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YENİLİKLERİN YAYGINLAŞMASI VE GENİŞLETİLMİŞ TEKNOLOJİ KABUL MODELİNİN BÜTÜNLEŞTİRİLMESİYLE TÜRKİYE’DE MOBİL UYGULAMA KULLANMA NİYETİNİN ARAŞTIRILMASI

Yıl 2021, Cilt , Sayı 59, 59 - 90, 31.08.2021
https://doi.org/10.18070/erciyesiibd.877730

Öz

Dünyadaki sosyal ve ekonomik değişimlerin temel belirleyicilerinden olan mobil teknolojiler oldukça büyük bir hızla gelişim göstermektedir. Mobil teknolojilerin en önemli göstergelerinden biri olan mobil uygulamalar günlük işlemlerin gerçekleştirilmesini destekleyen ve mobil cihazlarda yer alan programlar olarak tanımlanmaktadır. Bir mobil uygulamanın başarılı olarak kabul edilebilmesi için bireylerin mobil uygulamaları yüklemelerinin yanı sıra mobil uygulamaları benimsemiş olmaları ve uygulamaları sıklıkla kullanmaları gerekmektedir. Bu çalışmanın amacı tüketicilerin cep telefonlarının uygulama marketlerinde yer alan belirli bir uygulamayı kullanma kararı verme davranışlarını anlamak, yorumlak ve analiz etmektir. Bu bağlamda tüketiciler tarafından bir uygulamanın kullanım kararının verilmesini sağlayan direkt ve indirekt faktörler literatürdeki en etkili teorilerden olan Yeniliklerin Yaygınlaşması (Diffision of Innovations-DOI) ve Genişletilmiş Teknoloji Kabul Modeli (Extended Teknology Acceptance Model- Extended TAM) teorileri bütünleştirilerek araştırılmıştır. Çalışmada veri toplama aracı olarak anket yöntemi kullanılmıştır. 396 kişi ile gerçekleştirilen bu araştırmanın sonuçlarına göre imaj ve gözlenebilirliğin mobil uygulamaları benimseme niyetini doğrudan, anlamlı ve pozitif olarak etkilediği bulunurken, algılanan kullanım kolaylığının ve algılanan kullanışlılığın mobil uygulamaları benimseme niyetini anlamlı olarak etkilemediği bulunmuştur. Gençlerin imaj, ünlülük, bilinir olma gibi özelliklere önem verdikleri bilinmektedir. Bu sonuçlar mobil uygulamaları çoğunlukla gençlerin kullandığı genç nüfuslu Türkiye açısından oldukça anlamlı görülmektedir.

