Skip to main content

Advertisement

SpringerLink
Log in

Chatting with ChatGPT: decoding the mind of Chatbot users and unveiling the intricate connections between user perception, trust and stereotype perception on self-esteem and psychological well-being

  • Published:
Current Psychology Aims and scope Submit manuscript

Abstract

Artificial Intelligence (AI) technology has revolutionized how we interact with information and entertainment, with ChatGPT, a language model developed by OpenAI, being among its prominent applications. However, knowledge regarding the psychological impact of interacting with ChatGPT is limited. This study investigated the relationships between trust in ChatGPT; ChatGPT’s user perceptions; perceived stereotyping by ChatGPT; and two psychological outcomes, namely, psychological well-being and self-esteem. This study hypothesized that the former three variables exhibit a positive direct relationship with self-esteem. Additionally, the study proposed that job anxiety moderates the associations among trust in ChatGPT, user perceptions of ChatGPT, and psychological well-being. Using a survey design, data were collected from 732 participants and analyzed using SEM and SmartPLS analysis. Notably, perceived stereotyping by ChatGPT significantly predicted self-esteem, while user perceptions of ChatGPT and trust in ChatGPT exhibited a positive direct relationship with self-esteem. Additionally, job anxiety moderated the relationship between ChatGPT’s user perceptions and psychological well-being. These results provide important insights into the psychological effects of interacting with AI technology and highlight job anxiety’s role in moderating these effects. This study’s findings have implications for developing and using AI technology in various fields, including mental health and human-robot interactions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

The datasets generated and/or analyzed during the current study are available from the corresponding authors upon reasonable request.

References

  • Abd-Alrazaq, A. A., Alajlani, M., Alalwan, A. A., Bewick, B. M., Gardner, P., & Househ, M. (2019). An overview of the features of chatbots in mental health: A scoping review. International Journal of Medical Informatics, 132, 103978.

    Article  PubMed  Google Scholar 

  • Abd-Alrazaq, A. A., Alajlani, M., Ali, N., Denecke, K., Bewick, B. M., & Househ, M. (2021). Perceptions and opinions of patients about mental health chatbots: Scoping review. Journal of medical Internet research, 23(1), e17828.

  • Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part II 16.

  • Aljanabi, M., Ghazi, M., Ali, A. H., & Abed, S. A. (2023). ChatGpt: Open possibilities. Iraqi Journal For Computer Science and Mathematics, 4(1), 62–64.

    Google Scholar 

  • Arslan, G. (2019). Mediating role of the self–esteem and resilience in the association between social exclusion and life satisfaction among adolescents. Personality and Individual Differences, 151, 109514.

    Article  Google Scholar 

  • Babnik, K., Benko, E., & von Humboldt, S. (2022). Ryff’s psychological well-being scale. Encyclopedia of gerontology and population aging (pp. 4344–4349). Springer.

  • Baldner, C., & Pierro, A. (2019). The trials of women leaders in the workforce: How a need for cognitive closure can influence acceptance of harmful gender stereotypes. Sex Roles, 80(9–10), 565–577.

    Article  Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986(23–28).

  • Barrie, R. E., Langrehr, K., Jerémie-Brink, G., Alder, N., Hewitt, A., & Thomas, A. (2016). Stereotypical beliefs and psychological well-being of african american adolescent girls: Collective self-esteem as a moderator. Counselling Psychology Quarterly, 29(4), 423–442.

    Article  Google Scholar 

  • Błachnio, A., Przepiorka, A., & Pantic, I. (2016). Association between Facebook addiction, self-esteem and life satisfaction: A cross-sectional study. Computers in Human Behavior, 55, 701–705.

    Article  Google Scholar 

  • Borji, A. (2023). A Categorical Archive of ChatGPT Failures. arXiv preprint arXiv:2302.03494.

  • Brynjolfsson, E., & Mcafee, A. (2017). Artificial intelligence, for real. Harvard business review, 1, 1–31.

    Google Scholar 

  • Caldarini, G., Jaf, S., & McGarry, K. (2022). A literature survey of recent advances in chatbots. Information, 13(1), 41.

    Article  Google Scholar 

  • Capone, V., Joshanloo, M., & Sang-Ah Park, M. (2023). Job satisfaction mediates the relationship between Psychosocial and Organization factors and Mental Well-Being in schoolteachers. International Journal of Environmental Research and Public Health, 20(1), 593.

