Future Directions of Organizational Learning

Four new trends in research on organizational learning curves were noted in Chap. 1: examining a wider set of outcome measures and a broader set of organizational contexts, understanding why some organizations are better at learning than others, analyzing the dynamics of knowledge acquisition and loss by organizations, and understanding the conditions under which knowledge transfers across organizations. We have made significant progress in understanding these issues. In this section, I suggest how we might further advance our understanding of them.

Examining a wider set of outcome measures and a broader set of organizational contexts, including more service settings, advances our understanding of organiza- tional learning. For example, factors that contribute to quality improvements associ- ated with experience in service settings might overlap only partially with those contributing to productivity gains associated with experience in manufacturing. Investigating different dependent variables and different contexts leads to a more complete picture of factors affecting organizational learning.

Understanding why some organizations are better at learning than others continues to hold promise as an overarching research question. Specifying (and testing) the con- ditions under which key contextual variables affect organizational learning is likely to be fruitful. Thus, a “conditions-seeking” approach that identifies the conditions under which particular variables have desired effects on organizational learning processes and outcomes is needed (Greenwald, Pratkanis, Leippe, & Baumgardner, 1986).

Although we have made progress in understanding how many factors affect orga- nizational learning, research on other factors is in the early stages. For example, con- siderable progress has been made understanding how characteristics of experience, personnel movement, and social networks affect organizational learning. Research on how identity (Kane, Argote, & Levine, 2005) and emotion (Levin, Kurtzberg, Phillips, & Lount, 2010) affect organizational learning processes and outcomes is in the early stages. Further, while research on how performance relative to aspirations affects organizational learning has been an active research area for many years, research on how incentive systems (Quigley, Tesluk, Locke, & Bartol, 2007) and intrinsic motiva- tion (Osterloh & Frey, 2000) affect organizational learning and knowledge transfer is more recent. Further research on how identity, emotion and motivation affect organi- zational learning and knowledge transfer would be especially valuable.

Additional analyses of the dynamics of knowledge acquisition and depreciation in organizations are also needed. Identifying the conditions under which knowledge depreciation occurs would be a major contribution. For example, does knowledge acquired from different types of experience or embedded in different repositories decay at different rates? Studies that examine the micro processes through which knowledge is retained in organizations and the implications of where and how knowl-edge is retained for organizational performance are likely to be fruitful. Thus, analy- ses that increase our understanding of the conditions under which depreciation occurs and the implications of depreciation for organizational performance are needed.

A greater understanding of the dynamics of knowledge transfer is also needed. Advancing our understanding of the conditions under which knowledge transfers and the implications of knowledge transfer for firm and industry performance would be major contributions. Studies that examine the micro processes through which knowledge transfers within and across firms are likely to be fruitful. More macro analyses of knowledge transfer across populations (e.g., see Mezias & Lant, 1994; Miner & Haunschild, 1995) will also advance our understanding of learning at different levels and relationships across those levels. Additional studies that examine knowledge transfer in an international context would also make important contributions. These international knowledge transfer studies provide information about a phe- nomenon that is important in its own right as well as provide variation on independent variables, such as culture, that might explain differences in knowledge transfer patterns across organizations. Understanding the dynamics of knowledge transfer also sheds light on the dynamics of knowledge retention in organizations because an organiza- tion has to retain knowledge in order to transfer it.

Although we have separated the subprocesses of creating, retaining and transfer- ring knowledge for analytic purposes, these processes are related. Research has begun to examine their interrelationships. For example, Weigelt and Sarkar (2009) found a complementary relationship between knowledge creation and transfer: transferring knowledge led to new knowledge being created. By contrast, Levine and Prietula (2012) found that investments in organizational memory substituted for knowledge transfer. Research that examines the relationship between the subpro- cesses of knowledge creation, retention and transfer would greatly increase our understanding of organizational learning.

