Climate of innovation in government communities of practice

focusing on knowledge gains and relationships

Virginia Tech, Falls Church, Virginia

Abstract

A recurrent justification for knowledge management initiatives in the United States U.S. federal government workplace is the assertion that knowledge sharing groups, such as communities of practice, positively impact their members and benefit the organization by fostering a work environment that results in innovation. However, limited quantitative research exists to support the claims. The purpose of this research was to discover evidence for and explain the relationships between two of the dimensions of communities of practice (i.e., participation and connectivity) and a climate of innovation (e.g., vision, participative safety, task orientation, and support for innovation). This study provided empirical support for the relationship between participation and climate of innovation, as well as the relationship between connectivity and climate of innovation.

Perceptions were collected from 384 community of practice members within the U.S. federal government environment about participation, connectivity, and the community's climate of innovation. Items from three existing instruments, Communities Assessment Tool (Verburg & Andriessen, 2006), Sense of Community Index (Chipeur & Pretty, 1999; Peterson, Speer, & Hughey, 2006), and Team Climate Inventory (Kivimäki & Ellovainio, 1999), were consolidated into one online questionnaire. Once the data were collected from the respondents, they were checked for completeness, reorganized and relabeled as necessary, and then transported to SAS JMP, version 7. The reliabilities in this study were comparable to previously published reliabilities. Demographic data indicated that the respondents tended to see themselves as experts, were active within their community, and relied on virtual contact with community members, although they had the opportunity to meet face-to-face in the past. After a review of the correlations, a parsimonious model containing four variables (i.e., climate of innovation, perceived benefits of participation, nature of participation, and connectivity) was generated. In response to the research questions, multiple regression was conducted. The results showed that participation variables accounted for 22% to 26% of the variance in climate of innovation, with support for innovation being the best explained and vision following close behind with the second largest percentage of its variance explained. The connectivity variables explained 18% to 29% of the additional variance, with participative safety responsible for the largest percentages of the variance and vision having the second largest percentage. Together, the four participation variables explained about one quarter of the variance in each of the climate of innovation criteria. Adding the four connectivity variables explained more than an additional quarter of the variance for vision and participatory safety.

Given the results, two themes emerged: The first was the importance of connectivity within communities of practice and in relation to a community's climate of innovation. The second was the refinement of the contemporary definition of participation within communities of practice. The findings signify that social approaches to knowledge management, such as communities of practice, may contribute to a climate conducive to innovation. Suggestions for future research and implications for practitioners are discussed. Given the current economic and security challenges, such as the global recession, homeland protection, and industry bailouts, the need for innovative products and services is paramount. Incorporating the results of this study and placing an emphasis on building or solidifying relationships, members of knowledge sharing groups within and across the federal government environment may better develop and implement strategies to address the current stresses and work toward stabilizing the worldwide situation.

Background

Communities of practice are now a popular knowledge management tool within and across organizations, emphasizing the social aspects of knowledge creation, sharing, and application. The added value of a community of practice comes when knowledge is applied for a specific purpose such as to improve, change, or develop specific tasks. Innovation–or the intentional application of ideas–within organizations is a desired outcome and is frequently cited as a primary purpose for knowledge management activities. However, innovation can be a complex and uncertain process due to its dynamism and episodic nature at the initial phase of creativity or invention. It may also be disruptive and highly political at the diffusion and implementation phase. Innovation is also difficult to measure because of, among other reasons; the time involved tracking it and the need to control for external influences or mediating factors.

However, there is evidence to suggest that innovation often originates from a group and is subsequently developed by that group into routinized practice within organizations (West & Farr, 1990; King & Anderson, 1995). Therefore, it is assumed that a precursor to innovation is the climate of innovation, which may be examined through a group's internal environment or social climate in relation to innovation. The climate of innovation of a group or community of practice would then serve as a reasonable proxy for innovation (Chindgren, 2009).

Problem Statement

          Given this assumption, the purpose of this research study was to investigate the relationship between communities of practice and the climate of innovation within the United States (U.S.) federal government environment. Two features of communities (1) participation of the members and (2) the relationships among the members (also known as “connectivity”) were assessed. The focus was on examining quantitative evidence for the associations between these two features and climate of innovation (e.g., vision, participative safety, task orientation, and support for innovation) within communities of practice.

This study was not designed to compare communities of practice; evaluate strategies to promote, build, or facilitate communities of practice; explore techniques for fostering innovation in the workplace; or define and measure innovative products or services. However, the results may contribute to accomplishing these objectives.

Literature Review

Communities of Practice

A community of practice is a group of professionals who interact with each other within an organization, across organizational units, or even organizational boundaries; have a common interest or field of application in certain work-related topics; and share their knowledge on a regular basis (Andriessen, 2005; Chindgren & Hoffman, 2006; Lave & Wenger, 1991; Saint-Onge & Wallace, 2003; Wenger, 1998; Wenger, McDermott, & Snyder, 2002). This definition reflects key characteristics of communities of practice identified by researchers and practitioners in the last quarter century. (1) These units can exist within a specific organization or across an industry. (2) A community of practice is a knowledge sharing forum for practitioners of a discipline or topic, or those interested in addressing a specific concern. (3) Members have a shared purpose or common goal and are Foften internally motivated, as opposed to having some external driver. (4) Members value all kinds of knowledge (including, for instance, hunches as well as demonstrable scientific knowledge) that transpires within a community. (5) A community of practice is a joint enterprise that has its own identity, which is continually renegotiated by its members, and individuals become members through shared practices and involvement in common activities (e.g., storytelling). (6) Typically, relationships develop and trust is generated over time. (7) Generally, communities of practice have a long-term orientation on knowledge creation and knowledge sharing. (8) The community structure provides broad access to peers and experts who share experiences and innovative ideas and is not constrained by the conventions of traditional hierarchical structures.

