The Knowledge Management Environment

A few weeks ago, I wrote a blog about knowledge and tragedy. This post detailed issues of bounded awareness and trust issues during times of disaster. Mary also discussed trust during emergencies. We often think of the failure of individuals or systems during a tragedy or a botched disaster response. These failures happen, of course, but are there other factors involved?

Green_Izalco_VolcanoJones and Mahon (2012) detail the environment as a clear factor in knowledge management. These researchers describe high velocity, turbulent, and stable environments and how the inherent characteristics of these may hinder effective knowledge management practices. Jones and Mahon (2012) reiterate that decisions made in real time may have life or death consequences (e.g., military combat knowledge management). They describe the battlefield as a high-velocity situation, which is often short-lived; knowledge results from pattern-recognition across individual cases. Reflection on what happened occurs afterward when there is time to do so. Conversely, turbulent environments are long-lived with significant changes in communication among involved parties. Stable environments are low-pressure, with challenges occurring in communication and complacency when the environment shifts to turbulent or high-velocity. In a stable environment, complacency may cause something similar to bounded awareness. Regardless of success with standard operating procedures, we must recognize that conditions may change, and we must anticipate these changes (e.g., strategic planning, communication plans). Tacit and explicit knowledge are both critical in these environments, especially in high-velocity situations. In the face of tough circumstances, how do we transfer critical tacit knowledge quickly and effectively?

volcano-12In response to this question, Jones and Mahon (2012) created a model that includes the fundamental aspects of preparing for unstable environments. They highlight appropriate considerations for those preparing for such situations including (1) developing strategic communication plans, (2) considering organizational culture, (3) providing proper training to help people deal with ambiguity, (4) having access to appropriate technologies (e.g., social networks, databases), and (5) developing a central command area within the organizational structure that facilitates knowledge sharing and transfer. Together, these elements help organizations prepare for effective practice in the face of adversity, as Raphael recently discussed.

An interesting aspect of this model is the access to technologies. What happens when an organization doesn’t have access to the information needed? What if the organization doesn’t have the tools and resources necessary to create the information needed for an effective response to changing environments?

Lam and Chua (2009) discuss knowledge outsourcing as a response to these issues. Knowledge outsourcing is when organizations contract external entities for their expertise. They identify favorable conditions for knowledge outsourcing (e.g., lack of in-house experts) but also examine the risks. First, organizations must be able to identify their knowledge needs. Second, knowledge sourcing must occur. This process is challenging in that it requires finding qualified outlets to produce knowledge that have the time and resources to get the job done. The an organization must negotiate knowledge services, ensure timely and adequate knowledge delivery, and monitor the contracted services over time. Moreover, utilizing knowledge may bring challenges due to the appropriateness of the knowledge. This is especially important in high-velocity and/or turbulent environments. What good is knowledge if it cannot be used in changing environments?

volcanoTo illustrate these potential issues, think about an organization contracting an outside entity to construct a crisis response plan to anticipate a potential change in environment (i.e., stable to turbulent). This is a common practice, yet I think the focus is on explicit knowledge rather than tacit. Because of this, organizational members should be contributing to planning – engaging in a form of knowledge insourcing (Lam & Chua, 2009). I think this practice will also contribute to other dimensions of Jones and Mahon’s (2012) model such as developing strategic communication plans and considering organizational culture. In sum, for optimal knowledge management, organizations must consider individuals, systems, and environments.

References

Jones, N. B. & Mahon, J. F. (2012) Nimble knowledge transfer in high velocity/turbulent environments. Journal of Knowledge Management, 16(5), 774-788. doi:10.1108/13673271211262808

Lam, W., & Chua, A. Y. (2009). Knowledge outsourcing: An alternative strategy for knowledge management. Journal of Knowledge Management, 13(3), 28-43. doi:10.1108/13673270910962851

 

 

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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 & Tragedy

I took a crisis communication course the first semester of my doctoral program. Since then, I have developed a fascination with risk and crisis communication. In fact, I am continuing my research on Knowledge Management to fulfill the requirements for the Certificate of Risk Communication. The more I dive into risk and crisis literature, the more convinced I become that predicting, preventing, surviving, and learning from crises and disasters are primarily communicative processes. What can we learn from past crises and disasters? How does knowledge management play a role in our learning?

