Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Aspect-based Kano categorization
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.ORCID iD: 0000-0001-7918-003x
Hydcon KB.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.ORCID iD: 0000-0002-2342-1647
2019 (English)In: International Journal of Information Management, ISSN 0268-4012, E-ISSN 1873-4707, Vol. 46, p. 163-172Article in journal (Refereed) Published
Abstract [en]

Customers commonly share opinions and experiences about products via the internet by means of social media and networking sites. The generated textual data is often analysed by means of Sentiment Analysis (SA) as means to assess customer opinions on product features more efficiently than through surveys. To enable a more objective product target setting, the impact of product feature performance changes on customer satisfaction is essential. Kano et al. (1984) presented a survey-based model to classify product features based on their impact on customer satisfaction to aid designers in their product target setting. Approaches extending the Kano model rely on customer surveys as input data. In addition, existing studies classifying extracted product features from textual data (e.g. product reviews) rarely provide a clear separation in terms of Kano categories. Thus, the impact of identified product features on customer satisfaction remains unknown to product designers. This paper presents a methodology for autonomously classifying extracted aspects from textual data into Kano categories. For verification purposes, two examples using coffee machine and smartphone user reviews are presented. Results indicate that the proposed methodology efficiently provides product designers with insightful customer information through the proposed aspect categorization.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 46, p. 163-172
Keywords [en]
Aspect, Categorization, Kano, Sentiment Analysis (SA), Target setting
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
URN: urn:nbn:se:ltu:diva-71652DOI: 10.1016/j.ijinfomgt.2018.11.004ISI: 000461899300013Scopus ID: 2-s2.0-85058698469OAI: oai:DiVA.org:ltu-71652DiVA, id: diva2:1266572
Note

Validerad;2019;Nivå 2;2019-01-07 (svasva)

Available from: 2018-11-28 Created: 2018-11-28 Last updated: 2019-04-05Bibliographically approved
In thesis
1. Customer-focused data-driven target setting
Open this publication in new window or tab >>Customer-focused data-driven target setting
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

To develop products through a customer-centric strategy, early stages of product development such as target setting play an important role. In the target setting stage Customer Needs (CN) are gathered and translated into Design Requirements (DR) in order to subsequently set product targets that fit cost constraints and at the same time result in high Customer Satisfaction (CS). Continuous advances in information technology create new opportunities for companies to gather information about the customer, for example, for marketing purposes, or to assess customer reactions after the launch of new products. In addition, products are becoming complex systems that are successively equipped with more software and sensors offering opportunities for collecting data on how they are used. Knowing how customers use the product enhances a company’s ability to segment customers and customize products.

Despite customer information availability from different sources (sensors, social media, etc.), surveys and focus groups are considered today as the main data source to derive the set of CN statements during target setting. Further, the team’s interpretation of CNs, which are often described in abstract language, must be translated into DRs, which are described in a more technical language. Hence, the translation process of CNs into DRs is said to be subjective. To set product targets, CS sensitivity to changes in DR levels is also considered. Surveys and benchmarking data containing customer perceptions on competitors’ performance are often the main customer data input into the process. While insightful information may be obtained, surveys are costly and time consuming and only encompass a small part of the market population.

The research presented in this doctoral thesis explores how customer information obtained from sensors (e.g. product usage data) and text data (e.g. from websites, open-survey questionnaires) can be factored in the target setting process before concept generation to enhance customer focus without compromising product development time. The aim is to increase designers’ awareness of target population and in turn increase the quality of the design decisions on product targets. For this purpose, a customer-focused data-driven target setting methodology is proposed. The presented methodology changes the actual target setting methodology by means of indicators and autonomous activities on those parts of the process where marketing or design decisions are needed. The proposed methodology gives the incentive for a more integrated product development where marketing and designers need to work closely. This further allows a sustainable customer information gathering strategy that strives for missing customer information that is required for setting product targets. The indicators act as feedback channels for continuous product improvement. The use of such indicators and autonomous activities highlights the potential of a more efficient, less subjective and higher-quality target setting process.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2019
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
urn:nbn:se:ltu:diva-72655 (URN)978-91-7790-304-8 (ISBN)978-91-7790-305-5 (ISBN)
Public defence
2019-04-08, E632, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2019-03-19Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Marti Bigorra, AnnaKarlberg, Magnus

Search in DiVA

By author/editor
Marti Bigorra, AnnaKarlberg, Magnus
By organisation
Product and Production Development
In the same journal
International Journal of Information Management
Other Mechanical Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 262 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf