The effectiveness of different representations for managerial problem solving: comparing tables and maps

Geographic Information Systems enable decision makers to view tabular data geographically, as maps. This simple yet powerful visual format appears to facilitate problem solving, yet how it does so is not clear, nor do we know the types of problems that benefit from this representation. To begin to understand the contributions of geographic representations over tabular representations, we conducted a three-factor experiment in problem solving. The experiment contained two different representations (map and table), three different geographic relationships (proximity, adjacency, and containment), and three levels of task difficulty (low, medium, and high). We found that maps generally produced faster problem solving than tables, and that problem-solving time increased with task difficulty. Most importantly, for the proximity and adjacency geographic relationships we found that maps kept problem-solving time low, while tables tended to increase time dramatically. However, we found that the number of knowledge states for each task explains performance times quite well and is a useful tool for understanding performance differences and interaction effects. As tasks become more difficult, representing them as maps generally keeps the number of knowledge states small, while for tables, the number of knowledge states increases dramatically. Correspondingly, problem-solving times increase dramatically with tables, but not with maps. In sum, as difficulty increases, maps are more effective for problem-solving tasks. Using maps, the tasks are simplified using visual heuristics that keep problem-solving times and error rates from rising as quickly as they do with tables.