Abstract
Increasingly, spatial decision support systems (SDSS) help consumers, businesses and governmental entities make decisions involving geospatial data. Understanding if, and how, user- and task-characteristics impact decision-performance will allow developers of SDSS to maximize decision-making performance. Furthermore, scholars can benefit from a more comprehensive understanding of what specific characteristics influence decision-making when using an SDSS. This paper provides a synthesis of relevant research and presents a two-factor experiment (n = 200) designed to measure the impact of user- and task-characteristics on decision-performance. Using Cognitive Fit Theory (CFT) as the theoretical framework, we investigate the effect of geospatial reasoning ability (GRA), input complexity, task complexity, and user perceptions of task-technology fit (PTTF), on geospatial decision-making performance. Specifically, we measure GRA as a user-characteristic, while input- and problem-complexity are measured as task-characteristics. A partial least squares (PLS) analysis reveals the statistical significance of user- and task-characteristics on geospatial decision-making performance. Theoretical and managerial implications are discussed in detail.
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Erskine, M.A., Gregg, D.G., Karimi, J. et al. Individual Decision-Performance Using Spatial Decision Support Systems: A Geospatial Reasoning Ability and Perceived Task-Technology Fit Perspective. Inf Syst Front 21, 1369–1384 (2019). https://doi.org/10.1007/s10796-018-9840-0
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DOI: https://doi.org/10.1007/s10796-018-9840-0