Araştırma Makalesi
BibTex RIS Kaynak Göster

Güvenin, Akışın ve Kritik Yoğunluğun Mobil Sosyal Ağ Uygulamaları Sürekli Kullanım Niyetine Etkisi

Yıl 2023, Cilt: 3 Sayı: 2, 31 - 52, 18.10.2023

Görkem Erdoğan

https://doi.org/10.59597/akademikaci.1342819

Öz

Türkiye’de mobil sosyal ağ uygulamalarının sürekli kullanım niyetleri öncüllerinin incelenmesi hakkındaki araştırmalara çok az ilgi gösterilmiştir. Bu nedenle, bu çalışmanın temel amacı, güven, akış deneyimi ve kritik yoğunluğu değişkenlerinin sürekli kullanım niyeti üzerindeki etkisini inceleyerek bu boşluğu doldurmaktır. Çalışmada anket yöntemiyle 424 katılımcıdan toplanan veriler analiz edilmiştir. Sonuçlar hem yaygınlığın hem de güvenin, kullanıcıların sürekli kullanım niyetlerini belirleyen akış deneyimlerini etkilediğini göstermektedir. Ayrıca araştırma bulguları, güven ve kritik yoğunluğun sürekli kullanım niyeti üzerinde önemli etkileri olduğunu göstermiştir. Bu çalışmanın sonuçları mobil sosyal ağ uygulamalarının yöneticilerine ve pazarlamacılarına kullanıcıların sürekli kullanım niyetini anlamak için güven, akış deneyimi ve kritik yoğunluk hakkında güçlü bilgiler sağlayabilmektedir.

Anahtar Kelimeler

Mobil Sosyal Ağ Uygulamaları Sürekli Kullanım Niyeti Akış

Kaynakça

  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94.
  • Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2014). Trust transfer in the continued usage of public e-services. Information & Management, 51(6), 627-640.
  • Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 25(3), 351-370.
  • Chen, A., Lu, Y., Wang, B., Zhao, L., & Li, M. (2013). What drives content creation behavior on SNSs? A commitment perspective. Journal of Business Research, 66(12), 2529-2535.
  • Chen, S. C., Yen, D. C., & Hwang, M. I. (2012). Factors influencing the continuance intention to the usage of Web 2.0: An empirical study. Computers in Human Behavior, 28(3), 933-941.
  • Chinomona, R. (2013). The influence of perceived ease of use and perceived usefulness on trust and intention to use mobile social software: technology and innovation. African Journal for Physical Health Education, Recreation and Dance, 19(2), 258-273.
  • Csikszentmihalyi, M. and Csikszentmihalyi, I.S. (1988), Optimal Experience: Psychological Studies of Flow in Consciousness, Cambridge University Press, Cambridge.
  • Csikszentmihalyi, M. and Lefeyre, J. (1989). Optimal experience in work and leisure, Journal of Personality and Social Psychology, Vol. 56 No. 5, pp. 815-822
  • DataReportal, (2022). Global Social Media Statistics. https://datareportal.com/social-media-users. Finneran, C. M., & Zhang, P. (2005). Flow in computer-mediated environments: Promises and challenges. Communications of the association for information systems, 15(1), 4.
  • Fornell, C. and Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  • Gao, L., & Bai, X. (2014). An empirical study on continuance intention of mobile social networking services: Integrating the IS success model, network externalities and flow theory. Asia Pacific Journal of Marketing and Logistics.
  • Gefen, D. (2002). Customer loyalty in e-commerce. Journal of the association for information systems, 3(1), 2. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 51-90.
  • Hair, J., Anderson, R., Tatham, R., & Black, W. (1992). Multivariate data analysis with readings. New York, NY: Macmillan.
  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. New York, NY: Macmillan.
