The analytical Kano-IPA model

Section 1 Background and research purpose

The traditional Kano’s model is widely used by industries and researchers, but some controversy still exists surrounding the classification of quality attributes (Lee, et al., 2011;

Lee & Chen, 2009). Furthermore, the traditional Kano’s model focuses on the qualitative descriptions of various relationship curves; thus, only limited quantitative analysis or measurement of the relationships is discussed (Wang & Ji, 2010). Although Xu, et al.

(2009) proposed the analytical Kano model to mitigate the disputes and limitations of the traditional Kano’s model, the model still has room for improvements regarding the scoring scheme which is against the basic assumption of prospect theory. Consequently, the traditional Kano’s model is not capable of precisely reflecting the influence of quality attributes. Additionally, the Kano’s model cannot directly analyze improvement effects and prioritize quality attributes, and consequently must be improved.

Importance-performance analysis (IPA) is frequently used to discuss improvement effects and prioritize quality attributes. The aforementioned literature review revealed that numerous studies have made numerous significant contributions to IPA methodology. Their research not only demonstrates the linear relationships between quality attributes and satisfaction, but also confirms that quality attributes still have nonlinear effects on customer satisfaction. However, when nonlinear effects exist, the IPA model cannot accurately analyze the importance of the quality attributes and prioritize improvements, potentially leading to inaccurate decision making.

To mitigate the scoring scheme of the analytical Kano model disagrees with the basic assumption of prospect theory, and to address the issues surrounding IPA, this study attempts to reform the analytical Kano model, and then combines the revised analytical Kano model and traditional IPA to put forth a more rational methodology. The study also discusses the nonlinear relationship between quality attributes and customer satisfaction by employing the revised analytical Kano model. Meanwhile, for increasing the precision of the Kano indices, the scoring scheme by Xu et al. (2009) which evaluates customer

satisfaction and dissatisfaction would be revised at the initial stage of this study. This study aims to establish a novel analytical Kano-IPA model to precisely identify the priority of improvements.

Section 2 Methodology

This study takes account of quantified concept by Kano indices (Xu, et al., 2009) and modifies the traditional IPA model in order to explain the asymmetrical and non-linear effects between customer satisfaction and quality attributes. Considering that the scoring scheme of the original Kano indices is against the basic assumption of the prospect theory, this study revises the original Kano indices before using it to adjust the traditional IPA model.

First of all, by comparing Figure 3 and Figure 4, the similarity of the 2 two-dimensional diagrams is found between Berger, et al. (1993) and Xu, et al. (2009). The most significant difference between the 2 two-dimensional diagrams is the indicators of vertical axis and the horizontal axis. In the two-dimensional diagram of Xu, et al. (2009), where the horizontal axis indicates the dissatisfaction score and the vertical axis stands for the satisfaction score. In the two-dimensional diagram of Berger, et al. (1993), where the horizontal axis indicates the DDI and the vertical axis stands for the SII. As the aforementioned literature review, as specified by SII and DDI indexes, we can find which attributes can influence customer satisfaction. Meanwhile, the customer satisfaction index (SII and DDI) that were developed by Kuo (2004) are used to provide enterprises with valuable information for making decisions regarding the quality of products and services.

Therefore, this study adopts SII and DDI instead of the indicators of the vertical axis and the horizontal axis in the two-dimensional diagram of Xu, et al. (2009), which is shown in Figure 14, to revise the original Kano indices.

Second, the revised Kano indices would be used to adjust the importance and performance of quality attributes for the purpose of improving the analysis of the traditional IPA. A vectorrv, calculated through the results of dysfunctional and functional questionnaire, represents the degree of customer’s reaction toward the fulfillment of quality attributes, being used to modify the importance of IPA. α, an index to classify Kano category of quality attributes, takes account of the asymmetric relationships between customer satisfaction and quality attributes and the life cycle of quality attributes, being

used to modify the performance of IPA.

The using steps of the analytical Kano-IPA model are as follows:

Step1 Design three kinds of questionnaires:

1) The importance of quality attributes: the self-stated importance score falls within a range of 1-9, which is from “Not at all important” to “Extremely important”.

2) The performance of quality attributes: the service performance is evaluated by a 10-point scales and each question is closed.

