Algorithmic Control and Work Motivation in the Gig Economy: A Self-Determination Theory Approach among Chinese Food Delivery Platform Workers
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v7i2.5201
Abstract
The research objectives were 1) to examine the effect of algorithmic control on the intrinsic and extrinsic work motivations of Chinese food delivery platform workers, and 2) to explore the mediating role of the need for autonomy, competence, and relatedness in the relationship between algorithmic control and work motivation among Chinese food delivery platform workers. This study employed quantitative research methods, collecting data from a sample of 547 valid food delivery platform employees in China. Data analysis included descriptive statistics (frequency, percentage, mean, standard deviation) and inferential statistics (confirmatory factor analysis, correlation analysis, and structural equation modeling based on the Bootstrap method). The findings revealed that 1) algorithmic control had a significant negative direct impact on the intrinsic motivation of food delivery platform employees, but a significant positive direct impact on their extrinsic motivation, indicating an "intrinsic-extrinsic displacement" phenomenon, and 2) basic psychological needs played a complex mediating role: algorithmic control significantly inhibited intrinsic motivation by weakening the need for autonomy and relatedness, but simultaneously enhanced employees' sense of competence through clear task guidance and real-time feedback, thus supporting intrinsic motivation to some extent. Based on the findings of this study, to build a sustainable and human-centric gig economy ecosystem, the following three governance recommendations are proposed. First, reframe algorithmic design by integrating autonomy, competence, and relatedness into the core decision-making logic, transforming algorithms from "control tools" to "empowerment partners". Second, establish a hybrid governance system combining algorithms and human support through dedicated welfare officers and bidirectional negotiation mechanisms, addressing social isolation and psychological need deficits caused by algorithmic control. Third, implement a real-time monitoring and dynamic adjustment mechanism based on psychological needs, enabling platforms to shift from static monitoring to dynamic responsiveness and effectively preserve workers' intrinsic motivation.
Keywords
algorithmic control, basic psychological needs, work motivation, food delivery platform workers
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[39] Liang, Y., Luo, H., Duan, H., Li, D., Liao, H., Feng, J., ... & Wang, L. (2024). Meituan’s real-time intelligent dispatching algorithms build the world’s largest minute-level delivery network. INFORMS Journal on Applied Analytics, 54(1), 84-101.
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[42] Walker, M., Fleming, P., & Berti, M. (2021). ‘You can’t pick up a phone and talk to someone’: How algorithms function as biopower in the gig economy. Organization, 28(1), 26-43.
[43] Yan, J. (2025). From algorithmic control to psychological motivation: Reflection and reconstruction of incentive mechanisms for platform riders. Capital University of Economics and Business. https://doi.org/10.54254/2754-1169/2025.LH25609
[44] Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.
[45] Lehdonvirta, V. (2018). Flexibility in the gig economy: managing time on three online piecework platforms. New Technology, Work and Employment, 33(1), 13-29.
[46] Lin, Q., Sun, R., & Zhu, Q. (2025). Perceived algorithmic control and gig workers’ work engagement: assessing the mediating role of psychological empowerment and the moderating effect of deep acting. BMC psychology, 13(1), 1237.
[47] Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. (2019). Networked but commodified: The (dis) embeddedness of digital labour in the gig economy. Sociology, 53(5), 931-950.
[48] Rosenblat, A. (2018). Uberland: How algorithms are rewriting the rules of work. Univ of California Press.
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[50] Bond, M. H., & Hwang, K. K. (1986). The social psychology of Chinese people. Oxford University Press.
[51] Adler, P. S., & Borys, B. (1996). Two types of bureaucracy: Enabling and coercive. Administrative Science Quarterly, 61-89.
[52] Xie, X. Y., Zuo, Y. H., & Hu, Q. J. (2021). Human resource management in the digital era: A perspective based on human-technology interaction. Management World, 37(1), 200–216. https://doi.org/10.19744/j.cnki.11-1235/f.2021.0013
[2] De Stefano, V. (2016). The rise of the "just-in time workforce": On demand work, crowdwork, and labor protection in the "gig economy". Comparative Labor Law and Policy Journal, 37(3), 461-471.
[3] 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.
[4] Kihwa, S. (2022). The Gig Economy & Algorithmic Management; A Modern Version of Scientific Management? A Digital Taylorism?.
[5] Mbare, B., Perkiö, M., & Koivusalo, M. (2024). Algorithmic management, wellbeing and platform work: Understanding the psychosocial risks and experiences of food couriers in Finland. Labour and Industry, 34(4), 386-411.
