ABSTRACT:
This research asked the following question: Is there a correlation between organizational culture and the ability of an organization to efficiently and effectively transfer knowledge? In an attempt to answer this question, we surveyed 1,116 people in 23 organizations from the United States Air Force using an instrument that assessed organizational culture and factors known to influence knowledge transfer efficacy. We found evidence that organizations with cultural traits exhibiting an openness to change and innovation as well as a task-centered orientation tended to be conducive to knowledge transfer. Conversely, we found evidence that organizations with cultural traits exhibiting a confrontational and competitive orientation tended not to be conducive to knowledge transfer. Managerial implications and directions for future research are also provided.
Keywords: Knowledge management, knowledge transfer, organizational culture
1. Introduction
An organization’s knowledge may be one of its most important resources. In fact, numerous authors have pointed to knowledge as an organization’s best sustainable source of competitive advantage (Drucker, 1988; Nonaka, 1991; Morey & Frangioso, 1997; Zwass, 1999; Argote & Ingram, 2000; Argote, Ingram, Levine, & Moreland, 2000; Davenport & Prusak, 2000; Lahti & Beyerlein, 2000; Rulke, Zaheer & Anderson, 2000). Recent academic and popular media attention on organizational knowledge creation, capture, and transfer attest to a widespread acceptance of this idea (Davenport, DeLong, & Beers, 1998; Costa, 1999; Marchand & Davenport, 2000).
If knowledge is indeed as important as some argue it is, perhaps an organization should investigate ways to increase its use of the knowledge it already possesses. One interim step towards realizing this goal is to identify factors that encourage or discourage knowledge transfer in organizations. Once knowledge transfer is understood in this organizational context, managers might be able to implement strategies to boost organizational efficacy through better knowledge management.
Recently, there has been concern with how to increase organizational knowledge to gain a strategic advantage (Nonaka & Takeuchi, 1995; Bresman, Birkinshaw, & Nobel, 1999; Davenport & Prusak 2000). Thus far, interest has centered on knowledge creation and codification. There are two principle reasons for this emphasis. First, the idea that a competitive advantage stems from new inventions and management innovations has led to interest in knowledge creation (Nonaka & Takeuchi, 1995). Second, a competitive job market for technology professionals coupled with an aging technical workforce has led to numerous attempts to codify and capture knowledge before it leaves an organization (e.g., when individuals retire).
With respect to technically-oriented professionals, the United States Air Force (USAF) is similar to many civilian organizations. That is, the USAF requires a highly technically competent workforce to maintain and repair current systems, and it must also fill research and development positions to create future systems. Significantly, recent studies indicate that the USAF workforce is aging, and it is having an increasingly difficult time recruiting and retaining a knowledgeable workforce (Ryan, 2000).
Complicating the aging workforce and recruiting problems is the apparent failure of many early knowledge management efforts—at great cost in some cases (Davenport, DeLong, & Beers, 1998). Some authors have argued that the reasons for this breakdown have to do with treating knowledge either as (1) no different from information or (2) an asset that could be generated, codified, and transferred at no cost (i.e., no “friction”) (Von Hippel, 1994; Pan & Scarbrough, 1999; Szulanski, 2000). Some authors also note that a mismatch between the goals of knowledge management and organizational culture might cause a major conflict, reducing the effectiveness of knowledge projects (Davenport, DeLong, & Beers, 1998; Kostova, 1999; Pan & Scarbrough, 1999; Davenport & Prusak, 2000; Marchand & Davenport, 2000; King, 2001). Indeed, some even label this friction as the “biggest obstacle” (Costa, 1999). This stumbling block is another important reason to study a possible relationship between organizational culture and knowledge transfer.
Despite the difficulties associated with knowledge management, researchers and practitioners alike have come to believe that knowledge transfer within an organization might represent a low-cost alternative to the creation, codification, and capture of new knowledge. One practitioner put it this way: “We used to say knowledge is power. Now we say sharing is power” (Pederson, 1998, p. 20). This study is important because the ability to identify how conducive an organization’s culture is to the transfer of knowledge could provide management with the tools to determine whether it is worth the investment to implement knowledge transfer strategies. In some respects, our research parallels those studies that attempted to determine the initial success of information technology installation efforts based on organizational culture variables (e.g., Hoffman & Klepper, 2000), but it expands the scope of the investigation to include knowledge management efforts, which often require significant management changes in addition to the introduction of new information technology. To that end, this research also endeavors to demonstrate what cultural factors might be “manipulated” to make an organization more amenable to knowledge transfer.
