The Mediating Role of Cognitive Flexibility in the Association Between Technology-Related Difficulties and Positive Attitudes Towards the Metaverse: Structural Equation Modelling

Abstract

Emerging immersive environments may be evaluated positively by individuals who are highly involved in digital technologies, even when that involvement co-occurs with technology-related difficulties; cognitive flexibility may help explain this apparently contradictory pattern. This cross-sectional correlational study examined whether cognitive flexibility statistically mediated the association between technology-related difficulties and positive attitudes towards the Metaverse in 231 adults aged 18-40 recruited by convenience sampling at a state university. Data were collected using a Demographic Information Form, the Metaverse Attitude Scale, the Cognitive Flexibility Inventory, and the Technology-Based Disorders Screening List. The structural model contained three latent variables represented by theoretically defined subscale scores. Technology-related difficulties were positively associated with positive Metaverse attitudes (beta = .54), negatively associated with cognitive flexibility (beta = -.53), and cognitive flexibility was positively associated with positive Metaverse attitudes (beta = .53). The estimated indirect association was negative (beta = -.28), whereas the direct association was positive, yielding a smaller total association (beta = .26). The opposite signs of the direct and indirect estimates are consistent with competitive or inconsistent mediation. Because the data are cross-sectional and self-reported, the paths indicate statistical associations rather than temporal or causal processes.

Keywords

• Metaverse
• cognitive flexibility
• technology-related difficulties
• positive attitudes
• structural equation modelling

References

1. Akçay Özcan, D., & Kıran, B. (2016). The relationship between adolescents' cognitive flexibility levels and self- efficacy. International Journal of Eurasian Education and Culture, 1(1), 1–8.
2. Altunkol, F. (2011). The examination of the relationship between university students' cognitive flexibility and perceived stress levels [Master's thesis]. Çukurova University.
3. Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a Facebook addiction scale. Psychological Reports, 110(2), 501–517. https://doi.org/10.2466/02.09.18.PR0.110.2.501-517
4. Anlayışlı, C., & Serin, N. B. (2019). The examination of internet addiction and depression among high school students in terms of gender, academic achievement, and duration of internet use. Folklore/Literature, 25(97), 730–743.
5. Arslan, S., Savaşer, S., Hallett, V., & Balcı, S. (2012). Cyberbullying among primary school students in Turkey: Self-reported prevalence and associations with home and school life. Cyberpsychology, Behavior, and Social Networking, 15, 527–533. https://doi.org/10.1089/cyber.2012.0207
6. Bakioğlu, F., Ekinci, N., & Deniz, M. (2023). Fear of missing out (FOMO) and internet addiction: A study on the mediating role of cognitive flexibility. Bingöl University Journal of Social Sciences Institute, 26, 264–275. https://doi.org/10.29029/busbed.1298386
7. Balcı, Ş., & Gülnar, B. (2009). Internet addiction among university students and the profile of internet addicts. Selçuk Communication, 6(1), 5–22.
8. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

