Organizational Learning and Adaptation
- Henrich R. GreveHenrich R. GreveThe John H. Loundon Chaired Professor of International Management, INSEAD
Organizational learning theory is motivated by the observation that organizations learn by encoding inferences from experience into their behavior. It seeks to answer the questions of what kinds of experiences influence behaviors, how and under what circumstances behaviors change, and how new behaviors are stabilized and have consequences for organizations’ adaptation to their environment. Organizational learning research has as key mechanisms innovations and other triggering events that lead to major behavioral change, knowledge accumulation and experimentation that encourage incremental change, and interpretations that guide each of these processes. Organizational learning research has gained a central position in organizational theory because it has implications for organizational behaviors that also affect other theoretical perspectives such as institutional theory, organizational ecology, and resource dependence.
Key research topics in organizational learning and adaptation are (a) organizational routines and their stability and change, (b) performance feedback and its consequences for organizational search and change, (c) managerial goal formation and coalition building, (d) managerial attention to goals and organizational activities, and (e) adaptive consequences of learning procedures. Each of these topics has seen significant research, but they are far from completing their empirical agenda. Recently, organizational learning research has been very active, especially on the topics of routines, performance feedback, and attention, resulting in a strong increase in learning and adaptation research in management journals.
- Organizational Theory and Behaviour
- Organizational Theory and Behaviour
Organizational learning theory has its origins in the behavioral theory of the firm (Cyert & March, 1963) and related research in the Carnegie School (see review by Gavetti, Greve, Levinthal, & Ocasio, 2012). Its rise in management theory can be traced to the famous review by Levitt and March (1988), who laid out a research agenda composed by learning from direct experience, interpretation of experience, organizational memory, learning from the experience of others, ecologies of learning, and learning as a form of intelligence. Both the papers reviewed by Levitt and March (1988) and subsequent research have covered all these topics, but as organizational learning research crystallized further, some have become significantly more active than others. In this article, recent studies on the most active research traditions are discussed, followed by some remarks on the less active research traditions. Because there have been many reviews on organizational learning (Argote & Greve, 2007; Argote & Miron-Spektor, 2011; Cohen & Sproull, 1995; Gavetti et al., 2012; Huber, 1991; Miner & Mezias, 1996), the reader can refer back to them for more detail on earlier work.
This review examines the research on organizational learning and adaptation in steps, while also paying close attention to the connections between the steps. First, organizational routines are intuitively thought of as what is learned and held stable in order for the organization to perform predictably and economically. Organizational routines have this function and thus should lead a discussion of organizational learning, but they are more flexible than this depiction suggests, and they have consequences for organizational learning both through generating clashes between routinized behaviors and immediate needs for response and through their potential for directing change (Feldman & Pentland, 2003).
Second, performance feedback is a major trigger for change through its signaling of performance below aspiration levels (Greve, 1998), and thus it is commonly thought of as examining when to learn. However, performance feedback can be obtained on multiple goals and can direct learning to the behaviors most closely associated with a specific goal, hence directing the learning. Performance feedback also has the potential to adjust aspiration levels and the choice and attention to goals, as managers seek to pursue goals and aspiration levels that have a reasonable chance of success.
Third, goal formation is a decision on for what purpose to learn, as it determines what outcomes the organization sees as valuable and directs efforts toward. Goal formation is seen as including both the explicit and constitutional goals that are specified when an organization is founded, and the more implicit and informal goals that a managerial team may set as a result of politics and negotiations. The negotiation in turn directs the attention of the top management team toward specific goals.
Fourth, managerial attention determines situated learning (Ocasio, 1997), or where to focus learning. Managerial attention is directed by goals and performance feedback, but it is also affected by events in the environment. It directs change efforts that in turn are oriented toward improving organizational adaptation to the environment.
Fifth, organizational adaptation is what learning accomplishes. Organizational adaptation is a goal-oriented process made dynamic by the potential for improvement, even in a stable environment, because the organization is not fully adapted to begin with (Levinthal & March, 1981). It is made even more dynamic by the ecologies of learning, which forces each organization to learn in the new environment created by the learning of organizations with which it interacts, such as competitors and suppliers. Finally, environmental discontinuities, such as technological changes or market changes, create discontinuities in organizational adaptation as the organization seeks to reorient its goals, choice of activities, aspiration levels, and choice and execution of routines.
