An Empirical Test of Nonaka’s Theory of Organizational Knowledge

Richard G. Best
Sylvia J. Hysong
Charles McGhee
Frank I. Moore
Jacqueline A. Pugh

Recognition of organizational knowledge creation as a corporate resource has generated considerable interest in recent years. The evolution toward a “knowledge society” (Drucker, 1968; Bell, 1973; and Toffler, 1990) underscores the salience of the knowledge creation process within organizations. It has been argued that knowledge creation, an integral facet of organizational learning, is prerequisite in the face of on-going change (von Krogh & Grand, 2000). Nonaka’s (1994) Dynamic Theory of Organizational Knowledge Creation provides a theoretical backdrop on which to conceptualize the knowledge creation process. The bulk of Nonaka’s work, however, relies on case studies and observational methodologies to the exclusion of experimental validation. Thus, a primary goal for this research is to empirically test the conceptual propositions of Nonaka’s (1994) theory using a mixed methodology consisting of qualitative (i.e., interviews) and quantitative (surveys) information. Commensurate with that objective, a survey instrument was designed to operationalize the core concepts of Nonaka’s theory (i.e., autonomy, fluctuation and creative chaos, intention, redundancy and requisite variety).

Nonaka (1994) argues that the current paradigm in which organizations process information efficiently in an “input-process-output” cycle represents a “passive and static view of the organization” (p. 14). Alternatively, he asserts that organizational learning results from a process in which individual knowledge is transferred, enlarged, and shared upwardly to the organizational level. This process is characterized as a spiral of knowledge conversion from tacit to explicit. In the broadest sense, organizational knowledge creation may be explicated by the interchange between tacit and explicit knowledge. Tacit knowledge, according to Polanyi (1966) is a subtle conception rooted in cognitive schemata referred to as “mental models” and is rather difficult to articulate. According to Nonaka and Takeuchi (1995), “tacit knowledge is highly personal and hard to formalize, making it difficult to communicate or to share with others. Subjective insights, intuitions, and hunches fall into this category of knowledge” (p. 8). On the other hand, explicit knowledge is more easily transmitted as it is characteristically codified. As such, explicit knowledge is more easily processed and shared with others. Nonaka (1994) argues that knowledge conversion initiates at the individual level as a “justified true belief” and is expanded through social interactions to include a diversity of perspectives that ultimately represent shared knowledge at the organizational level (p. 15). According to the theory, the process of knowledge conversion proceeds through four different modes:

During the socialization mode, tacit knowledge is transferred through interactions between individuals, which may also be accomplished in the absence of language. According to Bandura (1982), individuals may learn and gain a sense of competence by observing behavior modeled by others. For example, mentoring and apprenticeships instruct tacitly through observation, imitation, and practice. The combination mode of knowledge conversion embodies the aggregation of multiple examples of explicit knowledge (Nonaka, 1994). Explicit knowledge may be exchanged during meetings or conferences in which a diversity of knowledge sources combine to shape a new and enhanced conception. The externalization mode of the knowledge conversion spiral references the translation of tacit knowledge into explicit. Because the conversion of tacit to explicit knowledge involves the reification of an esoteric, cognitive abstraction into a concrete concept, metaphors are recommended as a way to facilitate this translation (Nonaka, 1994). Metaphors assist individuals in explaining concealed (i.e., tacit) concepts that are otherwise difficult to articulate by assisting individuals in forming impressions based on “imagination and intuitive learning through symbols” (p. 21). In other words, metaphors create networks of related concepts as prototypes to facilitate the ability to understand abstract, imaginary concepts. The conversion of explicit to tacit knowledge (i.e., the internalization mode) occurs through a series of iterations in which concepts become concrete and ultimately absorbed as an integral belief or value. Where externalization utilizes metaphors to facilitate knowledge conversion, internalization represents an active process of learning. Nonaka (1994) describes this as “participants…..sharing explicit knowledge that is gradually translated, through interaction and a process of trial-and-error, into different aspects of tacit knowledge…..Tacit knowledge is thus mobilized through a dynamic entangling of the different modes of knowledge conversion” (p. 20).

The Knowledge Creation Process

The mutual exchange of tacit and explicit knowledge that describes the knowledge creation process is initiated at the level of the individual employee or organizational member. Because individuals are an integral component of this conversion process, their commitment to knowledge creation is critical. According to Nonaka (1994), individual commitment is generated through intention, autonomy, and environmental fluctuation. This suggests that knowledge creation may be activated when organizational members have freedom and sufficient purpose to pursue new knowledge, such as when confronted by change in the external environment. Indeed turbulence in the organizational environment may act as the catalyst for the knowledge creation process. According to Nonaka and Takeuchi (1995), a disruption in the status quo in the organizational environment triggers a need to adapt and learn. As we question the validity of traditionally held perspectives, “we turn our attention to dialogue as a means of social interaction, thus helping us create new concepts. This continuous process of questioning and reconsidering existing premises by individual members of the organization fosters organizational knowledge creation” (Nonaka & Takeuchi, 1995; p. 79).

