Título: Task-Technology Fit & Geospatial Reasoning
Palestrante: Michael Erskine (Middle Tennessee State University, USA)
Individuals, business enterprises, and public agencies frequently make decisions involving geospatial data using spatial decision support systems (SDSS). SDSS consist of spatial databases, analytic models, and interactive visualizations that should facilitate more effective decision-making when solving geospatial problems. These SDSS can range in sophistication from complex analytical software (e.g., ArcGIS Pro) to online mapping services (e.g., Google Maps). Yet, even with the proliferation and extensive application of SDSS technologies, inaccurate and even irrational decisions are frequently made when attempting to solve geospatial problems. Our recent investigation into the topic of decision-making using spatial data (e.g., Erskine et al., 2018; 2019) provides an opportunity to understand the spatial decision-making process more clearly. Moreover, using the lens of Goodhue and Thompson’s (1995) Task-Technology Fit Theory (TTFT), we explore effective and efficient spatial decision-making approaches. Scholars benefit from a comprehensive understanding of the individual characteristics that influence decision-making using geospatial data, including heuristics, cognitive abilities, personal innovativeness, self-efficacy, and intrinsic motivations. We will also evaluate task characteristics, such as input and task complexity, and examine technology characteristics such as the influence of visualization techniques on the decision-performance. Practical and academic implications, including future research directions, will be discussed.