9. Bilgiç, R., & Bilgin, M. (2016). The examination of the relationship between adolescents' cognitive flexibility levels and decision-making strategies according to gender and educational level. Uşak University Journal of Educational Research, 2(2), 39–55. https://doi.org/10.29065/usakead.232432
10. Braem, S., & Egner, T. (2018). Getting a grip on cognitive flexibility. Current Directions in Psychological Science, 27(6), 470–476. https://doi.org/10.1177/0963721418787475
11. Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. (2019). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond Internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience & Biobehavioral Reviews, 104, 1–10. https://doi.org/10.1016/j.neubiorev.2019.06.032
12. Brown, T., Smith, R., Zarate, D., Griffiths, M. D., & Stavropoulos, V. (2024). Exploring user-avatar bond profiles: Longitudinal impacts on internet gaming disorder. Computers in Human Behavior, 159, 108340. https://doi.org/10.1016/j.chb.2024.108340
13. Bulut, L., Zararsız, Ö. F., & Seyhan, A. S. (2022). Identity, pleasure, and metaverse: A semiotic analysis of the film Ready Player One. New Media, 2022(12), 258–282.
14. Cañas, J. J., Fajardo, I., & Salmerón, L. (2006). Cognitive flexibility. In W. Karwowski (Ed.), International encyclopedia of ergonomics and human factors (2nd ed., pp. 297–301). CRC Press.
15. Chen, C., Zhang, K. Z., Chu, Z., & Lee, M. (2024). Augmented reality in the metaverse market: The role of multimodal sensory interaction. Internet Research, 34(1), 9–38. https://doi.org/10.1108/INTR-08-2022-0670
16. Chen, Z., Gan, W., Sun, J., Wu, J., & Yu, P. S. (2024). Open metaverse: Issues, evolution, and future. In Companion Proceedings of the ACM Web Conference 2024 (WWW ’24 Companion), 1351–1360. https://doi.org/10.1145/3589335.3651898
17. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications.
18. Cutter, S. L., Tiefenbacher, J., & Solecki, W. D. (1992). En-gendered fears: Femininity and technological risk perception. Industrial Crisis Quarterly, 6(1), 5–22. https://doi.org/10.1177/108602669200600102
19. Çelikkol, Ş. (2022). Evaluation of metaverse world in terms of consumer purchasing behaviors. Istanbul Kent University Journal of Human and Social Sciences, 3(1), 64–75.
20. Dajani, D. R., & Uddin, L. Q. (2015). Demystifying cognitive flexibility: Implications for clinical and developmental neuroscience. Trends in Neurosciences, 38(9), 571–578. https://doi.org/10.1016/j.tins.2015.07.003
21. Dawson, C. (2023). Gender differences in optimism, loss aversion and attitudes towards risk. British Journal of Psychology, 114(4), 928–944. https://doi.org/10.1111/bjop.12668
22. Dennis, J. P., & Vander Wal, J. S. (2010). The cognitive flexibility inventory: Instrument development and estimates of reliability and validity. Cognitive Therapy and Research, 34(3), 241–253. https://doi.org/10.1007/s10608-009-9276-4
23. Deák, G. O. (2003). The development of cognitive flexibility and language abilities. Advances in Child Development and Behavior, 31, 271–327. https://doi.org/10.1016/S0065-2407(03)31007-9
24. Diril, A. (2011). The examination of high school students' cognitive flexibility levels in relation to sociodemographic variables, anger level, and anger expression styles [Master's thesis]. Çukurova University, Institute of Social Sciences.
25. Dong, G., Lin, X., Zhou, H., & Lu, Q. (2014). Cognitive flexibility in internet addicts: fMRI evidence from difficult-to-easy and easy-to-difficult switching situations. Addictive Behaviors, 39(3), 677–683. https://doi.org/10.1016/j.addbeh.2013.11.028
26. Elliot, A. J., & Thrash, T. M. (2002). Approach-avoidance motivation in personality: Approach and avoidance temperaments and goals. Journal of Personality and Social Psychology, 82(5), 804–821.
27. Elphinston, R. A., & Noller, P. (2011). Time to face it! Problematic mobile phone use linked to daily life impairments. Cyberpsychology, Behavior, and Social Networking, 14(12), 755–760. https://doi.org/10.1089/cyber.2010.0319
28. Eroğlu, Y., Aktepe, E., Akbaba, S., Işık, A., & Özkorumak, E. (2015). An examination of the prevalence and risk factors of cyberbullying and victimization. Education and Science, 40(177), 93–107. https://doi.org/10.15390/EB.2015.3698
29. Gülbol, D. (2024). The effects of the metaverse on social life [Master's thesis]. Ankara University, Institute of Social Sciences.