While individual commitment to knowledge creation is key to the process, the organization, as a whole must support this effort. In particular, Nonaka (1994) argues that an organizational climate that fosters the sharing of redundant information from a variety of requisite personnel enables a creative (rather than counterproductive) chaos to emerge from the changing environment. In the rubric of the theory of knowledge creation, the concepts of redundancy and requisite variety support a climate of social exchange necessary for sharing, and enlarging individual knowledge. That is, a social network of requisite perspectives and a diversity of viewpoints helps convert an otherwise turbulent organizational environment into a more “creative chaos” (Nonaka, 1994). The importance of an organizational climate that encourages constructive social exchange is underscored by the recognition that sharing individual concepts (i.e., in order to foster organizational knowledge creation) requires intrusion into others’ spheres of reality (Schrage, 1990). Cross-functional teams are actually regarded as a means to manage social collaboration and concept creation. Nonaka (1994) argues that self-organizing teams inherently accommodate diversity in thinking for resolving problems or enhancing creativity and innovation. In essence, team environments provide a social context conducive to enlarging individual perspectives. Combining multiple implicit perspectives into a common, socially constructed understanding is necessary but insufficient to the knowledge creation process. Socially shaped concepts (i.e., knowledge) must ultimately be internalized, as when other organizational members or departments validate its utility through testing and experimentation. Nonaka (1994) refers to this process as “crystallization” and recognizes that it furthers refinement of the concept. The extent to which the knowledge or concept is considered useful determines whether it becomes a justified true belief to the organization. Indeed, Nonaka (1994) considers the justification of shared knowledge as a quality criterion. Thus, organizational knowledge creation illustrates the conversion of individual to organizational knowledge through a process of enlargement aided by social interaction.

Organizational Knowledge Creation Within the Veterans Health Administration

Although the theory of organizational knowledge creation has roots in Japanese industry, its practicality is increasingly recognized in other domains. For example, Rippy and Baker (2003) argue that knowledge management, a key aspect of organizational learning, is now considered a “strategic imperative” among the healthcare industry. Current initiatives within healthcare to translate and implement evidence into daily practice embody the basic tenets of knowledge conversion affording the opportunity to investigate organizational knowledge creation in this domain. The implementation of evidence-based medicine represents a prominent cornerstone of quality improvement initiatives within the healthcare industry in general, and the Veterans Health Administration (VHA) more specifically. Viewed as a way to improve the quality, consistency and predictability of healthcare, the VHA is focusing major resources toward the implementation and utilization of evidence-based practice guidelines. Indeed, implementation of clinical practice guidelines has been mandated by the VHA as a means to optimize access, use best practices, and improve patient participation in decision making (VHA, 1996; Directive 96-053). Toward that end, compulsory adherence to evidence-based guidelines exemplifies the VHA’s commitment to this imperative by prescribing high quality, preventive care to assist the shift to outpatient services.

However, substantial variability in practice patterns and reporting of outcomes indicates that the implementation of evidence-based guidelines is widely discrepant. In one study, extensive variation (i.e., up to 400%) in practice patterns across VHA facilities could not be explained by differences in the clinical condition of patients and better outcomes were not predictably related to the amount of care delivered (Ashton, Petersen, Souchek, Menke, Yu, Pietz, Eigenbrodt, Barbour, Kizer, & Wray, 1999). The literature indicates that there is a wide range of strategies for implementing evidence-based medicine, highlighting a need for closer investigation (see Oxman, Thomson, Davis, & Haynes, 1995). To date, most studies of guideline implementation have targeted changes in provider behavior to the exclusion of the external environment, characteristics of the organization, and characteristics of the clinical practice (Rubenstein, Mittman, Yano, & Mulrow, 2000). While practice guidelines are admittedly implemented at the level of the patient provider interaction, a large body of literature documents the difficulties in changing physician practice behavior (VERDICT Brief, Spring, 1998). Research findings that attribute variation in practice patterns to facility level differences rather than at the provider level (Krein, Hofer, Kerr, & Hayward, 2002) suggest that studies investigating change efforts, such as the implementation of clinical guidelines, concentrate on organizational factors or influences. The few studies that have investigated the influence of organizational factors on guideline implementation have focused narrowly on structural characteristics such as size or staffing rather than systemic features such as culture or learning orientation. Thus, this study contributes to this area of scientific inquiry by examining conditions in the organizational environment that foster knowledge creation and diffusion.