[6] Chen, L. (2020). Labor order under "digital control"—A study on labor control of takeaway riders. Sociological Studies, (6), 113-135. https://doi.org/10.19934/j.cnki.shxyj.2020.06.006
[7] Luo, H. X. (2023). Dilemmas and solutions for identifying labor relations in platform employment from the perspective of algorithmic control. SJTU Law Review, (2), 74-88. https://doi.org/10.19375/j.cnki.31-2075/d.2023.02.001
[8] Goods, C., Veen, A., & Barratt, T. (2019). “Is your gig any good?” Analysing job quality in the Australian platform-based food-delivery sector. Journal of Industrial Relations, 61(4), 502-527.
[9] Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), 100838.
[10] Li, B. A., & Ye, J. T. (2023). Legal dilemmas and solutions for trade union formation in the intelligent platform economy. Journal of Hefei University of Technology (Social Sciences Edition), (3), 51-61.
[11] 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.
[12] Wu, Q. J., & Li, Z. (2018). Labor control and work autonomy in the sharing economy: A mixed-methods study on the work of online ride-hailing drivers. Sociological Studies, (4), 137-162. https://doi.org/10.19934/j.cnki.shxyj.2018.04.006
[13] Wood, A., Graham, M., Yan, Y. M., Shi, Z. X., Lehdonvirta, V., & Hjorth, I. (2022). Autonomy and algorithmic control in the global gig economy. Foreign Social Sciences Frontiers, (5), 43-57.
[14] Zhou, L., Lei, X., Hou, R., & Chen, Y. (2022). Research on the negative impacts and control strategies of algorithmic management in online labor platforms: From the perspective of algorithmic technical attributes. Human Resources Development of China, (6), 8-22.
[15] Jin, Z. J., Bian, X. N., Guan, Y. Y., & Yang, Q. (2023). A study on the influence mechanism of perceived algorithmic control on ride-hailing drivers' turnover intention in the gig economy era. In Abstracts of the 25th National Academic Conference on Psychology: Poster Presentations (pp. 236-238).
[16] Deci, E. L., & Ryan, R. M. (2013). Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media.
[17] Gagné, M., Parker, S. K., Griffin, M. A., Dunlop, P. D., Knight, C., Klonek, F. E., & Parent-Rocheleau, X. (2022). Understanding and shaping the future of work with self-determination theory. Nature Reviews Psychology, 1(7), 378-392.
[18] Hagger, M. S., Koch, S., & Chatzisarantis, N. L. (2015). The effect of causality orientations and positive competence-enhancing feedback on intrinsic motivation: A test of additive and interactive effects. Personality and Individual Differences, 72, 107-111.
[19] Zhou, X. J., Fu, F. L., & Hu, N. (2024). Algorithmic management and intrinsic motivation in the gig economy: A study based on self-determination theory. Journal of Hefei University of Technology (Social Sciences Edition), (3), 34-44.
[20] Deci, E. L., Olafsen, A. H., & Ryan, R. M. (2017). Self-determination theory in work organizations: The state of a science. Annual Review of Organizational Psychology and Organizational Behavior, 4, 19-43.
[21] Griesbach, K., Reich, A., Elliott-Negri, L., & Milkman, R. (2019). Algorithmic control in platform food delivery work. Socius, 5, 2378023119870041.
[22] Lu, S. (2020). Algorithmic opacity, private accountability, and corporate social disclosure in the age of artificial intelligence. Vanderbilt Journal of Entertainment & Technology Law, 23, 99.
[23] Chen, L. (2022). Labor order under digital control: Research on labor control of take-out platform riders. The Journal of Chinese Sociology, 9(1), 17.
[24] Pei, J. L., Liu, S. S., Cui, X., & Qu, J. J. (2021). Gig workers' perceived algorithmic control: Conceptualization, measurement, and verification of its impact on service performance. Nankai Business Review, 24(6), 14-27.
[25] Zhang, Y., Li, D., & Liu, S. (2024). Time evolution analysis of riders’ preference attention and satisfaction on real-time crowdsourcing logistics platform. SAGE Open, 14(3), 21582440241271145.
[26] Rabbi, M., Pfammatter, A., Zhang, M., Spring, B., & Choudhury, T. (2015). Automated personalized feedback for physical activity and dietary behavior change with mobile phones: A randomized controlled trial on adults. JMIR mHealth and uHealth, 3(2), e4160.
[27] Wu, X., Liu, Q., Qu, H., & Wang, J. (2023). The effect of algorithmic management and workers’ coping behavior: An exploratory qualitative research of Chinese food-delivery platform. Tourism Management, 96, 104716.
