About the Center

            The ability to learn is an important competitive advantage for firms. Firms that are able to innovate or create new knowledge, to retain that knowledge and to transfer it throughout the enterprise are more productive and more likely to prosper than their counterparts that are less adept at organizational learning.

            Researchers at Carnegie Mellon are making fundamental contributions to our understanding of learning and innovation in groups, organizations, and social networks. Because learning and innovation are phenomena that cut across disciplines, scholars in a variety of disciplines (organizational behavior, psychology, economics, computer science, sociology and information systems) and at several schools at Carnegie Mellon University (Tepper School of Business, School of Computer Science, the College of Humanities and Social Sciences, and the Heinz School of Public Policy and Management) are actively involved in center initiatives.

            Three interrelated research thrusts have been established for the Center for Organizational Innovation and Learning:

                        (1) Behavioral Foundations of Group and Organizational Learning

                        (2) Social Networks, Learning and Innovation

                        (3) Technology, Organizational Learning and Knowledge Transfer

Behavioral Foundations of Group and Organizational Learning

            Just as individuals learn at different rates, groups or organizations learn at different rates.  Some firms learn from their experience very rapidly, while others exhibit little or no learning. Researchers aim to open up the "black box" of organizational learning in order to understand why some firms are better at learning and innovating than others.  Because much of the learning that takes place in organizations occurs in teams or work groups, a particular focus of Center initiatives is group learning and knowledge transfer across groups. By identifying factors facilitating (or impeding) learning and innovation in groups and organizations, Center researchers advance understanding of these important topics. Such knowledge also has the potential to advance practice by identifying factors that increase team learning, innovation and performance in firms.

            Research by Ray Reagans (Tepper School of Business) and Linda Argote (Tepper School of Business) and Daria Brooks, M.D. (Northwestern University) on surgical teams.  Their research examines whether organizational “learning curves” are due to individuals getting better at their jobs, to team members learning how to communicate and coordinate their activities better, or to the organization transferring knowledge across different teams.  Experience working together in teams provides individuals the opportunity to learn who knows what and how to communicate and coordinate more effectively.  Results are described in “Individual experience and experience working together: Predicting learning rates from knowing who knows what and knowing how to work together,” Management Science, June, 2005.  Results have implications for how to design and staff teams to promote learning and performance.

            A second research project in this area examines the effect of transactive memory, or knowledge of who knows what, on group learning and performance.  The work examines the conditions under which investments in building a transactive memory are especially worthwhile for firms.  Computational models are used to assess how the value of transactive memory varies as a function of group size, group task, and the competitive environment.  The research conducted by Yuqing Ren (School of Computer Science), Kathleen Carley ( School of Computer Science) and Linda Argote (Tepper School of Business) is co-funded by the Army Research Institute.  Results from the study are described in “When is it more beneficial to know what others know? The contingent effects of transactive memory,” Management Science in 2006.  Results indicate, for example, that transactive memory is especially beneficial for large groups and for groups with changing tasks.  

            Roberto Weber (Social and Decision Sciences) examines how feedback affects learning. In "Reflective learning and transfer of learning in games played repeatedly without feedback,"  Weber analyzes ways in which people learn  in the context of strategic games.  Experiments using several games demonstrate that a type of learning that occurs without feedback, termed 'reflective learning', is more profound and transfers to new contexts while a kind of reinforcement-based learning (that occurs with feedback) does not.  These results have important implications for the design of feedback and reward systems in organizations.

            Another research project in this area focuses on knowledge transfer across groups. Organizations often have multiple groups or establishments performing similar tasks.  How can organizations leverage knowledge acquired in one unit so that all units benefit?  What facilitates the transfer of improvements made in one group to others?  Aimeé A. Kane (a Tepper graduate and faculty member at New York University ), Linda Argote (Tepper School of Business) and John Levine (Department of Psychology, University of Pittsburgh ) examine how creating a shared social identity affects knowledge transfer.  The work is jointly funded by the Decision, Risk and Management Sciences (DRMS) program of the National Science Foundation (NSF).  Results are described in “Knowledge transfer between groups via personnel rotation: Effects of social identity and knowledge quality, “Organizational Behavior and Human Decision Process, January 2005.  The researchers find that sharing a superordinate social identity dramatically increases receptivity to ideas and knowledge transfer across groups.

