The Moderating Role of a Knowledge Strategy

In Figure 8.3, we link operational capabilities to firm performance. Increasingly, dynamic capability researchers are agreeing that it is not dynamic capabilities, per se, that lead to competitive advantage, but it is the resulting configuration of operational capabilities and resources—including knowledge—that has an impact on performance (e.g. Eisenhardt and Martin, 2000; Winter, 2003). As described earlier, operational capabilities are rooted in knowledge and learning and, in particular, in single-loop learning. Thus, the link to examine is the relationship between learning/knowledge and performance.

Researchers have opposite views about the impact of learning and knowledge on firm performance. On one side of this discussion are those scholars who establish a positive link between these constructs. In their pioneering work, Cangelosi and Dill (1965) mention that improved performance is learning. Later, Fiol and Lyles (1985) propose that, irrespective of the underlying interpretations of organizational learning, ‘in all instances the assumption that learning will improve future performance exists’ (1985: 803). The perspective of the knowledge-based view further stresses a positive link between knowledge and performance. It is expected that a particular sub-category of knowledge, which is valuable, rare, inimitable, and non-substitutable (Barney, 1991), would lead to competitive advantage.

On the other side of the discussion are authors (Argyris and Schön, 1978; March and Olsen, 1975) who do not see a direct relationship between learning, knowledge, and performance. For example, Levitt and March (1988) state that ‘learning does not always lead to intelligent behavior’ (1988: 335) and Huber (1991) adds that ‘learning does not always increase the learner’s effectiveness or even potential effectiveness . . . Entities can incorrectly learn, and they can correctly learn that which is incorrect’ (1991: 89). Complementary to this view is Leonard’s (1992) description of how core rigidities are deeply embedded knowledge sets that hinder innovation. Arthur’s (1989) law of increasing returns also supports the equivocal link between knowledge and performance. While having a good base of knowledge means that a company can leverage it and increase its advantage over competitors, having a poor base of knowledge means that the company that is losing advantage can only lose further advantage. Finally, from their review of the OL literature, Crossan et al. (1995) conclude that good performance is not a sign of learning and that learning may negatively impact performance in the short term.

In conclusion, OL and KM views of the impact of learning and knowledge on performance are diverse. While the OL literature presents an equivocal link between the learning process and performance, the knowledge literature suggests that knowledge—if recognized as a source of competitive advantage—explains differences in performance. Essentially, the attention is given only to the knowledge that is valuable, rare, inimitable, and organized in a manner that enables exploitation.

Empirical efforts have found support for the direct impact of learning, knowledge, and human and social capital on performance (e.g. Appleyard, 1996; Bontis, Crossan, and Hulland, 2002; Decarolis and Deeds, 1999; Hitt, Bierman, Shimizu, and Kochhar, 2001; Yeoh and Roth, 1999). It is important to note that the conclusion of these studies is not that ‘the more learning the better’ or ‘the more knowledge the better,’ but that learning that is effective and that knowledge that is relevant may have positive effects on performance. In our model, we emphasize that when studying learning and knowledge as antecedents of firm outcomes, it is critical that contextual variables, and in particular strategic variables, be included. The effectiveness of learning can only be assessed on the basis of its utility in guiding behavior relative to the organization’s relevant domain (Crossan, 1991).

Capturing this thinking, we include in our integrative framework a fit construct, which represents the fit or mutual alignment between a firm’s business strategy and a firm’s knowledge management strategy. The notion of fit has been extensively used in contingency theories to study alignment among organizational factors such as the environment, structure, culture, leadership, and the strategy of firms (e.g. Thomas, Litschert, and Ramaswami, 1991; Venkatraman and Prescott, 1990). Argote, McEvily, and Reagans (2003) explicitly call for application of the fit or congruence concept in the field of OL/KM. In our third proposition, fit is a moderator of the impact of operational capabilities—rooted in learning and knowledge—on performance. Building on Teece (2007), we position a KM strategy as part of the development of the KM dynamic capability that allows firms to maintain competitiveness through enhancing, combining, protecting, and, when necessary, reconfiguring the business enterprise’s intangible and tangible assets (Teece, 2007). We propose that if learning and knowledge are not relevant to, and consistent with, the firm’s purpose, they do not guarantee positive results. For knowledge to become a source of competitive advantage, firms need to match their KM strategy with their business strategy. When a firm’s KM strategy matches its business strategy, the impact of knowledge and learning is positive. If this match is not achieved, knowledge and learning may have no impact or even have a negative impact on performance.

In the late 1990s, authors in the OL and KM fields started to develop the ‘learning strategy’ and ‘knowledge strategy’ constructs. These learning/knowledge strategies can be explicit or implicit. Bierly and Chakrabarti (1996) define a knowledge strategy as the set of strategic choices that shape and direct the organization’s learning process and determine the firm’s knowledge base. In contrast to Bierly and Chakrabarti’s definition, Zack’s (1999) definition of knowledge strategy explicitly includes the notion of fit with the firm’s business strategy. He suggests that a knowledge strategy describes the overall approach an organization intends to take to align its knowledge resources and capabilities with the intellectual requirements of its business strategy. Through a knowledge strategy, organizations identify the knowledge required to execute the firm’s strategic intent, compare that to its actual knowledge, and recognize its strategic knowledge gaps (Zack, 1999).

There are also initial efforts in the late 1990s and 2000s in the OL and KM fields towards understanding the dimensions of an OL/KM strategy. As part of their knowledge strategy taxonomy, Bierly and Chakrabarti (1996) describe four tensions in the learning process: the tension between external and internal learning, radical and incremental learning, fast and slow learning, and a narrow and wide knowledge base. Building on this work, Zack (1999) adds that a knowledge strategy includes decisions regarding the creation, development, and maintenance of a firm’s knowledge resources and capabilities. These decisions are the choices between internal and external knowledge, and between exploration and exploitation. These two pieces of research cut across the KM and the strategy fields. In addition, Argote (1999) lists several tensions or tradeoffs in the learning process, which define a learning strategy. These are the tensions between group and organizational learning, heterogeneity and standardization, learning by planning and learning by doing, and the tension between fast and slow learning. Argote’s (1999) work cuts across the OL and KM fields. Vera and Crossan (2004) propose a connection between different styles of strategic leadership (transformational and transactional) and the different components of an OL system. Although this work is positioned in the OL/strategy theoretical domain, it does not presuppose the deployment of a particular type of leadership as an intentional knowledge strategy but suggests mechanisms that leaders can use to support the elements of an OL system.

Given that these three lists of learning/knowledge choices barely overlap each other, it appears that neither list is comprehensive. However, for the purposes of this chapter, we are not interested in providing a comprehensive list of the dimensions of a KM strategy, but in emphasizing the importance of studying the impact of learning and knowledge on performance within the strategic context of the firm. In addition to the work of Bierly and Chakrabarti (1996), Argote (1999), and Zack (1999), other authors have introduced similar concepts such as ‘learning styles’ (Ribbens, 1997), ‘learning modes’ (Miller, 1996), ‘learning orientations’ (Nevis, DiBella, and Gould, 1995), and ‘knowledge management styles’ (Jordan and Jones, 1997). Table 8.2 summarizes the dimensions discussed in these conceptualizations. There is ample room for future research in the integration of these concepts and for new studies on this topic.

Table 8.2 Examples of dimensions incorporated into learning/knowledge strategies

The previous discussion leads to our third proposition:

In the next section, we provide conclusions and directions for future research based on the discussion in this chapter.