Identification of Telecom Service Quality Dimensions in India with Fuzzy Analysis

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1 Global Journal of Management and Business Studies. ISSN Volume 3, Number 5 (2013), pp Research India Publications Identification of Telecom Service Quality Dimensions in India with Fuzzy Analysis Amit Chaudhary and Indu Uprety* School of Management, Gautam Buddha University, Greater Noida , Uttar Pradesh, INDIA. * School of Management, Gautam Buddha University, Greater Noida , Uttar Pradesh, INDIA. Abstract Purpose The main purpose of this research is to find out the various Telecom Service Quality dimensions that affect on the Telecom Service Quality with the help of fuzzy TOPSIS and Fuzzy AHP. Design/methodology/approach The research identify the major dimensions of Telecom Service Quality those are more important for the Service Provider to survive in the market. The research used the TOPSIS and AHP method with trapezoidal fuzzy numbers to identify these dimensions. The research used the webmail survey method for data collection. Findings The Approach of Employee towards Customers, Technology Expertise of Employees, Information provided to understand the product, Sincerity of Service Provider, Timeline to provide the service, Network Quality of Service Provider, Time Frame to Solve Grievance, Company Trust are eight most important Service Quality Attributes. Research limitations/implications This research survey has done in India, the preference of quality telecom dimensions may be differing in other Countries. Practical implications The growth of literature in the field of Telecom Service Quality seems to have developed successively, providing a continuous updating and learning from the findings and

2 468 Amit Chaudhary & Indu Uprety observations of predecessors. This paper provides new directions to Telecom Service Quality researchers. Originality/value This paper explores new directions in Telecom Service Quality Research and offers practical help to researchers and practitioners in providing a direction for Telecom Service Quality improvement. Keywords: Telecom Service Quality, Network Quality, Entropy Weight Calculation, TOPSIS, Fuzzy TOPSIS, Fuzzy AHP. 1. Introduction Telecom Industries are analyzed and inventing how to satisfy their customers with service quality in the competitive market. The Service Quality dimensions were examined by the Parasuraman et al. (1988), with the formulation of SERVQUAL scale having five core dimensions tangibles, reliability, responsiveness, assurance, and empathy. The SERVQUAL have severally criticized by scientists (Babakus and Boller 1992) in the different sectors that the customer s expectations cannot be meet which they actually think. The scale SERVPERF was succeeded over SERVQUAL developed which measure only performance of the service. The researcher argued that Service Quality cannot be judged by primary dimensions, they insist to introduce the multi dimensional criteria for judgment of Service Quality (Brady and Cronin 2001, Dabholkar et al. 1996). During the last few years service quality has become an area of attention to managers and researchers to find out that the Service quality has strong impact on company performance, lower costs, customer satisfaction, customer loyalty and profitability (Cronin and Taylor, 1992). There has been a continued research on the definition, modeling, measurement, data collection procedure, data analysis etc., issues of service quality, leading to development of sound base for the researchers. The value, customer satisfaction are factors those impact the Service quality and Corporate Image of Telecom Industry (Fujun Lai, Mitch Griffin and Barry J. Babin, 2008), the service quality may have impact on the number of customers. Therefore it is obviously important to find out the dimensions of Telecom Service Quality and major factors which influence it, just to assure the higher market position. 2. Background An Organization continued success comes from new customers migrated from other Organizations as well as satisfied existing customers. The cost of customers retention is less than the cost of attraction, customer s retention is more critical than attraction. The key of the customer s retention is service satisfaction. Delighting customers are good advertiser than the highly paid advertisements in media (Kotler, 2006).

3 Identification of Telecom Service Quality Dimensions in India with Fuzzy Analysis Literature Review Quality of services introduces a bunch of opportunities to satisfy the consumer s perceptions. Service Quality is the output of an assessment process, where the customer compares his anticipation with the services which he perceives and acknowledged ( Brady and Cronin 2001, Jozée Lapierre, Pierre Filiatrault, Jean Perrien, 1996, Senga Briggs, Jean Sutherland, Siobhan Drummond,2007). Service generally has four distinctive properties such as Intangibility, Inseparability of Construction and Utilization, Heterogeneity, and Perishability (Zeithaml et al. 1985). However, most researchers considered service quality as a multidimensional concept. For example, Grönroos (1984) identified two dimensions of the Service Quality construct including Technical Quality and Functional quality-how the service is delivered? A recent study by Xin Shu (2010) reveals that Service Quality practices presents a comprehensive evaluation of Customer Perceptions of Service Quality in the Chinese mobile communications market through developing and estimating a Hierarchical and Multidimensional model and are useful in the Telecommunications Network that utilize more core dimension as well as sub dimensions. Pinaki Das, P.V. Srinivasan, 1999, in their paper on Demand for telephone usage in India, estimated the price elasticities of demand for aggregate telephone usage in India using alternatively national level time series data and a panel data set consisting of annual observations on Indian States. After literature review we have identified the seven dimensions and 21 sub dimensions to find out their priorities and their effectiveness on the Telecom Service Quality as mentioned in Table1. Table 1: Important dimensions and sub dimensions. Core Dimensions Interaction quality (Iq) Environment Quality (Eq) Provisioning(P) Communication(C) Value Added Services(Vas) Tariff (T) Sub Dimensions Approach(Ap) Unique Identification(UI) Company Trust(CT) IT tools(it) Equipment(Eq) Billing System(Bs) Sincerity(Si) Timeline(Tl) Feedback Support(Fs) Call Quality(Cq) Geographical Coverage(Gc) Data Bandwidth(Db) Entertainment Services(Ets) Financial Services(Fs) Educational Services (Eds) Product Cost(Pc) Promotional Offers(Po)

4 470 Amit Chaudhary & Indu Uprety Grievances(G) Service Cost (Sc) Mechanism(Ms) Time Frame to Solve(Tfs) Transparency(Tp) 4. Research Methodology 4.1 Purpose of Research: After literature review of Telecom Service quality we have identified the 21 telecom dimensions to find out their priorities in Indian Telecom Market. 4.2 Strategy of Research Strategy of research is a plan to answer the research problem accurately (Saunder s et. al. 2000). The strategy should include the experiment, survey, case study, ground theory and action. In this research the survey is used as a strategic tool to find out the important parameters those affect the telecom service quality. In study we have applied TOPSIS method first and find out the ranks of the dimensions then the ranks refined with fuzzy trapezoidal approach. After getting ranks we analyzed that out of top 10 ranks four dimensions 1, 7, 9 and 21 exists in both the methodology TOPSIS and Fuzzy TOPSIS it means our model is valid but fuzzy values are more reliable, then we take 8 top dimensions ranked accordingly with fuzzy TOPSIS and make comparative matrix of 8X8 then send to the 8 experts to get their feedback and applied AHP and fuzzy AHP approach to get their priorities. The comparison of fuzzy AHP and fuzzy TOPSIS as seen in the table 4, the ranks of the dimensions are agreed in both of the methodology that shows further validity of the model with minimum error. 4.3 Questionnaire Design The questionnaire was designed on the dimensions and sub dimensions identified in the review of literature. The sub dimensions were judged on the basis of 5 point scale 1-5(not important to important). 4.4 Pilot Testing and Data Collections The pilot study was done to refine the questionnaire. The minimum required respondents are 10 for pilot study (Fink 1995). The questionnaire was distributed to 10 different telecom experts on Linkden Network in the month of April May 2013 to finalize the questionnaire. The finalized questionnaire were distributed to 70 telecom customers and experts to extract the most important dimensions those show the significant effect on the Telecom Service Quality. The judgments of the 70 people are figure out in the table 2.

5 Identification of Telecom Service Quality Dimensions in India with Fuzzy Analysis 471 Table 2: The judgments of the 70 telecom people. Choice Not Imp Less Imp Avg Imp Most Imp Dimensions Validity and Reliability: The questionnaire found valid and reliable with.925 Cronbach's alpha value. TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) and Fuzzy TOPSIS: The TOPSIS methodology originated by Kwangsun Yoon and Hwang Ching-Lai in 80 s as chosen alternative should have the shortest distance from the ideal solution and the farthest from the negative-ideal solution with a defined five steps (Construct the Normalized Decision Matrix, Construct the Weighted Normalized Decision Matrix, Determine Ideal and Negative-Ideal Solutions, Calculate the Separation Measure, Calculate the Separation Measure, Rank the preference order) procedure. This research is based on the fuzzy version of similar to TOPSIS priority ordered ranks of the alternatives with trapezoidal membership functions. The calculated ranks of the alternatives are summarized in table 3.

