Ideas on Paper

“For a scientist, this is a good way to live and die, maybe an ideal way for any of us– excitedly finding we were wrong and excitedly waiting for tomorrow to come so we can start over.” (Maclean, 1992, p. 139)

When people ask me what I do, I tell them that I am a Ph.D. and MPH student studying Health Communication and Health Management and Policy. I often get confused looks and have to dive deeper into explaining my research, which only sometimes brings clarity the conversation. Generally speaking, I talk about my job as an instructor, which usually resonates a bit more. I even have a family member who refers to me as “The Professor,” and I have not corrected him because I like the way it sounds. As much as I love teaching, my research is what drives me to keep going through the labyrinth that is graduate school. As an undergraduate, theory and research existed on paper as a study guide for good marks on exams. Somewhere along the line, this all changed. In an unusual turn of events, I went from an over-achieving undergraduate student to a barely-achieving researcher. As a young scholar, intellectual curiosity is both a gift and a curse – I am filled with ideas yet frightened by failure. That said, I know from the sage advice from those who have gone before me that this feeling is natural and, unfortunately, never goes away.


I will continually encounter challenges of finding determinants and results and the linkages between them, and, perhaps most importantly, I must find ways to express why these are important to my field and to science (Zetterburg, 1965). Ideas are often only as good as the paper on which they are printed, which leads me to describe where I get my ideas from, discuss the problems that obsess me, and make a case for the why these ideas are important.

Personal experience is a primary driver of my interest in health communication. My family suffers a host of health issues, and I have lost too many family members to cancer. In my first undergraduate health communication class, I realized that there were evidence-based strategies to counter many different health issues among myriad populations. I began to consider how I may conduct unique research based on my experience of living in a rural area that is disproportionately affected by health disparities. For example, my thesis centered on the memorable messages about nutrition recalled by Appalachian adolescents. Listening to these children describe their lived experiences helped me to understand the unique challenges they face when making nutritional decisions and that eating “healthy” means something entirely different to them than it does to you and me. This type of research helps to better understand how communication influences nutritional behaviors and decision-making, which may inform culturally appropriate interventions and campaigns.

My personal experience was one of the compelling reasons behind my decision to go to graduate school. Not just that I had incredible undergraduate professors and mentors with whom I shared the passion of educating others, but also that they had given me something I had longed for years before. My first college experience was not ideal as I was intellectually and emotionally unprepared. I struggled greatly and ended up dropping out. This, in part, was likely because I did not have instructors who took an interest in me – I was just another face in the crowd. Now that I am an instructor, I want my students to master content (and hopefully learn how theory is pragmatic), but also to know that there is at least one person in their corner who understands what it is like to fail and recognizes that failure is often only the beginning. Each semester, I encounter students from all walks of life, who often face challenges similar to my own, which drives my interest in the ways that the instructor-student relationship may help students to achieve academic and personal success. Currently, I am leading a research project aimed at discovering the ways students’ sensitive self-disclosures affect both the student and the instructor. Reading the accounts of others in our qualitative findings give me hope that my research may shape positive communication practices among students and instructors. Ideally, this type of research may improve instructor-student relationships and contribute to better university policies for dealing with sensitive student issues.


Listening to and reading the accounts of others is also a source of my ideas as a communication scholar. My advisor, Dr. Elisia Cohen, once said, “every problem is a communication problem,” and I believe this to be true. Recently, I have become interested in the lived experiences of medical students, which I became acquainted with through my friends who are pursuing medical careers. I often listen as they detail the challenges they face, which most often are communication-based. Because of the stories of friends, I often search for the narratives of others, such as Poorman’s (2016) account of the “powerful culture of fear, stigma and lack of self-care that prevents [medical] residents from seeking help” managing their emotions (para. 8). These stories contribute to my unrelenting curiosity of how improved communication practices can make a difference in the lives of medical residents charged with caring for an ailing population.


Another driver of my ideas is thinking about where things went wrong, especially in public health crises. Often, public health professionals focus on education and access issues, which are key pieces of the puzzle. However, a systematic understanding of how communication may shape education, access, and policy is my primary interest. Recently, I conducted an analysis of the Indiana State Department of Health’s response to an intravenous drug use-driven HIV outbreak in Austin, Indiana. In dissecting the press releases during the height of the outbreak, I discovered exemplars that may help to inform other public health efforts necessary for similar situations. Analyzing the communication among the enormous number of entities (including now VP Pence) involved in these efforts may help to improve communication during crises. Moreover, this project stimulated my thinking about the ways these practices may apply in my hometown, which experiences issues with drug use similar to that of Austin.

In sum, many issues “obsess” me. So much so, that I often struggle with choosing my research foci. I am excited to continue my research, and I recognize that I must fail in order to succeed. As a young scholar, I take solace in knowing that failure is only the beginning.