30. Güler, O., & Savaş, S. (2022). All aspects of metaverse studies, technologies and future. Gazi Journal of Engineering Sciences, 8(2), 292–319. https://doi.org/10.30855/gmbd.0705011
31. Gülüm, I. V., & Dağ, İ. (2012). The Turkish adaptation, validity and reliability study of the Repetitive Thinking Questionnaire and the Cognitive Flexibility Inventory. Anatolian Journal of Psychiatry, 13(3), 216–223.
32. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
33. Harris, C. R., & Jenkins, M. (2006). Gender differences in risk assessment: Why do women take fewer risks than men?. Judgment and Decision Making, 1(1), 48–63.
34. Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). The Guilford Press.
35. He, Z., Li, M., Liu, C., & Ma, X. (2022). Common predictive factors of social media addiction and eating disorder symptoms in female college students: State anxiety and the mediating role of cognitive flexibility/sustained attention. Frontiers in Psychology, 12, 647126.
36. Hoyle, R. H. (Ed.) (2012). Handbook of structural equation modeling. Guilford Press.
37. Hu, L. T., & Bentler, P. M. (1999). Cut-off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
38. Indu, M. A. (2024). Stress reduction through virtual therapy centre in metaverse. In Examining the metaverse in healthcare: Opportunities, challenges, and future directions (pp. 329–352). https://doi.org/10.4018/979-8-3693-1515-6.ch013.
39. Jarvenpaa, S. L., & Keating, E. (2024). Fluid teams in the metaverse: Exploring the (un)familiar. Frontiers in Psychology, 14, 1323586. https://doi.org/10.3389/fpsyg.2023.1323586
40. Jim, J. R., Hosain, M. T., Mridha, M. F., Kabir, M. M., & Shin, J. (2023). Toward trustworthy metaverse: Advancements and challenges. IEEE Access, 11, 118318–118347.
41. Kalıp, K., & Çöl, M. (2020). New behavioral disorders related to technology. ESTÜDAM Public Health Journal, 5(2), 318–333.
42. Kayacan, Ş., & Batu, M. (2024). What is the metaverse not? An academic discussion on the virtual world. Journal of Türkiye Media Academy, 4(7), 88–110. https://doi.org/10.5281/zenodo.10715739
43. Kaymaz, E., & Şakiroğlu, M. (2020). The effects of mindfulness and cognitive flexibility on problematic smartphone use: The mediator role of self-control. Uludağ University Faculty of Arts and Sciences Journal of Social Sciences, 21(38), 79–108. https://doi.org/10.21550/sosbilder.600325
44. Kim, S., & Kim, E. (2023). Emergence of the metaverse and psychiatric concerns in children and adolescents. Journal of the Korean Academy of Child and Adolescent Psychiatry, 34(4), 215.
45. King, D., & Delfabbro, P. (2018). Internet gaming disorder: Theory, assessment, treatment, and prevention. Academic Press.
46. Kulakoğlu Dilek, N., & İstanbullu Dinçer, F. (2020). The supportive role of virtual reality applications in the participation of individuals with disabilities in recreational activities. CUDES Journal, 3(1), 1–9. https://www.cudesjournal.com/article/sanal-gerceklik-uygulamalarinin-engelli-bireylerin-rekreasyonel-faaliyetlere-katiliminda-destekleyici-rolu
47. Kun, Y. U. E. (2022). The metaverse in the psychoanalytic perspective: The unleashed human id in the metaverse. Psychology, 12(1), 6–10.
48. Kırcaburun, K., Tosuntaş, Ş. B., Emirtekin, E., Koç, G., Bahtiyar, M., & Griffiths, M. (2023). Social anxiety and social media addiction among emerging adults: The mediating role of ‘sofalising’. International Journal of Düzce Educational Sciences, 1(1), 13–19. https://izlik.org/JA98WX52ZE
49. Lai, M. H., & Hsiao, Y. Y. (2022). Two-stage path analysis with definition variables: An alternative framework to account for measurement error. Psychological Methods, 27(4), 568–588. https://doi.org/10.1037/met0000410
50. Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. Springer Publishing Company.
51. Lewin, K. (1936). A dynamic theory of personality: Selected papers. McGraw-Hill.
52. Loh, K. K., & Kanai, R. (2016). How has the Internet reshaped human cognition?. The Neuroscientist, 22(5), 506–520. https://doi.org/10.1177/1073858415595005
53. MacKinnon, D. P. (2012). Introduction to statistical mediation analysis. Routledge.
54. MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614. https://doi.org/10.1146/annurev.psych.58.110405.085542
55. Martin, M. M., & Anderson, C. M. (1998). The cognitive flexibility scale: Three validity studies. Communication Reports, 11(1), 1–9. https://doi.org/10.1080/08934219809367680
56. Martin, M. M., & Rubin, R. B. (1995). A new measure of cognitive flexibility. Psychological Reports, 76(2), 623–626. https://doi.org/10.2466/pr0.1995.76.2.623
