FACTORS INFLUENCING THE USAGE OF MOBILE SHOPPING APPLICATIONS AND THE IMPACT OF THESE FACTORS ON SATISFACTION AND INTENTION TO USE

Yıl 2018, Cilt: 7 Sayı: 1, 306 - 310, 01.09.2018

Tevfik Sukru Yaprakli Zeynep Kacer Musa Unalan

https://doi.org/10.17261/Pressacademia.2018.904

Öz

Purpose- The purpose of this study is to determine the impact of perceived risk, perceived enjoyment, perceived usefulness, perceived ease of use on satisfaction and intention to use in mobile shopping applications.

Methodology- For testing the proposed hypotheses, a survey instrument is designed to measure different constructs. Convenience sampling method was used in this study. Data collected from Erzurum in Turkey. After the validation and cleaning of data by removal missing entries, a total of 389 respondet data points is available for carrying out further analysis.

Findings- The multiple regression results show that satisfaction and intention to use are influenced by perceived usefulness, perceived ease of use and perceived enjoyment. Also, perceived risk have a significant effect on satisfaction whereas the effect of perceived risk is non-significant on intention to use.

Conclusion- The seven of eight hypotheses are accepted in our model. The results show that participants have hihger perceived risk when they use to mobile shopping applications.

Anahtar Kelimeler

Technology acceptance model, perceived risk, perceived enjoyment, satisfaction, mobile shopping applications

MOBİL ALIŞVERİŞ UYGULAMALARININ KULLANIMINI ETKİLEYEN FAKTÖRLER VE BU FAKTÖRLERİN MEMNUNİYET VE KULLANMA NİYETİ ÜZERİNDEKİ ETKİSİ

Öz

Amaç- Araştırmanın amacı, tüketicilerin mobil alışveriş uygulamaları kullanımında algıladıkları risk, kullanışlılık, kullanım kolaylığı ve keyifin, memnuniyet ve kullanma niyeti üzerindeki etkisini belirlemektir.

Yöntem- Veri toplama metodu olarak anket yöntemi seçilmiştir. Anket çalışması kolayda örnekleme yöntemi kullanılarak yapılmıştır. Araştırmanın ana kütlesini Erzurum il sınırlarında yaşayan 18 yaş ve üzeri katılımcılar oluşturmaktadır. 412 kişiye uygulanan anketin hatalı ve eksik cevapları elendikten sonra 389 adet anket formu değerlendirmeye tabi tutulmuştur.

Bulgular- Yapılan çoklu regresyon analizi sonucunda, algılanan kullanışlılık, algılanan kullanım kolaylığı ve algılanan keyif kullanma niyeti ve memnuniyet üzerinde etkili olduğu görülmüştür. Ayrıca algılanan riskin memnuniyet üzerinde etkisi olmasına rağmen kullanma niyeti üzerinde etkisinin olmadığı sonucuna ulaşılmıştır.

Sonuç- Araştırma için belirlenen 8 hipotezden 7’si kabul 1’i red edilmiştir. Katılımcıların mobil alışveriş uygulamalarını kullanırken algıladıkları riskin yüksek olduğu tespit edilmiştir.

Anahtar Kelimeler

Teknoloji kabul modeli, algılanan risk, algılanan keyif, memnuniyet, mobil alışveriş uygulamaları

