Relationships in Communities & Networks

As my interest in knowledge management and communities of practice grows, I am more interested in the ways technology may be used to facilitate work. I recently began a research project evaluating Asana as a tool to fulfill the needs of academic research teams. In this research, I detail the issues with email overload. For example, the meaning and information lost in those long chains of email communication among project members. Yuan, Zhao, Liao, and Chi (2013) found that social norms are a key dimension in the adoption and use of technologies like Asana. Simply put, change is difficult. Using tools like email and conference calls are what we have grown used to in contributing to academic research teams. In line with Yuan, Zhao, Liao, and Chi (2013), I argue that information and communication technology tools that integrate social media functionality are more in tune with the relational needs of contributors. For instance, with Asana you can “heart” the work of others, which acknowledges individual and collective work, and, perhaps most importantly, shows affinity for that contributor. Functionalities such as this may foster community among project members.

Finger CommunityI have frequently referred to communities of practice in many of my blogs because these are a gold-standard of sorts for effective academic research teams. However, there are clear differences between communities of practice and networks of practice. An electronic network of practice is much larger, more loosely knit, and often geographically distributed – the most significant difference is that in networks of practice, contributors are often strangers who may never expect to meet face-to-face (Brown & Duguid, 2001 ). An example of this is Wikipedia, where experts (maybe?) on particular subjects contribute to pages of shared knowledge. I’ve often wondered what rewards come from this type of contribution as it requires resources of both time and energy, which I do not have. Raphael recently discussed just that – maintaining that the dimensions of social exchange theory (i.e., costs and rewards of social interaction) are at play during these types of individual contributions to a larger network of knowledge.

Homer ThinkingMotivation plays an important role in the decision-making contribute to this type of knowledge network (Wasko & Faraj, 2005). In thinking about reasons why I would potentially contribute to an electronic network of practice, I stumbled upon a wiki dedicated to information sharing in partnership with the National Cancer Institute. In my own research of cancer-related prevention and policy, I could envision myself as a contributor to this site, which is due largely to intrinsic motivation. I want to create and share knowledge that allows other public health practitioners access to potentially valuable and pragmatic knowledge to inform their work. However, this is not necessarily indicative of the motivation of others, as only weak evidence is found to suggest that relational capital plays a role in networks of practice – stronger evidence suggests that professional reputation is a more significant predictor of participation (Wasko & Faraj, 2005).

Even in light of these contradictory findings, communication and relational maintenance are important (see Abigail’s thoughts). Whether you are contributing to a community or network of practice, facework is involved. In other words, a person may desire feelings of belonging in a community or respect in a network. Regardless, and once again, knowledge management is relational.

References

Brown, J. S., & Duguid, P. (2001). Knowledge and organization: A social-practice perspective. Organization Science, 12(2), 198-213.

 

Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1), 35-57. doi:http://www.jstor.org/stable/251486673

Yuan, Y. C., Zhao, X., Liao, Q., & Chi, C. (2013). The use of different information and communication technologies to support knowledge sharing in organizations: From e-mail to micro-blogging. Journal of the American Society for Information Science and Technology, 64(8), 1659-1670. doi:10.1002/asi.22863

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Knowledge, Risk, & Trust

One of my mentors once said that “every problem is a communication problem.” At the time, my mind was blown, but the more I progress in communication scholarship, the more I find this statement to ring true. This seems to be relevant in many discussions of knowledge management over the course of the semester. As a communication researcher, I find myself looking for ways the relationships we form as organizational members shape the ways we create and share knowledge. These relationships are especially important when approaching knowledge management in high-risk, emergency, or crisis situations.

Risk-Management Tight Rope

Massingham (2010) examined the effectiveness of a decision tree method for managing organizational risk within the Royal Australian Navy, which informed the development of an alternative model centered on constructs from the field of knowledge management. He details the similarities between risk and knowledge management. Both may inform employees, highlight the importance of action, and stress the significance of lessons learned. By marrying concepts from risk management and knowledge management, Massingham (2010) developed a Knowledge Risk Management (KRM) framework. This framework aids in evaluating how knowledge can lead to better risk management and helps to examine how the knowledge management process may inform risk management strategies.

