Yükseköğretim öğrencilerinin eğitim amaçlı Instagram kullanımlarını etkileyen faktörlerin yapısal bir modelle incelenmesi

Öz

Dijital yazılım ve teknolojilerin hızlı gelişimi, yeni sosyal ağların (SNS) tasarlanmasına neden olmuştur. Bu sosyal ağ araçları mevcut sosyal medya ortamlarına yenilikler katmış ve onları kullanıcı dostu hale getirmiştir. Yazılım teknolojilerinin hayatın her alanına entegre olması nedeniyle sosyal medya platformları özellikle yükseköğretim öğrencileri tarafından yaygın olarak kullanılır hale gelmiştir. YouTube ve Instagram gibi sosyal ağlar öncelikle eğlenceyle ilişkilendirilse de artık sıklıkla eğitici videolar ve gönderiler içeriyorlar. Bu çalışma, yükseköğretim öğrencilerinin eğitim amaçlı Instagram kullanımlarını etkileyen faktörleri önerilen bir yapısal model kullanarak araştırmaktadır. Çalışma literatüre dayalı bir araştırma modeli geliştirilerek başlamıştır. Model temel alınarak bir veri toplama aracı tasarlanmış ve veriler bir bağlantı aracılığıyla çevrimiçi olarak toplanmıştır. Önerilen araştırma modeli ve ilişkileri, kısmi en küçük kareler yapısal eşitlik modellemesi (PLS-SEM) kullanılarak test edildi. Analiz sonuçları, Instagram kullanımının algılanan kullanışlılığının içerik zenginliği, video kalitesi ve kaynak güvenilirliği arttıkça arttığını ortaya koydu. Ayrıca kullanıcı memnuniyeti, algılanan kullanışlılık ile pozitif yönde ilişkilidir ve bu da Instagram kullanım yoğunluğunun artmasına yol açmaktadır.

Anahtar Kelimeler

• instagram
• yükseköğrenim
• eğitimsel amaçlar

Exploring factors affecting higher education students' use of Instagram for educational purposes with a structural model

Öz

The rapid development of digital software and technologies has resulted in the design of new social networks (SNS). These social network tools have added innovations to existing social media environments and made them user-friendly. Due to the integration of software technologies into all areas of life, social media platforms have become widely used especially by higher education students. Although SNSs such as YouTube and Instagram are primarily associated with entertainment, they now frequently include educational videos and posts. This study investigates the factors affecting higher education students' use of Instagram for educational purposes using a proposed structural model. The study started by developing a research model based on the literature. A data collection tool was designed based on the model and data were collected online through a link. The proposed research model and its relationships were tested using partial least squares structural equation modelling (PLS-SEM). The results of the analyses revealed that the perceived usefulness of Instagram use increases with content richness, video quality and source credibility. Furthermore, user satisfaction is positively related to perceived usefulness, leading to an increase in Instagram usage intensity.

Anahtar Kelimeler

• ınstagram
• higher education
• educational purposes

Kaynakça

1. Abu-Taieh, E., AlHadid, I. Masa’deh, R., Alkhawaldeh, R.S., Khwaldeh, S., & Alrowwad, A. (2022). Factors Influencing YouTube as a Learning Tool and Its Influence on Academic Achievement in a Bilingual Environment Using Extended Information Adoption Model (IAM) with ML Prediction—Jordan Case Study. Applied Sciences, 12(12), 5856. https://doi.org/10.3390/app12125856
2. Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological bulletin, 82(2), 261-277. https://doi.org/10.1037/h0076477
3. Argın, F. S. (2013). Ortaokul Ve Lise Öğrencilerinin Sosyal Medyaya İlişkin Tutumlarının İncelenmesi (Yayımlanmış Yüksek Lisans Tezi, Yeditepe Üniversitesi, Sosyal Bilimler Enstitüsü, İstanbul).
4. Brecht, H. (2012). Learning from Online Video Lectures. Journal of Information Technology Education: Innovations in Practice, 11(1), 227-250.
5. Chaiken, S. (1980). Heuristic versus Systematic Information Processing and the Use of Source versus Message Cues in Persuasion. Journal of Personality and Social Psychology, 39, 752-766. http://dx.doi.org/10.1037/0022-3514.39.5.752
6. Chintalapati, N., & Daruri, V. S. K. (2017). Examining the use of YouTube as a Learning Resource in higher education: Scale development and validation of TAM model. Telematics and Informatics,34(6), 853-860. https://doi.org/10.1016/j.tele.2016.08.008
7. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.
8. Çömlekçi, M. F., & Başol, O. (2019). Gençlerin sosyal medya kullanım amaçları ile sosyal medya bağımlılığı ilişkisinin incelenmesi. Manisa Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 17(4), 173-188.

