Just released
Desouza, K.C., and Paquette, S. Knowledge Management: An Introduction, New York, NY: Neal-Schuman Publishers, Inc., 2011.
After spending over 5 years at the University of Washington, I will soon be making a change. Starting this autumn, I will assume a new role as the Director of the Metropolitan Institute at Virginia Tech. I will also hold an appointment as an associate professor (with tenure) at the Center for Public Administration and Policy within the College of Architecture and Urban Studies. While I am excited to assume my new role and take on different challenges in my academic career, I also recognize that I am leaving an excellent institution. I want to thank all of my colleagues and students at the Information School, Daniel J. Evans School of Public Affairs, and the College of Engineering, who have been wonderful collaborators and friends. I also want to acknowledge the generous support that I have received from private and government sponsors for my research institutes.
Over the next few months, I will be making the move from Seattle, WA to Alexandria, VA.
Yuan Lin, my doctoral student, and I have a paper accepted for presentation at the Annual Meeting of the Academy of Management in San Antonio, Texas (August 12-16, 2011).
Individuals’ Knowledge Transfer Behaviors and Social Networks: A Co-evolution Framework
The boom of the network concept in organizational research has resulted in a growing interest in the interplay between organizational members’ knowledge transfer and their social network structure. This paper treats such interplay as a co-evolution process and lay out a theoretical framework, CO-evolution of Individuals and Networks (COIN), to facilitate its modeling. Using a simplified example, we identify the components of a co-evolution model that should be constructed based on substantive theories: cross-level causal mechanisms, network structural factors, individual heterogeneity and autonomy, the relationships among model assumption, inputs and outputs. COIN synthesizes theoretical or empirical evidence that can help construct these components from multiple disciplines (e.g., organizational research, statistics, physics, economics, and sociology). It decomposes the co-evolution process into key constructs and mechanisms and organizes existing theories around them. It also exposes gaps in related work which once filled can facilitate studies on network-behavior co-evolutio
Lin, Y.A., and Desouza, K.C. “Individuals’ Knowledge Transfer Behaviors and Social Networks: A Co-evolution Framework,” In Proceedings of the Annual Meeting of the Academy of Management, San Antonio, TX (August 12-16, 2011).
My presentation, Building and Sustaining Agile Information Systems, as part of the Strategic Issues in Information Technology: Challenges and Innovations series, is now available online. In this presentation, I discuss practical design guidelines for building and sustaining agile information systems and agile organizations. I focus on four key levers that need to be managed towards this end: information, knowledge, work, and technology.
Desouza, K.C. (2011), "Building and sustaining agile information systems", in Galliers, R.D.(. (ed.), Strategic Issues in Information Technology: Challenges and innovations, The Marketing & Management Collection, Henry Stewart Talks Ltd, London (online at http://hstalks.com/lib.php?t=HST120.2632_1_3&c=250)
I just returned from a wonderful trip to Chicago. During my visit, I had the opportunity to talk with senior leaders of the University of Illinois at Chicago. At a special event called Celebrating the College of Business Administration (CBA), I was honored by being inducted into the Alumni Leadership Academy (ALA) as part of the 2011 Class. "The ALA celebrates those alumni who best exemplify the College of Business Administration's commitment to leadership, teaching, research and service. Inductions to the academy are held every year to honor outstanding CBA alumni whose contributions in these areas raise the stature and standing of the college."[1] I dedicated my award to the undergraduate advisors I had during my time at the CBA.
Next week, I will visit with executives, project managers, platform leaders, and employees at the Delta Faucet Company (Indianapolis, Indiana). I will be conducting an innovation audit, learning about innovation strategies employed by Delta Faucet, and making strategic recommendations on how to bolster the innovation quotient of the organization. Having just completed a book titled Intrapreneurship: Leveraging Ideas within the Organization, I am looking forward to using the models described in the book to study how ideas are generated, mobilized, advocated and screened for, experimented with, commercialized, diffused and implemented by the Delta Faucet Company.
Shh! It's vive la résistance, co-authored with Nicholas D. Sweers II, that appeared in Journal of Business Strategy, 31 (6), 2010 will re-printed in Strategic Direction.
I hope to use this post to begin a discussion on this question. Specifically, how do we define network resiliency when examining large-scale public sector networks. These networks span multiple-levels from individuals to organizations and may even involve consortiums. Consider the case of the US intelligence community (USIC). The USIC involves both public sector organizations (e.g. CIA, NSA, FBI, etc) but also collaborates with intelligence agencies in other countries (e.g. MI6, BND) and even private organizations (Xe Services LLC). The USIC must ensure that its network is resilient. Its resiliency is dependent not only how well it plans for, and executes, responses to changes in its internal and external, but also how well its network (which consists of many organizations it does not have formal control, or even influence, over) fairs in times of crises.
Today, I was examining the literature in telecommunication networks for concepts that we could draw on. The engineering literature has a myriad of concepts that we could draw on to build a framework for organizational network resiliency. For example, consider the concept of load-balancing. Load balancing is essential to the design of robust electronic networks. While its primary purpose is to allow us to plan for efficient usage of resources, load balancing also helps with managing against overload on devices. To describe the concept without getting too technical, one might conceptualize load balancing as follows: incoming information requests to a network are distributed to the appropriate device within the network by a load-balancer. The load-balancer is responsible for routing the request to the best available device (different algorithms might be used for this, and we can have different criteria for determining the best device to route a request to). Load balancing can help us design failsafe mechanisms (for e.g., if one node is down then traffic is routed to a backup node).
Should we have load-balancing mechanisms for organizational networks? Absolutely! I actually think that organizational networks do have implicit load-balancers. Some view these as gatekeepers? Gatekeepers play a vital role in determining how information moves within networks. Do you know of any organizations that manage their gatekeepers mindfully? If so, how do they do it? Also, are there other organizational concepts that are similar to load-balancing?
During my visit to the CIS @ LSE, I conducted an inquiry into how ecological models might help us understand robustness of networks, especially terrorist networks. One idea that I worked hard on is how do agents within a network adapt under conditions of duress. For example, assuming you took away a food source from an ecosystem, how might the various entities (species) adapt and create work-a-rounds? Would the nature of competition among the species change? Would the patterns that drive the reorganization of the ecosystem be predictable?
Along with my doctoral student, Yuan Lin, I have co-authored an article that describes how we might move towards evidence-driven policy design. This article draws from the keynote that I have at the 2010 Computational Social Science Society Conference.
Efforts to design public policies for social systems tend to confront highly complex conditions which have a large number of potentially relevant factors to be considered and rapidly changing conditions where continuous adaptation delays or obscures the effect of policies. Given unresolvable uncertainty in policy outcomes, the optimal solution is difficult, if ever possible, to nail down. It is more reasonable to choose a solution that is robust to as many future scenarios that might ensue from the decision. Arriving at such a solution requires policy makers to actively explore and exploit rich information to support their decision making in a cost-efficient, yet rigorous manner. We name this new working style as evidence-driven policy design and outline the characteristics of favorable evidence. We then argue that computational modeling is a potential tool for implementing evidence-driven policy design. It helps the study and design of solutions by simulating various environments, interventions, and the processes in which certain outcomes emerge from the decisions of policy makers. It allows policy makers to observe both the intended and, equally important, unintended consequences of policy alternatives. It also facilitates communication and consensus-building among policy makers and diverse stakeholders.