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Towards an understanding of decision complexity in IT configuration
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Source Computer Human Interaction for the Management of Information Technology archive
Proceedings of the 2007 symposium on Computer human interaction for the management of information technology table of contents
Cambridge, Massachusetts
SESSION: Studies of systems management table of contents
Article No.: 3  
Year of Publication: 2007
ISBN:1-59593-635-6
Authors
Bin Lin  Northwestern University
Aaron B. Brown  IBM T. J. Watson Research Center
Joseph L. Hellerstein  IBM T. J. Watson Research Center
Sponsor
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
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ABSTRACT

In previous work we laid out an approach to quantifying configuration complexity [3]. In that earlier work, we explicitly focused on complexity as experienced by expert systems managers, and thus looked at straight-line configuration procedures, ignoring the complexity faced by non-experts as they have to decide what configuration steps to follow. Decision complexity is the complexity faced by a non-expert system administrator---the person providing IT support in a small-business environment, who is confronted by decisions during the configuration process, and is a measure of how easy or hard it is to identify the appropriate sequence of configuration actions to perform in order to achieve a specified configuration goal. To identify spots of high decisionmaking complexity, we need a model of decision complexity for configuring and operating computing systems. This paper extends previous work on models and metrics for IT configuration complexity by adding the concept of decision complexity. As the first step towards a complete model of decision complexity, we describe an extensive user study of decision making in a carefully-mapped analogous domain (route planning), and illustrate how the results of that study suggest an initial model of decision complexity applicable to IT configuration. The model identifies the key factors affecting decision complexity and highlights several interesting results, including the fact that decision complexity has significantly different impacts on user-perceived difficulty than on objective measures like time and error rate. We also describe some of the implications of our decision complexity model for system designers seeking to automate the decision-making and reduce the configuration complexity of their systems.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Bin Lin: colleagues
Aaron B. Brown: colleagues
Joseph L. Hellerstein: colleagues