an organizational learning approach to product innovation

232 J PROD INNOV MANAG 1992:9:232-245

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An Organizational Learning Approach to Product Innovation

Daryl McKee

This article examines product innovation as an organizational learning process. It provides a .framework allowing managers and scholars to relate product-innovation learning skills to organizational goals. Darvl McKee shows how different types o f organizational learning skills are involved in incremental innovation, discontinuous innovation and institutionalization q f innovation within the organization. This conceptualization can help scholars and managers diagnose an 01"- ganization's learning skills and how they relate to new product management; direct the organization toward learning more efficient and effective product innovation; and provide scholars with a structure for future research.

Address correspondence to Daryl McKee, Department of Mar- keting, College of Business Administration, Louisiana State Univer- sity, Baton Rouge, LA 70803-6314.

Introduction

Scholars and practitioners alike appear to be reaching a consensus that organizational learning is a key strategic variable [22,45,60] and one that drives innovation [71]. Organizational learning has been called "an underlying variable explain- ing performance in strategic action" [60, p. 221] that may be the only competitive advantage avail- able to the company of the future [22]. The business press worries that "even in its current de- centralized, lean and mean version" the traditional organization won't have the learning skills required to compete effectively in the 1990s [45, p. 133].

Despite growing recognition of the importance of organizational learning, the business strategy literature has focused primarily on production- oriented learning [2,3,37,69]. Within this domain "learning includes the increasing efficiency of labor as a result of practice and the exercise of ingenuity, skill, and increased dexterity in repetitive activities" [24, p. 30]. However, learning is not limited to repetitive task situations.

Organizations also can learn to innovate. Al- though the learning concept has been applied pre- dominantly to production issues, it applies equally to product innovation. Product innovation learning is the increasing effectiveness of product development efforts as a result of practice and the refinement of innovation-related skills. Individual product innovations require organizational learning and, at a more general level, organizations can learn to institutionalize innova-

© 1992 Elsevier Science Publishing Co., Inc. 0737-6782/92/$5.00 655 Avenue of the Americas, New York, NY 10010

AN O R G A N I Z A T I O N A L L E A R N I N G APPROACH J PROD INNOV MANAG 233 1992:9:232-245

BIOGRAPHICAL SKETCHE

Daryl McKee is Assistant Professor of Marketing at Louisiana State

University, Baton Rouge. He received his B.A. from the University of

Texas at Austin, his M.B.A. from Louisiana Tech University and his

Ph.D. from Texas A&M University. He has served on the faculty of

LSU since receiving his Ph.D. in 1987. His research has been pub-

lished in Journal qf Marketing. Journal of the Academy of Marketing Science. Journal of Macromarketin~.. and Jo,rnal of Health Care Marketing.

tion. These different types of learning require different skills that can be identified and developed.

This article examines the skills needed for organizations to learn to innovate. It provides a framework that allows managers and scholars to relate product-innovation learning skills to organizational goals. Different types of organizational learning skills are shown to be involved in incremental innovation, discontinuous innovation and institutionalization of innovation within the organization. This conceptualization can help scholars and managers diagnose an organization's learning skills and how they relate to new product management, direct the organization toward learning more efficient and effective product-innovation activities and provide scholars with a framework for future research.

First, the organizational learning literature is reviewed in terms of the product innovation process, and the product innovation literature is reviewed in terms of organizational learning. Next, three levels of organizational learning are associated with three types of innovation: (1) '~single- loop" learning is associated with incremental product innovation; (2) "double-loop" learning is associated with discontinuous product innovation and (3) "meta-learning" is associated with institutionalizing innovation in organization. Organi- zational learning skills are then specified in terms of each of these levels. Finally, the implications for managers and researchers are examined.

Product Innovation and Organizational Learning Organizational Learning and Strategy." An Overview

Definitions of organizational learning emphasize three factors: (1) organizational interaction with

the environment; (2) changes in organizational modeling of the environment and (3) organizational action. For example, Argyris and Schon [5, p. 323] define organizational learning as "experience-based improvement in organizational task performance." Similarly, Shrivastava and Grant [70, p. 98] define it as " the autonomous capacity of organizations to create, share and use strategic information about themselves and their environments for decision making."

Individual learning is necessary but insufficient to produce organizational learning. Indeed, organizations often " k n o w " less than their members. For organizational learning to occur knowledge must be accessible to others beyond the individual discoverers, and it must be subject to application, change and adaptation by others in the organization [42]. In this sense, organizational learning is more than the sum of learning by individual members of the organization [42,69,71]. Organizational learning involves sharing assumptions, developing knowledge of act ion-outcome relationships and institutionalizing experience [69]. In short, organizational learning requires that information be shared and stored in a form convenient to all relevant organizational members [71].

