Short- and Long-Term Performance Feedback and Absorptive Capacity

Abstract

Research on organizational learning from performance feedback has produced findings on how organizational change is influenced by performance relative to aspiration levels, but has focused on short-term goal variables. In this article, we examine how short- and long-term goals are related to short- and long-term actions, respectively. We do so by predicting changes in absorptive capacity from performance relative to aspiration levels, and by testing whether long-term goals mainly affect potential absorptive capacity, which has long-term effects, while short-term goals mainly affect the realized absorptive capacity, which has short-term effects. Using data from surveys of 252 decision makers representing 129 Israeli early-stage high-tech organizations, our analysis yields supportive empirical findings. The findings imply that performance relative to aspiration levels has effects on long-term strategic actions as well as short-term ones, and thus argue against strict myopia.

Keywords

performance feedback organizational learning knowledge management absorptive capacity

Organizations learn from performance feedback when managers evaluate the organizational performance relative to an aspiration level, search for solutions when the performance is low, and make changes when a promising solution to the performance problem is found (Cyert & March, 1963; Greve, 2003b; Shinkle, 2012). This learning process has been shown to affect organizational risk taking (Fiegenbaum & Thomas, 1988), R&D and innovations (Greve, 2003a), strategic positions (Park, 2007), accident prevention (Baum & Dahlin, 2007), and growth (Desai, 2008; Greve, 2008). An important issue in performance feedback research is the role of multiple goal variables as a mechanism for guiding attention and search efforts (Greve, 2008; Ocasio, 1997). This issue has gained prominence because organizations monitor a wide range of goals, making the ability to predict how they pay attention to goals and respond through changes a key frontier of research. In particular, the charge that organizations have a short-term focus and do not allocate sufficient attention to exploration of new activities is related to the choice of goal variables (Levinthal & March, 1993; March, 1991). The current study addresses this gap in performance feedback research by systematically distinguishing between short- and long-term performance goals and their impact on organizational change, as well as the environmental contingency of dynamism.

Organizations may pursue different goals either simultaneously or sequentially (Cyert & March, 1963; Greve, 2008; Ocasio, 1997), and may react differently to performance feedback on long- and short-term goals, but the difficulty in finding outcomes that reveal such differences has held back research. Absorptive capacity, which is the organizational ability to identify, assimilate, and exploit external knowledge that affects an organization’s strategy and performance (Cohen & Levinthal, 1989, 1990), is a promising context for examining how organizational actions are influenced by long- and short-term goals. Absorptive capacity, as an outcome, may solve this difficulty in finding differential outcomes by using the distinction between potential absorptive capacity, which consists of knowledge acquisition and assimilation capabilities and affects long-term performance, and realized absorptive capacity, which consists of transformation and exploitation capabilities and affects short-term performance (Zahra & George, 2002). These differ in time perspective, and if managers search by matching problems to solutions, as suggested by the mechanism of the search in the proximity of the problem (Cyert & March, 1963), we expect to find potential absorptive capacity to be associated with feedback on long-term goals, while realized absorptive capacity is associated with feedback on short-term goals. Thus, while most previous studies have explored the performance consequences of absorptive capacity, the current study focuses on its generation using performance feedback theory. Moreover, using absorptive capacity to test performance feedback theory also advances our understanding of the search process. While most previous performance feedback studies have neglected the process behind problemistic search and directly linked the motivation to execute and the implementation, examining change in absorptive capacity, which is by itself a type of search process, rather than focusing only on the final result, promotes our understanding of the search stage.

Absorptive capacity has been viewed as important in itself because dynamic environments and high competition put a premium on the organizational ability to update its knowledge stocks. More than two decades of research has advanced a wide range of findings on its construct and scope (e.g., Lane, Koka, & Pathak, 2006; Todorova & Durisin, 2007; Zahra & George, 2002), environmental and organizational antecedents (e.g., Hill & Rothaermel, 2003; Jansen, Van Den Bosch, & Volberda, 2005), and most prominently, its performance consequences (e.g., Lane et al., 2006; Lane, Salk, & Lyles, 2001; Tsai, 2001). However, performance feedback has not yet been shown to influence the generation of absorptive capacity, perhaps because absorptive capacity generation is often thought to be a result of external opportunities and internal capabilities (Cohen & Levinthal, 1990; Hill & Rothaermel, 2003; Jansen et al., 2005). Absorptive capacity research has thus focused on the ability to absorb and exploit external knowledge rather than the motivation to do so. While both opportunities and capabilities matter for organizational actions, the motivation to absorb and exploit external knowledge in the form of adverse performance feedback has an effect as well (Greve, 1998).

In addition, investigation of moderators of the performance feedback relations has been noted as a high priority task in research, with environmental dynamism especially promising because it may affect learning processes (Shinkle, 2012). Although theoretical work has pointed out that organizational mechanisms may lead managers to focus too much on exploitation relative to exploration, and hence not to have the optimal level of exploration (March, 1991), it may still be true that managers recognize the level of dynamism in the environment and make (partial) adjustments in their absorptive capacity buildup in response to performance feedback. Thus we also consider how environmental dynamism moderates the effect of performance feedback.

The empirical context of the study is the Israeli early-stage high-tech sector. Israel has a globally acknowledged status as an entrepreneurial center, creating more than 3,000 early-stage firms within a decade (Fiegenbaum, 2007). This emergence, which has made the Israeli high-tech sector a significant worldwide player, is especially suitable for investigating absorptive capacity because knowledge acquisition and exploitation, as represented by absorptive capacity, is thought to be central in entrepreneurial ventures (Shane, 2000; Venkataraman, 1997; Zahra & Hayton, 2008). To enrich our knowledge regarding this phenomenon and to ensure the suitability of our theoretical arguments to this context, we supplemented the main data collection through secondary data and surveys with in-depth interviews with decision makers in the industry. We give representative quotes from these interviews below.

In sum, the current study makes three advances on the theory of organizational learning from performance feedback (Cyert & March, 1963; Greve, 2003b): (a) in contrast to previous studies, we theoretically and empirically distinguish between short- and long-term performance feedback; (b) we explore those two types of performance feedback impact on an unexamined organizational action (absorptive capacity); and (c) we consider the environmental contingency effects. Furthermore, our use of survey measures in addition to secondary data is rare in performance feedback research and hence constitutes an important complement to earlier research. Our findings indicate a very close link between organizational performance and absorptive capacity, which in turn implies that organizational performance influences the knowledge acquired by the organization down the road. For theory this matters because it links two theories that are often studied separately—the behavioral theory of the firm and absorptive capacity theory. In practice it matters because our findings show that, paradoxically, high-performing firms do not act to extend their lead over lower-performing firms, thus leaving an opening for the low-performing firms to catch up.

Theory and Hypotheses

Performance Feedback

Pursuit of goals is a central activity in organizations, and it is often treated as a defining element of formal organizations (Scott, 2003: 26-27). A landmark treatment of how goals affect organizations is the behavioral theory of the firm, which argues that organizational decision makers pursue multiple goals that result from internal bargaining processes (Cyert & March, 1963: 26-43). When an organization falls below the aspiration level of a goal variable, decision makers initiate problemistic search for actions that can solve the problem (Cyert & March, 1963: 120-123) and accept the risks inherent in changing the organization (Fiegenbaum & Thomas, 1988). Conversely, performance above the aspiration level can lead the organization to avoid making changes as a result of complacency or inertia. Thus, goal dimensions and their aspiration levels motivate organizational search and strategic changes, possibly including absorptive capacity.

