Virtual networks: Why do students use Instagram?
Published:
Mar 27, 2019
Keywords:
virtual networks Instagram user attitude social presence perceived pleasure perceived usefulness perceived value
Abstract
Instagram has become the bridge between consumers who share moments from their lives and companies that share their products and services with the users. Instagram stands out from the other social media networks thanks to user-friendly toolkit that provides photo editing, video sharing and Instagram stories. This conceptual model research investigates the impact of Instagram on user's attitude. Data were collected from young Instagram users in order to measure if there is a relationship between specific factors of Instagram and user attitude through Structural Equation Modeling (SEM). In this research, will be analyzed the consumer behavior in social media and particularly on Instagram. As a first stage of our research we are going to develop the theoretical study for Instagram users (n=200) at the age of 18-34, investigating the behavior of use Instagram which is determined by social presence. This study presents a theoretical conceptual model based on the theory of social presence, perceived pleasure, perceived usefulness and perceived value on Instagram and the potential connection of those factors to the user attitude.
Article Details
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Kaperonis, S. (2019). Virtual networks: Why do students use Instagram?. Homo Virtualis, 2(1), 43–49. https://doi.org/10.12681/homvir.20193
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References
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361–391.
Berthon, P., Pitt, L., & Campbell, C. (2008). Ad lib: When customers create the ad. California Management Review, 50(4), 6–30.
Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511–535.
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 1. Journal of Applied Social Psychology, 22(14), 1111–1132.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research.
Gefen, D., & Straub, D. W. (2004). Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services. Omega, 32(6), 407–424.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall.
Kaperonis, S. D. (n.d.). The Impact of Social Media on user’s Travel Purchase Intention. Retrieved from http://www.thinkmind.org/index.php?view=article&articleid=data_analytics_2018_4_10_68002
Kardaras, D. K., Barbounaki, S., Petrounias, I., & Kaperonis, S. (2018). An Approach to Web Adaptation by Modelling User Interests Using TF-IDF: A Feature Selection and Multi-Criteria Approach Using AHP. HUSO 2018, 67.
Kline, R. B. (2010). Promise and pitfalls of structural equation modeling in gifted research.
Lee, E. A., & Seshia, S. A. (2016). Introduction to embedded systems: A cyber-physical systems approach. Mit Press.
Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191.
Mazer, J. P., Murphy, R. E., & Simonds, C. J. (2007). I’ll see you on “Facebook”: The effects of computer-mediated teacher self-disclosure on student motivation, affective learning, and classroom climate. Communication Education, 56(1), 1–17.
Payne, A., & Holt, S. (2001). Diagnosing customer value: integrating the value process and relationship marketing. British Journal of Management, 12(2), 159–182.
Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6–30.
Sánchez-Fernández, R., & Iniesta-Bonillo, M. Á. (2007). The concept of perceived value: a systematic review of the research. Marketing Theory, 7(4), 427–451.
Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications.
Straker, K., & Wrigley, C. (2018). From a Mission Statement to a Sense of Mission: Emotion Coding to Strengthen Digital Engagements. Journal of Creating Value, 2394964318771783.
Straub, D., & Karahanna, E. (1998). Knowledge worker communications and recipient availability: Toward a task closure explanation of media choice. Organization Science, 9(2), 160–175.
Taylor, S., & Todd, P. (1995a). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137–155.
Taylor, S., & Todd, P. A. (1995b). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.
Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 695–704.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Yahia, I. Ben, Al-Neama, N., & Kerbache, L. (2018). Investigating the drivers for social commerce in social media platforms: Importance of trust, social support and the platform perceived usage. Journal of Retailing and Consumer Services, 41, 11–19.
