Characteristics of motives for the use of electronic social networks by students with different indicators of communication competency

  • Ivan V. Tkachov aBelarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus

Abstract

The article examines the differences in the intensity of motives for the use of electronic social networks by students with different indicators of communication competency. The hypothesis of the study was the assumption that students with high and low indicators of communication competency have stronger motives for using electronic social networks, which are based on social needs, than students with medium indicators of competency. To diagnose the phenomena under study, offline questionnaires were administered at four Belarusian universities (n = 488). It was found that students with average communication competency indicators have the strongest motives for using electronic social networks. The obtained results contribute to the understanding of the role of students’ communication competency in motivating the use of electronic social networks and can be used for the development of teaching materials on the topics of digital communication, as well as for psychological correction of problematic use of electronic social networks.

Author Biography

Ivan V. Tkachov, aBelarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus

postgraduate student at the department of social and organisational psychology, faculty of philosophy and social sciences

References

  1. Shevkun AV. [Communication competency as a means of social adaptation of first-year students]. Siberian Pedagogical Journal. 2007;3:158–164. Russian.
  2. Wright KB, Rosenberg J, Egbert N, Ploeger NA, Bernard DR, King S. Communication competence, social support, and depression among college students: a model of Facebook and face-to-face support network influence. Journal of health communication. 2013;18(1):41–57. DOI: 10.1080/10810730.2012.688250.
  3. Mat’yash OI, Pogol’sha VM, Kazarinova NV, Bibi S, Zaritskaya ZhV. Mezhlichnostnaya kommunikatsiya: teoriya i zhizn’ [Interpersonal communication: theory and life]. Saint-Petersburg: Rech’; 2011. 560 p. Russian.
  4. Tkachov IV. Component composition of communication competency and its measurement. Journal of the Belarusian State University. Philosophy and Psychology. 2022;2:85–92. Russian.
  5. Ellison NB, Boyd DM. Sociality through social network sites. In: Dutton WH, editor. The Oxford handbook of Internet studies. Oxford: Oxford University Press; 2013. p. 151–172. DOI: 10.1093/oxfordhb/9780199589074.013.0008.
  6. Katz E, Blumler JG, Gurevitch M. Uses and gratifications research. The Public Opinion Quarterly. 1973;37(4):509–523.
  7. Katz E, Haas H, Gurevitch M. On the use of the mass media for important things. American Sociological Review. 1973;38(2):164–181. DOI: 10.2307/2094393.
  8. Bae M. Understanding the effect of the discrepancy between sought and obtained gratification on social networking site users’ satisfaction and continuance intention. Computers in Human Behavior. 2018;79:137–153. DOI: 10.1016/j.chb.2017.10.026.
  9. Il’in EP. Motivatsiya i motivy [Motivation and motives]. Saint Petersburg: Piter; 2002. 512 р. Russian.
  10. McKenna K, Bargh JA. Plan 9 from cyberspace: the implications of the internet for personality and social psychology. Personality and Social Psychology Review. 2000;4(1):57–75. DOI: 10.1207/S15327957PSPR0401_6.
  11. Kraut R, Kiesler S, Boneva B, Cummings J, Helgeson V, Crawford A. Internet paradox revisited. Journal of Social Issues. 2002;58(1):49–74. DOI: 10.1111/1540-4560.00248.
  12. Williams D. On and off the ’Net: scales for social capital in an online era. Journal of computer-mediated communication. 2006;11(2):593–628. DOI: 10.1111/j.1083-6101.2006.00029.x.
  13. Cecilia Cheng, Hsinyi Wang, Leif Sigerson, Chorlam Chau. Do the socially rich get richer? A nuanced perspective on social network site use and online social capital accrual. Psychological bulletin. 2019;145(7):734–764. DOI: 10.1037/bul0000198.
  14. Hassan N, Sumardi N, Aziz RA. The influence of personality traits on communication competence. International Journal of Academic Research in Business and Social Sciences. 2019;13:493–505. DOI: 10.6007/IJARBSS/v9-i13/6999.
  15. Spitzberg BH, Canary DJ. Loneliness and relationally competent communication. Journal of Social and Personal Relationships. 1985;2(4):387–402. DOI: 10.1177/0265407585024001.
  16. Zakahi WR, Duran RL. All the lonely people: the relationship among loneliness, communicative competence, and communication anxiety. Communication Quarterly. 1982;30(3):203–209. DOI: 10.1080/01463378209369450.
  17. Rubin RB, Martin MM. Development of a measure of interpersonal communication competence. Communication Research Reports. 1994;11(1):33–44. DOI: 10.1080/08824099409359938.
  18. Hollenbaugh EE, Ferris AL, Casey DJ. How do social media impact interpersonal communication competence? A uses and gratifications approach. In: Desjarlais М, еditor. The psychology and dynamics behind social media interactions. Pennsylvania: IGI Global; 2020. p. 137–163. DOI: 10.4018/978-1-5225-9412-3.ch006.
  19. Spitzberg BH. Assessing the state of assessment: communication competence. In: Hannawa AF, Spitzberg BH, editors. Communication competence. Berlin: De Gruyter Mouton; 2015. p. 237–270. DOI: 10.1515/9783110317459-023.
