The Role of Visualization Tools in Spreadsheet Error Correction from a Cognitive Fit Perspective

Errors in spreadsheets pose a serious problem for organizations and academics. This has resulted in ongoing efforts to devise measures for reducing errors or efficient ways of correcting them. Visualization tools are often advertised as means for improving spreadsheet error correction performance. This study investigates the role of visualization tools in spreadsheet error correction. For this purpose, this study proposes a framework for classifying activities associated with spreadsheet error correction. The framework is to highlight the activities that are important for correcting different types of spreadsheet errors, and to show how different visualization tools can aid error correction by effectively supporting these activities. By identifying chaining as one of the most important activities from the framework, this study uses cognitive fit theory to examine the effects of a visualization tool that supports chaining on spreadsheet error correction performance. Experimental methodology is used to test the outcome of cognitive fit between the error correction task and the visualization tool. The results of the experiment highlight the importance of cognitive fit between the type of task and the visualization tool for attaining better performance. This study also provides guidelines for designing and developing tools for spreadsheet error correction

Spreadsheet Error Correction Using an Activity Framework and a Cognitive Fit Perspective

Errors in a spreadsheet constitute a serious reason for concern among organizations as well as academics. There are ongoing efforts toward finding ways to reduce errors, designing and developing visualization tools to support error correction activities being one of them. In this paper, we propose a framework for classifying activities associated with spreadsheet error correction. The purpose of this framework is to help in understanding the activities that are important for correcting different types of spreadsheet errors and how different visualization tools can help in error correction by effectively supporting these activities. An experiment is designed to test the effectiveness of a visualization tool that supports one of the most important activities from the framework – chaining activity. Two groups of subjects, with and without the visualization tool, are required to correct two types of errors. Our hypotheses are derived based on the notion of cognitive fit between problem representation and task, and the results of the experiment support most of the hypotheses. Thus, this study demonstrates the usefulness of the activity-based framework for spreadsheet error correction, and also provides guidelines for designing and developing tools for spreadsheet audit. It also provides empirical evidence to the cognitive fit theory by showing that performance is significantly better when visual support tools result in a match between problem representation and the task in hand, as in the case of correcting link errors with the tool used in this study. Theoretical and practical implications of the findings are discussed

The Role of Cognitive Effort in Decision Performance Using Data Representations :;a Cognitive Fit Perspective

A major goal of Decision Support (DSS) and Business Intelligence (BI) systems is to aid decision makers in their decision performance by reducing effort. One critical part of those systems is their data representation component of visually intensive applications such as dashboards and data visualization. The existing research led to a number of theoretical approaches that explain decision performance through data representation\u27s impact on users\u27 cognitive effort, with Cognitive Fit Theory (CFT) being the most influential theoretical lens. However, available CFT-based literature findings are inconclusive and there is a lack of research that actually attempts to measure cognitive effort, the mechanism underlying CFT and CFT-based literature. This research is the first one to directly measure cognitive effort in Cognitive Fit and Business Information Visualization context and the first one to evaluate both self-reported and physiological measures of cognitive effort. The research provides partial support for CFT by confirming that task characteristics and data representation do influence cognitive effort. This influence is pronounced for physiological measures of cognitive effort while it minimal for self-reported measure of cognitive effort. While cognitive effort was found to have an impact on decision time, this research suggests caution is assuming that task-representation fit is influencing decision accuracy. Furthermore, this level of impact varies between self-reported and physiological cognitive effort and is influenced by task complexity. Research provides extensive cognitive fit theory, business information visualization and cognitive effort literature review along with implications of the findings for both research and practic

The Role of Cognitive Effort in Decision Performance Using Data Representations :;a Cognitive Fit Perspective

