Tagging

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.

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

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

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

 

 

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

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).

Leonardo-Dicaprio-Cheers

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

 

 

 

 

References

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.