3) The categorization of attributes according to Kano’s model: the method suggested by Kano, et al., (1984) is used to design the questionnaire.

Step2 Collect data through questionnaires and define the importance of the quality attribute of element i asI_iand the performance of the quality attribute of element i asP_i. In addition, in the Kano survey，for each respondent j , the evaluation of quality attribute of element i is represented as eij =

(

xij,yij

)

, where x_ij is one of five alternative answers (Like; Must-be; Neutral; Live-with; and Dislike) given to a quality attribute for the dysfunctional form question; andy_ijis one of five alternative answers given to a quality attribute for the functional form question.

Step3 Calculate the magnitude of rv_i. From the customer’s perspective, the characteristics of the quality attribute of element i can be represented as a vector rv_i, the magnitude

Attractive

One-dimensional

Must-be Indifferent

α γ

0 1

1

Figure14 A revised analytical Kano model DDI SII

of rv_iis rv_i ; the formula is as follows:

i

i DDI

X = , Yi = SIIi (31) whereXi,Yi is the customer satisfaction index of element i, which are obtained

according to Kuo (2004).

2 2

i i

i X Y

rv = +

, 0≤ rr_i ≤ 2 (32) Step4 Calculate the magnitude of α_i. α_i is the angle between rv_iand the horizontal axis;

the formula is as follows:

(

_{i i}

)

i =tan⁻¹Y X

α , 0 ≤α_i ≤ π 2 (33) Step5 Determine appropriate values of Kano classifiers, k=

(

r₀,α_L,α_H

)

. This study uses sensitivity analysis to select and testify the appropriate settings of the Kano classifiers.

Step6 Modify the traditional IPA model and plot the new IPA matrix. rv_i denotes the overall importance of quality attribute of element i to the customers, being used to modify the importanceI_i of IPA. The angle α_i determines the relative level of satisfaction and dissatisfaction. According to Kano, et al., (1984)’s basic definition to the five categories of quality attributes, customers’ reaction of satisfaction toward the level of fulfillment of quality attributes: attractive attributes is apparently obvious and positive, the one-dimensional is on the second place, and the must-be attributes is lower. Therefore, here use

1 2 π

α_i

+ to modify the performanceP_i of IPA. The formulae are as follows:

i i

Mi I r

I = × v (34)

⎟⎟⎠

⎜⎜ ⎞

⎝

⎛ +

×

= 1 2

π α_i

i

Mi P

P (35) Step7 Summarize and categorize the results according to analytical Kano-IPA model.

Section 3 Practical example

The subject for this case study was the mobile telecommunication company FT which is one of the top three largest mobile telecommunication companies in Taiwan. Due to the

trend of loosening related regulations in telecommunications, the industry has become more prosperous and the varieties of telecommunication products are also highly productive. In addition to traditional local calls, customers also demand mobile telecommunications and internet connection. However, the size of the telecommunications market in Taiwan is quite limited, if telecommunication companies could exploit limited resources to upgrade their marketing competiveness, it would be the key to the success of the enterprise. Therefore, it is very important for telecommunication company to confirm the key service quality elements of telecommunication industry.

This study refers to the definition by Lee, Hsieh, and Huang (2010) of the telecommunication industry’s service quality elements and base on step1 to undergo relative questionnaires. There were 13 questions about service quality elements shown in the questionnaires used in this study, as shown in Table 9.

Table9

Service quality elements of telecommunication industry Item Service quality elements

Q1 successful connection to service center Q2 attitude of the service center clerk

Q3 professional knowledge of the service center clerk

Q4 online service provided by the telecommunications company Q5 service provided in the shop

Q6 quality of the mobile phone signal Q7 correctness of the mobile phone bill Q8 e-bill service

Q9 premium programs for mobile phones Q10 value-added service for mobile phones Q11 internet connection service for mobile phones Q12 3G visual service for mobile phones

Q13 service rate for mobile phones

Note. From “Using gap analysis and implicit importance to modify SIPA,” by Lee, et al., Poster session presented at the 2010 IEEE 17th International Conference on Industrial Engineering and Engineering Management, p.175-179.

