Investigating FactorsA consumer Behavior in Social Networks

Document Type : Research Paper

Authors

1 Ph.D. Student in Business Management Department, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

2 Assistant Professor, Center for Management Studies and Technology Development, Tarbiat Modares University, Tehran, Iran. Business Management Department, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

3 Assistant Professor, Department of Business Administration, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

Abstract

Extended Abstract
Abstract
The use of social media is increasing. In Iran, in recent years, different social media have been presented. These networks are mainly offered in the form of mobile platforms. The primary purpose of this study is to present a model of Iranian consumers' behavior on social networks. The method of this research was applied correlation and survey. In this study, the statistical population included social network users to examine the fit of the final model. Using the Morgan table, the sample size was 384 people. The sampling method was Simple random sampling. In order to measure the research indicators, a researcher-made questionnaire was prepared based on the research literature. Its Reliability was confirmed using Cronbach's alpha coefficients (0.75), and its validity was confirmed using CFA and fit indices. This research was implemented in 2021, and the questionnaire was also distributed in the summer of this year. The results of this research confirmed the relationship between 7 leading indicators in the form of functions, usability, privacy, network content, marketing, and psyxchological and environmental factors with consumer behavior in social networks. Based on the obtained results, the theoretical basis of the research was valid, and the developed model/scale was suitable for measuring the behavior of Iranian consumers in social networks.
Introduction
Today, social networks play an essential role in the general public's lives. The age of using social networks in Iran has reached below seven years. Moreover, this issue has caused attention to the need to implement and develop social networks. Meanwhile, knowing the behavior of Iranian consumers in social networks can be effective as an essential issue in knowing the behavior patterns of people in these networks. A subject that many domestic and international companies have always considered.
Case study
In this research, the statistical population included social network users to check the fit of the final model. Using Morgan's table, the sample size was determined to be 384 people. The sampling method in this research was random simple. 
Materials and Methods
In order to measure research indicators, a researcher-made questionnaire was prepared based on research literature, its reliability was confirmed using Cronbach's alpha coefficient (0.75), and its validity was confirmed using confirmatory factor analysis and fit indices. The period of research and distribution of questionnaires was 1400. In this research, AMOS software uses structural equation modeling to investigate the relationship between influential factors and consumer behavior. The results of this research confirmed the relationship between 7 main indicators in the form of functions, capabilities, privacy, network content, marketing, psychological and environmental factors with consumer behavior in social networks.
Discussion and Results
The research results have shown the appropriate validity and reliability of the research tool. Also, in the examination of the fit indices of the conceptual model in the first and second orders, the values of the fit indices, such as chi-square on the degree of freedom and RMSEA, were calculated at the standard level. Therefore, the investigated model also has a suitable fit. Also, the critical ratios for all seven variables, including content, privacy, usability, functions and marketing, and environmental and psychological factors, were calculated at a level above 1.96, which can be concluded that these variables have a positive and significant effect on explaining/forming the main structure. This research means the behavior of Iranian consumers on social networks. In other words, the developed model was suitable for measuring the behavior of Iranian consumers concerning social networks.
Conclusion
The results of this research have shown that consumer behavior can be through attention to privacy, network functions, network capabilities, content management, psychological and personal dimensions, marketing, and environmental factors that these factors are in a social network. It can influence the consumption behavior of users. Therefore, to implement or exploit internal and external social networks, paying attention to these factors can guide researchers and experts in the field of social networks.

