Managerial and Strategic Implications of Organizational Learning

This section develops managerial and strategic implications of new results in orga- nizational learning for market entry decisions, for the development of organiza- tional capabilities and competitive advantage, for the success of new entrepreneurial ventures and for the design of experience in organizations.

1. Market Entry

Researchers have examined the conditions under which it is better to be a first mover and enter the market first and when it is better to be a follower. Lieberman and Montgomery (1988), for example, concluded that several mechanisms created advantages for first movers: learning benefits associated with accumulating a large stock of cumulative output, benefits associated with patents, benefits associated with preempting scarce resources such as desirable locations, and reputational benefits or costs associated with changing products that bind customers to early movers (see also Lieberman & Montgomery, 1998).

Similarly, Cho, Kim, and Rhee (1998) grouped sources of early mover advantage into three categories: the market, the competition, and the firm itself. Market factors include reputational or loyalty effects as well as costs customers incur when they switch to a different product that favor early movers. Competitive effects include the ability of early movers to preempt limited opportunities in areas such as natural resources, location, suppliers, and employees. Firm-level factors include the benefits of learning how to develop, produce and market a product sooner than others. Conversely, sources of early mover disadvantage include “free-rider” effects through which a follower benefits from knowledge acquired by an early entrant, shifts in customer preferences or technology, and inertial forces that make it hard for an incumbent to change (Cho et al., 1998; Lieberman & Montgomery, 1988).

New results on organizational learning suggest an additional factor that should be considered in calculating the advantages of being a first mover: the extent of knowledge depreciation. If knowledge depreciates, the benefits of being a first mover are less than if knowledge is cumulative and persists through time. If knowl- edge depreciates, a follower would not be at a competitive disadvantage relative to a first mover if the recent output of the follower is comparable to that of the first mover. Knowledge depreciation might explain why late entrants to some industries have been so successful.

New results on knowledge transfer also have implications for market entry deci- sions. As noted in Chap. 6, knowledge can transfer across firms in an industry through a variety of mechanisms, including personnel movement, suppliers, consul- tants, trade associations, and the like. If knowledge transfer occurs, the benefits of being a first mover are less than when knowledge can be kept proprietary (see also Lieberman, 1987). When knowledge transfers, a follower can appropriate knowl- edge acquired by a first mover.

Korean firms provide examples of late entrants who competed successfully with early movers in the semiconductor industry, especially in the production of Dynamic Random Access Memory (DRAM) chips (Cho et al., 1998). Although Korean firms did not enter the semiconductor market until the 1980s, a Korean firm, Samsung, achieved the seventh position in total world semiconductor production and the first position in DRAM production by the 1990s (Cho et al., 1998). Two other Korean firms ranked among the world’s top ten producers of DRAM chips by the 1990s.

According to Cho et al. (1998), knowledge transfer figured prominently in the success of Korean firms. Korean chip makers transferred knowledge from universi- ties and firms in the USA by hiring Korean scientists who were trained and had worked there. Korean firms also transferred knowledge from Japanese firms, par- ticularly Toshiba. In addition, Korean firms learned from suppliers and contractors who had worked with early entrants to the industry. Further, Korean firms trans- ferred knowledge from previous products they manufactured in related industries. Thus, when knowledge transfers across firms in an industry, the benefits of being an early mover are considerably less than when knowledge does not transfer. Under conditions of knowledge transfer, a new entrant may be able to compete effectively against incumbent firms.

2. Competitive Advantage

An issue that many organizations, especially for-profit firms, face is how to promote the internal transfer of knowledge while blocking or minimizing external knowl- edge transfer to competitors (Kogut & Zander, 1992; Rivkin, 2001). As discussed in Chap. 6, firms that are able to transfer knowledge within their boundaries typically perform better that their counterparts that are less adept at knowledge transfer. Several mechanisms that facilitate the internal transfer of knowledge, such as embedding knowledge in tools, however, can make it easy for competitors to copy that knowledge. For example, Mansfield (1985) found that knowledge embedded in tools leaked our very quickly and more quickly than knowledge embedded in orga- nizational processes.