Some scholars contend that to be considered useful to an organization, the definition of knowledge must include at least three concepts. First, it must point out that knowledge is more than data or information. Second, it must describe the tacit and explicit nature of knowledge. Finally, it must describe the personal nature of knowledge (Nonaka & Takeuchi, 1995). Davenport and Prusak (2000) offer the following definition:
Knowledge is a fluid mix of framed
experience, values, contextual information, and expert insight that provides a
framework for evaluating and incorporating new experiences and
information. It originates and is
applied in the minds of knowers (p. 5).
This definition also addresses key cultural components of organizations. These factors include the varied experiences and values of the organization’s membership and a framework for evaluating and incorporating new experiences and information through embedded routines, processes, practices, and norms.
3.1. Knowledge Management
At the beginning of the information age, many organizations were content to simply manage their data and information by automating their storage and retrieval through transaction processing systems (Drucker, 1988). However, this practice led to three unintended consequences. First, as data and information storage increased and became more interconnected, organizations found it more and more difficult to interpret their meaning, a phenomenon popularly known as “information overload” (Davenport & Prusak, 2000, p. xiv). Second, as companies downsized in the latter decades of the Twentieth Century, they relied on their data and information to maintain a competitive market edge, while viewing their employees as expendable capital (Womack, Jones, & Roos, 1990). Lastly, studies began to reveal that in many cases there was apparently little correlation between expenditures on data and information storage and organizational efficiency and effectiveness, defined in the literature as the “productivity paradox” (Brynjolfsson, 1993). In order to combat these problems, many organizations adopted the learning model in which enterprises attempt to not only become skilled at creating, acquiring, and transferring knowledge but also modify their activities to reflect new knowledge and insights (Garvin, 1993).
Although knowledge transfer is an important component of knowledge management (Davenport & Prusak, 2000), it has quixotically received the least attention in the business community. In the field of psychology, however, the study of knowledge transfer predates the study of knowledge management by several decades (Argote, Ingram, Levine, & Moreland, 2000). Indeed, the notion that knowledge transfer could represent not only a competitive advantage within a firm but also a less expensive alternative to knowledge creation and acquisition is well documented in economics (Alchian & Demsetz, 1972) and organizational behavior literature (Argote & Ingram, 2000).
Interestingly, as organizations consume material assets, they often decrease in value and quantity. On the other hand, when organizations use knowledge resources, these assets tend to increase in that both the giver and receiver are enriched as a result of the transaction (Davenport & Prusak, 2000). For example, more than one individual can use knowledge at the same time, and shared knowledge stimulates the creation of new knowledge. More importantly, this process appears to reduce costs and significantly contribute to overall organizational success by preventing individuals from repeating the mistakes of other individuals (Baum & Ingram, 1998; Gruenfeld, Martorana, & Fan, 2000).
Knowledge transfer is nominally concerned with the process of moving useful information from one individual to another person. Notably, in order for this transferred information to have utility, it must be critical to the success of the organization (Davenport & Prusak, 2000). Extant literature provides several instances of organizations skillful at knowledge transfer (Zairi & Whymark, 2000), but most of these case studies do not fully explore why these organizations were successful at this endeavor. To fully understand how to grow this capability, it is probably necessary to understand what factors tend to affect knowledge transfer. A recent literature review within the knowledge domain provided the following five factors that might influence knowledge transfer:
1. Relational channels … frequency and depth of two-way human-to-human contact (Rulke, Zaheer, & Anderson, 2000)
2. Partner similarity … degree of similarity (e.g., interests, background, or education) between individuals (Almeida & Kogut, 1999; Darr & Kurtzberg, 2000)
3. Depreciation … loss of knowledge after transfer (Argote, Beckman, & Epple, 1990; Darr, Argote, & Epple, 1995)
4. Organizational self-knowledge … what do individuals know (Rulke, Zaheer, & Anderson, 2000)
5. Divergence of interests … congruency of individual and organizational goals (Alchian & Demsetz, 1972; Jensen & Meckling, 1976; Donaldson, 1990).
