Internet–based information and communication technologies (ICTs) and the information flows they support have played an important role in the advancement of society. In this paper we investigate the role of Internet–based ICTs in electoral revolutions. Employing a case study approach, we examine the part played by ICTs during the Orange Revolution in Ukraine (2000–2004). Roles and activities of the dissenters, as well as their associates, the incumbent authorities and their allies are analyzed with regard to Internet–based technologies during the electoral revolution in Ukraine. The case of the Orange Revolution is particularly salient, as even though only one to two percent of the Ukrainian population had access to the Internet, this was sufficient to mobilize the citizens towards an eventually successful revolution. This paper lays the groundwork for further investigations into use of ICTs by political dissenters.

Lysenko, V.V. and Desouza, K.C. “Role of Internet-based Information Flows and Technologies in Electoral Revolutions: The Case of Ukraine’s Orange Revolution,” First Monday, 15 (9), 2010, Available Online at: [LINK]

Abstract: Large IT projects, such as the US Government’s Internal Revenue Service Business Modernization Effort, can take a decade or more and consume billions of dollars. Traditional approaches to the study of such projects emphasize concerns such as requirements monitoring, progress tracking and risk mitigation. We propose an alternative approach guided by a digital ecosystems view instead of a hierarchical, decision-oriented view. We argue that this perspective is more suited to understand how such projects evolve and cause changes in the underlying digital ecosystem characterized by not only the IT infrastructure but also the transactional relationships among stakeholders. We illustrate our arguments by drawing on an archaeological case study of the IRS effort, and discuss implications of the digital ecosystem perspective for the study of large IT projects.

Reference: Purao, S., and Desouza, K.C. “Large IT Projects as Interventions in Digital Ecosystems,” In Proceedings of the International ACM Conference on Management of Emergent Digital EcoSystems (MEDES'10), Bangkok, Thailand (October 26-29. 2010).

Consider the stage of idea generation and mobilization: One example of a company that has successfully created numerous channels for communication of ideas is Whirlpool. One strategy used by Whirlpool explicitly for idea generation was having an Innovation Team (people conversant with desired business goals and objectives as well as current operational capacities) go to each department and solicit ideas from employees. The Innovation Team included a cross-section of the company, including members from many functional areas and levels of hierarchy. During the idea collection process, all ideas were recorded and listened to without evaluation. By having a team composed of people from across the company and having that team travel to each department, normal rules of hierarchy and ordinary routines were disrupted sufficiently that employees could communicate without needing to find a way to communicate across the hierarchy.  Ethicon Endo-Surgery conducts team events encouraging cross-team functionality. For instance, the "battle of the masterminds" allowed employees to collaborate in teams to solve a particular problem. This may not necessarily be  a medical problem, but it instigates analytical thinking and cross-team collaboration.

Mars, the candy company, hosted a conference for its employees, and gave each one a nametag with RFID components that lit up when the employee was near someone they didn't know. Social networks were mapped out on a huge overhead projection that changed in real-time as employees met new people. This project was backed by social network analysis done with academic researchers, who interviewed employees to find out their current connections and devised this plan to increase the networking for the entire organization. The technique of social network analysis can provide a way for organizations to see whether or not collaboration across hierarchies and divisions is happening, and if so where--thus allowing them to analyze why. Social network analysis can illustrate pockets of communication that could be particularly ripe for idea generation. Peer-to-peer networks which have been derived from this concept  are deemed the best forms of communication are now getting popular day by day.

The next stage is the advocacy and screening of ideas. The joint processes of advocacy and screening involve the bubbling up of ideas and the filtering out of ideas into separate categories. Advocacy leads to increased communication about potential innovations, as well as encouraging the refinement of scope and intent of ideas. Screening is the process of identifying which ideas are suitable for development at a particular time, with particular capacities in mind. These two processes must occur together, as a communicative endeavor. At the end of this stage of the innovative process, high priority and high probability ideas have been identified. More extensive screening processes will also include categorization of ideas for the future, high-risk but high-gain ideas and ideas for mobilization.

Creating groups of advocates can be a challenging process. At Boeing, when executives decided to support radical process innovation they chose to create a specific team designed for the sole purpose of finding and advocating for big, radical innovations -- the group was called Phantom Works. The goal of Phantom Works was not to be the sole source of innovations, but to inspire change throughout the organization by asking questions, supporting ideas and demanding radical changes. In effect, Phantom Works is an advocacy group, supporting the idea generation and advocacy stages of the innovation process. Phantom Works also helped with communication between departments and sought ideas and technologies that could be applied in new areas of the organization. The creation of a business unit for purposes of radical innovation demonstrates organizational commitment as well as creating an advocacy body that can help incumbent organizations develop and sustain advocacy and idea generation.

