prioritization of voice of customers by using kano questionnaire and data (1)

International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –

6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1, January - April (2013), © IAEME

1

PRIORITIZATION OF VOICE OF CUSTOMERS BY USING KANO

QUESTIONNAIRE AND DATA ENVELOPMENT ANALYSIS

Satyendra Sharma1, Dr.Jayant Negi

2

1(Mechanical Engineering Department, Swami Vivekanand College of Engineering/ Rajiv

Gandhi Proudyogiki Vishwavidyalaya, Bhopal/ MP, India) 2(Mechanical Engineering Department, Swami Vivekanand College of Engineering/ Rajiv

Gandhi Proudyogiki Vishwavidyalaya,Bhopal/ MP, India)

ABSTRACT

Service Quality has received increased attention as a means for service firms to attract

and retain customers and gain a competitive edge in the marketplace. The effect of the global

economic meltdown increased the pressure on industries to make right decisions about their

strategies for better performance. Quality service is a key factor of value that drives any

company's success. Measuring service quality is another challenge because customer

satisfaction is a function of many intangible factors. This research aims to prioritize the voice

of customers’ (VOC) for an Automobile service centre. Kano questionnaires were designed

and used for collecting the data, and Data Envelopment Analysis (DEA) has been used for

prioritization analysis.

Keywords: Customer satisfaction, Data Envelopment Analysis, Kano Questionnaires,

Service Quality, Voice of Customer

1. INTRODUCTION

Recently, design of Service Quality has become the most critical task for any

company. In this present competitive scenario, for any organization such as Automobile

service industries it is essential to provide quality service to retain their customers’. The

service sector is going through revolutionary change, and the future of economy depends on

the growth rate of service sector. The services sector now accounts for over 75% of the GDP

in the developed countries and the same trend is being observed in the majority of the

developing countries. Today’s market is so competitive that new services are continually

launched and advance services are readily available in terms of both cost and quality. For the

survival of any service organization it is necessary to respond quickly to the changes, and

deliver according to diverse customer requirements.

INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING

RESEARCH AND DEVELOPMENT (IJIERD)

ISSN 0976 – 6979 (Print) ISSN 0976 – 6987 (Online)

Volume 4, Issue 1, January - April (2013), pp. 01-09

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The measurement of service quality performance plays a significant role in each quality

improvement attempt. Measuring service quality is another challenge because customer satisfaction is

a function of many intangible factors. A product has physical features that can be independently

measured (e.g., the fit and finish of a car) and easily manageable, on the other hand service quality

contains many psychological features (e.g., the ambience of customer waiting lounge/room. Applying

measurable functions in their operations and practices, service industries are able to evaluate and

improve the service quality.

The main objectives of this paper are to prioritize voice of customers’ and identify the most

critical parameters for an Automobile service centre. Kano Questionnaires has been designed by

modifying the 22 items of the SERVQUAL model for collecting the data. In addition Data

Envelopment Analysis (DEA) has also been employed to determine the target values of the voice of

customers’ (VOCs) relative to the competitors. It has been utilized by several researchers for

evaluating nonprofit and public sector organizations. DEA can undertake numerous inputs and outputs

at a time and direct analyst in deciding the target values for the future/weaker areas. DEA is generally

to judge against decision-making units (DMU) and to evaluate managerial strategies to improve the

productive efficiency of those DMU’s that are not lying on the efficient frontier.

2. LITERATURE REVIEW

Service quality is a concept that has aroused considerable interest and debate in the research

literature because of the difficulties in both defining it and measuring it with no overall consensus

emerging on either (Wisniewski, 2001). One that is commonly used defines service quality as the

extent to which a service meets customers’ needs or expectations (Lewis and Mitchell, 1990; Dotchin

et al, 1994a). Mik Wisniewski, had study using an adapted SERVQUAL approach across a range of

Scottish council services. The use of SERVQUAL results by service managers reviewed and the

contribution of SERVQUAL to continuous improvement assessed [1].

