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

INTERNATIONAL JOURNAL Research and Development (IJIERD), ISSN 0976 –
International Journal of Industrial Engineering OF INDUSTRIAL ENGINEERING
6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1, January - April (2013), © IAEME
RESEARCH AND DEVELOPMENT (IJIERD)
ISSN 0976 – 6979 (Print)
ISSN 0976 – 6987 (Online)
Volume 4, Issue 1, January - April (2013), pp. 01-09
IJIERD
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PRIORITIZATION OF VOICE OF CUSTOMERS BY USING KANO
QUESTIONNAIRE AND DATA ENVELOPMENT ANALYSIS
Satyendra Sharma1, Dr.Jayant Negi2
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.
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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –

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

SC SC SC SC
S.No. VOC Question
1 2 3 4
Qc1 Vehicle delivery on-time
Qc2 Billing service
Qc3 Reliability Estimated delivery time
Qc4 Queuing/ waiting time
Qc5 Prior appointment (Booking)
Qc6 Response of SA
Qc7 Compensations for mistakes
Qc8 Responsiveness in customer lounge
Responsiveness
Qc9 Responsiveness at billing
Qc10 Responsiveness for additional
small repair work
Qc11 Knowledge of the SA
Qc12 Ability to convey trust
Qc13 Confidence of SA
Assurance
Qc14 Politeness & Respect to customer
Qc15 Effectiveness communication
with customer
Qc16 Sensitivity of SA
Qc17 Empathy Way of approach of SA
Effort to understand the need of
Qc18
customer
Qc19 Equipments at SC
Qc20 Surrounding environment of SC
Qc21 Facilities at SC
Tangible Communicating materials provided
by SC (visiting card, complaint ph
Qc22
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
Customer Importance
Voice of Customer C1 C2 C3 C4
(CI)
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

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

Planned Improvement Sales
Voice of Rating Point Overall
CI C1 C2 C3 C4 Factor
Customer Weighting
(PR) (IF) (SP)

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-
3 2.20 3.80 2.74 4.50 3.60 1.28 1.4 5.376
veness

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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REFERENCES

[1] Mik Wisniewski, Using SERVQUAL to assess customer satisfaction with public
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pp. 266–277].
[3] A.Parasuraman, Valarie A. Zeithaml, & Leonard L. Berry., “A Conceptual Model of
Service Quality and Its Implications for Future Research,” 50/Journal of Marketing, Fall
1985.
[4] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. “SERVQUAL: A multiple-item
scale for measuring consumer perceptions”. Journal of Retailing, 1988 64(1), 12-40.
[5] Nitin Seth and S.G. Deshmukh, and Prem Vrat, Service quality models: a review,
International Journal of Quality & Reliability Management Vol. 22 No. 9, 2005 pp. 913-949.
[6] Adele Berndt., Investigating Service Quality Dimensions in South African Motor
Vehicle Servicing, African Journal of Marketing Management, Vol. 1(1) pp. 001-009 April,
2009.
[7] Rajnish Katarne, Satyendra Sharma, Dr.Jayant Negi, Measurement of Service Quality
of an Automobile Service Centre, International Conference on Industrial Engineering and
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Quality Strategies in an Automobile Service, Eighth AIMS International Conference on
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[9] Julia E. Blose, William B. Tankersley, Leisa R. Flynn, “Managing Service Quality
using data Envelopment Analysis”, 8 QMJ Vol. 12 No. 2, 2005 ASQ.
[10] Thomas R. sexton, Christie L. Comunale, “An efficiency analysis of U.S. business
schools”, Journal of case studies in Accreditation and Assessment.
[11] Naveen Donthu, Boonghee Yoo, “Retail Productivity Assessment using Data
envelopment Analysis”, Journal of Retailing, Vol. 74(1), pp. 89-105, ISSN: 0022-4359, 1998.
[12] Sherman, H.D.; Zhu, J., Service Productivity Management, Improving Service
Performance using Data Envelopment Analysis, 2006, XXII, 328.64 illus.
http://www.springer.com/978-0-387-33211-6.
[13] Vani Haridasan.P and Dr. Shanthi Venkatesh , “Impact of Service Quality in
Improving the Effectiveness of CRM Practices Through Customer Loyalty – A Study on
Indian Mobile Sector” International Journal of Management (IJM), Volume 3, Issue 1, 2012,
pp. 29 - 45, ISSN Print: 0976-6502, ISSN Online: 0976-6510.
[14] Parul Gupta and R.K. Srivastava, “Analysis of Customer Satisfaction in Hotel Service
Quality Using Analytic Hierarchy Process (AHP)” International Journal of Industrial
Engineering Research and Development (IJIERD), Volume 2, Issue 1, 2011, pp. 59 - 68.

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