These are just a few examples of how to communicate the business value of innovation. To learn more about how to communicate the business value of innovation within your organization, please contact me…

Understanding the barriers to communication in these discrete phases of the innovation process allows executives and organizations to make rational choices about what types of communication to pursue. These stages of innovation each have particular challenges, but anticipating those challenges and taking steps to minimize them can significantly increase the success of long-term innovation in an organization. When discussing the business value of innovation, organizations must be sensitive to the current stage of the innovation process. A newly hatched idea simply cannot be talked about in the same way as an idea that has passed through advocacy, screening and experimentation and is currently being mobilized for use in a new area of the organization. Innovations have differing levels of maturity, and communication must reflect those levels. Furthermore, creating an open and collaborative culture can assist communication at all levels of the innovation process.

About the Presentation

As an organization prepares for the departure of valuable staff, a key challenge is how to capture, store, and transfer knowledge. Managing knowledge and ensuring its transfer will increase productivity. This session will provide useful tools and processes for selecting the best strategy to fit your organization’s culture. Participants will explore the use of technology as well as best practice approaches and tools to preserve and transmit institutional memory.

Topics include:

• Understanding the value proposition of investing in knowledge transfer mechanisms
• Creating appropriate knowledge creation and transfer strategies for various organizational contexts
• Measuring the impact of knowledge transfer on organizational outcomes (e.g. innovation, cost reduction, etc.)
• Deploying technological solutions to enable knowledge transfer
• Deploying social solutions to enable knowledge transfer
• Understanding the changing dynamics of knowledge transfer with social networking sites
• Leveraging knowledge transfer processes for sustainable competitive advantages

Unfortunately, few managers know how to write business cases that attract the necessary resources for their knowledge management and innovation programs.Business cases are strategic artifacts aimed to sell internal and/or external stakeholders on the merits of a project. Upon reading a business case, one should come away with a clear strategic understanding of the project and its value proposition, confidence in the project team, assurance that the budget for the project is reasonable, and awareness that the high-level project plan is sound. Based on my experience, I would suspect that out of every 20 business cases for a knowledge management related effort, about one is funded at the level requested, up to three are funded at 30% or below of what was requested, and the rest are not funded at all!

First, the scarcity problem means that organizations do not have unlimited resources (e.g., capital, or even more intangible resources, like managerial attention), meaning all needs are not going to be met. Recognizing the criticality of the scarcity problem means that when an organization considers a case for investing in knowledge management, it is going to be evaluated against every other case that is asking for resources. Too often, knowledge management business cases do not understand or account for this reality, and go by the wayside.

The second thing to understand is that knowledge management efforts need to show payoffs. In an organizational context, payoffs are compared across projects that are candidates for investments. Business cases that are able to demonstrate payoffs that are worthy of the effort (time, cost, personnel, etc) of the investment, and present convincing arguments on why the payoff will better the organization towards its future objectives, stand a high chance of being funded. Simply claiming a high payoff is not sufficient. The business case presented must be sufficiently evidenced to show that achieving the payoff is reasonable.

From the outset, one must realize that making the case for a knowledge management effort and calculating payoffs is not easy, when compared to making the business case for a new piece of manufacturing equipment, such as new welding machine or a color photocopier. Investing in a piece of new machinery can be directly tied to increases in product quality and/or quantity through multiple metrics (e.g., lower defect rates, finished products per hour, etc). Calculating the payoffs for investments in knowledge management efforts is not as easy, nor is it as direct, and first-order effects are difficult, if not impossible to measure. Knowledge management efforts lead to changes in behaviors, approaches, and methods that, on their own may not have direct bottom-line impacts. However, when these are mapped and traced to organizational processes, the impacts can be measured and articulated. Needless to say, this is often a more time consuming and creative effort than simply measuring direct impacts as in the case of outcomes from a new piece of manufacturing equipment.  Equally important is that there is a lag time between when one invests in a knowledge management effort and when one witnesses outcomes that result in payoffs. Accounting for this lag time is not easy, yet it is essential to building an adequate business case.