6 472 Amit Chaudhary & Indu Uprety Table 3: Comparison of Topsis and Fuzzy Topsis data. Topsis Ranking Ranking Fuzzy TOPSIS Ranking Dimensions Dimensions Analytical Hierarchal Process (AHP) and FUZZY AHP: The Analytic Hierarchy Process (AHP) is a structured technique for organizing and analyzing complex decisions. It was developed by Thomas L. Saaty in 1980s for the decision support system in the field of Industry, business, Government etc. This process has steps to find out the weighs of factors and sub-factors with a prescribed procedure with the equal or less than 0.1 consistencies. As the fuzzy numbers approach introduced by Zadeh, are used for the better result as compared with the human thoughts results. Here we are using the trapezoidal membership functions for finding out the fuzzy weights of the important telecom dimensions as indicated in table 4.

7 Identification of Telecom Service Quality Dimensions in India with Fuzzy Analysis 473 Table 4: Comparison of Fuzzy AHP and Fuzzy Topsis data. Criteria Rank in Rank in Fuzzy Weights Fuzzy AHP TOPSIS Company Trust Network Quality of Service Provider Time Frame to Solve Grievances Timeline to provide the service Information provided to understand the product Sincerity of Service Provider] Technology Expertise of Employees Approach of Employee towards Customers Concluding Remarks The Telecom market is growing in India drastically and significantly impacts on the customers and organizations. As a result most of the firms have started to formalize the strategies to tackle these kinds of situations. In this research we emphasized on to find out the important telecom dimension those affect the Organization growth in terms of sale and customers. The analysis has done with two methodology TOPSIS and AHP with fuzzy approach to refine the outcome of research. In study we have identified eight dimensions Approach of Employee towards Customers, Technology Expertise of Employees, Information provided to understand the product, Sincerity of Service Provider, Timeline to provide the service, Network Quality of Service Provider, Time Frame to Solve Grievance, Company Trust, those have a significant impact on the Telecom Service Quality. References [1] A. Parasuraman, Valarie A. Zeithaml, (1985) Leonard L. Berry, A Conceptual Model of Service Quality and Its Implications for Future Research, Journal of Marketing, Vol. 49, No. 4 (Autumn, 1985), pp A. Rozinat, W.M.P. van der Aalst (2008), Conformance checking of processes based on monitoring real behavior, Information Systems 33 (2008) [2] Adem Karahoca, Dilek Karahoca, (2011) GSM churn management by using fuzzy [3] c-means clustering and adaptive neuro fuzzy inference system, Expert Systems with Applications 38 (2011)

8 474 Amit Chaudhary & Indu Uprety [4] Ali Erbasi and Raif Parlakkaya (2012), The use of Analytic Hierarchy Process in the Balanced Scorecard: An Approach in a Hotel Firm, Business and Management Review Vol. 2(2) pp April, 2012 [5] Andreas H. Zins, (2001),"Relative attitudes and commitment in customer loyalty models: Some experiences in the commercial airline industry", International Journal of Service Industry Management, Vol. 12 Issue: 3 pp [6] Ashraf A. Bany Mohammed & Jörn Altmann (2010), A funding and governing model for achieving sustainable growth of computing e- infrastructures, Ann. Telecommun. (2010) 65: [7] Babakus, E., and Boller, G. W. An empirical assessment of the SERVQUAL scale, Journal of Business Strategies, 24, 3, 1992, [8] Brady, M. K., and Cronin, J. Some new thoughts on conceptualizing perceived service quality: a hierarchical approach. Journal of Marketing, 65, 2001, [9] Cronin, J. J., and Taylor, S. A. Measuring service quality: a reexamination and extension, Journal of Marketing, 56, 3, 1992, [10] Dabholkar, P. A., Thorpe, D. I., and Rentz, J. O. A measure of service quality for retailstores: scale development and validation. Journal of the Academy of Marketing Science, 24, 1, 1996, [11] M.C.Y. Tam, V.M.R. Tummala, (2001), An application of the AHP in vendor selection of telecommunications system, Omega, 29 (2001) [12] Parasuraman, A., Zeithaml, V. A., and Berry, L. L. SERVQUAL: a multipleitem scale for measuring consumer perceptions of service quality, Journal of Retailing, 64, 1, 1988, [13] R.P. Sundarraj (2002), A Web-based AHP approach to standardize the process of managing service-contracts, Decision Support Systems 37 (2004) [14] Saaty, T.L. and Vargas, L.G. (2000) Models, Methods, Concepts and Applications of the Analytic Hierarchy Process, Boston: Kluwer Academic Publishers. [15] Xuan Cheng, Bo Gong, Hong Zhang, 2012, Customer Value Assessment Using the Fuzzy AHP and TOPSIS Methods: Application in Bank Journal of Information & Computational Science 9: 12 (2012)

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