Keywords


References
Alarcón-del-Amo, M. D. C., Lorenzo-Romero, C., & Gómez-Borja, M. Á. (2011). Classifying and profiling social networking site users: A latent segmentation approach. Cyberpsychology, behavior, and social networking14(9), 547-553.
Alshourah, S., Jodeh, I., Swiety, I., & Ismail, A. (2022). Social Customer Relationship Management Capabilities and Performance: Moderating Social Media Usage among SMEs Jordanian. Decision Sciences, 25(S2), 1-8.
Baden, R., Bender, A., Spring, N., Bhattacharjee, B., & Starin, D. (2009, August). Persona: an online social network with user-defined privacy. In Proceedings of the ACM SIGCOMM 2009 conference on Data communication (pp. 135-146).
Barker, V. (2009). Older adolescents' motivations for social network site use: The influence of gender, group identity, and collective self-esteem. Cyberpsychology & behavior12(2), 209-213.
Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of computer‐mediated Communication13(1), 210-230.
Braddy, P. W., Meade, A. W., & Kroustalis, C. M. (2008). Online recruiting: The effects of organizational familiarity, website usability, and website attractiveness on viewers’ impressions of organizations. Computers in Human Behavior24(6), 2992-3001.
Breslin, J., & Decker, S. (2007). The future of social networks on the internet: The need for semantics. IEEE Internet Computing11(6), 86-90.
Brito, T. R. P. D., Nunes, D. P., Duarte, Y. A. D. O., & Lebrão, M. L. (2019). Social network and older people’s functionality: Health, Well-being, and Aging (SABE) study evidences. Revista Brasileira de Epidemiologia21.
Butts, C. T. (2008). Social network analysis with sna. Journal of statistical software24, 1-51.
Chen, Y. H., Hsu, I. C., & Lin, C. C. (2010). Website attributes that increase consumer purchase intention: A conjoint analysis. Journal of business research63(9-10), 1007-1014.
Choi, S. B., & Lim, M. S. (2016). Effects of social and technology overload on psychological well-being in young South Korean adults: The mediatory role of social network service addiction. Computers in Human Behavior61, 245-254.
Coyle, C. L., & Vaughn, H. (2008). Social networking: Communication revolution or evolution?. Bell Labs technical journal13(2), 13-17.
Currás‐Pérez, R., Ruiz‐Mafé, C., & Sanz‐Blas, S. (2013). Social network loyalty: evaluating the role of attitude, perceived risk and satisfaction. Online Information Review.
de Boer, J., & Aiking, H. (2018). Prospects for pro-environmental protein consumption in Europe: Cultural, culinary, economic and psychological factors. Appetite121, 29-40.
DeAndrea, D. C., Ellison, N. B., LaRose, R., Steinfield, C., & Fiore, A. (2012). Serious social media: On the use of social media for improving students' adjustment to college. The Internet and higher education15(1), 15-23.
Dickinger, A., & Stangl, B. (2013). Website performance and behavioral consequences: A formative measurement approach. Journal of business research66(6), 771-777.
Elling, S., Lentz, L., de Jong, M., & Van den Bergh, H. (2012). Measuring the quality of governmental websites in a controlled versus an online setting with the ‘Website Evaluation Questionnaire’. Government information quarterly29(3), 383-393.
Ezumah, B. A. (2013). College students' use of social media: Site preferences, uses and gratifications theory revisited. International journal of business and social science4(5).
Fu, P. W., Wu, C. C., & Cho, Y. J. (2017). What makes users share content on Facebook? Compatibility among psychological incentive, social capital focus, and content type. Computers in Human Behavior67, 23-32.
Houghton, D. J., & Joinson, A. N. (2010). Privacy, social network sites, and social relations. Journal of technology in human services28(1-2), 74-94.
Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business horizons53(1), 59-68.
Kim, S., Kandampully, J., & Bilgihan, A. (2018). The influence of eWOM communications: An application of online social network framework. Computers in Human Behavior80, 243-254.
Kiser, A. I., & Porter, T. (2011, May). Social networking: Integrating students and university professor utilization. In Global Conference on Business and Finance Proceedings (Vol. 6, No. 6, pp. 191-196).
Korhan, O., & Ersoy, M. (2016). Usability and functionality factors of the social network site application users from the perspective of uses and gratification theory. Quality & quantity50(4), 1799-1816.
Kwon, O., Min, D., Geringer, S., & Lim, S. K. (2013). Students perception of qualifications for successful social media coordinator. Academy of Marketing Studies Journal17(1), 109-128.
Lax, G., Russo, A., & Fasci, L. S. (2021). A Blockchain-based approach for matching desired and real privacy settings of social network users. Information Sciences557, 220-235.
Lee, Y., & Kozar, K. A. (2012). Understanding of website usability: Specifying and measuring constructs and their relationships. Decision support systems52(2), 450-463.
Liu, G. Z., Liu, Z. H., & Hwang, G. J. (2011). Developing multi-dimensional evaluation criteria for English learning websites with university students and professors. Computers & Education56(1), 65-79.
Liu, P., Xu, Y., Jiang, Q., Tang, Y., Guo, Y., Wang, L. E., & Li, X. (2020). Local differential privacy for social network publishing. Neurocomputing391, 273-279.
Mason, A. N., Narcum, J., & Mason, K. (2021). Social media marketing gains importance after Covid-19. Cogent Business & Management8(1), 1870797.
Pagani, M., Hofacker, C. F., & Goldsmith, R. E. (2006). The influence of personality on active and passive use of social networking sites. Psychology & Marketing28(5), 441-456.
Park, Y. A., & Gretzel, U. (2007). Success factors for destination marketing web sites: A qualitative meta-analysis. Journal of travel research46(1), 46-63.
Rahman, N. (2014). The usage and online behavior of social networking sites among international students in New Zealand. The Journal of Social Media in Society3(2).
Roetzel, P. G. (2019). Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework development. Business research12(2), 479-522.
Singh, R., Chauhan, A. N. S., & Tewari, H. (2021). Blockchain-enabled end-to-end encryption for instant messaging applications. arXiv preprint arXiv:2104.08494.
Smith, A. G. (2001). Applying evaluation criteria to New Zealand government websites. International journal of information management21(2), 137-149.
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 psychology29(6), 420-433.
Sundararaj, V., & Rejeesh, M. R. (2021). A detailed behavioral analysis on consumer and customer changing behavior with respect to social networking sites. Journal of Retailing and Consumer Services58, 102190.
Tang, Z., Miller, A. S., Zhou, Z., & Warkentin, M. (2021). Does government social media promote users' information security behavior towards COVID-19 scams? Cultivation effects and protective motivations. Government Information Quarterly38(2), 101572.
Terlutter, R., & Capella, M. L. (2013). The gamification of advertising: analysis and research directions of in-game advertising, advergames, and advertising in social network games. Journal of advertising42(2-3), 95-112.
Tirumala, L. N., & Youngblood, E. (2021). Captioning Social Media Video. Public Relations Education7(1), 169-187.
Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of operational research169(1), 1-29.
West, B. J., Massari, G. F., Culbreth, G., Failla, R., Bologna, M., Dunbar, R. I. M., & Grigolini, P. (2020). Relating size and functionality in human social networks through complexity. Proceedings of the National Academy of Sciences117(31), 18355-18358.
Yao, J., & Cao, X. (2017). The balancing mechanism of social networking overuse and rational usage. Computers in Human Behavior75, 415-422.
Yap, S. F., & Gaur, S. S. (2016). Integrating functional, social, and psychological determinants to explain online social networking usage. Behaviour & Information Technology35(3), 166-183.
Yin, X., Wang, H., Yin, P., & Zhu, H. (2019). Agent-based opinion formation modeling in social network: A perspective of social psychology. Physica A: Statistical Mechanics and its Applications, 532, 121786.