The objective is for members to learn and support one another to create, capture, spread, retain, and apply knowledge relevant to the organization. As a result, communities of practice have emerged as a key instrument for collaboration and knowledge sharing across conventional organizational boundaries. Traditionally, communities form to share ideas and insights as well as to solve problems and explore changes to their discipline or practice area (Lave & Wenger, 1991; Brown & Duguid, 1991). These communities were not obvious in the organizational structure, and organizations did very little to encourage, nurture, or sponsor them. In contrast, a recent study sponsored by the Knowledge and Innovation Network (KIN) at the Warwick Business School in the United Kingdom (2006) noted, “in the last half decade organizations seem to be paying increasing attention to the role communities can plan in helping to drive organizational performance” (p. 3). Organizational leaders are gradually advocating the formation of communities, aligning communities with formal organizational objectives, and supporting communities with resources and training. This is because there is increasing evidence suggesting organizations, work groups, and individual practitioners benefit from participation in communities of practice (Allen, 2003; Ardichvilli, Page, & Wentling, 2003; Malone, 2002; Swan, Scarbrough, & Robertson, 2002; Lesser & Everest, 2001; Zboralski & Gemünden, 2006).

Two key dimensions of communities of practice are (1) participation in the community by the members and (2) the “connectivity” or relationships among the members of the community. Regarding participation, four variables are examined in this study: (1) perceived benefits of participation, (2) the nature of participation, (3) the amount or level of participation, and (4) the primary mode of participation. To investigate connectivity, the four variables of interest include: (1) a feeling of belonging and identification, (2) an opportunity for influence, (3) an integration and fulfillment of individual and community needs, and (4) a shared emotional connection. All eight variables and their relationship with climate of innovation are discussed in this paper.

Climate of Innovation

Communities of practice may have a climate of innovation that could indicate their potential contribution to organizational innovation (Amin & Roberts, 2008; Hildreth & Kimble, 2004). Innovation is described as the “intentional introduction and application within a role, group, or organization of ideas, processes, products, or procedures new to the relevant unit of adoption, designed to significantly benefit role performance, the group, the organization, or the wider society” (West & Farr, 1990, p. 16).

Innovation is fostered by a combination of both personal qualities (e.g., cognitive style of individuals, openness to experience, and intrinsic motivation) and work environment (e.g., commitment to ambitious goals, freedom and autonomy regarding how tasks will be performed, encouragement of ideas, time to generate new ideas, permission for risk taking, and the opportunity to make errors) (Tesluk, Farr, & Klein, 1997; West & Richards, 1999). Organizations may cultivate an atmosphere in which innovation is fostered. West and Richards (1999) reported that the combination of a supportive and challenging environment has been found to sustain high levels of creativity in work groups. This type of environment is social and non-threatening, characterized by clear objectives, autonomy in accomplishing work, the encouragement of ideas, recognition and rewards for creative work, and a shared sense of quality (Amabile, Conti, Coon, Lazenby, & Herron, 1996; Tesluk, et al., 1997; West, 1990). In essence, there is a need for a climate conducive to innovation. The climate of an organization is based on the shared perceptions of how “the manner of working together” has evolved (Anderson & West, 1994, p. 3). The combination of a supportive climate and communities of practice within an organization would presumably lead to greater organizational effectiveness, including innovation.

The four factors of workplace climate of innovation that were explored in this study include: (1) vision, which is an idea of a valued outcome which represents a higher order goal and a motivating force at work; (2) participative safety, which refers to group members actively contributing to creating a non-threatening, trusting, and supportive group environment; (3) task orientation, which describes a commitment to excellence in task performance coupled with a climate that supports the adoption of improvements to established policies, procedures, and methods; and (4) support for innovation, which refers to the expectation, approval, and practical support of attempts to introduce new and improved ways of doing things in the work environment.

Research Method

Community of practice members within the U.S. federal government environment provided their perceptions about participation, connectivity, and their community's climate of innovation. Demographic data about the members' employers, workplace environments, expertise levels, and tenure in one's job and community were also collected.

The predictor variables (i.e., participation and connectivity) and the criterion variable (i.e., climate of innovation) were examined by combining items from three existing instruments into an online questionnaire: Communities Assessment Tool (CAT) (Verburg & Andriessen, 2006); Sense of Community Index (SCI) (Chipeur & Pretty, 1999; Peterson, Speer, & Hughey, 2006); and Team Climate Inventory Short Version (TCI) (Kivimäki & Ellovainio, 1999).