  20. Nasledov AD, Shamaev AN. [Adapting the Tromso Social Intelligence Scale for Russian psychology students]. In: Zashchirinskaya OV, Shaboltas AV, editors. Psikhologiya XXI veka: sistemnyi podkhod i mezhdistsiplinarnye issledovaniya. Sbornik nauchnykh trudov uchastnikov Mezhdunarodnoi nauchnoi konferentsii molodykh uchenykh; 18–20 aprelya 2017 g.; Sankt-Peterburg, Rossiya. Tom 2 [Psychology of the 21st century: a systematic approach and interdisciplinary research. Collection of scientific papers of the participants of the International scientific conference of young scientists; 2017 April 18–20; Saint Petersburg, Russia. Volume 2]. Saint Peterburg: Skifiya-print. 2017. p. 296–301. Russian.
  21. Lyusin DV. [«EmIn» emotional intelligence questionnaire: new psychometric findings]. In: Lyusin DV, Ushakov DV, editors. Sotsial’nyi i emotsional’nyi intellekt: ot protsessov k izmereniyam [Social and emotional intelligence: from process to measurement]. Moscow: Institute of Psychology Russian Academy of Sciences; 2009. p. 264–278. Russian.
  22. Magomed-Eminov MSh. Motivatsiya dostizheniya: struktura i mekhanizmy [Achievement motivation: structure and mechanisms] [dissertation]. Moscow: Lomonosov Moscow State University; 1987. 343 p. Russian.
  23. Tkachov IV, Fofanova GA. Adaptation of the questionnaire «motives for using electronic social networks» on a sample of Belarusian students. Journal of the Belarusian State University. Philosophy and Psychology. 2023;1:82–94. Russian.
  24. Nasledov AD. IBM SPSS Statistics 20 i AMOS: professional’nyj statisticheskij analiz dannyh [IBM SPSS Statistics 20 and AMOS: professional statistical data analysis]. Saint Petersburg: Piter; 2013. 416 р. Russian.
  25. Lumley T, Diehr P, Emerson S, Lu Chen. The importance of the normality assumption in large public health data sets. Annual review of public health. 2002;23(1):151–169. DOI: 10.1146/annurev.publhealth.23.100901.140546.
  26. Schmider E, Ziegler M, Danay E, Bühner M. Is it really robust? Reinvestigating the robustness of ANOVA against violations of the normal distribution assumption. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences. 2010;6(4):147–151. DOI: 10.1027/1614-2241/a000016.
  27. Blanca MJ, Alarcón R, Arnau J, Bono R, Bendayan R. Non-normal data: is ANOVA still a valid option? Psicothema. 2017;29(4):552–557. DOI: 10.7334/psicothema2016.383.
  28. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 7th еdition. New Jersey: Prentice Hall; 2013. 816 р.
  29. Awang Z. A handbook on structural equation modeling using AMOS graphic. 5th еdition. Kota Baru: Universiti Technologi MARA Press; 2012. 167 р.
  30. Litze Hu, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: a Multidisciplinary Journal. 1999;6(1):1–55. DOI: 10.1080/10705519909540118.
  31. Browne MW, Cudeck R. Alternative ways of assessing model fit. Sociological methods & Research. 1992;21(2):230–258. DOI: 10.1177/0049124192021002005.
  32. Satorra A, Bentler PM. Corrections to test statistics and standard errors in covariance structure analysis. In: von Eye A, Clogg CC, editors. Latent variables analysis: applications for developmental research. Thousand Oaks: Sage; 1994. p. 399–419.
  33. Cheung GW, Rensvold R. Evaluating Goodness-of-Fit indexes for testing measurement invariance. Structural Equation Modeling. 2002;9(2):233–255. DOI: 10.1207/S15328007SEM0902_5.
  34. Hout MC, Papesh MH, Goldinger SD. Multidimensional scaling. Wiley Interdisciplinary Reviews: Cognitive Science. 2013;4(1):93–103. DOI: 10.1002/wcs.1203.
  35. Şaliş İ, Bulut MT. Effects of communication competency and self-esteem on gaming addiction symptoms. Addicta: the Turkish Journal on Addictions. 2022;9(2):196–203. DOI: 10.5152/ADDICTA.2022.21097.
  36. Bubaš G, Spitzberg B. The relations of communication skills in face-to-face and computer-mediated communication. In: European Communication Research and Education Association. Communication Policies and Culture in Europe. Proceedings of the European Communication Research and Education Association 2 nd European Communication Conference; 2008 November 25–28; Barcelona, Spain. Barcelona: European Communication Research and Education Association; 2008. p. 1–19.
  37. Bouchillon BC. Social networking for interpersonal life: a competence-based approach to the rich get richer hypothesis. Social Science Computer Review. 2022;40(2):309–327. DOI: 10.1177/0894439320909506.
  38. Zywica J, Danowski J. The faces of Facebookers: investigating social enhancement and social compensation hypotheses; predicting Facebook™ and offline popularity from sociability and self-esteem, and mapping the meanings of popularity with semantic networks. Journal of Computer-Mediated Communication. 2008;14(1):1–34. DOI: 10.1111/j.1083-6101.2008.01429.x.
Published
2023-11-05
Keywords: competency, communication competency, electronic social networks, motives for using electronic social networks, social intelligence, emotional intelligence, affiliation motive, students
How to Cite
Tkachov, I. V. (2023). Characteristics of motives for the use of electronic social networks by students with different indicators of communication competency. Journal of the Belarusian State University. Philosophy and Psychology, 3, 67-82. Retrieved from https://journals.bsu.by/index.php/philosophy/article/view/5324