Hustle Culture and Work-Life Balance in the Digital Live Streaming Industry: The Role of Employee Engagement
Abstract
The rapid expansion of the digital live streaming industry has intensified productivity norms that encourage constant performance and sustained online presence. This study examines the relationship between hustle culture and work–life balance among digital live streamers, with employee engagement positioned as a mediating mechanism. Drawing on the Job Demands-Resources (JD-R) model and Conservation of Resources (COR) theory, a cross-sectional survey was conducted with 226 active live streamers. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that hustle culture has a significant negative direct effect on work-life balance, while positively predicting employee engagement. Employee engagement partially mediates the relationship, suggesting that although hustle-oriented norms enhance motivational involvement, they simultaneously generate strain that undermines balance. The structural model explains 38% of the variance in work-life balance. These findings highlight the dual role of hustle culture as both an energizing and strain-inducing force in algorithm-driven work environments. Promoting sustainable digital careers requires balancing performance expectations with recovery-supportive practices to protect psychological well-being.
Pesatnya perkembangan industri live streaming digital telah memperkuat norma produktivitas yang mendorong performa secara terus-menerus dan kehadiran daring yang berkelanjutan. Penelitian ini bertujuan untuk menganalisis hubungan antara hustle culture dan work–life balance pada para live streamer digital, dengan employee engagement sebagai mekanisme mediasi. Penelitian ini menggunakan pendekatan Job Demands-Resources (JD-R) Model dan Conservation of Resources (COR) Theory. Metode penelitian dilakukan melalui survei potong lintang (cross-sectional) terhadap 226 live streamer aktif. Data dianalisis menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM).
Hasil penelitian menunjukkan bahwa hustle culture memiliki pengaruh langsung negatif yang signifikan terhadap work–life balance, namun secara positif memengaruhi employee engagement. Employee engagement terbukti memediasi secara parsial hubungan tersebut, yang menunjukkan bahwa meskipun norma hustle culture dapat meningkatkan keterlibatan dan motivasi kerja, kondisi tersebut juga menimbulkan tekanan yang berdampak pada menurunnya keseimbangan kehidupan dan pekerjaan. Model struktural dalam penelitian ini mampu menjelaskan 38% varians work–life balance. Temuan ini menegaskan bahwa hustle culture memiliki peran ganda, yaitu sebagai faktor yang meningkatkan semangat kerja sekaligus menjadi sumber tekanan dalam lingkungan kerja berbasis algoritma. Oleh karena itu, pengembangan karier digital yang berkelanjutan memerlukan keseimbangan antara tuntutan performa dan praktik pemulihan yang mendukung kesejahteraan psikologis.
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Ashforth, B. E., Kreiner, G. E., & Fugate, M. (2000). All in a day’s work: Boundaries and micro role transitions. Academy of Management Review, 25(3), 472–491. https://doi.org/10.2307/259305
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
Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056
Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. I. (2023). Burnout and work engagement: The JD–R approach. Annual Review of Organizational Psychology and Organizational Behavior, 10, 391–417. https://doi.org/10.1146/annurev-orgpsych-012420-091355
Bakker, A. B., Schaufeli, W. B., Leiter, M. P., & Taris, T. W. (2014). Work engagement: An emerging concept in occupational health psychology. Work & Stress, 28(2), 187–200. https://doi.org/10.1080/02678373.2014.908354
Batubara, M., Aprilingga, F., & Fadlillah, A. (2022). Organizational commitment as a personal resource in forming of work engagement. Psikostudia: Jurnal Psikologi, 11(2), 259–269.
Bishop, S. (2021). Influencer labor and algorithmic visibility: Understanding digital content production. Social Media + Society, 7(3), 1–11. https://doi.org/10.1177/20563051211042372
Cheng, Y., Cheng, W.-J., Lin, R.-T., Wang, Y.-T., & Ko, J.-J. R. (2024). Associations between labor control through digital platforms and workers’ mental wellbeing: A survey of location-based platform workers in Taiwan. Safety and Health at Work. https://doi.org/10.1016/j.shaw.2024.08.003
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5, Ed.). Sage Publications.
Greenhaus, J. H., & Allen, T. D. (2011). Work–family balance: A review and extension of the literature. In J. C. Quick & L. E. Tetrick (Eds.), Handbook of occupational health psychology (pp. 165–183). American Psychological Association.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3, Ed.). Sage Publications.
Han, B.-C. (2015). The burnout society. Stanford University Press.