Once the data were gathered from the respondents, they were checked for completeness and reorganized and relabeled as necessary. The data were then transported to SAS JMP, version 7. Dummy coding was used for two of the predictor variables: participation level and participation mode. Reliability estimates for scales and subscales were calculated and were comparable to previously published reliabilities. Means and standard deviations were also determined for each scale. After a review of the correlations, a parsimonious model containing four variables was generated. Multiple regression was then conducted.

Highlights of the Results

Respondents

A total of 384 community members representing the U.S. federal government environment participated in the study. They primarily came from civilian government and military, yet industry, academia, and research centers that support the federal government participated as well. The communities addressed a variety of issues including transportation and logistics, technology security, and leadership. They supported a range of professionals such as acquisition specialists, F-16 officers, finance managers, ordinance experts, intelligence analysts, strategic thinkers, and human computer researchers. For this sample, 85% of the study respondents worked in their employer's organization while 15% worked outside their employer's organization, that is, they were contract workers in client organizations.

Nearly one-half (47%) of the 330 respondents described themselves as an “expert” in their field, followed by one-third (34%) who saw themselves as “proficient” and 14% saw themselves as “competent.” Small percentages of respondents reported that their expertise level was “advanced beginner” (4%) or “novice” (1%). The mean for the participants' tenure in their current job was 8.6 years (N = 241, SD 7.7) with the tenure ranging from less than one year to more than 35 years. The mean tenure as a community member was 3.2 years (N = 160, SD 3.9), with ranges from “newly joined” to 25.5 years. There was a fairly direct relationship with self-reported expertise level for both average job tenure and community tenure. In both cases, novices averaged less than one year in their jobs and one-half a year in their communities, while experts averaged 11 years in their jobs and four years in their communities of practice.

Criterion and Predictor Variables

Climate of Innovation.     The TCI Short Version with Likert scales was used in this study. Notably, the subscales' means were clustered closely together, which may indicate that the respondents saw these aspects in the same positive light. The highest mean was for participative safety at 3.8 (SD .68) on a five-point Likert scale. The lowest mean was for task orientation at 3.5 (SD .81). The reliabilities for the subscales were strong, ranging from .83 to .88.

Participation. Three of the participation variables, perceived benefits, amount/level of participation, and primary mode of participation items, were assessed with the CAT. The items for nature of participation were developed following a review of the literature. For perceived benefits of participating within communities of practice, respondents reported that “staying up to date in the topic of the community” was the most important. Of least importance were “having nice meetings, fun, and non-work-related activities” and “acquiring projects or customers.” The reliability was strong (.88).

Respondents reported that their nature of participation most often entailed “searching, accessing, or acquiring knowledge from relevant sources” with “monitoring the field” following close behind. “Interacting and communicating with fellow community members” and “organizing and packaging knowledge for others” were the least important types of participation. The reliability was also strong (.91).

The amount/level of participation data indicated that one-half of the respondents (50%) saw themselves as “active members” in their community, with one-third of the respondents (33%) reporting their level of participation as “peripheral” to the community. Less than one-fifth of the respondents (17%) believed they were “core members” of their community.

In examining the mode of participation variables, three-quarters (71%) of the respondents reported that virtual contact was primarily how they interacted with fellow community members. For the respondents who primarily participated virtually, 72% had previous face-to-face interaction with community members but about one-quarter of the respondents (28%) had not. The intranet or Internet were the main vehicle for participation for three-quarters of the participants (74%), followed by telephone or conference calls (20%).

Connectivity. Connectivity (i.e., the level of interaction between members and feelings of cohesion and belongingness) was assessed with a slightly modified version of the SCI. Like the means for the climate of innovation variables, the subscales' means were clustered closely together, which may again indicate that the respondents positively interpreted these factors similarly for their community. The highest mean was for integration/fulfillment of individual and community needs (mean 3.9, SD .62). The shared emotional connection subscale followed very closely with a mean of 3.89 (SD .63). The membership subscale had the items that the respondents least agreed with (mean 3.4, SD .78) and referred to the feeling of belonging and identification. The reliabilities for the connectivity subscales ranged from weak to strong (.25 to .68).

Relationships among Variables

Correlations within Criterion Variables. The four climate of innovation subscales were strongly related (.66 to .75) with participative safety and support for innovation having the strongest correlation (.75).

Correlations within Predictor Variables. The participation variables were fairly independent of each other, with the exception of participation benefits and nature of participation (.53) and participation levels (i.e., core and active) (.56), which had moderate correlations. The primary mode of participation had a weak relationship with the participation variables (-.02 to .20).

The connectivity subscales were fairly related with each other (.49 to .62) with influence having the strongest relationship with membership (.62) and shared emotional connection (.61).

Correlations between Predictor and Criterion Variables. Two of the connectivity variables, membership and shared emotional connection, were moderately correlated with participation levels (.46, .42 respectively). Influence also had a moderate correlation with nature of participation (.41). Both perceived participation benefits and nature of participation had moderate correlations with climate of innovation subscales (.31 to .47), indicating that these two participation variables had the strongest relationship with the criterion, as compared with the two other participation variables. The four climate of innovation subscales and the connectivity subscales had moderate to strong correlations with each other (.43 to .61). (See Table 1, Intercorrelations among Climate of Innovation, Participation, and Connectivity Variables.)