As a process, organizational learning occurs when individuals, acting from their images or maps of the organization's environment (1) "de- tect a match or mismatch of outcomes to expecta- tions which confirms or disconfirms" expecta- tions and (2) embed the resulting discoveries, inventions and evaluations in organizational memory [69, p. 12]. To perpetuate the organizational learning process parts of it are institutional- ized as formal learning systems [69].

In sum, there appears to be an emerging consensus that organizational learning (1) involves the ability of the organization to position itself vis-a-vis the environment; (2) is distinct from individual learning (i.e., organizational learning is not simply the sum of learning by organization members) and (3) responds to contextual factors such as organizational culture, strategy, structure and environment [28].

The concept of organizational learning is examined in the management literature mainly in terms of production. US Air Force production workshops dating back to 1936 demonstrate a relationship between the number of times a task is

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A. Production Cost per Unit

Cumulative Production Volume

B. Product Performance

Discontinuous S tnnovation (Double- Loop Learning) /

~..° .-, . . . . . I~-1 \ S J ~ F / Product-technology B

/ j , . \e ~ roduct Technology A

Product Development Effort

Figure 1. Production-(A) and innovation-based (B) learning relationships. *Adapted from Foster [29].

performed and the direct labor hours required to complete that task [69]. This relationship is referred to as a learning curve. The Boston Con- sulting Group extends this concept to include broader categories of organizational output and term the relationship between unit output cost and cumulative output the experience curve [10]. This relationship is illustrated in Figure IA. Be- sides learning, the Boston Consulting Group argues that specialization, investment and scale effects also influence the slope of the experience curve. Amit summarizes these influences as scale and learning effects [3]. Alberts argues that learning effects are the more powerful of the two forces [2].

The product innovation equivalent of the experience curve is the product-innovation learning curve or '~S"-curve suggested by Foster [29], illustrated in Figure lB. The predictor variable of the product-innovation learning curve is product development effort, instead of cumulative production as in the experience curve. The criterion variable is product performance, instead of unit production cost.

In the product-innovation learning curve, performance is seen in terms of customer benefits. For example, product performance for artificial hearts may be operationalized as survival time, and product performance for automobile tires may be operationalized as relative cord wear [29].

Thus, where the experience curve utilizes an internal perspective (i.e., unit cost) as the criterion, the product-innovation learning curve utilizes an external perspective (i.e., customer benefits).

Movement along the product-innovation learning curve represents incremental product innovation within a particular technology. Improve- ments in product performance increase rapidly during successive early versions of the product. Benefits from subsequent incremental product development efforts increase at a declining rate due to limitations inherent in the underlying technology [7]. At some point product innovation efforts result in diminishing marginal returns.

Movement between product-innovation learning curves represents discontinuous product innovation. This shift is required as a particular technology reaches an inherent upper performance limit. For example, after investment in development of nylon automobile tires reached a point of diminishing returns, the rate of industry return on investment in product investment increased only through a shift to polyester-based tire technology [29].

The distinction between production and product-innovation learning curves is strategically important. Each learning curve relates to a different type of organizational strategy: (1) the defender strategy, which focuses on production learning and (2) the prospector strategy, which focuses on innovation learning [55]. The defender strategy relies on production efficiencies to defend a stable product-market position. The prospector strategy relies upon innovation skills to develop viable new product-market positions.

For the defender, the production learning curve provides a useful analytic perspective. This concept and the more general concept of the experience curve form the basis for production volume and cost goals. These goals in turn are related to competitive pricing tactics that anticipate unit cost declines.

For the prospector, the product-innovation learning curve provides an analytic perspective. For firms following this strategy, movement along and between product-innovation learning curves is the basis for critical product investment decisions. However, the skills needed by a prospector firm are not clearly spelled out.

Fundamentally different o.:ganizational learning skills are needed to support each of the two

AN O R G A N I Z A T I O N A L L E A R N I N G APPROACH J PROD INNOV MANAG 235 1992 ;9:232-245

strategies. Where the defender strategy type at- tempts to maximize cumulative product ion volume for a low cost base, the prospector strategy type utilizes product deve lopment effort to maximize product performance. There is substantial research on the skills needed to move a firm down the product ion learning curve, but the research on learning skills needed to move along or between product- innovat ion learning curves lacks a systematic framework.

Product-Innovation Learning: An Overview

Nonetheless , the concept of organizational learning pervades the product innovation literature. Successful innovation is not due to environmental factors but to management action [18,20]. In particular, successful new product performance is seen as resulting from disciplined adherence to a product deve lopment process, which suggests that product innovation is a learnable activity [19].

Evidence that organizations learn to innovate is found in the historical decline of new product failure rates [9] as well as in the ability of some firms to develop new products with more consistent success than their competi tors [20,38]. These differences over time or among firms may relate to exposure to innovation learning experiences. For example, successful innovators are involved with more new products , technologies and markets than less successful innovators [59].