Kaynakça

  • Atkinson, N. L. (2007). Developing a questionnaire to measure perceived attributes of eHealth innovations. American Journal of Health Behavior, 31(6), 612–621.
  • Bao, Y., Zhou, K. Z., ve Su, C. (2003). Face consciousness and risk aversion: do they affect consumer decision‐making? Psychology & Marketing, 20(8), 733–755.
  • Chan, T. C. (2005). A Study of the Adoption E-learning Factors on Taiwan (Doctoral Thesis). Chaoyang University of Technology.
  • Chauhan, S., Mukhopadhyay, S., ve Jaiswal, M. (2018). The adoption of mobile app for B2C transaction in platform marketplace: An empirical examination of key drivers. Journal of Information Technology Case and Application Research, 20(1), 9–22.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319.
  • Davis, F. D., Bagozzi, R. P., ve 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., ve Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22(14), 1111–1132.
  • Davis, F D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral Thesis). Massachusetts Institute of Technology.
  • Davis, F. D. (1989). Perceived Usefulness , Perceived Ease of Use , and User Acceptance of Information Technology. MIS Quarterly ·, 13(3), 319–340.
  • Fagan, M. H. (2019). Factors Influencing Student Acceptance of Mobile Learning in Higher Education. Computers in the Schools, 36(2), 105–121.
  • Fishbein, M., ve Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley, Reading, MA
  • Fishbein, M., ve Ajzen, I. (1980). Predicting and understanding consumer behavior: Attitude-behavior correspondence. Understanding attitudes and predicting social behavior. Prentice Hall Englewood Cliffs, NJ.
  • Gürbüz, S. (2019). AMOS ile yapısal eşitlik modellemesi. Ankara: Seçkin Yayıncılık.
  • Gumussoy, C. A., Kaya, A., ve Ozlu, E. (2018). Determinants of mobile banking use: an extended TAM with perceived risk, mobility access, compatibility, perceived self-efficacy and subjective norms. In Industrial Engineering in the Industry 4.0 Era (pp. 225-238). Springer, Cham.
  • Hajiheydari, N., ve Ashkani, M. (2018). Mobile application user behavior in the developing countries: A survey in Iran. Information Systems, 77, 22–33.
  • Holak, S. L., ve Lehmann, D. R. (1990). Purchase intentions and the dimensions of innovation: An exploratory model. Journal of Product Innovation Management: An International Publication of the Product Development & Management Association, 7(1), 59–73.
  • Hsu, C. L., Lu, H. P., ve Hsu, H. H. (2007). Adoption of the mobile Internet: An empirical study of multimedia message service (MMS). Omega, 35(6), 715–726.
  • Joo., Y. J., Lim, K. Y., ve Lim, E. (2014). Investigating the structural relationship among perceived innovation attributes, intention to use and actual use of mobile learning in an online university in South Korea. Australasian Journal of Educational Technology, 30(4), 427–439.
  • Kalinic, Z., ve Marinkovic, V. (2016). Determinants of users’ intention to adopt m-commerce: an empirical analysis. Information Systems and E-Business Management, 14(2), 367–387.
  • Karahanna, E., Straub, D. W., ve Chervany, N. L. (1999). Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–213.
  • Kenny, Phan, Daim, ve Tugrul, U. (2011). Exploring technology acceptance for mobile services. Journal of Industrial Engineering and Management (JIEM), 4(2), 339–360.
  • Kim, J., Connolly, D. J., ve Blum, S. (2014). Mobile Technology: An Exploratory Study of Hotel Managers. International Journal of Hospitality & Tourism Administration, 15(4), 417–446.
  • Kim, S. H. (2008). Moderating effects of Job Relevance and Experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Information and Management, 45(6), 387–393.
  • Kim, Y. H., Kim, D. J., ve Wachter, K. (2013). A study of mobile user engagement (MoEN): Engagement motivations, perceived value, satisfaction, and continued engagement intention. Decision Support Systems, 56, 361–370.
  • Lee, M. S., McGoldrick, P. J., Keeling, K. A., ve Doherty, J. (2003). Using ZMET to explore barriers to the adoption of 3G mobile banking services. International Journal of Retail & Distribution Management, 31(6), 340-348.
  • Lee, S.-G., Park, B., Kim, S.-H., ve Lee, H.-H. (2012). Innovation and imitation effects in the mobile telecommunication service market. Service Business, 6(3), 265–278.
  • Lee, Y.C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517–541.
  • Lin, C., ve Bhattacherjee, A. (2010). Extending technology usage models to interactive hedonic technologies: a theoretical model and empirical test. Information Systems Journal, 20(2), 163–181.
  • Matthew Yaw Owusu, G., Amoah Bekoe, R., Amoasa Addo-Yobo, A., ve Otieku, J. (2020). Mobile Banking Adoption among the Ghanaian Youth. Journal of African Business. 1-22.