The dilemma is: How can organizations promote the internal transfer of knowl- edge while minimizing its external transfer or spillover to competitors? Paul Ingram and I used the framework of members, tools, tasks and their networks presented in Chap. 2 to theorize about how a firm could promote internal knowledge transfer while minimizing external knowledge spillover and thereby improve its competitive position (Argote & Ingram, 2000). As noted previously, knowledge can be embed- ded in members, tools, tasks, and the networks formed by crossing members, tools, and tasks. Knowledge can be transferred by moving members, tools, tasks, and their networks from one organizational unit to another.

Organizational performance depends on the internal and external compatibility of the networks (McGrath & Argote, 2001). For example, internal fit or compatibil- ity is achieved within the member–task network when tasks are allocated to the members most qualified to perform them. External compatibility is achieved when the various components of organizations fit each other. For example, when members have the appropriate tools to perform their tasks, the member–tool and member– task network would be compatible.

It is less likely that a network developed in one context fits another than that the basic elements of members, tools and tasks fit a new context. Networks involve more components than the basic elements and those components must be internally compatible as well as externally compatible with other elements and networks in the new context in order for knowledge transfer to succeed. For example, consider mov- ing a tool to a new context. In order for knowledge transfer to be effective, the tool would have to fit the elements and networks in the new context. Moving a member– task network successfully would also require that the member–task network fit the elements and networks in the new context. Moving the member–task network suc- cessfully would require internal compatibility in addition to external compatibility. A member–task network or transactive memory system developed in one organiza- tional context might not fit another where members have different knowledge and skills. Wegner, Erber, and Raymond (1991) found that imposing a transactive mem- ory system or member–task network on an existing dyad where members had already developed their knowledge about who was good at what hurt the perfor- mance of the dyad while imposing a member–task network on a newly formed dyad helped its performance. Thus, the member–task network did not fit the skills and knowledge of members of the existing dyad and was incompatible with the division of labor that had already been developed. This incompatibility or lack of fit hurt their performance.

Although moving networks successfully is more problematic than moving the basic elements, moving networks involving members is more likely to be effective within than between organizations. Processes of member selection, socialization, communications, and training that occur within organizations lead to members being more similar within than between organizations (Jackson et al., 1991). Because of greater member similarity within organizations, moving networks involving members is a more effective way to transfer knowledge within organizations than between organizations. Thus, embedding knowledge in the networks involving members facilitates internal knowledge transfer and impedes external knowledge spillover.

Argote and Ren (2012) argued that the networks involving members and tasks (the member–task, member–tool and member–task–tool networks) are especially valuable sources of competitive advantage in organizations. These networks meet the criteria for conveying competitive advantage to firms: path dependence, tacitness and social complexity, and context dependence (Dierickx & Cool, 1989). The networks are developed internally through experience members acquire working together to perform the organization’s tasks. The networks have many components, which fit each other (Rivkin, 2000), and therefore make it hard for other firms to discern the exact source of their advantage. Further, because transactive memory systems develop as organizational members gain experience working together, transactive memory systems are idiosyncratic to the particular organization and, therefore, are context- dependent. Different organizations have different members with different skills and expertise and therefore different transactive memory systems. Thus, transactive memory systems are difficult for competitors to imitate (Barney, 1986: Lippman & Rumelt, 1992) and are a source of competitive advantage for firms.

3. Success of Entrepreneurial Ventures

The sources of competitive advantage discussed in the previous section of networks involving members can help explain the success of entrepreneurial ventures. This section interprets evidence on the performance of entrepreneurial ventures (see Sorensen & Fassiotto, 2011, for a review) in light of networks involving members. Empirical studies have shown that new ventures that spin out from existing firms generally perform better than de novo entrants to an industry (Klepper & Sleeper, 2005). The question remains about what is being transferred to the new firms that facilitates their performance. Researchers have made some progress on this ques- tion. For example, Carroll, Bigelow, Seidel, and Tsai (1996) found that de novo entrepreneurial firms with preproduction experience performed better initially than de novo firms lacking experience or de alio firms that developed from a related industry. Preproduction experience in which organization members worked together on the product would have led to the creation of transactive memory systems.