Having an aspiration level of a goal variable means that organizations evaluate their performance in relative, rather than absolute, terms just as individuals do (Festinger, 1954). In the organization and strategy literature this assumption has been the foundation for the development of several theories of strategic choice such as prospect theory (Kahneman & Tversky, 1979), variable risk preferences (March & Shapira, 1992), goal setting (Locke & Latham, 1990), and strategic groups (Cool & Schendel, 1988). This comparison process leads to a view of performance relative to the aspiration level as the driving force for feedback learning, where the aspiration level is derived from comparisons with competitors’ performance and own past performance (Greve, 2003b). Gaps in these comparisons trigger a search process that leads to knowledge acquisition and deployment, such as the discovery of actions that could solve the problem and close the performance gaps in the next time period.

Much work has taken the characterization of search processes as myopic (e.g., Cyert & March, 1963; Levinthal & March, 1993) to mean that the solutions are found inside the organization. This implicit view is becoming increasingly out of date as empirical work shows that search processes also influence adoption of innovations observed externally (Massini, Lewin, & Greve, 2005; Salge, 2011). Indeed, it makes sense for managers in low-performing organizations to search for solutions both internally and externally. Therefore, as the performance falls below the aspiration level, organizations invest in developing capabilities to acquire and assimilate external knowledge, with the intention of exploiting it to improve firm performance (Winter, 2000). Our field interviews corroborate this inference:

We are very aware of the firm performance both by examining its progress relative to past and to the competitors (that in some ways can be also in our portfolio firms). Once the performance is inferior we usually either try to explore new knowledge or to better implement the existing. (a venture capital partner who serves as board chairman)¹

Absorptive Capacity

Absorptive capacity research was launched two decades ago and has since undergone significant development in its definition and measurement (Lane et al., 2006). Zahra and George (2002: 186) defined it as “a set of organizational routines and processes by which firms acquire, assimilate, transform, and exploit knowledge to produce a dynamic organizational capability” and saw absorptive capacity theory as central for strategic management. Their model focuses on absorptive capacity as a dynamic capability and the distinctive role of its two subconstructs—potential and realized absorptive capacity. Acquisition and assimilation capabilities constitute potential absorptive capacity and affect long-term performance through gaining strategic flexibility (Matusik & Hill, 1998), while transformation and exploitation capabilities constitute realized absorptive capacity and affect short-term performance through gaining process and product innovation (Kogut & Zander, 1992). Furthermore, this theoretical framework predicts that potential absorptive capacity produces capabilities that in turn are commercialized through realized absorptive capacity (Zahra & George, 2002). Thus, firm absorptive capacity has effects through a sequence in which the first and thus long-term step is supported by potential absorptive capacity, while the knowledge acquired in this first step is put to practical use (and affects shorter-term processes) through application of the realized absorptive capacity. This insight is also found in the innovation literature, which distinguishes between activities that feed knowledge into the organization and activities that exploit this knowledge (Fiol, 1996). This two-stage approach with its categorization into potential and realized absorptive capacity is thus a good match with the distinction between long- and short-term goals that we seek to explore because it implies that potential absorptive capacity has an earlier position in the commercialization process than realized absorptive capacity.

There are alternative operationalizations of absorptive capacity. Usually, researchers have adopted a single measure, mainly representing the technology knowledge domain such as R&D intensity (Cohen & Levinthal, 1990; Stock, Greis, & Fischer, 2001; Tsai, 2001). Lane et al. (2006) argued that this technology focus and its single-measure approach was a major cause of reification in absorptive capacity research. This argument is consistent with earlier suggestions that knowledge pertaining to the organization’s competitive landscape is also important (Hill & Rothaermel, 2003). Thus, absorptive capacity requires that the organization acquire not only technology-related knowledge, but also business knowledge referring to its customers, suppliers, competitors, partners, and general industry information (Fiegenbaum, Hart, & Schendel, 1996; Porter, 1980). Indeed, Zahra and Hayton (2008) highlighted how absorptive capacity can serve as a knowledge creation mechanism also outside the technology domain, as one of our interviews also illustrates:

The technological aspects are usually covered very well by our R&D team; our main goal as the decision makers is to make sure that our competitive landscape is always scanned. In order to do it we obtain information about our competitors and our industry through various sources then we analyze and discuss it in our meetings in order to integrate it in our decisions. (a chairman of the board)

Performance Feedback Driving Absorptive Capacity

Environmental and organizational influences have been considered in absorptive capacity research and have led to findings on how prior knowledge base, ongoing investments in learning, and learning environments affect absorptive capacity (e.g., Cohen & Levinthal, 1989; Lane et al., 2006; Todorova & Durisin, 2007; Zahra & George, 2002). Prior research has thus underscored the firm’s ability to learn while latently assuming that it is motivated to learn. Nevertheless, there is heterogeneity in firms’ motivations to learn from their external environment. In particular, performance feedback can explain this motivation. Hence, the focus here is on how low performance, although a negative event, intensifies an organization’s efforts to obtain new knowledge through learning processes that increase absorptive capacity (Chaudhuri & Tabrizi, 1999; Kim, 1998). Indeed, performance feedback can be linked to the narrow conception of absorptive capacity as R&D investment because a reduction in R&D expenditures as performance increases relative to the aspiration level is a robust finding (Antonelli, 1989; Chen & Miller, 2007; Greve, 2003a). However, the broader view of absorptive capacity as also including nontechnological capacity has not been explained by performance relative to aspiration levels.

Examining performance feedback as a driver of absorptive capacity buildup is especially important because it enables the organization to redirect its absorptive capacity based on the “bottom line”: the deviation of the organization’s performance from its aspirations. Although managers may not have the foresight to identify environmental opportunities and threats that can be met with a buildup of absorptive capacity, low performance relative to the aspiration level (performance feedback) is a strong signal that will produce a response even in myopic managers and inert organizations. Thus, an organization’s pursuit of absorptive capacity can be modeled as an outcome of organizational learning from performance feedback (Cyert & March, 1963; Greve, 2003b).

In sum, organizations search for external knowledge on their customers, suppliers, competitors, partners, and the industry in general. This management of externally based knowledge leads organizations to develop their own absorptive capacity that aims to improve their performance (Lane et al., 2001). Based on organizational learning from performance feedback, organizations evaluate their current performance relative to some aspiration level that may be either their own historical values or those of their competitors. Then, based on the comparison process, investments in absorptive capacity will be greater when the organization’s performance is low relative to its aspiration level. The formal hypothesis states,

Hypothesis 1: Lower performance relative to the aspiration level leads to increases in absorptive capacity.

Short- and Long-Term Goals

In spite of the central role of performance feedback in organizations and management theories, the theory of organizational learning from performance feedback (Greve, 2003b) has addressed a narrow scope of performance goals—mainly short-term performance goals such as return on assets or equity (Gavetti, Greve, Levinthal, & Ocasio, 2012). This narrow focus goes against the original concept of the behavioral theory of the firm, which argues that organizations have multiple goals (Cyert & March, 1963) and does not claim that short-term goals necessarily have top priority. Later work has noted the importance of measurable goals over less measurable ones for influencing organizational actions (March, 1994), but this is not the same as claiming a short-term bias, as measurable long-term goals also exist. Rather, the main claim in the literature is that organizations are myopic in the sense of being overly responsive to short-term fluctuations in performance and making overly short-term responses to low performance (Levinthal & March, 1993). Thus, current theory would appear to suggest that any kind of goal, short or long term, would affect organizational actions, but the actions would typically have a short-term orientation. What this leaves out is the possibility that long-term responses do occur, but only in response to shortfalls in long-term goals.

