When digital technology distracts: Impact on cognitive load and work productivity in Generation Z
DOI:
https://doi.org/10.12928/fokus.v16i1.14769Abstract
This study examines how digital technology distraction affects employees’ cognitive load and work productivity, focusing on Generation Z employees at Telkom Indonesia Company within a hybrid work setting. Grounded in cognitive load theory, the research evaluates whether frequent notifications, multitasking, and overlapping digital activities heighten cognitive strain and reduce performance. Using a quantitative approach, data were collected from 108 respondents via online surveys, and purposive sampling was used to ensure a representative sample. The proposed model was analyzed using SmartPLS for the structural equation model with partial least squares, and process model 5 was applied to test mediation effects using SPSS. The findings show that digital technology distraction significantly increases cognitive load, and higher cognitive load subsequently decreases work productivity. In addition, digital technology distraction also has a significant direct adverse effect on work productivity. Mediation results indicate partial mediation, confirming that cognitive load is a central mechanism through which digital distractions translate into productivity loss. The study advances workplace digital-distraction literature by validating cognitive load theory in a real-world hybrid work environment and by providing evidence specific to Generation Z employees, who face high digital exposure at work. In practice, the findings highlight the need for digital exposure management and attention regulation strategies to sustain Generation Z performance in hybrid workplaces.
References
Ahmed, O., Walsh, E. I., Dawel, A., Alateeq, K., Oyarce, D. A. E., & Cherbuin, N. (2024). Social media use, mental health and sleep: A systematic review with meta-analyses. Journal of Affective Disorders, 367, 701-712. https://doi.org/10.1016/j.jad.2024.08.193
Almassaad, A., Alajlan, H., & Alebaikan, R. (2024). Student perceptions of generative artificial intelligence: Investigating utilization, benefits, and challenges in higher education. Systems, 12(10), 385. https://doi.org/10.3390/systems12100385
Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability, 16(3), 1166. https://doi.org/10.3390/su16031166
Anand, P., Mishra, S., & Jena, L. K. (2025). Cognitive shifts in workplace learning and Gen Z: Challenges and opportunities. Development and Learning in Organizations: An International Journal. https://doi.org/10.1108/DLO-12-2024-0349
Ash, S., & Karmakar, R. (2025). Addressing digital distraction at work: A systematic literature review. Emotional Intelligence in the Digital Era, 263-281. https://doi.org/10.1201/9781032715377-17
Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831–A10. https://doi.org/10.2307/41409963
Bailey, B. P., & Konstan, J. A. (2006). On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state. Computers in Human Behavior, 22(4), 685–708. https://doi.org/10.1016/j.chb.2005.12.009
Bakker, A. B., & Demerouti, E. (2007). The job demands–resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10.1108/02683940710733115
Beyer, K. M., Kaltenbach, A., Szabo, A., Bogar, S., Nieto, F. J., & Malecki, K. M. (2014). Exposure to neighborhood green space and mental health: Evidence from the survey of the health of Wisconsin. International Journal of Environmental Research and Public Health, 11(3), 3453-3472. https://doi.org/10.3390/ijerph110303453
Cetindamar, D., Abedin, B., & Shirahada, K. (2021). The role of employees in digital transformation: A preliminary study on how employees’ digital literacy impacts use of digital technologies. IEEE Transactions on Engineering Management, 71, 7837-7848. https://doi.org/10.1109/TEM.2021.3087724
Çetinkaya, A., Kırık, A. M., & Gündüz, U. (2021). Fear of missing out and problematic social media use: A research among university students in Turkey. AJIT-e: Academic Journal of Information Technology, 12(47), 12-31. https://doi.org/10.5824/ajite.2021.04.001.x
Chang, P. C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-driven technostress promote or hinder employees’ artificial intelligence adoption intention? A moderated mediation model of affective reactions and technical self-efficacy. Psychology Research and Behavior Management, 413-427. https://doi.org/10.2147/PRBM.S441444
Dehais, F., Dupres, A., Di Flumeri, G., Verdiere, K., Borghini, G., Babiloni, F., & Roy, R. (2018). Monitoring pilot's cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI. In 2018 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 544-549). IEEE. https://doi.org/10.1109/SMC.2018.00102
Di Dalmazi, M., Mandolfo, M., Stringhini, C., & Bettiga, D. (2022). Influence of technostress on work engagement and job performance during remote working. In International Conference on Human-Computer Interaction (pp. 149–163). Springer. https://doi.org/10.1007/978-3-031-06086-1_12
Elshaer, I. A., Azazz, A. M., Ghaleb, M. M., Abdulaziz, T. A., Mansour, M. A., & Fayyad, S. (2024). The impact of work-related ICT use on perceived injustice: Exploring the effects of work role overload and psychological detachment. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100208. https://doi.org/10.3390/joitmc10010208