57. McNeish, D., & Wolf, M. G. (2020). Thinking twice about sum scores. Behavior Research Methods, 52, 2287–2305.
58. Mystakidis, S. (2022). Metaverse. Encyclopedia, 2(1), 486–497.
59. Pal, D., & Arpnikanondt, C. (2024). The sweet escape to metaverse: Exploring escapism, anxiety, and virtual place attachment. Computers in Human Behavior, 150, 107998.
60. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
61. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. Presidency of the Republic of Türkiye Directorate of Communications (2023). Social Network Map. https://sosyalagharitasi.gov.tr/
62. Reis, I. W., Romeiro, A. E., Berg, C. H., & Ulbricht, V. R. (2024). Sociodigital experiences and creativity in the metaverse: An integrative review. Heliyon, 10(7), e29047. https://doi.org/10.1016/j.heliyon.2024.e29047
63. Ritter, S. M., Damian, R. I., Simonton, D. K., van Baaren, R. B., Strick, M., Derks, J., & Dijksterhuis, A. (2012). Diversifying experiences enhance cognitive flexibility. Journal of Experimental Social Psychology, 48(4), 961–964. https://doi.org/10.1016/j.jesp.2012.02.009
64. Riva, G., Wiederhold, B. K., & Villani, D. (2024). Toward a humane metaverse: Challenges and opportunities. Cyberpsychology, Behavior, and Social Networking, 27(1), 3–8. https://doi.org/10.1089/cyber.2023.29303.editorial
65. Ryu, K., Kim, Y., Kim, J., & Woo, M. (2021). False accusation of online games: Internet gaming can enhance the cognitive flexibility of adolescents. Asian Journal of Sport and Exercise Psychology, 1(2–3), 116–121.
66. Sarıbay, B., & Durgun, G. (2020). Phobias of touchscreen societies: A content analysis of netlessphobia, nomophobia, and FOMO. E-Journal of Yaşar University, 15, 280–294.
67. Sağar, M. E. (2021). Predictive role of cognitive flexibility and self-control on social media addiction in university students. International Education Studies, 14(4), 1–10. https://doi.org/10.5539/ies.v14n4p1
68. Schaap, F. (2006). Disaggregation, technology, and masculinity: Elements of internet research. In D. Silver & A. Massanari (Eds.), Critical cyberculture studies. New York University Press.
69. Schumacker, R. E., & Lomax, R. G. (2016). A beginner's guide to structural equation modeling (4th ed.). Routledge.
70. Shu, S., & Woo, B. K. (2023). Pioneering the metaverse: The role of the metaverse in an aging population. JMIR Aging, 6(1), e40582.
71. Stephenson, N. (1992). Snow Crash. Bantam Books.
72. Sunar, D., & Fişek, G. O. (2005). Contemporary Turkish families. In J. E. Roopnarine & U. P. Gielen (Eds.), Families in global perspective (pp. 169–183). Pearson/Allyn & Bacon.
73. Söner, O., & Yılmaz, O. (2018). The relationship between social media addiction and psychological well-being levels among high school students. Ufuk University Journal of Social Sciences, 7(13), 59–73.
74. Şimşek, Ö. F. (2020). Introduction to structural equation modeling: Basic principles and LISREL applications.
75. Tabachnick, B. G., & Fidell, L. S. (2014). Using multivariate statistics (6th ed.). Pearson Education Limited.
76. Toffler, A. (1984). Previews and premises. Black Rose Books. Turkish Statistical Institute (2024). Statistical report. TÜİK.
77. Ümmet, D., & Ekşi, F. (2016). Internet addiction in young adults in Turkey: Loneliness and virtual-environment loneliness. Addicta: The Turkish Journal on Addictions, 3(1), 42–55. https://doi.org/10.15805/addicta.2016.3.0008
78. Wang, C., Zhang, Z., Wiley, J. A., Fu, T., & Yan, J. (2022). Gender differences in pleasure: The mediating roles of cognitive flexibility and emotional expressivity. BMC Psychiatry, 22(1), 320. https://doi.org/10.1186/s12888-022-03945-9
79. WHO Regional Office for Europe (2024). A focus on adolescent peer violence and bullying in Europe, central Asia and Canada: Health Behaviour in School-aged Children international report from the 2021/2022 survey (Vol. 2). World Health Organization. ISBN 978-92-890-6092-9
80. Wiederhold, B. K. (2022). Sexual harassment in the metaverse. Cyberpsychology, Behavior, and Social Networking, 25(8), 479–480.
81. Wiederhold, B. K. (2023). Treading carefully in the metaverse: The evolution of AI avatars. Cyberpsychology, Behavior, and Social Networking, 26(5), 321–322.
82. Yam, F. C., & İlhan, T. (2020). The holistic technological addiction of the modern era: Phubbing and its dynamics. Current Approaches in Psychiatry, 12(1), 1–15. https://doi.org/10.18863/pgy.551299
83. Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257