Algorithmic technology in the gig economy is widely used and adopted by digital platform companies as a new form of managing employees. However, this approach has changed the form of interaction between employees and employers, and led to changes in the individual psychology and work behaviors of gig workers. The study of the impact of algorithms on employees is of great significance for enterprises to optimize employee management, improve organizational efficiency, and optimize digital human resource management practices. Based on the social exchange theory, this study developed a research model, took the gig workers of digital gig platforms in Jiangsu Province as the research object and empirically analyzed the 377 valid questionnaires collected. It was found that algorithmic transparency can positively influence proactive service performance through the chain mediation effect of psychological contract and organizational identification, while techno-complexity has a negative moderating effect on relational and transactional contract fulfillment. The findings of this study theoretically expand the current understanding of algorithmic human resource management and provide practical advice to digital platform companies on how to manage the gig workers more effectively.
Ahonen, P., & Erkkilä, T. (2020). Transparency in Algorithmic Decision-Making: Ideational Tensions and Conceptual Shifts in Finland. Information Polity, 25, 419-432. https://doi.org/10.3233/IP-200259
Albert, S., & Whetten, D. A. (1985). Organizational Identity. Research in Organizational Behavior, 7, 263-295.
Ananny, M., & Crawford, K. (2018). Seeing Without Knowing: Limitations of the Transparency Ideal and Its Application to Algorithmic Accountability. New Media & Society, 20, 973-989. https://doi.org/10.1177/1461444816676645
Ashforth, B. E., & Mael, F. (1989). Social Identity Theory and the Organization. The Academy of Management Review, 14, 20-39. https://doi.org/10.2307/258189
Bal, P. M., Jansen, P. G. W., Van der Velde, M. E. G., De Lange, A. H., & Rousseau, D. M. (2010). The Role of Future Time Perspective in Psychological Contracts: A Study among Older Workers. Journal of Vocational Behavior, 76, 474-486. https://doi.org/10.1016/j.jvb.2010.01.002
Bari, M. W., Qurrah-tul-ain, Abrar, M., & Fanchen, M. (2022). Employees’ Responses to Psychological Contract Breach: The Mediating Role of Organizational Cynicism. Ec o nomic and Industrial Democracy, 43, 810-829. https://doi.org/10.1177/0143831X20958478
Blau, P. (2017). Exchange and Power in Social Life (2nd ed.). Routledge. https://doi.org/10.4324/9780203792643
Buell, R. W., Kim, T., & Tsay, C.-J. (2017). Creating Reciprocal Value through Operational Transparency. Management Science, 63, 1673-1695. https://doi.org/10.1287/mnsc.2015.2411
Bujold, A., Parent-Rocheleau, X., & Gaudet, M.-C. (2022). Opacity behind the Wheel: The Relationship between Transparency of Algorithmic Management, Justice Perception, and Intention to Quit among Truck Drivers. Computers in Human Behavior Reports, 8, Article 100245. https://doi.org/10.1016/j.chbr.2022.100245
Burrell, J. (2016). How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms. Big Data & Society, 3 . https://doi.org/10.1177/2053951715622512
Busuioc, M. (2021). Accountable Artificial Intelligence: Holding Algorithms to Account. Public Administration Review, 81, 825-836. https://doi.org/10.1111/puar.13293
Conroy, S. A., Roumpi, D., Delery, J. E., & Gupta, N. (2022). Pay Volatility and Employee Turnover in the Trucking Industry. Journal of Management, 48, 605-629. https://doi.org/10.1177/01492063211019651
Conway, N., & Coyle-Shapiro, J. A.-M. (2012). The Reciprocal Relationship between Psychological Contract Fulfilment and Employee Performance and the Moderating Role of Perceived Organizational Support and Tenure. Journal of Occupational and Organizational Psychology, 85, 277-299. https://doi.org/10.1111/j.2044-8325.2011.02033.x
Cram, W. A., Wiener, M., Tarafdar, M., & Benlian, A. (2022). Examining the Impact of Algorithmic Control on Uber Drivers’ Technostress. Journal of Management Inform a tion Systems, 39, 426-453. https://doi.org/10.1080/07421222.2022.2063556
Crowston, K., & Bolici, F. (2019). Impacts of Machine Learning on Work. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp. 5961-5970). https://doi.org/10.24251/HICSS.2019.719
D’Arcy, J., Herath, T., & Shoss, M. K. (2014). Understanding Employee Responses to Stressful Information Security Requirements: A Coping Perspective. Journal of Ma n agement Information Systems, 31, 285-318. https://doi.org/10.2753/MIS0742-1222310210
De Fine Licht, K., & De Fine Licht, J. (2020). Artificial Intelligence, Transparency, and Public Decision-Making. AI & Society, 35, 917-926. https://doi.org/10.1007/s00146-020-00960-w
Diakopoulos, N., & Koliska, M. (2017). Algorithmic Transparency in the News Media. Digital Journalism, 5, 809-828. https://doi.org/10.1080/21670811.2016.1208053
Diamond, S. S., & Zeisel, H. (1978). Review of Procedural Justice: A Psychological Analysis. Duke Law Journal, 1977, 1289-1296. https://doi.org/10.2307/1371953
Díaz-Soloaga, P., & Díaz-Soloaga, A. (2022). Forced Telecommuting during the COVID-19 Lockdown: The Impact on Corporate Culture in Spain and Kazakhstan. Corporate Communications: An International Journal, 28, 193-212. https://doi.org/10.1108/CCIJ-02-2022-0018
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err. Journal of Experimental Psychology: General, 144, 114-126. https://doi.org/10.1037/xge0000033