Various frameworks have been introduced, in order to measure the Service quality. However,

as Robinson (1999) states, it is impossible to construct a ‘global measurement approach’ of service

quality, as each organization is unique and as a result, altered practices are employed. Christian

Gronroos, (1984) gave a three-dimensional model of Service Quality, which includes three

components namely technical quality, functional quality, and image. He also emphasized the

importance of corporate image in the experience of service quality, similar to the idea proposed by

Lehtinen and Lehtinen (1982) [2]. A. Parasuraman, Valarie A. Zeithaml and Leonard L. Berry

(PZB,1985) developed the most popular instrument for measuring service quality named

SERVQUAL [3]. Initially they identifies ten dimensions regarding service quality in their model,

however these were reduced to five dimensions namely: Reliability, Assurance, Tangibles, Empathy

and Responsiveness (1988) [4]. Seth et.al critically examines different service quality models to

derive linkage between them, and highlight the area for further research. The review of various

service quality model revealed that the service quality outcome and measurement is dependent on

factors such as type of service setting, situation, time, need etc.[5].

Adele Berndt (2009) has used PZB’s instrument to determine the Service quality in vehicle

servicing in South Africa. However, limited published research has been conducted into service

quality in the motor industry with respect to the servicing of vehicles. This means that the issue of

service quality in the motor vehicle industry is a largely unknown factor [6]. Rajnish Katarne,

Satyendra Sharma et.al. (2010) measured service quality of an automobile service centre in an Indian

city. In that research, satisfaction/dissatisfaction of the customers, and its reason(s) had been

evaluated by applying root cause analysis [7]. In the continuation they did further research (2011) to

assess impact of service quality strategies made on the basis of earlier suggestion in the same service

organization [8].

Julia E. Blose et al. [9] using DEA proposes a new managerial tool for evaluating and

managing service quality levels. This new approach treats service quality as an intermediate



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variable, not the ultimate managerial goal of interest, and makes use of DEA, a nonparametric

technique that allows for the relative comparison of a number of comparable organizational

decision-making units (DMUs) (Sexton 1986).

Thomas R. Sexton et. al. [10] has presented an efficiency analysis of U.S. business schools using

DEA. Naveen Donthu and Boonghee Yoo, [11] suggest that DEA may be used to assess retail

productivity/efficiency and to address some of the problems with existing retail productivity

measures. While traditional approaches are more appropriate for macro-level analysis, DEA is a

micro-level or store-level productivity measurement tool that may have more managerial

relevance.

3. DATA ENVELOPMENT ANALYSIS

Data Envelopment Analysis (DEA) was originally introduced by Charnes, Cooper and

Rhodes based on the earlier work of Farrell (1957), in 1978 [12]. It is a brilliant and simply used

service management technique for evaluating nonprofit and public sector organizations. DEA

allows management to estimate the relative productive efficiency of a number of similar

organizational units based on a theoretical finest performance for each organization. The

organizational units in analysis are called Decision Making Units (DMUs) that are characterized

by multiple inputs and outputs.

Efficiency of any organization is the ratio of its output to input. More output for every

unit of input reflects relatively better efficiency. Optimum efficiency can be defined as the

maximum possible output per unit of input. Efficiency as indicated by DEA can be defined as the

maximum outputs for any specified quantity of inputs or the minimum use of inputs for any

specified quantity of outputs. The difference between DEA and simple efficiency ratio is that

DEA accommodates multiple inputs and outputs simultaneously, and make available significant

extra information about where efficiency improvements are required along with the extent of

improvements.

Objective of DEA is to find the most efficient DMUs, and construct an efficient frontier.

The efficient frontier is a curve, or a shell obtained by joining the points representing most

efficient DMUs. Efficient DMUs can be determined from the comparison of inputs and outputs of

all DMUs under consideration. As a consequence DEA generates the relative efficiency

boundaries, also called envelopes. Statistical methods can also be used for finding efficient

DMUs, but it evaluates them relative to an average one. While in DEA each DMU is compared

with only the paramount (best) DMUs.

