![]() ![]() One of the researchers’ findings was particularly intriguing: the technological dimension (whether the project designed and/or built its own equipment, and whether this technology advanced the state of the art) corresponded to all five of the success measures. Most of these categories had little relationship to success measures, and nor did they correspond strongly to particular subdisciplines. Their analysis sought to find relationships between these dimensions and the outcome measures of trust, stress, perceived conflict, documentary process, and perceived success. Chompalov and Shrum analyzed data from a subset of twenty-three of these projects, and performed a cluster analysis that made use of seven measured dimensions: project formation and composition, magnitude, interdependence, communication, bureaucracy, participation, and technological practice. This large-scale, three-phase study looked at a large number of collaborations in high-energy physics, space science, and geophysics. Ivan Chompalov and Wesley Shrum ( 1999) developed a larger-scale classification scheme based on data from phase one of the American Institute of Physics (AIP) Study of Multi-Institutional Collaborations ( 1992, 1995, 1999). ![]() Low-level task/technology matching may be one factor in a project’s success, but it is not a sufficient predictor of overall success. Any single project will at different times engage in negotiation, decision making, and brainstorming, and will make use of e-mail, face-to-face meetings, and real-time communication tools. The SOC project conducted a similar technology inventory as part of its research, but this level of classification is not as useful for large-scale projects because these projects perform many different task types using numerous tools over the course of their lives. This type of classification yields insights about what kinds of task/technology matches are most apt (e.g., text chat is a good choice for maintaining awareness, but a poor one for negotiation). Georgia Bafoutsou and Gregoris Mentzas ( 2002) reviewed this literature, and mapped it on to the specific technology functionalities of modern groupware systems. A great deal of previous work in computer-supported cooperative work (e.g., Grudin 1994 DeSanctis and Gallupe 1987) has classified technology as to how well it supported different task types as well as different configurations of local and distant workers. This is not the first typology of its kind, although it is unique in its scale and purpose. Scientific users were early adopters and promoters of many of the technologies that long-distance collaboration now relies on, including e-mail, FTP servers, and the World Wide Web. Mark Newman’s (2001) social network analyses of scientific communities in biomedicine, physics, and computer science showed that each of these fields formed a well-interconnected or “small world” network (Watts and Strogatz 1998). Diana Crane ( 1972) first described the loosely affiliated but highly interactive networks of scientists as “invisible colleges.” Compared to other peer groups, scientific communities are often surprisingly egalitarian and broadly international. Thomas Kuhn ( 1963) showed how scientific peer groups determine what theories will be accepted as well as make more mundane judgments about what papers will be published and what grants will be funded. Pioneers in the social studies of science documented how the basic activities of scientists, such as deciding what counts as evidence, are fundamentally social undertakings (Collins 1998 Latour and Woolgar 1979). Still, the difficulties of sustaining large-scale collaboratories were unexpected to many scientists and funders, partially because modern studies of science have repeatedly emphasized the social nature of scientific communities. Should we consider this surprising? Scientific progress is by nature uncertain, and long-distance collaboration always faces many barriers (Olson and Olson 2000).
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