A key assumption of this study is that evidence-based medicine, and specifically the implementation of practice guidelines, is facilitated by the generation and sharing of knowledge and evidence in continuous and systematic ways. In the rubric of the theory of organizational knowledge creation, the translation of clinical evidence into an organizationally shared vision must follow the knowledge conversion spiral from an individually tacit conception to organizationally explicit understanding. This is even more apparent to the extent that evidence-based practice represents a retranslation of existing ideologies (i.e., justified true beliefs). In other words, the implementation of and adherence to practice guidelines requires the internalization of the evidence backing the guidelines and their justification as a worthwhile strategy for quality of care. Thus, in order for VHA facilities and providers to continuously improve their capacity to synthesize, translate and implement evidence into primary care practice, the tenets of organizational knowledge creation offer theoretical guidance. That is, the turbulence in the organizational environment within the VHA has precipitated a need for knowledge creation and an opportunity to empirically examine the basic principles of Nonaka’s theory of organizational knowledge creation. We therefore hypothesized that the extent to which autonomy, intention, fluctuation and creative chaos, redundancy and requisite variety are present in the organizational environment will predict compliance with clinical practice guidelines.

Although Nonaka’s theory of organizational knowledge creation is premised on years of case studies and rigorous observational methodologies, we were not able to identify any formal measurement of its theoretical constructs. Because we were interested in investigating the impact of knowledge creation on efforts to implement practice guidelines, an important objective of this research was to operationalize the presence of autonomy, fluctuation and creative chaos, intention, redundancy, and requisite variety as enabling conditions in the organizational environment. Thus, we used a mixed methodology consisting of both questionnaire and interview procedures. We employed a dual approach methodology as a way to supplement quantitative results with qualitative information. Our intent was to utilize the qualitative information to triangulate our survey results by providing a contextual backdrop on which to interpret our findings. True to our theoretical framework, we sought to expand the individual knowledge generated by our survey results to create a new, enhanced understanding of these concepts.

In order to gain a more comprehensive picture of the process of implementing guidelines, we investigated VHA facilities with varying degrees of success toward that effort. That is, we selected facilities stratified on the basis of successful adherence to the practice guidelines to cover the full range of performance. We included facilities identified as “high performers” (those that maintained consistently high rank-order status over a two-year period), “improvers” (those that demonstrated consistent improvement in rank-order status over a two-year period), and “low performers” (those that maintained consistently low rank-order status over a two-year period). In total, 15 facilities and their satellite outpatient clinics were included for data collection.

One hundred ninety-seven employees from various levels within the fifteen VHA facilities participated in the study. The sample consisted of 62% women, 28% men, and 10% who did not report gender information. About three-quarters of the sample were between the ages of 38 and 57, with percentages dropping sharply both above and below that range (see Table 1 for the actual distribution). We solicited both qualitative and quantitative information from clinical and managerial personnel most actively involved in the implementation of practice guidelines. Facility leadership assisted in identifying employees with requisite knowledge and experience in guideline implementation. These employees were mailed a survey instrument and cover sheet inviting their participation in a one-hour semi-structured interview and completion of the survey. Small group interviews were scheduled during site visits to each of the participating facilities.

In order to empirically examine our hypothesis that the presence of autonomy, intention, fluctuation and creative chaos, redundancy and requisite variety in the organizational climate is associated with the implementation of clinical practice guidelines, we developed a survey instrument to measure these constructs as outlined by Nonaka and Takeuchi (1995).

Autonomy. Autonomy affords organizational members the freedom to pursue new knowledge that may ultimately translate into a shared, organizational conception. Organizations benefit from individual autonomy through “greater flexibility in acquiring, relating, and interpreting information” (Nonaka, 1994; p. 18).

Intention. Intention is described as purposive action by which individuals make sense of their environment. The objective is to acquire, create, cumulate, and exploit knowledge to facilitate adaptation to the surrounding environment (Nonaka & Takeuchi, 1995). Organizational members with intention are thus committed to the creation and adoption of new knowledge as a way to assimilate to changing external conditions.

Fluctuation & Creative Chaos. Individuals may commit to knowledge creation when they experience a “breakdown” in their routines, habits, or cognitive frameworks (Nonaka & Takeuchi, 1995). Fluctuations in the organizational context or environment can produce a creative chaos or tension that challenges fundamental ways of thinking. According to Nonaka and Takeuchi (1995), turbulence in the organizational environment triggers a disruption in the status quo and the need to adapt through organizational knowledge creation.