Organizational Routines: Stability and Change
A central part of organizational learning theory is the view of organizational action as being guided by routines, defined as repeated interdependent behaviors by multiple people that emerge from and are adapted to recurring situations. Organizational routines give regularity and efficiency to organizational behaviors, as organizational members can quickly go through preset behaviors instead of engaging in joint improvisation. Organizational routines research grew from the work of Cyert and March (1963) and Nelson and Winter (1982), and thus has foundations in both management theory and economics. In management, important progress was made through research examining the flexibility of routines and the distinction between routines as described and prescribed (ostentive) and as actually executed (performative) (Feldman & Pentland, 2003). As research on routines has developed, an important tension has been discovered between routines as management-imposed forms of regularity and as flexible foundations of organizational problem-solving (e.g., Reynaud, 2005), which in turn informs work examining the stability or decay of routines and the resulting organizational capability differences (e.g., Knott, 2001).
Organizational routines have been reviewed thoroughly in a handbook (Becker, 2008). Also, a more recent review of routine research was written by Parmigiani and Howard-Grenville (2011), who noted that substantial theoretical progress has been made, but empirical work is still scarce. Additional empirical work has since been reported in a special issue on routine dynamics and the introduction and review of this issue (Feldman, Pentland, D’Adderio, & Lazaric, 2016). Given these recent and very informative reviews, the comments here will center on the role of routines as a part of organizational learning and adaptation research in general.
Empirical research on routines has by now thoroughly documented that (a) they are an important part of organizational capabilities, (b) they are altered to circumstances and change over time, and (c) their execution and alteration result from an interplay between individual agency and organizational context (Feldman et al., 2016; Parmigiani & Howard-Grenville, 2011). This research has been fruitful, but also sufficiently thoroughly done that it does not represent the greatest gap in our understanding of routines. Instead, the gaps are found in the interfaces between routines and other parts of organizational behavior. These gaps are significant because organizational routines research has seen significant fragmentation, both internally and in relation to other research (Parmigiani & Howard-Grenville, 2011).
An important interface is between the alteration (or stability) of routines and performance feedback. Because performance feedback triggers organizational search and change, it affects routines through multiple processes. To the extent that organizational search is localized and bottom up, performance below aspiration levels allows greater flexibility and experimentation in routines, and hence feeds the variation in performative routines. To the extent that a search is centralized, it may instead lead to changes in ostensive routines that in turn trigger top-down adjustment processes as the organizational units develop new performative routines. Such adjustment processes are beginning to be documented in recent research (Nigam, Huising, & Pentland, 2016), but it is far from enough.
Another interface is between organizational routines and rules. Organizational rules evolve in systematic ways through learning processes (March, Schulz, & Zhou, 2000), and they affect routines because they constitute a framework for executing routines but are not sufficiently complete to specify the routine (Reynaud, 2005). Reciprocally, routines affect rules because routine execution gives ground both to behaviors that are sought (and hence are made into rules for easy referral and repetition) and behaviors that are avoided (and hence are proscribed through rules). There is currently little work on the interdependence of rules and routines.
Performance Feedback: Organizational Search and Change
A central part of learning theory is the view of organizations as not being in constant search for improvement, but rather as starting searches and considering changes as a result of triggering events. The main theory on triggering events is problemistic search. In this theory, organizations set aspiration levels for goal variables based on social comparison and their own historical performance, compare the actual performance with aspiration levels, and initiate problemistic search when performance is below aspiration levels (Cyert & March, 1963). This theory has led to a strong research tradition on performance feedback that saw its first book-length review by Greve (2003b), and has since grown to more than 100 empirical contributions.
Initial work on performance feedback showed that performance below aspiration levels on profitability and closely linked goal variables led to strategic changes such as mergers and acquisitions (Haleblian, Kim, & Rajagopalan, 2006; Iyer & Miller, 2008), growth (Audia & Greve, 2006; Desai, 2008; Greve, 2008), diversification (McDonald & Westphal, 2003), market position change (Greve, 1998; Park, 2007), product introduction (Gaba & Joseph, 2013), alliance initiation (Baum, Rowley, Shipilov, & Chuang, 2005; Shipilov, Li, & Greve, 2011; Tyler & Caner, 2016), and resource acquisition (Greve, 2011b). The findings strongly validate the role of performance below the aspiration level as a triggering event for organizational change, and also support the Cyert-March model of aspiration levels as adapting to the performance observed in both the focal organization and other comparable organizations.