4. DATA COLLECTION

Section 1: Kano questionnaire has been used for finding the relative importance of the

voice of customers. Data were collected by administering the questionnaire to adequate number

of respondents. Five dimensions of the service quality given by PZB in their SERVQUAL

instrument have been taken as VOCs. Customers were asked to rate each VOC on the scale (1-5)

as shown in fig. 1. This will facilitate in knowing the customers’ preference on five dimensions of

service quality.

1 2 3 4 5

|__________________________________________________________________________|

Worst Average Best

Fig. 1: rating scale



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Section 2: Another questionnaire was developed to collect the data for individual service

centre. For this purpose, each dimension of quality was subdivided into the factors on which it

depends. The opinion of customers was taken at each service center to find out the standing of a

particular service center on a given dimension.

Questionnaire was designed by modifying 22 items of the SERVQUAL model. The

questionnaire is shown in Table 1. Customers were requested to respond to each question by

using the scale in fig.1.

Table 1:Questionnaire

S.No. VOC Question SC

1

SC

2

SC

3

SC

4

Qc1

Reliability

Vehicle delivery on-time

Qc2 Billing service

Qc3 Estimated delivery time

Qc4 Queuing/ waiting time

Qc5 Prior appointment (Booking)

Qc6

Responsiveness

Response of SA

Qc7 Compensations for mistakes

Qc8 Responsiveness in customer lounge

Qc9 Responsiveness at billing

Qc10 Responsiveness for additional

small repair work

Qc11

Assurance

Knowledge of the SA

Qc12 Ability to convey trust

Qc13 Confidence of SA

Qc14 Politeness & Respect to customer

Qc15 Effectiveness communication

with customer

Qc16

Empathy

Sensitivity of SA

Qc17 Way of approach of SA

Qc18 Effort to understand the need of

customer

Qc19

Tangible

Equipments at SC

Qc20 Surrounding environment of SC

Qc21 Facilities at SC

Qc22

Communicating materials provided

by SC (visiting card, complaint ph

No, Suggestion/complain box,

schemes for customer etc.)



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5. DATA INTERPRETATION AND ANALYSIS

By interpreting and analyzing the data through Kano questionnaire following results

were found.

5.1. Customer Importance Rating The customer importance rating for each of the VOC has been calculated using the

data collected in Section 1. The results are exhibited in the Table 2. It is clear from the table

that Reliability has got the highest rating; hence it will be the most important VOC for

automobile service center. Empathy and Responsiveness are the other two VOCs rated with

more than average weights.

Table 2: Customer Importance Rating

Voice of Customer Customer Importance Rating

VOC1 Reliability 5

VOC2 Assurance 2

VOC3 Tangible 2

VOC4 Empathy 4

VOC5 Responsiveness 3

5.2. Customer Competitive Evaluation This section evaluates the current performance of the service centers (SC) under

study. Data collected under section 2 have been used to find out each SC’s score on

individual quality dimension. Table 3 shows comparative status. Here, C1 indicates the SC

under consideration. C2, C3, and C4 are the three competitor SCs.

Table 3: Customer Competitive Evaluation

Voice of Customer Customer Importance

(CI) C1 C2 C3 C4

Reliability 5 2.20 4.40 2.67 4.14

Assurance 2 2.40 3.74 2.67 3.20

Tangible 2 2.92 4.09 3.75 3.50

Empathy 4 2.56 4.23 3.00 3.67

Responsiveness 3 2.20 3.80 2.74 4.50

5.3. Determination of Planned Rating for VOC

Data Envelopment Analysis (DEA) will help in determining the standing of Service

Center C1 with respect to the best performer in similar set up. This will in turn help us to

determine the target value of VOCs. Data Envelope for each pair of VOC can be formed

using information from table 3. In this illustration, five VOCs have been considered.