Kamryn recently posted about the 1986 Challenger tragedy and the incompetency in managerial decision-making. I often use the communication surrounding this tragedy to illustrate the concept of groupthink to my students.

20th Anniversary Of The US Space Shuttle Challenger's Explosion

KENNEDY SPACE CENTER, UNITED STATES: BOB PEARSON/AFP/Getty Images

Kumar and Chakrabarti (2012) provide another interesting perspective in the context of the Challenger disaster about the ways tacit knowledge creates bounded awareness. Bounded awareness occurs when individuals “overlook relevant and readily available information, even while using other available information, and take a decision that is either suboptimal or entirely erroneous” (p. 935). They discuss the implications of prior successes, particularly how decision-makers “make important knowledge appear trivial and/or irrelevant and in turn reduce the perceived likelihood of failure risk (p. 943). In the case of the Challenger tragedy, NASA had experienced a wealth of prior success. The authors postulate that these experiences caused decision-makers to develop meta-knowledge that they were faultless. This meta-knowledge “blunts their sensitivity to risk and cripples their ability to recognize the relevance of critical new information even when it is readily given to them” (p. 945).

The investigation of NASA after the disaster was widely publicized and many case studies were written about the events leading up to the explosion that killed seven people. Other disasters have garnered just as much attention. A more recent tragedy was the events surrounding Hurricane Katrina. Chua (2007) provides a comparison of the disaster response to hurricanes Katrina and Rita. This researcher conducted a textual analysis investigating the prediction, implementation of disaster plans, and management of the relief and rescue operations related to hurricanes Katrina and Rita. There were obvious, glaring differences in the responses of local, state, and federal agencies. Chua (2007) highlights the importance of knowledge creation, reuse, and transfer in the context of disaster. An important aspect of knowledge creation is spanning the “knowing-doing” chasm. He also maintains that reusing knowledge as “lessons learned” is critical. Bridging the knowing-doing chasm and learning lessons from Katrina helped organizations better prepare for Rita.

Hurricane Katrina Hits Gulf Coast

NEW ORLEANS – AUGUST 31: (Photo by Mario Tama/Getty Images)

Wang and Lu (2010) ask important questions about what knowledge transfer channels are used during times of organizational crisis. During adverse events, “decision makers are often forced to make critical decisions, based on limited information and knowledge and with time pressure, in response to situations marked by a high level of ambiguity and uncertainty” (p. 3935). They identify the major challenge of knowledge transfer and crisis management as identifying those who have the knowledge needed to address crises. Finding these critical actors in organizational communities of practice “enables the organizations to identify and resolve organizational problems in a more efficient manner, and, in turn, reduces the impact of organizational crises” (p. 3938).

In addition to finding the right people and appropriate channels, there is also a socioemotional dimension of knowledge transfer. Transferring knowledge from one entity to another is deeply affected by trust and reciprocity (Chua, 2007). In considering the unacceptable circumstances that occurred during the Katrina disaster, I understand how it might be difficult to trust organizations like FEMA in the wake of another disaster. Knowledge plays a fundamental role in how we predict, respond, and learn from disaster. I look forward to continuing my scholarship in knowledge management as it aligns to risk, disaster, and crisis communication.

References

Chua, A. Y. K. (2007). A tale of two hurricanes: Comparing Katrina and Rita through a knowledge management perspective. Journal of the American Society of Information Science and Technology, 58(10), 1518-1528. doi:10.1002/asi.20640

Kumar J, A., & Chakrabarti, A. (2012). Bounded awareness and tacit knowledge: Revisiting Challenger disaster. Journal of Knowledge Management, 16(6), 934-949. doi:10.1108/13673271211276209

Wang, W. T., & Lu, Y. C. (2010). Knowledge transfer in response to organizational crises: An exploratory study. Expert Systems with Applications, 37(5), 3934-3942. doi:10.1016/j.eswa.2009.11.023

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 Transfer & Social Capital

Navigating the nuances of organizational knowledge management is often challenging. I find myself toying with the idea that we take knowledge for granted. Maybe the complexities of knowledge are just too much – individual, collective, explicit, tacit, organized, mediated, structured – the list goes on. In a recent exchange about potential changes to the scholarly peer review process, this complexity became apparent. Without people, there is no knowledge management. I know this is a bold statement, but I charge you to think about what the world would be like in the absence of those who organize our contributions to science. A world without librarians? No, thank you. Creating, sharing, and transferring knowledge is inherently human, existing in our realities and relationships.