Research has proceeded to broaden the scope of investigation by also examining (a) less strategic behaviors, (b) other goal variables than profitability, and (c) mechanisms that explain the link between low performance and organizational change. The mechanisms of change have seen the least research so far, but promising findings include a loss of chief executive officer (CEO) autonomy because boards monitor more when the performance is low (Tuggle, Sirmon, Reutzel, & Bierman, 2010), and clear career concerns in how individuals react to performance below the aspiration level (Kacperczyk, Beckman, & Moliterno, 2015). An early and especially promising study is the comprehensive examination of the sequence of organizational responses to low performance done in the “sharp bender” study in the United Kingdom (Grinyer & McKiernan, 1990), which showed that firms with performance significantly below aspiration levels engaged in a wide range of changes from operational improvements, through adjustment of the business scope, to fundamental rethinking of the strategy. These changes were in turn mediated by internal processes such as leadership change, information collection, and change in goals and incentives.
Among the less strategic behaviors, there has been an older line of research on adapting research and development (R&D) expenditures to performance (Antonelli, 1989) that has since been extended to show that performance below aspiration levels increases both R&D and innovation launches (Greve, 2003a). The role of low performance in driving innovations has also been shown by later studies (e.g., Gaba & Bhattacharya, 2012; Giachetti & Lampel, 2010; Salge, 2011). Similarly, operational changes such as managerial procedures occur more often when performance is below aspiration levels (Massini, Lewin, & Greve, 2005).
In addition to profitability, a number of other goals have been shown to affect organizational change. Accidents relative to aspiration levels influence organizational safety procedures (Baum & Dahlin, 2007; Madsen & Desai, 2010), quality problems lead to improved product quality (Rhee, 2009), growth below aspiration levels increases growth (Greve, 2008), status relative to aspiration levels leads to network changes that increase status (Baum et al., 2005), and social ventures increase their attention to social goals when they perform below aspiration levels (Stevens, Moray, Bruneel, & Clarysse, 2015). These findings are particularly interesting because they are in support of the theory of myopic search (Cyert & March, 1963) for solutions close to the problem indicated by low performance on a given goal variable, unlike studies of the effects of profitability, which show that organizations are willing to reach wide for solutions to profitability goals. Given the key role of problemistic search in learning theory and the plethora of goals in organizations, we should expect to see much more research on specific goals and solutions that are proximate to these goals.
There has also been significant recent work on the determinants of aspiration levels, which represents a renewal of such research since the first work examining how aspiration levels were set (Lant, 1992). Key issues in current research include whether the simplification of averaging historical and social aspiration levels is correct (Bromiley & Harris, 2014), whether there are circumstances that shift focus to social or historical aspiration levels (Kacperczyk et al., 2015; Rowley, Shipilov, & Greve, 2016), and whether reference groups for social aspiration levels should be made heterogeneous (Moliterno, Beck, Beckman, & Meyer, 2014). These studies represent methodological improvements, but also give insights into managerial cognitions and social proximity.
Like routine theory, performance feedback theory also has unresolved work in relation to other theoretical topics. A key part of performance feedback theory is the idea that the multiplicity of goals means that which performance measure gains attention at any specific time is far from a trivial question. Early and strong evidence shows that self-enhancement occurs, as managers place less emphasis on goal variables with performance below aspiration levels (Audia & Brion, 2007; Audia, Brion, & Greve, 2015). This matters because organizations are often exposed to goals that external actors seek to impose, and they tend to accept these when their performance is high but reject them when their performance is low (Rowley et al., 2016). The result is a shifting attention to goals that is highly relevant to the research on goal formation and goal attention and is a fundamental issue that remains unresolved despite having a long history in research on organizations (Selznick, 1948).