Tagging 2

This is my final blog post about my course in Knowledge Management. Additionally, this is likely the post that reflects the most vulnerability of my grasp of knowledge management concepts. Part of our course requirement was to develop a bibliographic reference account manager. As a researcher, I am familiar with other software to aid in reference management such as EndNote and Mendeley. However, I was not accustomed to the CiteULike interface, and I feel like I am still in uncharted territory.

The image above showcases my tagging of the 34 articles I read and synthesized for Knowledge Management over the course of the semester. I bring out many of the salient constructs and concepts that are apparent throughout many of our readings including tacit and explicit knowledge, the social aspects of knowledge management, and the relational maintenance required in creating and sharing knowledge. Clearly, there are terms I used more frequently to categorize my reading, but I feel it lacks structure and does not provide a clear picture of my understanding of the course content.

ThrougOscar-canhout many of our discussions this semester, we have talked about how people are reticent to change. In the context of managing my references, I have to say that I am guilty. In the past, I have tried to adopt several reference management technologies, but for whatever reason, I turn into Oscar the Grouch when navigating these systems. I think that this is just me being overwhelmed in the face of new knowledge AND a new way to manage this knowledge. I have an existing knowledge management system that is an amalgamation of Dropbox and OneNote technologies. Surprisingly enough, I can search and reference effectively, but I remain unconvinced this is the most efficient process.

In sum, I remain a CiteULike novice. In a course filled with future librarians, I am sure this is appalling. That said, in the future,  I would like to have a more closely guided instruction with reference management interfaces and creating efficient tagging systems. Maybe my summer to-do list?

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.


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:

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

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.


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

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.


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



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?


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


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.


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

The Information Society: A Cloudy Forecast

I recently shared an article with my Twitter community members about the post-work economy in response to Dr. Burns’ tweet about a hotel’s robot concierge. Since then, I have often thought about my place in the workforce and my value as a social scientist to the larger economic picture. In consideration of Tremblay (1995) and Rule and Besen (2008), I think rhugenwrites gets it right by saying the future they forecast concerning the “information society does not present a message of hope, but rather a darker perspective on the future” (2016, para. 1). road_cloudy_by_neonxlt-d39l3rb

Tremblay (1995) points out that due to fast-paced developments in technology, phrases such as  “the information society” and “the knowledge economy” are often used interchangeably. Although Tremblay doesn’t offer clear distinctions of each, he does provide interesting ways for us to consider the changes in our society by labeling our past in the context of Henry Ford and our present in the context of Bill Gates. These comparisons illustrate clear differences from past to present, but perhaps most importantly, it serves as a catalyst to question where we go from here. Tremblay (1995) discusses the fact that our society has been through changes in the way we think about work, providing the example that “laid-off workers in the primary sector shifted to the secondary sector, and those in the secondary sector moved on to the tertiary sector, after often long and painful transition periods. But there are no more sectors” (para. 60). If, in fact, there are no more sectors, what do we do? How do we prepare? How might we embrace and adapt to changes in our society?

If, in fact, there are no more sectors, what do we do? How do we prepare? How might we embrace and adapt to changes in our society? Cowan, David, and Foray (2000) discuss the economic issues associated with the “intellectual property rights regime and the disclosure conventions of various epistemic communities” (p. 250). Can we work together to foster new sectors in the face of these tensions?

As scholars, I believe it’s important to look at the bigger picture. Powell and Snellman (2004) define the knowledge economy as “production and services based on knowledge-intensive activities that contribute to an accelerated pace of technical and scientific advance, as well as rapid obsolescence”(p. 201). These authors point out that existing research on the knowledge economy focuses on knowledge production rather than its impact. It makes sense that Powell and Snellman (2004) assert that this shortcoming is neglectful. They maintain that “a key insight of the productivity debate is that significant gains in productivity are achieved only when new technologies are married to complementary organizational practices”(p. 215). As a health and risk communication researcher, I know that more communication is not always better. Studying dissemination and the impact of knowledge is crucial in the era of big data.

Regardless of all the gloom and doom I can muster in considering the future, I remain optimistic. Rule and Besen (2008) say  “those whose work involves social analysis are also inclined to believe that such understanding promotes all sorts of other good effects. Educated understanding of social life supposedly encourages economic growth and prosperity; it renders the individuals who incorporate it more productive and successful; it makes organizations more egalitarian and effective; and it reduces the role of destructive conflict in human affairs” (p. 341).


Here’s to the future and, hopefully, job security.






Cowan, R., David, P. A., & Foray, D. (2000). The explicit economics of knowledge codification and tacitness. Industrial & Corporate Change, 9(2), 211-253.

Powell, W. W., & Snellman, Kaisa. (2004). The knowledge economy. Annual Review of Sociology, 30, 199-220. doi:10.1146/annurev.soc.29.010202.100037

Rule, J. B., & Besen, Yasemin. (2008). The once and future information society. Theory and Society, 37(4), 317-342. doi:10.1007/s11186-007-9049-6

Tremblay, G. (1995). The information society: From Fordism to Gatesism. Canadian Journal of Communication, 20(4), 461-482.


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).


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.


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.


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