The third, critical realization that we need to appreciate is the fact that investing in knowledge management is akin to a group as a whole investing in a common effort. Consider the case of investing in initiatives such as the promotion of fair trade practices. Most people agree that increasing the adoption of fair trade practices benefits society. The challenge arises when we ask who wants to take responsibility for investing in these efforts. If taxes were raised to support these efforts, would you be happy? Rational individuals often want others to bear the cost of these common efforts and gladly enjoy the benefits, yet hesitate to initiate responsibility. A similar predicament faces knowledge management efforts. Departments within an organization want their peers’ units to invest in a common effort. Each department might see knowledge management as an effort someone else should put up resources for and hence defers spending its own resources. In some organizations, knowledge management efforts might be viewed as a tax levied on a department’s resources. This tax, is something every department either does not want to pay or wants to pay the lowest possible amount; yet any outcomes from the tax, such as infrastructure (e.g. a new intranet) is of interest to all. Moreover, the departments may get upset if they see the common effort they invest in does not perform up to par. This is akin to how one feels when one drives down a poorly maintained road, knowing that one has paid taxes for its upkeep. Knowledge management is seldom viewed as a profit center in an organization. It is important to remember that building a business case for a knowledge management effort is often similar to trying to build a case for increasing investment in an effort common to the whole organization.

The above three challenges, while severe, are not insurmountable. To learn more about how to build a good business case for knowledge management and innovation programs, please send me an email and stay tuned for my forthcoming article in Business Information Review.

Without a good business case, knowledge management will remain a theoretical, and even an impractical, concept in organizations. Good business cases give individuals a chance to put theory into practice, by providing resources for implementing knowledge management programs, processes, and technologies. Writing good business cases requires time, effort, and practice. Seldom is one born with the ability to write good business cases.

I will be giving a keynote address at the 2010 Computational Social Science Society Conference (CSSS). CSSS 2010 is hosted by the Center for Social Dynamics and Complexity and the Consortium for Biosocial Complex Systems at. For more information on the conference, please click here [LINK].

From Hunches to Evidence Driven Policy Design: Leveraging Information through Simulation

Constructing public policy, whether at the national or local level, is a complex undertaking. Complexity arises from the number of stakeholders involved, varying agendas and incentives, resource constraints, a multitude of interacting variables, multiple time horizons, and even political climates. Due to these complexities, we too often categorize political and social problems as ‘wicked’ and unanalyzable. The default option is to take a haphazard approach to policy design, most often the outcome of the ego-based debates and negotiations of the decision-makers. In this keynote address, I will argue for a move from hunches (or intuition) to evidence driven policy construction. Today, due to the advancement of computational power and modeling techniques, we can simulate complex scenarios. Simulation gives us an ability to move policy construction from an activity primarily driven by historic case analysis and intuitions, to more of an applied science, where we can actually predict and control phenomenon. Through simulation we can, with reasonable certainty, ascertain the cost, benefit, risk, impact, and value proposition of a given policy. Using examples from simulation projects, such as a project that examined strategic options for dismantling terrorist networks, I will demonstrate how we can move policy design from being an ‘art’ to more of a ‘science.’

Abstract
Backsourcing is motivated by opportunities arising from changes in the business situation, redefinition of the character of outsourced service declining in quality or due to the discovery of flaws in the contract. The situation of backsourcing clearly has major implications for an organization in terms of monetary investments, IS infrastructure and changes in employee requirements during and after the process. The research in this paper consequently acknowledges a serious challenge involving the management of systems within organizations following backsourcing events. This paper considers a detailed analysis of two case studies of backsourcing: JP Morgan Chase (USA) and Sainsbury (UK). A major contribution of the paper is to identify important strategies to be followed in backsourcing projects to ensure efficient knowledge re-integration. In this respect, it is believed the paper is unique in identifying emergent suggestions for strategic backsourcing decision making through a series of insightful observations.

Bhagwatwar, A., Hackney, R., and Desouza, K.C. “Considerations for Information Systems ‘Backsourcing’: A Framework for Knowledge Re-integration,” Information Systems Management, Forthcoming.

It is through the process of collecting data through the conduct of experiments that we can find support for the ideas. Data collected during the experimentation process will help us gather the necessary evidence to support decision-making. Today, there is a big movement in medicine called evidence-based medicine (EBM), which demonstrates that a move towards more scientific and data-driven decision-making can prove to be valuable, rather than purely relying on gut instincts. Dr. Dave Sackett, a pioneer in the field states “EBM is saying rather than just rely on tradition, expert opinion, wishful thinking, let's try and find the evidence and apply it.” [1] To build a culture of experimentation one must focus on the following principles: 1) do not just discard ideas without adequate evidence, 2) do not support or move ideas ahead without adequate evidence, and 3) always look to exploit data from experiments.
While at Amazon from 1997-2002, Greg Linden prototyped a system that would make personal recommendations to customers as they checked out. Linden commented, “I heard the SVP was angry when he discovered I was pushing out a test. But, even for top executives, it was hard to block a test. Measurement is good. The only good argument against testing would be that the negative impact might be so severe that Amazon couldn't afford it, a difficult claim to make.” [2] Linden’s experiment showed how much the customer liked the feature and it won praise – the end result is that this has become a signature design feature for Amazon, and most online web marketers have introduced a similar concept. This illustrates the value and capabilities of organizations to test incremental ideas and achieve innovation through “continual tiny experiments in such areas as business processes and customer relationships rather than a single, company-transforming idea.” [3]