Table 1. Intercorrelations among Climate of Innovation, Participation, and Connectivity Variables (N = 287)

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13
Climate of Innovation                          
1. Vision 1.00                        
2. Participative Safety 0.71 1.00                      
3. Task Orientation 0.67 0.70 1.00                    
4. Support for Innovation 0.66 0.75 0.70 1.00                  
Participation                          
5. Perceived Benefits 0.33 0.37 0.31 0.38 1.00                
6. Nature of Participation 0.43 0.46 0.44 0.47 0.53 1.00              
7. Participation Level/Core 0.30 0.32 0.25 0.27 0.20 0.37 1.00            
8. Participation Level/Active 0.09 0.12 0.09 0.06 0.11 0.11 0.56 1.00          
9. Mode of Participation 0.02 0.06 -0.01 0.01 0.04 -0.02 0.20 0.18 1.00          
Connectivity                          
10. Membership 0.50 0.53 0.49 0.47 0.31 0.37 0.47 0.25 0.26 1.00      
11. Influence 0.59 0.58 0.48 0.53 0.29 0.41 0.36 0.16 0.14 0.62 1.00    
12. Needs 0.57 0.56 0.43 0.47 0.26 0.37 0.32 0.17 0.02 0.49 0.52 1.00
13. Shared Emotional Connection 0.61 0.60 0.48 0.50 0.29 0.37 0.42 0.21 0.10 0.59 0.61 0.57 1.00

Note. Correlations < .13 are not statistically significant

 

Generating a Parsimonious Model

The results of the correlations led to the plan for developing a more simplistic model. Because there were high intercorrelations for the climate of innovation subscale scores, a composite of the subscale mean scores was made. In addition, because a factor analysis suggested one dimension for connectivity, and not four, a composite connectivity mean score was constructed. Lastly, perceived benefits of participation and nature of participation were the two participation variables with moderate correlations and, therefore, their mean scores were kept in the final model.

With this revised model, a moderate correlation remained for nature of participation and perceived benefits of participation (.53), signifying that the motivation for participating within communities is related to the method of participation. Nature of participation had a moderate correlation with climate of innovation (.51) and with perceived benefits (.40). Of particular interest, the correlation for the consolidated mean scores of climate of innovation and connectivity was very high (.73), indicating a strong association between creating the proper environment for innovation and the importance of relationships within communities (See Table 2, Intercorrelations among Climate of Innovation, Perceived Benefits, Nature of Participation, and Connectivity.)

Table 2. Intercorrelations among Climate of Innovation, Perceived Benefits, Nature of Participation, and Connectivity (N = 289)

  1 2 3 4
1. Climate of Innovation 1.00      
2. Perceived Benefits 0.40 1.00    
3. Nature of Participation 0.51 0.53 1.00  
4. Connectivity 0.73 0.36 0.47 1.00

Variance Explained

The purpose of this research was to discover evidence for and explain the relationships between sets of variables representing the two dimensions of communities of practice (i.e., participation and connectivity) and a set of variables representing a climate of innovation. In response to this purpose, multiple regression was conducted. Table 3 has the coefficients of determination and provides the variance explained for each climate of innovation factor (i.e., vision, participative safety, support for innovation, and task orientation). The Appendix contains the betas for the hierarchical regression.

Drawn from a standard least squares regression analysis, the column with R2 Step 1 denotes the proportion of each climate of innovation factor explained by the set of four participation variables (i.e., perceived benefits for participation, the nature of participation, the amount or level of participation, and the primary mode of participation). This column indicates that the participation variables accounted for 22% to 26% of the variance in climate of innovation, with support for innovation being the best explained and participative safety following close behind with the second largest percentage of its variance explained.

Table 3. Variance Explained for Climate of Innovation Variables by Participation and Connectivity Variables in Four Hierarchical Regressions

  R2 Step 1 Δ R2 R2 Step 2
Climate of Innovation Due to participation Due to connectivity Participation & connectivity
1. Vision .23 .29 .52
2. Participative safety .25 .27 .52
3. Support for innovation .26 .16 .42
4. Task orientation .22 .18 .40

The column with R2 Step 2 displays the proportion of each climate of innovation factor explained by the set of four connectivity variables (i.e., membership, influence, integration and fulfillment, and shared emotional connection), and the four participation variables. This column reflects the second step in each hierarchical regression analysis, which included all nine predictor variables. The change in R2 column shows the difference between the R2 scores, indicating how much of the variance in each criterion was explained by connectivity over and above that which was explained by participation. The connectivity variables explained 18% to 29% of the additional variance, with vision responsible for the largest percentages of the variance and participative safety having the second largest percentage.

Together, the four participation variables explained about one-quarter of the variance in each of the climate of innovation criteria, with only slight differences. Adding the four connectivity variables explained more than an additional quarter of the variance for vision and participatory safety but less than 20% for the other two criteria. It should be noted, however, that even the worst result was fairly remarkable, with 40% of the variance in task orientation being explained by participation and connectivity variables.

