Commercial software project managers design project organizational structure carefully, mindful of available skills, division of labour, geographical boundaries, etc. These organizational “cathedrals ” are to be contrasted with the “bazaarlike” nature of Open Source Software (OSS) Projects, which have no pre-designed organizational structure. Any structure that exists is dynamic, self-organizing, latent, and usually not explicitly stated. However, in large, complex, successful, OSS projects, we expect that sub-communities will form organically within the “bazaar ” of developer teams. Studying these sub-communities, and their behavior can shed light on how successful OSS projects self-organize. This phenomenon could even hold important lessons for how commercial software teams might be organized. Building on wellestablished techniques for detecting community structure in complex networks, we extract and evaluate latent subcommunities from the email social network of several projects: Apache HTTPD, Python, PostgresSQL, Perl, and Apache ANT. We then validate them with software development activity history. Our results show that subcommunities do indeed form within these projects. We find, in other words, that “chapels ” (if not cathedrals) spontaneously arise within the bazaar as OSS systems and the teams evolve. We also find that these subgroups manifest most strongly in technical discussions, and are significantly connected with collaboration behaviour. 1.

What is the network form of online discussion groups? What are the topological parameters delineating the interaction on such groups? We report an empirical examination of the form of online discussion groups. We are interested in examining whether such groups conform to the Small World and the Scale Free models of networks. Support for these expectations provides a formal expression of growth, survival potential and preferential attachment in the connection patterns in discussion groups. The research questions were tested with a sample of over 8,000 active participants, and over 30,000 messages. We find that the social network resulting from discussion groups is indeed a Scale Free Network, based on In, Out and All Degree distributions. We also find that, for the same sample, discussion groups are a Small World Network too. As expected, the clustering coefficients for these groups differ significantly from random networks, while their characteristic path lengths are similar to random networks. Implications of the topology for the design and understanding of discussion groups include the stability and control of such groups, as well as their

doi 10.1287/mnsc.1070.0712

Increasingly organizations are utilizing geographically distributed teams to accomplish their goals. To a great extent this new way of working has been made possible by electronic communication technology. Yet even while managers are leveraging electronic communication technology to gain access to new knowledge and to enable new team configurations, they are concerned about the knowledge acquisition of distributed team members who interact primarily via electronic communication. The objective of this study is to deepen our understanding of the relationship of electronic communication technology use and team configuration with knowledge access in distributed teams. We do so by examining the communication networks of individuals in distributed teams, and the relationship of team configuration on those networks. We extend prior work on social networks and propose that individuals in distributed teams have two distinct communication networks that influence knowledge access: face-to-face and electronic networks. We find that these two networks differentially influence an individual’s level of knowledge access from team members. In addition, we find that the relationship of each of these networks with knowledge access level is influenced by how the team is physically configured and the size of the team. These findings suggest that achieving higher knowledge access levels in distributed teams is more complex than just increasing electronic and face-to-face communication. Rather it involves understanding how communication patterns, communication mode and team configuration interact to influence the level of knowledge access for each individual in the team. Arling Subramani Working Paper January 2006 1

This study contrasts the structural perspective with the individual perspective in explaining group performance in a dynamic setting. The authors argue that these perspectives are not mutually exclusive but have different predictive powers at different group stages. Results from 45 project groups show (a) group structures provide stronger performance predictions at the later stage, whereas individual-based attributes do so at the earlier stage, and (b) different group structures and individual-based attributes provide distinctive insights at respective stages. This indicates the need to explore the potential bridge between the two perspectives in advancing group studies.

Computational Analysis of Social and Organizational Systems. To my wife, Libby, and to my family, whom I love beyond words… Computational methods combined with traditional empirical techniques offer a powerful new approach to the study of human performance. Scholars engaged in the study of work group and organizational behavior are increasingly calling for the use of integrated methods in conducting research, including the wider adoption of computational models for generating and testing new theory. In this collection of three studies, I first review the state of modern computational modeling and find a steady increase in the incorporation of dynamic, adaptive, and realistic behaviors of agents in social network settings. However, my analysis suggests areas that can be addressed in the next generation of organizational simulation systems. I compare 28 models according to more than 200 evaluation criteria, ranging from simple representations of agent demographic and performance characteristics, to more richly defined instantiations of behavioral attributes, interaction with non-agent entities, model

Abstract- Although team-based projects have become a popular practice in colleges with focus on helping students learn subjects collaboratively and effectively, the current education paradigm mainly adopts ad hoc collaboration practices. There lacks a quantitative methodology for enabling appropriate and scientific approaches to guide the educational community in teaching and transforming educational practices to support the development, implementation, and improvement of state-of-the-art collaborative-learning practices on campuses. This paper presents a new computational model and framework to quantitatively explore the collaboration-based behavioral dynamics of student teams and its impact on team performance. More specifically, a suite of mathematical models in the form of computations, structural equation modeling, and principal component analysis are created to describe and analyze a variety of learning circumstances, aimed at generating rules of thumb to guide student teams to retune collaborative practices in a proactive and realtime manner for improved performance.

We propose a sensor-based organizational design and engineering approach that combines behavioral sensor data with other sources of information such as e-mail, surveys, and performance data in order to design interventions aimed at improving organizational outcomes. The proposed system combines sensor measurements, pattern recognition algorithms, simulation and optimization techniques, social network analysis, and feedback mechanisms that aim at continuously monitoring and improving individual and group performance. We describe the system’s general specifications and discuss several studies that we conducted in different organizations using the sociometric badge experimental sensing platform. We have deployed such system under naturalistic settings in more than ten organizations up to this date. We show that it is possible to automatically capture group dynamics, and analyze the relationship between organizational behaviors and both subjective and objective outcomes (such as job satisfaction, quality of group interaction, stress, productivity, and group performance). We propose the use of static and dynamic simulation models of group behavior captured by sensors, in order to optimize group configurations that maximize individual