  • Mehra, A., Paul, J., ve Kaurav, R. P. S. (2020). Determinants of mobile apps adoption among young adults: theoretical extension and analysis. Journal of Marketing Communications, 00(00), 1–29.
  • Min, S., Kam Fung So, K., ve Jeong, M. (2019). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. Journal of Travel & Tourism Marketing, 36(7), 770–783.
  • Moore, G. C., ve Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
  • Nysveen, H., Pedersen, P. E. ve Thorbjørnsen, H. (2005). Intentions to use mobile services: Antecedents and cross-service comparisons. Journal of the Academy of Marketing Science, 33(3), 330–346.
  • Oliveira, T., Thomas, M., ve Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information and Management, 51(5), 497–510.
  • Premkumar, A. G., Ramamurthy, K., ve Nilakanta, S. (1994). Implementation of Electronic Data Interchange: An Innovation Diffusion Perspective. Journal of Management Information Systems, 11(2), 157–186.
  • Purcell, K. (2011). Half of adult cell phone owners have apps on their phones. Retrieved from http://pewinternet.org/Reports/2011/Apps-update.aspx
  • Radner, R., ve Rothschild, M. (1975). On the Allocation of Effort*. Journal of Economic Theory, 10(3), 358–376.
  • Ram, S., ve Sheth, J. N. (1989). Consumer resistance to innovations: the marketing problem and its solutions. Journal of Consumer Marketing.6(2), 5-14.
  • Robinson, L. (2009). A summary of Diffusion of Innovations. Enabling Change, 5(10), 1–7.
  • Rogers, E. M. (1995). Diffusion of Innovations: modifications of a model for telecommunications. In Die diffusion von innovationen in der telekommunikation (pp. 25–38). Berlin: Springer Berlin Heidelberg.
  • Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press.
  • Rogers, E. M., ve Shoemaker, F. (1983). Diffusion of innovation: A cross-cultural approach. New York: Free Press.
  • Roy, S. (2017). App Adoption And Swıtchıng Behavior: Applying The Extended Tam In Smartphone App Usage. JISTEM - Journal of Information Systems and Technology Management, 14(2), 239–261.
  • Sam, K. M., Chatwin, C. R., ve Ma, I. C. (2014). Mobile stock trading (MST) and its social impact: A case study in Hong Kong. In IEEE International Conference on Industrial Engineering and Engineering Management (ss. 437–441).
  • Social, W. are. (2020). Digital in 2020. Retrieved from https://wearesocial.com/digital-2020
  • Tamilmani, K., Rana, N. P., ve Dwivedi, Y. K. (2018). Mobile application adoption predictors: Systematic review of UTAUT2 studies using weight analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11195 LNCS). Springer International Publishing.
  • Taylor, D. G., Voelker, T. A., ve Pentina, I. (2011). Mobile Application Adoption by Young Adults: A Social Network Perspective. WCOB Faculty Publications.
  • Taylor, S., ve Todd., P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144–176.
  • Tornatzky, L. G., ve Klein, K. J. (1982). Innovation Characteristics and Innovation Adoption- Implementation : A Meta-Analysis of Findings. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, 29(1), 28–45.
  • Torres, C. A. (2011). Patient Health Portals for Personal Health in Health Consumers’ Intentions to Use Examining the Role of Anxiety and Apathy Information Management (Doctoral Thesis). Florida State University.
  • Van Slyke, C., Belanger, F., ve Comunale, C. L. (2004). Factors influencing the adoption of web-based shopping: the impact of trust. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 35(2), 32–49.
  • Venkatesh, V., Morris, M. G., Davis, G. B., ve Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478.
  • White, J. B., Tynan, R., Galinsky, A. D., ve Thompson, L. (2004). Face threat sensitivity in negotiation: Roadblock to agreement and joint gain. Organizational Behavior and Human Decision Processes, 94(2), 102–124.
  • Yang, H. C. (2013). Bon appétit for apps: Young American consumers’ acceptance of mobile applications. Journal of Computer Information Systems, 53(3), 85–96.
  • Yazıcıoğlu, Y., ve Erdoğan, S. (2004). SPSS uygulamalı bilimsel araştırma yöntemleri. Ankara: Detay Yayınevi. Yi, M. Y., ve Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human Computer Studies, 59(4), 431–449.
  • Yoon, V. Y., Hostler, R. E., Guo, Z., ve Guimaraes, T. (2012). Assessing the moderating effect of consumer product knowledge and online shopping experience on using recommendation agents for customer loyalty. Decision Support Systems, 55, 883–893.
  • Zarmpou, T., Saprikis, V., Markos, A., ve Vlachopoulou, M. (2012). Modeling users’ acceptance of mobile services. Electronic Commerce Research, 12(2), 225–248.
  • Zhang, L., Zhu, J., ve Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902–1911.