  • Han, S., Min, J., & Lee, H. (2015). Antecedents of social presence and gratification of social connection needs in SNS: a study of Twitter users and their mobile and non-mobile usage. International Journal of Information Management, 35(4), 459-471.
  • Hausman, A. V., & Siekpe, J. S. (2009). The effect of web interface features on consumer online purchase intentions. Journal of business research, 62(1), 5-13.
  • Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of marketing, 60(3), 50-68.
  • Hsieh, S. W., Jang, Y. R., Hwang, G. J., & Chen, N. S. (2011). Effects of teaching and learning styles on students’ reflection levels for ubiquitous learning. Computers & education, 57(1), 1194-1201.
  • Kim, D., & Ammeter, T. (2014). Predicting personal information system adoption using an integrated diffusion model. Information & Management, 51(4), 451-464.
  • Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: The Guilford Press. Ku, Y. C., Chen, R., & Zhang, H. (2013). Why do users continue using social networking sites? An exploratory study of members in the United States and Taiwan. Information & Management, 50(7), 571-581.
  • Lee, T. (2005). The impact of perceptions of interactivity on customer trust and transaction intentions in mobile commerce. Journal of Electronic Commerce Research, 6(3), 165-180.
  • Lee, K. C., Kang, I., & McKnight, D. H. (2007). Transfer from offline trust to key online perceptions: an empirical study. IEEE Transactions on Engineering Management, 54(4), 729-741.
  • Lee, S., & Kim, B. G. (2020). The impact of individual motivations and social capital on the continuous usage intention of mobile social apps. Sustainability, 12(20), 8364.
  • Lin, H., Fan, W., & Chau, P. Y. (2014). Determinants of users’ continuance of social networking sites: A self-regulation perspective. Information & Management, 51(5), 595-603.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York, NY: McGraw-Hill.
  • Qin, L., Kim, Y., & Tan, X. (2018). Understanding the intention of using mobile social networking apps across cultures. International Journal of Human–Computer Interaction, 34(12), 1183-1193.
  • Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York, NY: The Free Press.
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23-74.
  • Statista, (2021). Forecast of the number of smartphone users in the World from 2010 to 2025. https://www.statista.com/forecasts/1143723/smartphone-users-in-the-world.
  • Subrahmanyam, K., Reich, S. M., Waechter, N., & Espinoza, G. (2008). Online and offline social networks: Use of social networking sites by emerging adults. Journal of applied developmental psychology, 29(6), 420-433.
  • Yen, W. C., Lin, H. H., Wang, Y. S., Shih, Y. W., & Cheng, K. H. (2019). Factors affecting users’ continuance intention of mobile social network service. The Service Industries Journal, 39(13-14), 983-1003.
  • Zhou, T. (2012). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management, 13(1), 27-37.
  • Zhou, T. (2013). The effect of flow experience on user adoption of mobile TV. Behaviour & Information Technology, 32(3), 263-272.
  • Zhou, T., Li, H., & Liu, Y. (2010). The effect of flow experience on mobile SNS users' loyalty. Industrial Management & Data Systems.
  • Zhou, T. (2014). Understanding the determinants of mobile payment continuance usage. Industrial Management & Data Systems, 114(6), 936-948.
  • Zhou, Z., Fang, Y., Vogel, D. R., Jin, X. L., & Zhang, X. (2012). Attracted to or locked in? Predicting continuance intention in social virtual world services. Journal of management information systems, 29(1), 273-306.