Therefore, ten envelopes will be formed as shown in fig.3.



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Fig. 3 Envelopes



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For C1, these target values are calculated as shown in table 4. The Planned rating

(PR) quantifies the desired performance of the service centre under consideration in

satisfying each VoC.

Table 4: Planned rating

VOC Value 1 Value 2 Value 3 Value 4 Planned Rating (PR)

(Average)

Reliability 3.42 2.75 3.64 4.23 3.51

Assurance 3.74 3.40 3.20 3.74 3.52

Tangible 3.80 4.09 3.90 4.09 3.97

Empathy 4.23 3.40 3.40 4.23 3.82

Responsiveness 4.26 3.40 3.10 3.60 3.60

6. PRIORITIZATION OF VOC

Now it is required to select the most critical quality dimension out of all, and

assigning them a priority. Based on this analysis, it will be possible to devise the strategies

for meeting the targets. In order to get these priority scores, overall weightings are required to

be calculated. Overall weighting is a function of Customer Importance Rating, Improvement

Factor, and Sales Point.

Data in the planned rating column has been taken from the outcome of Data

Envelopment Analysis. The difference between Current Service level and target Service level

indicates the scope of improvement. The amount of work required to change the level of

Perceived Performance is generally calculated and stored as the Improvement Factor. It can

be determined by using equation (1) given below.

Improvement Factor (IF) = [1 + {0.2( PR – SC’s Current score of VOC)}] ------ (1)

Sometimes customers underestimate a particular VOC because of their unawareness of

the benefit likely to be derived through a quality dimension. In order to take this into account,

a factor known as Sales Point has been used. Its value ranges between 1.0 - 1.5. Value 1.0

show that VOC will not influence in marketing efforts and value 1.5 shows that VOC has

tremendous potential and will have high impact on marketing efforts. It should therefore be

used very carefully. Overall weighting can be calculated by using equation (2). These

calculations are represented in table 5 showing the Overall Weightings of all VOCs.

Overall weighting = [CI x IF x SP] …. (2)



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Table 5: Overall Weighting Matrix

Voice of

Customer CI C1 C2 C3 C4

Planned

Rating

(PR)

Improvement

Factor

(IF)

Sales

Point

(SP)

Overall

Weighting

Reliability 5 2.20 4.40 2.67 4.14 3.51 1.262 1.4 8.834

Assurance 2 2.40 3.74 2.67 3.20 3.52 1.224 1.3 3.183

Tangible 2 2.92 4.09 3.75 3.50 3.97 1.21 1.4 3.388

Empathy 4 2.56 4.23 3.00 3.67 3.82 1.252 1.4 7.012

Responsi-

veness 3 2.20 3.80 2.74 4.50 3.60 1.28 1.4 5.376

Maximum overall weighting is found to be 8.834 for Reliability. The other higher values

of overall weighting are 7.012 & 5.376 for Empathy and Responsiveness respectively.

Tangible and Assurance have got lower weights. Data shows that the most critical VOC is

Reliability. Table 6 depicts the Priority wise weightings of Voice of Customers.

Table 6: Final Prioritized Voice of Customer

Voice of Customer Overall Weighting Priority

Reliability (VOC1) 8.834 I

Empathy (VOC4) 7.012 II

Responsiveness (VOC5) 5.376 III

Tangible (VOC3) 3.388 IV

Assurance (VOC2) 3.183 V

7. CONCLUSION

The main aim of this research was to prioritize the voice of customers’ for an

Automobile service centre. Kano questionnaire and Data Envelopment Analysis has been

used for this purpose. The data interpretation and analysis show the prioritizations of Voice

of Customers. The results reveal that the first and foremost critical VoC to be considered is

Reliability. Now this can be used to devise the strategies to reach the target values of quality

dimensions which will ultimately yield desired service quality.



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prioritization of voice of customers by using kano questionnaire and data (1)

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