bingham et al-2007-strategic entrepreneurship journal

Copyright © 2007 Strategic Management Society

Strategic Entrepreneurship JournalStrat. Entrepreneurship J., 1: 27–47 (2007)

Published online 16 November 2007 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/sej.1

WHAT MAKES A PROCESS A CAPABILITY? HEURISTICS, STRATEGY, AND EFFECTIVE CAPTURE OF OPPORTUNITIES

CHRISTOPHER B. BINGHAM,1* KATHLEEN M. EISENHARDT,2 and NATHAN R. FURR2

1 Robert H. Smith School of Business, University of Maryland, College Park, Maryland, U.S.A.2 Department of Management Science and Engineering, Stanford University, Stanford, California, U.S.A.

While organizational processes, such as internationalization, acquisition, and alliance, are a fundamental concept within many literatures and central to fi rm capabilities, controversy exists regarding how they become high performing. One view emphasizes the role of experi-ence while a second view emphasizes cognition and, in particular, the role of articulated heuristics. Using qualitative and quantitative fi eld data on the internationalization process of entrepreneurial fi rms from three culturally distinct regions (Finland, U.S., Singapore), we juxtapose these two competing theoretical views to better gain insight into organizational processes and capabilities. The core contribution of our paper is insight into the structure of fi rm capabilities. Results show that organizational heuristics more closely relate to the development of a high performing process and hence a fi rm capability. At a broader level, we contribute to strategy by empirically validating the strategic logic of opportunity, a logic that is particularly relevant in dynamic markets and growth oriented fi rms. We also contribute to entrepreneurship by adding to the opportunity discovery vs. opportunity creation debate, and by shedding light on the relationship between structure and performance in new ventures. Overall, we contribute to the emerging but growing body of research emphasizing a more cognitive view of fi rms. Copyright © 2007 Strategic Management Society.

Organizational processes are a central concept within the strategy, organizations, and entrepreneurship lit-eratures (Eisenhardt and Martin, 2000; Helfat and Peteraf, 2003; Pentland, 1995; Zollo and Winter, 2002). By organizational processes, we mean the sets of actions that repeat over time and allow man-agers to accomplish some business task (Pentland and Rueter, 1994; Ray, Barney, and Muhanna, 2004;

Teece, Pisano, and Shuen, 1997). Common pro-cesses include acquisitions (Zollo and Singh, 2004), alliances (Kale, Dyer, and Singh, 2002), product development (Brown and Eisenhardt, 1997; Miner, Bassoff, and Moorman, 2001), and internationaliza-tion (Sapienza et al., 2006; Zahra, Ireland, and Hitt, 2000). Organizational processes have long been seen as central to how the work of organizations gets done (Miles and Snow, 1978; Weber, 1947).

Recent theoretical arguments go a step further to designate organizational processes as a central feature of capabilities. Amit and Schoemaker (1993: 35), for example, describe how ‘capabilities refer to a fi rm’s capacity to deploy resources . . . using orga-nizational process, to effect a desired end.’ Others

Keywords: organizational process; capabilities; experience; heuristics; opportunity capture; cognition* Correspondence to: Christopher B. Bingham, Department of Management and Organization, Robert H. Smith School of Business, University of Maryland, 4519 Van Munching Hall, College Park, MD 20742, U.S.A. E-mail: [email protected]

28 C. B. Bingham, K. M. Eisenhardt, and N. R. Furr

Copyright © 2007 Strategic Management Society Strat. Entrepreneurship J., 1: 27–47 (2007) DOI: 10.1002/sej

argue that organizational processes form dynamic capabilities that are crucial to strategy (Eisenhardt and Martin, 2000; Teece et al., 1997). Teece and col-leagues (1997: 524) state, ‘The essence of a fi rm’s competence and dynamic capabilities is presented here as being resident in the fi rm’s organizational processes.’ In addition, there is growing consensus that capabilities imply a threshold of performance (Grant, 1996; Helfat et al., 2007; Maritan, 2001), suggesting the particular relevance of high perform-ing processes to capabilities.

Some scholars have even suggested that organiza-tional processes are not just crucial to strategy, but rather are the strategy of fi rms, especially in entre-preneurial fi rms and dynamic markets (Bingham and Eisenhardt, 2007). In contrast to positioning (Porter, 1996) and leverage (Collis and Montgomery, 1995) strategic logics, the logic here is that organizational processes put fi rms in the midst of opportunity fl ows (e.g., fl ows of new product opportunities, alliance opportunities, and country opportunities). By select-ing processes with the most attractive fl ows of oppor-tunities and effectively executing those processes, fi rms can gain a series of temporary performance advantages (Wiggins and Ruefl i, 2005). Supporting this strategic logic of opportunity, Roberts (1999) found that pharmaceutical fi rms adopting a strategy of product development, where leaders continually repeated the process to create a series of new prod-ucts, were able to stay ahead of the competition, capture emergent product opportunities and gener-ate a string of short-term competitive advantages over time. Other popular examples of ‘organiza-tional process as strategy’ include Cisco’s acquisi-tion process, Hewlett-Packard’s alliance process and Starbuck’s internationalization process.

Despite their fundamental importance for fi rm action, capabilities and even strategy, it is unclear how organizational processes become high-perform-ing. Two streams of research are particularly rel-evant. One stream is organizational learning from experience. Numerous studies in this area emphasize the role of more experience in developing a high-performing organizational process. Research shows that as fi rms engage in more acquisitions (Haleblian and Finkelstein, 1999), country entries (Barkema, Bell, and Pennings, 1996) or alliances (Kale et al., 2002) process performance improves. Other organi-zational learning studies emphasize that particular types of experience (e.g., similar and paced) lead to a high performing process. For example, research on internationalization fi nds that this process is higher

performing when country entry experience is paced (Vermeulen and Barkema, 2002) and accumulates in culturally similar regions (Davidson, 1980; Hof-stede, 2001; Kogut and Singh, 1988). But, while studies in this stream are useful, it is unclear what is actually learned from experience and how that learned content leads to high process performance.

The second stream relates to organizational cog-nition. It emphasizes that fi rms must translate their experience into articulated heuristics in order to develop an organizational process. Simply gaining experience is not enough. By opening up the ‘black box’ of what is learned from experience, empiri-cal research fi nds that these heuristics are simple rules that focus on capturing opportunities within a given process (Bingham, Eisenhardt, and Davis, 2007). These heuristics delineate the selection, pri-ority, pacing and execution of specifi c opportunities from the larger set of possibilities that is especially common in dynamic markets where opportunities are often ‘super-abundant’ (Davis, Eisenhardt, and Bingham, 2007). The semi-structure of heuristics enables fl exibility to adjust to the unique demands of any particular opportunity while still retaining some coherence and effi ciency (Brown and Eisenhardt, 1997; Burgelman, 1996; Rindova and Kotha, 2001). But while work in this second stream fi nds that some fi rms develop heuristics as they gain process experience (Bingham et al., 2007), the link between these heuristics and high process performance lacks empirical validation.

The purpose of this paper is to compare experi-ence and heuristics as theoretical explanations of organizational process performance. Specifi cally, we ask whether fi rms learn high performing pro-cesses by simply gaining more or particular types of experience, or whether they have to translate experi-ence into articulated heuristics. We begin by devel-oping predictions for each theoretical explanation. We then examine these alternative explanations by focusing on the internationalization process of tech-nology-based entrepreneurial fi rms from three cul-turally distinct regions (Finland, U.S., Singapore). A unique feature of our study is combining both quali-tative and quantitative data. This research approach is particularly appropriate when the research aim is to reinvestigate measures from prior theory, and yet simultaneously test promising new measures from provisional theory (Edmondson and McManus, 2007). Such an approach provides not only granu-larity and depth of understanding, but also statistical precision and generalization.

What Makes a Process a Capability? 29


Our core contribution is the insight that heuristics are at the heart of fi rm capabilities. That is, fi rm members must actively translate their process expe-rience into shared heuristics for opportunity capture in order to develop a high performing process, and hence a fi rm capability. In contrast, experience per se is insuffi cient for creating high performing organizational processes. Thus, capabilities rest on an explicit structure of heuristics, and not just tacit knowledge built from accumulated experience. Broadly, we contribute to strategy by confi rming the strategic logic of opportunity, a logic that is particu-larly relevant in dynamic markets and growth-ori-ented fi rms. We also contribute to entrepreneurship by adding insights into the creation vs. discovery of opportunities debate, and the crucial importance of increased structure in new ventures. Overall, by combining rich fi eld insights with theory and evi-dence from psychology and cognitive science, we promote a fresh and empirically valid view where simple cognitive structures are central to fi rm capa-bilities and the effective capture of entrepreneurial opportunities.

THEORETICAL BACKGROUND

Organizational learning: experience

Extensive literature on organizational learning links experience with an organizational process to improvements in the performance of that process (for a review see Argote, 1999). Typically, these studies tie experience to performance while positing an underlying learning mechanism. Much of this research has theoretical roots in early psychologi-cal studies (e.g., Thorndike, 1898). Psychologists discovered that the amount of time individuals used when performing a task, as well as the extent of mis-takes when accomplishing the task, decreased with increased experience (Thurstone, 1919). Organiza-tional researchers later identifi ed learning curves at the fi rm level, and emphasized repeated experience as a primary mechanism for the creation of high per-forming processes. Through ongoing trial-and-error and repeat practice, fi rm members better comprehend the specifi c causal links between prior fi rm decisions and fi rm outcomes. They also gain insights about the production and management of processes such that these processes become more effi cient and reliable, and thus higher performing (Argote, 1999).

Much empirical evidence supports the tie between experience and process performance. For example,

one of the earliest pieces of research on organiza-tional learning curves is a study by Wright (1936) on the labor required to build airplanes. Wright found that, as more airplanes were produced, the amount of labor hours needed to produce a single plane decreased at a decreasing rate. Later research on the manufacturing process of automobiles (Levin, 2000), trucks (Epple, Argote, and Devadas, 1991), and ships (Rapping, 1965) also showed that, as fi rms produced more of a discrete product, the unit cost of production typically decreased at a decreasing rate. Beyond its historical roots in manufactur-ing settings, organizational learning research in a wide variety of settings, such as pizza assembly (Darr, Argote, and Epple, 1995), alliance forma-tion (Anand and Khanna, 2000), surgical procedures (Pisano, Bohmer, and Edmondson, 2001), semicon-ductor production (Chung, 2001; Gruber, 1994) and internationalization (Martin and Salomon, 2003) lends support for experience effects. To illustrate, Kale and colleagues (2002) analyzed approximately 1572 alliances from 78 fi rms. The authors found that cumulative alliance experience was signifi cantly related to abnormal stock gains following alliance announcements. As a whole, theoretical argument and empirical evidence suggest that more experience should lead to a higher performing organizational process.

