Araştırma Makalesi
BibTex RIS Kaynak Göster

A Study to Understand Factors Affecting Social Network Usage

Yıl 2020, Cilt: 7 Sayı: 13, 515 - 526, 25.12.2020

Öz

Users may join social networks (SNS) for many personal or professional purposes. While many individuals voluntarily join SNS, for some, this may also be a form of forced use. However, in both cases there are some factors that influence SNS use. This research uses Technology Acceptance Model and includes trust and satisfaction factors and the technical aspects of SNS. Data collected from university students by survey technique was analyzed. As a result; the research model has good fit indices, and the factors in the model have relationships of varying strengths and significance with each other. Trust in SNS positively affects the continuance intention to SNS. However, the relationship between trust in others and the continuance intention did not turn out as predicted. Informativeness affects both perceived usefulness and perceived enjoyment constructs in a positive and significant way. While interactivity significantly affects perceived usefulness, there is no significant relationship between it and perceived enjoyment. There are also positive and significant relationship between satisfaction, intention, perceived usefulness and perceived enjoyment.

Kaynakça

  • Al-Sharqi, L., Hashim, K., & Kutbi, I. (2015). Perceptions of social media impact on students’ social behavior: A comparison between Arts and Science students. International Journal of Education and Social Science, 2(4), 122-131.
  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
  • Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, 351-370.
  • Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach9s alpha. Bmj, 314(7080), 572.
  • Chahal, H., & Kumari, N. (2011). Consumer perceived value and consumer loyalty in the healthcare sector. Journal of Relationship Marketing, 10(2), 88-112.
  • Chang, S. E., Liu, A. Y., & Shen, W. C. (2017). User trust in social networking services: A comparison of Facebook and LinkedIn. Computers in Human Behavior, 69, 207-217.
  • Correa, T., Hinsley, A. W., & De Zuniga, H. G. (2010). Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247-253.
  • Davis, F. D., Bagozzi, R. P., & 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. (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.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 27(1), 51-90.
  • Hair, J. J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate data analysis (5thed.). NJ, United States: Prentice-Hall.
  • Han, B. (2012). An investigation of factors influencing the user's social network site continuance intention. University of North Texas.
  • Han, B. O., & Windsor, J. (2011). User's willingness to pay on social network sites. Journal of computer information systems, 51(4), 31-40.
  • Harris, J. A. (1993). Personalities of students in three faculties: Perception and accuracy. Personality and Individual Differences, 15(3), 351-352.
  • Helliwell, J. F., & Putnam, R. D. (2004). The social context of well-being. Philosophical Transactions of the Royal Society B: Biological Sciences, 359(1449), 1435.
  • Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Articles, 2.
  • Huang, C. C., Wang, Y. M., Wu, T. W., & Wang, P. A. (2013). An empirical analysis of the antecedents and performance consequences of using the moodle platform. International Journal of Information and Education Technology, 3(2), 217.
  • Kline, R. B. (1998). Principles and practice of structural modeling. Guilford publications.
  • Kline, P., & Lapham, S. L. (1992). Personality and faculty in British universities. Personality and Individual Differences, 13(7), 855-857.
  • Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 705-737.
  • Lomax, R. G., & Schumacker, R. E. (2004). A beginner's guide to structural equation modeling. Psychology press.
  • McKnight, D. H. (2005). Trust in information technology. The Blackwell encyclopedia of management, 7, 329-331.
  • McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information systems research, 13(3), 334-359.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (McGraw-Hill Series in Psychology) (Vol. 3). New York: McGraw-Hill.
  • Poushter, J., Bishop, C., & Chwe, H. (2018). Social network adoption varies widely by country. Retrieved from Pew Research Center: http://www. pewglobal.org/2018/06/19/3-social-network-adoption-varies-widely-by-country.
  • Ridings, C. M., Gefen, D., & Arinze, B. (2002). Some antecedents and effects of trust in virtual communities. The Journal of Strategic Information Systems, 11(3-4), 271-295.
  • Sharma, A., & Jaswal, I. (2015). Personality and Patterns of Facebook Usage. International Journal of Academic Research in Psychology, Vol. 2, No. 2.
  • Shi, N., Lee, M. K., Cheung, C. M., & Chen, H. (2010). The continuance of online social networks: how to keep people using Facebook?. In System sciences (HICSS), 2010 43rd Hawaii international conference on (pp. 1-10). IEEE.
  • Shipps, B. & Phillips, B. (2013). Social networks, interactivity and satisfaction: assessing socio-technical behavioral factors as an extension to technology acceptance. Journal of theoretical and applied electronic commerce research, 8(1), 35-52.
  • Sledgianowski, D., & Kulviwat, S. (2009). Using social network sites: The effects of playfulness, critical mass and trust in a hedonic context. Journal of Computer Information Systems, 49(4), 74-83.
  • Statista (2018). Active social network penetration in selected countries as of January 2018. https://www.statista.com/statistics/282846/regular-social-networking-usage-penetration-worldwide-by-country/
  • Sun, Y., Liu, L., Peng, X., Dong, Y., & Barnes, S. J. (2014). Understanding Chinese users’ continuance intention toward online social networks: an integrative theoretical model. Electronic Markets, 24(1), 57-66.
  • Ullman, J. B. (2001). Structural equation modeling. In B. G. Tabachnick & L. S. Fidell (Eds.), Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon.
  • Wei, H. L., Lin, K. Y., Lu, H. P., & Chuang, I. H. (2015). Understanding the intentions of users to ‘stick’to social networking sites: a case study in Taiwan. Behaviour & Information Technology, 34(2), 151-162.
  • Wu, J. J., & Chang, Y. S. (2005). Towards understanding members' interactivity, trust, and flow in online travel community. Industrial Management & Data Systems, 105(7), 937-954.
  • Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS quarterly, 695-704.