Requisite Variety. In defining a social context conducive to knowledge creation, it is important to regulate the number and composition of available input through the principle of requisite variety (Ashby, 1956). The key to this principle is to ensure a radial pattern of interaction among organizational members across relevant functional areas. This span of interaction can, and should extend beyond organizational boundaries to make use of knowledge from the external environment. Diversity in thinking is maximized when organizational members have ready access to the widest variety of relevant information with the least amount of effort (Nonaka & Takeuchi, 1995).

Redundancy. An important function of requisite variety is to ensure appropriate redundancy of information. Multiple implicit perspectives are combined to forge a shared conception through continuous dialogue, only possible through redundancy of information (Nonaka, 1994). By sharing redundant information, organizational members are allowed to invade each other’s functional boundaries and entertain alternative perspectives (Nonaka & Takeuchi, 1995).

Survey items were either developed de novo, or adapted from relevant existing scales (e.g., Breaugh’s Work Autonomy Scale and Dibella’s Learning Inventory) to operationalize these constructs as conceptualized. The resulting 31-item instrument was pilot tested among a small group of clinicians for clarity and ease of understanding (see Appendix).

We conducted semi-structured interviews among primary care providers and facility leadership to gain better insight into organizational processes for implementing practice guidelines. These interviews solicited information about strategies, barriers and facilitators as well as a description of a successful implementation effort to shed light on local adaptation. Although we used scripted questions to guide the interview process, respondents were invited to offer additional relevant information to illuminate processes for implementing clinical practice guidelines. With the consent of the participants, interviews were audio recorded for later transcription and content analysis.

We focused on compliance with practice guidelines as our measure of performance because it inherently requires organizational learning and is directly relevant to knowledge creation. That is, implementing practice guidelines involves the translation of new knowledge (i.e., the evidence base) into daily operations within the local constraints. Accordingly, the practice guidelines must be adapted to the local setting through a conversion process such as that outlined in the theory of knowledge creation. Thus, compliance with guideline implementation provides an index of coping with organizational change efforts. We used data from the External Peer Review Process (EPRP) to operationalize guideline compliance. Adherence to clinical practice guidelines is monitored nationally through the EPRP and used to document implementation effectiveness within VHA. This process involves random abstraction of charts for documenting whether or not a guideline was performed as required. For example, the EPRP identifies whether or not a foot sensation examination was performed on a diabetic patient per the guideline for diabetes mellitus. Quarterly reports are published by the Office of Quality Performance and document the performance of each facility relative to all others.

In sum, we used both qualitative and quantitative information to test our hypothesis that the presence of knowledge enabling conditions in the organizational setting is associated with adherence to clinical practice guidelines. Indeed this contention is premised on the doctrine that organizational knowledge creation and diffusion facilitates the translation of clinical evidence into practice (Nonaka, 1994).

The first step in the data analysis was to determine the psychometric properties of the Knowledge Enabler Survey. Data obtained from 197 completed instruments were examined for this purpose. Cronbach’s alpha was used to assess the internal consistency of each of the intended subscales in the questionnaire. Results indicated acceptable reliability estimates for the scales, ranging from .80 for fluctuation and creative chaos to .85 for both intention and requisite variety. In addition, a principal components exploratory factor analysis was conducted to assess the dimensionality of the survey instrument. As the knowledge enablers are highly interrelated in Nonaka’s theory, an oblique, rather than an orthogonal rotation was used. The 31 items resolved to a robust two-factor solution, rather than the five-factor solution originally intended by the scale construction. Parallel analysis of the eigenvalues confirmed the two-factor solution (Zwick & Velicer, 1986), therefore these two factors were retained for all subsequent analyses. Table 2 shows the loadings for these factors, labeled organizational intention and individual commitment, and their associated internal consistency estimates (i.e., α = .91 and α = .93, respectively).

Because our research design was inherently nested (i.e., we used patient- and organization-level data), we used hierarchical linear modeling to assess the impact of knowledge enablers on guideline compliance. More specifically, we tested several models in which both knowledge enabler factors were evaluated for their impact on compliance with the hypertension guideline, with the diabetes mellitus guideline, and with the prevention index. Results did not support our contention that knowledge enabling conditions (i.e., operationalized by our two-factor solution) are associated with compliance on the practice guidelines. As may be seen in Table 3, neither factor was significantly associated with differences in any of the measures of guideline compliance.

