Data driven design optimisation: an empirical study of demand discovery combining theory of planned behaviour and Bayesian networks.
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| Title: | Data driven design optimisation: an empirical study of demand discovery combining theory of planned behaviour and Bayesian networks. |
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| Authors: | Liu, Yitian1 (AUTHOR) 245780922@qq.com, Hu, Kang1 (AUTHOR), Zhou, Ruifeng2 (AUTHOR), Ai, Xianfeng1 (AUTHOR), Chen, Yunqing3 (AUTHOR) |
| Source: | International Journal of Production Research. Jul2024, Vol. 62 Issue 13, p4696-4716. 21p. |
| Subjects: | Planned behavior theory, Bayesian analysis, Control (Psychology), Empirical research, Behavioral assessment |
| Abstract: | Many theoretical methods have been applied to research user behaviour and requirements. However, the uncertainty associated with customer characteristics often biases the conclusions drawn from customer research and affects the effectiveness of product design. In this paper, Bayesian networks (BN) are introduced into the research on customer behaviour analysis based upon theory of planned behaviour (TPB), and an analysis model driven by customer research data is established from the perspective of user behaviour intention to guide design optimisation. Combining the User background Factor with the TPB Factor, the model analyses the uncertainty of the association between the two, and corrects the errors in the designer's prior knowledge through structural learning. By a case study the paper finds that the evaluations that enhance customers' subjective norms and perceived behavioural control lead to a greater probability of purchase or use. In addition, customers with specific characteristics are more inclined to generate behaviour intention. The paper finally provides a design optimisation plan based upon the result of the research and discusses about the advantages of the research approaches and the directions of future researches. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 177117501 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Data driven design optimisation: an empirical study of demand discovery combining theory of planned behaviour and Bayesian networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Yitian%22">Liu, Yitian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 245780922@qq.com</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Kang%22">Hu, Kang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Ruifeng%22">Zhou, Ruifeng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ai%2C+Xianfeng%22">Ai, Xianfeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Yunqing%22">Chen, Yunqing</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Jul2024, Vol. 62 Issue 13, p4696-4716. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Planned+behavior+theory%22">Planned behavior theory</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Control+%28Psychology%29%22">Control (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+assessment%22">Behavioral assessment</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Many theoretical methods have been applied to research user behaviour and requirements. However, the uncertainty associated with customer characteristics often biases the conclusions drawn from customer research and affects the effectiveness of product design. In this paper, Bayesian networks (BN) are introduced into the research on customer behaviour analysis based upon theory of planned behaviour (TPB), and an analysis model driven by customer research data is established from the perspective of user behaviour intention to guide design optimisation. Combining the User background Factor with the TPB Factor, the model analyses the uncertainty of the association between the two, and corrects the errors in the designer's prior knowledge through structural learning. By a case study the paper finds that the evaluations that enhance customers' subjective norms and perceived behavioural control lead to a greater probability of purchase or use. In addition, customers with specific characteristics are more inclined to generate behaviour intention. The paper finally provides a design optimisation plan based upon the result of the research and discusses about the advantages of the research approaches and the directions of future researches. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=177117501 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2023.2271093 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 4696 Subjects: – SubjectFull: Planned behavior theory Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Control (Psychology) Type: general – SubjectFull: Empirical research Type: general – SubjectFull: Behavioral assessment Type: general Titles: – TitleFull: Data driven design optimisation: an empirical study of demand discovery combining theory of planned behaviour and Bayesian networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Yitian – PersonEntity: Name: NameFull: Hu, Kang – PersonEntity: Name: NameFull: Zhou, Ruifeng – PersonEntity: Name: NameFull: Ai, Xianfeng – PersonEntity: Name: NameFull: Chen, Yunqing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 62 – Type: issue Value: 13 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
