The Use of Information Systems in Collocated and Distributed Teams: A Test of the 24-Hour Knowledge Factory

Abstract

Acknowledgements: We appreciate the vision provided by Professor Lester Thurow, former Dean of the MIT Sloan School of Management, in highlighting the importance of outsourcing and the global economy, and the support provided by him at several stages of the research. We also acknowledge the help provided by a number of companies with respect to implementing the proposed vision in their respective companies. Author Biographies:Satwik Seshasai is the Development Manager for QuickPlace, IBM's enterprise team collaboration platform, and is currently pursuing a doctoral degree in Engineering Systems at the Massachusetts Institute of Technology. He holds two Masters degrees from MIT -in Computer Science, and Technology and Policy -and has published research in leading journals on technology and managerial aspects of distributed teams and knowledge sharing. Assisting professors Amar Gupta and Lester Thurow, he helped found the nation's first MBA course on Offshore Outsourcing, at MIT. As an Advisory Software Engineer at IBM, he has worked with many Fortune 500 companies to develop and deploy software for team collaboration across many countries and individuals. At the University of Arizona, Professor Gupta is the chief architect of new multi-degree graduate programs that involve concurrent study of management, entrepreneurship, and one specific technical or scientific domain. He has nurtured the development of several key technologies that are in widespread use today, and is currently focusing on the area of the 24-Hour Knowledge Factory. 3The Use of Information Systems in Collocated and Distributed Teams:A Test of the 24-Hour Knowledge Factory Abstract Recent academic and policy studies focus on offshoring as a cost-of-labor driven activity that has a direct impact on employment opportunities in the countries involved. This paper broadens this perspective by introducing and evaluating the 24-hour knowledge factory as a model of information systems offshoring that leverages other strategic factors beyond cost savings. A true 24-hour knowledge factory ensures that progress is being made on information systems related tasks at all times of day by utilizing talented information systems professionals around the globe. Many organizations currently implement other variants of offshoring that appear similar but are fundamentally distinct. The typical model is a service provider framework in which an offshore site provides service to the central site, often with two centers and a distinction between a primary center and secondary center. Entire tasks are often outsourced to the lower-cost overseas site and sent back when completed. In contrast, the 24-hour knowledge factory involves continuous and collaborative round-the-clock knowledge production achieved by sequentially and progressively distributing the knowledge creation task around the globe, completing one cycle every 24 hours. Thus, the 24-hour knowledge factory creates a virtual distributed team, in contrast to a team that is collocated in one site, either onshore or o...

Computational studies to understand the role of social learning in team familiarity and its effects on team performance

“…In identifying which modes of social learning to examine, we found that social learning opportunities in teams may vary because of factors (Kleinbaum et al 2008) such as informal interactions (Marsick and Watkins 1997, Bobrow and Whalen 2002, Conlon 2004, social and cultural groups (Krishna et al 2004), and technology-mediated communication modes (Fulk and Boyd 1991, DeSanctis and Jackson 1994, Dede 1996, del Cerro et al 2001, Kreijns et al 2003, Smith 2003, Seshasai et al 2006. Across these situations, the following three modes reoccur as consistent patterns of actions, interactions and observations in social learning:…”

“…What is required is an analysis of the configuration of social relations that permit social learning to affect team performance, which is mediated by the formation and disruption of TM. Knowing the relative contributions of the different modes of social learning will enhance our understanding of technology-mediated collaborative teamwork, which is known to support and limit the modes of interaction and communication, and hence, the modes of social learning (Fulk and Boyd 1991, DeSanctis and Jackson 1994, Dede 1996, del Cerro et al 2001, McDonough et al 2001, Kreijns et al 2003, Smith 2003, Seshasai et al 2006. Aside from the differential effect of various modes of social learning, there are factors that interfere with or attenuate its effects.…”

Developing a computational model to understand the contributions of social learning modes to task coordination in teams

“…Thus, even if the teams are flat for the purpose of task allocation, the opportunities for social learning are skewed owing to the physical boundaries (McDonough et al, 2001; Leinonen et al, 2005; Sutherland et al, 2007). Examples of distributed flat teams can be found in global product development teams (McDonough et al, 2001) and the current practice of outsourcing (Seshasai et al, 2006; Sutherland et al, 2007). Virtual teams can be considered as a kind of distributed team where it is possible that all the team members are distributed geographically such that there are no collocated clusters (Clancy, 1994; Desanctis & Monge, 1999).…”