Know_Innov by Simantov Assessoria

Knowledge Reuse for Innovation Lynne P. Cooper Olivia E. Neece Ann Majchrzak

MANAGEMENT SCIENCE

informs

Vol. 50, No. 2, February 2004, pp. 174–188 issn 0025-1909 eissn 1526-5501 04 5002 0174

Knowledge Reuse for Innovation Ann Majchrzak

Department of Information and Operations Management, Marshall School of Business, University of Southern California, Los Angeles, California 90089-1421, majchrza@usc.edu

Lynne P. Cooper

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109-8099, lynne.p.cooper@jpl.nasa.gov

Olivia E. Neece

Claremont Graduate School, Claremont, California 91711, olivia.neece@earthlink.net

his study was conducted to better understand the knowledge reuse process when radical innovation (e.g., experiments to prepare for human exploration of Mars) is expected. The research involved detailing the knowledge reuse process in six case studies varying in degree of innovation. Across the six cases, a six-stage reuse-for-innovation process was identified consisting of three major actions: reconceptualize the problem and approach, including deciding to search for others’ ideas to reuse; search-and-evaluate others’ ideas to reuse; and develop the selected idea. Findings include (1) the need for an insurmountable gap in performance to stimulate the decision to reuse others’ knowledge; (2) the critical importance of an adapter to bridge the idea source and recipient; (3) three layers of search-and-evaluate activities in which the first layer of scanning to find ideas to reuse and the last layer of detailed analysis of ideas are bridged by a layer of brief evaluations of ideas assessing the presence (or absence) of targeted information about each idea; and (4) the differential use of metaknowledge about each idea to facilitate proceeding through each search-and-evaluate layer. In addition, reusers in the more (versus less) innovative cases redefined problems at the outset in nontraditional ways using analogies and extensions, rather than accepting the preexisting problem definition; used a substantially broader search strategy with a greater variety of search methods; and worked more closely with adapters during the latter stages of the reuse process. Key words: knowledge management; knowledge transfer; innovation History: Accepted by Linda Argote, former department editor; received March 1, 2001. This paper was with the authors for 8 months for 2 revisions.

to another (Argote and Ingram 2000). We adopt a broad definition of knowledge consistent with prior research: explicit knowledge such as drawings, analytic results, and scientific journal articles, as well as tacit knowledge such as insights, intuition, and implied assumptions (Beccerra-Fernandez and Sabherwal 2001, Grant 1996, Kogut and Zander 1992, Polanyi 1966, Teece 1981). Knowledge transfer can generally be subdivided into knowledge sharing (the process by which an entity’s knowledge is captured; Appleyard 1996) and knowledge reuse (the process by which an entity is able to locate and use shared knowledge; Alavi and Leidner 2001). We are focused on knowledge reuse. In this paper, we are interested in knowledge reuse for the express purpose of facilitating the development of radically innovative solutions. Innovative solutions are defined as solutions that represent creative (i.e., novel and useful) ideas that are implemented (Amabile 1996). Radical innovation is differentiated from incremental innovation by involving discontinuous development where unprecedented improvements or performance features are achieved

Pete, the project manager, read the Announcement of Opportunity, which called for the development of an instrument that autonomously detects and measures dust devils on Mars. Dust devils are notoriously difficult to predict and yet carry with them enough force to upset equipment, and dust so fine that it poses a hazard to future human exploration of the Red Planet. As a 20-year veteran of space mission development, Pete considered the use of “standard” (if there is a standard for Mars) meteorological solutions but they didn’t provide the advance-warning capability. Moreover, it wasn’t “sexy” enough for the NASA sponsors, so transfer of known practices wasn’t feasible. Pete considered developing a solution from scratch, but the project resources didn’t allow the time or money. So Pete embarked on a search for ideas that he could reuse and adapt to innovate. What did Pete do to find those ideas, how did he evaluate them when he found them, and what are the implications of Pete’s behaviors and decisions for knowledge management? These are the questions this paper addresses.

Introduction

Knowledge transfer is the process through which knowledge acquired in one situation is applied 174

(Leifer et al. 2000). From a knowledge reuse perspective, reuse for radical innovation is the exploitation of existing diverse ideas previously unknown to the innovator when creating a new product or service (Armbrecht et al. 2001). Kogut and Zander (1992) describe this ability of the firm to generate new combinations of existing knowledge as combinative capabilities. Grant (1996) argues that such a capability is a strategically significant resource to a competitive organization. Given the importance of understanding how these new combinations of knowledge are created, identifying ways to facilitate innovators’ search and reuse behavior is an appropriate objective. If innovators limit their search for solutions to their current personal knowledge base or existing network of sources, the extent to which radical innovation is achieved will be limited (Leifer et al. 2000, Clark and Fujimoto 1991). When innovators reuse others’ knowledge which was previously unknown to them, the creativity envelope is expanded beyond a small set of individuals (Armbrecht et al. 2001). Thus, a knowledge management system that expands the creativity envelope improves the research and development process through quicker access and movement of new knowledge. Moreover, improving the use of knowledge in innovation has benefits to the practical field of knowledge management. According to a 1997 Ernst & Young survey of executives (cited in Holsapple and Joshi 2000), innovation is seen as the area of greatest payoff from knowledge management, even though such efforts to date have mostly focused on operational productivity improvements (Davenport et al. 1996).

Existing Literature on Knowledge Reuse

There are several frameworks in the literature for understanding knowledge reuse. Grant (1996) categorizes these frameworks into those that focus on knowledge acquisition (or replication) and those that focus on knowledge integration. For example, studies on spillover effects of knowledge between related research programs, product generations, and manufacturing organizations, as well as studies of bestpractice transfers (see review by Argote 1999) focus on how a “recipient organization” acquires and applies the knowledge of the “source organization” in an effort to replicate the essential elements of the source’s knowledge. Szulanski (2000) provides an example of a study with this “knowledge reuse as replication” (KRR) focus. Grant (1996), however, argues that knowledge acquisition is not necessarily an efﬁcient approach when the objective is radical innovation. With radical innovation, knowledge is not