Massingham (2010) makes a clear case for the inclusion of knowledge management constructs in risk management. He stresses that this would (1) offer greater insight of organizational risk, (2) reduce the environmental complexity among organizations by identifying salient, significant risks, (3) address cognitive bias of risk perception on the individual level, (4) provide ways to navigate the boundaries of risk event to the knowledge management, and (5) foster inter-organizational collaboration among employees with the necessary expertise. The KRM model would ideally bolster dialogue and more objective assessments of organizational issues.

The question remains – what role does trust play in the marriage of knowledge & risk?

There are many fellow bloggers who have discussed the role that trust plays in crisis response (see Abigail’s thoughts). Additionally, during an emergency or crisis, who determines organizational leadership (see Rachel’s post here)? Who determines what knowledge to share and how?

Ibrahim and Allen (2012) address these questions in their research on crisis in the oil industry. They stress the central role that information sharing plays in emergency responses related to offshore oil drilling. Information sharing needs to (1) foster a shared understanding among emergency responders, (2) aid in collective decision-making, (3) allow for the coordination of action, and (4) contribute to how responders follow instructions. These functions look great on paper, but can we apply these practices in high-stress, and perhaps volatile, situations? Offshore oil drilling

There were many key issues that organizational members of a multinational oil company revealed in this study, including the importance of knowledge, training, and the application of emergency procedures. Moreover, they stressed the significance of human interaction during emergency. Ibrahim and Allen (2012) view human interaction in this context through a socio-physical lens, which includes situational, affective, and cognitive aspects. The affective element stands out to me as this includes how organizational members may feel about one another, which goes back to the relational maintenance required to achieve optimal information sharing and seeking. Ibrahim and Allen (2012) offer ways to effectively approach communication among emergency responders including sharing clear, concise, and accurate information in a timely manner with a calm and confident tone. However, they neglect to provide ways to foster relationships and trust among responders. As I have stated many times before, knowledge management is a relational process. Even good communication can fail in the presence of poor relationships.

References

Ibrahim, N. H., & Allen, D. (2012). Information sharing and trust during major incidents: Findings from the oil industry. Journal of the American Society of Information Science and Technology, 63(10), 1916-1928. doi:10.1002/asi.22676.

Massingham, P. (2010). Knowledge risk management: A framework. Journal of Knowledge Management, 14(3), 464-485. doi:10.1108/13673271011050166

Creating, Learning, & Unlearning

Recently, I have been working on research investigating the use of Knowledge Management Systems (KMS) among communities of practice (COP), particularly among academic research teams. This is especially practical for me, as I intend to spend my career working in interdisciplinary teams to create real solutions that address  health disparities in rural areas. The most important outcomes from these types of COP is creating pragmatic knowledge, innovation, and what we may learn from team successes and failures. I’m sure my readers may reflect on past projects as I discuss some challenges for working with and learning from research teams.

unlearningThere are many ways that COP may be defined. Amin and Roberts (2008) take issue with how researchers have conceptualized COP, saying that “the use of the term (COP) has become imprecise, having strayed far from the original definition of COPs as relatively stable communities of face-to-face interaction between members working in close proximity to one another, in which identity formation through participation and the negotiation of meaning are central to learning and knowledge generation”(p. 355). In my research, I use  Hara’s (2009) definition that defines COP as “collaborative, informal networks that support professional practitioners in their efforts to develop shared understandings and engage in work-relevant knowledge building” (p. 3). Similar to Hara (2009), Amin and Roberts (2008) focus on innovation and the creation of knowledge. Additionally, they detail the knowledge acquisition, nature of social interaction, innovation, and organizational dynamic of professional knowing in action. These dimensions are inextricably linked and vital to the way we work to acquire, create, and disseminate knowledge to interested publics. That said, what happens when a project is complete? Our tacit and explicit knowledge carries with us to the next task, project, team, and so forth. Perhaps what we learn from our experiences in COP is the most significant element of our work.