Comments from Respondents

At the end of the questionnaire, respondents were invited to provide comments about their participation within the community, the relationships within the community (i.e., connectivity), and the climate of innovation within their community. Seventy-three respondents, or 19% of the 384 participants, wrote comments. Interestingly, there was a pattern in the proportion of comments based on the level of community participation. The self-described “core” members were the group to offer the largest percentage of comments. As displayed in Table 4, 30% of the 61 core members responded to the invitation to provide written comments, although they only represented 17% of the total respondents. This is not surprising as indicated by previous research, core members are the most involved in the community and would most likely have some insights into participation, connectivity, and the climate of innovation within their community, and be willing to give their time to comment. Self-described “active” members were the group with the second highest percentage, with 21% of 181 active members providing written comments. Finally, 15% of 117 “peripheral” members supplied comments.

Table 4. Written Comments Provided by Members at Different Amounts/Levels of Participation

  Total N (359) % of Total Respondents N of Respondents Providing Comments (73) % of Respondents Providing Comments
Amount/Level of participation        
a) Core member 61 17% 18 30%
b) Active member 181 50% 38 21%
c) Peripheral member 117 33% 17 15%

The comments were placed in categories, quantified, and labeled accordingly. Nineteen of the 73 comments, or 26%, were relevant to the research questions. All addressed participation variables– perceived benefits of participation, nature of participation, level of participation, and participation mode. Some comments reflected a combination of participation variables such as perceived benefits and nature of participation. There were no overt comments on connectivity or the climate of innovation.

Overwhelmingly, respondents used the opportunity to provide feedback to their community. These comments were categorized as either “community-specific feedback,” which generally praised the workings and fellow members of the community, or labeled as “organizational barriers,” which tended to be critical of organizational obstacles to community performance. Because the community-specific feedback was extraneous to the research purpose, the comments were not further analyzed. However, both the community-specific feedback and organizational barriers comments were forwarded to the appropriate community liaisons for their review and distribution to the community.

Discussion of the Results

From the results, two themes emerged about communities of practice and climate of innovation within the U.S. federal government environment. The first was the importance of relationships, or connectivity, within communities of practice and in relation to a community's climate of innovation. The second was the refinement of the contemporary description of participation within communities of practice. In addition, two aspects of the respondents’ demographic information were of special interest and will be highlighted.

Regarding demographic information, the respondents in this study were community members within the U.S. federal government environment. Participation was dominated by military personnel (47%), followed by civilians (28%). There are a couple factors that may have accounted for the large military participation. First, the military has been a leader in knowledge management activities, such as community generation and facilitation (Dixon, Allen, Burgess, Kilner, & Schweitzer, 2005; Lubold, 2008). Their progressive community-related actions may have influenced the respondents’ experience, perhaps in ways that were not directly captured by this study. Second, the military communities' liaisons were interested in the study results and asked their members to participate. Historically, when military personnel are asked to complete a task, such as a questionnaire, they do.

Additionally, there was a fairly direct relationship with self-reported expertise level for both average job tenure and community tenure. The respondents tended to describe themselves as expert (47%) with the mean tenure for experts within their jobs just over 11 years and membership in the community for four years. This high percentage of participation in the study by experts suggests that these members may find the community to be especially helpful in keeping up to date in their field. In addition, it may be the experts within the community who believed that the research study results would be of interest or benefit to their community and volunteered to participate. It may also be possible that some of the members who self-described their level of expertise at the expert level perceived their skills to be stronger than they actually were.

Connectivity: The Importance of Relationships

This study empirically demonstrated that connectivity is related to nurturing and sustaining a climate of innovation. As introduced earlier, connectivity was described as a sense of community among the members and entails: (1) a feeling of belonging and identification, (2) an opportunity for influence, (3) an integration and fulfillment of individual and community needs, and (4) a shared emotional connection. The implication is that if members tend to feel positively about their colleagues, they are more likely to receive the benefits of participation and rely on their community to stay up to date on new knowledge and listen to experiences from other members. Similarly, Cross, Hangadon, Parise, and Thomas (2007) have shown that emotion, energy, and enthusiasm with another type of knowledge sharing group-- a social network--are important factors in organizational productivity and innovation.

Although all four factors of connectivity existed in this study, the respondents tended to agree that they experienced integration and fulfillment of needs and shared emotional connection slightly more than an opportunity for influence or a feeling of belonging. Early sense of community theory described these elements as distinct but influencing each other. Recent studies have shown that there may be different dimensions; however, given the complexity of connectivity, researchers continue to debate which dimensions are a part of sense of community. In this study, a principal components factor analysis was conducted and showed one factor of connectivity, instead of four. Therefore, a composite connectivity score was generated and used to examine the relationship among participation, connectivity, and climate of innovation. With the composite score, the importance of relationships was quantified and had an impressive 52% coefficient of determination for climate of innovation. Although connectivity within communities of practice was not quantified before this study, the empirical findings supported earlier qualitative research that report that connectivity was a feature of communities.

With the respondents reporting that their levels of subject-matter expertise was at the expert level, it may be that experts, in general, value their workplace relationships. They would have likely cultivated associations during many years. Perhaps connectivity had become second nature. The experts may not even be aware of the importance or strength of relationships as “an expert's skill has become so much a part of him that he need be no more aware of it than he is of his own body” (Dreyfus & Dreyfus, 1986, p. 30). This may be the reason that the regression analysis showed a strong relationship between connectivity and climate of innovation, but when asked to comment on participation, connectivity, and climate of innovation, respondents only remarked on participation within their communities.