Learning differences also are evident in cross- cultural studies of innovation. Imai et al. [41] view certain Asian firms as successfully practic- ing what Abernathy [1] terms a "learning-by- doing" approach, compared with an "analytic- s t rategy-synthesis" approach they see many American firms following. In these "learning-by- doing" firms there is an "a lmost fanatical devo- tion to learning" that " takes place continuously in a highly adaptive and interactive manner" (p. 353).

Three levels of organizational learning are dis- t inguished in the innovation literature: incremental, d iscont inuous and organizational. Each level is supported by different organizational processes [26,32,34,41]. For example, incremental innovation requires expertise focused close to opera- tional levels with "a great deal of highly specific information on a particular aspect of a business"

[32, p. 51]. It emphasizes integration of functions such as R&D and marketing [27,33,47,65].

Discontinuous innovation involves learning to relate the organization to its environment in a new way. This often requires utilization of high- level skills that originate outside the organization [34,53] and creation of an internal environment that allows "uns t ruc tured , playful, contentious and rambling" decision processes [32, p. 54]. For this reason, small cross-functional teams are thought to be more effective [63]. Indeed, many writers indicate a need to shield ongoing opera- tions from discontinuous innovation [38,43,63].

A third theme is the organization's overall ability to innovate, as opposed to its performance on individual product innovations. This is referred to as program-level innovation, as opposed to project-level innovation [43]. At this level, the organization learns to generalize innovation skills [12]. For example, Takeuchi and Nonaka [74] dis- cuss the importance of the transfer of learning to subsequent new product development projects by assigning key people to subsequent projects and by standardizing innovation practices.

Part of this organizational or "meta"-level learning concerns how organizations deal with product failure. As Schrage [68] notes, "organizations that learn how to fail intelligently outper- form organizations that seek to minimize the fre- quency of failure" (p. 46). This suggests a cycle of learning to innovate, where new product failure is instrumental to the success of subsequent at tempts at innovation [51]. For this type of learning to occur organizations must tolerate failure and encourage employee participation across projects [33].

Another part of this learning process concerns organizational goals. Schrage [68] notes that: "Almos t without exception, at the root of the fail- ures . . . is an organization that 's kidding itself about what it really wants . . . Organizations that succeed at innovation are those that make an unwavering commitment to it" [p. 47]. This commitment often is expressed through ambitious goals like 3M's goal that 25% of sales must come from products that did not exist 5 years before [30]. Innovat ion goals focus the attention of learning. Such goals also help overcome the ten- dency for depar tments to focus on functional issues that can be counterproduct ive at the organizational level [31,39,76].

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\

Decision

Network

Product / (Information Outputs Inputs

Figure 2. Cybernetic model of product development learning systems.

Learning Levels for Product Innovation

Organizational learning provides a three-tier framework--single-loop, double-loop and meta- learning--that juxtaposes with three types of innovation: incremental, discontinuous and institutional. A simple cybernetic model (Figure 2) depicts the first two levels of learning; it clarifies the meaning of the "single-loop" and "double- loop" language and provides a visual reminder of the separation of the organizational-norms com- ponent from the routine information flow.

This model, adapted from Beer ]8], includes four primary elements: information inputs, a decision network, organizational norms and technologies and product outputs. Information inputs may be characterized in terms of the scope and depth of information obtained from the environment. The decision network consists of the linkages among decision makers required to reach a decision on a given issue. Organizational norms and technologies consist of the accepted "way we do things around here ." Product outputs include the goods and services developed and mar-

keted by an organization. These elements may be related through two learning " loops ."

"Single-loop" learning occurs when the decision network utilizes information inputs to mod- ify products without changing existing organizational norms and technologies. "Double-loop" learning involves change in organizational norms and technologies as well.

A third level of learning involves institutionalizing the ability to learn [6]. This could be referred to as "meta-learning" because it refers to a comprehensive learning mode (Bateson also terms this level "deu te ro" learning). This level of learning is not focused on a particular task (e.g., a specific innovation) but on the organization's generalized ability to improve its performance at a class of tasks (e.g., to learn to innovate). A company may perform well on a specific innovation, for example, but still lack the ability to generalize what it has learned to other innovations.

These learning capabilities are hierarchical. Meta-learning includes double-loop learning, which, in turn, includes single-loop learning. Conversely, single-loop learning is necessary but insufficient for double-loop learning. This organizational learning hierarchy can be applied directly to the issue of product innovation. In the sections below, each level of organizational learning is characterized and related to corre- sponding product innovation issues.

Single-loop learning. Because single-loop learning occurs within a given organizational framework, it emphasizes the type of association building that results from repetition and routine [28]. The organization is open to its environment, but only in ways consistent with its guiding norms and the capabilities of its existing technology. In- compatibilities between these norms and the environment cannot be reconciled by single-loop learning systems. Indeed, allegiance of organization members to existing norms and an accepted technology may cause problems to be hidden, disguised or denied [5].

Double-loop learning. Double-loop learning involves changing "what the organization is doing" in terms of its underlying norms and technologies [6,60]. This type of learning tends to be associated with revolutionary rather than evolutionary changes; it involves "big bang" product innovations [32]. As a result of this type of learning, " the way we do things around here"

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(i.e., organizational norms) may be disrupted [32].

