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

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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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References

  • Alavi, M., & Joachimsthaler, E. A. (1992). Revisiting DSS implementation research: a meta-analysis of the literature and suggestions for researchers. MIS Quarterly, 16(1), 95–116.

    Google Scholar 

  • Albert, W. S., & Golledge, R. G. (1999). The use of spatial cognitive abilities in geographical information systems: the map overlay operation. Transactions in GIS, 3(1), 7–21.

    Google Scholar 

  • Arnott, D., & Pervan, G. (2008). Eight key issues for the decision support systems discipline. Decision Support Systems, 44(3), 657–672.

    Google Scholar 

  • Baloian, N., & Zurita, G. (2016). Achieving better usability of software supporting learning activities of large groups. Information Systems Frontiers, 18(1), 125–144.

    Google Scholar 

  • Benbasat, I., & Nault, B. (1990). An evaluation of empirical research in managerial support systems. Decision Support Systems, 6(3), 203–226.

    Google Scholar 

  • Chung, N., Tyan, I., & Han, H. (2017). Enhancing the smart tourism experience through geotag. Information Systems Frontiers, 19(4), 731–742.

    Google Scholar 

  • Clementini, E., Di Felice, P., & van Oosterom, P. (1993). A small set of formal topological relationships suitable for end-user interaction. In D. Abel, B. C. Ooi (Eds.), Advances in Spatial Databases: Third International Symposium, SSD '93 Singapore, Proceedings, Lecture Notes in Computer Science, 692, 1993, 277–295.

  • Compeau, D. R., & Higgins, C. A. (1999). Social cognitive theory and individual reactions to computing technology: a longitudinal study. MIS Quarterly, 23, 145–158.

    Google Scholar 

  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.

    Google Scholar 

  • Crossland, M. D., & Wynne, B. E. (1994). Measuring and testing the effectiveness of a spatial decision support system. Proceedings of the 27th Annual Hawaii International Conference on System Sciences, Volume IV, Los Alamitos, CA: IEEE Computer Society Press, 542–551.

  • Crossland, M. D., Wynne, B. E., & Perkins, W. C. (1995). Spatial decision support systems: an overview of technology and a test of efficacy. Decision Support Systems, 14(3), 219–235.

    Google Scholar 

  • Dennis, A. R., & Carte, T. A. (1998). Using geographical information systems for decision making: extending cognitive fit theory to map-based presentations. Information Systems Research, 9(2), 194–203.

    Google Scholar 

  • Densham, P. J. (1991). Spatial decision support systems. In D. J. Maguire, M. F. Goodchild, & D. W. Rhind (Eds.), Geographical information systems: Principles and applications (pp. 403–412). New York: John Wiley and Sons.

    Google Scholar 

  • Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information Management, 36(1), 9–21.

    Google Scholar 

  • Eom, S. B., & Lee, S. M. (1990). A survey of decision support system applications (1971–1988). Interfaces, 20(3), 65–79.

    Google Scholar 

  • Erskine, M. A., Gregg, D. G., Karimi, J., & Scott, J. E. (2015). Geospatial reasoning ability: definition, measurement and validation. International Journal of Human-Computer Interaction, 31(6), 402–412.

    Google Scholar 

  • Erskine, M. A., Gregg, D. G., & Karimi, J. (2016). Perceptions and attitudes toward online mapping services. Journal of Computer Information Systems, 56(2), 175–184.

    Google Scholar 

  • ESRI (2017). ESRI Industries, URL: http://www.esri.com/industries.

  • Evans, G. W. (1980). Environmental cognition. Psychological Bulletin, 88(2), 259–287.

    Google Scholar 

  • Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Google Scholar 

  • Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-Graph: tutorial and annotated example. Communications of the Association for Information Systems, 16, 91–109.

    Google Scholar 

  • GISCloud (2014). Orange County, CA Adopts GIS Cloud, URL: http://www.giscloud.com/blog/orange-county-gis-cloud-case-study/.

  • Gliem, J. A., & Gliem, R. R. (2003). Calculating, interpreting, and reporting Conbach’s alpha reliability coefficient for likert-type scales. 2003 Midwest Research to Practice Conference in Adult, Continuing and Community Education, Columbus, 82–88.

  • Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 213–236.

  • Goodhue, D. L., Klein, B. D., & March, S. T. (2000). User evaluations of IS as surrogates for objective performance. Information Management, 38(2), 87–101.

    Google Scholar 

  • Hahmann, S., & Burghardt, D. (2013). How much information is geospatially referenced? Networks and cognition. International Journal of Geographical Information Science, 27(6), 1171–1189.

    Google Scholar 

  • Hair Jr., J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate data analysis 7th Ed. Upper Saddle River: Pearson.

    Google Scholar 

  • Hair Jr., J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Los Angeles: Sage.

    Google Scholar 

  • Jarupathirun, S., & Zahedi, F. M. (2007). Exploring the influence of perceptual factors in the success of web-based spatial DSS. Decision Support Systems, 43(3), 933–951.

    Google Scholar 

  • Jarvenpaa, S. L. (1989). The effect of task demands and graphical format on information processing strategies. Management Science, 25(3), 285–303.

    Google Scholar 

  • Kozlowski, L. T., & Bryant, K. J. (1977). Sense of direction, spatial orientation and cognitive maps. Journal of Experimental Psychology: Human Perception and Performance, 3, 590–598.

    Google Scholar 

  • Lawton, C. A. (1994). Gender differences in way-finding strategies: relationship to spatial ability and spatial anxiety. Sex Roles, 30(11–12), 765–779.

    Google Scholar 

  • Lee, C. C., Cheng, H. K., & Cheng, H. H. (2007). An empirical study of mobile commerce in insurance industry: Task–technology fit and individual differences. Decision Support Systems, 43(1), 95–110.

