1. Expanded Set of Outcomes
Several important new trends have occurred in research on organizational learning curves in the 1990s. First, researchers expanded the set of outcome measures used as indicators of organizational learning. Research conducted before the 1980s had shown that outcomes in addition to the number of direct labor hours per unit followed a learning curve (e.g., see Greenberg, 1971; Preston & Keachie, 1964). Recent research further expanded the set of outcome measures examined as a function of experience. For example, in our research, we have examined the outcome measures of quality, as measured by complaints or defects per unit (Argote, 1993), and service timeliness, as measured by the number of “late” products per unit (Argote & Darr, 2000) as outcome variables. Baum and Ingram (1998) focused on the outcome of organizational survival and analyzed how organizations’ survival prospects were affected by experience.
A couple of figures illustrate the wide range of outcomes that have been found to follow a learning curve. Figure 1.3 shows an example of a learning curve for quality. The figure is based on data from the production of the same advanced jet aircraft discussed earlier. Figure 1.3 plots the number of complaints made to quality assur- ance per aircraft as a function of cumulative output. These complaints, which are made internally to the firm, identify problems that are to be corrected before the product is shipped. As Fig. 1.3 indicates, the number of quality complaints made per aircraft decreased at a decreasing rate as the organization gained experience in pro- duction. Thus, experience in production was associated with improvements in quality.
Figure 1.4 shows an example of a learning curve for service timeliness for a very different production process—pizza production. The figure is based on one and a half years of data from a pizza store. The cumulative number of pizzas produced is plotted on the horizontal axis; the number of “late” pizzas per unit is plotted on the vertical axis. The organization’s metric for assessing whether a pizza was late was adopted: if the number of minutes that elapsed from when an order was received to when the pizza was completely prepared exceeded a prespecified limit, then the pizza was coded as late. Figure 1.4 depicts the classic learning-curve pattern: the number of late pizzas per unit decreased at a decreasing rate as experience was gained in production.
Fig. 1.3 The relationship between number of complaints about quality per aircraft and cumulative output. Note: Reprinted with permission from L. Argote, Group and organizational learning curves: Individual, system and, environmental components, British Journal of Social Psychology: Special Issue on Social Processes in Small Groups, Volume 32 (March, 1993). Copyright 1993. Units omitted to protect confidentiality of data
Fig. 1.4 The relationship between proportion of late pizzas and cumulative output. Note: Units omitted to protect confidentiality of data
2. Understanding Productivity Differences
Another trend in recent research on organizational learning curves is identifying factors explaining the variation observed in organizational learning rates (e.g., see Adler & Clark, 1991; Argote, Beckman, & Epple, 1990; Bahk & Gort, 1993; Hayes & Clark, 1986; Ingram & Baum, 1997; Lester & McCabe, 1993; Lieberman, 1984; Pisano, Bohmer, & Edmondson, 2001). This work focuses on understanding why some organizations are more productive than others. Research on these productivity differences was stimulated by both practical and theoretical concerns. On the practi- cal side, many manufacturing organizations in the USA in the 1980s experienced enormous productivity problems (Minabe, 1986). Although the USA had once enjoyed a very large productivity advantage relative to other industrial countries, the productivity of firms in other countries caught up with and even surpassed US productivity in many sectors during this period (Krugman, 1991). Understanding sources of productivity differences became a central concern.
On the theoretical side, many scholars at this time were shifting to the view that interesting performance variation occurred at the firm rather than the industry level. Resource-based and evolutionary views of the firm were gaining momentum in the fields of strategy and organizational theory (Barney, 1991; Henderson & Cockburn, 1994; Lippman & Rumelt, 1982; Montgomery, 1995; Nelson, 1991; Prahalad & Hamel, 1990; Teece, 1998; Winter, 1995). These theoretical perspectives emphasize differences across firms and aim to understand the source of the differences. Thus, research on identifying factors contributing to organizational learning curves occurred against a backdrop of intense concern about productivity problems and a theoretical shift to the firm as a fundamentally important unit of analysis.
3. Organizational Forgetting
A third important new trend in research on organizational learning curves is exam- ining the dynamics of knowledge acquisition (and loss) by firms. Research in this area examines whether organizational knowledge is cumulative and persists through time or whether it decays or depreciates (e.g., Argote et al., 1990; Benkard, 2000). This stream of research occurred amidst the same currents discussed previously as motivating work on organizational productivity because differences in the ability to retain knowledge can contribute to productivity differences across organizations. In addition, the anticipated retirement of the baby boomer generation also stimulated work on knowledge retention. Research on the dynamics of knowledge acquisition and retention also occurred against a backdrop of increased interest on the part of many scholars in applying cognitive principles to understand organizational phe- nomena (Walsh, 1995). Developments in computing and information systems (Stein & Zwass, 1995) also stimulated and were stimulated by work on organizational knowledge acquisition and retention because a potential benefit of these systems is their enhanced capacity for capturing and retaining knowledge.
