies used by the firm to align the mask with the semicon-ductor. Our measure also considers how the introduction of a computer-aided engineering system may require changes in other activities for the production process to work properly (e.g., the introduction of computer-aided manufacturing workstations and/or flexible manufacturing systems). As such, our measure refers to a concept of interdependence that overlaps with that of interdependence between the activities of an industrys productive func-tion, as originally proposed by Lenox, Rockart, and Lewin (2006, 2010).

3. For an early discussion of the concordance table, see Scherer (1982). An alternative table, proposed by Silverman (2002), has the advantage of matching granted patents to four-digit SIC codes. However, since the concor-dance is built on data that range from 1989 to 1993, it is less useful for those studies whose time range is later than 1993. Furthermore, to associate a subclass to an industry, it is necessary to relate each subclass to a given International Patent Classification class and then, using another concordance table (e.g., a Yale table), relate that class to an industry.

4. Note that interdependencies can involve coupling between triples and higher-order groupings of elementary components. We restrict our attention to bilateral interactions and consider only the second-order approximations of more general interactions. Note that in our formulation a permutation of components does not change the cor-responding measure (Reiter & Sherman, 1962).

5. Consider an industry g. For the period t, we have ni,g,t and nj,g,t as the number of patents associated with subclasses i and j, respectively, and Ng,t as the total number of patent applications. The expected number of co-occurrences between subclasses i and j is then equal to Ei,j,g,t = ni,g,tnj,g,t/Ng,t and its variance is 2i,j,g,t = Ei,j,g,t(1 ni,g,t/Ng,t)(Ng,t nj,g,t)/(Ng,t 1). Hence, the normalized number of raw co-occurrences between subclasses i and j in the period t is equal to Ki,j,g,t = (Ri,j,g,t Ei,j,g,t)/i,j,g,t.




6. We assess the validity of our measure as an industry-level attribute of interdependency in productive activities by referring to Lenox et al. (2010). using data from the 1994 Carnegie Mellon Survey on industrial R&D, Lenox et al. (2010) derive managers perceptions of the complexity related to the introduction of new processes and products. The Pearson correlation coefficient between our measure of interdependence (for the comparable year 1995) and that of Lenox et al. (2010) equals 0.31. Such a correlation, although not perfect, presents a magnitude and significance level comparable to those found by Fleming and Sorenson (2004) when they correlated their mea-sure of coupling based on patents with a measure they derived from surveying inventors about the coupling of the components of their inventions.

7. As a test of robustness, we recalculate our measure in less conservative settings. In particular, we use shorter periods. As in Yayavaram and Ahuja (2008), we also employ two nonoverlapped periods and set t [t 2, t], t + 1 [t + 1, t + 3]. The Pearson correlation coefficients for our measure are positive and generally significant. Yet in these less conservative settings we observe greater standard deviations. Therefore, our proposed measure may only understate underlying moments and reduce the probability of finding statistically significant coefficient estimates, which makes our estimates more conservative.

8. Our measures require that organizations sharing the same SIC-based product field refer to the same set of constituent technologies in their combinations of productive activities. To verify this condition, we evaluate how representative the SIC-based product fields are for organizations in developing new combinations of productive activities. We randomly select 30 firms, acquire all patents granted to them in the period 19891999, and extract the associated subclasses with their relative frequencies. Then, for the whole period considered, we divide the number of times an organization quotes subclasses included in the assigned product fields by the total number of subclasses quoted. The average value of the ratio is 0.73, and its standard deviation is 0.12. We replicate the analy-sis by considering shorter subperiods. Although the standard deviation increases, the average value is stable.

9. With a subsample of 30 firms, we correlate for all available years our measure of exploration with firms tendency to experiment with new and unfamiliar technologies. Following Ahuja and Lampert (2001), we measure this tendency by dividing the number of new patent subclasses (relative to subclasses used in patents in the previous four years) by the total number of subclasses indicated in patents assigned to each firm in a given year. The Pearson correlation coefficient for our measure of exploration is equal to 0.42.

10. The effect of the removal of large outliers, at worst, is that it underestimates the underlying moments and the likelihood of finding statistically significant coefficient estimates (Lenox et al., 2010). However, as a robustness test, we estimate our empirical models on a less restrictive sample and observe comparable results. In addition, the composition of the panel changes over time because some firms have exited the sample. The estimations and results section tests the robustness of our estimates against these changes in the sample composition.

11. As suggested by Blundell and Bond (1998a), we instrumentalize our variables by initially considering lags from 0 to 2. We analyze the admissibility of more recent lags as instruments (i.e., l = 3 and l = 4) and note that increasing lags from 2 to 3 reduces the Hansen J test (and thus slightly improves the quality of our estimates); however, further increasing lags from 3 to 4 slightly increases the considered test. Therefore, there is no sensible advantage to the model including more recent lags of the variables considered. In addition, further enlarging lags used to instrumentalize our variables can boost the number of instruments exorbitantly compared with the number of observations currently available.

