Résumés
Abstract
This study explores the influence of emotional intelligence and privacy orientation on attitudes and intentions to learn with mobile technologies. Data were collected from 272 respondents in Kazakhstan, a country with a transitioning economy. The findings reveal that both emotional intelligence and privacy orientation positively affect attitudes and intentions, except for the dimension of concern about one’s own informational privacy. Additionally, a model incorporating both emotional intelligence and privacy orientation explains variations in attitudes and intentions more effectively than models with either factor alone. This research contributes to the understanding of the multidimensional constructs of mobile learning, privacy, and emotional intelligence in non-Western contexts, providing valuable insights for technology adoption in transitional economies.
Keywords:
- emotional intelligence,
- Kazakhstan,
- mobile technologies,
- privacy orientation,
- technology adoption
Résumé
Cette étude explore l'influence de l'intelligence émotionnelle et de l'orientation vers la vie privée sur les attitudes et les intentions d'apprendre avec les technologies mobiles. Des données ont été recueillies auprès de 272 répondants au Kazakhstan, un pays dont l’économie est en transition. Les résultats révèlent que l'intelligence émotionnelle et l'orientation vers la vie privée affectent positivement les attitudes et les intentions, sauf pour la dimension relative à la protection de la vie privée personnelle. De plus, un modèle intégrant l'intelligence émotionnelle et l'orientation vers la vie privée explique mieux les variations dans les attitudes et les intentions que les modèles les considérant séparément. Cette recherche contribue à la compréhension des construits multidimensionnels de l'apprentissage mobile, de la vie privée et de l'intelligence émotionnelle dans des contextes non occidentaux, offrant des perspectives pertinentes pour l'adoption technologique dans des économies en transition.
Mots-clés :
- adoption technologique,
- intelligence émotionnelle,
- Kazakhstan,
- orientation vers la vie privée,
- technologies mobiles
Parties annexes
Bibliography
- Abu-Shanab, E., & Abu-Shanab, A. (2022). The influence of emotional intelligence on technology adoption and decision-making process. International Journal of Applied Decision Sciences, 15(5), 604–622. https://doi.org/10.1504/IJADS.2022.10041125
- Adhikary, A., Diatha, K. S., Borah, S. B., & Sharma, A. (2021). How does the adoption of digital payment technologies influence unorganized retailers’ performance? An investigation in an emerging market. Journal of the Academy of Marketing Science, 49, 882–902. https://doi.org/10.1007/s11747-021-00778-y
- Akpan, I. J., Udoh, E. A. P., & Adebisi, B. (2020). Small business awareness and adoption of state-of-the-art technologies in emerging and developing markets, and lessons from the COVID-19 pandemic. Journal of Small Business & Entrepreneurship, 34(2), 123–140. https://doi.org/10.1080/08276331.2020.1820185
- Audrin, C., & Audrin, B. (2024). Emotional intelligence in digital interactions: A call for renewed assessments. Personality and Individual Differences, 223, 112613. https://doi.org/10.1016/j.paid.2024.112613
- Bali, S., Chen, T. C., & Liu, M. C. (2024). Behavioral intentions of low-achieving students to use mobile English learning: Integrating self-determination theory, theory of planned behavior, and technology acceptance model approaches. International Journal of Human–Computer Interaction, 1–11. https://doi.org/10.1080/10447318.2024.2364142
- Barth, S., & de Jong, M. D. T. (2017). The privacy paradox – Investigating discrepancies between expressed privacy concerns and actual online behavior – A systematic literature review. Telematics and Informatics, 34(7), 1038–1058. https://doi.org/10.1016/j.tele.2017.04.013
- Baruh, L., & Cemalcilar, Z. (2014). It is more than personal: Development and validation of a multidimensional privacy orientation scale. Personality and Individual Differences, 70, 165–170. https://doi.org/10.1016/j.paid.2014.06.042
- Bayaga, A., & du Plessis, A. (2024). Ramifications of the Unified Theory of Acceptance and Use of Technology (UTAUT) among developing countries’ higher education staffs. Education and Information Technologies, 29, 9689–9714. https://doi.org/10.1007/s10639-023-12194-6
- Briz, L., Pereira, A., Carvalho, L., Juanes, J., & García-Peñalvo, F. (2016). Learning with mobile technologies – Students’ behavior. Computers in Human Behavior, 72, 612–620. https://doi.org/10.1016/j.chb.2016.05.027
- Creswell, J. W., & Creswell, J. D. (2022). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE Publications.
