The Effects of Perceived Risk and Cost on Technology Acceptance: A Study on Tourists’ Use of Online Booking

Yıl 2015, Cilt: 13 Sayı: 2, 227 - 244, 06.07.2015

Assist. Prof. Volkan Özbek , Lect. Mustafa Günalan Assist. Prof. Fatih Koç Nisa Şahin , Eda Kaş

https://doi.org/10.18026/cbusos.49782

Cited By: 1

Öz

The aim of this study is to determine how tourists’ online booking sites related risk and cost perceptions affect their adoption level of this technology. In order to detect tourists’ adoption level, Technology Acceptance Model (TAM) was adapted to online reservation technology. In this context, relationships between perceived risk, perceived cost, and the variables of TAM which are perceived ease of use, perceived usefulness, and behavioral intentions were tested. The participants of the study were 242 Russian tourists visiting Antalya, which is an important touristic destination in Turkey. In the research, participants were determined with the convenience sampling method and the data was gathered through face to face survey method. In the analyses of relationships between and effects sizes of variables, Structural Equation Modeling was used. The results revealed that tourists’ risk perceptions about using online reservation technology have negative effects on TAM variables while cost perceptions have positive effects on these variables

Anahtar Kelimeler

Perceived risk, Cost, Technology acceptance model, Perceived usefulness, Perceived ease of use

RİSK VE MALİYET ALGILARININ TEKNOLOJİ KABULÜNE ETKİLERİ: TURİSTLERİN ONLİNE REZERVASYON KULLANIMI ÜZERİNE BİR ÇALIŞMA

Öz

Bu çalışmanın temel amacı, turistlerin online rezervasyon sitelerinin
kullanımına ilişkin risk ve maliyet algılarının, online rezervasyon teknolojisini
kabullerini nasıl etkilediğini belirlemektir. Turistlerin online rezervasyon
teknolojisini kabul seviyelerini belirlemek amacıyla, Teknoloji Kabul Modeli
online rezervasyon teknolojisine uyarlanmıştır. Bu bağlamda algılanan risk ve
algılanan maliyet değişkenleri ile Teknoloji Kabul Modelinin değişkenleri olan
algılanan kullanım kolaylığı, algılanan kullanışlılık ve kullanıma yönelik
davranışsal niyetler değişkenleri arasındaki ilişkiler test edilmiştir.
Araştırmanın katılımcıları, Türkiye’nin önemli bir destinasyon merkezi olan
Antalya’yı ziyaret etmekte olan 242 Rus turistten oluşmaktadır. Araştırmanın
katılımcıları kolayda örneklem yöntemiyle belirlenmiş ve veriler yüz yüze
anket yöntemi kullanılarak toplanmıştır. Değişkenler arası ilişki ve etkilerin
analizinde Yapısal Eşitlik Modellemesi kullanılmıştır. Analiz sonuçları,
turistlerin online rezervasyon teknolojisini kullanmaya ilişkin risk algılarının
Teknoloji Kabul Modeli değişkenleri üzerinde olumsuz etkisi olduğunu
gösterirken, maliyet algılarının bu değişkenler üzerinde olumu etkisi olduğunu
ortaya koymaktadır.

Anahtar Kelimeler

Algılanan risk, Maliyet, Teknoloji Kabul Modeli, Algılanan Kullanışlılık, Algılanan kullanım kolaylığı