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
Hu, Y. (2024). The effect of work connectivity behavior after-hours on emotional exhaustion: The role of psychological detachment and work–family segmentation preference. SAGE Open. https://doi.org/10.1177/21582440241281417
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
Noponen, N., Feshchenko, P., Auvinen, T., Luoma-aho, V., & Abrahamsson, P. (2024). Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management. Management Review Quarterly, 74, 1695–1721. https://doi.org/10.1007/s11301-023-00345-5
Nugroho, Y. A. W., Yanti, B. E. D., & Haryanto, F. (2025). A Systematic Literature Review on Workplace Expectations and Behavioral Characteristics of Generation Z Employees. Psikostudia: Jurnal Psikologi, 14(3), 367–375.
Parent-Rocheleau, X., Parker, S. K., Bujold, A., & Gaudet, M.-C. (2024). Creation of the algorithmic management questionnaire: A six-phase scale development process. Human Resource Management, 63(1), 25–44. https://doi.org/10.1002/hrm.22185
Parsama, C. I. A. M. S., Kristin, D., Ni’mah, N. U., Afsyari, B., & Hastuti, R. (2025). Self-Regulation as a Factor in the Realization of Work Engagement. Psikostudia: Jurnal Psikologi, 14(4), 493–501.
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
Poell, T., Nieborg, D., & Duffy, B. E. (2022). Platforms and cultural production. Polity Press.
Pramana, I. B. G. A. Y., Saraswati, I. A. A., & Pradhana, I. P. D. (2025). The Dilemma of Multi-Role Balinese Working Women in Achieving Self-Harmony. Psikostudia: Jurnal Psikologi, 14(2), 173–180.
Preacher, K. J., & Hayes, A. F. (2008). Contemporary approaches to assessing mediation in communication research. Communication Monographs, 76(4), 408–420. https://doi.org/10.1080/03637750903310360
Ravid, D. M., White, J. C., Tomczak, D. L., Miles, A. F., & Behrend, T. S. (2022). A meta-analysis of the effects of electronic performance monitoring on work outcomes. Personnel Psychology, 76(1), 5–40. https://doi.org/10.1111/peps.12514
Roberts, J. A., & David, M. E. (2020). The social media party: Fear of missing out, social media intensity, and job outcomes. Addictive Behaviors Reports, 12, 100289. https://doi.org/10.1016/j.abrep.2020.100289
Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross-national study. Educational and Psychological Measurement, 66(4), 701–716. https://doi.org/10.1177/0013164405282471
Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two-sample confirmatory factor analytic approach. Journal of Happiness Studies, 3, 71–92. https://doi.org/10.1023/A:1015630930326
Shimazu, A., Schaufeli, W. B., Kubota, K., & Kawakami, N. (2015). Do workaholism and work engagement predict employee well-being and performance? International Journal of Behavioral Medicine, 22(1), 18–23. https://doi.org/10.1007/s12529-014-9412-2
Siegel, R., & Larson, B. Z. (2022). The impact of electronic monitoring on employees’ job satisfaction and stress: A meta-analysis. Organizational Dynamics. https://doi.org/10.1016/j.orgdyn.2022.100911
Sonnentag, S., Cheng, B. H., & Parker, S. L. (2022). Recovery from work: Advancing the field toward the future. Annual Review of Organizational Psychology and Organizational Behavior, 9, 33–60. https://doi.org/10.1146/annurev-orgpsych-012420-091355
Sonnentag, S., & Fritz, C. (2007). The Recovery Experience Questionnaire: Development and validation. Journal of Occupational Health Psychology, 12(3), 204–221. https://doi.org/10.1037/1076-8998.12.3.204
Sumantri, O. R., Violi, L., Anastasia, V., Annissatya, K. A., & Saraswati, K. D. H. (2024). AI opportunity perception and workplace wellbeing: A study on student interns in the era of smart technology. Psikostudia: Jurnal Psikologi, 13(3), 464.
Vignola, E. F. (2023). Workers’ health under algorithmic management: Emerging findings and urgent research questions. International Journal of Environmental Research and Public Health, 20(2), 1239. https://doi.org/10.3390/ijerph20021239
Wijaya, P., & Soeharto, T. N. E. D. (2021). Kontribusi work life balance terhadap work engagement karyawan. Psikostudia Jurnal Psikologi, 10(3), 266–272.
Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. (2019). Good gig, bad gig: Autonomy and algorithmic control in the global gig economy. Work, Employment and Society, 33(1), 56–75. https://doi.org/10.1177/0950017018785616
Wu, X. (2023). The effect of algorithmic management and workers’ coping behavior: An exploratory qualitative research of Chinese food-delivery platform. Tourism Management, 96, 104716. https://doi.org/10.1016/j.tourman.2022.104716
DOI: http://dx.doi.org/10.30872/psikostudia.v15i2.26213
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