![]() It extracts the key insights from the body of CE research thus far, identifies significant areas of inquiry that have not yet been explored, and looks ahead at the CE research opportunities that are emerging as our society, organizations, technologies, and the nature of collaboration evolve.ĭata warehouses (DWs) are widely known for their powerful analysis capabilities that serve either for historic data investigation or for predictions of potentially continuous phenomena. This paper details the contributions from CE research and practice based on a literature assessment of 331 publications. ![]() Subsequent research focused on the development of theories to explain key phenomena, the development of a structured design methodology, training methods, technology support, design theories, and various field and experimental studies focusing on specific aspects of the CE approach. CE research started with studies on ways to transfer professional collaboration expertise to novices using a pattern language called thinkLets. Since 2001, CE has been an active and productive topic of research that has attracted scientists from different backgrounds and disciplines. The approach was successfully tried in a case study letting end-users add collaboration support to a system that did not provide it.Ĭollaboration Engineering (CE) is an approach for the design and deployment of repeatable collaborative work practices that can be executed by domain experts without the ongoing support of external collaboration professionals. The approach relies on Design Thinking, Web Augmentation and Collaboration Engineering. This work presents an approach to involve end-users in enhancing exiting web software to produce incremental innovations. Collaboration Engineering is an effective means to reuse design experience of collaboration strategies. Design thinking has proven to be an effective tool to support innovation on many domains. Augmenting the web is a widely adopted technique for enhancing existing applications with new features which are not available out-of-the-shelf. Existing software products, frequently implemented as web applications, are found to lack functionality, for example to support collaboration. Information systems innovation in agriculture is a challenging and very active area. By focusing research on thinkLets, rather than GSS, field and laboratory research may be more controllable, more replicable, and better able to inform GSS development and use.ĭecision making in agriculture increasingly relies on software, for example to gather important information or to weight alternatives. Each thinkLet creates some unique variation on its basic pattern. To date we have documented about 60 thinkLets that map to seven basic patterns of thinking: Diverge, Converge, Organize, Elaborate, Abstract, Evaluate, and Build Consensus. Field experience shows that thinkLets may be used to create repeatable, predictable patterns of thinking among people making an effort toward a goal. A thinkLet encapsulates three components of a GSS stimulus: The tool, its configuration, and the script. This paper argues that in GSS research, the thinkLet may be a more useful unit of comparison than the GSS. One cause of the conflict and ambiguity in GSS research results may be the result of focusing on a less-than-useful level of abstraction: GSS itself. However, research results are not unequivocal they have been ambiguous, and sometimes conflicting which makes it difficult for GSS research to inform GSS practice. Over the past decade, Group Support Systems (GSS) has shown that, under certain circumstances, teams using GSS can be far more productive than teams who do not use GSS.
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