The questionnaires were mailed out to 1300 customers randomly and 122 valid questionnaires were returned. The results of three kinds of questionnaires are listed in

Table 10. According to step3 to step6, calculate the results of three kinds of questionnaires, and then obtain the modified importance and performance, as shown in Table 11.

Sensitivity analysis is carried out to examine different settings of Kano classifiers. This study adopts a strategy wherer0 changes from 0.1 to 0.9 with an increment of 0.1,αL

changes fromπ ³⁶ toπ ⁴ with an increment ofπ ³⁶ , andαHchanges from^{5π 18} to π 2 with an increment ofπ ³⁶ .Through sensitivity analysis, the threshold values of the Kano classifiers are selected and tested such that the quality attributes are classified as four categories, i.e., k =

(

r₀^,α_L^,α_H

) (

= ^0.5,5π ^36,13π ³⁶

)

. According to the Table 10 and

(

₀,α ,α

) (

= 0.5,5π 36,13π 36

)

= r _{L H}

k , plot the two-dimensional Kano diagram, as

shown in Figure 15.

Table10

The survey results of service quality elements in telecommunications

Item Importance (I_i) mean

Performance (P_i)

mean ^{Xi Yi}

IPA strategy

Q1 8.12 3.83 0.92 0.03 C

Q2 8.71 3.83 0.95 0.11 C

Q3 8.05 2.50 0.73 0.48 C

Q4 7.05 5.17 0.35 0.27 P

Q5 7.71 4.50 0.59 0.11 K

Q6 8.89 5.67 0.85 0.15 K

Q7 8.67 6.67 1.00 0.11 K

Q8 6.49 4.67 0.17 0.18 P

Q9 7.65 3.17 0.50 0.64 L

Q10 6.27 3.33 0.09 0.29 L

Q11 6.70 3.17 0.36 0.80 L

Q12 6.58 3.23 0.23 0.56 L

Q13 8.85 5.17 0.86 0.70 K

mean 7.67 4.22

Q1 Q3

Q7 Q2 Q11

Q13

Q4

Q5 Q8 Q6

Q9

Q10 Q12

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00X(DDI) Y(SII)

Figure15 Quality attributes classification of Analytical Kano-IPA model

Attractive One-dimensional

Indifferent Must-be Table11

Analysis of analytical Kano-IPA model

Item rv_i α_i ¹⁺_π^αi₂ Adj. Importance IMi

Adj. Performance PMi

New strategy

Q1 0.92 1.88 1.02 7.51 3.91 C

Q2 0.96 6.34 1.07 8.37 4.10 C

Q3 0.87 33.69 1.37 7.03 3.44 C

Q4 0.44 38.05 1.42 3.12 7.36 P

Q5 0.60 10.18 1.11 4.63 5.01 L

Q6 0.86 10.12 1.11 7.67 6.31 K

Q7 1.01 6.05 1.07 8.72 7.12 K

Q8 0.25 47.49 1.53 1.60 7.13 P

Q9 0.81 51.84 1.58 6.19 5.00 C

Q10 0.30 72.47 1.81 1.89 6.01 P

Q11 0.88 65.64 1.73 5.90 5.48 C

Q12 0.60 67.93 1.75 3.98 5.67 L

Q13 1.11 38.90 1.43 9.82 7.40 K

mean 0.74 5.88 5.69

Q7

Q6

Q2 Q1

Q3 Q9

Q5

Q13 Q4

Q8

Q12Q11 Q10

2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50 6.00 6.50 7.00

6.00 6.20 6.40 6.60 6.80 7.00 7.20 7.40 7.60 7.80 8.00 8.20 8.40 8.60 8.80 9.00 Importance

Performance

Figure16 Traditional IPA map of service quality attributes

Q3

Q1 Q2

Q5 Q9 Q10

Q11 Q12

Q6

Q8 Q4

Q7 Q13

3.00 3.50 4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00

Adjusted Importance

Adjusted Performance

Through the results of Table 10and Table 11,depict the traditional IPA matrix and the analytical Kano-IPA matrix, as shown in Figure 16 and Figure 17. The differences between tow Figures will be discussed in the next session.