Interestingly, Carroll et al. (1996) did not find evidence that technology imported from the parent firm figured in the performance of its progeny.

Similarly, Phillips (2002) found that the failure rate of new firms decreased as the proportion of founding team members from a parent firm increased and that failure rates increased as the number of parent firms increased. Although Phillips (2002) attributed the survival advantages of new firms with a large proportion of founders from one parent firm to the importation of routines from the parent, I suggest that the advantage was more likely due to the importation of a transactive memory system. When members have a large proportion of founders from one firm, they could import a well-developed transactive memory that included a large proportion of their employees. By contrast, if founders came from multiple firms, they would import multiple transactive memory systems, which would likely conflict with each other and detract from performance.

Routines (task–task networks), recurring patterns of activity among interdepen- dent organization members, are less dependent on the particular skills and expertise of members than transactive memory systems. Feldman and Pentland (2003) defined a routine as a repetitive pattern of interdependent actions carried out by multiple individuals. For example, a firm might have a routine for producing its product that specifies the steps to assemble a product or a routine for acquisitions that specifies the processes for acquiring other firms. Although the performance of a routine might vary somewhat as a function of the individuals performing it, the processes or steps in the routines are specified independently of the members who perform them. Indeed, an advantage of relying on routines is that organizations are less affected by turnover when they rely on routines than when they do not (Rao & Argote, 2006; Ton & Huckman, 2008). By contrast, transactive memory systems depend on the particu- lar knowledge and skills of members. Their effectiveness is negatively affected by turnover (Moreland, Argote, & Krishnan, 1998; Lewis, Belliveau, Herndon, & Keller, 2007). Thus, the value of a transactive memory system in a new setting would depend more on the number of members of the system who move to the new context while the value of a routine would. Phillips (2002) found that new firms performed better when a greater percentage of their members came from the same parent firm.

Studies of personnel movement provide additional indirect support for the benefits of transactive memory systems. Wezel, Cattani, and Pennings (2006) found in a lon- gitudinal study of firms in the accounting industry that interfirm mobility had the greatest effect when collectives rather than individuals moved. Similarly, studying financial analysts who moved from one firm to another, Groysberg and Lee (2009) found that analysts who moved with their team performed better than analysts who moved solo. Arguably, collective movement or moving with their teams enabled members to keep their transactive memory systems intact and thereby improved their performance. Huckman and Pisano (2006) studied surgeons who performed the same operation in different hospitals and found dramatic differences in their performance. When individuals moved from one organization to another, a transactive memory sys- tem developed with one set of colleagues would not fit another. Thus, it seems likely that individuals moving across organizational contexts did not have the benefit of a transactive memory system, a deficit that contributed to their poor performance.

4. Design of Organizational Experience

March (2010) has written about the challenges of learning from experience (see also March, Sproull, & Tamuz, 1991). This section discusses how experience can be designed to facilitate learning. The section argues that organizations can take a proactive approach to designing experience to enhance the ability of organizations to learn from experience.

Many of the studies discussed in this monograph showing group or organiza- tional productivity gains with experience were conducted in production environ- ments. Several characteristics of these environments facilitate learning. First, the group or the organization typically makes many repetitions of the same product. For example, one truck plant we studied produced almost 1,000 trucks each day! These trucks provided many opportunities for learning since different modifications could be made on each truck and their effects assessed.

Second, feedback in these production environments is often more immediate and less forgiving than feedback in other contexts. One determines fairly quickly whether the windshield fits into its frame, whether the welding holds, or whether the lights work properly. This environment contrasts, for example, with research environments where it might take months or years to determine whether a new approach is effective.

A third reason why learning might be easier in production environments is that the factors determining organizational outcomes such as productivity and quality might be more under the organization’s control than those determining outcomes in service settings. Thus, it is easier to assess the effect of organizational action on outcomes and easier to determine effective responses.