Double-loop learning involves the organization in a new way of seeing the environment, and leads to invention, production and evaluation of responses compatible with these new viewpoints [5]. Such change requires that the organization "un learn" previously held beliefs [28]. Naviga- tion of the ambiguous and ill-defined cognitive contexts associated with double-loop learning may require a variety of new organizational learning skills [28].

Meta-learning. At a more general level, organizations are concerned with the problem of institutionalizing innovation. Single- and double-loop learning pertain to the performance of a particular task (e.g., a specific incremental or discontinuous product innovation). At the institutional level, management seeks to learn to improve the effectiveness of future innovation projects based on experience with previous product innovations, both successful and unsuccessful [6].

At each of these levels, learning must be managed; it is not automatic. A variety of skills has been shown to contribute to production-related learning. While learning skills have been identified in the product-innovation literature, they

have not been examined in a systematic framework. Such a framework would allow managers and scholars to relate product-innovation learning skills to organizational goals.

A useful framework of product-innovation learning skills should include at least two dimen- sions: learning levels and skill groups within each level. The importance of learning levels is demon- strated in both the organizational learning and product innovation literature. Normann identifies four groups of learning skills--interpersonal skills, analytic skills, organizational skills and ecological interfacing skil ls--based on the organizational learning literature [60]. In the following section, each of these product-innovation learning skill groups is linked to specific levels of product innovation: incremental, discontinuous and institutional (Table 1).

Learning Skills for Product Innovation

Four groups of organizational learning skills reflect the capabilities necessary to "enhance organizational learning and strategic action" [60, p. 226]. Interpersonal skills reflect organization members ' commitment to the organization as well as the extent to which they are free to search

Table 1. Innovation Learning Skills Framework

Innovation/learning level

Incremental/ Discontinuous/ Institutional/ Learning skill single-loop double-loop meta-learning

Interpersonal lnteJj~mctional Environmental Structuring Contact Contact Interaction Interfunctional teams Out-rotation Key contact linkage Job rotation Outsider involvement Cross-team contact

Analytic

Organizational

Ecological interfacing

Communication rewards Boundary-spanning rewards Analytic Depth Analytic Breadth Analytic training Skill acquisition Conclusive methods Exploratory methods "Competency trap" avoidance Confrontational methods System Maintenance Organizational System stability Adaptability "Camouflage" avoidance "'Unlearning"

Error tolerance Slack resources Contact Breadth Feedback diversity Feedback capacity Early warning

Contact Depth Computerization Informal networking Communications efficiency

Analytic Framework Inventory skills Create innovation chain Innovation training Meta-Norm Creation Flexible constitution Innovation goals

Domain Selection Skill relatedness Learning environments

Adapted from Normann [60].

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for valid information and to make an informed choice among alternatives [5]. Analytic skills reflect the ability of organization members to ana- lyze situations and formulate action plans. Orga- nizational skills reflect the extent to which the organization is amenable to change in structure and transfer of power. Ecological interfacing skills reflect the organization's ability to perceive and adapt to new types of stimuli. While an organization's mix of product-innovation learning skills varies with its competitive context, selected skills are associated with particular types of innovation.

Learning Skills for Incremental Innovation

Incremental innovation typically does not involve fundamental change in the norms or technological base of the organization. In this "single-loop" learning context an organization (1) focuses on interpersonal contacts within the organization; (2) analyzes relatively structured problems in depth; (3) maintains its existing structure, norms and technological base and (4) limits its attention to a stable product-market.

Interfunctional contact. Communications barriers between functional a reas- -such as marketing and R & D - - c a n inhibit incremental innovation. These barriers may occur due to inflexibility in the communications linkages and communications inefficiencies due to differences in functional perspectives.

The decision network requires flexible interpersonal contact (Figure 2). Linkages among decision makers should vary with the nature of the problem, which also may vary over time on a particular project. Traditional product development processes often fail to provide the needed flexibility, particularly on an interfunctional basis. This suggests why project teams and project matrix structures are judged more conducive to product innovation than linear functional ar- rangements [48] and why informal networks are judged substantially more important than formal channels in transferring learning among projects [541.

Innovative companies appear to enforce such informality and flexibility in decision networks. For example, at Merck, which has a $530 million R&D budget, no project team has a budget or formal authority. Project champions must con-

vince specialists from different disciplines to commit intellectual and financial resources to a particular project [16]. This arrangement appears to facilitate active and widespread networking targeted to specific project needs.

Interfunctional contact also may increase communications efficiency. For example, rotating employees among jobs in different functional areas expands their frame of reference, increases their ability to see problems from different perspectives and, as a result, improves cross-functional communications. For example, GE regularly rotates a portion of its R&D center 's 1,800 researchers to manufacturing [58]. Interfunc- tional communications may even be formally rec- ognized and rewarded in the organization. For example, Analog Devices builds such character- istics as teamwork, openness and objectivity into its performance appraisal process [71].