- D'Amico, A., & Geraci, A. (2023). Beyond emotional intelligence: The new construct of meta-emotional intelligence. Frontiers in Psychology, 14, 1096663. https://doi.org/10.3389/fpsyg.2023.1096663
- Davis, M., Lennerfors, T. T., & Tolstoy, D. (2022). Can blockchain-technology fight corruption in MNEs’ operations in emerging markets? Review of International Business and Strategy, 32(1), 39–56. https://doi.org/10.1108/RIBS-12-2020-0155
- Davis Le Brun, S., Butchard, S., Kinderman, P., Umeh, K., & Whittington, R. (2023). Applying the theory of planned behaviour to understand mental health professionals’ intentions to work using a human rights-based approach in acute inpatient settings. Journal of Mental Health, 33(3), 326–332. https://doi.org/10.1080/09638237.2023.2245910
- Dogruel, L., Joeckel, S., & Henke, J. (2023). Disclosing personal information in mHealth apps: Testing the role of privacy attitudes, app habits, and social norm cues. Social Science Computer Review, 41(5), 1791–1810. https://doi.org/10.1177/08944393221108820
- Fiori, M., Agnoli, S., & Davis, S. K. (2023). New trends in emotional intelligence: Conceptualization, understanding, and assessment. Frontiers in Psychology, 14, 1266076. https://doi.org/10.3389/fpsyg.2023.1266076
- Granić, A. (2022). Educational technology adoption: A systematic review. Education and Information Technologies, 27, 9725–9744. https://doi.org/10.1007/s10639-022-10951-7
- Hagger, M. S., Cheung, M. W.-L., Ajzen, I., & Hamilton, K. (2022). Perceived behavioral control moderating effects in the theory of planned behavior: A meta-analysis. Health Psychology, 41(2), 155–167. https://doi.org/10.1037/hea0001153
- Hameed, F., Qayyum, A., & Khan, F. A. (2024). A new trend of learning and teaching: Behavioral intention towards mobile learning. Journal of Computing in Education, 11, 149–180. https://doi.org/10.1007/s40692-022-00252-w
- Hou, Y., & Yu, Z. (2023). The unified theory of acceptance and use of DingTalk for educational purposes in China: An extended structural equation model. Humanities & Social Sciences Communications, 10, 733. https://doi.org/10.1057/s41599-023-02257-x
- Hsu, S.-H., Tang, K.-P., Lin, C.-H., Chen, P., & Wang, L.-H. (2023). Applying the theory of planned behavior to investigate type 2 diabetes patients' intention to receive injection therapy. Frontiers in Public Health, 11, 1066633. https://doi.org/10.3389/fpubh.2023.1066633
- Huang, R. T. (2022). Explore the moderating impact of learners’ anticipated emotions on mobile learning outcome: A moderated mediation model. Innovations in Education and Teaching International, 60(6), 872–882. https://doi.org/10.1080/14703297.2022.2076717
- Jeon, H., & Lee, C. (2022). Internet of Things Technology: Balancing privacy concerns with convenience. Telematics and Informatics, 70, 101816. https://doi.org/10.1016/j.tele.2022.101816
- Kowal, M. (2024). Translation practices in cross-cultural social research and guidelines for the most popular approach: Back-translation. Anthropological Review, 87(3), 19–32. https://doi.org/10.18778/1898-6773.87.3.02
- Krouska, A., Troussas, C., Kabassi, K., & Sgouropoulou, C. (2023). An empirical investigation of user acceptance of personalized mobile software for sustainability education. International Journal of Human–Computer Interaction, 1–8. https://doi.org/10.1080/10447318.2023.2241614
- Li, F., Zhu, D., Lin, M.-T., & Kim, P. B. (2024). The technology acceptance model and hospitality and tourism consumers’ intention to use mobile technologies: Meta-analysis and structural equation modeling. Cornell Hospitality Quarterly, 65(4). https://doi.org/10.1177/19389655241226558
- Li, Y., Zhang, X., Guo, X., & Wang, L. (2019). Underlying emotional mechanisms of routine m-health use in chronically ill patients. IEEE Transactions on Engineering Management, 1–12. https://doi.org/10.1109/TEM.2019.2940242
