INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH AND DEVELOPMENT (IJIERD)

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1 INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH AND DEVELOPMENT (IJIERD) International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 ISSN (Print) ISSN (Online) Volume 4, Issue 1, January - April (2013), pp IAEME: Journal Impact Factor (2013): (Calculated by GISI) IJIERD I A E M E PRIORITIZATION OF VOICE OF CUSTOMERS BY USING KANO QUESTIONNAIRE AND DATA ENVELOPMENT ANALYSIS Satyendra Sharma 1, 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. 1

2 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 2

3 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 Worst Average Best Fig. 1: rating scale 3

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

5 5. DATA INTERPRETATION AND ANALYSIS By interpreting and analyzing the data through Kano questionnaire following results were found 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 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 Assurance Tangible Empathy Responsiveness 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. 5

6 International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 Fig. 3 Envelopes 6

7 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 Assurance Tangible Empathy Responsiveness 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 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) 7

8 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 Assurance Tangible Empathy Responsi- veness Maximum overall weighting is found to be for Reliability. The other higher values of overall weighting are & 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) I Empathy (VOC4) II Responsiveness (VOC5) III Tangible (VOC3) IV Assurance (VOC2) 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. 8

9 REFERENCES [1] Mik Wisniewski, Using SERVQUAL to assess customer satisfaction with public sector services, Managing Service Quality, 2001 Vol. 11 Iss: 6, pp , [2] Gi-Du Kang and Jeffrey James: Service quality dimensions an examination of Gronroos s service quality model, Managing Service Quality, Volume 14 Number pp ]. [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 [4] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. SERVQUAL: A multiple-item scale for measuring consumer perceptions. Journal of Retailing, (1), [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 [6] Adele Berndt., Investigating Service Quality Dimensions in South African Motor Vehicle Servicing, African Journal of Marketing Management, Vol. 1(1) pp April, [7] Rajnish Katarne, Satyendra Sharma, Dr.Jayant Negi, Measurement of Service Quality of an Automobile Service Centre, International Conference on Industrial Engineering and Operations Management 2010 Dhaka, Bangladesh]. [8] Satyendra Sharma, Rajnish Katarne, Dr.Jayant Negi, Impact Assessment of Service Quality Strategies in an Automobile Service, Eighth AIMS International Conference on Management 2011, Ahmedabad, India. [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 , ISSN: , [12] Sherman, H.D.; Zhu, J., Service Productivity Management, Improving Service Performance using Data Envelopment Analysis, 2006, XXII, illus. [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 , ISSN Print: , ISSN Online: [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