In this study, respondents were asked about their primary mode of participation, face-to-face meetings or virtual interactions. A sense of community may result from both modes, serving as the ‘glue’ that holds members together within communities of practice. The ‘glue’ reflects the feelings of attachment and belonging that an individual has toward a community and refers to the trust and reciprocity that undergird a community. Respondents indicated that 72% of them had the opportunity in the past for face-to-face interactions and dialogue. This exposure may have helped generate or solidify a sense of community. This may be especially true because the respondents described themselves as “active” members and one would expect active community members to have–or look for–the opportunity for interactions with fellow members. Furthermore, with 3.2 years as the average tenure of community membership, it is possible that this length of time helped to expand the network of active members and solidify some relationships. Interestingly, the respondents reported that interacting and communicating with fellow members was of lesser importance to them, as compared with other benefits of community membership. Perhaps this was because, as implied earlier, it was almost ‘second nature’ or they had already formed close working relationships with colleagues and did not rely on the community for the interaction.

Research has begun to emerge measuring a sense of virtual community (SOVC), which takes into account the unique features of groups that primarily communicate electronically (Blanchard, 2007, 2008; Forster, 2004). Early findings suggest that virtual communities may have less pronounced feelings of influence than do members of face-to-face communities (Blanchard & Markus, 2004). The researchers propose that information and communication technology may affect these feelings. Yet the same research on SOVC has reported that community members feel that they experience and observe more personal relationships than do members of face-to-face communities. Although the SOVC was not used here, similar dynamics may have been present as the findings about influence and personal relationships, (i.e., connectivity) were reported by the respondents in this study.

Participation: Focusing on Knowledge Gains

Although the quantitative results generated from a regression analysis demonstrated the strongest relationship was between connectivity and climate of innovation, and not participation and climate of innovation, the written comments provided by participants indicated that these matters were on their minds. The four factors of participation examined in this study were perceived benefits, nature of participation, amount/level of participation, and primary mode of participation, and each was reflected in the written comments.

Early in this paper, a community of practice was defined as a group of professionals who interact with each other within an organization, and across organizational units or even organizational boundaries; have a common interest or field of application in certain work-related topics; and share their knowledge on a regular basis. In describing key participation characteristics, previous studies were cited purporting members shared a purpose or goal and described a community as having its own identity.

For these respondents, there was not an overt commitment to a shared purpose or identity. Instead, the respondents indicated that their communities of practice primarily served as a knowledge-retrieving forum. Study participants reported that “staying up to date in the topic of the community” and “saving time in finding all kinds of information” were the most important benefits of community membership. It is apparent that although contemporary communities of practice differ from the original model of apprenticeship envisioned by Lave and Wenger (1991), knowledge sharing or learning activities continue to be the most important benefit to members.

The type or nature of participation that was of most value was “searching, accessing, or acquiring knowledge from relevant sources” and “monitoring the field.” “Organizing and packaging knowledge for others or embedding it in a useful form” or “interacting and communicating with fellow community members” were the types of participation that were least likely to occur. These findings suggest the overarching goal that members shared was one that would serve their own best interest and reiterate conclusions from earlier research (Zboralski & Gemünden, 2006). An apparent contradiction is that although members valued the ability to access knowledge, they were only somewhat interested in helping others locate and acquire knowledge.

Community members contribute at different participation levels and this study used core, active, and peripheral groups to categorize respondents' participation. According to earlier research, the majority of members were peripheral, rarely actively participating and instead watching the interactions from the “sidelines” (Wenger, McDermott, and Snyder, 2002, p. 56). In this study, 50% of 359 respondents described themselves as “active.” It is possible that the active members saw the invitation whereas the members on the periphery were not aware of the questionnaire, were not interested in participating, or believed their observations were not appropriate, and therefore, did not participate.

Distributed, virtual communities have emerged as information and communication technology has improved, and for 71% of the research participants, it is now the primary mode of community participation. However, for these respondents, 72% had the opportunity for face-to-face interactions with fellow members in the past. This exposure may have influenced the members. For instance, extensive personal contact or co-location may have solidified relationships among the members and improved communication and knowledge sharing through information and communication technology mechanisms. Alternatively, if previous face-to-face interactions did not influence, or was minimally influential, it may be that the communities that participated in this study had been successful in creating shared cultural objects (e.g., stories) around which virtual communities coalesced (Brown & Duguid, 2000).

Climate of Innovation

Factors such as (1) shared vision, (2) participative safety, (3) support for innovation, and (4) task orientation have been identified as important in fostering a climate conducive to innovation within work groups and were applied in this study to communities of practice. In this study, two of the climate of innovation subscales, participative safety and vision, had an important role in the relationship with connectivity. This is illustrated by their high mean scores (3.81, SD .68 and 3.79, SD .73, respectively), the cross-loading of four of their items with connectivity items, and the strength of their R2 in a regression analysis (27% and 29% respectively).

Potential Effect of Range Restriction

All members of the 12 communities participating in the study were invited to respond to the questionnaire, yet, the members who chose to respond tended to be active within the community, described themselves as “experts,” and had the opportunity in the past to meet with fellow members face-to-face. As a result, the respondents may have been more inclined toward connectivity (i.e., establishing or maintaining relationships) and therefore, there may be a potential generalizability problem.