Analytic depth. The analytic skills of an organization represent the store of concepts with which organization members interpret and com- municate with the environment [60]. For example, the product-market matrix [4], the experience curve and portfolio matrix [10] and industrial organization approaches to competitive strategy analysis [62] provide organization members with shared perspectives on the competitive environment. De Geus refers to this as a process of language development [25].

Organizations involved in incremental innovation tend to trade off breadth for depth of analytic skill. Analytic skills often are highly specific to a particular product technology. Because learning occurs more readily in areas related to what is already known than in novel areas [17], innovations develop momentum. The stable product- market setting of incremental innovation permits development of research techniques capable of substantial analytic depth.

The danger is that this momentum may blind the organization to the emergence of a superior technology [29]. Levitt and March refer to this as a "competency trap" that occurs when "favor- able experience with an inferior procedure leads an organization to accumulate more experience with it . . . keeping experience with a superior procedure inadequate" [49, p. 322]. Cohen and Levinthal note a similar phenomenon they term " lockout ," which occurs when a firm focuses on a particular technological path and restricts in-

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flow of information about alternative technological opportunit ies [17]. This leads to a self-reinforcing cycle: As the firm invests in technical capabilities along a particular path, the apparent attractiveness of alternative paths (and its ability to move toward those paths) progressively dimin- ishes.

Organization maintenance. The learning process associated with incremental innovation emphasizes organizational maintenance. This stability permits, and derives from, efficient use of the firm's technology base. This can tend to make the organizational map of its environment and its responses rigid [75].

This rigidity can become dysfunctional. Under pressure to preserve the status quo, organization members may "camouf lage" problems by hiding, disguising or denying them, so that they become "uncorrec table e r rors" [5]. Certain problems can become unspoken open secrets, attributed to external factors, or are otherwise shifted outside of feasible action.

Contact Depth. The learning process associated with incremental innovation also leads to depth of contact with a selected environment. This may be fostered by (1) increasing the number of contacts the organization has in a given environment ; (2) increasing the velocity of information between these contact points and the organization and (3) increasing the reliability of information obtained by the organization.

Computers have accelerated the trend toward increasing both the number of contacts and the velocity of information. For example, Frito-Lay has instituted a system updated daily on hand- held terminals by 10,000 Frito salespeople gener- ating information on 100 product lines in 400,000 stores [14]. Similarly, Levi Stauss 's " L e v i L i n k " system enables the company to track product de- tails such as colors and fabrics [67].

These systems facilitate both speed and accu- racy in testing and reinforcing product changes associated with incremental product innovation. Frito 's sys tem allows the company to track performance of products by feature within regions and incrementally shift features (e.g., container size, ingredients and so on) overall and by geo- graphic area. Levi ' s system allows it to test the appeal of colors and fabrics in the field and systematically move toward popular combinations. The improved response time provided by such

systems is one of the "highest leverage points for improving per formance" [71, p. 65].

Limiting the number of changes made at any given time can improve the effectiveness with which product-related feedback is interpreted. When multiple changes occur simultaneously within a system, it is difficult to separate effects [50]. As a result, some incremental innovators deliberately reduce the number of product changes. For example, Connors Peripherals, a manufacturer of disk drives and one of the fastest-growing major firms in the United States, con- fines product innovations to one or two features at a time so that it can clarify the impact of product change and reduce risk [46].

These skills are important to a firm in the early stages of the product- innovation learning curve. At most times and in most places, incremental innovation is the driving force behind industry change. For a firm to conduct this type of innovation effectively, it must develop and maintain these "s ingle- loop" skills while avoiding the problems that can limit this type of learning. As an industry approaches diminishing returns from a particular technology, innovative firms must be prepared to engage in another level of learning.

Learning Skills for Discontinuous Product Innovation

While incremental innovation occurs within the context of existing organizational norms and technology, discontinuous innovation often requires change in an organization's norms (" the way we do things around here") and its technological base. This requires a shift in organizational structure and power.

The problems associated with discontinuous innovation are ill-structured. While incremental innovation occurs within stable p roduc t -marke t and technological domains, the domain of discontinuous innovation is inherently ambiguous.

Discontinuous innovation requires a special set of learning skills to deal with this ambiguity. The focus of market and technical contacts often is external rather than internal. A broad array of analytic approaches is considered. Organiza- tional adaptability is stressed. Breadth of outside contacts is as important as depth.

Environmental contact. Interpersonal contacts at an organization undertaking discontinuous in-


novation often are external. The organization is attempting to redefine the way it fits into its environment. This occurs because of weakening support for existing products or perceived opportunity for new products. The learning goal of the organization is to convert new environmental op- portunities into new organizational norms and technologies.