We know relatively more about knowledge transfer than we know about knowl- edge retention and creation in organizations so research on these latter processes would be especially useful. Further, the relationship between knowledge creation through organizational learning and creativity needs to be better articulated. Ella Miron-Spektor and I argued that learning researchers could learn from creativity researchers and vice versa (Argote & Miron-Spektor, 2011). The benefit of this cross-fertilization is most apparent in reviewing studies of how experience affects creativity (e.g., see Taylor & Greve, 2006). More generally, we need to articulate whether and how the processes underlying and factors affecting creativity are simi- lar to or different from those associated with organizational learning.

Entrepreneurial firms provide important opportunities for examining how knowl- edge is created in organizations. Most entrepreneurial firms are small enough that researchers can more readily observe learning processes in them than in large estab- lished organizations. In addition, entrepreneurial firms are new enough that one can study organizational learning from the beginning of a firm’s operation. Thus, researchers could observe the building of connections and establishment of relation- ships that are central to organizational learning models. Entrepreneurial firms are especially promising sites for studying knowledge creation.

New developments in technologies also provide opportunities for organizational learning and organizational learning researchers. For example, social media such as blogs and forums, are being used increasingly in organizations. These new tech- nologies facilitate connections across organizational members and thereby, have the potential to transmit richer knowledge than afforded by previous generations of technology such as document repositories. Another example of a new technology is crowdsourcing, which can harness innovative ideas from members outside the boundary of the firm. Research on how these new forms of technology affect orga-nizational learning and knowledge transfer is needed.

New developments in neuroscience also provide new tools that researchers can use to study learning (Senior, Lee, & Butler, 2011). Although these physiological techniques have advanced our understanding of individual learning (e.g., Walsh & Anderson, 2011), the extent to which they will advance our understanding of organizational learn-ing is yet to be determined. In my view, these tools show promise for studying learning processes. For example, we often invoke the concept of “mindful” processes in our theorizing about organizational learning. If a functional magnetic resonance imaging test or f(MRI) reveals that areas of the brain known to be associated with deliberative processes become active when an individual is performing a task, it would suggest that the individual is using controlled or deliberative processes. In order to realize the prom-ise of these new technologies, considerable work will have to be done to adapt the tools to study groups and organizations and/or to develop theory and methods of combining data from individuals to reflect phenomena at higher levels of analysis.

Developments in the area of practice theory also hold promise for enriching our understanding of organizational learning. Practice theory recognizes the centrality of individuals’ actions to organizational outcomes (Feldman & Orlikowski, 2011). Because practice theory focuses on what actors do, it can shed light on learning by doing. For example, Rerup and Feldman (2011) showed how trial and error learning connected organizational routines and interpretative schema through observable action. Practice theory shows promise for facilitating the articulation of the micro processes underlying organizational learning.

Finally, because learning from experience is a mechanism through with organi- zations develop capabilities (Salvato, 2009), organizational learning research has important implications for advancing understanding of organizational capabilities, an active research area in strategic management. Argote and Ren (2012) theorized that transactive memory systems provide a micro foundation for organizational capabilities. We argued that not only do transactive memory systems provide a foundation for operational capabilities, but they also provide a base for the develop- ment of dynamic capabilities. Dynamic capabilities have been defined as an organi-zation’s ability to build or reconfigure its competences to respond to changes in the environment (Helfat et al., 2007; Teece, Pisano, & Shuen, 1997). Simulation results have demonstrated that transactive memory systems are especially valuable in tur- bulent environments (Ren, Carley, & Argote, 2006) and that they facilitate adapta- tion to novel problems (Miller, Pentland, & Choi, 2012). Further, groups with well-developed transactive memory systems have been found to produce more cre- ative products than groups with less developed transactive memory systems (Gino, Argote, Miron-Spektor, & Todorova, 2010). Thus, research on transactive memory systems promises to contribute to the burgeoning research on the micro foundations of organizational capabilities (Felin, Foss, Heimeriks, & Madsen, 2012).

Source: Argote Linda (2013), Organizational Learning: Creating, Retaining and Transferring Knowledge, Springer; 2nd ed. 2013 edition.

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