INVESTIGATION OF INTENTION TO USE MOBILE APPLICATION IN TURKEY BY INTEGRATING DIFFISION OF INNOVATIONS AND EXTENDED TEKNOLOGY ACCEPTANCE MODEL

Yıl 2021, Cilt , Sayı 59, 59 - 90, 31.08.2021
https://doi.org/10.18070/erciyesiibd.877730

Öz

Mobile technologies, which are one of the main determinants of social and economic changes in the world, develop rapidly. Mobile applications, one of the most important indicators of mobile technologies, are defined as programs that support the realization of daily transactions and take place on mobile devices. In order for a mobile application to be considered successful, individuals must have adopted mobile applications and frequently use applications as well as installing mobile applications. The aim of this study is to understand, interpret and analyze the behaviors of consumers to decide to use a specific application in application markets of mobile phones. In this context, direct and indirect factors that enable consumers to decide on the use of an application have been investigated by integrating the Diffision of Innovations (DOI) and Extended Technology Acceptance Model (Extended TAM) theories, which are among the most effective theories in the literature. Questionnaire method was used as a data collection tool in the study. According to the results of this study conducted with 396 people, it was found that image and observability directly, significantly and positively affect the intention to adopt mobile applications, while perceived ease of use and perceived usefulness did not significantly affect the intention to adopt mobile applications. It is known that young people attach importance to features such as image, fame, and being known. These results are are seemed quite significant for Turkey which has young population and where mostly young people use mobile applications.

