Multiattribute Data Presentation and Human Judgment: A Cognitive Fit Perspective*

Abstract

We assessed the ability of the cognitive fit theory to explain the performance of certain display formats on multiattribute judgment tasks. This theory suggests that for most effective and efficient problem solving to occur, the problem representation and any tools or aids employed should all support the strategies (methods or processes) required to perfom that task. The theory was tested by assessing performance with schematic faces, graphs, and tables on a bankruptcy prediction task. Bankruptcy pndiction involves integrating a large amount of data (a number of financial indicators over a number of years), as well as refening to ranges and/or levels of financial indicators. Schematic faces provide a cognitive fit with such tasks since the information in a face can be processed holistically; however, they do not permit decision makers to refer to the underlying data. Graphs facilitate a different integrating process; further, they preserve characteristics of the underlying data. Tables, on the other hand, do not aid the decision maker in integrating information; they provide only the underlying data values. It was hypothesized that graphs would provide the best cognitive fit for the bankruptcy prediction task since they permit processing both integrated and discrete data. Participants made judgments with two of the three display formats, at two levels of information load, in a fractional factorial design. The information load manipulation was designed to provide meaningful and meaningful plus redundant information to the decision maker in a test of information load "per se." The research findings provided substantial support for the theory of cognitive fit. The findings also have implications for the study of information load.Wwne 25 Number 516 Printed in the U.S.A.

Complexity Factors and Intuition‐based Methods for Facility Network Design

“…We have discussed objective problem complexity as a function of the number of decision paths (i,e,, feasible solutions) available to the decision maker (Campbell, 1988), and as a function of the number of decision acts (Le., decision variables) (Umanath & Vessey, 1994;Wood, 1986). It appears that the latter conceptualization more accurately describes the effect of problem size on complexity in the given context.…”

Effects of Animations in Learning—A Cognitive Fit Perspective1

“…The theory of cognitive fit originated in a decision‐making context, but it has been applied to a varied set of contexts such as software maintenance (Shaft & Vessey, 2006), consumers' online shopping behavior (Hong et al, 2004), virtual reality on consumer learning (Su & Lee, 2005), multiattribute data presentation and human judgment (Umanath & Vessey, 1994), information acquisition tasks (Vessey & Galletta, 1991), and in the use of geographic information systems (Dennis & Carte, 1998). In these applications, the cognitive fit theory has been able to provide insight on the role and proper design of external representations.…”

“…The caveat, however, that we obtain from the cognitive fit theory is that, if the IT‐based external representations provide the stimulus and improve student attention but do not match the learning content (hereafter referred to as learning task ), it may only serve to increase the learners' cognitive load and not improve their understanding, that is, not enhance learning outcomes (Vessey, 1991; 2006). Although the cognitive fit theory was introduced in the domain of problem solving, the principles could be extended to learning, because, as in problem‐solving tasks, learning is an information‐acquisition task (Umanath & Vessey, 1994; Vessey, 2006; Vessey & Galletta, 1991). Hence, the learner tries to match the external task representations (problem representation), such as text, graphs, and animations, to the learning task (problem‐solving task).…”

“…But in learning tasks where the lecture content consists solely of descriptive knowledge, animations as external representations will no longer provide a superior cognitive fit compared with a text‐based representation. Descriptive knowledge tasks could be termed symbolic tasks wherein the learner has to acquire discrete information, not relationships between concepts (Umanath & Vessey, 1994; Umanath, Scamell, & Das, 1990). The movement and dynamic affordance of animations is not necessary in such symbolic learning tasks and may only cause higher cognitive load but not better learning outcomes when compared with other representations.…”

Toward a New Business Reporting Model

“…As an effective analysis tool of business intelligence (BI) systems, dashboards not only provide multivariate, timely, and often graphical displays of KPIs, but also have evolved to be interactive decision support systems built on companies' data warehouses such as ERPs (Yigitbasioglu and Velcu 2012). Because graphs are beneficial to financial predictions and mitigate the information overload problem (Hard and Vanecek 1991;Umanath and Vessey 1994), dashboards should help lesssophisticated investors understand financial information, given their limited data analysis capacity.…”