    Article  Google Scholar 

  • Catalano, L. T., Brown, C. H., Lucksted, A., Hack, S. M., & Drapalski, A. L. (2021). Support for the social-cognitive model of internalized stigma in serious mental illness. Journal of psychiatric research, 137, 41–47.

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen, Y. R. R., & Schulz, P. J. (2016). The effect of information communication technology interventions on reducing social isolation in the elderly: A systematic review. Journal of medical Internet research, 18(1), e4596.

  • Chen, J., & Wang, Y. (2021). Social media use for health purposes: Systematic review. Journal of medical Internet research, 23(5), e17917.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cheryan, S., & Bodenhausen, G. V. (2000). When positive stereotypes threaten intellectual performance: The psychological hazards of “model minority” status. Psychological science, 11(5), 399–402.

    Article  PubMed  Google Scholar 

  • Creswell, J. W., & Tashakkori, A. (2007). Differing perspectives on mixed methods research (1 vol., pp. 303–308). Los Angeles, CA: Sage publications Sage CA.

    Google Scholar 

  • Datu, J. A. D., Wong, G. S. P., & Rubie-Davies, C. (2021). Can kindness promote media literacy skills, self-esteem, and social self-efficacy among selected female secondary school students? An intervention study. Computers & Education, 161, 104062.

    Article  Google Scholar 

  • Dawson, J. F. (2014). Moderation in management research: What, why, when, and how. Journal of business and psychology, 29(1), 1–19.

    Article  Google Scholar 

  • Deley, T., & Dubois, E. (2020). Assessing trust versus reliance for technology platforms by systematic literature review. Social Media + Society, 6(2), 2056305120913883.

    Article  Google Scholar 

  • Dhimolea, T. K., Kaplan-Rakowski, R., & Lin, L. (2022). Supporting Social and Emotional Well-Being with Artificial Intelligence. In Bridging Human Intelligence and Artificial Intelligence (pp. 125–138). Cham: Springer International Publishing. Chicago.

  • Diener, E., & Emmons, R. A. (1984). The independence of positive and negative affect. Journal of personality and social psychology, 47(5), 1105.

    Article  PubMed  Google Scholar 

  • Du, H., Li, Z., Niyato, D., Kang, J., Xiong, Z., & Kim, D. I. (2023). Enabling AI-Generated content (AIGC) services in Wireless Edge Networks. arXiv preprint. arXiv:2301.03220.

  • Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American journal of theoretical and applied statistics, 5(1), 1–4.

    Article  Google Scholar 

  • Fallon, M., Spohrer, K., & Heinzl, A. (2019). Deep structure use of mHealth: a social cognitive theory perspective.

  • Faqih, K. M., & Jaradat, M. I. R. M. (2021). Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country. Technology in Society, 67, 101787.

    Article  Google Scholar 

  • Festinger, L. (1957). Social comparison theory. Selective Exposure Theory, 16, 401.

    Google Scholar 

  • Fiske, S. T., Cuddy, A. J., Glick, P., & Xu, J. (2002). A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition. Journal of personality and social psychology, 82(6), 878.

    Article  PubMed  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Los Angeles, CA: In: Sage Publications Sage CA.

    Google Scholar 

  • Gnambs, T., Scharl, A., & Schroeders, U. (2018). The structure of the Rosenberg self-esteem scale. Zeitschrift für Psychologie.

  • Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H. C., & Jeste, D. V. (2019). Artificial intelligence for mental health and mental illnesses: An overview. Current psychiatry reports, 21, 1–18.

    Article  Google Scholar 

  • Gulati, S., Sousa, S., & Lamas, D. (2019). Design, development and evaluation of a human-computer trust scale. Behaviour & Information Technology, 38(10), 1004–1015.

    Article  Google Scholar 

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. saGe publications.

  • 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.

    Article  Google Scholar 

  • Hair, J. F. Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

  • Harter, S. (2006). The development of self-esteem. Self-esteem issues and answers: A sourcebook of current perspectives, 144–150.

  • Hassan, M. S., Ariffin, R. N. R., Mansor, N., & Halbusi, A. (2021). H. The moderating role of willingness to Implement Policy on Street-level bureaucrats’ Multidimensional Enforcement Style and Discretion. International Journal of Public Administration, 1–15.