A major goal of Decision Support (DSS) and Business Intelligence (BI) systems is to aid decision makers in their decision performance by reducing effort. One critical part of those systems is their data representation component of visually intensive applications such as dashboards and data visualization. The existing research led to a number of theoretical approaches that explain decision performance through data representation\u27s impact on users\u27 cognitive effort, with Cognitive Fit Theory (CFT) being the most influential theoretical lens. However, available CFT-based literature findings are inconclusive and there is a lack of research that actually attempts to measure cognitive effort, the mechanism underlying CFT and CFT-based literature. This research is the first one to directly measure cognitive effort in Cognitive Fit and Business Information Visualization context and the first one to evaluate both self-reported and physiological measures of cognitive effort. The research provides partial support for CFT by confirming that task characteristics and data representation do influence cognitive effort. This influence is pronounced for physiological measures of cognitive effort while it minimal for self-reported measure of cognitive effort. While cognitive effort was found to have an impact on decision time, this research suggests caution is assuming that task-representation fit is influencing decision accuracy. Furthermore, this level of impact varies between self-reported and physiological cognitive effort and is influenced by task complexity. Research provides extensive cognitive fit theory, business information visualization and cognitive effort literature review along with implications of the findings for both research and practic

Using Visual Representations of Data to Enhance Sensemaking in Data Exploration Tasks

This paper explains how visual representations of data enable individual sensemaking in data exploration tasks. We build upon theories of human perception and cognition, including Cognitive Fit Theory, to explain what aspects of visual representations facilitate sensemaking for the viewer. We make three primary contributions. First, we give a general characterization of visual representations that would be used for data exploration tasks. These representations consist of a scene, objects within the scene, and the characteristics of those objects. Second, we extend Cognitive Fit Theory into the data exploration task domain. We explain that the data exploration task has a number of spatial subtasks including observing data points, looking for patterns or outliers, making inferences, comparing observed facts or patterns to one’s own knowledge, generating hypotheses about the data, and drawing analogies from the context being observed to another context. Third, we offer a set of theoretical propositions about how visual representations of data can serve the sensemaking goal. Specifically, visual representations best facilitate sensemaking in data exploration tasks when they (1) support the four basic human visual perceptual approaches of association, differentiation, ordered perception, and quantitative perception, (2) have strong Gestalt properties, (3) are consistent with the viewer’s stored knowledge, and (4) support analogical reasoning. We propose that visual representations should possess several of these four aspects to make them well-suited for the task of data exploration

Task-Representation Fit’s Impact on Cognitive Effort in the Context of Decision Timeliness and Accuracy: A Cognitive Fit Perspective

Cognitive fit theory (CFT) has emerged as a dominant theoretical lens to explain decision performance when using data representations to solve decision making tasks. Despite the apparent consensus regarding cognitive effort\u27s theoretical criticality in CFT-based research, researchers have made limited attempts to evaluate and empirically measure cognitive effort and its impact. Unlike prior CFT-based literature that has theorized only the role of cognitive effort, in our empirical study, we presented information and tasks to 68 participants and directly measured cognitive effort to understand how cognitive fit impacts it and how it impacts decision performance. We found that 1) cognitive fit had an impact on cognitive effort only for more complex tasks and 2) cognitive effort had an impact on decision performance time but not on decision performance accuracy. These findings enhance our understanding of an established IS theory and encourage more research on the cognitive underpinnings of CFT

Cognition Matters: Enduring Questions in Cognitive IS Research

We explore the history of cognitive research in information systems (IS) across three major research streams in which cognitive processes are of paramount importance: developing software, decision support, and human-computer interaction. Through our historical analysis, we identify “enduring questions” in each area. The enduring questions motivated long-standing areas of inquiry within a particular research stream. These questions, while perhaps unapparent to the authors cited, become evident when one adopts an historical perspective. While research in all three areas was influenced by changes in technologies, research techniques, and the contexts of use, these enduring questions remain fundamental to our understanding of how to develop, reason with, and interact with IS. In synthesizing common themes across the three streams, we draw out four cognitive qualities of information technology: interactivity, fit, cooperativity, and affordances. Together these cognitive qualities reflect IT’s ability to influence cognitive processes and ultimately task performance. Extrapolating from our historical analysis and looking at the operation of these cognitive qualities in concert, we envisage a bright future for cognitive research in IS: a future in which the study of cognition in IS extends beyond the individual to consider cognition distributed across teams, communities and systems, and a future involving the study of rich and dynamic social and organizational contexts in which the interplay between cognition, emotion, and attitudes provides a deeper explanation of behavior with IS