2.5 Information acquisition in collaborative GIS-MCDA
2.5.3 The effect of task complexity, information aids and decision modes in
As indicated in the previous section, there are a number of studies that have focused on studying the effects of task complexity on information acquisition behavior within the realm of non-spatial decisions. However, the research efforts examining task complexity effects in the field of spatial decision making in general and GIS-MCDA in particular have been rather limited. Crossland et al. (1995) examined the effects of task complexity on decision time and accuracy during the use of a spatial decision support system. The complexity of decision problem was manipulated on two levels. The first level required subjects to rank five facility sites based on three spatial criteria. The second level required ranking ten facility sites based on seven spatial criteria. The findings of this study suggested that an increase in task complexity resulted in an increase in decision time and a decrease in decision accuracy. Jankowski and Nyerges (2001a) employed a process tracing technique to study the influence of task complexity on dynamics of human- computer interaction (social-behavioral data analysis strategies) during a collaborative GIS-MCDA. They investigated how an increase in task complexity influences the use of information aids (e.g., maps, tables, diagrams) by decision participants, group work, and group conflict. In this effort, the task complexity was increased as a variation in both the number of spatial alternatives and criteria, with the simplest task involving eight sites and three evaluation criteria versus the most complex task being a choice among twenty sites based on eleven criteria. Results in this study demonstrated that the maps were used more in the simple task than the complex task by about twice as much.
As for the effect of information aids on decision making, it has been suggested that access to different tables, graphs and maps has an influence on the decision process and outcomes (Crossland et al., 1995; Smelcer & Carmel, 1997; Dennis & Carte, 1998; Speier, 2006; Andrienko et al., 2007). Speier (2006) argues that visualized data allows the decision-maker to shift some of the cognitive processing burden to perceptual operations that typically occur automatically and results in significantly lower mental workload that accelerate the speed and depth at which large amounts of data can be absorbed and
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comprehended. Therefore, it is reasonable to expect that the character of decision aids offered for use in the GIS-MCDA environment will have an influence on the number of times they are used and the way they are brought into use. The human-computer interaction (the pattern of decision aid moves) will likely be different between maps and decision tables because of the advantages or disadvantages of information associated with each (Contractor & Seibold, 1993). In an empirical study of socio-behavioral dynamics of using decision aids, Jankowski and Nyerges (2001b) examined the usage of four different types of geographic information structures including: map, MCDA (decision table), consensus (rank map), and table/text aids in a collaborative GIS-MCDA environment. In examination of the use of map and MCDA decision aids, they found that participants spent more time on exploring the MCDA aid than the map during the collaborative spatial decision making process. Dennis and Carte (1998) investigated the effect of map-based and tabular presentations on decision accuracy and speed. The study found that when data were presented in a map-based form and decision makers needed to consider the relationships among the geographic areas, the use of the map-based presentation led to both faster and more accurate decisions.
However, none of these studies has gone further to examine the effects of task complexity and information aids on the information acquisition metrics discussed above within the Web 2.0-based collaborative GIS-MCDA context. Also, these research efforts have not examined effect of decision mode (individual vs. group) on the information acquisition metrics. There is no empirical study exploring how decision makers’ information acquisition behavior can be affected by the use of different GIS-MCDA modes. There is, therefore, a need for further research to examine: (i) information acquisition metrics as a means of inferring the behaviors and strategies used by participants within the realm of collaborative spatial multicriteria decision making and (ii) the effect of task complexity, information aids and decision modes on the information acquisition metrics.
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Chapter 3
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OWA-based approach for collaborative GIS-MCDA
This chapter presents a collaborative GIS-MCDA procedure to be used in the empirical study. The procedure involves two stages: (i) each decision maker solves the problem individually, and (ii) the individual solutions are aggregated to obtain a group solution. The first stage is operationalized by an OWA (ordered weighted averaging)-based decision rule for the generation of individual solutions. The second stage employs a Borda-based method for aggregating the individual solutions into a consensus solution. During the process of individual decision making, decision makers have access to the decision information represented by means of a decision table or map. They are able to acquire and integrate decision-relevant information, specify their preferences, and arrive at a decision.