Although these features of production environments can be thought of as bound- ary conditions that specify when an organization or group is most likely to learn from experience, the features can also be regarded as desired attributes for learning that can be emulated in other settings. That is, rather than regard the extent of repeti- tion of production as immutable or “given,” organizations can aim to increase the number of parts or processes that are repeated on their products by using common “platforms” for a variety of products. Rather than have each product consist of unique parts and processes, products share a common base or platform of parts and processes. Because these common parts and processes are repeated on many prod- ucts, the organization has a much larger experience base from which to learn.

Mass customization also has the potential to increase an organization’s experience base for learning. In mass customization, a product is designed to consist of indepen- dent modules that can be assembled into different forms of the product, according to customer specifications (Feitzinger & Lee, 1997; Pine, Victor, & Boynton, 1993). Manufacturing processes are also designed to consist of independent modules that can be reconfigured to produce different designs. According to Feitzinger and Lee (1997), the key to effective mass customization is to postpone differentiating the product for a specific customer as long as possible. From a learning perspective, this postponement increases the number of times a part or process is repeated because the same parts and processes are used on many variants of the product.

Another way in which organizations can increase their ability to learn from experi- ence is to design experiments (natural or laboratory) that have the potential to inform one about the effect of critical factors. In actual organizations, factors often covary in ways that make it hard to assess their separate effects. For example, an organization might introduce new technology at a plant that produces a diverse product mix. The performance of this plant might be lower than its sister plant that uses a technology that is better understood to produce a less varied product mix. In this example, it is impossible to determine if the new technology or the complex product mix contributes to the lower productivity because the use of the new technology is perfectly correlated with the complex product mix. That is, the data from the naturally occurring experi- ment described above are structured in a way that makes it impossible to partial out the effect of the technology from the effect of the product mix.

In this example, the organization might have been able to structure its experience differently to enhance learning. For example, the organization could keep the prod- uct mix (and other critical factors to the extent possible) the same across the plants and only vary the technology. To the extent that other factors are constant or can be controlled for statistically, differences in productivity between the plants with and without the new technology can reasonably be attributed to the technology. Thus, the organization would be in a better position to learn from its experience.

Another issue related to the design of experience is the degree of experience heterogeneity that facilitates learning. Studying the mail service of a European country, Wiersma (2007) found that heterogeneous product mixes were associated with more learning on the part of delivery persons, as reflected in lower average costs per mail item, than homogeneous product mixes. Similarly, Schilling, Vidal, Ployhart and Marangoni (2003) found in a laboratory study that dyads learned more from similar experience than from identical or very different experiences. Heterogeneous experience can lead to richer and deeper understanding than homo- geneous experience (see also Haunschild & Sullivan, 2002).

Cautionary notes about the benefits of heterogeneity, however, are sounded in sev- eral studies. In contrast to studies finding a positive effect of heterogeneity on organi- zational learning outcomes, Fisher and Ittner (1999) reported negative effects of product heterogeneity in an automotive assembly plant. Greater product variety increased over- head and inventory requirements, the amount of rework, and the total labor hours required to produce each car. In their qualitative analysis of what differentiated high from low-performing hospitals using a new surgical procedure, Pisano, Bohmer, and Edmondson (2001) noted that the high-performing hospital chose similar cases on which to operate during the early phases of using a new procedure while the poor per- forming hospital did not. The performance of tasks in the automotive plant (Fisher & Ittner, 1999) and the hospital (Pisano et al., 2001) require the coordination of a larger number of interdependent members and tools than mail delivery (Wiersma, 2007) or laboratory tasks (Schilling et al., 2003). The benefits of variety seem harder to realize in these more complex systems. Understanding the degree of heterogeneity that is most conducive to learning in particular contexts and designing that heterogeneity into the context, where feasible, would facilitate organizational learning.

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

Leave a Reply

Your email address will not be published. Required fields are marked *