A promising extension of the theory is thus to differentiate between measures representing short-term performance (e.g., return on assets) and those representing long-term performance (e.g., market value). Although the long-term perspective has received little research attention in the theory of organizational learning from performance feedback, support for this aspect can be found in work arguing that there is a clear need to consider the “future-depth” of the impact of performance feedback on strategic choice (Denrell, Fang, & Levinthal, 2004; Levinthal & March, 1993) and that time is an acknowledged reference point in the strategy literature (Mosakowski & Earley, 2000). Therefore, the learning process of performance feedback should take into account the “timing problem” of how to best link performance variables to organizational outcomes (Greve, 2003b: 64). Accordingly, while current research emphasizes short-term performance goals and outcomes, it is useful to consider also the long-term performance goals and outcomes.

Clues on how to best do this can be gained from the theory of absorptive capacity. Potential and realized absorptive capacities affect different aspects of performance and competitive advantage. These relations are likely to be understood by managers, who typically distinguish between activities that are investments and activities that give immediate returns, and hence managers can strategically choose what kind of absorptive capacity to build depending on the time horizon of the problem they are facing. Potential absorptive capacity influences mainly long-term performance; it aids organizations in adopting new external knowledge and therefore allows adaptation to the environment (Lei, Hitt, & Bettis, 1996). In contrast, realized absorptive capacity has more influence on short-term performance through process and product innovation (Kogut & Zander, 1992). Hence, the enhancement of potential and realized absorptive capacity enhances flexibility and innovation capabilities, which affect long- and short-term performance, respectively (Zahra & George, 2002). If these relations are known to managers or can be guessed by them, the actions with relatively greater impact over the long run should be chosen more often when the performance is low on a goal indicating long-term performance. This occurs because managerial attention shifts sequentially between goals depending on which goal is currently not being met (Cyert & March, 1963; Greve, 2008), as exemplified by our interviews:

When I see that our market value valuation (by the investors) does not show good enough progress I try to understand what we are doing wrong, usually by testing our basic assumptions. . . . For example, a year ago, I spent a whole month only to collect new knowledge about my competitors and the industry in order to analyze again what should be our competitive position. (a chief executive officer)

Therefore, we suggest that long- and short-term performance feedback lead to different search mechanisms. Managers who perform below the aspiration level on a short-term goal variable will seek to find solutions to this problem that take effect early on, and hence will take actions that they expect will produce results quickly. Managers who perform below the aspiration on a long-term goal will suspect that actions with short-term effects are not sufficient as solutions and will instead seek actions that match the time perspective of the goal. The theoretical argument leading to this differential outcome is search in the proximity of the problem. The behavioral theory of the firm implies that the reaction to the problem will be in the organizational unit most closely identified with the goal, and it will proceed on the basis of a simple model of causality (Cyert & March, 1963: 121-122). Namely, the organization will consider solutions near the problem symptom or the current activity and its association with a particular goal (March, 1994). Consequently, because potential absorptive capacity consists of capabilities with more impact on long-term performance, falling below aspiration levels of long-term performance is expected to result in proximate search that mainly affects potential absorptive capacity. Conversely, since realized absorptive capacity has more impact on short-term performance, falling below the aspiration levels of short-term performance triggers proximate search that mainly affects realized absorptive capacity.² Consistent with this view, Baum and Dahlin (2007) examined proximate versus distant learning and found that different performance feedback (nearer to and farther from the aspiration level) initiated different search mechanisms, but they did not extend this idea to qualitatively different goals; we do here. Our formal hypotheses are as follows:

Hypothesis 2a: Lower long-term performance relative to aspiration results in greater impact on potential than on realized absorptive capacity.

Hypothesis 2b: Lower short-term performance relative to aspiration results in greater impact on realized than on potential absorptive capacity.

Environmental Dynamism

Strategic management emphasizes the fit of the organization’s internal resources and capabilities with the environmental contingencies as a major factor affecting organizational performance and even survival (Ginsberg & Venkatraman, 1985; Miles & Snow, 1994). To achieve this fit, organizations should develop resources and capabilities that trace the evolution of the new environmental knowledge (McGahan & Porter, 1997; Van den Bosch, Volberda, & De Boer, 1999). Dess and Beard (1984) suggested three environmental dimensions—dynamism, heterogeneity, and munificence—that have been used widely (e.g., Sirmon, Hitt, Arregle, & Campbell, 2010). We focus on dynamism because of its importance for entrepreneurial ventures. Dynamism is defined as the degree of instability of the environment, the rate of change, and the level of turbulence (Song, Droge, Hanvanich, & Calantone, 2005). The emphasis is on change that represents qualitative differences in competitive conditions rather than just fluctuations such as price, thus dynamic environments are characterized by changes in technologies, variations in customer preferences, and fluctuations in products demand or supply of materials. Early-stage and high-tech organizations face environments that are generally dynamic, but with levels of dynamism that vary across sectors. Because environmental dynamism is noticeable for managers through information collection and analysis (Hough & White, 2003), and affect organizations both directly through producing surprises and indirectly through making planning and decision making more difficult, it is reasonable to posit that environmental dynamism will moderate the effect of performance feedback on absorptive capacity.

In highly dynamic environments, customers’ needs change rapidly, new customers enter the market, and technologies evolve quickly and create new opportunities for firms (Tallon, 2008). Organizations that are facing environmental dynamism should be quick to adjust their products, services, and markets to avoid obsolescence (Jansen, Van Den Bosch, & Volberda, 2006). Thus, they need strategic flexibility, which in turn is supported by potential absorptive capacity because it helps the organization pick up early signals of changes and acquire the necessary knowledge to adapt (Zahra & George, 2002). Indeed, researchers have emphasized that developing potential absorptive capacities in a dynamic environment results in better performance (Lumpkin & Dess, 2001; Zahra & Bogner, 1999).

Conversely, based on the changes in the valued products and service in dynamic environments, promoting realized absorptive capacity under such conditions may actually decrease the organization performance. Organizations that develop their realized absorptive capacity in such an environment focus on shaping and improving current routines that might not be valuable for the changeable and turbulent external environment (Eisenhardt & Martin, 2000; Zahra & George, 2002). Namely, developing those realized capabilities sharpen the organizational capabilities that are valuable in a specific environment instead of developing capabilities that help adaptation to new environmental conditions, and thus the organization runs the risk of repeatedly seeking to catch up with the environment (Sorensen & Stuart, 2000). Therefore, although realized absorptive capacity leads to an increasing rate of innovation, these products and services may rapidly become obsolete relative to current environmental demands (Jansen et al., 2006). Those different effects of environmental dynamism on potential and realized absorptive capacity are also reflected in our interviews:

There are times that our industry changes rapidly; it usually follows significant change in the technology. In such circumstances I demand from my directors to increase discernibly the effort we invest in exploring new knowledge from our customers, suppliers and competitors. I do it because I know that it will lead us to brainstorming and to new ways of actions for the future of the firm. . . . In more stable external situations I request [from my directors] mainly to advise how to refine our current strategy. (a chairman of the board)