9. Davis, F. D. (1985). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Massachusetts Institute of Technology. http://hdl.handle.net/1721.1/15192
10. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008.
11. Eid, M.I.M., & Al-Jabri, I.M. (2016). Social networking, knowledge sharing, and student learning: The case of university students, Computers & Education, 99, 14-27. https://doi.org/10.1016/j.compedu.2016.04.007.
12. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(3), 39-50. https://doi.org/10.2307/3151312
13. Gold, A.H., Malhotra, A., & Segars, A.H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 187-214
14. Hair, J.F., Ringle, C.M. & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19, 139-151. https://doi.org/10.2753/MTP1069-6679190202
15. Hair, J.F., Risher, J.J., Sarstedt, M. & Ringle, C.M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
16. Jamil, R.A., & Qayyum, A. (2022). Word of mouse vs. word of influencer? An experimental investigation into the consumers’ preferred source of online information. Management Research Review, 45(2), 173–197. https://doi.org/10.1108/MRR-03-2021-0184
17. Jung, Y., Perez-Mira, B., & Wiley-Patton, S. (2009). Consumer adoption of mobile TV: Examining psychological flow and media content. Computers in Human Behavior, 25(1), 123-129. https://doi.org/10.1016/j.chb.2008.07.011
18. Kemp, S. (2018). Digital in 2018: World’s Internet Users Pass the 4 Billion Mark. We Are Social. 01.02.2019, Retrieved from: https://wearesocial.com/blog/2018/01/global-digital-report2018.
19. Kilis, S., Rapp, C., & Gülbahar, Y. (2014). Eğitimde Sosyal Medya Kullanımına Yönelik Yükseköğretim Düzeyindeki Eğitmenlerin Algısı: Türkiye-Almanya Örneklemi. Journal of Instructional Technologies and Teacher Education, 3(3), 20-28.
20. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration. 11(4), 1-10. https://doi.org/10.4018/ijec.2015100101
21. Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers and Education, 61, 193-208. https://doi.org/10.1016/j.compedu.2012.10.001
22. Lim, J. & Richardson, J.C. (2016). Exploring the effects of students' social networking experience on social presence and perceptions of using SNSs for educational purposes. The Internet and Higher Education, 29, 31-39. https://doi.org/10.1016/j.iheduc.2015.12.001
23. Lovelock, C., & Wright, L. (2002). Principles of Service Marketing and Management and Management. Edition, 2, illustrated. Publisher, Prentice Hall.
24. Mazman, S. G., & Usluel, Y. K. (2010). Modeling educational use of Facebook. Computers and Education, 55(2), 444–453. https://doi.org/doi:10.1016/j.compedu.2010.02.008
25. Morkoç, D., & Erdönmez, C. (2014). Web 2.0 Uygulamalarının Eğitim Süreçlerine Etkisi: Çanakkale Sosyal Bilimler Meslek Yüksekokul Örneği. AJIT-E: Academic Journal of Information Technology, 5(15), 25-48. https://doi.org/10.5824/1309-1581.2014.2.002.x
26. Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated guide and practical guide to statistical analysis (2nd ed.). Kuala Lumpur, Malaysia: Pearson.
27. Raykov, T., & Marcoulides, G. A. (2006). On multilevel model reliability estimation from the perspective of structural equation modeling. Structural Equation Modeling, 13(1), 130-141. https://doi.org/10.1207/s15328007sem1301_7
28. Ringle, C.M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management and Data Systems, 116(9), 1865-1886. https://doi.org/10.1108/IMDS-10-2015-0449
29. Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: a comparison of college faculty and student uses and perceptions of social networking sites. Internet and Higher Education, 13(3), 134–140. https://doi.org/10.1016/j.iheduc.2010.03.002
30. Saraçoğlu, M., & Aküzüm, C. (2017). Üniversite öğrencilerinin sosyal medyaya ilişkin tutumlarının incelenmesi. Dicle Üniversitesi Ziya Gökalp Eğitim Fakültesi Dergisi, 32, 803-817.
31. Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. J. of Family Business Strategy, 5(1), 105-115. https://doi.org/10.1016/j.jfbs.2014.01.002
32. Sarsar, F., Başbay, M. & Başbay, A. (2015). Öğrenme-öğretme sürecinde sosyal medya kullanımı. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 11(2), 418-431.
33. Shee, D. Y. & Wang, Y. S. (2008). Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers and Education, 50, 894-905. https://doi.org/10.1016/j.compedu.2006.09.005
34. Solmaz, B., Tekin, G., Herzem, Z., & Demir, M. (2013). İnternet ve sosyal medya kullanımı üzerine bir uygulama. Selçuk İletişim, 7(4), 23-32.
35. Statista (2024). https://www.statista.com/statistics/325587/instagram-global-age-group/. Accessed on January 2024.
36. Sussman, S.W., & Siegal, W.S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65. https://doi.org/10.1287/isre.14.1.47.14767
37. Tung, F. C., Chang, S. C., & Chou, C. M. (2008). An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. International journal of medical informatics, 77(5), 324-335. https://doi.org/10.1016/j.ijmedinf.2007.06.006
38. Üstün, Ö, Çam, H., Can, D., & Dönmez, Ö. (2018). Uzaktan yükseköğretim öğrencilerinin sosyal medyanın eğitim boyutu konusundaki algıları ve eğitim amaçlı sosyal medya kullanımlarının belirlenmesi. The Journal of International Scientific Researches, 3(1), 64-72.
39. Yang, Y., Wang, Q., Woo, H. L., & Quek, C. L. (2011). Using Facebook for teaching and learning: a review of the literature. International Journal of Continuing Engineering Education and Life-Long Learning, 21(1), 72–86. https://doi.org/10.1504/IJCEELL.2011.039695
40. Yılmazsoy, B., & Kahraman, M. (2017). Üniversite öğrencilerinin sosyal medya bağımlılığı ile sosyal medyayı eğitsel amaçlı kullanımları arasındaki ilişkinin incelenmesi: Facebook örneği. Journal of Instructional Technologies and Teacher Education, 6(1), 9-20.
41. Yu, A. Y., Tian, S. W., Vogel, D., & Kwok, R. C. W. (2010). Can learning be virtually boosted? an investigation of online social networking impacts. Computers and Education, 55(4), 1494–1503. https://doi.org/10.1016/j.compedu.2010.06.015
42. Wang, Y. (2016). Information Adoption Model, a Review of the Literature. Journal of Economics, Business and Management, 4(11), 618–622. https://doi.org/10.18178/joebm.2016.4.11.462
43. Wu, J. H., Tennyson, R. D., & Hsia, T. L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers and education, 55(1), 155-164. https://doi.org/10.1016/j.compedu.2009.12.01