Sosyal Ağ Kullanımını Etkileyen Faktörleri Anlamaya Yönelik Bir Araştırma

Yıl 2020, Cilt: 7 Sayı: 13, 515 - 526, 25.12.2020

Öz

Kullanıcılar sosyal ağlarda çeşitli kişisel veya personal amaçlarla yer alabilirler. Birçok birey sosyal ağlarda gönüllü kullanıcılar olarak yer alıp katılım gösterirken, kimi kullanıcıları zorlayan daha farklı durumlar olabilmektedir. İki tür katılım ve kullanım sürecini de etkileyen faktörler bulunmaktadır. Bu araştırma, Teknoloji Kabul Modeli’ni kullanmakta ancak buna tatmin, güven ve teknik bazı boyutları eklemektedir. Araştırmada üniversite öğrencilerinden anket tekniği ile toplanan veriler analiz edilmiştir. Sonuçlara göre araştırma modeli iyi bir uyum göstermektedir. Modelde yer alan faktörler birbirleriyle çeşitli güçlerde ilişkilere sahiptir. Sosyal ağlara yönelik kullanıcıların duyduğu güven, bu ağları kullanımı olumlu etkilerken; bu ağlarda yer alan diğer kullanıcılara olan güven beklendiğinin aksine olumsuz bir etkiye sahiptir. Bilgilendiriciliğin algılanan fayda ve algılanan haz üzerinde ise olumlu yönlü bir etkisi vardır. Bu ağların etkileşimli yapısını ön plana çıkaran kullanıcılar ise algılanan faydaları ön plana çıkarmaktadır. Ayrıca bu ağlardan alınan tatmin ile devamlılık niyeti, algılanan fayda ve algılanan haz arasında da olumlu ilişkiler elde edilmiştir.