Kaynakça

  • Atkinson, N. L. (2007). Developing a questionnaire to measure perceived attributes of eHealth innovations. American Journal of Health Behavior, 31(6), 612–621.
  • Bao, Y., Zhou, K. Z., ve Su, C. (2003). Face consciousness and risk aversion: do they affect consumer decision‐making? Psychology & Marketing, 20(8), 733–755.
  • Chan, T. C. (2005). A Study of the Adoption E-learning Factors on Taiwan (Doctoral Thesis). Chaoyang University of Technology.
  • Chauhan, S., Mukhopadhyay, S., ve Jaiswal, M. (2018). The adoption of mobile app for B2C transaction in platform marketplace: An empirical examination of key drivers. Journal of Information Technology Case and Application Research, 20(1), 9–22.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319.
  • Davis, F. D., Bagozzi, R. P., ve 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., ve Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22(14), 1111–1132.
  • Davis, F D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral Thesis). Massachusetts Institute of Technology.
  • Davis, F. D. (1989). Perceived Usefulness , Perceived Ease of Use , and User Acceptance of Information Technology. MIS Quarterly ·, 13(3), 319–340.
  • Fagan, M. H. (2019). Factors Influencing Student Acceptance of Mobile Learning in Higher Education. Computers in the Schools, 36(2), 105–121.
  • Fishbein, M., ve Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley, Reading, MA
  • Fishbein, M., ve Ajzen, I. (1980). Predicting and understanding consumer behavior: Attitude-behavior correspondence. Understanding attitudes and predicting social behavior. Prentice Hall Englewood Cliffs, NJ.
  • Gürbüz, S. (2019). AMOS ile yapısal eşitlik modellemesi. Ankara: Seçkin Yayıncılık.
  • Gumussoy, C. A., Kaya, A., ve Ozlu, E. (2018). Determinants of mobile banking use: an extended TAM with perceived risk, mobility access, compatibility, perceived self-efficacy and subjective norms. In Industrial Engineering in the Industry 4.0 Era (pp. 225-238). Springer, Cham.
  • Hajiheydari, N., ve Ashkani, M. (2018). Mobile application user behavior in the developing countries: A survey in Iran. Information Systems, 77, 22–33.
  • Holak, S. L., ve Lehmann, D. R. (1990). Purchase intentions and the dimensions of innovation: An exploratory model. Journal of Product Innovation Management: An International Publication of the Product Development & Management Association, 7(1), 59–73.
  • Hsu, C. L., Lu, H. P., ve Hsu, H. H. (2007). Adoption of the mobile Internet: An empirical study of multimedia message service (MMS). Omega, 35(6), 715–726.
  • Joo., Y. J., Lim, K. Y., ve Lim, E. (2014). Investigating the structural relationship among perceived innovation attributes, intention to use and actual use of mobile learning in an online university in South Korea. Australasian Journal of Educational Technology, 30(4), 427–439.
  • Kalinic, Z., ve Marinkovic, V. (2016). Determinants of users’ intention to adopt m-commerce: an empirical analysis. Information Systems and E-Business Management, 14(2), 367–387.
  • Karahanna, E., Straub, D. W., ve Chervany, N. L. (1999). Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–213.
  • Kenny, Phan, Daim, ve Tugrul, U. (2011). Exploring technology acceptance for mobile services. Journal of Industrial Engineering and Management (JIEM), 4(2), 339–360.
  • Kim, J., Connolly, D. J., ve Blum, S. (2014). Mobile Technology: An Exploratory Study of Hotel Managers. International Journal of Hospitality & Tourism Administration, 15(4), 417–446.
  • Kim, S. H. (2008). Moderating effects of Job Relevance and Experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Information and Management, 45(6), 387–393.
  • Kim, Y. H., Kim, D. J., ve Wachter, K. (2013). A study of mobile user engagement (MoEN): Engagement motivations, perceived value, satisfaction, and continued engagement intention. Decision Support Systems, 56, 361–370.
  • Lee, M. S., McGoldrick, P. J., Keeling, K. A., ve Doherty, J. (2003). Using ZMET to explore barriers to the adoption of 3G mobile banking services. International Journal of Retail & Distribution Management, 31(6), 340-348.
  • Lee, S.-G., Park, B., Kim, S.-H., ve Lee, H.-H. (2012). Innovation and imitation effects in the mobile telecommunication service market. Service Business, 6(3), 265–278.
  • Lee, Y.C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517–541.
  • Lin, C., ve Bhattacherjee, A. (2010). Extending technology usage models to interactive hedonic technologies: a theoretical model and empirical test. Information Systems Journal, 20(2), 163–181.
  • Matthew Yaw Owusu, G., Amoah Bekoe, R., Amoasa Addo-Yobo, A., ve Otieku, J. (2020). Mobile Banking Adoption among the Ghanaian Youth. Journal of African Business. 1-22.
  • Mehra, A., Paul, J., ve Kaurav, R. P. S. (2020). Determinants of mobile apps adoption among young adults: theoretical extension and analysis. Journal of Marketing Communications, 00(00), 1–29.
  • Min, S., Kam Fung So, K., ve Jeong, M. (2019). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. Journal of Travel & Tourism Marketing, 36(7), 770–783.
