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A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory
Along with the rapid application of new information technologies, the data-driven era is coming, and online consumption platforms are booming. However, massive user data have not been fully developed for design value, and the application of data-driven methods of requirement engineering needs to be...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8784405/ https://www.ncbi.nlm.nih.gov/pubmed/35082724 http://dx.doi.org/10.3389/fpsyg.2021.783116 |
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author | Cao, Enguo Jiang, Jinzhi Duan, Yanjun Peng, Hui |
author_facet | Cao, Enguo Jiang, Jinzhi Duan, Yanjun Peng, Hui |
author_sort | Cao, Enguo |
collection | PubMed |
description | Along with the rapid application of new information technologies, the data-driven era is coming, and online consumption platforms are booming. However, massive user data have not been fully developed for design value, and the application of data-driven methods of requirement engineering needs to be further expanded. This study proposes a data-driven expectation prediction framework based on social exchange theory, which analyzes user expectations in the consumption process, and predicts improvement plans to assist designers make better design improvement. According to the classification and concept definition of social exchange resources, consumption exchange elements were divided into seven categories: money, commodity, services, information, value, emotion, and status, and based on these categories, two data-driven methods, namely, word frequency statistics and scale surveys, were combined to analyze user-generated data. Then, a mathematical expectation formula was used to expand user expectation prediction. Moreover, by calculating mathematical expectation, explicit and implicit expectations are distinguished to derive a reliable design improvement plan. To validate its feasibility and advantages, an illustrative example of CoCo Fresh Tea & Juice service system improvement design is further adopted. As an exploratory study, it is hoped that this study provides useful insights into the data mining process of consumption comment. |
format | Online Article Text |
id | pubmed-8784405 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87844052022-01-25 A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory Cao, Enguo Jiang, Jinzhi Duan, Yanjun Peng, Hui Front Psychol Psychology Along with the rapid application of new information technologies, the data-driven era is coming, and online consumption platforms are booming. However, massive user data have not been fully developed for design value, and the application of data-driven methods of requirement engineering needs to be further expanded. This study proposes a data-driven expectation prediction framework based on social exchange theory, which analyzes user expectations in the consumption process, and predicts improvement plans to assist designers make better design improvement. According to the classification and concept definition of social exchange resources, consumption exchange elements were divided into seven categories: money, commodity, services, information, value, emotion, and status, and based on these categories, two data-driven methods, namely, word frequency statistics and scale surveys, were combined to analyze user-generated data. Then, a mathematical expectation formula was used to expand user expectation prediction. Moreover, by calculating mathematical expectation, explicit and implicit expectations are distinguished to derive a reliable design improvement plan. To validate its feasibility and advantages, an illustrative example of CoCo Fresh Tea & Juice service system improvement design is further adopted. As an exploratory study, it is hoped that this study provides useful insights into the data mining process of consumption comment. Frontiers Media S.A. 2022-01-10 /pmc/articles/PMC8784405/ /pubmed/35082724 http://dx.doi.org/10.3389/fpsyg.2021.783116 Text en Copyright © 2022 Cao, Jiang, Duan and Peng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Cao, Enguo Jiang, Jinzhi Duan, Yanjun Peng, Hui A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory |
title | A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory |
title_full | A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory |
title_fullStr | A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory |
title_full_unstemmed | A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory |
title_short | A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory |
title_sort | data-driven expectation prediction framework based on social exchange theory |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8784405/ https://www.ncbi.nlm.nih.gov/pubmed/35082724 http://dx.doi.org/10.3389/fpsyg.2021.783116 |
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