However, again these arguments presuppose motivation to invest in the development of potential and realized absorptive capacity. What a performance feedback view adds is that the performance below the aspiration level causes problemistic search, which in turn influences the absorptive capacity buildup. This suggests a moderation effect of dynamism rather than a direct one. Namely, we suggest a contingent role of environmental dynamism on the impact of performance feedback processes on the development of potential and realized absorptive capacity. Therefore, organizations that operate in dynamic environments will be more sensitive to the performance relative to the aspiration level on potential absorptive capacity in contrast with realized absorptive capacity. Again we assume that organizations act on a simple model of causality, but we also assume that experience and reasoning lead the decision makers to (on average) correctly infer the type of search behavior most suitable for their environment (Cyert & March, 1963: 121-122). Because organizations with well-developed potential absorptive capacity improve their performance in dynamic environments, falling below the aspiration level is expected to result in intensive search for improvements to the potential absorptive capacity to close the performance gap. Conversely, because the realized absorptive capacity may become irrelevant when the knowledge it is built on becomes obsolete in a dynamic environment, search to increase the realized absorptive capacity is reduced to focus attention on the more relevant potential absorptive capacity. Therefore, environmental dynamism increases the effect of performance below the aspiration level on potential absorptive capacity and decreases the effect on realized absorptive capacity. Our formal hypotheses are as follows:

Hypothesis 3a: Environmental dynamism increases the impact of performance relative to aspirations on potential absorptive capacity.

Hypothesis 3b: Environmental dynamism decreases the impact of performance relative to aspirations on realized absorptive capacity.

Method

Sample and Data Collection

During the 1990s, the state of Israel established its high-tech sector initiated by the decision to invest in and develop two complementary industries: technological incubators and venture capital (VC) funds (Fiegenbaum, 2007). The state funded 26 technological incubators across the country with the role of bridging entrepreneurial preliminary ideas to VC investments. There was no existing VC industry, so the state invested $100 million in “Yozma,” the first governmental VC. From its establishment until the data collection in 2007, more than 80 private VC funds have been operating in Israel, with total accumulated capital raised of about $10 billion, and investments have been made in more than 1,000 Israeli start-up firms. Many of these start-ups went through successful IPOs and acquisitions. Approximately 70 Israeli firms are traded on the NASDAQ stock exchange, and about 30 are traded on various European stock exchanges (Israel Venture Association, 2007). We chose to focus our sample on VC-funded early-stage high-tech firms. Firms that were successful in the past in terms of either IPOs or acquisition came mainly from this population, with only a small proportion coming from other sources such as angels and corporate entrepreneurship (D&A High Tech Information, 2007). There is ample research on VC-backed firms attributing their success to factors such as their selection process, expertise in strategy formulation, and ability to implement strategy by using more resources relative to nonbacked VC firms (Gompers, Kovner, & Lerner, 2009).

Our data collection procedure required the management’s willingness to participate in our research. We contacted the chairman of the Israel Venture Association (IVA), which represents the VC funds industry and their invested early-stage firms. With her approval, we presented our research to the IVA executive committee, which is composed of IVA’s 12 committees’ chairmen, representing varied VC funds in terms of their focus of investment and the size of the invested capital. As a result, the IVA committees’ chairmen wrote a letter to their VC-backed firms asking them to cooperate with us in our research. This cooperation gave us access to approximately 180 out of 470 operative early-stage backed VC firms (IVA, 2007) as our research sample. The data collection was done through a customized website that provided an overview of the research, introduced the survey questions in random mode, and compensated for the invested time (more than 180 items were collected) by offering free new software donated by one of the early-stage firms.

Studying such a focused population increases the reliability of the findings at potential cost of generalizability. As a result, the findings need verification in other contexts in the future. Our approach nevertheless has significant strengths: First, we had the opportunity to thoroughly learn about early-stage firms, in particular their performance feedback and knowledge management processes. Second, the close-knit Israeli early-stage business network and the familiarity of its leaders gave us good access to the required data through interviews, surveys, and financial statements. Third, this context is especially suitable for investigating absorptive capacity, which is thought to be central in entrepreneurial ventures. Fourth, culture and other country-specific differences are kept constant, whereas in-sample variation in these might have masked significant effects. Finally, this context has received minimal research attention, although it gives rich opportunities for examining entrepreneurial ventures in knowledge-intensive sectors.

To construct the survey,³ we reviewed the relevant literature and generated a pool of scales for each construct. Then, we conducted 20 in-depth interviews with managers, directors, venture capitalists, entrepreneurs, and government regulators. This step was used to help us better understand this early-stage high-tech industry in terms of its components, performance feedback mechanisms, and knowledge processes. We also examine this industry suitability to the research constructs and especially its common use of performance feedback measures and the manner of potential and realized absorptive capacity in those firms and to pretest the questionnaire and ask for further recommendations and improvements in selecting the survey in regard to its measures. This step was used to improve the choice of items and constructs in the final questionnaire. To ensure confidentiality, we agreed not to reveal the managers’ names and to present the research results only in an aggregated form. Due to the important role of the board of directors in early-stage organizations in formulating and implementing the new organization strategy (Daily, McDougall, Covin, & Dalton, 2002), we took strict care that for each firm one of the responders was the chairman of the board.

To ensure the causality of the data (i.e., performance feedback in time t will influence absorptive capacity in time t+1), we collected the data in three consecutive years. We needed the first of these years to assess the historical aspiration level, the second for the other independent variables, and the third for the outcome variables. Thus, although our data are a cross-section, they were obtained through three survey waves to get the correct temporal order of aspiration levels (first wave), performance (second wave), and behaviors (third wave). This procedure also means that the regressions have different surveys contributing items on the independent and dependent variables, making common-method bias unlikely. We include in our final sample only the firms that survived and responded at all three time points by the same respondents. A total of 252 complete responses were obtained, representing 129 early-stage firms (123 firms with two repeated respondents and 6 with only one). This survey response rate of 71.6% comprises 27.4% of the firms in all of the early-stage firms population (IVA, 2007). The characteristics of our sample are typical of early-stage firms (Shane, 2003), with a mean firm age of 4.1 years (SD = 1.0), 10.4 full-time employees on average (SD = 3.3), and a mean market value based on last investment of $6.4 million (SD = 2.6).

We also performed four statistical tests to ensure against biases. For nonresponse bias, we examined differences between responder and nonresponder firms in the entire population of the Israeli early-stage backed VC firms by using IVA databases. Results of t tests showed no statistically significant differences of firm age, number of full-time employees, and market value. We also compared early and late responders in terms of demographic characteristics and model variables. These comparisons did not reveal any statistically significant differences (p < .01), indicating that the sample does not suffer from response biases. To test for responders’ agreement bias on the subjective measures we calculated reliability statistics (Cronbach’s α) and interrater agreement scores (Rwg) for each subjective study variable (James, Demaree, & Wolf, 1984), and give these for each variable. Both indices have the same cutoff point of .7, above which they are considered to be acceptable (Lance, Butts, & Michels, 2006). In addition, examination of intraclass correlations revealed a strong level of interrater reliability: Correlations were consistently statistically significant at .001 levels.