Despite the concern of a possible range restriction problem, this study did demonstrate that the climate of innovation was related to connectivity. The degree to which it is connected requires further study. It is worthwhile to reiterate, however, that earlier qualitative studies and accepted practices of practitioners supported the idea that a relationship exists among participation, connectivity, and climate of innovation. For instance, knowledge creation and application were widely seen as a social process and having moderate to strong ties within communities of practice would likely be of value to the members (Cohen & Prusak, 2001; Cross, Parker, Prusak, & Borgatti, 2001; Dixon, 2000). Furthermore, trust, commitment, shared meaning, and understandings were believed to be important aspects of communities and provided the foundation for authentic connectivity (Weick, 1990).

Suggestions for Additional Research

The correlations from this study suggested no relationship between participation mode and the other participation variables, with the exception of a weak relationship with participation level. They also indicated no relationship with climate of innovation and a weak relationship with the connectivity variables. However, with 72% of the respondents reporting that they had the opportunity to meet with fellow members in the past, this exposure may have nullified the distinction between face-to-face contact and virtual interaction. Further research is needed to clarify the influence of the participation mode within a community of practice and on a climate of innovation.

In addition, because the majority of the respondents in this study relied on virtual interaction as their primary mode of community participation, supplementary research could compare and contrast the results from this study using the Sense of Community Index with the results from studies using the Sense of Virtual Community questionnaire. In particular, it may be valuable to explore why the opportunity for influence appeared to be less likely to occur, yet members tended to report that they observed and experienced more connectivity when online.

The majority of respondents in this study described themselves as experts and further research could validate the findings as well as dissect nuanced differences in how the novice, advanced beginner, competent, and proficient members participate in their communities. In addition, a content analysis on the written comments provided by core, active, and peripheral members may offer further insights into expectations, goals, and needs of the different levels of participation.

Two of the climate of innovation subscales, participative safety and vision, had a prominent role in the relationship with connectivity. Participative safety with its non-threatening, trusting, and supportive group environment is clearly a feature of connectivity. Vision with its composition of clarity, visionary nature, attainability, and sharedness is a part of building a sense of community; however, the respondents in this study did not report a shared purpose and this apparent contradiction warrants further examination. There may be a distinction between a specific purpose (e.g., create a new payroll form) and a more generalized shared sense of goal and purpose (e.g., improve battlefield decision-making.)

In this study, the respondents tended to be self-described experts, active within the community, and had the opportunity to meet face-to-face in the past. As discussed earlier, these factors may have influenced their sense of connectivity and contributed to a potential generalizability problem of the research results. Therefore, another study perhaps largely replicating this one is needed to determine if the respondents are typical of the federal government environment population or are exceptional in this regard and more inclined toward connectivity.

Finally, the reason for this research was to discover evidence for and explain the relationship between sets of variables representing two dimensions of communities of practice and a set of variables representing a climate of innovation. The results from this study produced a parsimonious model containing perceived benefits of participation, nature of participation, connectivity, and the climate of innovation. Supplementary research is needed to verify the factors, and then begin to explore additional aspects that may need to be included. For example, this research examined two key features of communities of practice (i.e., participation and connectivity) and their correlations, without any consideration of causality. Additional research may illuminate any mediating or mitigating factors to fostering a climate of innovation.

Implications for Practitioners

Knowledge is situated within a social context and the creation, sharing, and application of it depends on the context in which it is employed. Unlike data or information, knowledge is embedded in practice, and it is reconstructed in each new situation. Therefore, “while knowledge can be actively shared or constructed through the interaction between people or groups, it cannot be passively transferred” (Newell, Robertson, Scarbrough, & Swan, 2002, p. 103). With this understanding and these research results, practitioners can better organize, support, and facilitate communities of practice.

Nearly a decade ago, Wenger and Synder (2000) provided some general advice that still offers value today. Practitioners (e.g., community liaisons and members) as well as organizational leadership, program managers, and knowledge management experts or human resource development professionals can undertake a variety of activities, such as: hosting public events that engage the community, including formal meetings or problem-solving sessions; establishing internal community leadership roles, such as the coordinator who facilitates the community process, as well as members who serve to document practices, act as thought leaders, and network with other communities and knowledge sharing groups; brokering relationships or providing information and communication technology resources to facilitate introductions to and connections between different groups; participating in learning projects, such as tool development; developing artifacts, such as stories, documents, and Websites that support and communicate specific community goals.

Additionally, with the findings from this research, practitioners can offer more customized solutions. Specifically, practitioners should reassess precisely how members are using the community. For example, communities frequently rely on members to organize and package knowledge for others or embed it in a useful form. However, these findings show that members are less inclined to do this, preferring to search or acquire knowledge from relevant sources for themselves. The result could be a community that has members searching for knowledge that no one is posting or sharing. This would quickly lead to an ineffective community. It would be more useful to dedicate individuals to serve in the role of facilitating conversation, shared work, or demonstration and if needed, collecting, organizing, and distributing knowledge.