  • Moore, G. C., ve Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
  • Nysveen, H., Pedersen, P. E. ve Thorbjørnsen, H. (2005). Intentions to use mobile services: Antecedents and cross-service comparisons. Journal of the Academy of Marketing Science, 33(3), 330–346.
  • Oliveira, T., Thomas, M., ve Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information and Management, 51(5), 497–510.
  • Premkumar, A. G., Ramamurthy, K., ve Nilakanta, S. (1994). Implementation of Electronic Data Interchange: An Innovation Diffusion Perspective. Journal of Management Information Systems, 11(2), 157–186.
  • Purcell, K. (2011). Half of adult cell phone owners have apps on their phones. Retrieved from http://pewinternet.org/Reports/2011/Apps-update.aspx
  • Radner, R., ve Rothschild, M. (1975). On the Allocation of Effort*. Journal of Economic Theory, 10(3), 358–376.
  • Ram, S., ve Sheth, J. N. (1989). Consumer resistance to innovations: the marketing problem and its solutions. Journal of Consumer Marketing.6(2), 5-14.
  • Robinson, L. (2009). A summary of Diffusion of Innovations. Enabling Change, 5(10), 1–7.
  • Rogers, E. M. (1995). Diffusion of Innovations: modifications of a model for telecommunications. In Die diffusion von innovationen in der telekommunikation (pp. 25–38). Berlin: Springer Berlin Heidelberg.
  • Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press.
  • Rogers, E. M., ve Shoemaker, F. (1983). Diffusion of innovation: A cross-cultural approach. New York: Free Press.
  • Roy, S. (2017). App Adoption And Swıtchıng Behavior: Applying The Extended Tam In Smartphone App Usage. JISTEM - Journal of Information Systems and Technology Management, 14(2), 239–261.
  • Sam, K. M., Chatwin, C. R., ve Ma, I. C. (2014). Mobile stock trading (MST) and its social impact: A case study in Hong Kong. In IEEE International Conference on Industrial Engineering and Engineering Management (ss. 437–441).
  • Social, W. are. (2020). Digital in 2020. Retrieved from https://wearesocial.com/digital-2020
  • Tamilmani, K., Rana, N. P., ve Dwivedi, Y. K. (2018). Mobile application adoption predictors: Systematic review of UTAUT2 studies using weight analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11195 LNCS). Springer International Publishing.
  • Taylor, D. G., Voelker, T. A., ve Pentina, I. (2011). Mobile Application Adoption by Young Adults: A Social Network Perspective. WCOB Faculty Publications.
  • Taylor, S., ve Todd., P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144–176.
  • Tornatzky, L. G., ve Klein, K. J. (1982). Innovation Characteristics and Innovation Adoption- Implementation : A Meta-Analysis of Findings. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, 29(1), 28–45.
  • Torres, C. A. (2011). Patient Health Portals for Personal Health in Health Consumers’ Intentions to Use Examining the Role of Anxiety and Apathy Information Management (Doctoral Thesis). Florida State University.
  • Van Slyke, C., Belanger, F., ve Comunale, C. L. (2004). Factors influencing the adoption of web-based shopping: the impact of trust. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 35(2), 32–49.
  • Venkatesh, V., Morris, M. G., Davis, G. B., ve Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478.
  • White, J. B., Tynan, R., Galinsky, A. D., ve Thompson, L. (2004). Face threat sensitivity in negotiation: Roadblock to agreement and joint gain. Organizational Behavior and Human Decision Processes, 94(2), 102–124.
  • Yang, H. C. (2013). Bon appétit for apps: Young American consumers’ acceptance of mobile applications. Journal of Computer Information Systems, 53(3), 85–96.
  • Yazıcıoğlu, Y., ve Erdoğan, S. (2004). SPSS uygulamalı bilimsel araştırma yöntemleri. Ankara: Detay Yayınevi. Yi, M. Y., ve Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human Computer Studies, 59(4), 431–449.
  • Yoon, V. Y., Hostler, R. E., Guo, Z., ve Guimaraes, T. (2012). Assessing the moderating effect of consumer product knowledge and online shopping experience on using recommendation agents for customer loyalty. Decision Support Systems, 55, 883–893.
  • Zarmpou, T., Saprikis, V., Markos, A., ve Vlachopoulou, M. (2012). Modeling users’ acceptance of mobile services. Electronic Commerce Research, 12(2), 225–248.
  • Zhang, L., Zhu, J., ve Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902–1911.

Ayrıntılar

Birincil Dil Türkçe
Konular Sosyal
Bölüm Makaleler
Yazarlar

Busra KUTLU KARABIYIK (Sorumlu Yazar)
ADNAN MENDERES UNIV
0000-0002-6691-2921
Türkiye


Mustafa ÇETİN
AYDIN ADNAN MENDERES ÜNİVERSİTESİ
0000-0001-8264-7657
Türkiye

Yayımlanma Tarihi 31 Ağustos 2021
Yayınlandığı Sayı Yıl 2021, Cilt , Sayı 59

Kaynak Göster

APA Kutlu Karabıyık, B. & Çetin, M. (2021). YENİLİKLERİN YAYGINLAŞMASI VE GENİŞLETİLMİŞ TEKNOLOJİ KABUL MODELİNİN BÜTÜNLEŞTİRİLMESİYLE TÜRKİYE’DE MOBİL UYGULAMA KULLANMA NİYETİNİN ARAŞTIRILMASI . Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi , (59) , 59-90 . DOI: 10.18070/erciyesiibd.877730

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