Measures

Dependent measures

Given the very few studies that have operationalized absorptive capacity through its processes, we adopted the procedure used by Jansen et al. (2005). They developed and verified the validity of a questionnaire with 21 items using 7-point scales that included reverse-coded questions. This measure’s distinctiveness as compared with R&D intensity is in its ability to acquire data across a broad range of competitive activities and to classify the data to potential and realized absorptive capacity. This categorization allowed us to examine our suggested differences in feedback effects on potential and realized absorptive capacity. Details on the questions are given in Appendix A. For potential absorptive capacity, we used six items that measured the effort spent in knowledge acquisition from competitive sources such as firms belonging to the business network, employees and firm facilities, customers, and third parties such as accountants and consultants (reliabilities = .95, Rwg = .96), and three items that estimated the ability to analyze and understand this new external knowledge by identifying new opportunities to serve the firm’s clients and interpreting changes in market demands as presented in the assimilation process (reliabilities = .92, Rwg = .95). For realized absorptive capacity, we used six items that assessed the frequency of meetings and their content in terms of discussions and considerations of the usefulness of new external knowledge referring to new products and services and its consequences as suggested in the transformation construct (reliabilities = .94, Rwg = .92), and six items referring to the exploitation of the absorbed knowledge in terms of division of roles and responsibilities of managers that direct the suggested course of actions as well as the consideration of implementing the new knowledge (reliabilities = .93, Rwg = .85).

For convergent validity, we conducted confirmatory factor analysis of the items pertaining to the potential and realized absorptive capacity. We took strict care that each of the 21 items loaded on its relevant factor. Results indicate that a two-factor model representing potential and realized absorptive capacity fits the data extremely well (χ²/df = 2.48, GFI = .89, CFI =.91). Items loading were statistically significant (p < .001), providing evidence for convergent validity. The possibility that all the items converged on one common factor was also examined and rejected (Δχ² = 1097.00, p < .001). Accordingly, potential and realized absorptive capacity are not only theoretically distinct concepts but also empirically distinguishable through a survey methodology. Repeating these validation steps on a measure that has already been validated seemed important to us because we are using it in a very different context: small entrepreneurial firms rather than a large firm. These results increase our confidence in the broad applicability of the instrument, but it is still useful to keep in mind that these firms are sufficiently small that absorptive capacity measures refer to knowledge investments made by significantly fewer individuals than in medium or large firms.

Independent measures

For our performance feedback variables, we considered performance over the short and long term, as well as two referential comparisons: the organization’s history (through objective data) and its social comparison group (through surveys). We examined whether this theoretical separation of historical and social comparisons also existed in our population. From the interview stage we found that in addition to the common use of historical comparison through financial statement analysis, the organizations in our population are also comparing their performance relative to the competitors, as demonstrated by one of our informants:

I examine the firm progress by analyzing the improvement from the last financial statement and relative to my stated milestones every quarter before the meeting with the VC representatives. I also compare my performance relative to the rivals, both in my industry and the firms that are in my VC portfolio . . . from my point of view they are also competitors. Indeed, we aren’t always competing on the same customers but we are competing on the same resources of the VC. Accordingly, I know that the VC representatives will compare my firm with the other firms in their portfolio. (a chief executive officer)

Hence, short- and long-term performance variables were measured using four different types of data, which helps avoid bias and generalize the findings: data collection through objective data and survey, and aspirations levels represented by historical and social comparisons (Greve, 2003b). That way, we also contribute to performance feedback literature since we asked, rather than assumed, the uses and decisions about aspirations and goals, while the majority of performance feedback studies are based only on secondary data analysis. The few studies using survey measures of aspirations has been identified as a shortcoming of current research on performance feedback (Shinkle, 2012), so this aspect of our methodology is important. The contribution goes beyond methodology because the theory indicates that the motivation to initiate search depends mainly on the perceived performance feedback.

For the short-term performance goals we used the firm’s sales as objective data and the return on assets (ROA) as survey data, as these measures are often used to operationalize short-term performance (Fiegenbaum & Thomas, 1988; Greve, 2008). ROA equals the operating profits divided by the total assets of the firm. In preliminary analysis, we also considered the use of return on equity, which represents the viewpoint of the shareholders. However, we found that the two ratios are highly correlated in our data (0.89, p < .001), and hence we used ROA for our empirical analyses for comparability with previous research.

For the long-term performance goals we used two measures: market value (MV) as objective data and chief executive officer (CEO) performance as survey data. MV is an objective measure that reflects the market’s expectations of future cash flows to shareholders. Organizations referring to this measure are more concerned with affecting the long-term impact of strategic choices (Amit & Zott, 2001) because it indicates learning processes that take into account cognitive representations (Denrell et al., 2004; Gavetti & Levinthal, 2000). Our sampled firms are not traded in stock markets, and hence we calculated this measurement based on the last investment (Certo, Daily, & Dalton, 2003). Another indicator for long-term performance feedback is CEO performance in management, marketing, and technology by using a three-item scale (reliabilities: .94, Rwg = .93) as suggested in the literature (Shen, 2003).

We chose these four variables partly for theoretical reasons and partly because they were reported as important by our informants. ROA and sales (or market share) are frequently used measures in performance feedback research, as they are theoretically important high-level goals that managers will often prioritize ahead of other goals (e.g., Greve, 2008). It also helps accumulation of evidence that many studies use the same measures. MV is theoretically a long-term goal comprising future returns and was emphasized in our interviews as an important performance measure by the VCs for early-stage firms, while CEO performance was mainly noted as an important long-term evaluation metric by our informants. For example,

We are examining the performance in our portfolio firms, in which we are usually serving as the chairmen of the board, by using several performance measures. The most important to us is the market value of the venture, as reflected by the investments, since our goal is to enhance it and commonly to sell our holdings in profit for our investors. We also monitor continually the CEO management and capabilities. Our experience is that shortcoming in this measure is an indication for future decrease in the venture valuation. . . . We also examine those traditional performance measures [returns and sale], which indicate for us if the venture and its management team achieved our goals in the last quarter. (a board chairman of a VC fund who serves as board chairman in several portfolio firms)

Environmental dynamism was based on previous literature (Jansen et al., 2006) and measured by a five-item scale including a reverse-coded question (reliabilities: .85, Rwg = .92). Details on the questions are given in Appendix B. Respondents were asked to assess the changes in the environment for such important aspects as market structure, clients’ requests, and the rate of change to indicate the instability of the external environment.

Control measures

Two levels of control variables, firm and industry, were used in the analyses since they could be expected to be related to both the independent and dependent variables. At the firm level we controlled for age, measured as number of years since the firm’s foundation, and size, measured as the number of employees, following the entrepreneurship literature (Shane, 2003). Moreover, since the theory predicts that potential absorptive capacity sequentially influences the development of realized absorptive capacity (Zahra & George, 2002), we also assessed regression models with potential absorptive capacity as a control on realized absorptive capacity and vice versa to examine the possibility of cross-effects. At the industry level we controlled for the industry of the firms using five dummy variables.⁴ We included these variables as control variables to ensure consistent estimates free of spurious firm- or industry-level effects in all our regression models.

Analysis

To empirically examine the hypotheses, we calculated the performance feedback measures and estimated their impact on potential and realized absorptive capacity. The performance feedback, defined as performance relative to aspirations, is calculated in relative terms to avoid confounding due to a possible size effect. It is defined as,

{PF}_{i, j, t} = ({CP}_{i, t} - {AL}_{i, j, t}) / {AL}_{i, j, t}

Performance feedback (PF_i,j,t) measures the distance between current performance (CP_i,t) and the aspiration level (AL_i,j,t) divided by the aspiration level, all at time period t. The subscripts refer to the ith referential goal (short vs. long term) and the jth referential comparison (historical by comparison to previous-year performance through objective data vs. social by comparison to the perceived performance gap from the competitors through surveys). Our dependent variable of absorptive capacity (ACAP) is also defined in relative terms, as follows:

({ACAP}_{t + 1} - {ACAP}_{t}) / {ACAP}_{t}

We use linear regression analysis to model the effects of relative performance feedback, environmental dynamism, and the control variables on relative change in absorptive capacity.