Hypothesis 1: Organizational experience is posi-tively associated with process performance.

The timing of experience is also likely to infl u-ence the learning of a high performing organiza-tional process. On the one hand, insuffi cient time between discrete experiences (e.g., specifi c alliances, acquisitions or country entries) may lower process performance since it limits a fi rm’s ability to absorb new knowledge (Gersick, 1994). When experiences occur in quick succession, fi rm members do not have time to draw inferences or assimilate learning from the past (Hayward, 2002; Levitt and March, 1988). A rapid internationalization process consisting of multiple country entries within the same quarter, for example, can lead to poor internationalization performance by exceeding the cognitive limits of managers to internalize the learning from each new country. Consistent with this logic, Vermeulen and Barkema (2002) found that the development of a high performing internationalization process depends on the fi rm’s rhythm of expansion. Using a panel of 572 observations covering the country



entries of 22 Dutch fi rms over 26 years, the authors discovered that irregularity in movement abroad negatively moderated internationalization perfor-mance. Similarly, research on the acquisition process (Haunschild, Davis-Blake, and Fichman, 1994) also suggests that fi rms cannot learn effectively when acquisitions follow each other too rapidly.

On the other hand, too much time between experiences may lower process performance since fi rms may forget lessons from the past (Argote, 1999). Such organizational forgetting may occur for two reasons. First, when the time interval between experiences is long, individuals believe that the experiences are one-time events that are unlikely to repeat. In this situation, fi rm members are less prone to encode action steps in memory since they foresee little future performance payoff (Winter and Szulanski, 2001). Organizational for-getting can also occur when individuals learn some action steps from their experience, but then leave the fi rm or move on to other activities within the fi rm. Thus, unless knowledge is currently relevant for those individuals involved with the focal process, knowledge from prior experience with that process is likely to be seen as less salient, inappropriate, or even obsolete, and to be forgotten (Hayward, 2002).

Hypothesis 2: The period of time between experi-ences has an inverted U-shaped relationship with process performance.

Similarity of experience also infl uences the devel-opment of a high performing process. Unlike rela-tively homogeneous experiences (e.g., production of automobiles, ships, or planes) where experiences are highly similar to one another, experiences with organizational processes such as product develop-ment, alliance formation, acquisitions, and country entries are often heterogeneous. For example, the acquisitions of a given fi rm may be undertaken for many reasons, involve different negotiation challenges, and present distinct integration issues (Graebner, 2004). Therefore, the learning benefi ts of prior acquisitions exist only to the extent that these past experiences are suffi ciently similar to provide insight to the focal one. In particular, research suggests that, when the focal experience is similar to those of the past, processes improve through specialization (Haleblian and Finkelstein, 1999; Zollo, Reuer, and Singh, 2002). Small devia-

tions in context permit fi rm members to focus their time and effort, elaborate on existing knowledge, and develop deeper causal understandings for how to accomplish tasks. The result is often steeper organizational learning curves and greater gains in effi ciency (Von Hippel, 1998). This relationship is consistent with psychological research which shows that becoming expert in a given fi eld (e.g., chess) often requires at least ten years of dedicated study (Hayes, 1989). Overall, these arguments imply that similarity of experiences is helpful for developing a high performing organizational process. Studies on internationalization, for example, show that fi rms entering culturally similar countries develop a better internationalization process than fi rms enter-ing countries at random (Johanson and Vahlne, 1977).

But, if experiences are too similar, fi rms are unable to form the generalist skills needed to cope with het-erogeneity (Hayward, 2002). Ongoing experience with the same alliance partners or entering culturally similar countries may lead to a lower performing process by stifl ing creativity and making it diffi cult to capture a broad range of future opportunities. This occurs because the ability to understand, integrate, and effectively leverage new knowledge is largely dependent on the state of prior related knowledge (Cohen and Levinthal, 1990). Thus, the more similar a fi rm’s process experiences are to each other, the more likely that the fi rm is to develop core rigidities (Leonard-Barton, 1992), under invest in exploration (March, 1991), and fail to recognize fresh opportu-nities for growth and profi t (Schilling et al., 2003). Research on individual learning supports these argu-ments by showing that reinforcing a specialized knowledge base dampens problem solving skills by strengthening connections among existing cognitive nodes without fostering connections with new nodes that are essential for bridging knowledge gaps and assimilating new information (Martindale, 1995). Research on personal insight likewise shows that new understandings of problems are less likely to arise when domains are too closely related (Simon-ton, 1999). Taken together, these arguments suggest that moderately similar experience is likely to lead to a high performing organizational process (Schilling et al., 2003).

Hypothesis 3: The similarity of experiences has an inverted U-shaped relationship with process performance.



Organizational cognition: heuristics

In contrast to the fi rst stream, a second and more recent research stream deals directly with the content of learning from experience. This research emphasizes cognition, and the related artic-ulation of what is learned as experience is gained (Bingham et al., 2007; Zollo and Singh, 2004). Although studies identify elaborated and codi-fi ed tools such as check-lists, integration manuals, and training books (Kale et al., 2002; Szulanski and Jensen, 2006; Zollo and Singh, 2004) as the articulated learning in stable environments and large fi rms, we focus on dynamic markets and entrepreneurial fi rms where studies highlight that articulated learning takes the form of simple heuristics. By heuristics, we mean articulated and often informal rules-of-thumb shared by multi-ple participants within the fi rm. In the context of organizational processes, these heuristics center on capturing discrete opportunities (e.g., entering specifi c countries, developing specifi c products, acquiring particular fi rms) (Bingham et al., 2007; Burgelman, 1996; Rindova and Kotha, 2001), and become increasingly ‘expert’ as experience with opportunities accumulates (Bingham et al., 2007). Our theoretical argument is that, while experience may improve process performance, active learning by which fi rm members translate their accumulat-ing experience into increasingly honed heuristics that are expected to apply across multiple country entries is more likely to be associated with a higher performing process.

There are several reasons why heuristics create a high performing organizational process. First, heuristics focus attention and save time. The reason is that heuristics are cognitive structures that categorize stimuli (e.g., types of countries, customers, mode of entry). Such categorization frees up time to improvise unexpected aspects of opportunities (Daft and Weick, 1984; Friedman, 1979), and so enables rapid and accurate troubleshooting when surprises and problems (e.g., ‘this is an acquisition integration error’ or ‘this mode of country entry is incorrect’) arise.

Second, heuristics allow for improvisation (Brown and Eisenhardt, 1997; Miner et al., 2001). Their semi-structure enables the fl exibility and responsive-ness necessary for the effective capture of attractive but novel opportunities that are common in dynamic

markets.1 But they also allow for at least some effi -ciency since that limited structure provides guid-ance that keeps behavior partially constrained. This creates the coherence necessary during adaptation to achieve congruence with new opportunities (e.g., new countries, new products) (Teece et al., 1997).

Third, heuristics limit errors. They provide guide-lines and rough preliminary plans for how individuals should respond to future events, thereby reducing the amount of learning that needs to take place through pure trial-and-error (Eysenck and Keane, 1995). In their studies of expertise, for example, Chi and col-leagues (1981) found that physicists who spent more time creating cognitive representations of the problem situation before beginning were more successful solving problems than those who began without such representations. Similarly, Gitomer (1988) described how electronics technicians who engaged in trouble-shooting after creating a conceptual model of the task were more successful than those technicians who began immediately using trial-and-error. As a whole, these arguments suggest that, while experi-ence is probably needed to learn a high performing process, it is the articulation of that experience into heuristics by fi rm members that leads to a high per-forming process. The use of heuristics focuses atten-tion, reduces errors, and provides structure, and leads to ‘expertness’ that improves process performance. This leads to the following hypothesis:

Hypothesis 4: A greater number of heuristics is positively associated with process performance.2

1 The differences between extensive codifi ed knowledge and simple articulated heuristics may relate to differences in envi-ronments. In stable environments, codifi ed knowledge is ben-efi cial since there is greater predictability in situations and thus greater effi ciencies to be had from using many standardized steps. In dynamic environments however, codifi cation may be less useful as situations are less predictable (more hetero-geneous). Here maintaining plasticity, not just effi ciency, is important. Therefore, we argue that effective knowledge is probably simpler in dynamic environments, and probably more complicated and elaborated in stable ones.2 The more general prediction is that the total number of heu-ristics has an inverted U relationship with performance because many heuristics may become too constraining and so lower performance (Davis et al., 2007). But since we study fi rms that are just beginning, we do not observe a suffi cient range to test this prediction. That is, in the setting investigated for the study, entrepreneurial organizations suffered from having too few heuristics: managers generally struggled to learn new processes, making the development of suffi cient heuristics the primary challenge. Accordingly, we did not observe organiza-tions with enough heuristics to test a curvilinear (inverted-U) shaped relationship to performance.



Prior research shows that fi rms learn several types of process heuristics. Some of these are lower order heuristics – i.e., selection and procedural heuristics that relate to the capture of one particular opportunity such as a single country or acquisition (Bingham et al., 2007). Selection heuristics are defi ned as rules for choosing an opportunity, such as which types of countries to enter, which types of customers to target, and which product to develop. They narrow the range of opportunity choices by specifying which to pursue and which to ignore. Thus, they provide focus, and lead to higher process perfor-mance because they channel the efforts of dispersed fi rm members into similar kinds of opportunities. For example, fi rms may rely on selection heuris-tics to restrict their product development activities to retail software products and not fi nancial ones, or low-cost mobile semiconductor products, not all semiconductor products. Or, fi rms may use selection heuristics to focus on specifi c customer types (e.g., large fi nancial institutions or telecom operators), or on targeted geographic locations (e.g., Asia, big cities, or Scandinavia).