Godard, R., & Holtzman, S. (2024). Are active and passive social media use related to mental health, wellbeing, and social support outcomes? A meta-analysis of 141 studies. Journal of Computer-Mediated Communication, 29(1), zmad055. https://doi.org/10.1093/jcmc/zmad055
Goryunova, E., & Jenkins, D. (2023). Digital natives: Shaping the future of leadership-followership dyad worldwide. In Handbook of Global Leadership and Followership (pp. 431–447). Springer. https://doi.org/10.1007/978-3-031-21544-5_22
Guo, W., Ren, J., Wang, B., & Zhu, Q. (2015). Effects of relaxing music on mental fatigue induced by a continuous performance task: Behavioral and ERPs evidence. PloS one, 10(8), e0136446. https://doi.org/10.1371/journal.pone.0136446
Hair, J., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Halbesleben, J. R. B., Neveu, J. P., Paustian-Underdahl, S. C., & Westman, M. (2014). Getting to the “COR”: Understanding the role of resources in conservation of resources theory. Journal of Management, 40(5), 1334–1364. https://doi.org/10.1177/0149206314527130
Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs, 85(1), 4–40. https://doi.org/10.1080/03637751.2017.1352100
Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44(3), 513–524. https://doi.org/10.1037/0003-066X.44.3.513
Hobfoll, S. E., Halbesleben, J., Neveu, J. P., & Westman, M. (2018). Conservation of resources in the organizational context: The reality of resources and their consequences. Annual Review of Organizational Psychology and Organizational Behavior, 5(1), 103–128. https://doi.org/10.1146/annurev-orgpsych-032117-104640
Huang, Y., & Gursoy, D. (2024). How does AI technology integration affect employees’ proactive service behaviors? A transactional theory of stress perspective. Journal of Retailing and Consumer Services, 77, 103700. https://doi.org/10.1016/j.jretconser.2023.103700
Ioannou, A., Lycett, M., & Marshan, A. (2024). The role of mindfulness in mitigating the negative consequences of technostress. Information Systems Frontiers, 26(2), 523-549. https://doi.org/10.1007/s10796-021-10239-0
Isaac, S., & Michael, W. B. (1995). Handbook in research and evaluation: A collection of principles, methods, and strategies useful in the planning, design, and evaluation of studies in education and the behavioral sciences. Edits publishers.
Kabakus, A. K., Bahcekapili, E., & Ayaz, A. (2025). The effect of digital literacy on technology acceptance: An evaluation on administrative staff in higher education. Journal of Information Science, 51(4), 930-941. https://doi.org/10.1177/01655515231160028
Kassa, S. T. (2026). Cognitive benefits and risks of digital technology engagement. Discover Psychology. https://doi.org/10.1007/s44202-025-00527-0
Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., van Buuren, S., van der Beek, A. J., & de Vet, H. C. W. (2013). Development of an individual work performance questionnaire. International Journal of Productivity and Performance Management, 62(1), 6–28. https://doi.org/10.1108/17410401311285273
Kumar, P. S. (2024). Technostress: A comprehensive literature review on dimensions, impacts, and management strategies. Computers in Human Behavior Reports, 16, 100475. https://doi.org/10.1016/j.chbr.2024.100475
Lee, H. P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. In Proceedings of the 2025 CHI conference on human factors in computing systems (pp. 1-22). https://doi.org/10.1145/3706598.3713778
Leroy, S. (2009). Why is it so hard to do my work? The challenge of attention residue when switching between work tasks. Organizational Behavior and Human Decision Processes, 109(2), 168–181. https://doi.org/10.1016/j.obhdp.2009.04.002
Liu, Y., Li, Y., Song, K., & Chu, F. (2024). The two faces of Artificial Intelligence (AI): Analyzing how AI usage shapes employee behaviors in the hospitality industry. International Journal of Hospitality Management, 122, 103875. https://doi.org/10.1016/j.ijhm.2024.103875
Magni, D., Del Gaudio, G., Papa, A., & Della Corte, V. (2024). Digital humanism and artificial intelligence: The role of emotions beyond the human–machine interaction in Society 5.0. Journal of Management History, 30(2), 195-218. https://doi.org/10.1108/JMH-12-2022-0084
Mandal, S., Srinija, M., & Sukruthi, M. (2025). Deconstructing barriers to digital well-being among Gen Z. In World Conference on Information Systems for Business Management (pp. 392-402). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-13006-8_38
Mark, G., Gudith, D., & Klocke, U. (2008). The cost of interrupted work: More speed and stress. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 107–110). https://doi.org/10.1145/1357054.1357072
Mark, G., Iqbal, S. T., Czerwinski, M., Johns, P., Sano, A., & Lutchyn, Y. (2016). Email duration, batching and self-interruption: Patterns of email use on productivity and stress. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1717–1728). https://doi.org/10.1145/2858036.2858262
Méndez, M. L., Padron, I., Fumero, A., & Marrero, R. J. (2024). Effects of internet and smartphone addiction on cognitive control in adolescents and young adults: A systematic review of fMRI studies. Neuroscience & Biobehavioral Reviews, 159, 105572. https://doi.org/10.1016/j.neubiorev.2024.105572