Results

Table 1 presents descriptive statistics and correlations for the study variables. The performance feedback variables were significantly correlated, indicating that the feedback processes have similar effects, although to lesser degrees, for all performance variables. However, the correlations are not at levels that would affect the fitted model, and the potential for bias has been further reduced by the relative-centralized definition of performance feedback (Cortina, 1993). We also analyzed the data using separate models for each performance feedback measure and short-term and long-term measures as pairs, and got similar results.

Table 1

Descriptive Statistics and Zero-Order Correlations

	M	SD	1	2	3	4	5	6	7	8
1. Sale	0.37	0.51
2. ROA	5.19	1.23	.67
3. MV	0.71	0.75	.25	.21
4. CEO	5.29	0.85	.29	.24	.39
5. Environmental dynamism	5.67	0.51	−.33	−.14	−.37	−.74
6. Firm age	4.06	0.99	−.02	.02	−.23	−.07	.13
7. Employees	10.38	3.27	.17	.14	−.17	.07	−.03	.53
8. Potential absorptive capacity	0.31	0.17	−.21	−.03	−.25	−.57	.7	.18	−.02
9. Realized absorptive capacity	0.27	0.16	−.63	−.61	−.25	−.43	.5	.15	−.01	.39

Note: n = 129. Correlation coefficients greater than .21 are significant at p < .05. Performance feedback measures (sales, return on assets [ROA], market value [MV], and CEO) and absorptive capacity measures (realized and potential) are modeled as relative measures as mentioned in methodology section.

Table 2 summarizes the results for the regression analyses for the four performance feedback measures on each of the two dependent variables, potential absorptive capacity (Models 1–3) and realized absorptive capacity (Models 4–6), as shown in the columns of the table. For each absorptive capacity type, we have three models: Models 1 and 4 serve as the baselines that contain the control variables, Models 2 and 5 contain the performance feedback measures, and Models 3 and 6 also contain the cross-effects of potential and realized absorptive capacity since Zahra and George’s (2002) theoretical framework predicts that potential absorptive capacity sequentially affects realized absorptive capacity. The six models allow us to analyze separately short- and long-term performance feedback effects on potential and realized absorptive capacity and compare the findings.

Table 2

The Impact of Performance Feedback on Potential and Realized Absorptive Capacity

	Potential Absorptive Capacity			Realized Absorptive Capacity
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Variables	Control	All	All + Realized	Control	All	All + Potential
Intercept	0.22**	0.31***	0.21**	0.23**	0.28***	0.20**
	(0.07)	(0.01)	(0.03)	(0.07)	(0.01)	(0.02)
Age	0.05**	0.03	0.02	0.03*	0	0
	(0.01)	(0.03)	(0.03)	(0.01)	(0.02)	(0.02)
Employees	−0.01	0	0	0	0	0
	(0)	(0)	(0)	(0.04)	(0)	(0)
Industry	None	None	None	None	None	None
Potential absorptive capacity						0.24**
						(0.07)
Realized absorptive capacity			0.34**
			(0.1)
Sales		−0.06	−0.02		−0.10**	−0.09**
		(0.03)	(0.03)		(0.03)	(0.02)
ROA		−0.02	−0.03		−0.04***	−0.06***
		(0.02)	(0.03)		(0.01)	(0.01)
MV		−0.08**	−0.07**		−0.01	−0.01
		(0.02)	(0.02)		(0.02)	(0.02)
CEO		−0.11***	−0.10***		−0.05**	−0.03*
		(0.02)	(0.02)		(0.01)	(0.01)
Adj. R²	.02	.38	.42	0	.51	.56

Note: N = 129. Standard errors are in parentheses. Performance feedback measures (sales, return on assets [ROA], market value [MV], and CEO) and absorptive capacity measures (realized and potential) are modeled as relative measures as mentioned in methodology section.

p < .05. **p < .01. ***p < .001.

H1 focuses on the negative relation between performance feedback and absorptive capacity enhancement with no distinction between short- and long-term performance. Specifically, the hypothesis states that the lower the performances relative to aspiration, the higher the investment in absorptive capacity. The regression results support our hypothesis, since five out of eight performance feedback measures have a significantly negative impact on absorptive capacity enhancement (MV and CEO performance on potential absorptive capacity; sales, ROA, and CEO performance on realized absorptive capacity). Looking forward to H2a and H2b, we also note that there is a difference in the supporting of H1 depending on whether the time perspective of the performance variable and the absorptive capacity type match or not. For those that match (MV and CEO performance on potential absorptive capacity, sales and ROA on realized absorptive capacity), the support for H1 is complete with four out of four hypotheses tests supported. For those that do not match, the support is weak, with only one out of four in support (CEO performance on realized absorptive capacity).

H2a and H2b distinguish between the differential impacts of long- versus short-term performance on potential and realized absorptive capacity. As noted already, the patterns of significance levels suggest support. However, since the hypothesis states that there will be a greater effect on the matched goals, it is also tested by a comparison of effects in the case of dual impact. For H2a, both long-term measures (MV p < .01; CEO performance p < .001) have a significant impact on potential absorptive capacity while only the CEO performance has a significant impact on realized absorptive capacity (dual impact). The statistical test for mean differences of the coefficients on CEO performance reveals that the impact on potential absorptive capacity is not only more than three times that of realized absorptive capacity but also statistically significant (p < .05).⁵ Since the other long-term measure, MV, was not significant for realized absorptive capacity, the findings suggest that long-term performance feedback, reflected by the two measures of MV and CEO performance, has a greater impact on potential than on realized absorptive capacity, thus supporting H2a.

For H2b, again Table 2 provides the summary findings showing that short-term performance feedback has a greater impact on realized than on potential absorptive capacity. In terms of feedback extent, while the two short-term performance feedback measures were found to be significantly related to realized absorptive capacity (sales p < .001; ROA p < .05), none were found to significantly influence potential absorptive capacity. Those results support H2b.

The control variables in our models include six industry categories (not shown in table) and three firm-level measures. The industry control variables were not significant in any of the regressions, suggesting similar effects in all industries. The control for cross-effects of potential and realized absorptive capacity was highly significant (p < .01), giving evidence of their coevolutionary effects. Regarding the other firm-level control variables, employees was not significant while age was significant (p < .01 on potential, p < .05 on realized) in the control models, consistent with the literature on the development of absorptive capacity over time. The relatively weak effects of control variables may be attributed to low variation in size as well as generally high levels of absorptive capacity in the industries under investigation. The importance of performance feedback in explaining the enhancement of absorptive capacity was emphasized again by the raise of the adjusted R-squares from the control models (2.2% and 0%, respectively) to the models that include performance feedback measures (38% and 51%).