References

Abernathy, W. J., & Clark, K. B. 1985. Mapping the winds of creative destruction. Research Policy, 14: 3-22.Abrahamson, E., & Amir, E. 1996. The association between the information contained in the presidents letter to

shareholders and accounting market variables. Journal of Business Finance and Accounting, 23: 1157-1182.Adner, R., & Kapoor, R. 2010. Value creation in innovation ecosystems: How the structure of technological interde-

pendence affects firm performance in new technology generations. Strategic Management Journal, 31: 306-333.Aguinis, H. 1995. Statistical power problems with moderated multiple regression in management research. Journal

of Management, 21: 1141-1158.Ahuja, G., & Lampert, C. M. 2001. Entrepreneurship in the large corporation: A longitudinal study of how estab-

lished firms create breakthrough inventions. Strategic Management Journal, 22: 521-543.




Aiken, L. S., & West, S. G. 1991. Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.

Aldrich, H. E., & Pfeffer, J. 1976. Environments of organizations. Annual Review of Sociology, 2: 79-105.Anderson, P. 1999. Complexity theory and organization science. Organization Science, 10: 216-232.Arellano, M. 2003. Panel data econometrics. New York: Oxford university Press.Baldwin, C. Y., & Clark, K. B. 2000. Design rules. Cambridge, MA: MIT Press.Baltagi, B. H. 2001. Econometric analysis of panel data (2nd ed.). New York: John Wiley.Baron, R. M., & Kenny, D. A. 1986. The moderator-mediator variable distinction in social psychological research:

Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51: 1173-1182.

Beinhocker, E. D. 1999. Robust adaptive strategies. Sloan Management Review, 40: 95-106.Bergh, D. D. 1993. Watch the time carefully: The use and misuse of time effects in management research. Journal

of Management, 19: 683-705.Bierly, P., & Chakrabarti, A. 1996. Generic knowledge strategies in the u.S. pharmaceutical industry. Strategic

Management Journal, 17: 123-135.Bierly, P., & Daly, P. S. 2007. Alternative knowledge strategies, competitive environment, and organizational per-

formance in small manufacturing firms. Entrepreneurship Theory and Practice, 31: 493-516.Blundell, R. W., & Bond, S. R. 1998a. GMM estimation with persistent panel data: An application to production

functions. IFS Working Paper Series W99-4, Institute for Fiscal Studies, London.Blundell, R. W., & Bond, S. R. 1998b. Initial conditions and moment restrictions in dynamic panel data models.

Journal of Econometrics, 87: 115-143.Bowman, E. H. 1984. Content analysis of annual reports for corporate strategy and risk. Interfaces, 14: 61-71.Boyd, B. K., & Fulk, J. 1996. Executive scanning and perceived uncertainty: A multidimensional model. Journal

of Management, 22: 1-21.Brown, S. L., & Eisenhardt, K. M. 1997. The art of continuous change: Linking complexity theory and time-paced

evolution in relentlessly shifting organizations. Administrative Science Quarterly, 42: 1-34.Brusoni, S., & Prencipe, A. 2006. Making design rules: A multidomain perspective. Organization Science, 17:

179-189.Burgelman, R. A. 1991. Intra-organizational ecology of strategy-making and organizational adaptation: Theory and

field research. Organization Science, 2: 239-262.Cannon, A. R., & John, C. H. S. 2007. Measuring environmental complexity. Organizational Research Methods,

10: 296-321.Carley, K. M. 1997. Extracting team mental models through textual analysis. Journal of Organizational Behavior,

18: 533-558.Cassar, G. 2010. Are individuals entering self-employment overly optimistic? An empirical test of plans and projec-

tions on nascent entrepreneur expectations. Strategic Entrepreneurship Journal, 31: 822-840.Castrogiovanni, G. J. 2002. Organization task environments: Have they changed fundamentally over time? Journal

of Management, 28: 129-150.Chakravarthy, B. 1986. Measuring strategic performance. Strategic Management Journal, 7: 437-458.Chen, W.-R. 2008. Determinants of firms backward- and forward-looking R&D search behavior. Organization

Science, 19: 609-622.Chung, K. H., & Pruitt, S. W. 1994. A simple approximation of Tobins q. Financial Management, 23: 70-74.Chung, W., & Yeaple, S. 2008. International knowledge sourcing: Evidence from u.S. firms expanding abroad.

Strategic Management Journal, 29: 1207-1224.Cohen, J. 1978. Partialed products are interactions: Partialed vectors are curve components. Psychological Bulletin,

85: 858-866.Courtis, J. K. 1982. Private shareholder response to corporate annual reports. Accounting and Finance, 22: 53-72.Davis, J., Eisenhardt, K., & Bingham, C. 2007. Developing theory through simulation methods. Academy of

Management Review, 32: 480-499.Devinney, T. M., & Stewart, D. W. 1988. Rethinking the product portfolio: A generalized investment model.

Management Science, 34: 1080-1095.Devinney, T. M., Yip, G. S., & Johnson, G. 2010. using frontier analysis to evaluate company performance. British

Journal of Management, 21: 921-938.




Duriau, V. J., Reger, R. K., & Pfarrer, M. D. 2007. A content analysis of the content analysis literature in organiza-tional studies: Research themes, data sources, and methodological refinements. Organization

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Exploration and Long-Run Organizational Performance: The Moderating Role of Technological Interdependence