175 only acquired but also integrated across disparate sources of specialized knowledge. While acquisition may benefit from Nonaka’s (1994) conversion of tacit into explicit knowledge, for example, and from Brown and Duguid’s (1991) communities of practices, Grant argues that knowledge integration requires different—as yet underresearched—mechanisms. In addition to Grant’s claims, there is research from the literature on innovation to indicate that the frameworks and findings from the KRR literature may actually restrict, rather than facilitate, effective reuse for innovation. As one example, knowledge reuse is often thought to increase with initial shared experiences between source and recipient (Argote 1999, Brown and Duguid 1991, Hansen 1999, Kogut and Zander 1992, Nonaka 1994). That is, the more familiar you are with a source, the more likely you are to reuse the source’s knowledge. However, research on innovation diffusion (Rogers 1983), new product development (Dougherty 1992), creativity (Amabile 1996, Unsworth 2001), and how people reuse knowledge when innovating (Swan 2001, Gray 2000) comes to the opposite conclusion: divergence and lack of shared experiences are critical for developing new ideas. For example, Hargadon and Sutton (1997) explain why IDEO is so productive at repeatedly generating innovative ideas: employees identify solutions in other domains that have nothing in common with their focal domain. Allen’s (1977) research demonstrates that innovators get creative ideas from a variety of sources, including searches of knowledge bases with unknown sources. Thus, knowledge reuse for radical innovation (KRI) may not rely on shared experiences between sources and recipients as completely as KRR. KRI may also differ from KRR in the evaluation criteria applied to knowledge being reused. While any knowledge is likely to be reinvented as it is reused (Argote 1999, Rice and Rogers 1980), adaptation of knowledge is likely to be greater and more deliberate in radical innovation than in the transfer of best practices. Does this greater need for adaptation affect how knowledge is evaluated? For example, is knowledge that is being considered for reuse in a KRI process evaluated not only for its reuse potential but also for its adaptation potential? KRI may also differ from KRR in the content of the knowledge being transferred. Szulanski (2000) and Zander and Kogut (1995) found that practices with clear cause and effect relationships that were codified and trainable were more easily transferred. However, because radical innovation involves the transfer and integration of largely tacit knowledge (Leonard and Sensiper 1998), the knowledge being transferred is likely to be ambiguous, incompletely codified, and complex. This requires reusers in a KRI

176 process to ﬁnd ways to understand the tacit knowledge being transferred. Clark (1996) and Star and Griesemer (1989) have theorized that tacit knowledge transfer is facilitated by the use of shared artifacts. These artifacts convey contextual information about the knowledge being shared, helping to clarify the meaning underlying ambiguous knowledge. This would suggest that physical artifacts may play a particularly important role in the KRI process. Finally, we do not know what a staged model for the KRI process looks like. Radical innovation proceeds less as stages from conceptualization to commercialization (as is found with incremental development) and more as sporadic trajectory changes in response to unanticipated events (Cheng and Van de Ven 1996, Leifer et al. 2000). For example, in radical innovation, idea generation and opportunity recognition do not occur at the front end as in incremental development, but sporadically throughout and often in response to organization, technical, and market discontinuities (Dougherty 1992). Thus, knowledge reuse that occurs within a radical innovation work process may not proceed as a staged model ﬂowing from opportunity recognition to execution (Szulanski 2000) or tacit-to-explicit-to-tacit conversion (Nonaka 1994). For example, Thomke (1998) and Kelly (1970) suggest that tacit conversion in the form of experiments occur throughout the KRI process, not just at the beginning and end. This review suggests that researchers need to study KRI in its own right by examining how knowledge is reused during the actual work process of innovating. In particular, we are interested in answering the following questions: How is knowledge reused for radical innovation? Is this reuse process fundamentally different from previous studies depicting a KRR process?

Research Design

Our research question suggests a research design in which we build rather than test theory. Moreover, our research design requires examining reuse as part of the actual work process of innovation, requiring ethnographic research methods (Blackler 1995, Schultze and Boland 2000). Thus, we apply Eisenhardt’s (1989) guidelines for theory-building case study research in conjunction with the guidelines for hypothesis-generation case study research offered by Yin (1984) and Klein and Myers (1999). Table 1 summarizes our study design, comparing it to recommendations made by Eisenhardt (1989). Case Selection. Our case study research involved collecting and comparing data from six cases of reuse for innovation at the Jet Propulsion Laboratory (JPL). JPL is a federally funded research and development

Table 1

Our Study Design Compared to Eisenhardt’s Recommendations

Eisenhardt’s (1989) recommended steps

Our study design

(1) Getting started: Deﬁne research question with a priori constructs. (2) Select cases based on speciﬁc population and sampling to replicate or extend emergent theory.

Two research questions deﬁned. Used constructs from literature.

(3) Craft instruments to promote triangulation among data sources and investigators.

Initial semistructured interviews with JPL knowledge management staff and innovators; identiﬁed cases with archival data; examined archival data to derive initial thoughts on research questions; conducted structured interviews with at least 2 people per case; participant-observer in cases involved in interpreting data.

(4) Enter ﬁeld in such a way as to overlap data collection and analysis.

Collected verbatim transcripts; after each case, discussed notes to determine if additional questions, data, or cases were needed.

(5) Analyze data within and across cases.

Wrote up each case separately. Created matrices to identify patterns across cases.

(6) Shape hypotheses by looking for replication not sampling logic; iterative tabulation of evidence for each construct; refine definition of constructs. (7) Enfold literature by comparing results with conflicting and similar literature. (8) Reach closure about when to stop adding cases and iterating between theory and data. Guideline is 6–10 cases.

As each case unfolded, discussed notes extensively to shape emerging relationships among constructs; used next cases to replicate or modify emerging hypotheses.

JPL speciﬁcally picked as innovative organization. Selected 6 cases of successful reuse to vary along adopt/adapt continuum.

Compared our theory with innovation, NPD, and knowledge transfer literatures. Stopped adding cases and iterating when conclusions matched evidence, were practical, and interpretable to participants not involved in analysis. Ended with 6 cases.

center of over 5,000 employees (and on-site contractors) with a $1 billion budget, and is managed by the California Institute of Technology for the National Aeronautics and Space Administration (NASA). Started in the 1930s, JPL has conceived and executed missions to use robotic spacecraft to explore all of the solar system’s known planets (except Pluto). Thus, JPL specializes in developing technologies and concepts that have not been used previously, i.e., radical innovation. The six cases were selected from an initial pool of 15 cases identiﬁed through interviews with managers at JPL. The pool of cases came from two projects that had been supported by internal JPL funds. These funds were earmarked to develop detailed proposals in response to a NASA announcement of opportunity (AO). In addition, only cases involving reuse

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Table 2

Brief Description of Six Cases

Case description∗ Case 1. Lidar design Took notion of a Laser Radar (Lidar) prototype that was previously used to detect landing hazards and significantly adapted it to operate as an early warning system for detecting the presence of dust devils on Mars. Case 2. Electrometer (ELM) design Used concepts and principles of industrial devices that measure electrostatic discharge on earth, and significantly adapted them to measure electrostatic properties of Mars dust and its interaction effects on materials (for equipment and space suits) for use on Mars. Case 3. AFM design Took notion of an atomic force microscope (AFM) originally used in the semiconductor industry to test surface smoothness, and adapted it to characterize particles on Mars. Case 4. AFM tip array Took concept of multiple AFM tips originally used to increase scan speed by operating in parallel in semiconductor industry, and adapted it to instead provide redundancy for operation on Mars through an array of single replaceable tips for AFM. Case 5. Electrometer (ELM) materials Adopted existing set of materials from Kennedy Space Center collection for use in electrometer. Actual materials as well as test data were already available. Case 6. Magnetic (MAG) patches Adopted previously used Mars magnetic experiment to fit on new Mars mission in different size package.