Each of the aforementioned factors could be influenced by an organization’s culture, defined here as shared values and beliefs (Schein, 1990). Of course, if an organization has a discernable culture, the leadership of that organization might be able to influence it in such a way as to increase knowledge transfer efficacy.
An organization’s culture is an important guiding force in an organization. It grows and remains stable over relatively long periods and is often discernable at all levels of an organization (Schein, 1990; Lundberg, 1996). A number of studies have identified a variety of organizational cultures—each using different terminologies and methods to describe seemingly similar concepts. A recent study consolidated much of this research using factor analysis (Xenikou & Furnham, 1996). The authors identified four basic organizational cultures:
Type 1:
Openness to Change/Innovation
Humanistic orientation, affiliation, achievement, self-actualization, task support, task innovation, and hands-on management (i.e., managers who not only plan but also participate).
Type 2:
Task-Oriented
Being the best, innovation, attention to detail, quality orientation, profit orientation, and shared philosophy.
Approval, conventionality, dependence, avoidance, and lack of personal freedom.
Type
4:
Competition/Confrontation
Oppositional orientation, power, competition, and perfectionism (Xenikou & Furnham, 1996, p. 363).
5. Knowledge Transfer And Organizational
Culture Intersection
We now put forward a rationale to explain which organizational cultures might be more or less conducive to knowledge transfer.
5.1. Openness To Change/Innovation
An organization whose culture is characterized by openness to change and innovation would likely foster human-to-human contact and stress similarities between individuals. In addition, this culture promotes self-actualization, which is likely to increase individual knowledge. For these reasons, we hypothesized that organizations with openness to change/innovation cultures would be positively correlated to high knowledge transfer environments.
5.2. Task-Oriented
Similar to the openness to change/innovation culture, this cultural type fosters a shared philosophy, which should increase the convergence of the goals shared by an organization and its membership. Also, an organization that stresses quality and attention to detail would likely attempt to maximize knowledge transfer efficiency (i.e., minimize depreciation). Again, we hypothesized that an organization with a task-oriented culture would be positively correlated to a high knowledge transfer environment.
An organization that discourages interpersonal communication is likely to diminish relational channels. Also, a culture that encourages dependence is likely to discourage the pursuit of individual knowledge. Hence, we hypothesized that an organization with a bureaucratic culture would be negatively correlated to a high knowledge transfer environment.
5.4. Competition/Confrontation
As with the bureaucratic culture type, competitive/confrontational cultural types tend to discourage interpersonal relationships. Also, a culture that fosters a pursuit of power may put individual goals (e.g., advancement solely for personal gain) at odds with organizational goals. For these reasons, we hypothesized that an organization with a competition/confrontation culture would be negatively correlated to a high knowledge transfer environment.
6. Method
A linear regression model was used to analyze the data. There were four cultural types examined. Each cultural type could influence four knowledge transfer efficacy factors. For example, an organization exhibiting openness to change/innovation cultural traits could influence relational channels, partner similarity, self-knowledge, and interest divergence. This same analysis was performed on the remaining three cultural types. Therefore, four linear regression models were created to test each of the four hypotheses for a total of sixteen models for the entire study.
6.1. Population and Sample
The USAF has over 8,350 military units (e.g., squadrons, wings, groups) stationed around the world and is staffed by more then 760,000 active duty, civilian, guard, reserve, and ready reserve personnel (“People,” 2001). To reach a representative sample of this population, this research used a cross-sectional survey design, which is outlined below.
Because the goal of this study was to explore the relationship between organizational culture types and various environmental factors influencing knowledge transfer, it was naturally important to assess only those organizations that one would expect to have a discernable culture. Consider, it is possible for an organization not to have a discernable culture. One organization may be “nested” within a larger organization, and this subsumed organization may simply follow the dictates of the parent organization’s culture. It is also possible that an organization has not had the time to develop a culture (Ott, 1989). Recent research conducted by the USAF demonstrates that squadrons typically have robust and discernable cultures, containing a like-minded membership with similar organizational goals (Smith, 1998). For this reason, the population of interest was the 3,881 squadrons in the USAF, with the “squadron” being the unit of analysis.
