Location selection using heat maps: Relative advantage, task-technology fit, and decision-making performance

Abstract

Increasingly, consumers, businesses and governmental entities make decisions involving geospatial data using spatial decision support systems (SDSS). One frequent geospatial decision task is that of location selection, which may involve determining the best site for a new retail store or locating an ideal neighborhood for a future city park. Online and mobile SDSS often do not provide the capability to visualize information using heat maps. Understanding if, and how, heat maps influence the geospatial decision-making process provides an opportunity for SDSS developers to achieve competitive advantages. Such knowledge may also identify individual characteristics needed to effectively utilize heat maps. Scholars can benefit from a more comprehensive understanding of the characteristics that influence geospatial decision-making. Task-Technology Fit Theory (TTFT) provides the theoretical foundation of the study. Subjects (N = 294) participated in an experiment designed to model geospatial decision-making performance with and without the aid of data visualized using heat maps. Individual characteristics and perceptions, including personal innovativeness, self-efficacy, intrinsic motivation, relative advantage, and geospatial reasoning ability are examined as direct and indirect antecedents of perceived task-technology fit. Decision-making accuracy, time and satisfaction are measures of decision-performance. A partial least squares structural equation modeling approach reveals the significant influence of an individual's characteristics on decision-making performance. Furthermore, decision-making accuracy is shown to be influenced positively through the use of heat maps. Theoretical and managerial implications, as well as future research directions, are also discussed.