∗

Degree of innovativeness

Informants and informant role

High (change to form, ﬁt, and function)

Project Manager (Reuser) Scientist (Reuser) Engineer (Participant)

High (change to form, ﬁt, and function)

Engineer (Reuser/Adapter) Project Manager (Participant) Scientist (Participant)

Medium (change to ﬁt and form; some change to function)

Project Manager (Reuser) Scientist/Engineer (Reuser)

Medium (change function and minor change in form)

Scientist/Engineer (Reuser) Project Manager (Participant)

Low (change to ﬁt and minor change in form)

Engineer (Reuser/Adapter) Project Manager (Participant) Scientist (Participant)

Not innovative (minor changes to ﬁt)

Scientist (Reuser) Engineer (Reuser) Engineer (Participant)

All but the ﬁrst case came from Project #1.

of knowledge which was not initially known to the innovator and that led to an innovation were considered. The cases in the pool were stratified by degree of innovation; innovation was defined as degree of discontinuity in form, fit, or function from generally accepted approaches to the same problems. The manager of both projects along with the participantobserver made judgments about the degree of innovation in each case and the level of confidence that the current approach would continue beyond the proposal selection process into development.1 From this pool, we selected six cases where confidence in the solution being continued into development was high, and which ranged from little innovation to substantial radical innovation. We stopped at studying six cases when theoretical saturation was achieved. This number of six cases falls well within Eisenhardt’s (1989) suggestion of 3–10 cases required for theory building. The six cases are described in Table 2. 1 The JPL product development life cycle begins with concept development and proceeds through preliminary design, development, integration and testing, and operations. To reduce risk, proposal teams strive to create as mature a concept as possible during proposal generation. In the two projects we picked, concept development and preliminary design were part of the proposal development, after which the project team underwent a NASA competitive selection process. Thus, the six stages of knowledge reuse occurred during the concept development and preliminary design activities and, for Project 1, continued after selection.

In sum, by selecting six reuse cases arrayed along a continuum of innovation, we believe we have met Yin’s (1984) call for replication logic in case selection. In addition, we have met Eisenhardt’s (1989) call for selection to be based on a population (i.e., as the population of cases of reuse for innovation) that controls for extraneous variation. The six cases came from the same organization, JPL, thus controlling for organizational culture that encourages innovation (Amabile 1996). The six cases came from two similar projects, thus controlling for task differences, a factor found by Becerra-Fernandez and Sabherwal (2001) to affect knowledge reuse. Finally, all six cases were led by the same manager, thus controlling for the important role played by project managers in new product development efforts (Clark and Fujimoto 1991). Data Collection. Building on a tradition in the innovation literature of using retrospective tracer studies (Rogers 1983), data collection focused on developing a detailed timeline for each case. These timelines were developed based on review of documents (AO, ﬁnal proposals, e-mails, and engineering notes) and repeated interviews with reusers in each case. A minimum of two informants per case were used in addition to the archival information. The informants included reusers and team members participating in team discussions with the reusers.

178 The interview protocol consisted of a set of structured, open-ended questions asking informants to describe the knowledge they reused, reasons why they reused this knowledge, the problem that they were trying to solve with the reused knowledge, the initial state of their domain knowledge for solving the problem prior to finding the reused knowledge, a description of the reused knowledge (in terms of the form, fit, and function it was serving when they discovered it), how the reused knowledge was discovered and evaluated, and what they did with the reused knowledge. Because the events had taken place a relatively short time before the interviews were conducted, the informants were able to answer these detailed questions. Because multiple informants were used for each case, differences between the informants arose; however, these differences were generally attributable to the different roles that the different informants played, rather than to conflicts. Nevertheless, when differences arose, we shared these with the informants to determine if our interpretations of the differences were correct. Thus, the multiple informants allowed us to generate a more comprehensive timeline of events than we could have obtained from any single informant.2 In addition to the use of multiple informants, standardized interview protocol, and standardized data collection format of timelines, Eisenhardt (1989) recommends that theory-building case study research should consider the use of multiple investigators with complementary insights. We were fortunate because our research team consisted of a senior faculty member, a doctoral student intern who was given permission to join JPL’s knowledge management team for a year and spend time at JPL learning the culture and context, and a development engineer who had participated in an operational (rather than innovationgeneration) capacity on the two projects and thus served as a participant observer. To avoid bias during the interviewing process, the participant-observer did not conduct the interviews; however, her first-hand experience with the team provided a perspective not typically obtained through interviews. An example of a timeline (for the most-innovative case) is shown in Figure 1.3

Results

Because Szulanski (2000) provides one of the few operationalizations of stages in the knowledge 2 The timelines span different periods, but all start with concept development and include preliminary design activities which culminated in a high-conﬁdence commitment to a speciﬁc design approach. During the study period, the instruments under development reached varying degrees of maturity ranging from conceptual design to actual hardware. 3

The remaining timelines can be accessed on the senior author’s website at www-rcf.usc.edu/∼majchrza.

transfer process, and his process is conceptually similar to Rogers’s (1983) well-respected innovationdecision process, we initially tried to code each action taken in the timeline by the stages offered by Szulanski (2000): “Formation of transfer seed,” “Decision to transfer,” “First day of use,” and “Achievement of satisfactory performance.” During the coding process, we found that the timeline actions did not fall neatly into Szulanski’s stages; nevertheless, there were sufficiently similar actions across the six cases that actions could be coded, then grouped by precedence ordering. The actions for all the cases were then displayed on a single chart to observe commonalities across the cases. A summary of this chart is shown in Table 3 and is described in the remainder of this section.4 Reconceptualization Stage For each case, the reuse process began not with the formation of a “transfer seed,” as suggested by Szulanski (2000), but with a definition of the problem. The participants had the option to narrowly interpret the problem as portrayed in the NASA AO. In the least-innovative case (mag patches), they chose that option. In the other five cases, however, the respondents chose to redefine the problem in a way that would benefit from an infusion of as-yet unknown ideas, requiring the need for radical innovation. For example, in the most-innovative case (Lidar), the participants chose not only to characterize the meteorological phenomenon as called for in the AO but also to create an early warning system. In the atomic force microscope (AFM) design case, the reuser describes his reconceptualization: The AO called for measurement to be taken on particles of <1 micron. The traditional approaches were rejected as imposing too many constraints (size, sample type and preparation, high vacuum and voltage requirements). So the scientist considered a new approach, atomic force microscope.

Respondents offered several reasons why they believed radical redefinitions were necessary. First, these redefinitions were motivated by the competitive nature of the “marketplace,” in which proposed Mars projects that were perceived as having greater impact on the scientific community were more likely to be selected in the NASA competitive process. The respondents had some belief that radically different ideas would offer the greatest benefits to the scientific community. Characteristics of the individual (i.e., recipient) also played a role in the redefinition 4 The specific pattern-matching tables on which this summary table is based can be accessed on the senior author’s website at wwwrcf.usc.edu/∼majchrza.

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Figure 1

Example Timeline for Lidar Design

1.1 [C] R reads AO. Problem is: “In 45 days develop a tiny lightweight instrument that will autonomously detect and measure dust devils on Mars to characterize strength and frequency of hazard to later human exploration.” 1.10 Project engineer's husband serendipitously installs on his computer initial results from a prototype laser range finder which provides info about where rocks and hazards are to autonomously guide a lander during landing.

1.13 [BE] R asks himself: maybe we can convert a scanning laser range finder from scanning for rocks to scanning for dust devils (Dec to adapt Alt#3).

1.2[C] R considers traditional soln: measuring inside weather phenomenon using standard meteorological solns. Decides soln isn’t sufficient because it doesn’t provide advanced warning, 3d imaging, or measures of velocity, size, and accompanying phenomenon. 1.11 Project engineer suggests to R to look at husband's data to see the kind of data one gets from laser.

Evaluate Alt#3.

1.15 [C] R meets with team expert on Lidar to determine costs and risks of Lidar approach.

1.6 [S] R defines search to include finding lidars with different functions (sky, hazards terrain) in different conditions (stationary, scanning)

1.4[C] R uses radar as an analogy for the operating principle: wants a system to do for dust devils what radars do for thunderstorms. Because radar can’t measure dust, substitutes Lidar because knows Lidar measures dust.

1.7 [S] R contacts engineers, scientists, rover operations, researchers via Internet, friends.

1.5 [DS] R asks: can we do it ourselves? (Alt#2 discarded as too expensive.)

1.16 [S] R goes to see “old buddy” involved in lasar range finder project to learn more about it.

1.8 R remembers Russian experiment on Mars 98 using Lidar (Alt#1).

1.9 [BE] Russian space instrument not available. (Alt#1discarded.)

1.12 [C] R looks at husband’s data which visually indicates benefits of concept of using laser range finder.

1.14 [BE] R asks other team members to see husband’s data to

1.17 [BE] R discovers 2 prototypes for rock scanning had already been built by 2 firms identified by buddy as reputable: one in U.S. (Alt#3a) and one in Canada (Alt#3b).

1.18 [BE] R remembers that AO says Canadian Space Agency (CSA) willing to contribute to mission. Has idea CSA might be interested. 1.19 [BE] R contacts Can. firm for info and to see if interested.

1.20 [IA] R examines data from Alt#3b firm’s prototype and meets with firm about Alt#3b. In-house ballpark costing indicates possibility of cost overrun if idea #3a developed (Alt#3a discarded). 1.25 [IA] R examines Alt#3b firm’s proposals for prototype for Alt #3b and determines it’s too big/heavy for size/mass of instrument (dec to adapt Alt#3b).

1.3 [C] R defines problem to require innovation: provide info on dust storms so humans will have enough info to understand and predict hazardous weather conditions.

1.21 [IA] Team expert conducts analytic studies of Alt#3b concluding meets “borderline” mass, volume, and power requirements.

1.26 [IA] R works with team members to come up with ideas to make Alt#3b smaller by integrating with a camera from U of A.

1.22 [S] Team member examines AO to get names to contact to get CSA names. Contacts CSA and asks them to contribute Lidar for Alt#3b.

1.27 [IA] Team expert has several subsequent meetings with Alt#3b firm to determine if adaptations to Alt#3b can be made. 1.28 [BE] U of A scientists suggest Alt#4. Team looks at Alt#4 but because Alt#3b is “free,” Alt #4 considered fallback.

because all participants considered themselves innovators, had knowledge about the scientific market for their work as well as the current technologies that serve the market, and had histories of innovating (as evidenced by the patents they held). Thus, they had significant interest in innovating as well as the requisite experience in their field to know what was innovative and what was not. Project factors also appeared to influence the degree of innovation desired. There was only a limited amount of funding and risk that was acceptable. Innovators, together with other project members, decided which ideas (and AO problems) would be handled in a more-or-less innovative fashion. Finally, the organizational factor of JPL affected which problems to redefine: JPL was perceived in the science community as having a special expertise in space instruments (which was the focus of the redefinition for the three most-innovative cases), rather than in space materials (the focus in the two least-innovative cases). As a first step in the KRI process, then, radical reconceptualization was necessary to set the stage for innovation to occur, regardless of whether the solution would eventually be reused or new. The radical reconceptualization established a challenging vision—

1.23 [BE] CSA agrees if advocacy comes from Canadian scientists.

1.24[S] R searches internet and contacts Canadian scientists

1.29[F] Team expert & Alt#3b firm work to make adaptations to Alt#3b. 1.30 [F] Team expert and Alt#3b firm build separate software models to improve performance of Lidar.

1.31 [F] Data exchanged by e-mail/phone to converge on final solution .

a goal that excited, motivated, and directed the scientists’ efforts to strive for an unknown future state. Simultaneously with their radical redeﬁnitions, reusers in the more innovative cases developed conceptual approaches which were ambitious and not tied to the past; they used analogies and extensions to anchor the concepts. For example, the reuser in the most-innovative case used the analogy of an earthbased thunderstorm warning radar to describe an instrument to detect dust devils on Mars: Our focus was on developing an instrument to study dust devils on Mars. But the problem was how do you get a machine to tell when the dust devil has arrived and turn the machine on to take pictures and measurements. Well, I thought about radars at airports. Isn’t this what radars do? But radar can’t be used here because it has a much longer wave length and needs harder things like airplanes. Lidar, however, could be used. Point it at the sky, swivel it around, and tell whether there is a dust devil. This is a novel use of Lidar.

To use analogies and extensions required reusers in the more-innovative cases to not only be knowledgeable about traditional approaches, but also to be aware of, and open to, nontraditional approaches that

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Table 3

Brief Summary of Actions Taken in Each Case

Reconceptualize problem (C) Case 1. Lidar design (most innovative) R reads AO and rejects traditional approach as insufﬁcient. R redeﬁnes problem innovatively to not just characterize meteorological phenomenon but also create early warning system. R uses analogy (radar) to suggest new approach.

Case 4. AFM tips R reads AO and deﬁnes problem as the challenge of needing to autonomously change tips (not done previously). No traditional approach available. R considers new approach (replacing tips).

Brieﬂy evaluate ideas (BE)

In-depth analysis of ideas (IA)

Develop idea fully (F)

R acknowledges too expensive to invent without reuse.

R searches alternative functions, providers, geographies, and conditions of Lidar; makes contacts through Internet, AO, colleagues.

R brieﬂy evaluates 4 ideas for existence of data, source, and adapter attesting to ideas’ credibility, relevance, and adaptability.

R and adapter examine data, models, and studies on 2 ideas to identify adaptations required. R and adapter generate and evaluate prototypes of possible adaptations.

R and adapter work together to implement adaptations into ﬁnal solution.

R recognizes current expertise insufﬁcient.

R searches seemingly unrelated industries (semiconductor, ESD, British textiles) using Internet, catalog, and experts to generate ideas about electrometers. R ﬁnds a handheld electrometer with which to experiment. R connects with electrostatic community.

R brieﬂy evaluates 4 ideas for existence of drawings, source, and adapter attesting to ideas’ relevance, credibility, and adaptability. R decides will adapt himself.

R (as reuser and adapter) conducts experiments and analyses of data and models for 6 prototypes of Alt #2b. R generates and evaluates prototypes.

R (as reuser and adapter) meets with sources (KSC and ES community) to create ﬁnal solution.

R decides time too short to invent own solution; needs to use external AFM manufacturer.

R uses variety of methods (Internet, phone, visits) to search outside space community to ﬁnd adapter and adaptation ideas.

R brieﬂy evaluates 5 ideas for existence of prototypes, source, and data attesting to ideas’ credibility, relevance, and adaptability.

R and adapter examine data and models of Alt #4c. R and adapter generate and evaluate prototypes.

Weekly telecons between adapter and reuser to create ﬁnal solution.

R decides time too short to invent own solution.

R uses variety of methods (Internet, phone, visits) to ﬁnd anyone doing tip array research even though not for Mars.

R brieﬂy evaluates 3 ideas for existence of prototypes, data, and source attesting to ideas’ credibility, relevance, and adaptability applications. R decides will adapt himself.

R (as reuser and adapter) examines data, models, and prototypes for 2 ideas (Alt #1a, 1b).

R ﬁnalizes tip array design in discussions with source.

Case 3. AFM design R reads AO and rejects traditional approach. R considers new approach (AFM) not used in space before. R conducts quick experiment to evaluate concept as feasible.

Scan for reusable ideas (S)

Majchrzak, Cooper, and Neece: Knowledge Reuse for Innovation

Case 2. ELM design R reads AO and rejects traditional approach. R redeﬁnes problem innovatively as creating a new measurement device. R uses analogies (pressure gauges, robot arm) to suggest new approach. R checks with expert to evaluate implications of concept for generating useful science.

Decide to search (DS)

Reconceptualize problem (C) Case 5. ELM materials R reads AO and deﬁnes problem innovatively as selecting materials to embed in ELM. Traditional approach of using representative materials doesn’t work. Consider new approach (materials as references).

Case 6. mag patches (least innovative) R reads AO. R accepts deﬁnition of problem in AO as conducting dust experiments in ways done previously. R accepts traditional Mars approach by basing experiment on magnetic patches.

Decide to search (DS)

Scan for reusable ideas (S)

Brieﬂy evaluate ideas (BE)

In-depth analysis of ideas (IA)

Develop idea fully (F)

R decides time too short to invent own solution.

R uses variety of methods (catalogues, KSC database, visits) to ﬁnd materials that may be used as reference for Mars. R searches seemingly unrelated industries (space suits, semiconductor, and British textiles) to generate ideas about electrostatic materials.

R brieﬂy evaluates 2 ideas for existence of source, adapter, and data attesting to ideas’ credibility, relevance, and adaptability. R decides must be own adapter.

R (as reuser and adapter) examines data, models, and prototypes from source on 4 ideas (Alt #2, 3, 4, 5). Generate and evaluate prototypes.

R has close discussions with source (KSC) to yield concurrence on material selection.

R decides not to invent own experiment because mag patches already exist and insufﬁcient funds to pursue alternatives.

R uses scientiﬁc journals to get names of scientists who have done traditional approach in past.

R verifies benefits, costs, and risks of using mag patches from scientific journals and discussions with source. Source agrees to adapt.

R and adapters exchange speciﬁcation requirements for adaptations, jointly developing prototypes and conducting experiments.

Adapters transfer adapted artifacts and lessons learned to R.

Same: Assess each idea in terms of credibility, relevance, and adaptability; make assessments based on existence of supportive data without examining data in detail; identify need for adapter role. Different: More-innovative cases evaluate multiple ideas.

Same: Examine and manipulate data, models, and prototypes; interactions with source or adapter to generate prototypes. Different: None.

Same: Discussions between reusers and adapters. Different: Moreinnovative cases focus discussions on joint technology development; less-innovative cases focus discussion on technology transfer.

Summary of similarities and differences between more- and less-innovative reuse cases Same: Read AO; knowledge about Same: Decide not to Same: Engage in scanning traditional approaches; generate a invent own solution behavior to identify potentially conceptual approach before and to seek out reusable idea(s). deciding to search and proceeding reusable ideas when Different: Rs in more-innovative to evaluate speciﬁc ideas. insurmountable perf cases conduct broader Different: Rs in more innovative cases gap exists. searches of nontraditional aware of nontraditional approaches; Different: None. communities, using variety of redeﬁne problem innovatively and search methods. develop a conceptual approach which is ambitious, not tied to past, that postpones detailed consideration of constraints.

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(Continued).

Table 3

Note. Cases are listed from most innovative to least innovative.

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182 might lead to greater levels of innovation. For example, in the second-most innovative case (electrometer (ELM) design), the reuser generally knew of devices that measure electrostatic properties on earth. This inspired him to believe that there may be a way to measure electrostatic properties on Mars and thus propose such a conceptual approach. He extended the basic principles via an analogy to other measurement devices such as pressure gauges, and by using a robot arm as an analogy for the motion of an astronaut on the surface of Mars. In sum, at this early reconceptualization stage, individuals needed to balance the apparent paradox of suggesting wildly ambitious conceptualizations, while having conﬁdence that someone, somewhere, would have an idea that would help them operationalize their ideas. Decision to Search Having reconceptualized the problem and approach, the respondents proceeded to the next stage: the decision to initiate a search for reusable knowledge. Unlike Szulanski’s (2000) decision to transfer, this second stage involved individuals considering whether to invent their own solution—a clearly preferred strategy as inventors—or to examine others’ ideas for possible reuse. For our respondents to even consider examining others’ ideas required that they acknowledge that they did not have the personal expertise in the required area and that their aim in searching others’ ideas was not simply to support personal learning (so they could invent), but to actually reuse another’s ideas. We found that before engaging in a search for reusable knowledge, our respondents needed to experience an “insurmountable performance gap,” resulting from severe time and/or cost constraints. For example, in both the most- and least-innovative cases, inventing one’s own solution was deemed too expensive. In the words of the scientist involved in the most innovative (Lidar) case: The major problem was the cost cap. Full-up development would have broken the bank.

