Verifying dynamic Kano’s model to support new product/service development

Yuri Borgianni


Purpose: Although firms try to shorten time-to-market, the duration of product development projects might anyway jeopardize the assumptions made at the beginning of the design process. This includes the definition of product attributes for ensuring customer satisfaction, thus forecasting techniques could be worthwhile. Within Kano’s method, trajectories of quality attributes have been identified and they can be potentially useful to the scope, but they have not been carefully verified.

Design/methodology/approach: The paper takes on the above verification challenge by exploring studies of customer satisfaction conducted by means of Kano’s model regarding manifold industrial fields. The paper focuses on changes in the relevance of customer requirements reported in different contributions and analyses data statistically.

Findings: The dynamic trajectories outlined in Kano’s model are partially confirmed and they are valuable in the mid-term to predict changes in customer preferences. The use of quantitative indicators portraying the extent of customer satisfaction and dissatisfaction leads to more reliable predictions.

Research limitations/implications: In order to use as many data as possible, information has been used from different industrial fields, which can exhibit different paces in changes of customer preferences.

Practical implications: The results benefit firms willing to have a clearer picture of customer main drivers for customer satisfaction at the time of market launch, although customer surveys are conducted at the beginning of product development projects.

Originality/value: The paper puts into question previous assumptions about modifications of customer preferences, which, however are just empirically supported and assesses how these can be exploited in a reliable way.


Kano's theory, customer requirements, customer satisfaction, product design, dynamic preferences, forecasting

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This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2018

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience