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

    • INTERNATIONAL JOURNAL Research and Development (IJIERD), ISSN 0976 –International Journal of Industrial Engineering OF INDUSTRIAL ENGINEERING6979(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© IAEME: www.iaeme.com/ijierd.aspJournal Impact Factor (2013): 5.1283 (Calculated by GISI) ©IAEMEwww.jifactor.com 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 attractand retain customers and gain a competitive edge in the marketplace. The effect of the globaleconomic meltdown increased the pressure on industries to make right decisions about theirstrategies for better performance. Quality service is a key factor of value that drives anycompanys success. Measuring service quality is another challenge because customersatisfaction is a function of many intangible factors. This research aims to prioritize the voiceof customers’ (VOC) for an Automobile service centre. Kano questionnaires were designedand used for collecting the data, and Data Envelopment Analysis (DEA) has been used forprioritization analysis.Keywords: Customer satisfaction, Data Envelopment Analysis, Kano Questionnaires,Service Quality, Voice of Customer1. INTRODUCTION Recently, design of Service Quality has become the most critical task for anycompany. In this present competitive scenario, for any organization such as Automobileservice industries it is essential to provide quality service to retain their customers’. Theservice sector is going through revolutionary change, and the future of economy depends onthe growth rate of service sector. The services sector now accounts for over 75% of the GDPin the developed countries and the same trend is being observed in the majority of thedeveloping countries. Today’s market is so competitive that new services are continuallylaunched and advance services are readily available in terms of both cost and quality. For thesurvival of any service organization it is necessary to respond quickly to the changes, anddeliver according to diverse customer requirements. 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 The measurement of service quality performance plays a significant role in each qualityimprovement attempt. Measuring service quality is another challenge because customer satisfaction isa function of many intangible factors. A product has physical features that can be independentlymeasured (e.g., the fit and finish of a car) and easily manageable, on the other hand service qualitycontains many psychological features (e.g., the ambience of customer waiting lounge/room. Applyingmeasurable functions in their operations and practices, service industries are able to evaluate andimprove the service quality. The main objectives of this paper are to prioritize voice of customers’ and identify the mostcritical parameters for an Automobile service centre. Kano Questionnaires has been designed bymodifying the 22 items of the SERVQUAL model for collecting the data. In addition DataEnvelopment Analysis (DEA) has also been employed to determine the target values of the voice ofcustomers’ (VOCs) relative to the competitors. It has been utilized by several researchers forevaluating nonprofit and public sector organizations. DEA can undertake numerous inputs and outputsat a time and direct analyst in deciding the target values for the future/weaker areas. DEA is generallyto judge against decision-making units (DMU) and to evaluate managerial strategies to improve theproductive 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 researchliterature because of the difficulties in both defining it and measuring it with no overall consensusemerging on either (Wisniewski, 2001). One that is commonly used defines service quality as theextent to which a service meets customers’ needs or expectations (Lewis and Mitchell, 1990; Dotchinet al, 1994a). Mik Wisniewski, had study using an adapted SERVQUAL approach across a range ofScottish council services. The use of SERVQUAL results by service managers reviewed and thecontribution 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 servicequality, as each organization is unique and as a result, altered practices are employed. ChristianGronroos, (1984) gave a three-dimensional model of Service Quality, which includes threecomponents namely technical quality, functional quality, and image. He also emphasized theimportance of corporate image in the experience of service quality, similar to the idea proposed byLehtinen and Lehtinen (1982) [2]. A. Parasuraman, Valarie A. Zeithaml and Leonard L. Berry(PZB,1985) developed the most popular instrument for measuring service quality namedSERVQUAL [3]. Initially they identifies ten dimensions regarding service quality in their model,however these were reduced to five dimensions namely: Reliability, Assurance, Tangibles, Empathyand Responsiveness (1988) [4]. Seth et.al critically examines different service quality models toderive linkage between them, and highlight the area for further research. The review of variousservice quality model revealed that the service quality outcome and measurement is dependent onfactors 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 vehicleservicing in South Africa. However, limited published research has been conducted into servicequality in the motor industry with respect to the servicing of vehicles. This means that the issue ofservice 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 Indiancity. In that research, satisfaction/dissatisfaction of the customers, and its reason(s) had beenevaluated by applying root cause analysis [7]. In the continuation they did further research (2011) toassess impact of service quality strategies made on the basis of earlier suggestion in the same serviceorganization [8]. Julia E. Blose et al. [9] using DEA proposes a new managerial tool for evaluating andmanaging service quality levels. This new approach treats service quality as an intermediate 2