  • Hassan, M. S., Halbusi, A., Razali, H., Ariffin, A., R. N. R., & Williams, K. A. (2022). The swedish gamble: Trust in the government and self-efficacy in the battle to combat COVID-19. Current Psychology, 1–16.

  • Hegner, S. M., Beldad, A. D., & Brunswick, G. J. (2019). In automatic we trust: Investigating the impact of trust, control, personality characteristics, and extrinsic and intrinsic motivations on the acceptance of autonomous vehicles. International Journal of Human–Computer Interaction, 35(19), 1769–1780.

    Article  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115–135.

    Article  Google Scholar 

  • Hewitt, J. P. (2020). 22 The Social Construction of Self-Esteem. The Oxford handbook of positive psychology, 309.

  • Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of service research, 21(2), 155–172.

    Article  Google Scholar 

  • Inkster, B., Sarda, S., & Subramanian, V. (2018). An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study. JMIR mHealth and uHealth, 6(11), e12106.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jackson, L. A., von Eye, A., Fitzgerald, H. E., Zhao, Y., & Witt, E. A. (2010). Self-concept, self-esteem, gender, race and information technology use. Computers in Human Behavior, 26(3), 323–328.

  • Jeng, C. R. (2019). The role of trust in explaining tourists’ behavioral intention to use e-booking services in Taiwan. Journal of China Tourism Research, 15(4), 478–489.

    Article  Google Scholar 

  • Jiang, F., Wang, L., Li, J. X., & Liu, J. (2022). How Smart Technology affects the Well-Being and supportive learning performance of Logistics Employees? Frontiers in Psychology, 12, 6646.

    Article  Google Scholar 

  • Jones, M. K., Latreille, P. L., & Sloane, P. J. (2016). Job anxiety, work-related psychological illness and workplace performance. British Journal of Industrial Relations, 54(4), 742–767.

    Article  Google Scholar 

  • Kahneman, D. (2011). Thinking, fast and slow. macmillan.

  • Kim, J., & Kim, E. (2022). Relationship between Self-Esteem and Technological Readiness: Mediation Effect of readiness for change and moderated mediation effect of gender in south korean Teachers. International Journal of Environmental Research and Public Health, 19(14), 8463.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kim, E. E. K., Seo, K., & Choi, Y. (2022). Compensatory travel post COVID-19: Cognitive and emotional effects of risk perception. Journal of Travel Research, 61(8), 1895–1909.

    Article  Google Scholar 

  • Kim, M. J., Bonn, M., & Lee, C.-K. (2020). The effects of motivation, deterrents, trust, and risk on tourism crowdfunding behavior. Asia Pacific Journal of Tourism Research, 25(3), 244–260.

  • Lavoie, C. E., Vallerand, R. J., & Verner-Filion, J. (2021). Passion and emotions: The mediating role of cognitive appraisals. Psychology of Sport and Exercise, 54, 101907.

    Article  Google Scholar 

  • Leary, M. R. (1999). Making sense of self-esteem. Current directions in psychological science, 8(1), 32–35.

    Article  Google Scholar 

  • Lent, R. W., & Brown, S. D. (2008). Social cognitive career theory and subjective well-being in the context of work. Journal of career assessment, 16(1), 6–21.

    Article  Google Scholar 

  • LePine, J. A., LePine, M. A., & Jackson, C. L. (2004). Challenge and hindrance stress: Relationships with exhaustion, motivation to learn, and learning performance. Journal of applied psychology, 89(5), 883.

    Article  PubMed  Google Scholar 

  • Luszczynska, A., & Schwarzer, R. (2015). Social cognitive theory. Fac Health Sci Publ, 225–251.

  • Marques, S. C., Pais-Ribeiro, J., & Lopez, S. J. (2011). The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents: A two-year longitudinal study. Journal of happiness studies, 12, 1049–1062.

    Article  Google Scholar 

  • Mason, J., Classen, S., Wersal, J., & Sisiopiku, V. P. (2020). Establishing face and content validity of a survey to assess users’ perceptions of automated vehicles. Transportation research record, 2674(9), 538–547.

    Article  Google Scholar 

  • Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management review, 20(3), 709–734.

    Article  Google Scholar 

  • Mohammed Salah, H. (2021). Regulatory enforcement of minimum wage policy: An examination of street-level bureaucrats’ discretion in Malaysia/Mohammed Salah Hassan Universiti Malaya.