Mary recently discussed Brown and Duguid’s (1991) explication of working and learning through collaboration. These researchers say that learning cannot be separated from our work because “individual learning is inseparable from collective learning” (p. 46). Moreover, Amin and Roberts (2008) maintain that practice-based innovation and learning have considerable potential. Although these assertions make sense, I have experience working in groups where individuals have low expectations of what they may learn from the project. Often, there may be team members who rely on their own tacit or explicit knowledge, refusing to learn from others because of their prior personal or institutional experiences. For example, from a health communication perspective, health care providers may be reticent to engage with communication experts if they believe there is nothing to be learned from them.

yoda unlearnThe reticence for collaboration among individuals involved in COP may lend to Huber’s (1991) of unlearning, which Mary  recently discussed. If we are unable to shift from individual thinking based on prior collective knowledge and practices, how may we be active, productive members of academic research COP? Huber (1991) offers many avenues for organizational learning, some of which may support teams that run into issues with uncollaborative members. One way to facilitate learning among COP is through experimentation. In the context of academic research teams, this may take the form of program evaluation. If we are able to retrospectively see challenges and failures, may we learn from this? Is it possible to see the value of others to the extent that we desire to unlearn? If there are clear gaps in the experience-based learning curves, how do we respond as individuals? How do we respond collectively?

References

Amin, A., & Roberts, J. (2008). Knowing in action: Beyond communities of practice. Research Policy, 37(2), 353–369. doi:10.1016/j.respol.2007.11.003

Brown, J. S., & Duguid, P. (1991). Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation. Organization Science, 2(1), 40-57. doi:10.1287/orsc.2.1.40

Hara, N. (2009). Communities of practice: Fostering peer-to-peer learning and informal
knowledge sharing in the work place. Information Science and Knowledge Management
(Vol. 13). Berlin: Springer-Verlag.

Huber, G. P. (1991). Organizational learning: The contributing processes and the literatures. Organization Science, 2(1), 88-115.

Knowledge Management is Relational

Engaging in reading literature and research of knowledge management continues to offer many philosophical questions of knowledge, the individual, and the collective. As Tsoukas (2001) points out, “it is not quite evident how knowledge becomes an individual possession and how it is related to individual action, nor is it clear in what sense knowledge merits the adjective organizational” (p. 974). The more I engage in thoughtful consideration of knowledge management, the more I return to the argument that these processes, whether tacit or explicit, are intrinsically relational.

customer-relationshipTsoukas (2001) characterizes organizations by their concrete settings, abstract rules, and historical communities. I want to emphasize community within this characterization in this post. In past blogs, I have described the importance of the individual in creating productive communities of knowledge. I maintain this view as I read of organizational stories, which include the narratives of employees and their managers and the subsequent interactions that take place (Colon-Aguirre, 2015). Talk about the importance of stories is crucial as the cultural knowledge within an organization is “central to the organization’s own existence” (Colon-Aguirre, 2015, p. 431).

Blackler (1995) largely supports activity theories that argue knowledge is constantly evolving due to the nuances of language among organizational members. He points out that language is essential for enabling collective interpretations, negotiating behavioral priorities, signaling group membership, and helping to create community. The importance of community is stressed in the description of knowing as a pragmatic tool for developing communal narratives in the face of expanded knowledge systems. Although this article was written before big data, I believe the importance of the individual contribution to the collective still rings true. Tsoukas (2001) supports this assertion by saying, “in knowledge management digitalization cannot be a substitute for socialization” (p. 991).

The culture of knowledge communities in organizations shapes the beliefs, norms, and values among organizational members (Colon-Aguirre, 2015). A great portion of the literature concerning knowledge management in organizations is inherently positive by nature. I have detailed the power of individuals in an almost motivational manner in prior blog posts. Despite my optimistic views of the ways we may bolster knowledge creation, sharing, and transfer, there are examples of negative knowledge behaviors in organizations. Connelly, Zweig, Webster, and Trougakos (2012) describe the nature of knowledge hiding, or “an intentional attempt by an individual to withhold or conceal knowledge that has been requested by another person” (p. 65). Hiding is not merely the absence of sharing – these attempts can include playing dumb, evasive hiding, and rationalized hiding. These behaviors may hinder the productivity and negatively affect the culture of an organization. cat hiding

So what may managers do to facilitate positive knowledge behavior and culture? Colon-Aguirre (2015) advocates for the use of organizational stories to employ change management, increase motivation through communication of triumphs and survival, perpetuate belief systems and attitudes based on organizational history. Emphasis on culture and the narratives within may aid in emphasizing shared identity, increasing employees opportunities for social interactions, and highlighting instances where trust has been created and nurtured (Connelly, Zweig, Webster, & Trougakos, 2012). This emphasis adds to the significance of heuristic knowledge described by Tsoukas (2001) in that organizational knowledge “crucially depends on employees’ experiences and perceptual skills, their social relations, and their motivation” (p. 990-991).

