Experts’ Evaluation: Datasets of Rating Questionnaires for the HEUROBOX Heuristics to Evaluate Industrial Collaborative Robotic Environments

Apraiz, Ainhoa; Mulet Alberola, Jose Antonio; Lasa, Ganix; Mazmela Etxabe, Maitane; Nguyen Thi Ngoc, Hien

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Experts’ Evaluation: Datasets of Rating Questionnaires for the HEUROBOX Heuristics to Evaluate Industrial Collaborative Robotic Environments (833.0Kb)

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Título

Experts’ Evaluation: Datasets of Rating Questionnaires for the HEUROBOX Heuristics to Evaluate Industrial Collaborative Robotic Environments

Autor-a

Apraiz, Ainhoa

Mulet Alberola, Jose Antonio

Lasa, Ganix

Mazmela Etxabe, Maitane

Nguyen Thi Ngoc, Hien

Grupo de investigación

Innovación en Diseño Industrial

Versión

Version publicada

Tipo de documento

Conjunto de datos

Idioma

Inglés

Fecha de publicación

2023

Derechos

Acceso

Acceso abierto

URI

https://hdl.handle.net/20.500.11984/14500

Versión de la editorial

https://doi.org/10.17632/t2nwpwwsmg.1

Identificador

https://data.mendeley.com/datasets/t2nwpwwsmg/1

Palabras clave

Applied Mathematics
Decision Science
Psychometrics

Materia (Tesauro UNESCO)

Diseño industrial

Descripción

This study presents a dataset derived from expert evaluation aimed at assessing two aspects: (1) the usability of a newly developed set of heuristics (HEUROBOX), and (2) the relative importance of heuristic categories in evaluating collaborative robotic environments for industrial applications. The experts were carefully selected based on their expertise in various relevant fields, including collaborative robotics, human factors, biomechanics, and manufacturing. Their evaluations were obtained through the System Usability Scale (SUS) questionnaire and category-rating questionnaires, which were employed in conjunction with the Analytical Hierarchy Process (AHP) methodology. The resulting dataset was analyzed using AHP algorithms implemented in the R programming language. The transparency of both the data and the code allows design practitioners and researchers to utilize them for replication and further analysis, thereby adhering to the principles of reproducible research in the context of multiple-criteria decision analysis.