Without selection heuristics, fi rm members may chase too many, widely varying opportunities, and so lose effi ciency and lower process performance. Or, individuals may become confused about which opportunities to pursue, and so may be reluctant to do anything at all. Indeed, the inability to structure uncertainty can signifi cantly reduce decision making speed and process effectiveness in dynamic markets (Eisenhardt, 1989). Hence, the absence of selection heuristics reduces effi ciency, engenders confu-sion, and leads to lowered process performance. In contrast, selection heuristics enhance process per-formance by helping fi rm members allocate scare resources to a more focused opportunity set and eliminate poor opportunities that do not fi t the fi rm well.

Procedural heuristics are defi ned as rules that specify the actions a fi rm should take to execute a chosen opportunity (Bingham et al., 2007). Proce-dural heuristics focus attention on how to capture selected opportunities, and refl ect learning on the part of fi rm members about past actions and their effi cacy for process execution. Their use leads to improved process performance by specifying behaviors likely to prove helpful during execution. Examples include ‘hold weekly meetings between engineers and marketers’ in a product development process (Brown and Eisenhardt, 1997) and ‘do no exclusive deals’ in an alliance process (Rindova and

Kotha, 2001). More generally, procedural heuris-tics lead to improved process performance because they structure action, improve sensemaking, and aid problem solving. Psychological research shows that making sense out of life requires that individuals learn not only what to do, but also how to do it (Siegler, Deloache, and Eisenberg, 2003). It also suggests that effective problem solving involves problem structures that are defi ned by a goal and knowledge necessary to achieve the goal (Newell and Simon, 1972). Poor performance arises when one of these elements is missing (Simon, 1973). Thus, procedural heuristics improve process per-formance by elucidating understanding about how to capture opportunities within the fi rm’s purview. Like selection heuristics, procedural heuristics focus attention, structure action, and eliminate errors.

In sum, these arguments imply that fi rms with selection and procedural heuristics will have a higher performing organizational process due to more focused choice of particular opportunities from the larger set of possibilities, and more effi -cient guidance for actions regarding what to do (and not to do) while attempting to capture the selected opportunities.

Hypothesis 5: A greater number of lower order heuristics (i.e., selection and procedural) is posi-tively associated with process performance.

While lower order heuristics focus on the capture of a single opportunity, higher order heuristics such as those dealing with time and priorities link mul-tiple opportunities together. As such, they require greater cognitive sophistication, and are associated with higher expertise (Bingham et al., 2007). Tem-poral heuristics are defi ned as rules for opportunity capture that relate to sequence, pace, or synchroniza-tion (Brown and Eisenhardt, 1997; Gersick, 1994). Examples of sequence rules include ‘Use the U.K. as a launching pad into France and Germany,’ or ‘Move from tier-three to tier-two to tier-one coun-tries,’ while examples of pace and synchronization rules include ‘Enter one country every two months’ and ‘Build up enough strength in current markets before making a high-cost commitment to a new market,’ respectively.

Temporal heuristics improve process performance for several reasons. First, they synchronize various work groups (e.g., sales, engineering, marketing) with each other and the market. Such timing allows fi rm members to regulate the tempo of their actions,



and so lower the likelihood of confusion, fatigue and wasted effort. Second, temporal heuristics provide natural break points that are tied with market rhythms such as the product development cycle of customers. This allows fi rm members to entrain with the environment, and thus reassess their efforts and the competitive landscape at regular time intervals (Ancona and Caldwell, 1992). Temporal heuristics are especially valuable in dynamic markets where ongoing organizational change is critical to capture fl eeting opportunities, but where it is easy to either change too often (Sastry, 1997) or too little (Gersick, 1994). Finally, temporal heuristics are effective because they help managers maintain momentum. Momentum provides focus and direction about when and where to move forward from current opportunities. Overall, specifying the sequence, synchronization and pace by which a process takes place is likely to improve performance (Barkema et al., 1996; Chang, 1995; Vermeulen and Barkema, 2002).

In contrast, processes may be lower performing when fi rms lack temporal heuristics. Firm members may try to capture too many opportunities at once, or execute the appropriate actions but in the wrong order. Chang (1995), as one illustration, found that when Japanese electronic fi rms entering the U.S. fol-lowed a sequence of starting with small investments and then increasing the scale of those investments over time, they performed better than other fi rms that did not follow any sequence. Finally, without heuristics for sequence, pace, and synchronization, fi rm members may not be able to effectively cho-reograph the switch from one opportunity to another and properly coordinate different parts of the fi rm to manage the transitions (Brown and Eisenhardt, 1998).

Priority heuristics are defi ned as rules that specify the ranking of opportunities (Bingham et al., 2007). Specifi cally, they involve the identifi cation of a fi rm’s most important opportunities within the limits proscribed by its selection heuristics. Priority heuristics increase the probability of high process performance because they target effort on the most attractive opportunities. Since their creation and use require thoughtful evaluation and comparison of multiple opportunities, fi rm members come to better understand which are more important to the perfor-mance of an organizational process when they rank opportunities in relation to others. Thus, they are likely to create heuristics that focus their efforts on the best opportunities. Intel, for example, relied on

a priority heuristic of ‘maximize-margin-per-wafer’ to allocate capacity in its manufacturing process (Burgelman, 1996). When memory margins fell dramatically, Intel followed their heuristic and real-located capacity to microprocessors where margins were higher. Priority heuristics may also improve process performance by stipulating where within its proscribed scope of operations the fi rm should begin its opportunity search such as ‘Enter regions with the highest mobile penetration fi rst,’ ‘Start with the automotive sector,’ or ‘Enter English speaking markets fi rst.’ Alternatively, priority heuristics can specify where to end such as ‘Work towards enter-ing China.’ Firms lacking priority heuristics may not have high process performance because they pursue low-value opportunities when better opportu-nities are available or because they pursue too many opportunities in parallel, thereby failing to capture the value of any single opportunity. Collectively, these arguments suggest that higher order heuristics are likely to lead to a higher performing organiza-tional process because they help fi rm members to synchronize and order their efforts more effectively across multiple opportunities.

Hypothesis 6: A greater number of higher order heuristics is positively associated with process performance.

METHODS

Sample

Our setting is entrepreneurial fi rms – i.e., small and young organizations. We chose these fi rms because they offer methods advantages. Their young age avoids left censoring by allowing observation of process development and learning from fi rm incep-tion. Their small size enhances transparency (Argote, 1999), and so ensures better identifi cation of shared process heuristics if they exist.

We focus on the internationalization process in which each discrete experience is a unique country entry. Our sample consists of 67 country entries performed by 12 entrepreneurial fi rms between 1997 and 2003. Consistent with the internationalization literature (e.g., Root, 1994), we defi ne a country entry as a fi rm’s physical entry into a foreign country through institutional arrangements (e.g., joint ven-tures, acquisitions, Greenfi eld investments) for the primary purpose of enabling sales. Although often



overlooked as a setting in which to study organi-zational processes, internationalization is a good choice for several reasons. First, since each country entry is a discrete event, it can be analyzed in isola-tion as a single opportunity and/or as part of a larger set of experiences. Second, since performance data exist for each country entry, process performance can be measured. Finally, a discrete experience such as a country entry, acquisition or alliance is an appropriate, commonly used unit of analysis for the study of organizational processes (Hayward, 2002; Vermeulen and Barkema, 2001).

Country entries were compiled from a random sample of entrepreneurial fi rms operating in four global technology industries (IT hardware, software, medical equipment, and computer security) from three key entrepreneurial metropolitan areas (Singapore, Helsinki, San Jose). We sample fi rms operating across industries and locales to improve the generalizability of the results. We also sample fi rms that had experi-enced a minimum of three entries during the sample period, and that started internationalization within fi ve years prior to data collection. This helps to ensure that we study fi rms where learning an internationalization process is important, and where company informants would be likely to recall the events surrounding the process in each country entry.

A unique feature of our research design is the collection of multiple types of data (Edmondson and McManus, 2007). This helps to ensure greater measurement accuracy, and instill confi dence in the fi ndings. There are currently no databases tracking international country entries in suffi cient detail to measure learned content. Therefore, we collected data from multiple primary and secondary sources: quantitative and qualitative data from semi-structured interviews with fi rm management; archival data from public and private documents such as annual reports, business press, Federal Reserve Economic Data and the Hofstede index (2001) on cultural differences; and emails, phone calls, and follow-up interviews to track the real-time internationalization process. The primary source of data is over 70 interviews during a 15-month period with corporate executives of our focal fi rms on three different continents. We conducted all interviews according to well-estab-lished research procedures including ‘courtroom’ questioning, event tracking, and non-directive ques-tioning from multiple informants at different levels of hierarchy in order to ensure robustness, reliabil-ity, and internal consistency (Golden, 1992; Huber, 1985; Huber and Power, 1985; Miller, Cardinal,

and Glick, 1997; Schwenk, 1985). Our interviews yielded about 900 pages of single-spaced pages of transcript data.

All research designs make tradeoffs due to the practical limits of data collection. Although archival sources often yield extensive data, they do not allow direct, rich observation of organizational processes in the fi eld, and therefore, provide distant measures of the presence, content, and effects of constructs such as learned heuristics. Since a primary goal was to examine the relationship between learned heuristics and process performance, archival data that would afford a large sample size is inadequate. Therefore in order to ensure depth of understanding and accurate measures, we elected to gather fi eld-based measures across a varied sample of industries and geographies for our empirical test of the infl u-ence of heuristics on process performance. Hence, although our sample is small, this was a necessary tradeoff to get accurate measures of constructs like heuristics. Our qualitative data, in particular, play a critical role in uncovering unique insights that are unavailable from archival data and quantitative mea-sures alone (Jick, 1979). Thus, we sacrifi ce some statistical power in exchange for more measurement accuracy. This choice increases the likelihood that our results will be not only generalizeable and thus externally valid, but also fresh and insightful.