- Liébana-Cabanillas, F., Japutra, A., Molinillo, S., Singh, N., & Sinha, N. (2020). Assessment of mobile technology use in the emerging market: Analyzing intention to use m-payment services in India. Telecommunications Policy, 44(9), 102009. https://doi.org/10.1016/j.telpol.2020.102009
- Lin, S.-H., Lee, H.-C., Chang, C.-T., & Fu, C. J. (2020). Behavioral intention towards mobile learning in Taiwan, China, Indonesia, and Vietnam. Technology in Society, 63, 101387. https://doi.org/10.1016/J.TECHSOC.2020.101387
- Lin, X., Wang, Q., Limniou, M., Huijser, H., Yu, J. J., & Gu, H. (2024). The mediating role of technology acceptance and moderating role of emotion regulation in faculty’s online professional development. Australasian Journal of Educational Technology, 40(2), 37–54. https://doi.org/10.14742/ajet.9060
- Ling, E. C., Tussyadiah, I., Tuomi, A., Stienmetz, J., & Ioannou, A. (2021). Factors influencing users' adoption and use of conversational agents: A systematic review. Psychology & Marketing, 38(7), 1031–1051. https://doi.org/10.1002/mar.21491
- Liu, G., & Wang, Y. (2024). Modeling EFL teachers’ intention to integrate informal digital learning of English (IDLE) into the classroom using the theory of planned behavior. System, 120, 1–14. https://doi.org/10.1016/j.system.2023.103193
- Lopes, R., Navarro, J., Caetano, A., & Silva, A. J. (2017). Forecasting the influence of customer-related micro-events on employees’ emotional, attitudinal and physiological responses. European Journal of Work and Organizational Psychology, 26(6), 779–797. https://doi.org/10.1080/1359432X.2017.1360286
- Pekaar, K., Linden, D., & Born, M. (2018). Self- and other-focused emotional intelligence: Development and validation of the Rotterdam Emotional Intelligence Scale (REIS). Personality and Individual Differences, 120, 222–233. https://doi.org/10.1016/j.paid.2017.08.045
- Ried, L., Eckerd, S., & Kaufmann, L. (2022). Social desirability bias in PSM surveys and behavioral experiments: Considerations for design development and data collection. Journal of Purchasing and Supply Management, 28(1), 100743. https://doi.org/10.1016/j.pursup.2021.100743
- Roberts, R., Flin, R., Millar, D., & Corradi, L. (2021). Psychological factors influencing technology adoption: A case study from the oil and gas industry. Technovation, 102, 102219. https://doi.org/10.1016/j.technovation.2020.102219
- Sivakumar, C. L. V., Mone, V., & Abdumukhtor, R. (2024). Addressing privacy concerns with wearable health monitoring technology. WIREs Data Mining and Knowledge Discovery, 14(3), e1535. https://doi.org/10.1002/widm.1535
- Svenningsson, J., Höst, G., Hultén, M., & Hallström, J. (2022). Students’ attitudes toward technology: exploring the relationship among affective, cognitive and behavioral components of the attitude construct. International Journal of Technology and Design Education, 32, 1531–1551. https://doi.org/10.1007/s10798-021-09657-7
- Tarhini, A., AlHinaį, M., Al-Busaidi, A. S., Govindaluri, S. M., & Al Shaqsi, J. D. (2024). What drives the adoption of mobile learning services among college students: An application of SEM-neural network modeling. International Journal of Information Management Data Insights, 4(1), 100235. https://doi.org/10.1016/j.jjimei.2024.100235
- Yang, M., Al Mamun, A., Gao, J., Rahman, M. K., Salameh, A. A., & Alam, S. S. (2024). Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology. Scientific Reports, 14, 339. https://doi.org/10.1038/s41598-023-50436-2
- Zhang, X., & Zhang, Z. (2024). Leaking my face via payment: Unveiling the influence of technology anxiety, vulnerabilities, and privacy concerns on user resistance to facial recognition payment. Telecommunications Policy, 48(3), 102703. https://doi.org/10.1016/j.telpol.2023.102703
- Zhu, Z., & Huang, W. (2023). A meta-analysis of mobile learning adoption using extended UTAUT. Information Development, 41(2). https://doi.org/10.1177/02666669231176428