The Effects of Trust, Flow, and Critical Mass on Continuous Usage Intention of Mobile Social Networking Applications

Yıl 2023, Cilt: 3 Sayı: 2, 31 - 52, 18.10.2023

Görkem Erdoğan

https://doi.org/10.59597/akademikaci.1342819

Öz

Little attention has been paid to research on the antecedents of continued use intentions of mobile social networking applications in Turkey. Therefore, the main aim of this study is to fill this gap by examining the effect of trust, flow experience and critical mass variables on continuous usage intention. In the study, data collected from 424 participants by questionnaire method were analyzed. The results show that both ubiquity and trust significantly affect users’ flow experiences, which further determine their continous usage intention. In addition, research findings show that trust and critical mass have significant effects on continuous intention to use. The results of this study can provide managers and marketers of mobile social networking applications with powerful information about trust, flow experience and critical mass to understand users' continuous usage intention.

Anahtar Kelimeler

Mobile Social Networking Applications Continuous Usage Intention Flow

Kaynakça

  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94.
  • Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2014). Trust transfer in the continued usage of public e-services. Information & Management, 51(6), 627-640.
  • Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 25(3), 351-370.
  • Chen, A., Lu, Y., Wang, B., Zhao, L., & Li, M. (2013). What drives content creation behavior on SNSs? A commitment perspective. Journal of Business Research, 66(12), 2529-2535.
  • Chen, S. C., Yen, D. C., & Hwang, M. I. (2012). Factors influencing the continuance intention to the usage of Web 2.0: An empirical study. Computers in Human Behavior, 28(3), 933-941.
  • Chinomona, R. (2013). The influence of perceived ease of use and perceived usefulness on trust and intention to use mobile social software: technology and innovation. African Journal for Physical Health Education, Recreation and Dance, 19(2), 258-273.
  • Csikszentmihalyi, M. and Csikszentmihalyi, I.S. (1988), Optimal Experience: Psychological Studies of Flow in Consciousness, Cambridge University Press, Cambridge.
  • Csikszentmihalyi, M. and Lefeyre, J. (1989). Optimal experience in work and leisure, Journal of Personality and Social Psychology, Vol. 56 No. 5, pp. 815-822
  • DataReportal, (2022). Global Social Media Statistics. https://datareportal.com/social-media-users. Finneran, C. M., & Zhang, P. (2005). Flow in computer-mediated environments: Promises and challenges. Communications of the association for information systems, 15(1), 4.
  • Fornell, C. and Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  • Gao, L., & Bai, X. (2014). An empirical study on continuance intention of mobile social networking services: Integrating the IS success model, network externalities and flow theory. Asia Pacific Journal of Marketing and Logistics.
  • Gefen, D. (2002). Customer loyalty in e-commerce. Journal of the association for information systems, 3(1), 2. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 51-90.
  • Hair, J., Anderson, R., Tatham, R., & Black, W. (1992). Multivariate data analysis with readings. New York, NY: Macmillan.
  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. New York, NY: Macmillan.
  • Han, S., Min, J., & Lee, H. (2015). Antecedents of social presence and gratification of social connection needs in SNS: a study of Twitter users and their mobile and non-mobile usage. International Journal of Information Management, 35(4), 459-471.
  • Hausman, A. V., & Siekpe, J. S. (2009). The effect of web interface features on consumer online purchase intentions. Journal of business research, 62(1), 5-13.
  • Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of marketing, 60(3), 50-68.
  • Hsieh, S. W., Jang, Y. R., Hwang, G. J., & Chen, N. S. (2011). Effects of teaching and learning styles on students’ reflection levels for ubiquitous learning. Computers & education, 57(1), 1194-1201.
  • Kim, D., & Ammeter, T. (2014). Predicting personal information system adoption using an integrated diffusion model. Information & Management, 51(4), 451-464.
  • Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: The Guilford Press. Ku, Y. C., Chen, R., & Zhang, H. (2013). Why do users continue using social networking sites? An exploratory study of members in the United States and Taiwan. Information & Management, 50(7), 571-581.
  • Lee, T. (2005). The impact of perceptions of interactivity on customer trust and transaction intentions in mobile commerce. Journal of Electronic Commerce Research, 6(3), 165-180.
  • Lee, K. C., Kang, I., & McKnight, D. H. (2007). Transfer from offline trust to key online perceptions: an empirical study. IEEE Transactions on Engineering Management, 54(4), 729-741.
  • Lee, S., & Kim, B. G. (2020). The impact of individual motivations and social capital on the continuous usage intention of mobile social apps. Sustainability, 12(20), 8364.
  • Lin, H., Fan, W., & Chau, P. Y. (2014). Determinants of users’ continuance of social networking sites: A self-regulation perspective. Information & Management, 51(5), 595-603.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York, NY: McGraw-Hill.
  • Qin, L., Kim, Y., & Tan, X. (2018). Understanding the intention of using mobile social networking apps across cultures. International Journal of Human–Computer Interaction, 34(12), 1183-1193.
  • Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York, NY: The Free Press.
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23-74.
  • Statista, (2021). Forecast of the number of smartphone users in the World from 2010 to 2025. https://www.statista.com/forecasts/1143723/smartphone-users-in-the-world.
  • Subrahmanyam, K., Reich, S. M., Waechter, N., & Espinoza, G. (2008). Online and offline social networks: Use of social networking sites by emerging adults. Journal of applied developmental psychology, 29(6), 420-433.
  • Yen, W. C., Lin, H. H., Wang, Y. S., Shih, Y. W., & Cheng, K. H. (2019). Factors affecting users’ continuance intention of mobile social network service. The Service Industries Journal, 39(13-14), 983-1003.
  • Zhou, T. (2012). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management, 13(1), 27-37.
  • Zhou, T. (2013). The effect of flow experience on user adoption of mobile TV. Behaviour & Information Technology, 32(3), 263-272.
  • Zhou, T., Li, H., & Liu, Y. (2010). The effect of flow experience on mobile SNS users' loyalty. Industrial Management & Data Systems.
  • Zhou, T. (2014). Understanding the determinants of mobile payment continuance usage. Industrial Management & Data Systems, 114(6), 936-948.
  • Zhou, Z., Fang, Y., Vogel, D. R., Jin, X. L., & Zhang, X. (2012). Attracted to or locked in? Predicting continuance intention in social virtual world services. Journal of management information systems, 29(1), 273-306.
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İnovasyon Yönetimi
Bölüm Araştırma Makaleleri
Yazarlar

Görkem Erdoğan 0000-0002-2417-2718

Yayımlanma Tarihi 18 Ekim 2023
Gönderilme Tarihi 14 Ağustos 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 3 Sayı: 2

Kaynak Göster

APA Erdoğan, G. (2023). Güvenin, Akışın ve Kritik Yoğunluğun Mobil Sosyal Ağ Uygulamaları Sürekli Kullanım Niyetine Etkisi. Akademik Açı, 3(2), 31-52. https://doi.org/10.59597/akademikaci.1342819
 Kapak Resmi İndir