Dependent variable

PerformanceConsistent with prior studies of the international-ization process, we rely on multiple measures of performance (Brush and Vanderwerf, 1992; Delios and Beamish, 2001; Dunning, 1980; Geringer and Herbert, 1990; Zahra and Dess, 2001). First, we measured country entry performance as the log of average revenue generated by the international entry into the specifi c country, adjusted for infl ation. We chose the log of average annual revenue because it provides a reliable, objective measure of perfor-mance available across the sample. Although we considered other fi nancial measures such as CAGR, we found them unreliable or unavailable. Second, we measured country entry performance using a Likert scale. We asked top management team members responsible for the entry to rate the ‘overall success of the entry’ on a 10-point Likert scale (0 = very poor, 5 = moderate, 10 = excellent), and then computed the mean response. Third, we measured country entry performance with a qualitative assess-



ment of entry performance. For this measure, we asked the informants to describe the performance of each country entry that they knew (mean informants per entry = 5). One author then coded all responses as positive, negative, or neutral. Examples of posi-tive responses include ‘We have done very well in Malaysia,’ or ‘I think Taiwan has been very suc-cessful.’ Examples of negative responses include ‘In China, we were not very successful,’ or ‘We got nothing working in the Czech Republic.’ Examples of neutral responses include ‘Diffi cult to estimate at this point,’ or ‘Not making losses, but not making too much profi t.’ A second author confi rmed this categorization. We then calculated two qualitative performance measures: proportion of all comments that were positive and proportion that were negative. These measures are likely to be accurate because they span informants and hierarchical levels, thereby providing a robust and rich assessment of process performance from multiple vantage points.

We combined the performance measures (log of average revenue, Likert scale rating, and qualita-tive performance assessments) using factor analysis. This produced a single factor with an eigenvalue >1 (i.e., 2.2). Loadings occurred as expected with very high positive loadings (0.8) for both the Likert scale rating and the proportion of positive comments, a very negative loading (−0.8) for the proportion of negative comments, and a positive loading for log of average revenue growth (0.6). The performance factor score has a mean of 0 and a standard deviation of 0.9. Using multiple, independent assessments of performance that are congruent provides convergent validation and a more reliable performance measure than is possible from objective or subjective mea-sures alone.

Independent variables

Cumulative experienceWe measured cumulative experience as a count of the total country entries performed by a fi rm prior to the focal entry. Our measure is consistent with existing literature which also measures cumulative experience as the sum total of events undertaken by a fi rm (Haleblian and Finkelstein, 1999; Hayward, 2002).

Time between experiences Consistent with prior research (Vermeulen and Barkema, 2002), we measured time between expe-riences as the number of quarters between the focal country entry and the prior entry. If the entry were

a fi rm’s fi rst venture into another country, this lag was coded as the time between founding and the fi rst entry (the lag between founding and fi rst entry was, on average, not signifi cantly different than the lag between other entries).

Similarity of experiences Similar to others (Brockner et al., 2000; Gibson and Zellmer-Bruhn, 2001), we measured similar-ity of experiences as the cultural similarity between the focal country entry and the fi rm’s headquarter country. To measure cultural similarity, we fi rst used Hofestede’s (2001) index of cultural distance and calculated index rankings for each country relative to other countries. We then collapsed Hofstede’s four dimensions into a single measure of cultural distance for each country entry using a methodol-ogy developed by Kogut and Singh (1988). This measure calculates the sum of the squared differ-ences for each of Hofstede’s (1980) four primary cultural dimensions.3

Heuristics We assessed the existence of heuristics in two dif-ferent ways: behavioral and cognitive (Cyert and March, 1963; Miner et al., 2001). First, we cap-tured the behavioral action patterns used to enter new countries. Specifi cally, we analyzed the actions of individuals before and during each country entry. We then coded actions as heuristics only if they consisted of a recognized guide for internationaliza-tion that was articulated by more than one informant in each fi rm.4 Second, we assessed the existence of

3 Studies on the infl uence of cultural distance often use the four dimensions of Hofstede (1980): power distance (the degree to which people accept the unequal distribution of power inside organizations), uncertainty avoidance (the degree to which people tolerate uncertainty and ambiguity in situations), indi-vidualism (the preference of people to belong to a loosely versus tightly knit social framework), and masculinity (the degree to which people prefer values of success and competi-tion over modesty and concern for others). Although Hofstede (2001) includes a recently developed fi fth dimension, we calcu-lated the index using only Hofstede’s original four dimensions to facilitate comparability to previous empirical studies.4 To illustrate, to enter new countries one Singapore-based security software fi rm followed a heuristic of targeting ‘gov-ernment and fi nancial institutions.’ As a senior executive stated about the fi rm’s entry into Malaysia, ‘Banks have the money to spend, so you got to focus on them.’ He also added, ‘Govern-ment is again by design . . . If so much is going through the IT infrastructure, then protection is needed.’ The CEO noted the same target group when he said, ‘What we want is, whether you are a government agent or bank, when you think about info-security call (fi rm name).’



heuristics from a cognitive perspective. We analyzed each informant’s articulated descriptions of what the fi rm had learned in each country entry that could be used in other country entries. These descriptions occurred in response to our open-ended questions about the chronology of each country entry (e.g., Tell me the story of how you received your fi rst sale?). They also occurred in response to our wrap-up questions where we focused on lessons learned in the country (e.g., What, if any, were the lessons/insights your fi rm gained from its experience in this country?). As with behavioral patterns, we consid-ered these to be articulated heuristics when two or more informants independently described the same lessons regarding how to enter new countries.5 Each author independently examined the data with the aid of charts, tables, and cell designs to accurately identify heuristics from both behavioral and cogni-tive perspectives. Two authors then iterated the dif-ferences in assessments using the defi nition criteria. The third verifi ed this iteration independently. If there were still disagreements, the authors iterated until they agreed (less than 1% of heuristics required iteration). Finally, we identifi ed and categorized heuristics independent of the performance data. In general, the combination of behavioral and cognitive approaches for assessing heuristics is consistent with research describing how organizational processes improve through the development of behavior con-sistencies, in the form of repeated action patterns, and through the improvement of cognitive frames, in the form of enhanced mental models and causal theories (Miner et al., 2001). This dual approach

provides ‘thick description’ of heuristics with prox-imity to the data, and illumination of the learned content in context.

Total heuristics is the count of heuristics a fi rm employed in a focal country entry. It was calculated as the sum of both lower (selection and procedural) and higher (temporal and priority) order heuristics used in a focal entry.

Lower order heuristics is the count of selection and procedural heuristics employed in a country entry. We categorized heuristics as ‘selection heu-ristics’ if the articulated knowledge specifi ed which countries to enter, which customers to pursue, or which products to promote in each new country. Examples include ‘Enter English speaking markets,’ ‘Focus on large original device manufacturers,’ and ‘Promote electronic diary solutions.’ We categorized heuristics as ‘procedural heuristics’ if consistencies existed regarding how to enter new countries such as, ‘Use acquisitions,’ ‘Use joint venture partner-ships,’ or ‘Hire experienced locals using the advi-sory board.’

Higher order heuristics is the count of temporal and priority heuristics used in each country entry. We categorized heuristics as ‘temporal heuristics’ if con-sistencies emerged about (1) pacing (i.e., executing processes in cadence with some internal timing); (2) synchronization, (i.e., executing a process in cadence with some external timing); and (3) sequence, (i.e., the order of experiences that the fi rm needs to follow). Examples include, ‘Enter one country at a time,’ ‘Regulate entry according to the market readiness of the country,’ and ‘Build up Europe, move to U.S., then China.’ We categorized heuristics as ‘priority heuristics’ if consistencies emerged about opportu-nity rankings such as ‘Tier-three countries’ or ‘Large European retail markets.’

Control variables

We controlled for the country entry team because internationalization research indicates that these management teams may affect process performance (e.g., Carpenter, Sanders, and Gregersen, 2001; Daily, Certo, and Dalton, 2000). Consistent with this work, we defi ne a country entry team as those individuals directly involved with deciding and executing the country approach, and who had direct responsibility for the performance of the country entry. We control for three effects suggested by the literature: team size, team functional diversity and team international experience.

5 For example, during the aforementioned fi rm’s preinterna-tionalization experience in Singapore, management had been successful by targeting their sales approach to IT groups within customer organizations. Looking forward, managers believed IT managers in other countries would also most appreciate their technology and would have the greatest incentive to purchase the company’s software solutions since they were responsible for information security. However, after entering Hong Kong, the TMT realized that the sales approach of targeting IT groups was ineffective. Because new technology guidelines required senior executives to understand the risks associated with their technology, many Hong Kong corporations had shifted respon-sibility for information security from IT and into audit. Thus, the new belief was that managers should target audit groups, not IT groups (heuristic). The CEO stated, ‘We learned from experience who makes the decision – the auditors of govern-ments and banks instead of IT. In more and more organiza-tions, IT security is out of IT . . . The audience is the CFO, the group-auditor, and the CEO.’ Another leader recounted the same lesson when he said, ‘Instead of talking to the technical people, we learned that we need talk to the CEO and business people . . .’



Team size is the count of country entry team members. Controlling for team size is important since research demonstrates that a larger team increases the quantity of skills and attention available to the fi rm (Eisenhardt and Schoonhoven, 1990).

Team functional diversity is an index measuring the diversity of functional roles in the country entry team. A diverse team is better able to manage the multi-faceted nature of international entry as well as lead to more effective problem solving by adding more divergent views (Bantel and Jackson, 1989; Carpenter and Fredrickson, 2001). Consistent with previous research, we operationalize functional diversity by fi rst classifying individuals using func-tional classifi cations established in the literature such as backgrounds in sales, engineering, and marketing (Bantel and Jackson, 1989; Michel and Hambrick, 1992; Wiersema and Bantel, 1992). We then cal-culated a team heterogeneity measure using Blau’s (1977) method (heterogeneity is measured as 1 − Σ (Pi)2 where Pi represents the percentage of individu-als with a background in category i).

Team international experience is the number of country entry team members who had lived and worked outside their home country for over one year (Carpenter, Pollock, and Leary, 2003; Sambharya, 1996). Controlling for team international experi-ence is important since prior research suggests that such experience leads to a greater understanding of foreign customers, employees, and competitors and so can improve process performance (Carpenter et al., 2001; Daily et al., 2000).

In results available from the authors, we also examined additional controls beyond those reported. These include year indicators for temporal effects; fi rm indicators for fi rm-specifi c effects and for fi rm size, age, and headquarters location; and country entry indicators such as entry mode. None of these controls was signifi cant or infl uenced the hypoth-esized results, and so they were eliminated from the models in order to focus on the estimation of theoretically relevant variables.