  • Muriana, L. M., & Baranauskas, M. C. C. (2021). Technological Influence on Self-esteem: Towards a Research Agenda Through a Systematic Literature Review. Human-Computer Interaction. Theory, Methods and Tools: Thematic Area, HCI 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part I 23.

  • Patchin, J. W., & Hinduja, S. (2010). Cyberbullying and self-esteem. Journal of school health, 80(12), 614–621.

    Article  PubMed  Google Scholar 

  • Patchin, J. W., & Hinduja, S. (2011). Traditional and nontraditional bullying among youth: A test of general strain theory. Youth & Society, 43(2), 727–751.

    Article  Google Scholar 

  • Pellert, M., Lechner, C. M., Wagner, C., Rammstedt, B., & Strohmaier, M. (2023). AI psychometrics: Using psychometric inventories to obtain psychological profiles of large language models.

  • Podsakoff, N. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879–903.

    Article  PubMed  Google Scholar 

  • Qiu, J., Shen, B., Zhao, M., Wang, Z., Xie, B., & Xu, Y. (2020). A nationwide survey of psychological distress among chinese people in the COVID-19 epidemic: Implications and policy recommendations. General psychiatry, 33(2).

  • Ringle, C., Da Silva, D., & Bido, D. (2015). Structural equation modeling with the SmartPLS. Bido, D., da Silva, D., & Ringle, C.(2014). Structural Equation Modeling with the Smartpls. Brazilian Journal Of Marketing, 13(2).

  • Roothman, B., Kirsten, D. K., & Wissing, M. P. (2003). Gender differences in aspects of psychological well-being. South African journal of psychology, 33(4), 212–218.

    Article  Google Scholar 

  • Ryan, C. (2020). Refereeing articles including SEM–what should referees look for? Tourism Critiques: Practice and Theory, 1(1), 47–61.

    Article  Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of research on hedonic and eudaimonic well-being. Annual review of psychology, 52(1), 141–166.

    Article  PubMed  Google Scholar 

  • Ryff, C. D. (2014). Psychological well-being revisited: Advances in the science and practice of eudaimonia. Psychotherapy and psychosomatics, 83(1), 10–28.

    Article  PubMed  Google Scholar 

  • Schepman, A., & Rodway, P. (2022). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 1–18.

  • Salah, M., Al Halbusi, H., & Abdelfattah, F. (2023). May the force of text data analysis be with you: Unleashing the power of generative AI for social psychology research. Computers in Human Behavior: Artificial Humans, 1, 100006. https://doi.org/10.1016/j.chbah.2023.100006.

  • Schiff, D., Ayesh, A., Musikanski, L., & Havens, J. C. (2020). Ieee 7010: A new standard for assessing the well-being implications of artificial intelligence. 2020 IEEE international conference on systems, man, and cybernetics (SMC).

  • Schunk, D. H. (2012). Social cognitive theory.

  • Serafini, G., Adavastro, G., Canepa, G., De Berardis, D., Valchera, A., Pompili, M., Nasrallah, H., & Amore, M. (2018). The efficacy of buprenorphine in major depression, treatment-resistant depression and suicidal behavior: A systematic review. International journal of molecular sciences, 19(8), 2410.

    Article  PubMed  PubMed Central  Google Scholar 

  • Serafini, G., Parisi, V. M., Aguglia, A., Amerio, A., Sampogna, G., Fiorillo, A., Pompili, M., & Amore, M. (2020). A specific inflammatory profile underlying suicide risk? Systematic review of the main literature findings. International Journal of Environmental Research and Public Health, 17(7), 2393.

    Article  PubMed  PubMed Central  Google Scholar 

  • Serlachius, A., Boggiss, A., Lim, D., Schache, K., Wallace-Boyd, K., Brenton-Peters, J., Buttenshaw, E., Chadd, S., Cavadino, A., & Cao, N. (2021). Pilot study of a well-being app to support New Zealand young people during the COVID-19 pandemic. Internet Interventions, 26, 100464.

    Article  PubMed  PubMed Central  Google Scholar 

  • Shin, D. (2020). How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance. Computers in Human Behavior, 109, 106344.

    Article  Google Scholar 

  • Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551.