Managerial Goal Formation
Goal formation during the decision process was an important part of the early behavioral theory of the firm, as seen through its emphasis on the dominant coalition in decision-making, and so was the view of organizations following a set of basic business goals (profitability, market share, etc.… ) (Cyert & March, 1963). Interestingly, while the most influential later statement of the theory has been interpreted to omit managerial goal formation from the discussion, it actually examined goal formation and especially shifting goals under the title “ambiguity of success” (Levitt & March, 1988, p. 325). The main barrier to progress is that there has been little empirical evidence on managerial goal formation taking a learning perspective. As a result, there has been an increased emphasis on examining learning processes while taking organizational goals for granted (Argote & Greve, 2007; Levinthal & March, 1993).
Some research has provided clues to what an examination of the learning processes involved in managerial goal formation would yield. Early learning ideas on goal formation saw managerial power as an important determinant of goal formation by firms (Cyert & March, 1963), and this idea was expanded to include external actors as sources of power (Pfeffer & Salancik, 1978), along with internal resolution of uncertainty (Hickson, Hinings, Lee, Schneck, & Pennings, 1971). An important integration of these concerns was done by Fligstein (1987), who examined how different organizational subunits dominated the recruitment of chief executive officers (CEOs) in different time periods. His explanation of the rise of subunit dominance started with legal structures directing strategic choices, which in turn favored some searches for solutions over others, leading to subunits learning which strategies worked best and organizations making overall changes to the power distribution in response to the discovery that some subunits were better placed to solve organizational problems and set organizational goals. This process involves responses to institutional conditions (notably the state), along with learning from experiments with strategy and structure, and integrates multiple theories to examine goal formation.
Other research on goal setting has examined more specific processes. A key issue has been the degree to which external influence on organizations, as modeled in institutional theory (Scott, 2001), can alter organizational goals or just lead to compliance without real goal adoption. An important finding in this research is the interaction between the external influences from institutions and employees and managers who act as internal promoters of institutions. Together, these act to orient the organization more strongly toward new institutions (Briscoe, Chin, & Hambrick, 2014; Lounsbury, 2001). This finding is consistent with the theory of intraorganizational formation of dominant coalitions, as the external pressure is exactly what an executive would use to promote his or her own goals (Greve & Zhang, 2016). A more subtle effect, and a possible one with a shorter time horizon, is that organizations that do well in external evaluations in the form of publicized rankings subsequently invest heavily in compliance, suggesting that ranking placement has actually become a goal (Espeland & Sauder, 2007; Rowley et al., 2016). This finding could be responsible for observed overinvestment (relative to financial returns) in behaviors that lead to high rankings (e.g., Bermiss, Zajac, & King, 2014; Rossman & Schilke, 2014).
The main suggestion for extending research on learning of managerial goals is simple: do more of it. Research on goal formation is a part of management theory with little descriptive behavioral theory in general, as opposed to an abundance of prescriptive and applied theory (e.g., Kaplan & Norton, 1996), and there also is insufficient work on the contributing processes of organizational power dynamics (Wry, Cobb, & Aldrich, 2013) and institutions leading to organizational goals (Greve & Teh, 2016). The current state of the field is that we do not have sufficient empirical evidence to know the speed of goal formation and the duration of the goals driven by these processes, nor their strength relative to simple goal inertia or competing sources of organizational goals.
The interfaces of learning theory of goal formation to other theories are also important to understand. As noted earlier, performance feedback already has a documented effect on goal formation (Rowley et al., 2016), and we should expect goal formation and organizational attention to be closely related as well. Looking at the phenomenon more broadly, as Fligstein (1987) did, we also should examine links to other features of the top management dynamics. Clearly, there is potential for examining the effects of both top management teams and boards of directors, combined with learning (e.g., Chatterjee & Hambrick, 2011; McDonald & Westphal, 2003; Zhu & Chen, 2015).
Managerial Attention: Multiple Sources
Whereas goal formation is a theory of organizational determination of enduring goals, attention theory looks at how organizations allocate attention to different issues (including goals) (Ocasio, 1997). Organizational attention matters because it determines the collection and interpretation of information, the search for alternatives, and the direction of change efforts. Organizational attention is influenced by performance feedback as a triggering device, and hence also by organizational goal selection. In addition, it is affected by organizational structure for both information distribution and decision-making, and it is affected by the agendas and decision-making procedures (Ocasio & Joseph, 2005). Research on organizational attention has recently been reviewed (Ocasio, 2011), so next are given a few main insights and pending research questions.