Estimation technique

Given that we treat our data as cross-sectional rather than panel data, we use ordinary least squares regression (OLS) which, similar to random effects, combines an un-weighted average of between and fi xed effects (Kennedy 2003). In early estimations, we included indicator variables to control for fi rm effects but these variables dropped out of signifi -

cance. Therefore, we concluded that we had enough variables controlling for fi rm effects, and that OLS was the most appropriate technique in this situa-tion. OLS results were tested for violations of the standard assumptions including autocorrelation and heteroskedasticity. We found no violations. We further tested for the correct specifi cation using the Ramsey (1969) omitted variable test and found no support for omitted variables. In results available from the authors, we confi rmed our results using cluster analysis and ordered probit for three and six clusters of performance. Overall, our use of OLS is consistent with prior studies exploring sequential, discrete activities (e.g., country entries, acquisitions) performed by single fi rms (Hayward, 2002; Ver-meulen and Barkema, 2001).6

RESULTS

Table 1 reports descriptive statistics and correla-tions. Table 2 presents the OLS regression results. Model 1 includes the team control variables. Of the three team control variables (i.e., size, functional diversity, and international experience) only team functional diversity is signifi cant (p < 0.01). The overall model is signifi cant (p < 0.10).

Model 2 adds the results for the experience hypotheses (H1–H3). Hypothesis 1 predicted that cumulative experience is positively associated with country performance. This hypothesis is not sup-ported. Hypothesis 2 predicted that the time between experiences has an inverted U-shaped relationship with country performance. Results show that the linear term is not supported and the quadratic term is

6 We considered several alternative statistical techniques. A fi xed effects model, which is used primarily with panel data, was viewed as less appropriate for several reasons. First, fi xed-effects models eliminate across-fi rm variation, a key source of interest to the study. Second, although we tested fi xed effects, resulting models were generally poor and failed to reject the null hypothesis that the mean of fi rm indicator variables was equal to zero, suggesting that our current variables may suffi ciently control for fi rm effects. Third, we tested a between fi xed-effects model, which models between fi rm variation without account-ing for within fi rm variation, and found that, although in line with our hypotheses, the overall model was only weakly sig-nifi cant due to the heavy loss of degrees of freedom. Besides fi xed effects, we tested a random effects model which combines a matrix-weighted average of the between and fi xed effects estimators. The results supported our hypotheses but we did use random effects models because they are generally appropriate for panel data, not cross sectional data.



Tab

le 1

. D

escr

iptiv

e st

atis

tics

and

biva

riat

e co

rrel

atio

ns

Var

iabl

eM

ean

S.D

.1

23

45

67

89

1011

12

1Pe

rfor

man

ce−0

.02

0.89

2T

eam

Siz

e2.

311.

440.

17

3T

eam

Fun

ct. D

iver

sity

0.18

0.25

0.29

0.53

4T

eam

Int

ern.

Exp

6.37

7.07

−0.0

7−0

.35

−0.0

9

5C

umul

ativ

e E

xper

ienc

e3.

752.

24−0

.16

−0.4

5−0

.37

0.49

6T

ime

Btw

n E

xper

ienc

e3.

073.

930.

010.

190.

28−0

.16

−0.2

8

7T

ime

Btw

n E

xper

ienc

e224

.63

54.0

2−0

.04

0.26

0.22

−0.1

9−0

.28

0.94

8Si

mila

r E

xper

ienc

e1.

691.

300.

010.

050.

030.

100.

010.

040.

10

9Si

mila

r E

xper

ienc

e24.

546.

31−0

.03

0.08

0.04

0.06

−0.0

50.

020.

070.

94

10T

otal

Heu

rist

ics

7.71

4.10

0.49

0.18

0.10

−0.2

8−0

.23

−0.1

6−0

.21

−0.0

7−0

.01

11L

ower

Ord

er H

euri

stic

s5.

882.

390.

470.

210.

13−0

.31

−0.2

7−0

.11

−0.1

7−0

.16

−0.1

30.

88

12H

ighe

r O

rder

Heu

rist

ics

1.69

2.06

0.41

0.09

0.02

−0.2

0−0

.13

−0.1

7−0

.20

0.07

0.12

0.87

0.54



only weakly supported (p < 0.10).7 Hypothesis 3 pre-dicted that similarity in experience has an inverted U-shaped relationship with country performance, and is supported (p < 0.05). As a robustness check, we also measured similarity in experience as the cultural distance between a focal entry and the entry immediately preceding it (Kogut and Singh, 1988). Because this measure provided roughly equivalent results, we do not report them in our tables. The overall model is not signifi cant.

Model 3 presents the results of total heuristics (H4), including the team control variables. Hypoth-esis 4 predicted that an increase in the total number of heuristics would positively impact process per-formance. The overall model is highly signifi cant (p < 0.001) and provides strong support for Hypoth-esis 4 (p < 0.001) indicating that the greater the number of heuristics a fi rm employs when enter-ing a new country, the greater its performance in that country. Model 3 also shows that the relation-ship between heuristics and process performance is much more signifi cant than the effects of experi-ence alone (adjusted R-square value of 0.23 versus 0.06).

Table 2. OLS regression results

Variable Model 1 (Controls)

Model 2 (Exp &

Controls)

Model 3 (Controls & Heuristics)

Model 4 (Controls &

Heuristic Types)

Model 5 (Full Model)

Model 6 (Parsimonious

Model)

Model F Value 2.54* 1.53 6.07**** 4.96**** 3.6**** 7.47****Adjusted R-square 0.06 0.06 0.23 0.23 0.28 0.33N 67 67 67 67 67 67

Team Size −0.02 0.05 −0.05 −0.05 0.00

(0.09) (0.10) (0.08) (0.08) (0.09)

Team Functional Diversity 1.19*** 0.74* 1.21*** 1.22**** 0.76* 0.87***(0.50) (0.56) (0.45) (0.45) (0.49) (0.36)

Team International Experience −0.00 −0.00 0.00 0.00 0.01

(0.01) (0.01) (0.01) (0.01) (0.01)

Cumulative Experience −0.05 −0.02

(0.06) (0.05)

Time Between Experience 0.09 0.04

(0.09) (0.08)

Time Between Experience2 −0.00* −0.00

(0.00) (0.00)

Similarity of Experience 0.51** 0.55*** 0.54***

(0.26) (0.23) (0.21)

Similarity of Experience2 −0.10** −0.11*** −0.11***

(0.05) (0.04) (0.04)

Total Heuristics 0.09****

(0.02)

Lower Order Heuristics 0.09** 0.11** 0.11***

(0.04) (0.05) (0.04)

Higher Order Heuristics 0.11** 0.12*** 0.12***

(0.05) (0.05) (0.05)

Coeffi cients with standard errors listed under coeffi cients

****p < 0.001; ***p < 0.01; **p < 0.05; *p < 0.10

7 We further tested linear models for experience inverted Us that were not signifi cant (i.e., time between experience) and found that they were still not signifi cant.



Model 4 presents the results of the lower and higher order heuristics (H5–H6), including the control variables from Model 1 regressed on process performance. We entered these variables as a sepa-rate theoretical block from total heuristics due to high correlation between total heuristics and heu-ristic types (see Table 1). Hypotheses 5 and 6 pre-dicted that lower and higher order heuristics would each have a positive effect on performance. Results show that the model is highly signifi cant (p < 0.001) with an adjusted R-square of 0.23. There is strong and signifi cant support for Hypothesis 5 (p < 0.05). The greater the number of lower order heuristics, the higher the country performance. Model 4 also strongly and signifi cantly supports Hypothesis 6 (p < 0.05) indicating that the greater the number of higher order heuristics, the higher the performance.

Model 5 presents a full model with all control and hypothesized variables. This model is highly signifi cant (p < 0.001) with continued statistical sig-nifi cance for similarity of experience (inverted U) (H3), lower order heuristics (H5) and higher order heuristics (H6). However, time between experiences (H2) is no longer signifi cant. Model 6 is a parsimo-nious version of Model 5, and includes only those variables with statistical signifi cance in previous models. This model is also highly signifi cant (p < 0.001) with an adjusted R-square of 0.33. Together, Models 5 and 6 suggest that heuristics and experi-ence similarity have signifi cant and positive rela-tionships with country performance, with heuristics having much stronger effects.

DISCUSSION

Organizational processes such as internationaliza-tion, acquisition, alliance, and product develop-ment are central within the strategy, organizations, and entrepreneurship literatures. They enable fi rm members to perform tasks more effectively (Pentland, 1995; Ray et al., 2004; Teece et al., 1997), capture fresh opportunities for growth (Gilbert, 2006), adapt to changes in the market (Brown and Eisenhardt, 1997) and, when high-performing, con-stitute a primary feature of capabilities (Amit and Schoemaker, 1993; Eisenhardt and Martin, 2000; Maritan, 2001). Yet, the source of high performing organizational processes is unclear. One research stream emphasizes organizational learning from experience. In contrast, an emerging research

stream emphasizes organizational cognition and, in particular, heuristics. Our purpose is to juxta-pose these competing theoretical explanations to gain insight into organizational processes and capabilities.

Principal results: heuristics-basis of capabilities

Our core contribution is the insight that heuristics are at the heart of high performing organizational processes, and so are central to fi rm capabilities. Specifi cally, we fi nd that high performing organiza-tional processes consist of heuristics – i.e., informal rules-of-thumb that center on the capture of opportu-nities within fl ows of process-specifi c opportunities (e.g., new countries, acquisition targets, or product development projects). We also fi nd that more heuris-tics relate to higher process performance. Moreover, high performing organizational processes consist of particular types of heuristics. These are lower order heuristics for choosing (selection) and executing (procedural) opportunities. For example, in one very high performing country entry, fi rm members used several selection heuristics such as ‘Focus on low cost chips for mobile devices,’ and ‘Target countries that have ODMs or OEMs for mobile devices,’ and several procedural heuristics such as ‘Use a consul-tant to provide introductions and insight about the local market,’ and ‘Segment customers into tiers.’ We also fi nd that higher order heuristics for pacing, sequencing, synchronizing (temporal) and ranking (priority) multiple opportunities are especially related to higher process performance. To illus-trate, in one very high performing entry described as ‘perfect,’ fi rm members used several temporal and priority heuristics such as ‘Take one continent at a time (i.e., build up Europe, move to U.S., then China),’ ‘Synchronize entry pace with country’s retail lifecycle,’ and ‘Begin with direct sales (then move to indirect sales).’ In contrast, in low perform-ing country entries, fi rm members have very few heuristics that apply across country entries. Rather, they mostly engage in country-specifi c learning and related behaviors without developing or adapting more generalized heuristics. Firm members are par-ticularly unlikely to have higher-order heuristics that signal cognitive sophistication and expertness. For example, in one very low performing country entry (e.g., ‘not really any sales’), heuristics were notably absent. As the CEO noted, ‘We’re taking it oppor-tunistically.’ Thus, we fi nd that simply accumulat-



ing experience (even when paced and appropriately similar) is weakly or not at all associated with a high performing process.