Experimentation needs to be made part of every employee’s work and has to move beyond the R&D Labs. The R&D Labs have natural constraints that leave a lot to be desired in terms of experimentation. For example, most of the R&D personnel are detached from the day-to-day running of the business and hence are not the best people to experiment on the problems and solutions of interest for today. In addition, these labs are often physically secluded from the operational centers of the business. This separation leads to difficulty when you try to transport (mobilize) ideas from the lab in order to address problems that are happening on the ground. Finally, you also have a numbers issue. The number of individuals working in an R&D lab is minimal compared to employees who are involved with the day-to-day running of the business. As such, no matter how brilliant your R&D lab personnel are, you will be at a loss if you cannot find ways to tap into the 85-90% of your organization’s employees who do not work in the lab. At the Engaged Enterprise, we had a R&D lab, the Institute for Engaged Business Research (IEBR). IEBR was focused on working on applied management problems that had value propositions to our clients. We determined upfront that simply relegating experimenting and innovation to the labs was not optimal. We needed to find a way to blend the experiences of those working on the consulting side with the R&D side. In addition, we needed to find ways to take knowledge that was being generated on the consulting side (as experiments were conducted “live” while projects were being done – i.e. as we tried to install a new service or strategize with a client – we were in essence engaging in experimentation) and move these into the R&D lab. We did this by having people share their time between consulting and R&D. Also, we had our R&D folks develop a method and handbook that could be used for experimentation which explained the basics of experimentation and how to capture and store results. We also encouraged sharing of results from experimentation efforts so that others might use the results, or provide their reflections on the experiments.

The effort to move experimentation beyond the R&D labs needs to be a conscious one. Both, organizational (management) and employee level interventions need to be in place to promote this concept. Managers should not only encourage their employees to experiment with their ideas, but even go so far as making it a requirement when ideas are being developed and proposed. In addition, employees should take responsibility to engage with the experimentation process, and be aware of methods and practices for conducting experiments.

please stay tuned for my new book or send me a note via email…

[1] Sackett, Dr. Dave. (October 30, 2009). Interviewed by André Picard. Available at: http://www.theglobeandmail.com/life/health/when-we-began-we-were-almost-pariahs/article1344833/
[2] http://glinden.blogspot.com/2006/04/early-amazon-shopping-cart.html
[3] McCann, David. (March 15, 2010). Testing, Testing: The New Innovation Game. Available at: http://cfo.com/article.cfm/14482988?f=search

Intelligence and Nuclear Non-Proliferation Programs: The Achilles Heel?
Intelligence is a critical component of all counter-proliferation activities. It allows us to assess and determine what makes up the current threat environment in terms of the proliferation of nuclear weapons and technology. When informed with an accurate assessment of the situation, policy makers are better suited to counter the proliferation threat. However, success and failure hinge upon how well information is managed during the intelligence process. The intelligence process as it relates to estimating nuclear capabilities or intentions is wrought with many challenges and complications. The denial and deception techniques employed by states running covert weapons programs and the dual-use nature of many weapons components create many difficulties for intelligence organizations. Additionally, illicit transnational networks obscure the situation further by serving as a source, for both nation states and non-state actors, for acquiring dual-use commodities and technologies. These challenges can lead to the miscalculation of a state’s capabilities or intentions. As was seen with the case of Iraq in 2003, western intelligence services grossly overestimated the capabilities of Saddam’s regime. This paper presents a comparative analysis of three cases of nuclear proliferation: India, Pakistan and Iran. Drawing from the analysis, the authors examine the lessons learned and propose recommendations for future counter proliferation policy and strategy.

To read prior papers published on this topic, please see:
• Desouza, K.C., and Lau, K.A.* “Managing the Proliferation of Weapons of Mass Destruction: An Information Management Perspective,” International Journal of Public Administration, 31 (13), 2008, 1457–1512. [LINK]
• Desouza, K.C. “Information and Knowledge Management in Public Sector Networks: The Case of the US Intelligence Community,” International Journal of Public Administration, 32 (14), 2009, 1219–1267. [LINK]