A central feature of this research is a focus on internal and external mechanisms that draw the attention of managers, and especially how they work in combination. The classical focus on managerial cognition (e.g., Porac & Rosa, 1996) is in this research combined with examination of how cognition leads to the discovery of environmental cues, which in turn direct competitive responses (Marcel, Barr, & Duhaime, 2010). Work on power in organizations (Pfeffer & Salancik, 1978) is extended by examining how subunit power interacts with influence and attention-seeking behavior to shape corporate behaviors (Bouquet & Birkinshaw, 2008). Performance feedback theory is combined with examination of shifting board monitoring to examine how firms have reduced chief executive officer (CEO) discretion following performance below aspiration levels (Tuggle et al., 2010).
This work has been particularly strong in reminding learning theory that the environment is also a source of information that drives learning. This topic was earlier covered extensively in work on learning from the behaviors of others, often through imitation (Baum & Ingram, 1998; Greve, 1996; Haveman, 1993), but attention research examines a wider scope of learning processes. Major institutional changes can drive organizational attention and change processes (Thornton, 2004; Thornton & Ocasio, 1999), but they require some degree of matching with the decision-makers’ cognitive patterns (Jonsson, 2009). Consistent with the literature on social movements, influence efforts directed at organizations also draw attention and often lead to changes (e.g., Hoffman, 1999; Ingram, Yue, & Rao, 2010; King, 2008).
Attention theory has focused on how organizational changes are directed by contextual and time-dependent factors, such as performance feedback, and environmental change, such as institutional change or competitor behaviors. This has made it one of the branches of organizational learning theory that is most engaged in interaction with other theoretical perspectives. Interestingly, it has also led to some shortfalls in the research into foundational questions such as how organizational structure and procedures, including decision-making routines, stabilize organizational attention patterns. This is an area in which much more work can be done, and it is particularly promising because it would accumulate more findings on how organizations build stable responses to routine change and performance feedback. It would also offer insights that could be applied to research on the adaptive consequences of learning, discussed next.
Adaptive Consequences of Learning
Organizational learning has consequences on the adaptation of the organization to its environment, and important topics in studying these adaptive consequences include the degree (and the speed) of finding the best behaviors in a given environment, as well as the ability to discover environmental change and adapt to it. A number of classical simulation studies have examined these questions. Cohen, March, and Olsen (1972) examined how different organizational structures searched for problems and solutions, showing that decision-making took the forms of problem resolution, oversight, and flight. They showed that each of the organizational structures had distinct behavioral patterns, but randomness was also a significant factor in their behaviors.
Subsequent work took a more macro-approach, examining the organization as a unitary decision-maker, showing important tradeoffs in adaptation. Organizations can halt learning by change of either strategies or goals, giving adaptive outcomes at multiple levels of performance (Levinthal & March, 1981). When organizations also accumulate expertise through learning, a final source of adaptive outcomes below top levels of performance is the competence trap, in which the organization becomes so adept at an inferior alternative that it rejects the better one (Levinthal & March, 1981). A similar tradeoff is seen in the balance between exploration (innovative changes) and exploitation (incremental changes), where rapid socialization of individuals to the organizational code helps them perform, but also prevents the organization from learning from them (March, 1991). An important conclusion from this paper was that organizations will typically be biased toward excessive exploitation and less exploration, a proposal that since has seen substantial empirical research with supportive findings (Gupta, Smith, & Shalley, 2006).
A series of simulation studies have also examined the difficulties in organizational adaptation that follow from environments that have multiple suboptimal adaptation opportunities that can lead to prematurely halted searches (local peaks), or interdependence among organizational actions that confuse the interpretation of search feedback. The difficulty of learning under such conditions is well documented, and the efficacy of various solutions to it has been explored (Gavetti & Levinthal, 2000; Levinthal & Marino, 2015; Rivkin, 2001). The results typically suggest that search processes are improved by the presence of an irregular element such as managerial cognition or learning from others, though the design of organizational structures can also improve search processes (Rivkin & Siggelkow, 2003).
Research in organizational adaptation has a very solid foundation of learning models that apply classic frameworks for searching on continuous surfaces (hill-climbing) and discrete spaces (NK models), and these general models have been adapted well to answer questions on organizational learning and adaptation. There have also been research efforts using models with lower generalizability, and they have typically been too specialized to gain impact. What have seen less research are models that closely mimic learning patterns with strong empirical support and examine their consequences. Although such models also have some degree of specialization, their close relation to empirical findings would give them significant practical value in assessing the adaptive consequences of observed organizational learning patterns.