A related contribution is the insight that ‘opportu-nity-capture’ heuristics are also central to capabili-ties. Although there is much debate on capabilities in the literature, there is convergence on several themes. First, capabilities rely extensively on orga-nizational processes (Amit and Schoemaker, 1993; Eisenhardt and Martin, 2000; Helfat et al., 2007; Teece et al., 1997). For example, Stalk and col-leagues (1992: 62) stated that ‘a capability is a set of business processes strategically understood,’ while Eisenhardt and Martin (2000: 1106) noted that ‘dynamic capabilities consist of specifi c strategic and organizational processes . . .’ Second, capabili-ties are learned from experience. Therefore, while preexisting endowments (e.g., human capital of founding team) may set the stage for capability cre-ation (Gavetti, 2005; Helfat and Lieberman, 2002), capabilities are primarily learned through ‘doing’ as fi rm members enhance their understanding of causal relationships with accumulated experience (Chang, 1995; Helfat and Peteraf, 2003; Martin and Salomon, 2003). Finally, there is consensus on the need for high performance if a capability is to exist. Thus, several studies show how the presence of a capability results in increased effectiveness (Zollo and Winter, 2002), productivity (Galunic and Eisen-hardt, 2001; Makadok, 2001) or effi ciency (Collis, 1994). In short, a central purpose of capabilities is ‘to improve performance’ (Maritan, 2001: 514). Yet while there is convergence on these aspects of capabilities, their actual content is unclear. Some empirical research equates the content of capabili-ties with outcomes such as measures of fi rm per-formance (e.g., abnormal stock market returns) or non-fi nancial measures of process performance (e.g., subjective measures of project development performance) (for a review see Ethiraj, Kale, and Krishnan, 2005). But this approach focuses on the results of capabilities, not their actual content. Some theoretical research argues that capabilities consist of resources, but lacks specifi cs about content (e.g., types of resources, organization of tacit knowledge) (Barney, 1991; Helfat and Peteraf, 2003). Still other research points to the content of capabilities as being best practice (Szulanski, 1996) or codifi ed knowledge (Kale and Singh, 2007; Zollo and Singh, 2004; Zollo and Winter, 2002), but also generally neglects details about content (e.g., structure and types of codifi ed knowledge). In contrast, by using

a methodology that opens the ‘black box’ of what is learned from organizational process experience, we can be more specifi c about the actual content of fi rm capabilities. Thus, a core contribution of our study is that ‘opportunity-capture’ heuristics are central to the structure of capabilities, especially in dynamic markets and among entrepreneurial fi rms.

Another contribution is expanding the role of cog-nition in the creation of capabilities. While prior work highlights how cognitive phenomena such as threat vs. opportunity framing (Gilbert, 2006), analogy (Gavetti, Levinthal, and Rivkin, 2005), and identity (Tripsas and Gavetti, 2000) can infl uence capability development, we contribute heuristics to this emergent cognitive emphasis. In particular, we show how the creation of heuristics involves active cognitive engagement and results in the formation of a robust organizational memory needed for effec-tively coping in dynamic markets. Memory is asso-ciated with the brain’s efforts to impose structure on experience (Edelman, 1992). Numerous studies in the cognitive sciences show that memory perfor-mance is highly infl uenced by the encoding of expe-rience (Broadbent, 1958; Miller, 1956). Without some form of encoding, individuals often refl ect less on their experiences and thus retain only incom-plete and impoverished recollections of what they have done and how they did it (Schachter, 1996). This in turn increases the likelihood that short-term experiential lessons will not make it to longer-term memory and be inaccurately recalled (if at all) in the future (Braddeley and Hitch, 1974; Broadbent, 1958). Intriguingly however, research also suggests that only a certain type of encoding generates high long-term memory performance – a cognitively sophisticated encoding consisting of simple (not elaborate) lessons that allow individuals to combine new information with existing knowledge (Bradd-eley and Hitch, 1974; Broadbent, 1958). Heuristics provide such an encoding because their semi-struc-ture gives shape to organizational processes, but also leaves room for heedful cognitive engagement such as improvisation. Encoding of experience into generalizeable heuristics may, therefore, not only improve the quantity of organizational memory, but also its quality such that fi rms are better able to adapt to uncertain situations. Overall, while prior work on heuristics in the entrepreneurship domain often points to their maladaptive nature (Busenitz and Barney, 1997), we spotlight their adaptive nature as simple, deep, and fl exible knowledge structures that underpin capabilities.



Implications for strategy: extending the strategic logic of opportunity

Our work also contributes to strategy. Recent theory has sketched a typology of theoretical logics leading to competitive advantage: position, leverage and opportunity (Bingham and Eisenhardt, 2007). Yet while strategic logics of position (Porter, 1996; Rivkin, 2000) and leverage (Barney, 1991; Peteraf 1993) are well-known, the strategic logic of oppor-tunity is less well-developed. Although this logic is implicit in empirical research (Burgelman, 1996; Rindova and Kotha, 2001) and explored by simu-lation (Davis et al., 2007), this study is the fi rst to validate the logic empirically. Specifi cally, we confi rm the theory underpinning the strategic logic of opportunity – i.e., superior performance in dynamic markets and among entrepreneurial fi rms comes from choosing one or a few key organizational pro-cesses that put the fi rm in abundant and attractive opportunity fl ows, and developing simple heuristics to guide the effective capture of those opportunities. While past empirical research reveals that fi rms learn ‘opportunity capture’ heuristics from accumulating experience (Bingham et al., 2007), this study ties those heuristics to performance. That is, we support the strategic logic of opportunity that links heuristics (i.e., their number and types) to effective opportu-nity capture (and thus, high performance) through an improvisational mix of effi cient rule adherence and fl exible action.

At a broader level, this study may begin to reveal the essence of strategy in dynamic markets. As Porter (1996) notes, strategy is about ‘being differ-ent.’ But while he focuses on ‘different’ positions in stable markets, we focus on ‘different’ heuris-tics in dynamic markets – i.e., the distinctive set of heuristics developed by fi rms as their idiosyncratic decisions for how to compete. Although some heu-ristics may relate to best practices (e.g., conduct due diligence when entering a country through an acquisition mode), most are unique choices about how to be different in a competitively advanta-geous way. Hence, while all of our fi rms identifi ed the internationalization process as being vital and possessed the same types of heuristics (i.e., selec-tion, procedural, temporal, and priority), the specifi c detail of their heuristics varied by fi rm and formed the basis of their strategy for ‘being different.’ For example, three Singapore-based fi rms had different procedural heuristics for entering countries, even when they entered the same country (Japan). One

fi rm’s heuristics focused on using U.S. venture capi-talists for insight (e.g., as the Japan country manager stated, ‘We have American investors, like DFJ and Walden International. They gave us some great advice about who to work with, what to do, and what to start with.’) In contrast, a second fi rm relied on the local subsidiaries of multi-national corpora-tions (e.g., their Japan country manager noted, ‘We talk to quite a few people such as IBM Japan . . . It gave us a very good sense of the Japanese market.’) The third company watched the moves of promi-nent competitors. Overall, the uniqueness (and likely inimitability) of heuristics reinforces the key point that organizational processes such as internation-alization and their related heuristics are not just relevant for strategy, but perhaps are the strategy, especially in dynamic markets and among entrepre-neurial fi rms.

Implications for entrepreneurship: opportunity capture and the role of structure

Our work contributes to entrepreneurship in several ways. One contribution is sharpening understand-ing of opportunity capture in dynamic markets. Prior research debates whether opportunities are discovered or created (Kirzner, 1997; Shane, 2000). In contrast, we note that our entrepreneurs had a super-abundance of opportunities (e.g., many potential countries). For example, one leader said his fi rm could potentially ‘sell in 139 countries.’ Since abundant opportunities imply that opportuni-ties are readily discovered, entrepreneurs can be dis-criminating and pick the most promising. Moreover, although we saw many easily discovered opportuni-ties, these opportunities were also ill-formed, and so were more effectively captured when fi rm members relied on heuristics. Heuristics appeared to guide the extensive improvisational behavior needed to adjust to the unique aspects of each opportunity. As a whole, these arguments suggest that opportu-nity capture for fi rms in dynamic markets may be more about appropriate selection and execution of opportunities, and less about discovery or creation. Since these environments are opportunity-rich, dis-covery is not diffi cult, and creation can be ineffi cient when it leads to the under-exploitation of present possibilities. Rather, the imperative for managers in these unpredictable settings is using organizational heuristics as improvisational referents to provide a fl exible constraint within which opportunity capture may unfold.



Another contribution is insight into the fundamen-tal value of more structure for entrepreneurial fi rms. While cutting structure is often vital for established fi rms entering dynamic markets (Gilbert, 2005), our fi ndings suggest that the reverse is actually true for new fi rms in these settings. They need to add structure, especially since new fi rms lack the coher-ence, direction and control that structure brings. For example, a leader in a Finnish security software fi rm lacking heuristics remarked that there was ‘No clear picture of the major countries that we should target.’ In several country entries, leaders in this fi rm relied on only a few procedural heuristics, and suffered uniformly poor performance across countries. By contrast, in a Singapore security fi rm, leaders quickly developed many and different types of heuristics including selection and procedural heuristics such as ‘Restrict internationalization to Asia,’ ‘Target gov-ernment and fi nancial institutions,’ ‘Target the IT group within customer organizations to get sales,’ and ‘Always use a partnership entry mode when entering new countries.’ Over time, they added more selection and procedural heuristics such as ‘Hire an experienced country manager,’ and ‘Employ a consultative sales approach that highlights value over technology.’ They also developed temporal and priority heuristics such as ‘Use Hong Kong as a springboard to enter larger markets,’ ‘Synchronize entry with the pace of the local macro economy,’ ‘Push 24 × 7 security monitoring, then security systems integration,’ and ‘Enter one country at a time.’ Not surprisingly, country entry performance for this Singaporean security fi rm was positively related to their use of heuristics, and was consis-tently higher (e.g., ‘we own the market’) than that of its industry counterpart in Finland. Overall, an important insight of this study is that entrepreneur-ial fi rms need to quickly develop structure such as opportunity capture heuristics. Without structure, new ventures fl ounder since they experience neither order nor traction.