Section 4 Discussion

Regarding the analysis based on Table 10, Table 11, Figure 15, Figure 16 and Figure 17, analytical Kano-IPA model and a comparison with traditional IPA model are described as the following. From the results shown in Table 11 and Figure 15, Q4: online service provided by the telecommunications company, Q8: e-bill service, and Q10: value-added service for mobile phones are elements with smallerrv_i and rvi ^<0.50

, so Q4, Q8, and Q10 are viewed as Indifferent quality attributes. Q1: successful connection to service center, Q2: attitude of the service center clerk, Q5: service provided in the shop, Q6:

quality of the mobile phone signal, and Q7: correctness of the mobile phone bill are elements with smaller

α

and theα_i ≤5π 36, so Q1, Q2, Q5, Q6, and Q7 are viewed as Must-be quality attributes. Q3: professional knowledge of the service center clerk, Q9:

premium programs for mobile phones, and Q13: service rate for mobile phones are elements with moderate

α

_and^{5π 36<α}i^{≤13π 36}, so Q3, Q9, and Q13 are viewed as One-dimensional quality attributes. Q11: internet connection service for mobile phones and Q12: 3G visual service for mobile phones are elements with larger

α

and theα_i >13π 36_, so Q11 and Q12 are viewed as Attractive quality attributes.

On the other hand, with the argument of the importance index rv_i, Q1: successful connection to service center, Q2: attitude of the service center clerk, Q3: professional knowledge of the service center clerk, Q6: quality of the mobile phone signal, Q7:

correctness of the mobile phone bill, Q9: premium programs for mobile phones, Q11:

internet connection service for mobile phones, and Q13: service rate for mobile phone are elements with larger rv and _i rvi ^>0.80

, so Q1, Q2, Q3, Q6, Q7, Q9, Q11, and Q13 are the quality elements which are more important for the customer perspective. Taking the customer satisfaction index (SII and DDI) into the analysis, the DDI value of Q1, Q2, Q3, Q6, Q7, and Q13 are higher and thus, improving these quality attributes could decrease customer dissatisfaction. The SII value of Q9, Q11, and Q13 are higher and thus, improving these quality attributes could increase customer satisfaction.

A comparison of quality elements in the traditional IPA matrix and the analytical Kano-IPA matrix reveals that results of four elements were different. Q5: service provided in the shop, for instance, the original result is “Keep up the Good Work”, but after

modified is “Low Priority”. The use of network communication and E-commerce is extensive; therefore, the number of customers pro to shop in stores is at a lower rate; and it is more reasonable that Q5 is “Low Priority.” Q9: premium programs for mobile phones, the original result is “Low Priority”, but after modified is “Concentrate Here”. Due to the high competitiveness of mobile telecommunication industry,，premium programs has been an important marketing method. Moreover, Q9 is One-dimensional quality attribute, so Q9 should be “Concentrate Here”. Q10: value-added service for mobile phones, the original result is “Low Priority”, but after modified is “Potential Overkill”. Becauserv_i of Q10 is extremely small, which means the importance of Q10 is relatively low and value-added service is developed diversely, Q10 should be “Potential Overkill”. Q11: internet connection service for mobile phones, the original result is “Low Priority”, but after modified is “Concentrate Here”. Because the use of network communication and E-commerce is extensive and the continuing improvement of the functions of mobile phone, such like the Wi-Fi connection, the demand of “online” is increasing promptly.

Therefore, Q11 should be “Concentrate Here”.

Finally, through Figure 17 can find it that Q1: successful connection to service center, Q2: attitude of the service center clerk, Q3: professional knowledge of the service center clerk, Q9: premium programs for mobile phones, and Q11: internet connection service for mobile phones are in the quadrant of “Concentrate Here”, which should be listed first on the improvement list. According to Berger, et al. (1993), the impact of the category of quality attributes to the products or services is: M＞O＞A＞I, further with the judgment of rvi andα_i can find it that Q1 and Q2 are Must-be quality attributes, so Q1 and Q2 have to be improved first. Though Q3 and Q9 are both One-dimensional quality attributes, rv_i of Q3 is larger than the value of Q9; Q3 is the second to be improved, and Q9 is third to be improved. Q11 is the Attractive quality attribute, so Q11 is the last.

CHAPTER 5 REFINING KANO’S MODEL

在文檔中 狩野模型之精煉－三種改善方法與應用 Refining Kano’s Model－Three improvements (頁 48-58)