Furthermore, the respondents in this study believed that the most important benefits of participation were the ability to stay up to date in the topic of the community and saving time in finding all kinds of information. If these respondents are representative of other community members across the federal government environment, practitioners must focus on offering value with the latest news and efficient access to information, instead of spending time and resources to help members with career progression or gain new projects or customers. This research suggests that these activities are of little to moderate importance for community members.

Creating forums for formal dialogue and relaxed conversations and even humor continue to be needed, as are opportunities for collaboration around a shared idea or goal. Information and communication technology can help distributed members communicate and establish a sense of community, but this study quantitatively demonstrated the importance of relationships and underscored the need to consider social aspects when fostering knowledge sharing, which may be more challenging via technology. In addition to fostering productivity within communities, there is the potential of positively affecting multiple communities. If “innovation occurs at the boundaries between mind sets, not within the provincial territory of one knowledge and skills base” (Leonard-Barton, 1995, p. 64), then relationships between communities are important. The findings of this research may be applied to those relationships. For instance, explicitly establishing a shared vision between groups to develop a new product might increase the likelihood of success.

In addition to empirical contributions of this study, there may be practical downstream applications for communities of practice and other knowledge sharing groups. For example, the questionnaire used for assessing characteristics of a community of practice may also serve as an instrument for program managers or other practitioners to gain insight into and to establish a baseline of community of practice perspectives within organizations. Research will also contribute to a better understanding of the dynamics of communities of practice, which will help knowledge management, human resource development, or organizational learning professionals identify or develop programs to foster tacit knowledge sharing throughout a community of practice and encourage an environment where employees can create and innovate. Additionally, this research may shed light on other barriers to knowledge sharing, such as an unhealthy organizational culture, management roadblocks, or insufficient information and communication technology systems. With barriers identified, organizations can explore options to improve knowledge sharing that will result in improved organizational performance across the U.S. federal government environment. Such an environment will likely enable employees to launch a new product, implement a new system, or improve service to customers.

Finally, although these research findings may contribute to a prescription for generating an environment conducive to innovation, practitioners are cautioned to consider their community's unique goals and needs. This study examined participation, connectivity, and climate of innovation using the individual member's perspective as the unit of analysis (N = 384). If the examination was at the group level, the 12 communities of practice that participated in this study may have differences that would need to be considered by practitioners. For example, as indicated by the written comments, some of the communities in this study served mainly to distribute information whereas others offered a collaboration platform. This simple distinction in perspective underscores the need for practitioners to customize these research findings to best support their communities.

Concluding Thoughts

While researchers and practitioners recognize that a relationship exists between knowledge management and innovation, most of the communities of practice investigations have resulted in untested conceptual and theoretical models. For the most part, the research has been largely anecdotal evidence, reflected by case studies from practitioners focusing on “industry best practices.” Fortunately, there is now an emergence of empirical research on knowledge sharing groups, including communities of practice.

This study provided empirical support for the association between communities of practice and a climate of innovation. Notably, the importance of relationships, as captured by the connectivity construct, along with participation, was quantified for a climate of innovation within a coefficient of determination at 52%. These findings indicate social approaches of knowledge management, including knowledge sharing groups such as community of practice, may contribute to a climate conducive to innovation. Although the focus of this study was on communities of practice, it is likely that other knowledge sharing groups (e.g., social networks), and even those who use knowledge sharing tools (e.g., blogs, wikis), could apply these findings by recognizing the association between connectivity and climate of innovation.

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Appendix

Hierarchical Regression of Climate of Innovation on Participation and Connectivity Variables

1) Vision Beta
Step 1
Beta
Step 2
2) Participative Safety Beta
Step 1
Beta
Step 2
Participation Variables     Participation Variables    
Perceived Benefits .16 .06 Perceived Benefits .17 .07
Nature of Participation .27 .10 Nature of Participation .28 .14
Participation Level/Core .22 .00 Participation Level/Core .22 -.01
Participation Level/Active -.07 -.06 Participation Level/Active -.03 -.03
Mode of Participation -.01 .04 Mode of Participation -.02 .02
Connectivity Variables     Connectivity Variables    
Membership   .06 Membership   .11
Influence   .24 Influence   .20
Needs   .22 Needs   .20
Shared Emotional Connection   .26 Shared Emotional Connection   .24
R2 .23 .52 R2 .25 .52
Δ R2 .23 .27 Δ R2 .25 .27
3) Support for Innovation Beta
Step 1
Beta
Step 2
4) Task Orientation Beta
Step 1
Beta
Step 2
Participation Variables     Participation Variables    
Perceived Benefits .18 .10 Perceived Benefits .13 .04
Nature of Participation .32 .20 Nature of Participation .33 .20
Participation Level/Core .19 .01 Participation Level/Core .15 -.05
Participation Level/Active -.09 -.08 Participation Level/Active -.03 -.02
Mode of Participation .02 .06 Mode of Participation .04 .10
Connectivity Variables     Connectivity Variables    
Membership   .12 Membership   .23
Influence   .20 Influence   .13
Needs   .12 Needs   .11
Shared Emotional Connection   .14 Shared Emotional Connection   .16
R2 .26 .42 R2 .22 .40
Δ R2 .26 .16 Δ R2 .22 .18
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© 2010 Project Management Institute

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