Because we employed a combination of qualitative and quantitative methods, we were able to use the interview transcripts to more fully explore the results of our quantitative findings. In particular, passages from interview transcripts that were coded for examples of intention, autonomy, fluctuation and creative chaos, redundancy and requisite variety and content analyzed. Our hope was that personalized interview data would capture information about the knowledge creation process where survey examination could not. In fact, we used the interview data to qualitatively assess our two-factor solution and to evaluate the validity of our theoretical supposition. With regard to triangulating the unexpected two-factor solution, we examined the interview data for the co-occurrence of passages coded as intention, autonomy, and fluctuation and creative chaos and the co-occurrence of passages coded as both redundancy and requisite variety. The frequency of such co-occurrences provides support for the quantitative clustering we found with the survey instrument. Of 6354 coded passages, 405 were coded as illustrative of one or more knowledge enablers. Of these 405, 37 or nine percent were assigned more than one knowledge enabler code.

Table 4 presents frequency counts of each combination of singly and doubly coded passages for the five knowledge enablers proposed in the theory. As shown in Table 4, eight passages were coded both redundancy and requisite variety. This is slightly higher than the co-occurrence of either redundancy or requisite variety with other enablers (i.e., autonomy, intention, or fluctuation and creative chaos). In terms of other noteworthy coding combinations, intention co-occurred more with autonomy and fluctuation and creative chaos (i.e., 4 and 7 respectively) than with redundancy or requisite variety (i.e., 1 and 3 respectively). And finally, autonomy combined with intention more often than either requisite variety or redundancy (i.e., 4 versus 3 in both cases). In terms of additional qualitative support for the validity of the knowledge enablers, we content analyzed the interview data from the top and bottom facilities according to the EPRP data. That is, interview data were coded for each of the five knowledge enablers for the purpose of developing profiles for both facilities. These profiles were then compared and contrasted in the context of the theory of knowledge creation.

Bottom Performing Facility: The poorest performing facility from our sample revealed a markedly different profile from our top performer. In general, this facility employs a hierarchical line of authority. Autonomy is inhibited by an autocratic style of management as evidenced by statements such as “nothing gets done unless it goes through me first!”. The hierarchical line of authority ensures that the delivery of clinical practice reflects the prevailing ideology of leadership. In this case, practice guidelines are promoted as “cookbook medicine” and therefore perceived as constraining the freedom to practice independently; i.e., they basically dictate clinical practice. As a result, autonomy for clinicians in this facility means deciding whether or not to perform the guideline in the face of other competing demands, such as the presenting issues of the patients. Similarly, the strategic imperative to comply with guidelines for this facility is premised on their merit as a performance measure. That is, adherence to practice guidelines is motivated primarily by regular performance monitoring by VA headquarters. There is basically no effort to make the guideline materials readily accessible to providers, indicating a lack of intention to cumulate and share the latest evidence for delivering primary care. The general theme endorsed by this facility is that guidelines are an added responsibility and inhibit independent care, thus any intention to disseminate and exploit evidence-based guidelines is censored by the general climate.

One way that fluctuation may emerge in the organizational environment is through leadership (e.g., benchmarking, or setting higher internal standards). As reported by one clinician, tension in this facility is created internally by leadership, “I think it was threats….’I will kill you’…’you’re making me look bad’….you’d better start making me look good” as a way to motivate performance. Even more compelling is the implicit suggestion that clinical guidelines must be performed to make leadership ‘look good’, not because they ensure a higher quality of care for the patient! In many ways, an autocratic management style runs contrary to the basic premise of sharing and enlarging individual knowledge to promote organizational learning. Decision-making is essentially hierarchical, discouraging input from the diversity of requisite personnel. As a result, individual knowledge is not shared, nor is it enlarged through redundant perspectives.

Top Performing Facility: By way of comparison, our top performing facility may be characterized by its team-based approach to guideline implementation. In fact, responsibility for guideline performance is delegated to a local committee composed of a diversity of personnel with requisite expertise in their implementation. In contrast to the vertical line of authority and communication in hierarchical organizations, team-based approaches are inherently horizontal. The very nature of this structure invites input from a variety of requisite personnel. The strategic imperative at this facility is to implement practice guidelines as a means to ensure the highest quality of care for the patients. According to one clinician, leadership commitment to practice guidelines is indicated in statements by management such as, “this is really good…I don’t care whether it’s a measure or not….we’re not going to make the providers put them on it (beta-blockers) just to meet the performance measure…..people need to get their flu shots because it’s going to improve their care.” Commensurate with the goal of providing high quality care to the patient, providers are free to develop strategies such as the “quick order set” to accommodate multiple labs. In this facility, providers are motivated to perform clinical practice guidelines because they regard them as a way to ensure their patients receive care based on the latest evidence, rather than as a measure to make leadership look good or bad.