Given the size of the population, it was determined that in order to obtain a random sample representative of the population (confidence interval equals 90 percent), 20 squadrons (membership of at least 80 individuals) had to be surveyed (McClave & Benson, 1991). The anticipated return rate required us to contact 50 squadrons, and one additional squadron was contacted to conduct a pilot test. That is, in order for a squadron to be representative of the population, a certain number of individuals had to submit usable survey data (confidence interval equals 90 percent) from that squadron. Given a historical response rate of 25 percent from similar surveys of USAF personnel, individuals in at least 20 of the 50 squadrons would have to provide sufficient information in order for us to claim the data were representative of the population. Every individual in all 50 squadrons were asked to participate in the study. Of the 50 squadrons randomly selected from the population of 3,881, 23 submitted usable data. Individual responses from these 23 squadrons were apparently random, and no particular type of squadron (e.g., fighter versus bomber) or geographic location was over- or underrepresented in the usable responses.
6.2. Survey Development
And Administration
As noted earlier, the four types of organizational culture most likely to be found in organizations are openness to change/innovation, task-oriented, bureaucratic, and competition/confrontation. None of the four instruments listed by Xenikou and Furnham (1996) were available for use in this research. For this reason, a previously validated instrument was sought that measured similar domains. The FOCUS questionnaire (van Muijen et al., 1999) captured two of the four types of organizational culture, openness to change/innovation, and task-oriented. Twenty-three questions were derived or adapted directly from this source. To capture the other two types of organizational culture, bureaucratic and competition/confrontation, questions were devised using the FOCUS questionnaire as a starting point, with heavy emphasis on the descriptions of those factors identified in Xenikou and Furnham’s (1996) research. Twenty-four questions were written in this manner. Finally, a third source of questions was consulted to assist in phrasing of specific questions to capture items not covered by the FOCUS questionnaire (Hofstede & Neujen, 1990).
To capture the four indicators of knowledge transfer, we used the descriptions of the indicators given in the original research documents previously referenced. Ten questions were written based on the research on relational channels (Morey & Frangioso, 1997; Rulke, Zaheer, & Anderson, 2000). Ten questions were written based on the research on partner similarity (Almeida & Kogut, 1999; Darr & Kurtzberg, 2000). Twelve questions were written based on the research on organizational self-knowledge (Moreland & Myaskovsky, 2000; Rulke, Zaheer, & Anderson, 2000). Finally, eleven questions were written based on the research on divergence of interests (Alchian & Demsetz, 1972; Jensen & Meckling, 1976; Donaldson, 1990).
The survey instrument consisted of 90 items using a 5-point Likert-type scale. The 5-point scale was chosen to keep the survey consistent with the FOCUS questionnaire, which also used a 5-point Likert-type scale. The survey was administered through an electronic form sent by e-mail and conducted from January 30, 2002 to February 14, 2002. In our e-mail, we promised to protect the anonymity of all individuals as well as all organizations. That is, none of the results would be attributed to a specific individual or squadron.
7. Analysis and Results
Mean scores were calculated for each of the 23 squadrons for each of the
constructs measured. The 23
squadrons included in this analysis, representing 1,116 responses, were those
who met or exceeded an 88 percent level of confidence. This confidence interval was used
instead of 90 percent because it allowed the inclusion of more squadrons, which
increased the posterior probability of squadrons in the USAF multiplied by the
members in each squadron (.92 x .88 = .81). Each of the constructs’ mean scores was used to produce a
correlation and linear fit plot corresponding to each of the four
hypotheses. The fit was used to
make a determination about whether or not the data either supported each
hypothesis. Note that an analysis
was conducted on all 1116 responses to assess questionnaire reliability, and
with the exception of the bureaucratic culture construct (discussed later), no
significant discrepancies were uncovered.
7.1. Hypothesis
1: Openness to Change/Innovation
Overall, hypothesis one was strongly supported by the data, with the exception of partner similarity, which showed no statistically significant correlation with openness to change/innovation. In other words, a squadron exhibiting an openness to change/innovation culture traits tended to increase an organization’s ability to transfer knowledge. Table 1 summarizes these results.
