Understanding big consumer opinion data for market-driven product design.
Saved in:
| Title: | Understanding big consumer opinion data for market-driven product design. |
|---|---|
| Authors: | Jin, Jian1,2 (AUTHOR) jinjian.jay@bnu.edu.cn, Liu, Ying3 (AUTHOR), Ji, Ping4 (AUTHOR), Liu, Hongguang4 (AUTHOR) |
| Source: | International Journal of Production Research. May2016, Vol. 54 Issue 10, p3019-3041. 23p. 1 Diagram, 9 Charts, 7 Graphs. |
| Subjects: | Product design, Consumer attitudes, Big data, Product management research, Business requirements analysis, Amazon.com Inc. |
| Abstract: | Big consumer data provide new opportunities for business administrators to explore the value to fulfil customer requirements (CRs). Generally, they are presented as purchase records, online behaviour, etc. However, distinctive characteristics of big data, Volume, Variety, Velocity and Value or ‘4Vs’, lead to many conventional methods for customer understanding potentially fail to handle such data. A visible research gap with practical significance is to develop a framework to deal with big consumer data for CRs understanding. Accordingly, a research study is conducted to exploit the value of these data in the perspective of product designers. It starts with the identification of product features and sentiment polarities from big consumer opinion data. A Kalman filter method is then employed to forecast the trends of CRs and a Bayesian method is proposed to compare products. The objective is to help designers to understand the changes of CRs and their competitive advantages. Finally, using opinion data in Amazon.com, a case study is presented to illustrate how the proposed techniques are applied. This research is argued to incorporate an interdisciplinary collaboration between computer science and engineering design. It aims to facilitate designers by exploiting valuable information from big consumer data for market-driven product design. [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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 114435033 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Understanding big consumer opinion data for market-driven product design. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jin%2C+Jian%22">Jin, Jian</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jinjian.jay@bnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Ying%22">Liu, Ying</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ji%2C+Ping%22">Ji, Ping</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Hongguang%22">Liu, Hongguang</searchLink><relatesTo>4</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>. May2016, Vol. 54 Issue 10, p3019-3041. 23p. 1 Diagram, 9 Charts, 7 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Product+design%22">Product design</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+attitudes%22">Consumer attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Product+management+research%22">Product management research</searchLink><br /><searchLink fieldCode="DE" term="%22Business+requirements+analysis%22">Business requirements analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Amazon%2Ecom+Inc%2E%22">Amazon.com Inc.</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Big consumer data provide new opportunities for business administrators to explore the value to fulfil customer requirements (CRs). Generally, they are presented as purchase records, online behaviour, etc. However, distinctive characteristics of big data, Volume, Variety, Velocity and Value or ‘4Vs’, lead to many conventional methods for customer understanding potentially fail to handle such data. A visible research gap with practical significance is to develop a framework to deal with big consumer data for CRs understanding. Accordingly, a research study is conducted to exploit the value of these data in the perspective of product designers. It starts with the identification of product features and sentiment polarities from big consumer opinion data. A Kalman filter method is then employed to forecast the trends of CRs and a Bayesian method is proposed to compare products. The objective is to help designers to understand the changes of CRs and their competitive advantages. Finally, using opinion data in Amazon.com, a case study is presented to illustrate how the proposed techniques are applied. This research is argued to incorporate an interdisciplinary collaboration between computer science and engineering design. It aims to facilitate designers by exploiting valuable information from big consumer data for market-driven product design. [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=114435033 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2016.1154208 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 3019 Subjects: – SubjectFull: Product design Type: general – SubjectFull: Consumer attitudes Type: general – SubjectFull: Big data Type: general – SubjectFull: Product management research Type: general – SubjectFull: Business requirements analysis Type: general – SubjectFull: Amazon.com Inc. Type: general Titles: – TitleFull: Understanding big consumer opinion data for market-driven product design. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jin, Jian – PersonEntity: Name: NameFull: Liu, Ying – PersonEntity: Name: NameFull: Ji, Ping – PersonEntity: Name: NameFull: Liu, Hongguang IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 54 – Type: issue Value: 10 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