To get in touch with market and technological change, organizations may use mechanisms such as "out - ro ta t ion ," outsider involvement and rewards for boundary-spanning. Out-rotation involves placing employees into positions that require direct contact with customers, competitors and other key outside groups. For example, Hewlett-Packard regularly rotates design engi- neers to retail sales positions on a temporary basis to allow for cus tomer contact. Other organizations actively involve "ou ts iders" in planning or reward external contact [57,66].

Analytic Breadth. Discontinuous product innovation changes the firm and the way it fits into its environment . This requires techniques that en- able employees to "make novel associations and linkages" [17, p. 131]. These analytic techniques often are exploratory. For example, De Geus notes that Shell utilizes scenarios and simulations to trigger organizational learning [25]. These exploratory techniques allow Shell to test the valid- ity of new product assumptions.

Confrontational techniques for decision making, such as dialectical inquiry and Devil's advocacy, also may aid discontinuous innovation. Devil 's advocacy involves deliberate criticism of proposed actions. Dialectical inquiry involves specifying countersolutions and counterassump- tions to proposed actions [21, 22]. Because these techniques are confrontational, they can help move organizational thinking outside of existing frameworks. Learning may then be directed along new paths.

Organization adaptability. Discontinuous innovation also may require a shift in organizational paradigms that underlie the "way we do things around here ." Johnson [44, p. 84] defines an organizational paradigm as

the set of beliefs and assumptions, held relatively commonly through the organization, taken for granted, and discernible in the stories and expla- nations of the managers, which plays a central

role in the interpretation of environmental stimuli and configuration of organizationally relevant strategic responses.

A shift in paradigms associated with discontinuous innovation may require that the organization facilitate "unlearning," embrace errors, build in slack resources needed for transitions and create new goal structures.

"Unlearn ing" is the first step toward new organizational behavior [36]. Hedberg [35] notes that unlearning and " ref raming" enables an organization to move between environments or adapt to change in a given environment . Removing people is an important way in which organizations get "rid of their past and unlearn" [35, p. 18]. Listening to dissent within the organization and adopting experimental frames of reference also contribute to unlearning [61].

An "er ror -embracing" control structure also encourages change [40]. "Error-avoiding" control structures focus on the rationalization of decisions and avoidance of adverse consequences to the decision maker. "Error-embracing" organizations emphasize innovation rather than de- fensibility of actions and utilize self-control rather than top-down control.

Hrebiniak's [40] error-avoiding organization is likely to be incapable of double-loop learning. Members of error-avoiding organizations learn to avoid ill-structured situations (e.g., discontinuous innovation) and to defend necessary decisions with highly analytic support.

In error-embracing organizations, members are encouraged to seek out ill-structured situations and mistakes are seen as a natural by-product of an uncertain operating environment [40]. Thus, companies with high-level innovation goals such as Johnson & Johnson and 3M emphasize error-tolerance [13,15].

Availability of organizational slack also may encourage discontinuous innovation [23]. Based upon a review of the literature, Bourgeois [11] defines organizational slack in terms of a "cush- ion of actual or potential resources" that allows the organization to adapt to or initiate change. Stoner [73] suggests that the need for slack resources is directly related to the level of uncer- tainty confronted by an organization. Miles [56] suggests several methods for creating organizational slack, including bringing new skills into the

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organization, diverting resources internally to create new skills and generalizing existing skills to meet new situations.

Contact breadth. Diverse points of reference help in interpreting an ambiguous environment. For this reason, firms engaged in discontinuous innovation may increase (1) the diversity of information obtained from the environment; (2) the capacity of feedback systems and (3) the sensitiv- ity of the organization to remote signals.

Diversity increases the " r i chness" of information. For example, by allowing a problem to be seen from multiple perspectives, qualitative analytic approaches (e.g., case studies and focus group discussions) may provide a broader understanding than can be obtained from quantitative studies. The capacity of feedback systems may be increased by using "high context" communications methods (e.g., traveling to face-to-face meetings and use of televideo conferencing). Sen- sitivity to remote signals stretches organizational attention across time rather than topics of attention.

As at tempts to improve a product technology reach a point of diminishing returns, the firm faces the transition to a new technology. The skills needed for this transition often are not like those used to improve the old technology. They are more exploratory and far-ranging. These skills must help the firm reexamine " the way we do things around here . " Beyond this, the firm can at tempt to learn from all its innovation projects and p rog rams- - t o institutionalize innovation.

Learning Skills for Institutionalizing Innovation

What a manager or team learns from a particular innovation project can be lost to the organization as a whole. Just as a new product development team knows less than the sum of what its members know, so an organization knows less than its product deve lopment teams. To be effective over the long run, an organization must learn the innovation process. This ability to learn to innovate is a strategic skill. It sets the limits within which innovation will occur.

As with individual innovations, learning the innovation process involves sets of skills. Instead of communicat ions that cut across functions, the concern is organizational communicat ions that cut across teams. Instead of finding techniques to

solve problems, the concern is on developing new techniques to discover problems. Instead of em- phasizing maintenance and adaptation of the organization, the emphasis is on creation of a meta- norm within which the organization can evolve.