Kaynakça

• AGARWAL, R., and PRASAD, J. (1999). Are individual
• differences germane to the acceptance of new information
• technologies? Decision Sciences, 30, pp. 361-392.
• AHN, T., SUH, Y.I., LEE, J.K. and PEDERSEN, P.M. (2014). Understanding purchasing intentions in secondary sports ticket websites. International Journal of Sports Marketing & Sponsorship, 16, pp. 40-54.
• AJZEN, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, pp. 179- 211.
• AKÇA, Y. and ÖZER, G. (2012). Teknoloji kabul modelinin kurumsal kaynak planlaması uygulamalarında kullanılması. Business and Economics Research Journal, 3, pp. 79-96.
• AMARO, S. and DUARTE, P. (2015). An integrative model of consumers’ intentions to purchase travel online. Tourism Management, 46, pp. 64-79.
• BELDONA, S., NUSAIR, K. and DEMICCO, F. (2009). Online travel purchase behavior of generational cohorts: A longitudinal study. Journal of Hospitality Marketing & Management, 18, pp. 406- 420.
• BELKHAMZA, Z. and WAFA, S.A. (2009). The effect of perceived risk on the intention to use e-commerce: The case of Algeria. Journal of Internet Banking and Commerce, 14, pp. 1-10.
• BURTON-JONES, A., and HUBONA, G.S. 2006. The mediation of external variables in the technology acceptance model. Information&Management, 43, pp. 706-717.
• CEYLAN, H.H., GENÇ, E. and EREM, I. (2013). Tüketicilerin internet bankacılığını benimsemesini etkileyen faktörlerin yapısal eşitlik modeli ile araştırılması. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 13, pp. 143-154.
• CHANG, H. H. and CHEN, S. W. (2008). The Impact of Online Store Environment Cues on Purchase Intention. Online Information Review, 32 (6), pp. 818-845.
• CHAU, P.Y.K., HU, P.J-H., LEE, B.L.P. and AU, A.K.K. (2007). Examining customers’trust in online vendors and their dropout decisions: An empirical study. Electronic Commerce Research and Applications, 6, pp. 171-182.
• CLEMES, M.D., GAN, C. and ZHANG, J. (2014). An empirical analysis of online shopping adoption in Beijing, China. Journal of Retailing and Consumer Services, 21, pp. 364-375.
• ÇELIK, H. (2009). Bireysel müşterilerin sanal mağazaları kullanma eğilimlerini açıklarken çevirim içi alışveriş yapma kaygısını daha ne kadar görmezlikten gelebiliriz? Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 18, pp. 93-118.
• DAVIS, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterely, 13, pp. 319-340.
• DAVIS, F.D., BAGOZZI, R. and WARSHAW, P. (1989). User acceptance of computer technology: acomparison of two theoretical models. Management Science, 35, pp. 982-1003.
• DAVIS, F.D. and VENKATESH, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45, pp. 19-45.
• ERDOĞMUŞ, N. and ESEN, M. (2011). An investigation of the effects of technology readiness on technology acceptance in e-hrm. Procedia Social and Behavioral Sciences, 24, pp. 487-495.
• FORSYTHE, S.M. and SHI, B. (2003). Consumer patronage and risk perceptions in Internet shopping. Journal of Business Research, 56, pp. 867-875.
• GÜMÜŞSOY, Ç.A. and ÇALIŞIR, F. (2009). E-açık eksiltme teknolojisinin kabulünü etkileyen faktörlerin belirlenmesi. İTÜ Dergisi, 8, pp. 107-118.
• IZQUIERDO-YUSTA, A. and CALDERON-MONGE, E. (2011). Internet as a distribution channel: Emprical evidence from the service sector and managerial opportunities. Journal of Internet Commerce, 10, pp. 106-127.
• JOO, J., and SANG, Y. (2013). Exploring koreans’ smartphone usage: An integrated model of the technology acceptance model and uses and gratifications theory. Computers in Human Behavior, 29, pp. 2512-2518.
• LAM, J.M.S., TAN, S.H. and OH, Y.L. (2014). Exploring internet influence towards travel satisfaction. Procedia-Social and Behavioral Sciences, 130, pp. 542-551.
• LEGRIS, P., INGHAM, J. and COLLERETTE, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Technology, 40, pp. 191- 204.
• LI, Y-H. and HUANG, J-W. (2009). Applying Theory of Perceived Risk and Technology Acceptance Model in the online shopping channel. World Academy of Science, Engineering and Technology, 53, pp. 919-925.
• LIM, K-S., LIM, J-S. and HEINRICHS, J.H. (2008). Testing a integrated model of e-shopping web site usage. Journal of Internet Commerce, 7, pp. 291-312.
• LIN, W-B. (2008). Construction of online consumer behavior models: A comparative study of industries in Taiwan. International Journal of Commerce and Management, 18, pp. 123-149.
• LU, ,J-L., CHOU, H-Y. and LING, P-C. (2009). Investigating passengers’ intentions to use technology-based self check-in services. Transportation Research Part E, 45, pp. 345-356.
• MOROSAN, C. and JEONG, M. (2008). Users’ perceptions of two types of hotel reservation Web Sites. International Journal of Hospitality Management, 27, pp. 284-292.
• NUNKOO, R. and RAMKISSOON, H. (2013). Travelers’ e- purchase intent of tourism products and services. Journal of Hospitality Marketing & Management, 22, pp. 505-529.
• PAVLOU, P.A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7, pp. 101-134.
• TETT, R.P. and MEYER, J.P. (1993). Job satisfaction, organizational commitment, turnover intention, and turnover: Path analysis based on meta-analytic findings. Personnel Psychology, 46, pp. 259-293.
• VIJAYASARATHY, L.R. and JONES, J.M. (2000). Print and internet catalog shopping: Assessing attitudes and intentions. Internet Research, 10, pp. 191-202.
• WANG, H-Y. and WANG, S-H. (2010). Predicting mobile hotel reservation adoption: Insight from a perceived value standpoint. International Journal of Hospitality Management, 29, pp. 598-608.
• WU, S-I. (2002). Internet marketing involvement and consumer behaviour. Asia Pasific Journal of Marketing and Logistics, 14(4), pp. 36-53.
• WU, J-H. and WANG, S-C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42, pp. 719-729.
• YILMAZ, Ö. (2014). The effect of websites on customer preferences related to tourism products within the framework of Technological Acceptance Model (TAM). IIB International Refereed Academic Social Sciences Journal, 5, pp. 46-59.
• YOUSAFZAI, S.Y., FOXALL, G.R., and PALLISTER, J.G. 2007. Technology acceptance: a-meta analysis of the TAM: part 1. Journal of Modelling in Management, 2, pp. 251-280.
• ZHU, D.H. and CHANG, Y.P. (2014). Investigating consumer attitude and intention toward free trials of technology-based services. Computers in Human Behavior, 30, pp. 328-334.