Kaynakça

  • Al-Sharqi, L., Hashim, K., & Kutbi, I. (2015). Perceptions of social media impact on students’ social behavior: A comparison between Arts and Science students. International Journal of Education and Social Science, 2(4), 122-131.
  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
  • Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, 351-370.
  • Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach9s alpha. Bmj, 314(7080), 572.
  • Chahal, H., & Kumari, N. (2011). Consumer perceived value and consumer loyalty in the healthcare sector. Journal of Relationship Marketing, 10(2), 88-112.
  • Chang, S. E., Liu, A. Y., & Shen, W. C. (2017). User trust in social networking services: A comparison of Facebook and LinkedIn. Computers in Human Behavior, 69, 207-217.
  • Correa, T., Hinsley, A. W., & De Zuniga, H. G. (2010). Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247-253.
  • Davis, F. D., Bagozzi, R. P., & 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. (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.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 27(1), 51-90.
  • Hair, J. J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate data analysis (5thed.). NJ, United States: Prentice-Hall.
  • Han, B. (2012). An investigation of factors influencing the user's social network site continuance intention. University of North Texas.
  • Han, B. O., & Windsor, J. (2011). User's willingness to pay on social network sites. Journal of computer information systems, 51(4), 31-40.
  • Harris, J. A. (1993). Personalities of students in three faculties: Perception and accuracy. Personality and Individual Differences, 15(3), 351-352.
  • Helliwell, J. F., & Putnam, R. D. (2004). The social context of well-being. Philosophical Transactions of the Royal Society B: Biological Sciences, 359(1449), 1435.
  • Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Articles, 2.
  • Huang, C. C., Wang, Y. M., Wu, T. W., & Wang, P. A. (2013). An empirical analysis of the antecedents and performance consequences of using the moodle platform. International Journal of Information and Education Technology, 3(2), 217.
  • Kline, R. B. (1998). Principles and practice of structural modeling. Guilford publications.
  • Kline, P., & Lapham, S. L. (1992). Personality and faculty in British universities. Personality and Individual Differences, 13(7), 855-857.
  • Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 705-737.
  • Lomax, R. G., & Schumacker, R. E. (2004). A beginner's guide to structural equation modeling. Psychology press.
  • McKnight, D. H. (2005). Trust in information technology. The Blackwell encyclopedia of management, 7, 329-331.
  • McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information systems research, 13(3), 334-359.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (McGraw-Hill Series in Psychology) (Vol. 3). New York: McGraw-Hill.
  • Poushter, J., Bishop, C., & Chwe, H. (2018). Social network adoption varies widely by country. Retrieved from Pew Research Center: http://www. pewglobal.org/2018/06/19/3-social-network-adoption-varies-widely-by-country.
  • Ridings, C. M., Gefen, D., & Arinze, B. (2002). Some antecedents and effects of trust in virtual communities. The Journal of Strategic Information Systems, 11(3-4), 271-295.
  • Sharma, A., & Jaswal, I. (2015). Personality and Patterns of Facebook Usage. International Journal of Academic Research in Psychology, Vol. 2, No. 2.
  • Shi, N., Lee, M. K., Cheung, C. M., & Chen, H. (2010). The continuance of online social networks: how to keep people using Facebook?. In System sciences (HICSS), 2010 43rd Hawaii international conference on (pp. 1-10). IEEE.
  • Shipps, B. & Phillips, B. (2013). Social networks, interactivity and satisfaction: assessing socio-technical behavioral factors as an extension to technology acceptance. Journal of theoretical and applied electronic commerce research, 8(1), 35-52.
  • Sledgianowski, D., & Kulviwat, S. (2009). Using social network sites: The effects of playfulness, critical mass and trust in a hedonic context. Journal of Computer Information Systems, 49(4), 74-83.
  • Statista (2018). Active social network penetration in selected countries as of January 2018. https://www.statista.com/statistics/282846/regular-social-networking-usage-penetration-worldwide-by-country/
  • Sun, Y., Liu, L., Peng, X., Dong, Y., & Barnes, S. J. (2014). Understanding Chinese users’ continuance intention toward online social networks: an integrative theoretical model. Electronic Markets, 24(1), 57-66.
  • Ullman, J. B. (2001). Structural equation modeling. In B. G. Tabachnick & L. S. Fidell (Eds.), Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon.
  • Wei, H. L., Lin, K. Y., Lu, H. P., & Chuang, I. H. (2015). Understanding the intentions of users to ‘stick’to social networking sites: a case study in Taiwan. Behaviour & Information Technology, 34(2), 151-162.
  • Wu, J. J., & Chang, Y. S. (2005). Towards understanding members' interactivity, trust, and flow in online travel community. Industrial Management & Data Systems, 105(7), 937-954.
  • Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS quarterly, 695-704.
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İletişim ve Medya Çalışmaları
Bölüm Araştırma Makaleleri
Yazarlar

Kemal Elciyar 0000-0002-7820-2978

Yayımlanma Tarihi 25 Aralık 2020
Gönderilme Tarihi 9 Ekim 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 7 Sayı: 13

Kaynak Göster

APA Elciyar, K. (2020). A Study to Understand Factors Affecting Social Network Usage. Intermedia International E-Journal, 7(13), 515-526.

Creative Commons Lisansı Intermedia International E-journal

Bu eser Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.