CONCLUSION

While organizational processes are important to fi rm action, fi rm capabilities and even strategy, controversy exists about how they become high performing. One view emphasizes organizational learning from experience, while a more recent view emphasizes organizational cognition and the use of heuristics. Our core contribution is the insight

that heuristics are at the heart of high-performing processes, and thus fi rm capabilities. Experience per se is not enough. Rather experience must be articulated into ‘opportunity capture’ heuristics to achieve high-performance. At a broader level, we offer support for the view that strategy in dynamic markets and among entrepreneurial fi rms rests on a strategic logic of opportunity, and may well be distinctive heuristics and organizational processes. Most important, we add heuristics to the increas-ingly infl uential cognitive paradigm in organization theory, strategy, and entrepreneurship.

ACKNOWLEDGEMENT

Support for this research was generously provided by the National Science Foundation (IOC Award #0323176), the Robert H. Smith School’s Business and International Education grant, and the Stanford Technology Ventures Program.

REFERENCES

Amit R, Schoemaker PJH. 1993. Strategic assets and orga-nizational rents. Strategic Management Journal 14(1): 33–46.

Anand BN, Khanna T. 2000. Do fi rms learn to create value? The case of alliances. Strategic Management Journal 21(3): 295–315.

Ancona DG, Caldwell DF. 1992. Bridging the boundary: external activity and performance in organizational teams. Administrative Science Quarterly 37(4): 634–665.

Argote L. 1999. Organizational Learning: Creat-ing, Retaining and Transferring Knowledge. Kluwer Academic Publishers: Norwell, MA.

Bantel K, Jackson S. 1989. Top management and innova-tions in banking: does the composition of the top team make a difference? Strategic Management Journal, Summer Special Issue 10: 107–124.

Barkema HG, Bell JH, Pennings JM. 1996. Foreign entry, cultural barriers, and learning. Strategic Management Journal 17(2): 151–166.

Barney J. 1991. Firm resources and sustained competitive advantage. Journal of Management 17(1): 99–120.

Bingham CB, Eisenhardt KM. 2007. Position, leverage and opportunity: a typology of strategic logics linking resources with competitive advantage. Managerial and Decision Economics (forthcoming).

Bingham CB, Eisenhardt KM, Davis JP. 2007. Opening the black box of organizational expertise: understanding what fi rms learn from their process experience and how that learning unfolds over time. University of Maryland working paper.



Blau PM. 1977. Inequality and Heterogeneity. Free Press: New York.

Braddeley A, Hitch GJ. 1974. Working memory. In Psychology of Learning and Motivation, Bower GA (ed). Academic Press: New York; 47–89.

Broadbent DE. 1958. Perception and Communication. Pergamon Press: Oxford, U.K.

Brockner J, Chen Y-R, Mannix EA, Leung K, Skarlicki DP. 2000. Culture and procedural fairness: when the effects of what you do depend on how you do it. Administrative Science Quarterly 45(1): 138–163.

Brown SL, Eisenhardt K. 1998. Competing on the Edge – Strategy as Structured Chaos. Harvard Business School Press: Boston, MA.

Brown SL, Eisenhardt KM. 1997. The art of continuous change: linking complexity theory and time-paced evolu-tion in relentlessly shifting organizations. Administrative Science Quarterly 42: 1–34.

Brush CG, Vanderwerf PA. 1992. A comparison of methods and sources for obtaining estimates of new venture per-formance. Journal of Business Venturing 7: 157–170.

Burgelman RA. 1996. A process model of strategic business exit: implications for an evolutionary theory of strategy. Strategic Management Journal, Summer Special Issue 17: 193–214.

Busenitz LW, Barney JB. 1997. Differences between entrepreneurs and managers in large organizations: biases and heuristics in strategic decision-making. Journal of Business Venturing 12(1): 9–30.

Carpenter M, Fredrickson J. 2001. Top management teams, global strategic posture, and the moderating role of uncertainty. Academy of Management Journal 44(3): 533–545.

Carpenter M, Pollock TG, Leary MM. 2003. Testing a model of reasoned risk-taking: governance, the experi-ence of principals and agents, and global strategy in high-technology IPO fi rms. Strategic Management Journal 24(9): 803–820.

Carpenter M, Sanders WG, Gregersen HB. 2001. Bundling human capital with organizational context: the impact of international assignment experience on multinational fi rm performance and pay. Academy of Management Journal 44(3): 493–511.

Chang S-J. 1995. International expansion strategy of Japanese fi rms: capability building through sequential entry. Academy of Management Journal 38(2): 383–407.

Chi MTH, Feltovich PJ, Glaser R. 1981. Categorization and representation of physics problems by experts and novices. Cognitive Science 5: 121–152.

Chung S. 2001. The learning curve and the yield factor: the case of Korea’s semiconductor industry. Applied Eco-nomics 33: 473–483.

Cohen WM, Levinthal DA. 1990. Absorptive capacity: a new perspective on learning and innovation. Administra-tive Science Quarterly 35(1): 128–152.

Collis DJ. 1994. How valuable are organizational capa-bilities? Strategic Management Journal, Winter Special Issue 15: 143–152.

Collis DJ, Montgomery CA. 1995. Competing on resources. Harvard Business Review 73(4): 118–128.

Cyert RM, March JG. 1963. A Behavioral Theory of the Firm. Prentice-Hall: Englewood Cliffs, NJ.

Daft RL, Weick KE. 1984. Toward a model of organiza-tions as interpretation systems. Academy of Management Review 9(2): 284–295.

Daily CM, Certo ST, Dalton DR. 2000. International experience in the executive suite: The path to prosperity? Strategic Management Journal 21(4): 515–523.

Darr E, Argote L, Epple D. 1995. The acquisition, transfer, and depreciation of knowledge in service organizations: productivity in franchises. Management Science 41(11): 1750–1762.

Davidson W. 1980. The location of foreign direct investment activity: country characteristics and experience effects. Journal of International Business Studies 11(1): 9–22.

Davis JP, Eisenhardt KM, Bingham CB. 2007. Complex-ity theory, market dynamism, and the strategy of simple rules. MIT working paper

Delios A, Beamish PW. 2001. Survival and profi tability: the roles of experience and intangible assets in foreign sub-sidiary performance. Academy of Management Journal 44(5): 1028–1038.

Dunning JH. 1980. Toward an eclectic theory of interna-tional production: some empirical tests. Journal of Inter-national Business Studies 11(1): 9–31.

Edelman G. 1992. Bright Air, Brilliant Fire: The Matter of Mind. Basic Books: New York.

Edmondson AC, McManus SE. 2007. Methodological fi t in management fi eld research. Academy of Management Review 32(4): 1155–1179.

Eisenhardt K. 1989. Making fast strategic decisions in high-velocity environments. Academy of Management Journal 32(3): 543–576.

Eisenhardt KM, Martin JA. 2000. Dynamic capabilities: what are they? Strategic Management Journal 21(10/11): 1105–1121.

Eisenhardt KM, Schoonhoven CB. 1990. Organizational growth: linking founding team, strategy, environment, and growth among U.S. semiconductor ventures. Admin-istrative Science Quarterly 35(3): 504–529.

Epple D, Argote L, Devadas R. 1991. Organizational learn-ing curves: a method for investigating intra-plant transfer of knowledge acquired through learning by doing. Orga-nization Science 2(1): 58–70.

Ethiraj SK, Kale P, Krishnan MS. 2005. Where do capabilities come from and how do they matter? A study in the software services industry. Strategic Manage-ment Journal 26(1): 25–45.

Eysenck MW, Keane MT. 1995. Cognitive Psychology: A Student’s Handbook (3rd edn). Erlbaum Taylor & Francis: Hove and New York.



Friedman A. 1979. Framing pictures: The role of knowledge in automatized encoding and memory for gist. Journal of Experimental Psychology 108: 316–355.

Galunic C, Eisenhardt KM. 2001. Architectural innovation and modular corporate forms. Academy of Management Journal 44(6): 1229–1250.

Gavetti G. 2005. Cognition and hierarchy: rethinking the microfoundations of capabilities’ development. Organi-zation Science 16(6): 599–617.

Gavetti G, Levinthal DA, Rivkin JW. 2005. Strategy making in novel and complex worlds: the power of analogy. Stra-tegic Management Journal 26(8): 691–712.

Geringer JM, Herbert L. 1990. Measuring performance of international joint ventures. Journal of International Business Studies 22(2): 249–264.

Gersick CJG. 1994. Pacing strategic change: The case of a new venture. Academy of Management Journal 37(1): 9–45.

Gibson CB, Zellmer-Bruhn ME. 2001. Metaphors and meaning: an intercultural analysis of the concept of teamwork. Administrative Science Quarterly 46(2): 274–306.

Gilbert CG. 2005. Unbundling the structure of inertia: resource versus routine rigidity. Academy of Manage-ment Journal 48(5): 741–763.

Gilbert CG. 2006. Change in the presence of residual fi t: Can competing frames coexist? Organization Science 17(1): 150–167.

Gitomer DH. 1988. Individual differences in technical trou-bleshooting. Human Performance 1: 111–131.

Golden BR. 1992. The past is the past – or is it? The use of retrospective accounts as indicators of past strategy. Academy of Management Journal 35(4): 848–860.

Graebner M. 2004. Momentum and serendipity: how acquired leaders create value in the integration of tech-nology fi rms. Strategic Management Journal 25(8–9): 751–777.

Grant RM. 1996. Prospering in dynamically-competitive environments: organizational capability as knowledge integration. Organization Science 7(4): 375–387.

Gruber H. 1994. The yield factor and the learning curve in semiconductor production. Applied Economics 26: 837–843.

Haleblian J, Finkelstein S. 1999. The infl uence of organiza-tional acquisition experience on acquisition performance: a behavioral learning perspective. Administrative Science Quarterly 44(1): 29–56.

Haunschild PR, Davis-Blake A, Fichman M. 1994. Mange-rial overcommitment in corporate acquisition process. Organization Science 5: 528–540.

