HİZMET KALİTESİ ÖLÇÜM ARAÇLARININ SİNİR AĞLARI KULLANILARAK MODELLENMESİ. Aşiyan Meriç ENDÜSTRİ MÜHENDİSLİĞİ ANABİLİM DALI YÜKSEK LİSANS TEZİ

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1 öz HİZMET KALİTESİ ÖLÇÜM ARAÇLARININ SİNİR AĞLARI KULLANILARAK MODELLENMESİ Aşiyan Meriç ENDÜSTRİ MÜHENDİSLİĞİ ANABİLİM DALI YÜKSEK LİSANS TEZİ Ankara, 2003 Hizmet kalitesi, iş yönetimi alanında yıllardır araştırılmaktadır. Pazardaki rekabet ve pazarın iç performanstan müşteri memnuniyeti gibi dış kazançlara transferi "hizmet kalitesi" kavramına daha fazla ilgiye neden olmaktadır. Fakat, şu zamana kadar araştırmacılar arasında hizmet kalitesinin kavramsallığı üzerinde bir fikir birliğine varılamamıştır. En yaygın tanım kaliteyi müşterinin hizmetten algısı olarak gören geleneksel fikirdir (Parasuraman, Zeithaml ve Berry, 1988). Bu yaklaşıma göre, hizmet üründen farklıdır çünkü dokunulamaz, heterojendir ve eş zamanlı olarak üretilmekte ve tüketilmektedir. Hizmet kalitesinin bu geleneksel tanımına dayanarak Parasuraman, vd. (1985) algılanan hizmet kalitesinin "Fark Modeli"ni geliştirdiler. Çalışmalarında geliştirdikleri ve SERVQUAL olarak adlandırdıkları hizmet kalitesi ölçüm aracı hizmet kalitesini beklenti ve algı arasındaki bir fark olarak ölçmektedir. Bu araç, hizmet kalitesi araştırmalarında bir ilktir ve hizmet kalitesi araştırmaları içinde oldukça popüler olmuştur. Ancak, başka araştırmacılar bir hizmet kalitesi ölçümü olarak SERVQUAL'in kullanışlılığına kusurlarını işaret ederek karşı çıkmışlardır (Carman, 1990; Cronin ve Taylor, 1992, Teas, 1993). Hizmet kalitesini ölçmek için SERVPERF olarak adlandırılan bir başka araç Cronin ve Taylor (1992) tarafından ortaya atılmıştır ve bu araştırmacılar SERVPERF'in SERVQUAL'dan daha iyi performans gösterdiğini iddia etmişlerdir. Her ne kadar şu zaman kadar gerek iş gerekse de akademik alanda çok sayıda çaba gösterilmiş olsa da; hizmet kalitesinin ölçümü, yapısının karmaşıklığı nedeniyle hala bir düzensizlik halindedir.

2 Yapay sinir ağları araştırmalarındaki hızlı gelişme bazı ticaret uygulamalarının çözümlerine imkan vermiştir. Bu çalışma, sinir ağları yardımıyla hizmet kalitesi ölçüm modellerinin modellenmesini ve değerlendirilmesini amaçlamaktadır. Hangi modelin diğerlerine nazaran daha iyi performans gösterdiğini belirlemek amacıyla dört hizmet kalitesi modeli (algılar-eksibeklentiler fark modeli, sadece-algılar modeli, beklentiler-eksi-algılar fark modeli, ve beklentiler ve algılar modeli) arasında karşılaştırmalı bir araştırma incelenmektedir. Bunu başarmak suretiyle, hangi modelin en iyi performans gösterdiğine dair devam eden tartışmaya katkıda bulunulacağı düşünülmektedir. Bu çalışmada, matematiksel bir yazılım olan Matlab 6.5 kullanılarak en iyi sonuçlar doğruluk ve uygunluk istatistiği bakımından P-E fark modeli kullanıldığında elde edilmektedir. Aynı mantıksal yaklaşım bir istatistiksel yazılım olan SPSS 11.0 kullanılarak en iyi performans gösteren sinir ağı modelleriyle aşamalı ayırma analizinin karşılaştırılmasında uygulanmaktadır. Analizin sonuçları sinir ağlarının sınıflandırma performansının ayırma analizinden daha yüksek olduğunu göstermektedir.

3 ABSTRACT MODELLING SERVICE QUALITY MEASUREMENT INSTRUMENTS BY USING NEURAL NETWORKS Aşiyan Meriç MASTER THESIS DEPARTMENT OF INDUSTRIAL ENGINEERING Ankara, 2003 Quality of service has been studied in the field of business management for years. Competition in the market and transfer of market from internal performance to external interests like customer satisfaction bring about more attention in this concept of "service quality". However, so far no consensus has been reached among researchers on the conceptualization of service quality. The most common definition is the traditional notion that views quality as the customers' perception of service (Parasuraman, Zeithaml and Berry, 1988). According to this approach, services are different from goods because they are intangible, heterogeneous, and are simultaneously produced and consumed. Based on this traditional definition of service quality, Parasuraman et al. (1985) developed the "Gap Model" of perceived service quality. Service quality measurement instrument, developed in their study and named as SERVQUAL, measures service quality as a gap between expectation and perception. This scale is the first in service quality studies and it become very popular among service quality researches. However, several researchers disagreed with the usefulness of the SERVQUAL instrument as a measure of service quality by pointing out its shortcomings (Carman, 1990; Cronin and Taylor, 1992; Teas, 1993). Another instrument, called as SERVPERF, in measuring service quality has been suggested by Cronin and Taylor (1992) and these researchers claimed that SERVPERF outperformed SERVQUAL. Although, numerous efforts have been made in both business and academic field up to now, the measurements of service quality is still at a state of confusion because of the complexity of its construct.

4 The rapid development in artificial neural networks studies has given the opportunity for the solution of some business applications. This study aims at modeling and evaluating service quality measurements models by means of neural networks. A comparative study among for service quality models (perceptions-minus-expectations gap model, perceptions-only model, expectations-minus-perceptions gap model, and expectations and perceptions model) is examined so as to determine which model outperforms than the others. By achieving this, it is thought to be contributed on the ongoing debate concerning which model performance is the best. In this study, the best results are obtained by using mathematical software Matlab 6.5, in terms of accuracy and goodness-of-fit statistic when P-E gap model is used. The same logical approach is applied in the comparison of the best-performed neural network models and stepwise discriminant analysis, by using statistical software SPSS The results of the analysis show that neural network classification performances become superior to these of discriminant analysis. Keywords: Service quality measurement, service quality models, neural networks, discriminant analysis.

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