    • International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1, January - April (2013), © IAEMEvariable, not the ultimate managerial goal of interest, and makes use of DEA, a nonparametrictechnique that allows for the relative comparison of a number of comparable organizationaldecision-making units (DMUs) (Sexton 1986).Thomas R. Sexton et. al. [10] has presented an efficiency analysis of U.S. business schools usingDEA. Naveen Donthu and Boonghee Yoo, [11] suggest that DEA may be used to assess retailproductivity/efficiency and to address some of the problems with existing retail productivitymeasures. While traditional approaches are more appropriate for macro-level analysis, DEA is amicro-level or store-level productivity measurement tool that may have more managerialrelevance.3. DATA ENVELOPMENT ANALYSIS Data Envelopment Analysis (DEA) was originally introduced by Charnes, Cooper andRhodes based on the earlier work of Farrell (1957), in 1978 [12]. It is a brilliant and simply usedservice management technique for evaluating nonprofit and public sector organizations. DEAallows management to estimate the relative productive efficiency of a number of similarorganizational units based on a theoretical finest performance for each organization. Theorganizational units in analysis are called Decision Making Units (DMUs) that are characterizedby multiple inputs and outputs. Efficiency of any organization is the ratio of its output to input. More output for everyunit of input reflects relatively better efficiency. Optimum efficiency can be defined as themaximum possible output per unit of input. Efficiency as indicated by DEA can be defined as themaximum outputs for any specified quantity of inputs or the minimum use of inputs for anyspecified quantity of outputs. The difference between DEA and simple efficiency ratio is thatDEA accommodates multiple inputs and outputs simultaneously, and make available significantextra information about where efficiency improvements are required along with the extent ofimprovements. 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 mostefficient DMUs. Efficient DMUs can be determined from the comparison of inputs and outputs ofall DMUs under consideration. As a consequence DEA generates the relative efficiencyboundaries, also called envelopes. Statistical methods can also be used for finding efficientDMUs, but it evaluates them relative to an average one. While in DEA each DMU is comparedwith only the paramount (best) DMUs.4. DATA COLLECTION Section 1: Kano questionnaire has been used for finding the relative importance of thevoice of customers. Data were collected by administering the questionnaire to adequate numberof respondents. Five dimensions of the service quality given by PZB in their SERVQUALinstrument 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 ofservice quality.1 2 3 4 5|__________________________________________________________________________|Worst Average Best Fig. 1: rating scale 3
    • 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 Section 2: Another questionnaire was developed to collect the data for individual servicecentre. For this purpose, each dimension of quality was subdivided into the factors on which itdepends. The opinion of customers was taken at each service center to find out the standing of aparticular service center on a given dimension.Questionnaire was designed by modifying 22 items of the SERVQUAL model. Thequestionnaire is shown in Table 1. Customers were requested to respond to each question byusing 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.) 4
    • International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1, January - April (2013), © IAEME5. DATA INTERPRETATION AND ANALYSIS By interpreting and analyzing the data through Kano questionnaire following resultswere found. 5.1. Customer Importance Rating The customer importance rating for each of the VOC has been calculated using thedata collected in Section 1. The results are exhibited in the Table 2. It is clear from the tablethat Reliability has got the highest rating; hence it will be the most important VOC forautomobile service center. Empathy and Responsiveness are the other two VOCs rated withmore 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) understudy. Data collected under section 2 have been used to find out each SC’s score onindividual quality dimension. Table 3 shows comparative status. Here, C1 indicates the SCunder 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 4.14Assurance 2 2.40 3.74 2.67 3.20Tangible 2 2.92 4.09 3.75 3.50Empathy 4 2.56 4.23 3.00 3.67Responsiveness 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 ServiceCenter C1 with respect to the best performer in similar set up. This will in turn help us todetermine the target value of VOCs. Data Envelope for each pair of VOC can be formedusing information from table 3. In this illustration, five VOCs have been considered.Therefore, ten envelopes will be formed as shown in fig.3. 5
    • 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 Fig. 3 Envelopes 6
    • 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 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 insatisfying 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 3.82Responsiveness 4.26 3.40 3.10 3.60 3.606. PRIORITIZATION OF VOC Now it is required to select the most critical quality dimension out of all, andassigning them a priority. Based on this analysis, it will be possible to devise the strategiesfor meeting the targets. In order to get these priority scores, overall weightings are required tobe calculated. Overall weighting is a function of Customer Importance Rating, ImprovementFactor, and Sales Point. Data in the planned rating column has been taken from the outcome of DataEnvelopment Analysis. The difference between Current Service level and target Service levelindicates the scope of improvement. The amount of work required to change the level ofPerceived Performance is generally calculated and stored as the Improvement Factor. It canbe 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 ofthe 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 1.0show that VOC will not influence in marketing efforts and value 1.5 shows that VOC hastremendous potential and will have high impact on marketing efforts. It should therefore beused very carefully. Overall weighting can be calculated by using equation (2). Thesecalculations are represented in table 5 showing the Overall Weightings of all VOCs. Overall weighting = [CI x IF x SP] …. (2) 7
    • 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 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 valuesof 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 isReliability. Table 6 depicts the Priority wise weightings of Voice of Customers. Table 6: Final Prioritized Voice of Customer Voice of Customer Overall Weighting PriorityReliability (VOC1) 8.834 IEmpathy (VOC4) 7.012 IIResponsiveness (VOC5) 5.376 IIITangible (VOC3) 3.388 IVAssurance (VOC2) 3.183 V7. CONCLUSION The main aim of this research was to prioritize the voice of customers’ for anAutomobile service centre. Kano questionnaire and Data Envelopment Analysis has beenused for this purpose. The data interpretation and analysis show the prioritizations of Voiceof Customers. The results reveal that the first and foremost critical VoC to be considered isReliability. Now this can be used to devise the strategies to reach the target values of qualitydimensions which will ultimately yield desired service quality. 8
    • International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –6979(Print), ISSN 0976 – 6987(Online) Volume 4, Issue 1, January - April (2013), © IAEMEREFERENCES[1] Mik Wisniewski, Using SERVQUAL to assess customer satisfaction with publicsector services, Managing Service Quality, 2001 Vol. 11 Iss: 6, pp.380 – 388,[2] Gi-Du Kang and Jeffrey James: Service quality dimensions an examination ofGronroos’s service quality model, Managing Service Quality, Volume 14 ·Number 4 · 2004 ·pp. 266–277].[3] A.Parasuraman, Valarie A. Zeithaml, & Leonard L. Berry., “A Conceptual Model ofService Quality and Its Implications for Future Research,” 50/Journal of Marketing, Fall1985.[4] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. “SERVQUAL: A multiple-itemscale 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 MotorVehicle 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 Qualityof an Automobile Service Centre, International Conference on Industrial Engineering andOperations Management 2010 Dhaka, Bangladesh].[8] Satyendra Sharma, Rajnish Katarne, Dr.Jayant Negi, Impact Assessment of ServiceQuality Strategies in an Automobile Service, Eighth AIMS International Conference onManagement 2011, Ahmedabad, India.[9] Julia E. Blose, William B. Tankersley, Leisa R. Flynn, “Managing Service Qualityusing data Envelopment Analysis”, 8 QMJ Vol. 12 No. 2, 2005 ASQ.[10] Thomas R. sexton, Christie L. Comunale, “An efficiency analysis of U.S. businessschools”, Journal of case studies in Accreditation and Assessment.[11] Naveen Donthu, Boonghee Yoo, “Retail Productivity Assessment using Dataenvelopment Analysis”, Journal of Retailing, Vol. 74(1), pp. 89-105, ISSN: 0022-4359, 1998.[12] Sherman, H.D.; Zhu, J., Service Productivity Management, Improving ServicePerformance 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 inImproving the Effectiveness of CRM Practices Through Customer Loyalty – A Study onIndian 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 ServiceQuality Using Analytic Hierarchy Process (AHP)” International Journal of IndustrialEngineering Research and Development (IJIERD), Volume 2, Issue 1, 2011, pp. 59 - 68. 9