Table 1 Organizational Openness to Change/Innovation Culture: Summary of Four Linear Regression
Analyses Predicting Knowledge Transfer Efficacy (N = 23) |
|||
|
Variable |
Adj. R2 |
B |
SE B |
|
Relational Channels |
.99 |
.836* |
.067 |
|
Partner Similarity |
<.01 |
.151 |
.331 |
|
Self Knowledge |
.63 |
.872* |
.144 |
|
Interest Divergence |
.87 |
.943* |
.081 |
|
Note. *p < .01. |
|||
7.2. Hypothesis 2: Task-Oriented
Overall, hypothesis two was strongly supported by the data, with the exception of partner similarity, which showed a moderate but significant correlation with openness to change/innovation. In other words, a squadron exhibiting task-oriented cultural traits tended to increase an organization’s ability to transfer knowledge. Table 2 summarizes these results.
|
Table 2 Task-Oriented Culture:
Summary of Four Linear Regression Analyses Predicting Knowledge
Transfer Efficacy (N = 23) |
|||
|
Variable |
Adj. R2 |
B |
SE B |
|
Relational Channels |
.79 |
.794* |
.088 |
|
Partner Similarity |
.21 |
.740* |
.289 |
|
Self Knowledge |
.91 |
1.036* |
.072 |
|
Interest Divergence |
.88 |
.951* |
.076 |
|
Note. *p <.01. |
|||
7.3. Hypothesis 3: Bureaucratic
Overall, hypothesis three was not supported by the data. In other words, a squadron exhibiting bureaucratic cultural traits did not tend to decrease an organization’s ability to transfer knowledge.
Results may have been weakened by the reliability of the instrument used to measure the construct of bureaucratic culture. Although the pilot study demonstrated a reliability of .6172, once the overall results were tabulated, the reliability rate dropped to a disappointing .3575. Such results suggest that with respect to the individuals surveyed in this study, the instrument used ambiguous terms open to multiple interpretations. Significantly, the elimination of one or more questions concerning the bureaucratic construct did not increase overall reliability with these data.
In addition to these reliability problems, it is possible that the assumptions about an organization with a bureaucratic culture were incorrect. For example, although bureaucracies may encourage dependence, they also formalize the flow of information. In this sense, individual knowledge may actually increase because of the predictable nature of communicating information in organizations exhibiting this particular cultural type. On the other hand, most public concerns, especially military units, have more bureaucratic tendencies than other non-military organizations (i.e., private and third sector). Hence, the lack of support for this particular hypothesis may simply be an idiosyncratic artifact of the data. Table 3 summarizes these results.
|
Table
3 Bureaucratic Organizational
Culture: Summary of Four Linear
Regression Analyses Predicting Knowledge Transfer Efficacy (N = 23) |
|||
|
Variable |
Adj. R2 |
B |
SE B |
|
Relational Channels |
<.01 |
.277 |
.195 |
|
Partner Similarity |
<.01 |
.081 |
.333 |
|
Self Knowledge |
<.01 |
.011 |
.242 |
|
Interest Divergence |
<.01 |
.138 |
.225 |
|
|
|||
7.4. Hypothesis 4:
Competition/Confrontation
Overall, hypothesis four was moderately supported with the data collected, with the exception of partner similarity, which showed no significant correlation with competition/confrontation. In other words, a squadron exhibiting competition/confrontation cultural traits tended to decrease an organization’s ability to transfer knowledge. Table 4 summarizes these results.
|
Table
4 Competition/Confrontation
Organizational Culture: Summary
of Four Linear Regression Analyses Predicting Knowledge Transfer Efficacy (N
= 23) |
|||
|
Variable |
Adj. R2 |
B |
SE B |
|
Relational Channels |
.36 |
.782* |
.154 |
|
Partner Similarity |
.03 |
.564 |
.321 |
|
Self Knowledge |
.13 |
.624* |
.221 |
|
Interest Divergence |
.39 |
.917* |
.172 |
|
Note. *p < .01. |
|||
The data in this study demonstrated a correlation between some types of organizational culture and factors influencing knowledge transfer. This research could lay the groundwork for practitioners interested in increasing the efficacy of knowledge transfer in their organizations. For example, many tools already exist to assist practitioners in measuring organizational culture (e.g., van Muijen et al., 1999). If managers can accurately assess the culture of their organizations, these leaders can attempt to institute changes to make their concerns more conducive to knowledge transfer. Of course, although this research has pushed certain assertions about organizational culture and its relationship to knowledge transfer past the speculative stage, this is still only one study, and it is unclear how generalizable or replicable the results will be in future research. Further, even if this theory has merit, it still remains that changing an organization’s culture is a notoriously lengthy and difficult process (Goh, 1998). Future research should also examine the costs and benefits associated with changing an organization’s culture (costs) and, thereby, increasing this organization’s knowledge transfer efficacy (benefits). Indeed, it may be more beneficial for managers to learn how to develop knowledge management strategies that attempt to overcome obstacles created by their cultures—at least in the short term, rather than trying to change their cultures.
