Enhancing the analysis of online product reviews to support product improvement: integrating text mining with quality function deployment

Asadabadi, MR; Saberi, M; Sadghiani, NS; Zwikael, O; Chang, E

Enhancing the analysis of online product reviews to support product improvement: integrating text mining with quality function deployment

Asadabadi, MR Saberi, M

Sadghiani, NS Zwikael, O Chang, E

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Publisher:: EMERALD GROUP PUBLISHING LTD
Publication Type:: Journal Article
Citation:: Journal of Enterprise Information Management, 2023, 36, (1), pp. 275-302
Issue Date:: 2023-01-27

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Field	Value	Language
dc.contributor.author	Asadabadi, MR
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Sadghiani, NS
dc.contributor.author	Zwikael, O
dc.contributor.author	Chang, E
dc.date.accessioned	2023-04-03T13:07:35Z
dc.date.available	2023-04-03T13:07:35Z
dc.date.issued	2023-01-27
dc.identifier.citation	Journal of Enterprise Information Management, 2023, 36, (1), pp. 275-302
dc.identifier.issn	1741-0398
dc.identifier.issn	1758-7409
dc.identifier.uri	http://hdl.handle.net/10453/169082
dc.description.abstract	Purpose: The purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews. Design/methodology/approach: This paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process. Findings: The paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes. Originality/value: Online product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.
dc.language	English
dc.publisher	EMERALD GROUP PUBLISHING LTD
dc.relation.ispartof	Journal of Enterprise Information Management
dc.relation.isbasedon	10.1108/JEIM-03-2021-0143
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0806 Information Systems, 0807 Library and Information Studies, 1503 Business and Management
dc.subject.classification	Business & Management
dc.title	Enhancing the analysis of online product reviews to support product improvement: integrating text mining with quality function deployment
dc.type	Journal Article
utslib.citation.volume	36
utslib.for	0806 Information Systems
utslib.for	0807 Library and Information Studies
utslib.for	1503 Business and Management
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-03T13:07:33Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	36
utslib.citation.issue	1

Abstract:

Purpose: The purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews. Design/methodology/approach: This paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process. Findings: The paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes. Originality/value: Online product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.

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http://hdl.handle.net/10453/169082