Statistical formats to optimize evidence-based decision making: A behavioral approach

Arribas, Iván; Comeig, Irene; Urbano, Amparo; Vila, José

doi:10.1016/j.jbusres.2013.11.046

Cited by 4 publications

(2 citation statements)

References 17 publications

(12 reference statements)

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“…Correspondingly, if evidence is not analyzed at a frequent and efficient way, employees and managers may be drifted towards incorrect decisions. Moreover, evidence in specific forms such as statistical formats, has a significant impact on improving EBDM in terms of time required and accuracy of decisions (Arribas et al 2014) but if the results of evidence analysis are not fully incorporated into decision making, it may lead to a severe loss of financial and organizational resources (Julnes and Holzer 2001). Finally, Rousseau (2018) further categorizes EBDM into three major decision processes, the 'routine decisions' for clear cause-effect understanding, the 'non-routine decisions' for complicated decisions for which their information is existent but not currently available to the decision maker and the 'truly novel decisions' which require critical but currently non-existent information.…”

Section: Introductionmentioning

confidence: 99%

Heuristics and Evidences Decision (HeED) Making: a Case Study in a Systemic Model for Transforming Decision Making from Heuristics-Based to Evidenced-Based

et al. 2020

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Studies refer to Heuristics and Evidences Decision Making approaches in a comparative manner, however it is identified that these two approaches are inseparable and are applied in parallel. The objective of this paper is to provide a qualitative analysis of a systems thinking framework that defines a transition path from either a heuristic dominated or evidence-based dominated decision making approach to a balanced one. The aims are to demonstrate the stages of change and prepare managers and executives for the resistance that will be evident during the transition. We do not claim that this is the only path of change, however it provides a structured model that can be repeated under similar context. We use abductive reasoning in order to make logical inferences and construct the framework's theory based on a case study company, and then system dynamics that help us proceed to the modeling approach of this framework. The holistic modeling approach reveals the need to base decision making in both evidence and heuristics. Furthermore, it demonstrates actions to manage resistance and to make this system a self-regulated and continuous decisionmaking tool.

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Section: Introductionmentioning

confidence: 99%

Heuristics and Evidences Decision (HeED) Making: a Case Study in a Systemic Model for Transforming Decision Making from Heuristics-Based to Evidenced-Based

et al. 2020

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“…The use of graphical representation and visualization of information have had increasing significance in the recent years with our new information environments where variety and complexity of information are increasing. Former studies on graphic representations used for decision-making focused on the bar chart representations (Jarvenpaa, 1989 , 1990 ; Arribas et al, 2014 ). In particular, Jarvenpaa ( 1989 , 1990 ) reported the efficacy of graphic representations in multi-attribute decision-making.…”

Section: Introductionmentioning

confidence: 99%

Qualitatively Coherent Representation Makes Decision-Making Easier with Binary-Colored Multi-Attribute Tables: An Eye-Tracking Study

et al. 2017

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We aimed to identify the ways in which coloring cells affected decision-making in the context of binary-colored multi-attribute tables, using eye movement data. In our black-white attribute tables, the value of attributes was limited to two (with a certain threshold for each attribute) and each cell of the table was colored either black or white on the white background. We compared the two natural ways of systematic color assignment: “quantitatively coherent” ways and “qualitatively coherent” ways (namely, the ways in which the black-white distinction represented the quantitative amount distinction, and the ways in which the black-white distinction represented the quality distinction). The former consists of the following two types: (Type 1) “larger is black,” where the larger value-level was represented by black, and “smaller is white,” and (Type 2) “smaller is black.” The latter consisted of the following two types: (Type 3) “better is black,” and (Type 4) “worse is black.” We obtained the following two findings. [Result 1] The qualitatively coherent black-white tables (Types 3 and 4) made decision-making easier than the quantitatively coherent ones (Types 1 and 2). [Result 2] Among the two qualitatively coherent types, the “black is better” tables (Type 3) made decision making easier; in fact, the participants focused on the more important (black) cells in the case of “black is better” tables (Type 3) while they did not focus enough on the more important (white) ones in the case of the “white is better” tables (Type 4). We also examined some measures of eye movement patterns and showed that these measures supported our hypotheses. The data showed differences in the eye movement patterns between the first and second halves of each trial, which indicated the phased or combined decision strategies taken by the participants.

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Extracting business information from graphs: An eye tracking experiment

Vila

Gómez

2016

Journal of Business Research

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Statistical formats to optimize evidence-based decision making: A behavioral approach

Cited by 4 publications

References 17 publications

Heuristics and Evidences Decision (HeED) Making: a Case Study in a Systemic Model for Transforming Decision Making from Heuristics-Based to Evidenced-Based

Heuristics and Evidences Decision (HeED) Making: a Case Study in a Systemic Model for Transforming Decision Making from Heuristics-Based to Evidenced-Based

Qualitatively Coherent Representation Makes Decision-Making Easier with Binary-Colored Multi-Attribute Tables: An Eye-Tracking Study

Extracting business information from graphs: An eye tracking experiment

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