    Google Scholar 

  • Lee, J., & Bednarz, R. (2009). Effect of GIS learning on spatial thinking. Journal of Geography in Higher Education, 33(2), 183–198.

    Google Scholar 

  • Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1–55.

    Google Scholar 

  • Linn, M. C., & Petersen, A. C. (1985). Emergence and characterization of sex differences in spatial ability: a meta-analysis. Child Development, 56, 1479–1498.

    Google Scholar 

  • Liu, Y., Lee, Y., & Chen, A. N. (2011). Evaluating the effects of task–individual–technology fit in multi-DSS models context: a two-phase view. Decision Support Systems, 51(3), 688–700.

    Google Scholar 

  • Marcolin, B. L., Compeau, D. R., Munro, M. C., & Huff, S. L. (2000). Assessing user competence: conceptualization and measurement. Information Systems Research, 11(1), 37–60.

    Google Scholar 

  • Mäntylä, T. (2013). Gender differences in multitasking reflect spatial ability. Psychological Science, 24(4), 514–520.

    Google Scholar 

  • McGill, T. J., & Klobas, J. E. (2009). A task–technology fit view of learning management system impact. Computers & Education, 52(2), 496–508.

    Google Scholar 

  • Mennecke, B. E. (1997). Understanding the role of geographic information technologies in business: applications and research directions. Journal of Geographic Information and Decision Analysis, 1(1), 44–68.

    Google Scholar 

  • Mennecke, B. E., Crossland, M. D., & Killingsworth, B. L. (2000). Is a map more than a picture? The role of SDSS technology, subject characteristics, and problem complexity on map reading and problem solving. MIS Quarterly, 24(4), 601–629.

    Google Scholar 

  • Ozimec, A. M., Natter, M., & Reutterer, T. (2010). Geographical information systems-based marketing decisions: effects of alternative visualizations on decision quality. Journal of Marketing, 74(6), 94–110.

    Google Scholar 

  • Poblet, M., García-Cuesta, E., & Casanovas, P. (2017). Crowdsourcing roles, methods and tools for data-intensive disaster management. Information Systems Frontiers, 1–17.

  • Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0.M3. Hamburg: SmartPLS. Retrieved from http://www.smartpls.com.

  • Rogers, E. M. (1983). Diffusion of Innovations (3rd ed.). New York: The Free Press.

    Google Scholar 

  • Rusch, M. L., Nusser, S. M., Miller, L. L., Batinov, G. I., & Whitney, K. C. (2012). Spatial ability and map-based software applications. Proceedings of the Fifth International Conference on Advances in Computer-Human Interactions, 35–40.

  • Shih, H. P., Lai, K. H., & Cheng, T. E. (2015). Examining structural, perceptual, and attitudinal influences on the quality of information sharing in collaborative technology use. Information Systems Frontiers, 17(2), 455–470.

    Google Scholar 

  • Slocum, T. A., Blok, C., Jiang, B., Koussoulakou, A., Montello, D. R., Fuhrmann, S., & Hedley, N. R. (2001). Cognitive and usability issues in geovisualization. Cartography and Geographic Information Science, 28(1), 61–75.

    Google Scholar 

  • Smelcer, J. B., & Carmel, E. (1997). The effectiveness of different representations for managerial problem solving: comparing tables and maps. Decision Sciences, 28(2), 391–420.

    Google Scholar 

  • Speier, C., & Morris, M. G. (2003). The influence of query interface design on decision-making performance. MIS Quarterly, 27(3), 397–423.

    Google Scholar 

  • Speier, C. (2006). The influence of information presentation formats on complex task decision-making performance. International Journal of Human-Computer Studies, 64(11), 1115–1131.

    Google Scholar 

  • Swink, M., & Speier, C. (1999). Presenting geographic information: effects on data aggregation, dispersion, and users’ spatial orientation. Decision Sciences, 30(1), 169–196.

    Google Scholar 

  • Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144–176.

    Google Scholar 

  • Tonkin, T. (1994). Business geographics impacts corporate America. Business Geographics, 2(2), 27–28.

    Google Scholar 

  • Vessey, I. (1991). Cognitive fit: a theory-based analysis of the graphs vs. tables literature. Decision Sciences, 22(2), 219–240.

    Google Scholar 

  • Whitney, K. C., Batinov, G. J., Miller, L. L., Nusser, S. M., & Ashenfelter, K. T. (2011). Exploring a map survey task’s sensitivity to cognitive ability. Proceedings of the Fourth International Conference on Advances in Computer-Human Interactions, Gosier, Guadeloupe, France, 63–68.

  • Yang, C., Raskin, R., Goodchild, M., & Gahegan, M. (2010). Geospatial cyberinfrastructure: past, present and future. Computers, Environment and Urban Systems, 34(4), 264–277.

    Google Scholar 

  • Yang, C., Wong, D. W., Yang, R., Kafatos, M., & Li, Q. (2005). Performance-improving techniques in web-based GIS. International Journal of Geographical Information Science, 19(3), 319–342.

    Google Scholar 

  • Zigurs, I., & Buckland, B. (1998). A theory of task/technology fit and group support systems effectiveness. MIS Quarterly, 22(3), 313–334. https://doi.org/10.2307/249668.

    Article  Google Scholar 

  • Zipf, A. (2002). User-adaptive maps for location-based services (LBS) for tourism. In K. W. Wöber, A. J. Frew, & M. Hitz (Eds.), Proceedings of the 9th international conference for information and communication technologies in tourism, ENTER 2002 (pp. 329–338). Heidelberg: Springer Verlag.

    Google Scholar 

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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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