REIMAGINING JOB HAPPINESS: THE ROLE OF ALGORITHMIC DESIGN IN HUMAN-CENTERED ORGANIZATIONAL WELL-BEING
DOI:
https://doi.org/10.55197/qjssh.v6i4.756Keywords:
algorithmic management, job happiness, employee well-being, human-cantered AIAbstract
Significant changes to the structure, management, and experience of work have resulted from the increasing use of algorithms in the workplace. Although algorithms can enhance efficiency and objectivity, their influence on employee satisfaction remains a critical yet unexplored domain. This paper examines the impact of algorithmic systems on job satisfaction and suggests design principles for the development of algorithms that promote employee well-being. The study integrates recent discoveries on algorithmic management to comprehend its dual function in either enhancing or undermining job satisfaction, drawing from the Job Demands-Resources (JD-R) model, Self-Determination Theory (SDT), and Organisational Justice Theory. The systematic review employs the PRISMA framework to identify and assess pertinent literature, with an emphasis on empirical studies published between 2020 and 2024. Key themes include transparency, fairness, autonomy, human oversight, and feedback mechanisms. Algorithmic systems have the potential to reduce bias and optimise task allocation; however, they frequently result in job dissatisfaction and stress by reducing employee autonomy, increasing surveillance, and obscure decision-making processes. In contrast, algorithms can promote personal development, provide consistent feedback, and enhance fairness when they are developed with ethical considerations and employee input. The paper suggests a framework for human-cantered algorithm design that fosters job satisfaction, as evidenced by the literature. This framework prioritises five principles: maintaining human-in-the-loop oversight, providing meaningful feedback, integrating fairness, ensuring transparency, and enhancing autonomy. Each principle is based on psychological and organisational theories and is connected to practical design recommendations. The study concludes that algorithmic systems must consider human values and workplace happiness in addition to productivity objectives. Organisations are encouraged to implement participatory design methodologies, engage employees in algorithm development, and consistently evaluate well-being outcomes. Organisations can more effectively align technological advancement with sustainable performance and employee happiness by incorporating these values into algorithmic design.
References
[1] Bakker, A.B., Oerlemans, W.G.M. (2011): Subjective well-being in organizations. – The Oxford Handbook of Organizational Psychology 1(3): 178-197.
[2] Binns, R., Veale, M., Van Kleek, M., Shadbolt, N. (2020): Like trainer, like bot? Inheritance of bias in algorithmic content moderation. – Proceedings of the Conference on Fairness, Accountability, and Transparency 2020: 153-163.
[3] Brynjolfsson, E., McElheran, K. (2022): The rapid advance of digital tools: A new era for organizational design. – Management Science 68(1): 1-18.
[4] Choudhury, P., Foroughi, C., Larson, B.Z. (2021): Work-from-anywhere: The productivity effects of geographic flexibility. – Strategic Management Journal 42(4): 655-683.
[5] Colquitt, J.A. (2001): On the dimensionality of organizational justice: A construct validation of a measure. – Journal of Applied Psychology 86(3): 386-400.
[6] Deci, E.L., Ryan, R.M. (2013): Intrinsic motivation and self-determination in human behavior. – Springer Science & Business Media 371p.
[7] Deci, E.L., Ryan, R.M. (2000): The what and why of goal pursuits: Human needs and the self-determination of behavior. – Psychological Inquiry 11(4): 227-268.
[8] Duggan, J., Sherman, U., Carbery, R., McDonnell, A. (2020): Algorithmic management and app-work in the gig economy: A research agenda. – Human Resource Management Journal 30(1): 114-132.
[9] Felix, B., Dourado, D., Nossa, V. (2023): Autonomy-security fit and gig-worker well-being. – Frontiers in Psychology 14: 12p.
[10] Fisher, C.D. (2010): Happiness at work. – International Journal of Management Reviews 12(4): 384-412.
[11] Glikson, E., Woolley, A.W. (2020): Human trust in artificial intelligence: Review of empirical research. – Academy of Management Annals 14(2): 627-660.
[12] Jabagi, N., Kergroach, S., Lee, S. (2020): The impact of surveillance on employee well-being: A systematic review. – Employee Relations 42(1): 5-23.
[13] 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.
[14] Kinowska, S., Sienkiewicz, N. (2023): Algorithmic control as a double-edged sword. – Journal of Business Research 155: 113-125.
[15] Lee, M.K., Kusbit, D., Metsky, E., Dabbish, L. (2021): Algorithmic management in the workplace: A review of recent developments. – Journal of Management 47(1): 45-74.
[16] Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Scherer, A., Wildhaber, I., Bruch, H. (2022): The challenges of algorithm-based HR decision-making for personal integrity. – Journal of Business Ethics 171(3): 507-523.
[17] Milanez, A., Lemmens, A., Ruggiu, C. (2025): Algorithmic management in the workplace: New evidence from an OECD employer survey. – OECD Publishing 73p.
[18] Möhlmann, M., Zalmanson, L., Henfridsson, O., Gregory, R. (2021): Algorithmic management of work on online labor platforms. – MIS Quarterly 45(4): 1999-2022.
[19] Newlands, G. (2022): Constructive feedback in algorithmic management: Enhancing employee development. – Human Resource Management Journal 32(2): 345-361.
[20] Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Moher, D. (2021): The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. – BMJ 372: 9p.
[21] Parent-Rocheleau, X., Parker, S.K. (2023): Human-in-the-loop algorithmic design: Enhancing autonomy and worker well-being in gig work. – Journal of Occupational Health Psychology 28(1): 50-66.
[22] Peng, J., Wei, M., Loi, T.I., Li, J. (2025): Burnout from algorithmic evaluation: A self-determination perspective. – Journal of Managerial Psychology 40(3): 243-258.
[23] Riedl, M. (2022): The CARE framework for ethical AI in the workplace: comprehensibility, autonomy, responsiveness, and equity. – AI & Society 37(2): 507-520.
[24] Robert, L.P., Pierce, C., Morris, E., Kim, S., Alahmad, R. (2020): Designing fair AI for managing employees in organizations: A review, critique, and design agenda. – Human-Computer Interaction 35(5-6): 1-34.
[25] Rosenblat, A., Stark, L. (2020): Algorithmic labor and information asymmetries: A case study of Uber’s drivers. – International Journal of Communication 14: 3758-3784.
[26] Seligman, M.E.P., Csikszentmihalyi, M. (2014): Positive psychology: An introduction. – American Psychologist 55(1): 5-14.
[27] Stryker, S. (2020): Introduction: Trans* studies now. – Transgender Studies Quarterly 7(3): 299-305.
[28] Vaassen, B. (2022): AI, opacity, and personal autonomy. – Philosophy and Technology 35(4): 1-20.
[29] Voss, M., Rothe, N. (2024): Workers’ health under algorithmic management: Emerging insights. – Journal of Public Health Policy 45(1): 23-39.
[30] Warr, P., Inceoglu, I. (2012): Job engagement, job satisfaction, and contrasting associations with person-job fit. – Journal of Occupational Health Psychology 17(2): 129-138.
[31] 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.
[32] Zhang, A., Boltz, A., Lynn, J., Wang, C.W., Lee, M.K. (2023): Stakeholder-centered AI design: Co-designing worker tools with gig workers through data probes. – In Proceedings of the 2023 Chi Conference on Human Factors in Computing Systems 19p.
[33] Zhang, X., Zhengwei, L. (2025): Conceptual model of algorithmic management influence on behavior. – Journal of Economics and Management Sciences 8(2): 99-110.
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