  • Sibley, C. G. (2011). The BIAS–Treatment scale (BIAS–TS): A measure of the subjective experience of active and passive harm and facilitation. Journal of personality assessment, 93(3), 300–315.

    Article  PubMed  Google Scholar 

  • Suls, J., & Wheeler, L. (2013). Handbook of social comparison: Theory and research. Springer Science & Business Media.

  • Suseno, Y., Chang, C., Hudik, M., & Fang, E. S. (2022). Beliefs, anxiety and change readiness for artificial intelligence adoption among human resource managers: The moderating role of high-performance work systems. The InTernaTIonal Journal of human resource managemenT, 33(6), 1209–1236.

    Article  Google Scholar 

  • Tredinnick, L. (2017). Artificial intelligence and professional roles. Business Information Review, 34(1), 37–41.

    Article  Google Scholar 

  • Van der Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: Contributions from technology and trust perspectives. European journal of information systems, 12(1), 41–48.

    Article  Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425–478.

  • Wagner, A. R., Borenstein, J., & Howard, A. (2018). Overtrust in the robotic age. Communications of the ACM, 61(9), 22–24.

    Article  Google Scholar 

  • Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634.

    Article  Google Scholar 

  • Wang, C., Xu, J., Zhang, T. C., & Li, Q. M. (2020). Effects of professional identity on turnover intention in China’s hotel employees: The mediating role of employee engagement and job satisfaction. Journal of Hospitality and Tourism Management, 45, 10–22.

    Article  Google Scholar 

  • Wang, G., Xie, S., & Li, X. (2022). Artificial intelligence, types of decisions, and street-level bureaucrats: Evidence from a survey experiment. Public Management Review, 1–23.

  • Watson, B., & Osberg, L. (2018). Job insecurity and mental health in Canada. Applied Economics, 50(38), 4137–4152.

    Article  Google Scholar 

  • Wintersberger, P., Frison, A. K., Riener, A., & Sawitzky, T. (2018). Fostering user acceptance and trust in fully automated vehicles: Evaluating the potential of augmented reality. PRESENCE: Virtual and Augmented Reality, 27(1), 46–62.

    Article  Google Scholar 

  • Wright, K. B. (2005). Researching internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. Journal of computer-mediated communication, 10(3), JCMC1034.

    Google Scholar 

  • Yang, T. C., Chen, I. C., Choi, S., & Kurtulus, A. (2019). Linking perceived discrimination during adolescence to health during mid-adulthood: Self-esteem and risk-behavior mechanisms. Social Science & Medicine, 232, 434–443.

    Article  Google Scholar 

  • Zhai, X. (2022). ChatGPT user experience: Implications for education. Available at SSRN 4312418.

  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Available at SSRN 3312874.

  • Zhuo, T. Y., Huang, Y., Chen, C., & Xing, Z. (2023). Exploring AI Ethics of ChatGPT: A Diagnostic Analysis. arXiv preprint arXiv:2301.12867.

Download references

Funding

The author(s) received no funding for this work.

Author information

Authors and Affiliations

  1. Business and Economics Department, Modern College of Business and Science (MCBS), Muscat, Oman

    Mohammed Salah & Fadi Abdelfattah

  2. Department of Management, Ahmed Bin Mohammad Military College, Doha, Qatar

    Hussam Alhalbusi

  3. Department of Political Science, Public Administration and Development Studies, Faculty of Business and Economics, University of Malaya, Kuala Lumpur, Malaysia

    Maria Mohd Ismail

  4. Management Department, College of Business Administration (COBA), A’Sharqiyah University (ASU), Ibra, Oman

    Fadi Abdelfattah

Authors
  1. Mohammed Salah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hussam Alhalbusi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria Mohd Ismail
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fadi Abdelfattah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mohammed Salah or Hussam Alhalbusi.

Ethics declarations

Ethical approval

This study was approved by the Modern College of Business and Science Community of Ethics and Integrity.

Conflict of Interest

The authors declare that they have no conflicts of interest.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salah, M., Alhalbusi, H., Ismail, M.M. et al. Chatting with ChatGPT: decoding the mind of Chatbot users and unveiling the intricate connections between user perception, trust and stereotype perception on self-esteem and psychological well-being. Curr Psychol 43, 7843–7858 (2024). https://doi.org/10.1007/s12144-023-04989-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12144-023-04989-0

Keywords

Search

Navigation