References

Blackler, F. (1995). Knowledge, knowledge work and organizations: An overview and interpretation. Organization Studies, 16(6), 1021-1046. doi:10.1177/017084069501600605

Connelly, C. E., Zweig, D., Webster, J., & Trougakos, J. P. (2012). Knowledge hiding in organizations. Journal of Organizational Behavior, 33(1), 64–88. doi:10.1002/job.737

Colon-Aguirre, M. (2015). Knowledge transferred through organizational stories: a typology. Library Management, 36(6/7), 421-433. doi:10.1108/LM-06-2014-0073

Tsoukas, H. (2001). What is organizational knowledge. Journal of Management Studies, 38(7), 973-993. doi:10.1111/1467-6486.00268

Knowledge Power for the People

In an era of WEB 2.0, the power of sharing knowledge is driven by technology and created by individuals and the organizations or communities of practice that guide them. All the technology at our disposal creates an exponential opportunity for knowledge management practice and application. So, what is WEB 2.0? Furthermore, what is WEB 1.0? What makes these technologies so different?

internet-of-things-concept-illustration

Levy (2009) details the differences between these technologies. Think of version one as a vehicle for commerce and version two as a driving force for people. The Internet has become a platform for building networks that engage users. If we think about how we use the Internet, we may reflect on how our needs vary and how this impacts the responsive design of platforms. The passive user approach collects our activity history and provides an added value, such as recommendations for products based on our past purchase behaviors. This passive user is becoming a relic of the WEB 1.0 era. As our expectations progress, so does technology. We are now active users – we add to the content of others (e.g. hashtags) and collaborate with others (e.g., Google Docs, Wikis). The ways we evolve as users affect individuals and organizations.

For the individual, platforms for social media provides a communication infrastructure that is malleable and impressionable (Hemsley & Mason, 2013). We can build and change our social networks, gain social interaction and feedback, and share and re-share the knowledge and perceptions ourselves and our networks. Because of these functions, we can reduce the tangible and intangible costs of social exchanges. Platforms like Facebook, Twitter, Instagram, and YouTube allow us to self-publish and collaborate toward a collective intelligence (Levy, 2009).

infrastructureSo, how do these technologies supporting knowledge management affect organizations? First, we live in a world the connected consumer. Organizations that seek to stay connected and relevant to these consumers must exist in the WEB 2.0 ecosystem. Because the organization lives with us in this complex system, the customer becomes part of the knowledge management equation (Chua & Banerjee, 2013). We live in a viral world where knowledge spreads similar to that of a disease epidemic (Hemsley & Mason, 2013). Grass-root viral events often occur, most notably when hashtags are hijacked. These events may spiral out of control, which makes crisis communication a critical element of an organization’s strategic social media planning. Recognizing the role of the customer in knowledge management has a significant impact on how we view brands. For example, Chua and Banerjee (2013) details the success of Starbucks in their customer knowledge management approach, recognizing that “Starbucks redefines the roles of its customers through the use of social media by transforming them from passive recipients of beverages to active contributors of innovation” (p. 245).

In addition to customer knowledge management, it is critical for organizations to recognize the value WEB 2.0 brings to the employee experience. As millennials filter into today’s workplace, WEB 2.0 services are expected (Levy, 2009). Not only are these services commonplace, but they also provide a way for the employee to assimilate to and participate in the larger organizational culture. For example, Grace (2009) details the advantages of using Wikis. Wikis are “a democratic, accessible community of uses responsible for its own content, support by an open model of knowledge creation and communication” (p. 64). These communities come in various shapes and sizes as detailed by Kamryn in her recent blog. Despite security and data migration issues, the Wiki offers novel and easy solutions for knowledge management and organizational culture-building.

The savvy organization recognizes the importance of WEB 2.0 to the customer, the employee, and the brand. Viewing the ecosystem as a web of active users benefits organizational and customer knowledge management. An awareness of the strengths and challenges of new platforms will help organizations find their place in the modern communication infrastructure.