Structuring interaction. Institutionalizing innovation brings interpersonal contacts to a new level. The new product development team provides a structure for contact among functional areas that is focused on a particular project. For the organization to learn from the experience of these teams, however , they must be linked. Team learning about innovation must be shared for the organization to learn to innovate. Periodic de- briefings among product development t e a m s - - a forum for shared learning about successful and unsuccessful approaches to innova t ion- -can provide one mechanism for this to occur.

Linking the organization to key outside contacts also may enhance meta-learning. These con- tac ts - -consui tan ts , trainers, outside board mem- b e r s - o f f e r multiple learning benefits. Their access to other organizations allows them to serve as "learning agents ." Moreover, their independence allows them to avoid learning barriers.

The Council of Economic Advisers (CEA) has been cited as an example of excellent use o f " o u t - s iders" [77]. Established by the Employment Act of 1946 to give the President and Congress independent expert advise on general economic policy, the three-person CEA is uncommit ted to de- partmental or special viewpoints. Because of its small size, its access to the top levels of "management , " its professional independence, its ties with universities and its broad mission, the CEA "epi tomizes organizational defenses against in- formational pathologies" [77]. It enhances organizational learning because it is outside the organization. The success of the CEA suggests the value of an outside technical advisory council (TAC) to provide the firm with independent advise on innovation alternatives.

Institutionalizing analysis. Top management creates an analytic f ramework within which innovations are developed. This f ramework is built by (1) cultivating particular technical skills within the organization; (2) encouraging an innovative mindset among particular employee groups (e.g., new product deve lopment teams) and (3) spon- soring ongoing new product experiments and linkages among experiments. Decisions in these

242 J PROD INNOV MANAG D. M c K E E 1992;9:232-245

areas shape the innovations of an organization. They are seldom given systematic attention.

Management of this type of "intellectual capital" begins with locating, defining and linking skills within the organization [72]. It extends to matching skills with the organization's strategic plans. Most firms neither inventory nor plan for skills.

The difficulty of managing skill is its intangibil- ity. Comments one consultant: "When you buy a machine, you know exactly how much value it adds. When you hire a r e s e a r c h e r . . , you don' t know where it will lead" [72, p. 46]. When they cannot be replicated by competitors, such skills can provide a unique source of competit ive advantage. Managing skills is a way of creating this advantage.

Innovativeness is independent of technique. An organization may foster the shared learning of technical skills but fail to convert those skills into commercially viable new products. The missing dimension is an innovative perspective. Individ- uals with functional skills in marketing and R&D may fail to "make new connect ions" until they learn to think innovatively, rather than just tech- nically. This implies a need for continuous and creative innovation training.

While an organization may hire "intellectual capital ," it develops its own unique skills by linking new product development projects over time. These projects, both successful and unsuccessful, probe alternative product innovation paths. It has been argued that, in effective organizations, decisions result from a sequence of experiments that continue past the point where "acceptable" solutions have been reached [35]. The function of these experiments is to change " the mental models that . . . decision makers carry in their heads" [25, p. 71].

Cultivating the development of skills within the organization provides the resources for organizational learning. Creating a climate of innovation is still necessary to convert these learning resources into consistent patterns of behavior.

Creating an innovation meta-norm. In innovative organizations, notes Schrage [68, p. 47], "innovation is as much a core value as the route to an acceptable return on inves tment ." Only top management can establish innovation as a central va lue - - a " m e t a - n o r m " - - o f the organization. Unfortunately, top management often has a stake in the preservation of past products.

New products can shift power within an organization. Established managers often build their position based on the success of past products. New products , championed by up-and-coming managers, can be seen as a threat. New products can threaten to divert resources from existing products, and they can threaten the power of the executives who developed them.

A natural response to this threat is for established managers to hold on to power. They can do this by denying attention and resources to new projects. They also can seek to avoid criticism of existing products. As the rest of the industry moves up the product- innovation learning curve, this avoidance becomes increasingly obvious, but the rigid power structure creates a "learning block" that makes the problem difficult to correct.

A flexible organizational "cons t i tu t ion" may help to resolve this problem. Normann [60] suggests that such a "cons t i tu t ion" would include multiple power centers and the ability to shift power among them. Classic portfolio models have shown the importance of investing funds generated by mature products in new products. This has been exemplified in companies like Gil- lette, which maintains its dominance of the wet- shave market by regularly using revenue from existing products to support new products. Such fluid movement of power can be propelled by new product goals.

From an organizational learning perspective, challenging goals are a substitute for organizational failure. In many organizations quantum shifts in learning only occur after failure. This is the essence of a " tu rn -a round" with its new management, its new power structure and its new ways of seeing the market.