Other Topics in Learning Research
There are other branches of organizational learning theory that have seen less treatment here but have future research opportunities that seem promising. An important branch is learning curve research, which examines how the efficiency of producing goods increases as a function of accumulative production. This research tradition was established long ago (see review by Yelle, 1979), but it was significantly improved by learning scholars who used it as a tool to examine how organizational learning involves transfer across shifts and across organizational units, and it is forgotten when a production process is halted (Argote, 1999; Argote, Beckman, & Epple, 1990). This research tradition has a very strong foundation of empirical work, and the main opportunities for progress lie in interfaces with other parts of learning theory, such as in examining the effect of performance feedback on learning curves and the effect of learning curves on organizational adaptation. For example, recent work has combined formal modeling and experiments to explain learning curve effects in complex information environments (Fang, 2012).
Organizational memory was marked as an important research topic early on (Levitt & March, 1988; Walsh & Ungson, 1991), and it is important because many other learning mechanisms rely on assumptions about how organizations change and retain memory. It seems fair to say that until recently, this topic has seen less research than its importance suggests, although important contributions have been made through knowledge decay in learning curves (Benkard, 2000; Darr, Argote, & Epple, 1995). This work has recently been supplemented by research on the different forms of organizational forgetting (de Holan & Phillips, 2004), which has seen a special issue and a review (Martin de Holan, 2011). A key lesson from this research is that forgetting is not just limited to loss of effective routines, as most research has established (e.g., Argote, 1999; Madsen & Desai, 2010); it also acts as a form of unlearning that helps organizations adapt (Easterby-Smith & Lyles, 2011). Clearly, there is substantial room for additional progress on the forms of memory change, retention, and forgetting, and their effects on organizational learning processes and adaptation.
There is much research on organizational learning at the micro- and meso-levels of analysis. Some of this has already been reviewed, as the research on routines often examines the meso-level of behaviors done by organizational groups or departments. In addition, research on performance feedback has gained a rich research stream, using experiments to examine the mechanisms mediating organizational resistance to change (Audia, Locke, & Smith, 2000), as well as specific mechanisms such as self-enhancement (Audia & Brion, 2007; Audia et al., 2015). Recent research on performance feedback effects in economics has also followed an experimental approach (Selten, Pittnauer, & Hohnisch, 2012). The emphasis on field studies in most branches of organizational learning research means that the potential to use experiments to validate findings and gain additional information about the contributing mechanisms is very high.
Future Learning and Adaptation Research
An important feature of organizational learning is that it currently has an odd position of being used as a set of assumptions in important theories, while being poorly integrated with them. Institutional theory has a baseline assumption of bounded rationality and a central process of learning from others that mirror those of earlier statements in learning theory (DiMaggio & Powell, 1983). Organizational ecology often seeks minimal assumptions on organizational behavior, but when these are made explicit, they typically involve bounded rationality and learning and adaptation processes that are familiar in learning theory (Barnett, Greve, & Park, 1994; Barnett & Hansen, 1996). In spite of this common ground, integration of these theories is less common than one might expect (but see Greve & Rao, 2006; Sauder & Espeland, 2009; Vasudeva, Alexander, & Jones, 2015; Wezel & Saka-Helmhout, 2006).
It seems clear that there is much to learn by examining each of these interfaces. For example, the question of how learning is initiated by institutional change is beginning to see answers, but the question of how learning by an organization and across the organization add up to institutional change has seen only a few contributions (Holm, 1995). Similarly, in organizational ecology there is a clear contribution from learning theory to the construction of Red Queen theory on how interorganizational learning becomes specialized as a result of competitor homogeneity (Barnett, 2008), consistent with the discussion of ecologies of learners in Levitt and March (1988). However, the processes of legitimation and competition discussed in population ecology should leave a trace in the organizational learning that distinguishes early and late entrants to the population, but such effects have not been investigated thoroughly.