One limitation of a statistical correlation is that it does not prove causation (Kachigan, 1986). Of course, this concern is mediated by the notion that we posited a theory that is, at the very least, not contradicted (i.e., no correlation) by the data. Another limitation of our study is that we did not measure knowledge transfer. Instead we measured factors that provide a more or less conducive environment to knowledge transfer. We relied on previous studies (e.g., Rulke, Zaheer, & Anderson, 2000) that made the link between factors that provide a favorable atmosphere for knowledge transfer and knowledge transfer efficacy. It should also be noted that an inherent problem exists in attempting to measure organizational culture constructs and knowledge transfer through a cross-sectional, self-report survey instrument. Previous research has uncovered anecdotal evidence that suggests a high degree of correlation between culture scores and employee disenfranchisement (Key, 1999). In other words, empirically assessments of organizational culture are often confounded by the employee satisfaction construct, which could significantly influence the measurement of the former. As mentioned previously, our instrument did not exhibit a high degree of inter-item reliability with respect to the bureaucratic construct (.3575). This may have been a result of several factors, including the idiosyncratic nature of the unit of analysis and data, the USAF squadron. In addition, none of the linear regression models showed a significant correlation between any of the organizational cultures and partner similarity. USAF squadrons are relatively homogeneous organizations, which might skew the data to one extreme with respect to partner similarity, but precisely why (or even if) this would vitiate a statistical test of this construct is unclear at the moment. Finally, since our data only looked at a public sector institution, a prudent person may want to approach the generalizability of this research, especially to private and third-sector concerns, with caution.
This study points to several key areas that require further analysis. For example, subsequent research might involve conducting a longitudinal study of organizational culture and knowledge transfer, providing a more empirical causal link between these two constructs. Along these same lines, a longitudinal case study might involve actually manipulating an organization’s culture in an attempt to foster an environment conducive to knowledge transfer. Another potentially fruitful avenue of research would involve having managers first assess the type of culture their organizations have and subsequently attempting to implement strategies to maximize knowledge management practices without manipulating their organizations’ cultures, which is not feasible in the short term. Future studies would also benefit from more eclectic data, gathering it from both private and third-sector organizations.
This research indicates a correlation exists between some types of organizational culture and factors influencing knowledge transfer. In one sense, this study appears to bolster what academics and practitioners have stated about the importance of considering organizational culture when implementing knowledge management projects. In addition, this research leads to new questions: In the short term, if managers are armed with information about their respective organizational cultures, can they modify their knowledge management strategies to make their organizations more efficient and effective? If organizational culture and knowledge transfer are indeed correlated, what can managers do to assess and manipulate organizational culture when developing their long-term knowledge management strategies?
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Contact The Authors:
Darin A. Ladd, Mark A. Ward, Air Force Institute of Technology (AFIT)/ENV, Graduate School of Engineering and Management, 2950 P Street Bldg 640, Wright-Patterson AFB, Ohio 45433-7765 USA
Phone number: (937) 255-3636, ext. 4742; Fax number: (937) 656-4699; E-mail address: mark.ward@afit.edu
Darin A. Ladd is a graduate of the Air Force Academy in Colorado. He recently graduated from the Air Force Institute of Technology with a Master’s degree in Information Resource Management. He is currently assigned to Headquarters Air Combat Command, Directorate of Communication and Information, Langley Air Force Base, Virginia.
Mark A. Ward is an assistant professor of Information Resource Management at the Air Force Institute of Technology (AFIT) Graduate School of Engineering and Management in Dayton, Ohio. He holds a Ph.D. in Business Administration in MIS and Organizational Studies from Southern Illinois University at Carbondale and a Master’s degree in acquisition logistics management from AFIT. His current research focus is the management of information systems professionals and technology.