Kaynakça

• Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decision support systems, 32(2), 201-214.
• Bruner II, G. C., Kumar, A. (2005). Explaining consumer acceptance of handheld Internet devices. Journal of business research, 58(5), 553-558.
• Childers, T. L., Carr, C. L., Peck, J., Carson, S. (2002). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of retailing, 77(4), 511-535.
• Churchill Jr, G. A., Surprenant, C. (1982). An investigation into the determinants of customer satisfaction. Journal of marketing research, 491-504.
• Dai, H., Palvi, P. C. (2009). Mobile commerce adoption in China and the United States: a cross-cultural study. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 40(4), 43-61.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
• Davis, F. D., Bagozzi, R. P., Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.
• Davis, F. D., Bagozzi, R. P., Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of applied social psychology, 22(14), 1111-1132.
• Dowling, G. R., Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of consumer research, 21(1), 119-134.
• Featherman, M. S., Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International journal of human-computer studies, 59(4), 451-474.
• Fishbein, M., Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research.
• Gefen, D., Straub, D. W. (2003). Managing user trust in B2C e-services. E-service Journal, 2(2), 7-24.
• Gillett, P. L. (1976). In-home shoppers. An overview. The Journal of Marketing, 81-88.
• Giovannini C. J., Ferreira J. B., Silva J. F., Ferreira D. B. (2015). The effects of trust transference, mobile attributes and enjoyment on mobile trust. BAR, Rio de Janeiro, 12 (1/5), 88-108.
• Han, J., Han, D. (2001). A framework for analyzing customer value of internet business. JITTA: Journal of Information Technology Theory and Application, 3(5), 25.
• Kang, H., Lee, M. J., Lee, J. K. (2012). Are you still with us? A study of the post-adoption determinants of sustained use of mobile-banking services. Journal of Organizational Computing and Electronic Commerce, 22(2), 132-159.
• Khalifa, M., Ning Shen, K. (2008). Explaining the adoption of transactional B2C mobile commerce. Journal of enterprise information management, 21(2), 110-124.
• Kim, C., Mirusmonov, M., Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322.
• Kim, D. J., Ferrin, D. L., Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision support systems, 44(2), 544-564.
• Kim, H. W., Chan, H. C., Gupta, S. (2007). Value-based adoption of mobile internet: an empirical investigation. Decision support systems, 43(1), 111-126.
• King, W. R., He, J. (2006). A meta-analysis of the technology acceptance model. Information & management, 43(6), 740-755.
• Kucukusta, D., Law, R., Besbes, A., Legohérel, P. (2015). Re-examining perceived usefulness and ease of use in online booking: the case of Hong Kong online users. International Journal of Contemporary Hospitality Management, 27(2), 185-198.
• Kurtuluş, K. (1998). Pazarlama Araştırmaları. İÜ İşletme Fakültesi Yayınları No 274, 6. Baskı İstanbul.
• Lee, M. K., Cheung, C. M., Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & management, 42(8), 1095-1104.
• Lee, Y., Kozar, K. A., Larsen, K. R. (2003). The technology acceptance model: past, present, and future. Communications of the Association for information systems, 12(1), 50.
• Mitchell, V. W. (1992). Understanding consumers’ behaviour: can perceived risk theory help?. Management Decision, 30(3).
• Moore, G. C., Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
• Novak, T. P., Hoffman, D. L., Duhachek, A. (2003). The influence of goal-directed and experiential activities on online flow experiences. Journal of consumer psychology, 13(1-2), 3-16.
• Oliver, R. L. (1981). Measurement and evaluation of satisfaction processes in retail settings. Journal of retailing.
• Pai, F. Y., Huang, K. I. (2011). Applying the technology acceptance model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650-660.
• Ross, I. (1975). Perceived risk and consumer behavior: a critical review. ACR North American Advances.
• Shih, H. P. (2004). An empirical study on predicting user acceptance of e-shopping on the Web. Information & Management, 41(3), 351-368.
• Thakur, R., Srivastava, M. (2013). Customer usage intention of mobile commerce in India: an empirical study. Journal of Indian Business Research, 5(1), 52-72.
• Tsu Wei, T., Marthandan, G., Yee-Loong Chong, A., Ooi, K. B., Arumugam, S. (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370-388.
• Venkatesh, V., Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management science, 46(2), 186-204.
• Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS quarterly, 425-478.
• Westbrook, R. A., Oliver, R. L. (1991). The dimensionality of consumption emotion patterns and consumer satisfaction. Journal of consumer research, 18(1), 84-91.
• Wu, J. H., Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & management, 42(5), 719-729.