References

Chua, A. Y. K., & Banerjee, S. (2013). Customer knowledge management via social media: The case of Starbucks. Journal of Knowledge Management, 17(2), 237-249. doi:10.1108/13673271311315196

Grace, T. P. L. (2009). Wikis as a knowledge management tool. Journal of Knowledge Management, 13(4), 64-74. doi:10.1108/13673270910971833

Hemsley, J., & Mason, R. M. (2013). Knowledge and knowledge management in the social media age. Journal of Organizational Computing and Electronic Commerce, 23(1), 138-167. doi10.1080/10919392.2013.748614

Levy, M. (2009). Web 2.0 implications on knowledge management. Journal of Knowledge Management, 13(1), 120-134. doi:10.1108/13673270910931215

Chua, A. Y. K., & Banerjee, S. (2013). Customer knowledge management via social media: The case of Starbucks. Journal of Knowledge Management, 17(2), 237-249. doi:10.1108/13673271311315196

Knowledge Continuity

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“Knowledge continuity is analogous to business continuity” (Dalkir, 2009, p. 3137).

Nonaka (1994) believes that social knowledge exists on both the individual and collective levels and is created by and fundamental to the collective actions of a group. Alavi and Leidner (2001) cite three common applications of knowledge management including the coding and sharing of best practices, the creation of corporate knowledge directories, and the creation of knowledge networks (p. 114). Knowledge creation and retention are incredibly valuable for organizations, particularly in the age of big data and highly varied employee turnover rates.

In consideration of the need to create and retain intellectual capital, it is important to note that knowledge includes various perspectives. These frames of reference distinguish knowledge as being (1) a state of mind, (2) an object, (3) a process, (4) a condition for accessing information, or (5) a capability (Alavi & Leidner, 2001). Although all of these perspectives are valuable viewpoints of knowledge, perhaps the most important is knowledge as a capability. Developing ways to “enhance intellectual capital by supporting development of individual and organizational competencies” is crucial (Alavi & Leidner, 2001, p. 111). I believe that viewing knowledge as a capability contributes more to the pragmatic knowledge of an organization. A repertoire of best practices is handy to have in a pinch.

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Planning and implementing ways to manage organizational knowledge contributes to sustainable shared meaning among organizational members. Developing practical systems for employees provides a database of organizational language and its uses. According to Hara (2009), “a common language not only indicates a shared comprehension of explicit knowledge (e.g., meaning of words), but also signifies the existence of tacit knowledge (e.g., metaphors and values)” (p. 14).

Establishing a knowledge management system (KMS), which is an information technology-based system “developed to support and enhance the organizational processes of knowledge creation, storage/retrieval, transfer, and application” seems like a daunting task (Alavi & Leidner, 2001, p. 114). Several years ago, I worked for two of the largest telecommunications companies in the United States. Both organizations provided intranet access to vast information systems designed to help me perform my job duties. Even with these systems, I often consulted Google for help with technology issues for which I could not find answers. Individual, procedural knowledge was crucial in helping me to resolve customer issues. My gargantuan corporate system just didn’t have what it takes. That said, if a hugely successful tech company doesn’t have all the knowledge needed to sustain employee success, what does this mean for smaller businesses?

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Hansen, Nohria, and Tierney (1999) offer a codification (people-to-documents) versus a personalization (person-to-person) perspective in What’s Your Strategy for Managing Knowledge?  It seems as if codification was the strategy in my prior experiences, but what of personalization? The personalization approach is a way to transfer knowledge that cannot be codified into “brainstorming sessions and one-on-one conversations” (Hansen, Nohria, & Tierney, 1999, p. 2). Personalization seems more appropriate than codification for smaller businesses or those with more innovative organizational structures. Nevertheless, these authors offer several questions organizations must address before deciding to adopt a specific strategy.

In addition to organizational size, other considerations must be made in the decision-making process of KMS development. Chalmeta & Grangel (2008) propose a five-phase methodology for organizations considering adopting and developing a Knowledge Management System. This proposal is thorough and helpful in adopting best practices; however, the authors acknowledge limitations such as organizational culture and the type of stakeholders involved with the organization.

In my future as a member of many communities of practice, I foresee further challenges in how to manage intellectual capital. I base this prediction on the fact that I currently struggle with managing shared Dropbox folders. Nonetheless, I know that my intellectual contributions and those of my colleagues are an important product of education and hard work. In the words of Dalkir (2009), “these tangible by-products need to flow from individual to individual, between community of practice (CoP) members and, of course, back to the organization itself, in the form of lessons learned, best practices, and corporate memory” (p. 3131).

References

Alavi, M., & Leidner, D. E. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107-136. URL: http://www.jstor.org/stable/3250961 (Links to an external site.)