Table 3 summarizes the results of H3a and H3b, dealing with the interaction effects of environmental dynamism and performance feedback on potential and realized absorptive capacity. Models 1 and 3 contain the performance feedback measures, and Models 2 and 4 also contain the cross-effects of potential and realized absorptive capacity. The findings reveal that in the model without cross-effect (Model 1), four out of four interactions between performance feedback measures and environmental dynamism positively affect potential absorptive capacity and in the model with cross-effect (Model 2) the support is only marginally weaker, with three of the four, thus the findings support H3a. However, only one out of the four interactions were found to negatively affect realized absorptive capacity (Models 3 and 4) and hence H3b has limited support. Yet the results on realized absorptive capacity provide further support for earlier work showing that well-developed realized absorptive capacity does not necessarily increase the performance in dynamic environments (Jansen et al., 2005). Namely, since enhancing realized absorptive capacity is not an effective form of adaptation to environmental dynamism, the search in this domain will not be influenced. To summarize the interaction effects, combining our findings with the findings of Jansen et al. (2005) not only provides empirical evidence of search in the proximity of the problem mechanism, but also enables us to close the performance feedback loop, as the findings show that organizations focus on learning from performance feedback that in turn improves their performance. Namely, in dynamic environments the impact of learning from performance feedback is greater on potential absorptive capacity (the current study); subsequently the increased potential absorptive capacity in dynamic environments enhances the performance (Jansen et al., 2005).

Table 3

Interaction Models of Performance Feedback and Environmental Dynamism on Potential and Realized Absorptive Capacity

	Potential Absorptive Capacity		Realized Absorptive Capacity
	Model 1	Model 2	Model 3	Model 4
Variables	All	All + Realized	All	All + Potential
Intercept	−0.36^†	−0.27	−0.44*	−0.38^†
	(0.21)	(0.21)	(0.19)	(0.20)
Age	0.01	0.01	0	0
	(0.01)	(0.01)	(0.01)	(0.02)
Employees	0	0	0	0
	(0)	(0)	(0)	(0)
Industry	None	None	None	None
Potential absorptive capacity				0.18^†
				(0.09)
Realized absorptive capacity		0.19^†
		(0.09)
Sales	−0.29	−0.33	−0.21	−0.26
	(0.44)	(0.43)	(0.44)	(0.43)
ROA	−0.33^†	−0.32^†	−0.04	−0.01
	(0.17)	(0.17)	(0.17)	(0.17)
MV	−0.39	−0.51^†	−0.61*	−0.68*
	(0.30)	(0.31)	(0.30)	(0.30)
CEO	−0.71***	−0.66***	−0.25	−0.12
	(0.18)	(0.19)	(0.18)	(0.19)
Dynamism	0.11**	0.08*	0.13***	0.11**
	(0.04)	(0.03)	(0.03)	(0.04)
Sales × dynamism	−0.11^†	−0.07	0.02	0.03
	(0.07)	(0.08)	(0.08)	(0.08)
ROA × dynamism	−0.06*	−0.06*	0	0
	(0.03)	(0.03)	(0.03)	(0.03)
MV × dynamism	−0.07^†	−0.09^†	0.11*	0.12*
	(0.05)	(0.05)	(0.05)	(0.05)
CEO × dynamism	−0.13***	−0.12***	0.04	0.02
	(0.03)	(0.03)	(0.03)	(0.03)
Adj. R²	.60	.61	.58	.59

†

p < .1. *p < .05. **p < .01. ***p < .001.

Discussion

Performance Feedback

The empirical findings reported here support our three hypotheses. First, when organizations’ performance relative to aspirations decreases, organizations increase their absorptive capacity. Second, long-term performance has greater effect on potential absorptive capacity, while short-term performance has greater effect on realized absorptive capacity. Third, environmental dynamism positively interacts with performance feedback in its impact on potential absorptive capacity. The finding that low performance relative to an aspiration level leads to organizational change is in line with most empirical studies that have explored organizational learning from performance feedback (Gavetti et al., 2012; Greve, 2003b), but we also present theory and evidence that make novel contributions to this research.

This work is one of few studies that have examined multiple goals at once (e.g., Greve, 2008; Rowley, Greve, Rao, Baum, & Shipilov, 2005). This is an important arena of research because it is not realistic to assume that organizational action is guided by one goal alone, even such an important one as ROA. Among work showing effects of multiple goals, our novel contribution is to also show effects on multiple outcomes, and to demonstrate a correspondence between the type of goal and the type of outcome. Short-term goals affect actions that have a relatively short-term effect, in our case realized absorptive capacity, while long-term goals affect actions that have a longer-term effect, in our case potential absorptive capacity. These findings suggest that relevance heuristic is at work when managers respond to low performance. They also change the meaning of myopic search. Whereas myopic search in the behavioral theory of the firm has often been interpreted as search with a short-term perspective, we reinforce the original idea of myopic as meaning actions that are seen as relevant to the problem at hand (Cyert & March, 1963). By doing so we increase the relevance of performance feedback theory for strategic management research because strategy is about taking long-term actions (Lubatkin, Simsek, Ling, & Veiga, 2006).

The interaction effect of performance and environmental dynamism is important because it moves research into exploring how performance feedback processes depend on the organization and its context (Shinkle, 2012). Performance feedback theory has discussed this question to some extent, but little formal research has been attempted to develop theoretical propositions and examine them empirically. The findings reveal that environmental dynamism is an important contingency for how organizations react to performance feedback. Specifically, environmental dynamism positively interacts with low performance to produce increases in potential absorptive capacity. Hence, in highly dynamic environments organizations focus on the type of learning that produces general knowledge that can be used across a wide range of future environmental states. This finding makes sense when one considers that environmental dynamism increases the risk that knowledge specific to the current environment becomes obsolete. Although it is not possible to claim that managers are correctly calibrated in their emphasis on potential versus realized absorptive capacity, the qualitative pattern of the findings is consistent with the actions that would produce the best adaptation for the organization.

Performance feedback theory suggests that low performance relative to aspiration initiates problemistic search, which in turn affects the action (Greve, 2003b). However, most previous studies have neglected the process behind the problemistic search and directly linked the motivation to execute and the implementation itself. The current study opens the “black box” of problemistic search by examining change in absorptive capacity, which is by itself a search process, rather than focusing only on the final result of the organizational change. Our findings suggest that two different routes of search can exist simultaneously, in our case long- and short-term search represented by potential and realized absorptive capacity, respectively. We encourage future research to further develop the theoretical and empirical insights behind the search stages.

Generally, we found relatively stronger effects of long-term performance feedback than of short-term feedback, as one might expect from our sample of early-stage organizations. Their shareholders accept that the primary goal is to enhance long-term performance through increasing the value of the organization during the money-raising process (Alvarez & Barney, 2008). It appears that this leads to a focus on long-term goals, unlike the short-term goals seen to affect established firms. This finding might be different in organizations whose shareholders have a shorter-term focus. Hence, further research is needed to establish whether the findings presented here are generalizable to established organizations.

Absorptive Capacity

This study used absorptive capacity as an outcome to examine the performance feedback process. It also extends absorptive capacity theory by offering performance feedback as an antecedent, in addition to the two other major categories of antecedents discussed in the literature: external antecedents of the industry (e.g., Cohen & Levinthal, 1989, 1990) and organizational antecedents (e.g., Jansen et al., 2005). This places absorptive capacity theory firmly into the literature on organizational adaptation (Gavetti & Levinthal, 2000) and responds to the critique that earlier work on absorptive capacity antecedents has lacked adaptive mechanisms (Todorova & Durisin, 2007).

Our study also takes a broader view of absorptive capacity management, which enables us to extend research beyond the current focus on the technology domain and the usual use of a single measure such as R&D intensity or number of patents (Lane et al., 2006). As some work has done, we explore a wide range of learning processes from the competitive environment, including general industry trends, customers, competitors, and suppliers (Jansen et al., 2005) by using the two subconstructs of potential and realized absorptive capacity (Zahra & George, 2002). Our use of established scales to form the dependent variables and the adaptation of existing questionnaires from the literature promotes cumulative research knowledge.