4. Knowledge Transfer
The area of research on organizational learning curves that has exploded in recent years is work on the transfer of knowledge across organizations (e.g., Argote et al., 1990; Argote & Ingram, 2000; Baum & Ingram, 1998; Borgatti & Cross, 2003; Bresman, 2010; Carlile, 2004; Darr et al., 1995; Hansen, 1999; Haunschild and Miner, 1997; Henderson & Cockburn, 1994; Powell, Koput, & Smith-Doerr, 1996; Reagans & McEvily, 2003; Szulanski, 1996; Tsai, 2002; Zander & Kogut, 1995). This research examines whether productivity gains acquired in one organization (or unit of an organization) transfer to another. That is, the research examines whether organizations learn from the experience of other organizations—whether organiza- tions benefit from knowledge acquired at others. For example, research might exam- ine whether one shift learns from another at a manufacturing plant (Epple et al., 1996), whether one hotel learns from others in its chain (Baum & Ingram, 1998), or whether one biotechnology firm learns from others linked to it through a Research and Development (R&D) alliance (Powell et al., 1996).
Research on knowledge transfer was shaped in part by the same concerns about productivity that shaped the trends noted previously. An organization that is able to transfer successfully a productivity improvement made at one establishment to another will be more productive than its counterparts who are ineffective at knowl- edge transfer. Advances in computing and in information systems also stimulated and were stimulated by interest in knowledge transfer because these systems have the potential for facilitating knowledge transfer across geographically distributed sites (e.g., Goodman & Darr, 1996).
In addition, interest in knowledge transfer was stimulated by a shift in the mode of organizing used at many firms from large integrated facilities to small, distrib- uted sites (Galbraith, 1990). This shift enables firms to take better advantage of differences in expertise, labor costs, and demand for their product around the world. For example, rather than have all product-development activities occur at one centralized site, an organization might have small teams with expertise in aspects of product development distributed around the world. Similarly, aspects of manufacturing are becoming separate and distributed rather than concentrated at one site (“Survey of manufacturing,” 1998). More manufacturing is being done by multinational companies that are able to capitalize on differences in capabilities around the world (Blumenstein, 1997). Effective operation of global firms requires that distributed expertise be coordinated—that knowledge be transferred from one expert to another and from one site to another. Thus, the successful use of this organizational form requires the ongoing transfer of knowledge (Argote, Denomme, & Fuchs, 2011).
Another trend that contributed to interest in knowledge transfer is the growth of the multiunit, multimarket organizational form, which includes franchises and chains. This organizational form is used increasingly in both the manufacturing and service sector in industries including hospitality and health care (Baum & Greve, 2001). Because multiunit organizations produce the same product or deliver the same service in different markets, knowledge acquired at one establishment can be transferred to others. Thus, the performance of the units can be improved through knowledge transfer.
Increased interest in organizational learning curves occurred in the context of increased interest in the more general topic of organizational learning. Levitt and March (1988) published an influential theoretical piece on organizational learning in 1988. Huber (1991) provided a review and integration of literature on organizational learning (see also Easterby-Smith, 1997). The amount of empirical work on organi- zational learning increased dramatically in the 1990s (Miner & Mezias, 1996). Numerous books and articles on organizational learning and knowledge were pub- lished (Argyris, 1990; Davenport & Prusak, 1998; Easterby-Smith & Lyles, 2011; Garvin, 2000; Gherardi, 2006; Greve, 2003; Lipshitz, Friedman, & Popper, 2007; March, 2010; Nonaka & Takeuchi, 1995; Senge, 1990). Research on factors affect- ing organizational learning curves and the persistence and transfer of productivity gains from learning has occurred in the context of increased interest in the general topic of organizational learning.
This monograph aims to present and integrate research on these new trends in research on organizational learning curves. This chapter provides evidence that organizational learning varies across firms and discusses theoretical models aimed at explaining the variation. Chapter 2 provides a theoretical framework for analyzing organizational learning. Chapter 3 describes and integrates work on the dynamics of knowledge acquisition and loss in organizations. Chapter 4 discusses organizational memory and develops the implications of where knowledge resides for organiza- tional performance. Chapter 5 discusses the micro underpinnings of organizational learning, with particular attention to information processing by groups. Research on knowledge transfer in organizations is presented in Chap. 6. Strategic and manage- rial implications of research on organizational learning, tensions and trade-offs in the learning process and promising future directions are developed in Chap. 7. Before these new research findings are presented, an explanation of how knowledge is measured and how learning is assessed in the learning-curve framework is required. The next section addresses these issues.
Source: Argote Linda (2013), Organizational Learning: Creating, Retaining and Transferring Knowledge, Springer; 2nd ed. 2013 edition.