Chalmeta, R., & Grangel, R. (2008). Methodology for the implementation of knowledge management systems. Journal of the American Society for Information Science and Technology, 59(5), 742-755. doi:10.1002/asi.20785

Dalkir, K. (2010). Knowledge management. Encyclopedia of Library and Information Science (3rd Ed.). doi:10.1081/E-ELIS3-120043816

Hansen, M. T., Nohria, N., & Tierney, T. (1999). What’s your strategy for managing knowledge. Harvard Business Review. URL: http://consulting-ideas.com/wp-content/uploads/Whats-your-strat-art.pdf (Links to an external site.)

Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14-37. URL: http://www.jstor.org/stable/10.2307/2635068

Tacitness & Shared Meaning

total-awareness

“In all our waking moments we are relying on our awareness of contacts of our body with things outside for attending to these things. Our own body is the only thing in the world which we normally never experience as an object, but experience always in terms of the world to which we are attending from our body. It is by making this intelligent use of our body that we feel it to be our body, and not a thing outside.” (Polanyi, 1969, p. 16)

This excerpt, like many others in Polanyi’s The Tacit Dimension, tends to prompt stepping a bit outside your comfort zone to consider implicit and explicit knowing. I agree with Mary, who recommends a piecemeal approach to Polanyi’s text. Kimble’s (2013) unpacking of tacit knowledge is helpful in connecting Polanyi’s ideas to present reality. Perhaps the strongest connection is a critical view of positivist ideas. There exists a divide among researchers who agree to disagree about the researcher’s role in research. As social scientists, are we separated from our subjects?

I believe that a researcher is an instrument, no matter how much we try to remove ourselves from the discovery of knowledge. In both qualitative and quantitative studies, my personal interpretation is salient in my discussion of findings. I agree that knowing is a marriage of the tacit and explicit. Although I do not personally like to be ascribed to a certain “camp”, I assume you would describe me as a constructivist or an interpretivist.

I am still finding my way in the scholarly world, and recently I engaged in discussion with those embarking on similar decision-making for qualitative methods. To answer burning questions regarding sample size in qualitative research, Baker and Edwards (2012) reviewed the “tacit knowledge of a series of renowned social scientists who come from a range of epistemological and disciplinary positions but who share an expertise in qualitative research” (p. 3). This research fueled Fugard and Potts (2015) development of a quantitative tool to aid in study planning. It seems clear that the tacit has epistemic value for improving research methods and subsequent knowledge management.

Big Ts and little ts aside, if you are unable to express knowledge, does it exist? I believe Polanyi is correct in supporting both proximal and distal knowledge. Following the dense reading of Polanyi’s work, I did some searching for extensions of the ideas he presented in the sixties. I found interesting points from Haldin-Herrgard, who describes the epitomes of tacit knowledge with the help of an iceberg-style illustration. These epitomes include intuition, skills, insight, know-how, beliefs, mental models, and practical intelligence – all of which are placed on a spectrum of the extent of abstraction and the activities they affect. It is interesting to see know-how described as tacit knowledge.

My husband is quite the handyman – a bricoleur of sorts. His tacit knowledge abounds as he takes on projects without prior experience with the specific task at hand. I watch as he turns the distal into proximal, the tacit to the explicit, as he explains to me how he installed our attractive security lights onto the outside of our garage. This know-how exists, creates shared meaning, and lights my path through the snowy driveway to my car.

References

Baker, S. E., & Edwards, R. (2012). How many qualitative interviews is enough? Expert voices and early career reflections on sampling and cases in qualitative research. Retrieved from http://eprints.ncrm.ac.uk/2273/

Fugard, A. J., & Potts, H. W. (2015). Supporting thinking on sample sizes for thematic analyses: A quantitative tool. International Journal of Social Research Methodology, (ahead-of-print), 1-16.

Haldin-herrgard, T. (2004). Diving under the surface of tacit knowledge. In Fifth European Conference on Organizational Knowledge, Learning, and Capabilities.

Kimble, C. (2013). Knowledge management, codification and tacit knowledge. Information Research, 18(2). URL: http://www.informationr.net/ir/18-2/paper577.html (Links to an external site.)

Polanyi, Michael. (2009). The tacit dimension. Chicago: University of Chicago Press. (Original work published 1966) URL: http://www.worldcat.org/oclc/844340336