[28] Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2019). Algorithmic decision-making and the control problem. Minds and Machines, 29(4), 555-578.
[29] Dong, J., Zhang, G., & Wu, L. (2025). Life against algorithmic management: A study on burnout and its influencing factors among food delivery riders. Frontiers in Public Health, 13, 1531541.
[30] Maier, C., Thatcher, J. B., Grover, V., & Dwivedi, Y. K. (2023). Cross-sectional research: A critical perspective, use cases, and recommendations for IS research. International Journal of Information Management, 70, 102625.
[31] Lang, J. J., Yang, L. F., Cheng, C., Cheng, X. Y., & Chen, F. Y. (2023). Are algorithmically controlled gig workers deeply burned out? An empirical study on employee work engagement. BMC Psychology, 11(1), 354.
[32] Sun, G. (2023). Quantitative analysis of online labor platforms’ algorithmic management influence on psychological health of workers. International Journal of Environmental Research and Public Health, 20(5), 4519.
[33] Hair, J. F. (2009). Multivariate data analysis.
[34] Van den Broeck, A., Vansteenkiste, M., De Witte, H., Soenens, B., & Lens, W. (2010). Capturing autonomy, competence, and relatedness at work: Construction and initial validation of the Work-related Basic Need Satisfaction scale. Journal of Occupational and Organizational Psychology, 83(4), 981-1002.
[35] Gagné, M., Forest, J., Vansteenkiste, M., Crevier-Braud, L., Van den Broeck, A., Aspeli, A. K., ... & Westbye, C. (2015). The Multidimensional Work Motivation Scale: Validation evidence in seven languages and nine countries. European Journal of Work and Organizational Psychology, 24(2), 178-196.
[36] 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.
[37] Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
[38] Challender, L., & Viskum, T. (2025). A comparative study of algorithmic management and control mechanisms. A platform-centric review, comparing food delivery platforms in the UK and Sweden.
[39] Liang, Y., Luo, H., Duan, H., Li, D., Liao, H., Feng, J., ... & Wang, L. (2024). Meituan’s real-time intelligent dispatching algorithms build the world’s largest minute-level delivery network. INFORMS Journal on Applied Analytics, 54(1), 84-101.
[40] Huang, K., & Sun, Y. (2024). Resolution of labour disputes involving new forms of employment in China (No. 126). ILO Working Paper.
[41] Wu, P. F., Zheng, R., Zhao, Y., & Li, Y. (2022). Happy riders are all alike? Ambivalent subjective experience and mental well‐being of food‐delivery platform workers in China. New Technology, Work and Employment, 37(3), 425-444.
[42] Walker, M., Fleming, P., & Berti, M. (2021). ‘You can’t pick up a phone and talk to someone’: How algorithms function as biopower in the gig economy. Organization, 28(1), 26-43.
[43] Yan, J. (2025). From algorithmic control to psychological motivation: Reflection and reconstruction of incentive mechanisms for platform riders. Capital University of Economics and Business. https://doi.org/10.54254/2754-1169/2025.LH25609
[44] Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.
[45] Lehdonvirta, V. (2018). Flexibility in the gig economy: managing time on three online piecework platforms. New Technology, Work and Employment, 33(1), 13-29.
[46] Lin, Q., Sun, R., & Zhu, Q. (2025). Perceived algorithmic control and gig workers’ work engagement: assessing the mediating role of psychological empowerment and the moderating effect of deep acting. BMC psychology, 13(1), 1237.
[47] Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. (2019). Networked but commodified: The (dis) embeddedness of digital labour in the gig economy. Sociology, 53(5), 931-950.
[48] Rosenblat, A. (2018). Uberland: How algorithms are rewriting the rules of work. Univ of California Press.
[49] Möhlmann, M., & Zalmanson, L. (2017). Hands on the wheel: Navigating algorithmic management and Uber drivers’ autonomy. In Proceedings of the International Conference on Information Systems (ICIS), Seoul, South Korea (pp. 10-13).
[50] Bond, M. H., & Hwang, K. K. (1986). The social psychology of Chinese people. Oxford University Press.
[51] Adler, P. S., & Borys, B. (1996). Two types of bureaucracy: Enabling and coercive. Administrative Science Quarterly, 61-89.
[52] Xie, X. Y., Zuo, Y. H., & Hu, Q. J. (2021). Human resource management in the digital era: A perspective based on human-technology interaction. Management World, 37(1), 200–216. https://doi.org/10.19744/j.cnki.11-1235/f.2021.0013
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