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Individual Decision-Performance Using Spatial Decision Support Systems: A Geospatial Reasoning Ability and Perceived Task-Technology Fit Perspective

Author

Listed:
  • Michael A. Erskine

    (Middle Tennessee State University)

  • Dawn G. Gregg

    (University of Colorado Denver)

  • Jahangir Karimi

    (University of Colorado Denver)

  • Judy E. Scott

    (University of Colorado Denver)

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.

Suggested Citation

  • Michael A. Erskine & Dawn G. Gregg & Jahangir Karimi & Judy E. Scott, 2019. "Individual Decision-Performance Using Spatial Decision Support Systems: A Geospatial Reasoning Ability and Perceived Task-Technology Fit Perspective," Information Systems Frontiers, Springer, vol. 21(6), pages 1369-1384, December.
    • Handle: RePEc:spr:infosf:v:21:y:2019:i:6:d:10.1007_s10796-018-9840-0
    DOI: 10.1007/s10796-018-9840-0
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    Cited by:

    1. Yuan Li & William J. Kettinger, 2022. "Testing the Relationship Between Information and Knowledge in Computer-Aided Decision-Making," Information Systems Frontiers, Springer, vol. 24(6), pages 1827-1843, December.

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