Paper Type

ERF

Paper Number

1676

Description

Nowadays, many organizations invest in data visualization tools as decision support systems for decision-makers. We can also see a rise in the user base and revenue of popular visualization providers. A recent report states that the data visualization market will increase to USD 19.20 billion by the year 2027 from USD 8.85 billion in 2020. However, there is also a growing concern about whether using visualization as a decision tool can lower decision-making performance where managers are prone to cognitive biases. Too much information without enough meaning in visualization makes it difficult to organize data and make informed decisions. Because of these contrasting beliefs, it is important for us to understand whether different visualization styles lead to different decision quality. Research advances of visual perception and cognition, design pattern, and semiotics help us to define different visualization styles. Visualization styles are different representations of data such as time series, comparisons, relationships, ratios, deviations, multivariate, and spatial with different visualization elements such as labels, colors, annotations, and interactivity. Different visualization styles fit different types of information and task. Psychology and behavioral economics literature contribute to the idea of cognitive bias, which is a systematic error that clouds a decision maker's thinking and decision-making. Managerial cognition deals with the decision maker’s mental model and how that mental model interacts with the decision-making outcome, such as decision accuracy. There is still an important research avenue that explains how different visualization styles (such as dashboard and data label) can impact managerial cognition through the accumulation of cognitive bias. Thus, the objective of this research is to identify how different visualization styles impact managerial cognition. We integrate information overload, working memory capacity, and cognitive bias theories to formulate the research's causal model. A 2x2 randomized experiment will be conducted to test the hypotheses. The proposed research has significant theoretical and practical contributions.

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Aug 9th, 12:00 AM

Impact of the Interplay Between Visualization Styles and Cognitive Bias on Managerial Cognition

Nowadays, many organizations invest in data visualization tools as decision support systems for decision-makers. We can also see a rise in the user base and revenue of popular visualization providers. A recent report states that the data visualization market will increase to USD 19.20 billion by the year 2027 from USD 8.85 billion in 2020. However, there is also a growing concern about whether using visualization as a decision tool can lower decision-making performance where managers are prone to cognitive biases. Too much information without enough meaning in visualization makes it difficult to organize data and make informed decisions. Because of these contrasting beliefs, it is important for us to understand whether different visualization styles lead to different decision quality. Research advances of visual perception and cognition, design pattern, and semiotics help us to define different visualization styles. Visualization styles are different representations of data such as time series, comparisons, relationships, ratios, deviations, multivariate, and spatial with different visualization elements such as labels, colors, annotations, and interactivity. Different visualization styles fit different types of information and task. Psychology and behavioral economics literature contribute to the idea of cognitive bias, which is a systematic error that clouds a decision maker's thinking and decision-making. Managerial cognition deals with the decision maker’s mental model and how that mental model interacts with the decision-making outcome, such as decision accuracy. There is still an important research avenue that explains how different visualization styles (such as dashboard and data label) can impact managerial cognition through the accumulation of cognitive bias. Thus, the objective of this research is to identify how different visualization styles impact managerial cognition. We integrate information overload, working memory capacity, and cognitive bias theories to formulate the research's causal model. A 2x2 randomized experiment will be conducted to test the hypotheses. The proposed research has significant theoretical and practical contributions.

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