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


Knowledge Power for the People

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


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

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

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

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

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


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

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

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

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

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

Knowledge Continuity


“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.


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?


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).


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

Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14-37. URL:

Tacitness & Shared Meaning


“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.


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

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: (Links to an external site.)

Polanyi, Michael. (2009). The tacit dimension. Chicago: University of Chicago Press. (Original work published 1966) URL:

First Day

The beginning of a new semester is a fresh start. With a laptop and hundreds of dollars of books in hand, I am ready for the odyssey before me. I am arriving at the halfway mark of my first doctoral semester. The memes were right – a Master’s program pales in comparison to a doctoral program. Have you had a nice, long, relaxing winter break?


The to-do list NEVER ends. Research, data analysis, writing, course prep, self-loathing, etc.

I am experiencing a lot of anxiety prior to this semester. What will my classes be like? Will my research be accepted? Why am I doing this? Can I do this? Symptoms of the dreaded impostor syndrome.


My most important discovery is that a positive attitude goes a long way. Also sleep. And coffee. And wine.


All kidding aside, I am ready for what this semester will bring. New information and new faces. Happy spring semester!