Furthermore, the top performer emphasizes interdisciplinary cooperation as a strategy for successful guideline implementation. In terms of the knowledge enabling conditions, team-based approaches ensure greater requisite variety and redundancy of information. The participative approach employed by this facility highlights the salience of sharing and amplifying individual knowledge through a process of social exchange. Finally, the emphasis on education at this facility illustrates organizational intention to acquire and disseminate knowledge. Education is just one feature of an organizational climate that supports guideline implementation. Internal chart audits ensure comprehensive delivery of primary care while posters on the walls and screensavers on computers illustrate the value of practice guidelines.

It has been argued that coping with the uncertainty of a dynamically changing environment requires the capability “to create new knowledge, disseminate it through the organization, and embody it in products, services, and systems” (Nonaka & Takeuchi, 1995). While the theory of knowledge creation has intuitive appeal in terms of organizational learning, empirical scrutiny of this tenet has been limited to case studies and observational methodologies. Thus, this research contributes to the knowledge management literature in two important ways. First, it tests this framework in a health care setting in a Western culture, and second, it utilizes a mixed methodology consisting of a combination of qualitative and quantitative approaches.

With regard to the quantitative approach, the survey instrument for measuring the enabling conditions espoused by the theory fell short of our intended objective for both reliable and valid measurement. Psychometrically, the instrument revealed good internal reliability, though its validity was not established in the hypothesis testing. There are two possible interpretations of this particular finding: either the theory is invalid, or our test of the theory was not sufficient. It is our belief that the lack of association between our measure of knowledge enabling constructs and compliance with guidelines is in no way an indictment of the theory, rather an issue of measurement. There are several potential explanations for its inability to validly predict performance. According to Hunter and Schmidt (1990), measurement error in either the predictor or the criterion spuriously lowers ability to detect effect sizes. With regard to our measure of performance (i.e., EPRP data), there is reason to question its accuracy in terms of documentation. Anecdotally, we were told that EPRP data does not necessarily reflect the true state of affairs in reference to guideline adherence. For example, performance may actually occur even though it is not formally documented (e.g., conducting depression screening, though neglecting to record this activity). Conversely, providers admitted to documenting performance that may not have actually occurred (e.g., offering smoking cessation counseling when none was actually given). Because our outcome data may have suffered from documentation errors, the actual effect of knowledge creation on guideline compliance may not have been realized. Also, statistical power was not sufficient to detect potentially meaningful effects. Saal and Knight (1996) recommend a 10:1 case to predictor ratio for estimating statistical power for collection of predictor data. A 31-item instrument would therefore require 310 cases or respondents to yield sufficient power, clearly more than we were able to acquire. Finally, this research must be quantitatively regarded as pilot work toward the development of a survey instrument. Indeed, survey development is an iterative process in which this project was the first iteration.

This was also illustrated in the dimensional structure captured by the instrument. The fact that it resolved to a robust, two-factor solution rather than the five factors for which it was intended suggests the need for further development. It may, however, be argued that the two-factor solution is not entirely inconsistent with the theoretical proposition. That is, the fact that the items intended to measure autonomy, intention, and fluctuation and creative chaos generally clustered together to form one factor is consistent with Nonaka (1994) in that these concepts are proposed to describe individual commitment to knowledge creation. Furthermore, the finding that the items proposed to measure redundancy and requisite variety combined to form the other factor may be indicative of characteristics in the organizational environment that encourage social amplification of individual knowledge. This interpretation is purely speculative and requires further empirical confirmation, again supporting the iterative nature of scale development.

Support for the utility of the theory of knowledge creation in a health care setting was observed in the qualitative analyses. Profile analyses constructed from qualitative coding of interview data revealed themes consistent with the theoretical premises. Comparisons of the top and bottom performing facilities included in our study shed additional light on this contention. The top-performing facility attributed the creation and implementation of a local, interdisciplinary team as the reason for their success. In the rubric of the theory of knowledge creation, cross-functional teams allow individual knowledge to be “articulated and amplified through social interaction” (Nonaka, 1994; p. 22). The interdisciplinary composition of teams accommodates a diversity of perspectives useful for concept enhancement through social collaboration. Indeed, Nonaka and Takeuchi (1995) regard the concept of self-organizing teams as a conduit for acquiring, interpreting, and relating information necessary for organizational knowledge creation. Conversely, the bottom performing facility employed a more autocratic style of knowledge creation. This reflects a unidimensional approach to organizational learning that suffers from limited perspective (i.e., lacking both requisite variety and redundancy). Further, an autocratic style is inherently antithetical to autonomy, thus constraining individual freedom to create knowledge. Resistance to guideline compliance emanates from leadership in this facility as evidenced by performance on the diabetic guideline. As an example, a clinic-level leader rebuked a diabetic guideline requiring retinal imaging in favor of his “forty years of experience”. Indeed, his resistance to change with the evidence and the hierarchical style of knowledge creation resulted in the poorest performance on the diabetes guideline among the facilities sampled in our project.