Successful organizations that adopt challenging innovation goals create their own sense of urgency. For example, Johnson & Johnson sets the goal that products introduced within the previous 5 years account for 22% of sales, and 3M sets a goal of 25% [13,15]. To motivate, such goals must offer the possibility for failure. March and Simon [52] suggest that this recognition drives innovation.

Fluid structure and challenging goals provide the context for development of an innovative organizational culture. Such a culture provides an advantage that is difficult for competi tors to imi- tate. In any field of competi tors, the same organi-

AN O R G A N I Z A T I O N A L LEARNING APPROACH J PROD INNOV MANAG 2 4 3 1992;9:232-245

zational barriers to learning exist. Because it helps overcome these barriers, an innovative organizational culture may be relatively rare. Once in place, it requires direction.

Domain selection. Management sets a direction for organizational learning by establishing boundaries. Typically these boundaries are expressed through decisions to f u n d - - o r not f u n d - - particular projects. Innovat ion tends to occur within these sanctioned areas, but the rate of innovation will be affected by how these areas are defined. This is seen in product ion as well.

Product ion-oriented learning is measured in terms of the relationship between cumulative production o f a given produc t and unit cost. Switch- ing among product ion of unrelated products reduces the rate of learning. This occurs because shortcuts that work in one product ion process may not work in another. Finding these shortcuts requires repeated trials. Switching production runs limits the number of trials and reduces learning.

Learning to develop new products is similar. With product innovation, however , " t r ia ls" consist of using a technological skill in a new way. Aimless product innovat ion--swi tch ing among technolog ies - - reduces the opportunity for a firm to improve a particular skill. When a firm switches among skill bases, the rate at which the organization learns to innovate based on that skill is correspondingly reduced. This is seen in studies of diversification.

Successful diversification tends to occur in areas related to the organization's underlying skill base [for a summary see 64]. Product innovation is a special case of diversification where the skill base involves use of a technology. For example, an important skill at 3M is the development and application of adhesives. This is a technological domain within which much of the organization's innovation occurs. Because it focuses innovation within a particular domain, the organization's ability to learn to innovate is accelerated. It also can look to the outside environment for learning opportunit ies.

Certain environments seem to influence the rate at which organizations learn. Typically organizations select p roduc t -marke t domains based on profitability or other performance criteria. An additional considerat ion may include learning op- portunities. Normann [60] suggests that compet- ing in difficult environments promotes organiza-

tional excellence and raises the level of organizational ambitions by confronting it with new technologies, new needs and new ideas. Por- ter [62] also has emphasized the value of challenging competi tors and customers. Selection of competi tors and buyers may be as important to innovation as deve lopment of technological skills. Probably these forces interrelate.

Summary and Conclusions

Scholars and practitioners have worked exten- sively on learning to produce but have done little systematic work on learning to innovate. This may be because product ion skills lend themselves to study. Learning is defined in terms of the results of a repetitive task; it may be easier to see and measure product ion as a repetitive task than innovation. Nonetheless , it seems clear that organizations learn to innovate. The purpose of this article is to clarify how this type of learning occurs.

A starting point for examining an organiza- t ion's learning agenda is its strategy. Firms with a defender-type strategy are more likely to emphasize production-oriented learning. Firms with a prospector- type strategy are more likely to emphasize innovation-oriented learning at both the product and organizational levels. A mismatch of organizational strategy and learning style can be the foundation for innovation failure.

Even when an organization's strategy is consistent with its style of learning, the process must be managed. This has been demonstra ted in the production-oriented learning literature. The slope of the product ion learning curve depends upon management effort; it is not automatic. The same would seem to hold true for innovation learning.

This article provides a f ramework to diagnose organizational learning of innovation. Firms engaged in incremental innovation must be supported by single-loop learning skills, such as those identified in Table 1. Firms engaged in discontinuous innovation must have these skills, as well as the identified double-loop learning skills. The few firms that do both routinely must have additional skills as well; they must somehow generalize what is learned from particular innovation projects to the firm's next innovation. This also suggests opportunit ies for research.

Academic research on innovation has a strong learning orientation. The problem is that much of


the work that has been done is not organized in terms of underlying learning theory. Perhaps as a result, research on innovation learning has lagged research on production learning. This article may help correct the research imbalance. For example, the alignment of organizational strategy and particular learning skills proposed in this article lends itself to empirical testing. Similarly, the relationship between strategy-learning fit and organizational performance would seem to deserve empirical study. Learning-based innovation research could also be of practical help.

Practitioners require a way of visualizing their strategy. For organizations pursuing a defender strategy, this is provided by the experience curve. For organizations pursuing a prospector strategy, it is provided by the innovation curve. The experience curve is supported by guidelines that relate management action to the slope of the experience curve. Guidelines for the innovation curve, such as those suggested in this article, allow firms to systematically examine and improve the effectiveness of their product innovation efforts.

The author expresses his appreciat ion to Thomas P. Hustad

for his editorial suggest ions and to two anonymous review- ers for their valuable commen t s on earlier drafts of this

work.

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