Social Networks, Learning and Innovation

            Some organizations are designed with dense social networks where there are many connections between members; other organizations are designed with sparser networks that do not overlap much but reach into more knowledge pools.  Center research in this area is aimed at identifying the advantages and disadvantages of such structures for innovation, learning and knowledge transfer.

            A dissertation project in this stream by Marco Tortoriello (Tepper School of Business) examines “The social underpinning of absorptive capacity: External knowledge, social networks and individual innovativeness.”  The project, which is jointly funded by a grant from the Kaufmann Foundation to Marco Tortoriello, examines how knowledge acquired from outside the organization is internalized and used to create commercializable innovations inside the firm.  Data were collected from 16 R&D labs of a multinational semi-conductor firm.  The structure of an organization’s social network was found to affect innovation, which involves new combinations of knowledge.  The research sheds light on how to design social networks to promote learning and innovation in firms.  Marco successfully defended his dissertation, which was chaired by Bill McEvily, in 2006.

            Another dissertation project by Michael J. Ashworth ( School of Computer Science ) examines how social networks affect organizational learning. The research analyzes how social networks interact with team experience to predict learning and performance gains in teams in the electric utility industry.  The work, which is jointly funded by a Sloan Foundation grant to Lester Lave, uses a combination of field and computational methods.  The fundamental goal of the research is to determine how to design social networks to promote learning from experience and its translation into performance improvements in organizations. Mike’s dissertation is co-chaired by Kathleen Carley and Linda Argote.

Technology, Organizational Learning and Knowledge Transfer

            How can technology be used to enhance learning, innovation and performance in firms?  Does technology facilitate the ability of firms to retain knowledge?  Does technology enhance the ability of firms to transfer knowledge across geographically distributed units?   These questions are addressed by Center researchers.

            Michael J. Ashworth ( School of Computer Science ), Linda Argote (Tepper School of Business) and Tridas Mukhopadhyay (Tepper School of Business) analyze how technology affects organizational learning, knowledge retention, and knowledge transfer.  Their research, which is jointly funded by a grant from the Innovation and Organizational Change Program of the National Science Foundation, is based on data from a financial services firm that introduced the same technology in a staggered fashion in different locations.  Initial results from the project were presented at the December 2004 International Conference on Information Systems and published in the Proceedings of the 25th Annual International Conference on Information Systems.

            The introduction of the technology increased the rate of learning and performance gains at the firm.  Further, the technology increased the transfer of knowledge across the six locations and reduced knowledge decay or depreciation.  Lessons learned from experience at one location were embedded in the technology and benefited other locations.  Not only do the results increase understanding of the role of technology in learning and knowledge transfer, they also enable improved forecasts of the impact of technology and better decision making about technology consumption by firms.

            Youngsoo Kim (Heinz School of Public Policy and Management), R. Krishnan (Heinz School of Public Policy and Management) and Linda Argote (Tepper School of Business) analyze learning curves of knowledge workers who provide information technology technical support services.  The researchers aim to understand whether learning occurs in the service domain, whether learning rates vary as a function of the type of information system problem, and whether knowledge transfer occurs across different problem types.  Data were collected over three and a half years from a university computing services help center.

            Results were presented at the November 2005 meeting of The Institute for Operations Research and Management Sciences (INFORMS).  The researchers find that learning occurs in the call center: as the organization gains experience, problem resolution times decrease. In addition, workers solving problems involving application-level knowledge learned at a faster rate than workers solving problems involving underlying technical knowledge.  Further, knowledge transfer across problems occurred: experience on one type of problem reduced problem resolution times on others.  These results provide information about how to staff service centers to promote learning and performance gains.