Hayes JR. 1989. The Complete Problem Solver (2nd edn). Erlbaum: Hillsdale, NJ.

Hayward MLA. 2002. When do fi rms learn from their acquisition experience? Evidence from 1990–1995. Stra-tegic Management Journal 23(1): 21–39.

Helfat CE, Finkelstein S, Mitchell W, Peteraf MA, Singh H, Teece DJ, Winter S. 2007. Dynamic Capabilities: Under-standing Strategic Change in Organizations. Blackwell Publishing: Malden, MA.

Helfat CE, Lieberman MB. 2002. The birth of capabilities: market entry and the importance of pre-history. Indus-trial and Corporate Change 11(4): 725–760.

Helfat CE, Peteraf MA. 2003. The dynamic resource-based view: capability lifecycles. Strategic Management Journal 24(10): 997–1010.

Hofstede G. 1980. Culture’s Consequences. Sage: Beverly Hills, CA.

Hofstede G. 2001. Culture’s Consequences (2nd edn). Sage: Beverly Hills, CA.

Huber G. 1985. Temporal stability and response-order biases in participant descriptions of organizational decisions. Academy of Management Journal 28(4): 943–950.

Huber G, Power DJ. 1985. Retrospective reports of stra-tegic-level managers: guidelines for increasing their accuracy. Strategic Management Journal 6(2): 171–180.

Jick TD. 1979. Mixing qualitative and quantitative methods: triangulation in action. Administrative Science Quarterly 24(4): 602–611.

Johanson J, Vahlne JE. 1977. The internationalization process of the fi rm – a model of knowledge development and increasing foreign market commitments. Journal of International Business Studies 8(1): 23–32.

Kale P, Dyer JH, Singh H. 2002. Alliance capability, stock market response, and long term alliance success: the role of the alliance function. Strategic Management Journal 23(8): 747–767.

Kale P, Singh H. 2007. Building fi rm capabilities through learning: the role of the alliance learning process in alli-ance capability and fi rm-level alliance success Strategic Management Journal 28(10): 981–1000.

Kennedy P. 2003. A Guide To Econometrics (5th edn). MIT Press: Cambridge, MA.

Kirzner I. 1997. Entrepreneurial discovery and the com-petitive market process: an Austrian approach. Journal of Economic Literature XXXV: 60–85.

Kogut B, Singh H. 1988. The effect of national culture on the choice of entry mode. Journal of International Busi-ness Studies 19(3): 411–432.

Leonard-Barton D. 1992. Core capabilities and core rigidi-ties: a paradox in managing new product development. Strategic Management Journal, Summer Special Issue 13: 111–125.

Levin DZ. 2000. Organizational learning and the transfer of knowledge: an investigation of quality improvement. Organization Science 11(6): 630–647.

Levitt B, March JG. 1988. Organizational learning. American Review of Sociology 14: 319–340.

Makadok R. 2001. Toward a synthesis of the resource-based and dynamic-capability views of rent creation. Strategic Management Journal 22(5): 387–401.



March JG. 1991. Exploration and exploitation in organiza-tional learning. Organization Science 2(1): 71–87.

Maritan CA. 2001. Capital investment as investing in orga-nizational capabilities: an empirically grounded process model. Academy of Management Journal 44(4): 513–531.

Martin X, Salomon R. 2003. Tacitness, learning, and inter-national expansion: a study of foreign direct investment in a knowledge intensive industry. Organization Science 14(3): 297–311.

Martindale C (ed). 1995. Creativity and Connectionism. MIT Press: Cambridge, MA.

Michel JG, Hambrick DC. 1992. Diversifi cation posture and top management team characteristics. Academy of Management Journal 35: 9–37.

Miles RE, Snow CC. 1978. Organizational Strategy, Struc-ture, and Process. McGraw-Hill: New York.

Miller CC, Cardinal LB, Glick WH. 1997. Retrospective reports in organizational research: a reexamination of recent evidence. Academy of Management Journal 40(1): 189–204.

Miller GA. 1956. The magical number seven, plus or minus two: some limits on our capacity for information process-ing. Psychological Review 63: 81–97.

Miner AS, Bassoff P, Moorman C. 2001. Organizational improvisation and learning: a fi eld study. Administrative Science Quarterly 46: 304–337.

Newell A, Simon HA. 1972. Human Problem Solving. Prentice Hall: Englewood Cliffs, NJ.

Pentland BT. 1995. Grammatical models of organizational processes. Organization Science 6(5): 541–556.

Pentland BT, Rueter HH. 1994. Organizational routines as grammars of action. Administrative Science Quarterly 39(3): 484–510.

Peteraf MA. 1993. The cornerstones of competitive advan-tage: a resource-based view. Strategic Management Journal 14(3): 179–191.

Pisano GP, Bohmer RM, Edmondson AC. 2001. Organiza-tional differences in rates of learning: evidence from the adoption of minimally invasive cardiac surgery. Manage-ment Science 47(6): 752–768.

Porter ME. 1996. What is strategy? Harvard Business Review 74(6): 61–78.

Ramsey JB. 1969. Tests for specifi cation errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society 31(2): 350–371.

Rapping L. 1965. Learning and World War II production functions. The Review of Economics and Statistics 47(1): 81–86.

Ray G, Barney JB, Muhanna WA. 2004. Capabilities, business processes, and competitive advantage: choosing the dependent variable in empirical tests of the resource-based view. Strategic Management Journal 25(1): 23–37.

Rindova V, Kotha S. 2001. Continuous morphing: competing through dynamic capabilities, form, and

function. Academy of Management Journal 44(6): 1263–1280.

Rivkin JW. 2000. Imitation of complex strategies. Manage-ment Science 46(6): 824–844.

Roberts PW. 1999. Product innovation, product-market competition and persistent profi tability in the U.S. phar-maceutical industry. Strategic Management Journal 20(7): 655–670.

Root FR. 1994. Entry Strategies for International Markets. Jossey-Bass: San Francisco, CA.

Sambharya R. 1996. Foreign experience of top manage-ment teams and international diversifi cation strategies of U.S. multinational corporations. Strategic Management Journal 17(9): 739–746.

Sapienza H, Autio E, George G, Zahra S. 2006. A capabili-ties perspective on the effects of early internationalization on fi rm survival and growth. Academy of Management Review 31(4): 914–933.

Sastry MA. 1997. Problems and paradoxes in a model of punctuated organizational change. Administrative Science Quarterly 42(2): 237–275.

Schachter DL. 1996. Searching for Memory: The Brain, the Mind, and the Past. Basic Books: New York.

Schilling MA, Vidal P, Ployhart RE, Marangoni A. 2003. Learning by doing something else: variation, relatedness, and the learning curve. Management Science 49(1): 39–56.

Schwenk CR. 1985. The use of participant recollection in the modeling of organizational decision processes. Academy of Management Review 10(3): 496–503.

Shane S. 2000. Prior knowledge and the discovery of entrepreneurial opportunities. Organization Science 11(4): 448–469.

Siegler R, Deloache J, Eisenberg N. 2003. How Children Develop. Worth Publishers: New York.

Simon HA. 1973. The structure of ill-structured problems. Artifi cial Intelligence 4: 181–202.

Simonton DK. 1999. Creativity as blind variation and selec-tive retention: is the creative process Darwinian? Psycho-logical Inquiry 10(4): 309–328.

Stalk G, Evans P, Shulman LE. 1992. Competing on capabilities: the new rules of corporate strategy. Harvard Business Review 70(2): 57–69.

Szulanski G. 1996. Exploring internal stickiness: impedi-ments to the transfer of best practice within the fi rm. Strategic Management Journal, Winter Special Issue 17: 27–43.

Szulanski G, Jensen RJ. 2006. Presumptive adaptation and the effectiveness of knowledge transfer. Strategic Man-agement Journal 27(10): 937–957.

Teece DJ, Pisano G, Shuen A. 1997. Dynamic capabili-ties and strategic management. Strategic Management Journal 18(7): 509–533.

Thorndike EL. 1898. Animal intelligence: an experimental study of the associative processes in animals. The Psy-chological Review: Series of Monograph Supplements 2: 1–109.



Thurstone LL. 1919. The learning curve equation. Psycho-logical Monographs 26: 1–51.

Tripsas M, Gavetti G. 2000. Capabilities, cognition and inertia: evidence from digital imaging. Strategic Man-agement Journal 21(10/11): 1147–1162.

Vermeulen F, Barkema HG. 2001. Learning through acquisi-tions. Academy of Management Journal 44(3): 457–476.

Vermeulen F, Barkema HG. 2002. Pace, rhythm, and scope: process dependence in building a profi table multinational corporation. Strategic Management Journal 23(7): 637–653.

Von Hippel E. 1998. Economics of product development by users: the impact of ‘sticky’ local information. Manage-ment Science 44: 629–645.

Weber M. 1947. Max Weber: The Theory of Social and Economic Organization (Henderson AM, Parsons T, Trans.). Free Press: New York.

Wiersema MF, Bantel KA. 1992. Top management team demography and corporate strategic change. Academy of Management Journal 35(1): 91–121.

Wiggins RR, Ruefl i TW. 2005. Schumpeter’s ghost: is hypercompetition making the best of times shorter? Strategic Management Journal 26(10): 887–911.

Winter SG, Szulanski G. 2001. Replication as strategy. Organization Science 12(6): 730–743.

Wright TP. 1936. Factors affecting the costs of air-planes. Journal of the Aeronautical Sciences 3: 122–128.

Zahra SA, Dess GG. 2001. Entrepreneurship as a fi eld of research: encouraging dialogue and debate. Academy of Management Review 26(1): 8–10.

Zahra SA, Ireland RD, Hitt MA. 2000. International expan-sion by new venture fi rms: international diversity, mode of market entry, technological learning, and perfor-mance. Academy of Management Journal 43(5): 925–950.

Zollo M, Reuer JJ, Singh H. 2002. Interorganizational rou-tines and performance in strategic alliances. Organiza-tion Science 13(6): 701–713.

Zollo M, Singh H. 2004. Deliberate learning in corporate acquisitions: post-acquisition strategies and integration capability in U.S. bank mergers. Strategic Management Journal 25(13): 1233–1256.

Zollo M, Winter S. 2002. Deliberate learning and the evolu-tion of dynamic capabilities. Organization Science 13(3): 339–351.

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