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Article

iKeyCriteria: A Qualitative and Quantitative Analysis Method to Infer Key Criteria since a Systematic Literature Review for the Computing Domain

by
Mayra Carrión-Toro
1,
Jose Aguilar
2,3,
Marco Santórum
1,
María Pérez
1,
Boris Astudillo
1,
Cindy-Pamela Lopez
1,
Marcelo Nieto
1 and
Patricia Acosta-Vargas
4,*
1
Departamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito 170525, Ecuador
2
Centro de Estudios en Microelectrónica y Sistemas Distribuidos, Universidad de Los Andes, Mérida 5101, Venezuela
3
GIDITIC, Universidad EAFIT, Medellín 050022, Colombia
4
Intelligent and Interactive Systems Laboratory, Universidad de Las Américas, Quito 170125, Ecuador
*
Author to whom correspondence should be addressed.
Data 2022, 7(6), 70; https://doi.org/10.3390/data7060070
Submission received: 25 April 2022 / Revised: 16 May 2022 / Accepted: 17 May 2022 / Published: 26 May 2022
(This article belongs to the Section Information Systems and Data Management)
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Abstract

A systematic literature review is a synthesis of the available evidence, in which a review of quantitative and qualitative aspects of primary studies is carried out, to summarize the existing information regarding a particular topic. The researchers extract key criteria from papers collected about their study area, answering research questions and conducting document analysis. Nonetheless, in some cases, these criteria are improperly justified, unknowing their true level of importance in the study subject. Hence, an additional study is necessary to explain the criteria relevance in the papers studied using qualitative and quantitative premises. The correct identification of these key criteria is a critical factor in prioritizing and achieving appropriate results in any scientific research work. In our paper, a new method to determine key criteria from a literature review is proposed, composed of three components: input-process-output. First, the inputs are a set of criteria to evaluate and a set of documents to analyze. Next, the process component examines the document set to indicate whether the criteria to be analyzed are found. The process component produces a Boolean matrix, which is the input of the mathematical logic process that will get the key criteria considered necessary and sufficient as the output component. The iKeyCriteria method has been applied in different computing domains, particularly for serious games design and virtual organizations, giving positive results in each context. Finally, we developed an online tool that provides global support to the execution of our method.
Keywords: data; information retrieval; systematic literature review; key criteria; meta-analysis data; information retrieval; systematic literature review; key criteria; meta-analysis

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MDPI and ACS Style

Carrión-Toro, M.; Aguilar, J.; Santórum, M.; Pérez, M.; Astudillo, B.; Lopez, C.-P.; Nieto, M.; Acosta-Vargas, P. iKeyCriteria: A Qualitative and Quantitative Analysis Method to Infer Key Criteria since a Systematic Literature Review for the Computing Domain. Data 2022, 7, 70. https://doi.org/10.3390/data7060070

AMA Style

Carrión-Toro M, Aguilar J, Santórum M, Pérez M, Astudillo B, Lopez C-P, Nieto M, Acosta-Vargas P. iKeyCriteria: A Qualitative and Quantitative Analysis Method to Infer Key Criteria since a Systematic Literature Review for the Computing Domain. Data. 2022; 7(6):70. https://doi.org/10.3390/data7060070

Chicago/Turabian Style

Carrión-Toro, Mayra, Jose Aguilar, Marco Santórum, María Pérez, Boris Astudillo, Cindy-Pamela Lopez, Marcelo Nieto, and Patricia Acosta-Vargas. 2022. "iKeyCriteria: A Qualitative and Quantitative Analysis Method to Infer Key Criteria since a Systematic Literature Review for the Computing Domain" Data 7, no. 6: 70. https://doi.org/10.3390/data7060070

APA Style

Carrión-Toro, M., Aguilar, J., Santórum, M., Pérez, M., Astudillo, B., Lopez, C.-P., Nieto, M., & Acosta-Vargas, P. (2022). iKeyCriteria: A Qualitative and Quantitative Analysis Method to Infer Key Criteria since a Systematic Literature Review for the Computing Domain. Data, 7(6), 70. https://doi.org/10.3390/data7060070

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