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Understanding the relationship between Kano model’s customer satisfaction scores and self-stated requirements importance

Overview of attention for article published in SpringerPlus, February 2016
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  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

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157 Mendeley
Title
Understanding the relationship between Kano model’s customer satisfaction scores and self-stated requirements importance
Published in
SpringerPlus, February 2016
DOI 10.1186/s40064-016-1860-y
Pubmed ID
Authors

Emmanuel O. C. Mkpojiogu, Nor Laily Hashim

Abstract

Customer satisfaction is the result of product quality and viability. The place of the perceived satisfaction of users/customers for a software product cannot be neglected especially in today competitive market environment as it drives the loyalty of customers and promotes high profitability and return on investment. Therefore understanding the importance of requirements as it is associated with the satisfaction of users/customers when their requirements are met is worth the pain considering. It is necessary to know the relationship between customer satisfactions when their requirements are met (or their dissatisfaction when their requirements are unmet) and the importance of such requirement. So many works have been carried out on customer satisfaction in connection with the importance of requirements but the relationship between customer satisfaction scores (coefficients) of the Kano model and users/customers self-stated requirements importance have not been sufficiently explored. In this study, an attempt is made to unravel the underlying relationship existing between Kano model's customer satisfaction indexes and users/customers self reported requirements importance. The results of the study indicate some interesting associations between these considered variables. These bivariate associations reveal that customer satisfaction index (SI), and average satisfaction coefficient (ASC) and customer dissatisfaction index (DI) and average satisfaction coefficient (ASC) are highly correlated (r = 96 %) and thus ASC can be used in place of either SI or DI in representing customer satisfaction scores. Also, these Kano model's customer satisfaction variables (SI, DI, and ASC) are each associated with self-stated requirements importance (IMP). Further analysis indicates that the value customers or users place on requirements that are met or on features that are incorporated into a product influences the level of satisfaction such customers derive from the product. The worth of a product feature is indicated by the perceived satisfaction customers get from the inclusion of such feature in the product design and development. The satisfaction users/customers derive when a requirement is fulfilled or when a feature is placed in the product (SI or ASC) is strongly influenced by the value the users/customers place on such requirements/features when met (IMP). However, the dissatisfaction users/customers received when a requirement is not met or when a feature is not incorporated into the product (DI), even though related to self-stated requirements importance (IMP), does not have a strong effect on the importance/worth (IMP) of that given requirement/feature as perceived by the users or customers. Therefore, since customer satisfaction is proportionally related to the perceived requirements importance (worth), it is then necessary to give adequate attention to user/customer satisfying requirements (features) from elicitation to design and to the final implementation of the design. Incorporating user or customer satisfying requirements in product design is of great worth or value to the future users or customers of the product.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 157 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 <1%
Unknown 156 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 25 16%
Student > Ph. D. Student 18 11%
Student > Bachelor 12 8%
Student > Doctoral Student 8 5%
Lecturer 7 4%
Other 21 13%
Unknown 66 42%
Readers by discipline Count As %
Business, Management and Accounting 23 15%
Engineering 21 13%
Computer Science 17 11%
Social Sciences 5 3%
Economics, Econometrics and Finance 4 3%
Other 22 14%
Unknown 65 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 March 2017.
All research outputs
#14,336,352
of 22,958,253 outputs
Outputs from SpringerPlus
#780
of 1,853 outputs
Outputs of similar age
#157,029
of 297,927 outputs
Outputs of similar age from SpringerPlus
#63
of 161 outputs
Altmetric has tracked 22,958,253 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,853 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has gotten more attention than average, scoring higher than 55% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 297,927 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 161 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.