In the other cases, there was insufﬁcient time to invent a solution. Only with the insurmountable performance gap were the respondents willing to admit that they could not invent their own solution and that they would therefore consider reusing others’ ideas. Search and Evaluate Stage Having decided to consider reusing others’ ideas, respondents proceeded into a stage of active searching. In this third stage of the knowledge reuse process, we found that three layers of search and analysis were needed for reuse to occur: (1) Scanning the environment to become aware of possible ideas,

(2) Conducting brief evaluations of ideas to determine if the idea was worth pursuing, and (3) Conducting an in-depth analysis of the idea. Scanning. Respondents in all six cases engaged in scanning behavior, during which ideas and individuals with potential relevance to the conceptual approach were identified. Reusers in the more innovative cases conducted much broader searches extending into nontraditional areas, resulting in ideas that came from sectors beyond their immediate space community, did not fit the immediate functional requirements, or did not have the expected or needed form. For example, in the second-most innovative case (ELM design), ELMs to measure the electrostatic properties of dust (the conceptual approach) did not exist at the outset of the timeline. Therefore, the reuser could not simply look for the right ELM. Instead, he searched for information about how ELMs work in general and how various industries use ELMs. One of the ideas he discovered from this search was that the British textile industry worries a great deal about the electrostatic properties of chair covers; consequently, the industry routinely uses ELMs to measure the electrostatic properties of various materials. Thus, a space scientist developing an instrument for Mars reused ideas from the British textile industry! In the words of this scientist: I worked by analogy. I looked around to see what others were doing in the field: semiconductor industry, electrostatic discharge industry. [There are] a number of companies that deal with clean room garments; chair covers [that result in] minimal static build up. [There was] some help from the textile industry [for example, an individual] from the British textile industry.

In addition to broad search criteria, reusers in the more-innovative cases used a wide variety of search methods ranging from the Internet to face-to-face visits, using both strong and weak ties. For example, in the most-innovative case (Lidar design), strong and weak ties, formal “introductions” via the AO, telephone, e-mail, and the Internet were all used to find people and artifacts related to Lidar. Conducting Brief Evaluations. Having scanned the environment to become aware of others’ ideas, the reuser proceeded to briefly evaluate each selected idea. Three criteria were applied to decide if an idea should be either discarded or continued into an indepth analysis: credibility (the idea is valid and replicable), relevance (there is some degree of match with problem needs in terms of form, such as shape and materials; fit, such as size and weight; and/or technical functionality), and adaptability (the extent to which the idea can be modified to fit the new problem within time and cost constraints). When evaluating

for adaptability, we found that reusers assessed not just if the idea could be adapted, but more importantly for them, who (source, recipient or a third party) would do the adaptation. During this layer of search, the reuser was often trading off the cost, capabilities, and interests of a source or third party to do the adaptation against the cost, capabilities, and interests of the solo reuser. For example, in the AFM tip array case, one reusing scientist commented on the value of ﬁnding an adapter: They had an operational system of tip arrays and a fabrication process to make them [even though it had not been used on Mars or for our purpose]. This is a huge step forward. We immediately knew that we should team up with [the adapter] as it would save time and energy.

To assess the credibility, relevance, and adaptability of the ideas identified during the scanning step, we found that reusers looked for data, models, prototypes, and other contextual cues (such as whether the concept had been flown in space previously). We refer to these cues as the metaknowledge for the reused idea. Similar to metadata, meaning “data about the data,” which is used to facilitate retrieval (Heery 1996), we define metaknowledge as “knowledge about the knowledge,” which is used to facilitate evaluation and use (e.g., to assess relevance, credibility, or adaptability). While types of metaknowledge such as document author and date (e.g., Tiwana 2002) were of value to the reusers in the six cases, the types of metaknowledge that were more valuable were physical artifacts such as data, models, and prototypes. Though metaknowledge was used to evaluate ideas, there were too many ideas identified during the scanning step for metaknowledge to be closely examined for each idea. Therefore, during the brief evaluation phase, the reusers were primarily interested in determining if the metaknowledge even existed; they inferred from the existence of the metaknowledge that the idea would be judged positively. For example, when reusers learned that they could access the data or prototype for an idea, they inferred that the idea was more credible than an idea without data— even before they analyzed the data. When reusers learned that they could find someone (such as a manufacturer) able to adapt an idea, they judged the idea to be more adaptable than ideas without adapters—even before they spoke with the adapter. When reusers learned that contextual data describing the constraints and environmental conditions under which the original knowledge was generated existed, they inferred that the idea was more reliable. This finding suggests that reusers do not need or want access to metaknowledge when initially evaluating

183 ideas for reuse; rather, they only want to be informed that the metaknowledge exists and can be accessed at a later point in time. In the words of one reuser: The key [at this point in the evaluation process] was not the availability of the instrument but [knowing] the fact that the instrument was in development.

Conducting In-Depth Analysis. Finally, ideas that successfully met the three criteria for use (credibility, relevance, and adaptability) progressed into the final layer of search: in-depth analysis. The goal of this layer was to determine if any of the ideas being considered could in fact be adapted to meet the problem as formulated. In this final layer, reusers accessed and manipulated the metaknowledge to test it against the constraints and challenges of the problem. For example, a hand-held commercial ELM for the ELM design case, a software model for the Lidar case, and laboratory prototypes for the AFM design case were acquired and manipulated to provide the necessary confidence for the reuser to commit to the development approach. During this layer of activity, the reuser often needed to turn to the source and adapter for advice and artifacts. Summary of Three Layers. This pattern of three layers of evaluation suggests that a reusable idea must successfully traverse “gates” to be eventually reused. It must first be found on the “scanning radar,” then it must meet criteria expected during a brief evaluation, and finally it must continue to show promise in a more in-depth analysis. Thus, for knowledge to be reused not one, but three evaluations must take place. Full Development In the final stage, the continued ideas were developed and incorporated into a final solution. This stage is characterized by the full commitment of the team to the chosen implementation approach. The work now shifts from “Is this feasible?” to “Make it happen.” It is at this point that sharing common experiences between reusers and either the source or adapter becomes valuable. Shared experiences took the form of sessions in which prototypes were tested, reviewed, and improved. In the words of one reuser: We worked with the people at Kennedy Space Center to design the electrometer experiments on various materials. While working with the data was important, it was equally if not more important to have a lot of discussions and meetings.

While shared experiences were important for all six cases, the focus of the shared experience varied depending on the degree of innovation: for the least-innovative case, the shared experience focused on transferring best practices, while for the moreinnovative cases, the shared experience focused on

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codevelopment of the solution. Moreover, who was involved in these shared experiences varied across the cases. Sources were only included in these shared experiences in four of the cases; in the other cases, reusers shared experiences with adapters.