It is important to recognize that use of cross-sectional methodologies preclude causal inferences, therefore caution is advised in interpreting our results. Additionally, the relatively small sample used for our predictor data raises concern for a lack of power as well as the possibility of sample-dependent artifacts. Because we conducted this research in a primary care setting in the VHA, generalizability beyond this domain is not possible. One final caveat is offered with regard to our instrument development. While we used it to help empirically test the theory of knowledge creation in a health care setting, its focus was specific to evidence-based practice guidelines as a type of knowledge creation. A more appropriate measure of the theoretical concepts should focus on knowledge creation in general, rather than a specific application.

Although further scale development is clearly necessary, it is our hope that this research has established some foundation for empirical scrutiny of the theory of knowledge creation. We employed a mix of qualitative and quantitative methodologies to more eclectically examine this theory, and provide a richer context within which to interpret our results. Qualitatively, we witnessed stark contrasts in knowledge creating contexts among top and bottom performing facilities. While this should not be taken as definitive support for the theory, it provides descriptive profiles based on the knowledge creating taxonomy. Our frustration in developing a quantitative measure of the knowledge creating process is emphasized by two principle considerations. First, Nonaka (1994) conceptualizes organizational knowledge creation as a process involving the conversion of individual knowledge to a socially constructed understanding. Methodologically, cross-sectional procedures are not sensitive to processes and longitudinal designs are recommended. However, the process espoused by the theory of knowledge creation is conceptualized as spiral rather than linear in orientation, thus limiting the suitability of even longitudinal designs. That is, the process of knowledge creation may demonstrate bi-directionality at any point during the spiral, obfuscating any attempt to capture its temporal unfolding. Second, we used an ostensibly Western philosophy of hypothesis testing to examine an Eastern way of thinking. According to Nonaka and Takeuchi (1995), “Japanese companies remain an enigma to most Westerners…..Japanese companies have been successful because of their skills and expertise at ‘organizational knowledge creation’…..this view goes against the grain of the way most Western observers think of Japanese companies” (p. 3). Indeed, future work should consider alternative approaches to measure this spiraling process. While we clearly tried to take a snapshot of this process from our Western way of thinking, we also used qualitative information to triangulate and expand on that method. Perhaps more advanced, structural models with latent growth capabilities offer guidance. In the meantime, empirical scrutiny of the theory of knowledge creation remains an enigma!

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The research reported here was based on an Investigator Initiated Research (IIR) grant supported by the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Services, Verdict Field Program. The views expressed in this article are those of the author(s) and do not necessarily represent the views of the Department of Veterans Affairs. Portions of this research have been presented at the Health Services Research and Development Annual Meeting in Washington, DC, 2003, and the Academy of Health Conference in Nashville, TN, 2003. The authors wish to thank Christine Aguilar for her invaluable assistance in coordinating and conducting this research, the reviewers for their insightful comments, and Dr. Michael Parchman for his constructive feedback.

Table 2. Factor loadings for items in Knowledge Enabler Survey

Questionnaire Item	Loading
*Factor 1: Organizational Intention*
30. Clinical practice guidelines are an important part of our daily operations.	.92
17. Our facility views CPGs as tools to ensure a higher quality of care delivery.	.86
5. One of our priorities for improving patient care is to encourage the use of clinical practice guidelines (CPGs) in daily practice.	.74
8. Our facility commits resources and personnel to the implementation of CPGs as part of daily clinical practice (e.g., scheduling, clinical reminder systems, computers in each exam room, etc).	.73
29. An important goal in our facility/clinic is to continuously improve on guideline adherence or performance (e.g., EPRP) measures.	.70
15. Employees from a cross-section of services (e.g., medical nursing, MAS, IRM, etc.) form teams to determine ways to implement CPGs.	.68
26. Multiple sources of information (e.g., CPG champions, clinical reminders, copies of guidelines, patient education materials, etc.) facilitate CPG implementation in our facility/clinic.	.62
10. Our facility/clinic uses a variety of sources of information (e.g., CPG champions, clinical reminders, copies of guidelines, patient education materials, etc.) when implementing CPGs.	.62
28. Different services in our facility (e.g., MDs, RNs, MAS, IRM, etc.) work together to accomplish quality of care objectives (like CPG implementation).	.62
16. There is an information network in our facility/clinic that allows employees from a variety of services (e.g., MDs, RNs, MAS, IRM, etc.) to communicate about how to implement CPGs.	.61
25. Our facility recognizes that we can offer a higher quality of care than is currently provided.	.54
24. Several different services in our facility/clinic (e.g., medical nursing, MAS, IRM, etc.) offer unique perspectives about how to implement CPGs.	.49
Cronbach’ alpha	α = .91