knowledge-light-bulb

Nahapiet and Ghoshal (1998) present a theoretical frame for the ways in which human, intellectual, and social capital intertwine in the processes of creating and sharing knowledge. It is important to discern each of these types of capital to recognize the unique contribution of each to knowledge. Human capital refers to acquired knowledge, skills, and capabilities that enable novel interaction. Intellectual capital refers to a larger social collectivity of knowledge and expertise of knowing, in particular, the types of knowledge and the levels of analysis and knowing. Types of knowledge include “know-how” and “procedural” knowledge, which are critical to knowledge continuity (Dalkir, 2010). Spender (1996) presents a matrix for understanding the levels of analysis and knowing, which “concerns the degree to which it is possible to consider a concept of organizational, collective, or social knowledge that is different from that of individual, organizational members” (Nahapiet & Ghoshal, 1998, p. 246). These categories, which discern the explicit and the tacit, include conscious, automatic, objectified, and collective knowledge. Conscious knowledge refers to facts, concepts, and frameworks stored and retrieved from memory or records. Automatic knowledge is theoretical and practical, often in the form of different kinds of artistic, athletic, or technical skills. Objectified knowledge is a collection of explicit knowledge. Collective knowledge is “embedded in the forms of social and institutional practice, and that resides in the tacit experiences and enactment of the collective” (Nahapiet & Ghoshal, 1998, p. 247). All of these integral elements combine to form intellectual capital.

Social capital is a more complex, multidimensional construct that includes structural, relational, and cognitive dimensions (Nahapiet & Ghoshal, 1998). The structural dimension refers to the overall pattern of connections including network ties, network configuration, and appropriable organization. Th relational dimension reflects how relationships influence behavior. These relationships are affected by trust, norms, obligations, and identification. Lastly, the cognitive dimension refers to resources that facilitate shared languages, codes, and narratives. From an organizational perspective, intellectual and social capital are critical to organizational advantage. In reflecting on these types of capital, it is important to recognize that knowledge must transfer from the individual to the collective, from tacit to explicit, and vice versa in order to foster Hara’s (2009) common language.

knowledge sharingKnowledge transfer is inherent in many of the above categories and dimensions of social and intellectual capital. Knowledge transfer “is the process through which one unit (e.g., group, department, or division) is affected by the experience of another.” ( Argote & Ingram, 2000, p. 151). As we know, knowledge is anchored in many organizational functions including its tools, technology, tasks, relationships, and networks. The embedded nature of knowledge affects the way it is transferred to organizations including (1) characteristics of the source of knowledge, the recipient, the context, and the knowledge itself, (2) causal ambiguity, (3) the characteristics of individual members (i.e., ability and motivation), and (4) the strong and weak ties in social networks.

Lucas (2005) utilizes social information processing theory to argue that “prior experiences help us to determine what accurately reflects the facts and what does not” (p. 89). More importantly, Lucas (2005) demonstrates the significance of social capital in knowledge transfer, namely the relational dimension, in a study investigating a Fortune 500 company. He discovered the importance of trust and the reputation of knowledge providers and recipients.  Lucas (2005) also explains that dilemmas in knowledge transfer may occur as a direct result of technology, which supports the significance of relational, structural, and cognitive social capital. He maintains that “access to information does not guarantee its use. There must be some other basis upon which trust is developed” (p. 97).

No matter how technology progresses, people create trust in and build a reputation for organizational knowledge management systems.

References

Argote, L, & Ingram, P. (2000). Knowledge transfer: A basis for competitive advantage in firms. Organizational Behavior and Human Decision Processes, 82(1), 150-169. doi:10.1006/obhd.2000.2893

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

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.

Lucas, L. M. (2005). The impact of trust and reputation on the transfer of best practices. Journal of Knowledge Management, 9(4), 87-101. doi:10.1108/13673270510610350

Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. The Academy of Management Review, 23(2), 242-266.

Spender, J. C. (1996). Making knowledge the basis of a dynamic theory of the firm. Strategic Management Journal, 17, 45–62. doi:10.1002/smj.4250171106

 

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.

dt131231

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?

240px-Man-inside-note-head

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