Discussion

In summary, Figure 2 presents a staged KRI process that was discernible across the six reuse cases. This process included six stages: (1) reconceptualize the problem and approach for innovation, (2) decide to search for reusable ideas, (3) scan for reusable ideas, (4) briefly evaluate reusable ideas, (5) conduct in-depth analysis on reusable ideas and select one, and (6) fully develop reused idea. Within each stage, reusers involved in the moreinnovative cases behaved differently from those involved in the less-innovative cases. Thus, even though the unique culture of JPL may seem to limit the generalizability of the findings, radical innovation is by its very nature unique (Leifer et al. 2000). Developing atom-sized computers or biotechnology innovations require equally unique cultures and individuals. As revelatory cases (Yin 1984), then, these six cases from JPL provide the opportunity to develop new theory about knowledge reuse for innovation that focuses attention on reuse for all radical innovation processes. Therefore, the utility of this research should be judged by the degree to which it fosters new insights and stimulates new questions and propositions for future research (Eisenhardt 1989). We use these questions and propositions to summarize key points as well as to extend our findings into new areas. Figure 2

A Process Model for KRI. Our findings first suggest a process model for KRI. This model is composed of six stages starting with Reconceptualize the problem and ending with Develop the idea. That is, despite the nonlinear, chaotic nature of radical innovation, a sixstage model was identifiable. This suggests our first proposition: Proposition 1. An individual who proceeds through all six stages in the manner described in Figure 2 is more likely to reuse others’ ideas in ways that foster innovation than individuals who skip any stage or perform any stage differently from how it is portrayed in Figure 2. The uniqueness of the JPL context suggests that testing this proposition is worthy of future study. In addition, we examined only cases in which reuse actually happened, as opposed to cases in which innovation occurred without reuse, or failed to occur because of improper reuse. As such, we cannot conclude from our findings that these six stages are both necessary and sufficient for fostering reuse. Future research that makes such comparisons is needed. Three Levels of Search. One of the features of our staged process is the three levels of search: initial scan for possible reusable ideas, brief evaluation, and in-depth analysis. Clearly, such a three-tiered strategy benefits innovation by allowing innovators to be exposed to a diverse range of inputs quickly before they converge on a single idea, but it may be inefficient if a known point solution is desired, such as with KRR. This raises a proposition for future research: Proposition 2. Reusers who are interested in knowledge reuse for innovation have a greater need to proceed through the three layers of scanning, brief evaluation, and in-depth analysis than reusers interested in replication. Role of Adapters. Our research found that there are three criteria used to decide to reuse knowl-

Model of Knowledge Reuse Process for Innovation

Search and Evaluate

Scan

Reconceptualize Problem for Innovation

Briefly Evaluate

Analyze In Depth

Fully Develop

Decide to Search Experience insurmountable performance gap Awareness of traditional and nontraditional

Conduct broad, nontraditional search

Awareness that meta-knowledge exists

Shared experience with adapter Access t o metaknowledge

edge for innovation: relevance, credibility, and adaptability. While the first two criteria have been found elsewhere (e.g., Szulanski 2000), the criterion of adaptability has not been mentioned in the literature previously (although Rice and Rogers suggest, as early as 1980, that more research attention should be paid to the reinvention process). We found that the absence of credible adapters, or the absence of ways to quickly determine if adapters are available and credible, can create a barrier to reuse for innovation. This suggests, then, that if an idea is to be reused for innovation, adapters need to be readily identifiable. Thus, instead of simply storing an idea in a knowledge repository, names of agents (which may be manufacturers, institutions, or other researchers) willing and interested in adapting this idea should be stored as well, or included as part of a discussion forum on the idea. This notion needs further testing and thus we propose: Proposition 3. An innovator who considers how an idea will be adapted and who will do the adaptation is more likely to reuse ideas to facilitate innovation than reusers who ignore these issues during the reuse process. Role of Metaknowledge. Our findings that metaknowledge plays a different role at the different stages in the KRI process is another area for future research. We confirmed previous research that metaknowledge (describing context, credibility of source, etc.) about a potentially reusable idea is important to reusers (Markus 2001). We also confirmed previous research that embodying this metaknowledge in physical artifacts such as models, data, and prototypes instead of text facilitates understanding and reuse (e.g., Star and Griesemer 1989). The additional insight provided by our research is that metaknowledge is accessed differently at different stages in the reuse process. We found that when reusers first became aware of an idea they wanted only to know that the metaknowledge on the idea existed. It was only later when reusers conducted in-depth analyses of the idea that they accessed and manipulated this metaknowledge. This suggests that to facilitate knowledge reuse for innovation, the decision about what metaknowledge to capture should be based on which metaknowledge would provide, by its mere presence, some evidence for the credibility, relevance, and adaptability of the idea. This is a notion worthy of future research and thus we propose: Proposition 4. Ideas that are structured to indicate the presence (or absence) of metaknowledge will be more readily considered by reusers during the KRI process than ideas that are not so structured. If this finding that awareness of metaknowledge is more important than acquisition at early

185 stages in the knowledge reuse process generalizes to other sites and research studies, this could suggest how information overload might be avoided. Reusers can be initially provided with a checklist of what metaknowledge exists on the idea, allowing access to the metaknowledge when the reuser returns to the idea for a more detailed analysis. This may also suggest that metaknowledge can be constructed and presented in a way to facilitate or inhibit knowledge reuse. Borrowing from Culnan (1983), we call this structuring the “chauffeuring” of knowledge through the reuse process, with metaknowledge serving as chauffeur. Just as a technologytransfer professional is able to chauffeur an idea from initial awareness to implementation (Rogers 1983), or Toyota’s knowledge-sharing network is able to chauffeur Toyota suppliers from awareness to adoption of best practices (Dyer and Noveoka 2000), properly constructed metaknowledge might be able to chauffeur an idea through the knowledge reuse process and increase its chances of being reused. Metaknowledge, viewed in this way, becomes the “boundary object” that links the source to the reuser (Star and Griesemer 1989). To facilitate reuse, each idea could be coupled with its metaknowledge, organized around evaluation needs, and layered either for mere indication of presence or for access and manipulation. Factors Affecting Reuse. We found many factors that affected KRI, including searching nontraditional communities of practice, use of a variety of search methods, weak ties as well as strong ties, and shared experiences with sources and/or adapters (but primarily only at the end of the process, not at the beginning as found by Szulanski 2000). Additional research on these factors is needed to confirm that they are specific to KRI. We also found that innovators were motivated to consider reusing others’ knowledge only when (1) they confronted a problem that was insurmountable with their current knowledge and resources, (2) they reconceptualized the problem and approach to require an ambitious new perspective, and (3) they believed that existing ideas were likely to be found somewhere that would be useful. While the effects of the first two factors have been found previously (Gupta and Govindarajan 2000, Osterloh and Frey 2000), the third factor has important implications for building a theory of KRI. Why one innovator believes ideas exist that would be useful, while another believes the opposite, is not clear. It may be, as Allen (1977) observed, that those who believe there are existing ideas that would be helpful have been exposed, through experience and networking, to a wide range of inputs. Alternatively, as Leifer et al. (2001) recently observed, these individuals may have been exposed not just to a range of inputs but