*Factor 2: Individual Commitment*
20. In our facility, individual clinics are able to set their own goals for performance measures related to guidelines.	.82
12. In our facility/clinic, employees are allowed to decide how to improve the quality of patient care in their areas.	.79
3. Employees in our facility/clinic are free to determine the best methods for implementing clinical practice guidelines.	.75
27. Individual clinics in our facility/clinic are free to prioritize CPG activities within their own areas.	.74
6. Our facility/clinic is always scanning the medical literature to assess whether our care is up-to-date.	.72
22. Employees in our facility/clinic actively seek new ways to provide care in an effort to improve quality.	.72
9. Employees in our facility/clinic are encouraged to question traditional methods of the care delivery process.	.68
18. Employees in our facility/clinic have some control over their duties related to CPG implementation.	.63
23. CPG implementation has reinforced the desire to seek out other strategies for improving the quality of care in our facility.	.51
14. Each service in our facility/clinic provides its own guidance about CPGs to its employees.	49
19. Our senior management (i.e., VISN headquarters) has shared with us in face-to-face conversations their support of CPGs to improve patient care.	.44
7. In our facility/clinic, employees from different services meet informally to discuss CPG implementation.	.41
Cronbach’ alpha	α = .93

Table 3. Probability estimates for assessing the impact of knowledge enablers on guideline compliance.

Dr. Richard G. Best is a health services investigator for the Veterans Evidence Based Research Dissemination and Implementation Center (VERDICT), a research institution funded by the Health Services Research and Development within the Veterans Health Administration. His role as an investigator with the VERDICT involves the examination of organizational factors that facilitate, or impede the transfer of evidence into daily practice. Dr. Best is industrial and organizational psychologist with interest and expertise in job stress and burnout. He has conducted numerous organizational research projects designed to investigate the conditions that precipitate affective outcomes, such as job satisfaction and burnout. He has presented his research at international conferences such as the National Institute for Occupational Safety and Health/American Psychological Association Work Stress and Health Conference, Toronto, the Academy of Management, the Society for Industrial & Organizational Psychology, and the Annual Meetings for the Veterans Health Services Research and Development Meetings.

Dr. Sylvia J. Hysong is a visiting Assistant Professor of Psychology at the University of Houston, and an investigator with the Veterans' Evidence-Based Research Dissemination and Implementation Center in San Antonio. A member of the executive board of the Houston Area Industrial/Organizational Psychologists, and the editorial board of the Texas Journal of Distance Learning, Dr. Hysong specializes in the impact of technology on human resource systems, particularly recruiting and selection, training, and knowledge management. She has presented her work at various national conferences, including the Academy of Management, the Society for Industrial/Organizational Psychology, and Veterans' Affairs Health Services Research and Development Meeting.

Dr. Charles McGhee is an assistant professor of Biometry at the University of Texas School of Public Health and received his Ph.D. from the University of Texas at the Houston Health Science Center. His research interests involve the delivery of health services, stochastic modeling, and longitudinal models for quality of life transplants.

Dr. Frank I. Moore is an associate professor at the University of Texas School of Public Health and an investigator for the Veterans' Evidence-Based Research Dissemination and Implementation Center. Dr. Moore serves as the Director of the Center for Health Policy Studies and has been the principle investigator or co-investigator on several grants ranging from the evaluation and management information system development to community-based public health initiatives.

Dr. Jacqueline Pugh is the Director of the Veterans Evidence-based Research, Dissemination, and Implementation Center (a VA Health Services Research Center of Excellence) at the Audie Murphy Division of the South Texas Veterans Health Care System and a Professor of Medicine at the University of Texas Health Science Center at San Antonio. Her research has examined the epidemiology of the complications of diabetes, effective dissemination of guidelines on diabetes to health professionals, and most recently how to facilitate learning organizations’ adoption guidelines.

Age Range	Frequency	Percent
0	1	.5
18-27	2	1.0
28-37	17	8.5
38-47	65	32.3
48-57	89	44.3
58-67	9	4.5
Total	183	91.0

Knowledge Enablers	Guideline Compliance
Knowledge Enablers	Prevention Index	Diabetes Mellitus	Hypertension
Factor 1: Individual Commitment	p < .095	p < .14	p < .79
Factor 2: Organizational Intention	p < .13	p < .18	p < .38