A current interface of special interest is research on institutional environments in which multiple institutional logics are in conflict (Greenwood et al., 2011). Learning processes in such environments must involve either compromise between conflicting logics, and hence internal inconsistency (Battilana & Dorado, 2010; Battilana & Lee, 2014; D’Aunno, Sutton, & Price, 1991; Dunn & Jones, 2010); increased commitment to the logic that is gaining influence (Briscoe & Safford, 2008; Davis, Diekmann, & Tinsley, 1994; Greve & Zhang, 2016); or selecting one logic that matches the organizational identity (Negro, Hannan, & Rao, 2011; Pedersen & Dobbin, 2006). Because all these patterns have been observed, how organizations choose one or the other is an open question ready for theoretical and empirical contributions. It is likely that learning theory can be applied to explain the behaviors through an emphasis on myopic search and feedback from each adaptive step (Levinthal & March, 1993).
An interesting and potentially challenging question is whether organizations even have the control over goals and aspiration levels that learning theory assumes (Gavetti et al., 2012). With the increasing rise of market logics, securities analyst scrutiny, and shareholder value orientations (Davis, 2009), organizations may be primarily accountable to externally observable and shareholder-relevant goals such as profitability, and may see their aspiration levels pushed upward through external pressures. There is already comparative evidence on such influences gained from examining organizations that are affiliated with business groups or have family ownership, and as a result are less responsive to shareholder goals (Khanna & Palepu, 2000; Lincoln, Gerlach, & Ahmadjian, 1996; Luo & Chung, 2005). This tension deserves further examination, especially because a simple test of examining whether aspiration levels show upward pressure, as one would expect from shareholder influence, found that no such pressure was discernible (Bromiley & Harris, 2014). Thus, the degree of managerial control over goals and aspiration levels is uncertain.
Organizational learning theory could also benefit from better connections with network theory, especially because learning from the experience of others clearly is accelerated by observability and communications (e.g., Greve, 2011a; Greve & Seidel, 2014; Ingram & Baum, 2001; Powell, Koput, & Smith-Doerr, 1996). It is already well established that interpersonal ties facilitate learning across organizations (Beckman & Haunschild, 2002; Darr et al., 1995; Davis & Greve, 1997; Tuschke, Sanders, & Hernandez, 2014), but many studies do not go beyond showing the diffusion of practices across organizations. From a learning theory perspective, additional insights could be gained by examining how network learning interacts with own learning (e.g., Tuschke et al., 2014; Zhu & Chen, 2015). Learning theory could also contribute to network theory by helping further develop theory on network change. Although there is substantial evidence on how factors such as social similarity promote the establishment of network ties (Gulati & Gargiulo, 1999; Mitsuhashi & Greve, 2009; Powell, White, Koput, & Owen-Smith, 2005), a natural next step is to examine the effects of learning from the experience of prior tie establishment (e.g., Schwab & Miner, 2008). Work on this topic is so scarce that there are significant opportunities to make progress.
Organizational learning research has a remarkable history, ranging from one of the earliest management theories dating back to the Carnegie School (Cyert & March, 1963; Simon, 1947) to its current status as a very active stream of research showing youthful exuberance in activity level and experimentation with new topics. As noted throughout this review, a result of the great recent increase in research is that future research opportunities have come more clearly into view. Many of these are not further elaborations of the core theory (which at this point is becoming well specified theoretically and supported empirically), but rather examinations of how learning processes work jointly with each other and with adjacent theories. Because learning is a fundamental feature of organizations, any organizational theory is in principle adjacent to learning theory, but the initial progress has been greatest with respect to institutional theory, population ecology, and network theory, with significant potential for new work in power theory, top management team theory, and mesotheories of organizational behavior as well. Each of these theories either has a learning component already or can be reasonably rephrased to include one, so there is a theoretical interface that can be explored further and provide new theoretical and empirical contributions.
Because so much of this article has focused on showing the gaps in our knowledge and suggesting promising approaches for filling them, it is unnecessary to elaborate on the idea that learning theory is ready for new contributions. The key issue that should be kept in mind is that the division into topics and research traditions used here (or in any review of the literature) should not be taken too seriously. First, many papers gain leverage from crossing such boundaries. Second, one of the key features of learning theory is the capacity of scholars to find new and promising ideas, so any division into topics and identification of future areas of activity is prone to obsolescence (Greve, 2015). Thus, although this review helps map out the field in its current state, it is only a temporary description of the research on organizational learning and adaptation. More research will come and will bring new and exciting insights.
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