186 to a particular type of input, an “opportunity recognizer.” These are individuals who recognize the business potential of an innovator’s idea and who motivate the innovator to pursue his or her ideas, including sharing them with others. These individuals are different from traditional technology gatekeepers or brokers because they are focused on helping sources pursue their own interests. Innovators who make contact with an opportunity recognizer may, in the course of a discussion, learn enough about what various other sources may be doing or what various business opportunities may exist to increase their belief that an idea useful for their approach is likely to exist. Our findings suggest that the notion of an opportunity recognizer can be applied to the KRI process. In this role, the individual helps innovators recognize both that a conceptual approach has validity and that reusable ideas are likely to be found with a reasonable search strategy. This raises a proposition for future research: Proposition 5. Innovators who are encouraged to pursue innovation through reuse by opportunity recognizers are more likely to reuse others’ ideas than innovators without that encouragement. Role of Project-Level Decisions. Our findings suggest that the decision to reuse others’ ideas to solve a particular problem needs to be examined with respect to decisions made about other problems in the same project or organizational unit. In our sample, the amount of innovation desired by a reuser for a particular case was constrained in part by the amount of innovation incurred by the entire project. Too much risk in too many areas might damage the viability of the project. For example, in the least-innovative case (mag patches), the reusers intentionally chose to limit the innovativeness of the solution due to the degree of risk already present in other aspects of the project. This suggests that knowledge reuse research should broaden its focus beyond characteristics of the specific best practices being transferred or the specific knowledge and participants involved in the transfer process. This broadening of focus should take into account the web of decisions being made by the individual, project colleagues, and project managers. Just as Argote and Ingram (2000) suggest that the transfer process needs to be understood within an embedded network of elements, our findings suggest including projectwide decisions into this network, especially for innovation. We phrase this implication as a proposition: Proposition 6. A key determinant of reuse for innovation in any one case will be the degree of innovation incurred in other cases within the same project or organizational unit.

Reconciling Our Findings with Previous Research. We argued at the outset of this paper that Szulanski’s (2000) model was unlikely to provide a good fit with an innovation environment because the model was more linear in flow, and more deterministic about the knowledge being transferred, than is typically experienced in radical innovation. Our findings support this initial argument by identifying a model quite different from that of Szulanski. There are, however, commonalities between our model and that of Szulanski: they both begin with the recognition of a gap, end with integration of the transferred knowledge into an organizational routine (or finalized solution), and include characteristics of the recipient as important factors affecting the transfer process. Despite these similarities, there are many differences: Szulanski says little about the search and evaluation activities that were so critical to our reusers, and the role of source and recipient are different (in our study, recipient characteristics such as an openness to nontraditional approaches were critical early in our process, and shared experiences with the source were needed at the end, while Szulanski found the opposite to be true). The differences between our model and Szulanski’s may be caused by Szulanski’s assumption that metaknowledge and alternative reusable ideas were gathered prior to the decision to transfer, and didn’t model it. We offer an alternative explanation for the differences—and one we find more generative of theory building. We suggest that KRI requires more attention to the search and evaluation stages because the idea that is transferred is not committed to until late in the process. It may be that, when integration and innovation are the drivers of the process, the reuse process focuses on how the problem is conceptualized and what alternative reusable ideas can be identified. As such, characteristics of recipients are critical because the recipients define the level of aspiration (March and Shapira 1987) that motivates them to find innovative ways of achieving the desired objective, even if it means not inventing it themselves. In contrast, KRR focuses on the practices being transferred, and thus recipient traits may be less important early on. This suggests that the two models and the two streams of research on knowledge reuse (for innovation and for replication) can be reconciled by stringing them together into a single model. Our process model can be used to describe early stages of the reuse process and Szulanski’s model can be used to describe later stages. That is, early in the reuse process when innovation may be desired the reusers devote efforts to problem definition and search activities. Later in the process when routinization of the selected idea is desired the best practices underlying this idea (if best

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practices exist) can be transferred by removing as many of Szulanski’s barriers as possible. By stringing the two models together, we may be able to explain the difference in the impact of recipient and source characteristics between the two studies. It may be that the recipient’s and source’s characteristics are inﬂuential at several points in a complete reuse process: early in the innovation phase and later in the routinization phase for the recipient, and the inverse for the source. This suggests that future research should consider the entire lifecycle of the transfer process—from innovation to routinization—to understand the process by which transfer occurs.

Conclusion

In this study, we have offered several research questions and propositions to stimulate future research on KRI. We have suggested that reusers in a KRI process act differently from those in a KRR process. The unique context of JPL raises a challenge for future research to test the generalizability of these propositions. In the JPL context, the reusers redefined the problem to provide the greatest value to the customer (scientist and funder) relative to competitors. We propose that these same drivers exist in most industries; we would expect to see similar results in other contexts of radical innovation. We found that reusers in the JPL context balanced the paradox of identifying a nontraditional untested conceptual approach to the problem against the need for risk reduction by picking only those approaches in which they had some confidence that someone, somewhere, would have a relevant idea. We propose that risk reduction is a major factor in most radical innovations and thus would expect to see similar results in future studies. In the JPL context, we found reusers engaging in three levels of search and evaluation that require a broad search of nontraditional communities of practice and the ability to quickly scan metaknowledge. Creativity researchers (e.g., Amabile 1996) have long argued for the need to search in nontraditional communities; the use of metaknowledge to do this searching quickly deserves further study. Is the set of metaknowledge we identified a highly contextualized one? Finally, we found in the JPL context that reusers sought out adapters (who were not often the source) to bridge the gap between a source’s original idea and the final solution. Whether this finding is generalizable is an interesting question. In the JPL context, the reuser and adapter were often not the same person because skills, funding, and experience for the two were quite different. In other fields and industries, the two roles may overlap. Is there some characteristic of a discipline that drives adapters’ and reusers’ roles?

In addition to generating research questions for future work in this area, our ﬁndings are also practical. First, due to the importance of being able to use metaknowledge of potentially reusable ideas, such metaknowledge that quickly communicates credibility, relevance, and adaptability needs to be captured and presented. Second, because reusers for innovation need to deﬁne problems broadly, be aware of both traditional and nontraditional approaches, conduct broad and nontraditional searches, and use a variety of search methods, our research suggests that organizations should consider providing training and incentives to their innovators in these areas. Finally, new roles such as adapters, chauffeurs, and opportunity recognizers need to become part of the community of practice encouraging knowledge reuse. In conclusion, this research is intended to ground knowledge transfer and reuse research in a relatively unexplored context: innovation. In this context, as Grant (1996) so aptly explains, the intention is not spillover, replication, or acquisition, but recombinative integration. Knowledge is clearly being reused, but how? This study is an effort to address this question and stimulate future research. Acknowledgments

The research described in this paper was carried out by the Jet Propulsion Laboratory (JPL), California Institute of Technology, under contract with the National Aeronautics and Space Administration. The authors thank the MECA and MITCH teams and the Knowledge Management Project team at the JPL. The authors also thank Rajiv Sabherwal, M. Lynne Markus, T. Ravichandran, and the anonymous reviewers for their helpful comments. An earlier version of this paper received the 2001 Academy of Management Best Paper Award for the Organizational Communication and Information Systems Division.

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