Nastjuk, I., Trang, S., Grummeck-Braamt, J. V., Adam, M. T., & Tarafdar, M. (2024). Integrating and synthesising technostress research: A meta-analysis on technostress creators, outcomes, and IS usage contexts. European Journal of Information Systems, 33(3), 361-382. https://doi.org/10.1080/0960085X.2022.2154712
O’Keeffe, K., Hodder, S., & Lloyd, A. (2020). A comparison of methods used for inducing mental fatigue in performance research: Individualised, dual-task and short duration cognitive tests are most effective. Ergonomics, 63(1), 1-12. https://doi.org/10.1080/00140139.2019.1687940
Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 15583–15587. https://doi.org/10.1073/pnas.0903620106
Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24(1), 27–45. https://doi.org/10.1007/s10648-011-9179-2
Poupard, M., Larrue, F., Sauzéon, H., & Tricot, A. (2025). A systematic review of immersive technologies for education: Learning performance, cognitive load and intrinsic motivation. British Journal of Educational Technology, 56(1), 5-41. https://doi.org/10.1111/bjet.13503
Pratama, F. C., Herman, R. T., Prabowo, R., & Permana, F. (2024). Unveiling the transformative impact of digital technology on productivity: A bibliometric analysis of trends and future implications. In 2024 IEEE Technology & Engineering Management Conference-Asia Pacific (TEMSCON-ASPAC) (pp. 1-5). IEEE. https://doi.org/10.1109/TEMSCON-ASPAC62480.2024.11025085
Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1–6. https://doi.org/10.1108/10748120110424816
Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841–1848. https://doi.org/10.1016/j.chb.2013.02.014
Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948–958. https://doi.org/10.1016/j.chb.2012.12.001
Rubinstein, J. S., Meyer, D. E., & Evans, J. E. (2001). Executive control of cognitive processes in task switching. Journal of Experimental Psychology: Human Perception and Performance, 27(4), 763–797. https://doi.org/10.1037/0096-1523.27.4.763
Sala, A., Porcaro, L., & Gómez, E. (2024). Social media use and adolescents' mental health and well-being: An umbrella review. Computers in Human Behavior Reports, 14, 100404. https://doi.org/10.1016/j.chbr.2024.100404
Sedarmayanti. (2017). Human resource planning and development to improve competence, performance, and work productivity. Bandung: PT Refika Aditama.
Shaleha, R. R. A., & Roque, N. (2024). Cognitive performance in the digital era: Generational differences, stress, and distraction's impact on cognitive performance. International Journal of Cyber Behavior, Psychology and Learning (IJCBPL), 14(1), 1-14. https://doi.org/10.4018/IJCBPL.341788
Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160, 108386. https://doi.org/10.1016/j.chb.2024.108386
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Measuring cognitive load. In Cognitive Load Theory (pp. 71–85). Springer. https://doi.org/10.1007/978-1-4419-8126-4
Tangen, D. (2005). Demystifying productivity and performance. International Journal of Productivity and Performance Management, 54(1), 34–46. https://doi.org/10.1108/17410400510571437
Tarafdar, M., Pullins, E. B., & Ragu-Nathan, T. S. (2015). Technostress: Negative effect on performance and possible mitigations. Information Systems Journal, 25(2), 103–132. https://doi.org/10.1111/isj.12042
Verma, B., & Chatterji, N. (2024). Cognitive unburdening: Investigating the mediated pathway from digital detox to psychological well being through reduced cognitive load. In Business Drivers in Promoting Digital Detoxification (pp. 36-53). IGI Global Scientific Publishing. https://doi.org/10.4018/9798369311073.ch004
Wu, T. J., Liang, Y., & Wang, Y. (2024). The buffering role of workplace mindfulness: How job insecurity of human-artificial intelligence collaboration impacts employees’ work–life-related outcomes. Journal of Business and Psychology, 39(6), 1395-1411. https://doi.org/10.1007/s10869-024-09963-6
Yaseen, H., Mohammad, A. S., Ashal, N., Abusaimeh, H., Ali, A., & Sharabati, A. A. A. (2025). The impact of adaptive learning technologies, personalized feedback, and interactive AI tools on student engagement: The moderating role of digital literacy. Sustainability, 17(3), 1133. https://doi.org/10.3390/su17031133
Yu, L., Chen, Y., & Gong, M. (2023). The duality of ICT-mediated overload: Its nature and consequences. Information & Management, 60(8), https://doi.org/10.1016/j.im.2023.103864
Zheng, Y., Wang, Y., Liu, K. S. X., & Jiang, M. Y. C. (2024). Examining the moderating effect of motivation on technology acceptance of generative AI for English as a foreign language learning. Education and Information Technologies, 29(17), 23547-23575. https://doi.org/10.1007/s10639-024-12763-3
Zhou, S., Yi, N., Rasiah, R., Zhao, H., & Mo, Z. (2024). An empirical study on the dark side of service employees’ AI awareness: Behavioral responses, emotional mechanisms, and mitigating factors. Journal of Retailing and Consumer Services, 79, 103869. https://doi.org/10.1016/j.jretconser.2024.103869
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Bagus M. Fatah Kertarajasa, Imas Soemaryani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:Â
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