Exploration and Exploitation

Our findings are also relevant to the literature on the organizational balance between exploration of new organizational routines and exploitation of current ones (March, 1991). Potential absorptive capacity consists of organizational capabilities that can support exploration, while realized absorptive capacity consists of organizational capabilities needed for exploitation. The difference of these pairs of concepts is that whereas exploration and exploitation are actions, potential and realized absorptive capacity are capabilities that support those actions. Work on performance effects on exploration and exploitation is scarce, but it has shown that both are increased by short-term (ROA) performance below aspiration level (Greve, 2007), although exploration innovations were generally scarcer than exploitation innovations. Our findings are distinct because we find a clear difference in the antecedents of potential and realized absorptive capacity. We think the difference is because potential absorptive capacity is causally prior to exploration (it is a capacity that supports exploration), and hence the results of Greve (2007) may have been influenced by unmeasured variation in the potential absorptive capacity. Our study thus goes closer to the origin of how performance influences organizational adaptation than one of exploration/exploitation does. Also, contrary to a major stream in the literature arguing that there is a trade-off between exploration and exploitation (Gupta, Smith, & Shalley, 2006), our findings suggest that the organization builds up capabilities for both exploration and exploitation, as argued in ambidexterity theory (Lubatkin et al., 2006). More research is needed to establish whether the clearer findings on absorptive capacity are related to risk differences, as suggested above, or whether they are simply because this study took the pioneering approach of also examining effects of long-term performance measures.

Implications for Management

Our findings have one broad implication concerning actions that managers appear to take correctly and one on actions that they appear to be doing incorrectly. Our finding that managers matched time perspective of goal and investment in specific kinds of absorptive capacity is intuitive, and it suggests that they are able to match problems and solutions adaptively. It is not completely obvious that the finding would come out that way because we are examining effort to build up capacities for knowledge acquisition, not the knowledge acquisition itself. Thus, they are correctly identifying a necessary investment step in the process.

The finding that firms that do poorly will invest more may also seem natural, but it is actually problematic. We understand that those firms would want to catch up. But why are the firms that are currently ahead not trying to pull away from them? They will normally be ahead by virtue of successful past investments in absorptive capacity, and hence we can assume that they will get at least the same rents from additional investments as firms with a poorer track record. The success normally attracts resources, so constraints should not be the explanations. Rather, what we observe is that leaders let down their guard. This is a recurring theme in research on performance and aspiration levels. The efforts made by the lagging firm are understandable at some psychological level (we do try to catch up), but as a description of competitive dynamics between leading and lagging firms they suggest a risky form of complacency from the leading firm’s point of view. We would predict many more lagging firms catching up and dethroning leaders when this behavioral pattern is followed than if the performance-feedback curve were horizontal. Of course, when the lagging firms invest more we also expect more firm failures, as these investments are risky. Thus there are lessons here for firms with both high and low performance relative to the aspiration level, but especially so for those with high performance.

Limitations and Further Research

Some limitations of this study merit discussion. First, our data were partially self-reported assessments. We took several steps in both the design and testing phases to limit bias, but it cannot be totally ruled out. A key step in avoiding this potential was the collection of independent and dependent variables in different years. Moreover, the high scores of the Rwg test for interrater reliability, together with the confidentiality that was ensured for respondents, reduced our concerns that respondents artificially inflated or disguised their responses. Presence of such bias would also have produced consistent effects of the same variables, both performance and environmental dynamism, on both components of absorptive capacity, yet we found differential effects of several variables on potential and realized absorptive capacity. A second limitation is that our survey research was conducted at multiple organizations, but all of them are Israeli organizations. This kept constant country-specific differences that might have otherwise masked significant effects, but left verification of the findings in other contexts for future research. Third, the data employed in this study captured only three consecutive years. In spite of the consistency of the reported results with the theoretical predictions, further longitudinal research should provide empirical extensions of the theory over organizations’ and industries’ life cycles.

The study raises other research opportunities. First, it has demonstrated that different goals affect different outcomes, and specifically that there may be a match between the time perspective of a goal and the time perspective of the actions taken when performance falls below aspiration levels. This suggests potential for more work on the effects of multiple goals in organizations. Second, it has explored the important role of environmental dynamism in affecting organizational responses to performance feedback. Future research can extend the theoretical and empirical work to other environmental contingencies to develop a more comprehensive view of how performance feedback affects organizational responses, both absorptive capacity and the usual final outcomes such as organizational changes. Finally, it demonstrates the utility of a broader view of absorptive capacity that considers competitive knowledge as well as technological knowledge, and shows that the distinction of potential and realized absorptive capacity exists in that domain as well. It would be a promising but challenging project to explore the multilevel nature of organizations’ potential and realized absorptive capacity management as they respond to short- and long-term performance.

Footnotes

Appendix A

Scales and Items of Potential and Realized Absorptive Capacity

Potential absorptive capacity—Acquisition

We have frequent interactions with companies belonging to our business network to acquire new knowledge

We regularly visit employees and firm facilities

We collect industry information through informal means (e.g., lunch with industry friends, talks with trade partners)

We rarely visit our company units (r)

We periodically organize special meetings with customers or third parties to acquire new knowledge

We regularly approach third parties such as accountants, consultants, or tax consultants

Potential absorptive capacity—Assimilation

We are slow to recognize shifts in our market (e.g., competition, regulation, demography) (r)

New opportunities to serve our clients are quickly understood

We quickly analyze and interpret changing market demands

Realized absorptive capacity—Transformation

We regularly consider the consequences of changing market demands in terms of new products and services

We record and store newly acquired knowledge for future reference

We quickly recognize the usefulness of new external knowledge to existing knowledge

We hardly share (with each other) our practical experiences (r)

We laboriously grasp the opportunities for the firm from new external knowledge (r)

We periodically meet to discuss consequences of market trends and new product development

Realized absorptive capacity—Exploitation

It is clearly known how activities within our firm should be performed

Client complaints fall on deaf ears (r)

We have a clear division of roles and responsibilities

We constantly consider how to better exploit knowledge

We have difficulties in implementing new products and services (r)

We have a common language regarding our products and services

Source: Jansen, Van Den Bosch, and Volberda (2005).

Note: All items were measured on a 7-point scale, anchored by 1 = strongly disagree and 7 = strongly agree. (r) = reverse-coded item.

Appendix B

Items of Environmental Dynamism

Environmental changes in our market are intense

Our clients regularly ask for new products and services

In our market, changes are taking place continuously

In a year, nothing has changed in our market (r)

In our market, the volumes of products and services to be delivered change fast and often

Source: Jansen, Van Den Bosch, and Volberda (2006).

Note: All items were measured on a 7-point scale, anchored by 1 = strongly disagree and 7 = strongly agree. (r) = reverse-coded item.

Acknowledgements

This article was accepted under the editorship of Deborah E. Rupp. This article is in memoriam of Prof. Avi Fiegenbaum. Avi’s research has built an important body of knowledge, and we mourn the loss of a scholar, and Chanan’s advisor, whose ideas were instrumental to this article. May he rest in peace knowing that his insights and thoughts are still spreading in the academic world. We thank Dovev Lavie, seminar participants at Technion and the 2010 annual meeting of Strategic Management Society in Rome, Associate Editor Annette Ranft, and the anonymous reviewers for their helpful and insightful comments on this article.

Notes

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