Edwards, J. R., & Lambert, L. S. (2007). Methods for Integrating Moderation and Mediation: A General Analytical Framework Using Moderated Path Analysis. Psychological Methods, 12, 1-22. https://doi.org/10.1037/1082-989X.12.1.1
Epitropaki, O. (2013). A Multi-Level Investigation of Psychological Contract Breach and Organizational Identification through the Lens of Perceived Organizational Membership: Testing a Moderated-Mediated Model. Journal of Organizational Behavior, 34, 65-86. https://doi.org/10.1002/job.1793
Eslami, M., Krishna Kumaran, S. R., Sandvig, C., & Karahalios, K. (2018). Communicating Algorithmic Process in Online Behavioral Advertising. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-13). Association for Computing Machinery. https://doi.org/10.1145/3173574.3174006
Felzmann, H., Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020). Towards Transparency by Design for Artificial Intelligence. Science and Engineering Ethics, 26, 3333-3361. https://doi.org/10.1007/s11948-020-00276-4
Fieseler, C., Bucher, E., & Hoffmann, C. P. (2019). Unfairness by Design? The Perceived Fairness of Digital Labor on Crowdworking Platforms. Journal of Business Ethics, 156, 987-1005. https://doi.org/10.1007/s10551-017-3607-2
Gal, U., Jensen, T. B., & Stein, M.-K. (2020). Breaking the Vicious Cycle of Algorithmic Management: A Virtue Ethics Approach to People Analytics. Information and Organ i zation, 30, Article 100301. https://doi.org/10.1016/j.infoandorg.2020.100301
Gillespie, T. (2014). The Relevance of Algorithms. In T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.), Media Technologies: Essays on Communication, Materiality, and S o ciety . The MIT Press. https://doi.org/10.7551/mitpress/9780262525374.001.0001
Harms, P. D., & Han, G. (2019). Algorithmic Leadership: The Future Is Now. Journal of Leadership Studies, 12, 74-75. https://doi.org/10.1002/jls.21615
Hobfoll, S. E. (1989). Conservation of Resources: A New Attempt at Conceptualizing Stress. American Psychologist, 44, 513-524. https://doi.org/10.1037/0003-066X.44.3.513
Höddinghaus, M., Sondern, D., & Hertel, G. (2021). The Automation of Leadership Functions: Would People Trust Decision Algorithms? Computers in Human Behavior, 116, Article 106635. https://doi.org/10.1016/j.chb.2020.106635
Homans, G. C. (1958). Social Behavior as Exchange. American Journal of Sociology, 63, 597-606. https://doi.org/10.1086/222355
Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic Management in a Work Context. Big Data & Society , 8 . https://doi.org/10.1177/20539517211020332
Kim, K., & Moon, S.-I. (2021). When Algorithmic Transparency Failed: Controversies over Algorithm-Driven Content Curation in the South Korean Digital Environment. American Behavioral Scientist, 65, 847-862. https://doi.org/10.1177/0002764221989783
Kitchin, R. (2014). The Real-Time City? Big Data and Smart Urbanism. GeoJournal, 79, 1-14. https://doi.org/10.1007/s10708-013-9516-8
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, Article No. 354. https://doi.org/10.1186/s40359-023-01402-0
Lapostol Piderit, J. P., Garrido Iglesias, R., & Hermosilla Cornejo, M. P. (2023). Algorithmic Transparency from the South: Examining the State of Algorithmic Transparency in Chile’s Public Administration Algorithms. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 227-235). Association for Computing Machinery. https://doi.org/10.1145/3593013.3593991
Lee, M. K., Jain, A., Cha, H. J., Ojha, S., & Kusbit, D. (2019). Procedural Justice in Algorithmic Fairness: Leveraging Transparency and Outcome Control for Fair Algorithmic Mediation. Proceedings of the ACM on Human-Computer Interaction , 3, 1-26. https://doi.org/10.1145/3359284
Liu, B., & Wei, L. (2021). Machine Gaze in Online Behavioral Targeting: The Effects of Algorithmic Human Likeness on Social Presence and Social Influence. Computers in Human Behavior, 124, Article 106926. https://doi.org/10.1016/j.chb.2021.106926
Liu, N. T. Y., Kirshner, S. N., & Lim, E. T. K. (2023). Is Algorithm Aversion WEIRD? A Cross-Country Comparison of Individual-Differences and Algorithm Aversion. Jou r nal of Retailing and Consumer Services, 72, Article 103259. https://doi.org/10.1016/j.jretconser.2023.103259
Liu, W., He, C., Jiang, Y., Ji, R., & Zhai, X. (2020). Effect of Gig Workers’ Psychological Contract Fulfillment on Their Task Performance in a Sharing Economy—A Perspective from the Mediation of Organizational Identification and the Moderation of Length of Service. International Journal of Environmental Research and Public Health, 17, Article 2208. https://doi.org/10.3390/ijerph17072208
Lo, S., & Aryee, S. (2003). Psychological Contract Breach in a Chinese Context: An Integrative Approach. Journal of Management Studies, 40, 1005-1020. https://doi.org/10.1111/1467-6486.00368
Mael, F., & Ashforth, B. E. (1992). Alumni and Their Alma Mater: A Partial Test of the Reformulated Model of Organizational Identification. Journal of Organizational Beh a vior, 13, 103-123. https://doi.org/10.1002/job.4030130202
Mahmud, H., Islam, A. K. M. N., Ahmed, S. I., & Smolander, K. (2022). What Influences Algorithmic Decision-Making? A Systematic Literature Review on Algorithm Aversion. Technological Forecasting and Social Change, 175, Article 121390. https://doi.org/10.1016/j.techfore.2021.121390
Masterson, S. S., & Stamper, C. L. (2003). Perceived Organizational Membership: An Aggregate Framework Representing the Employee-Organization Relationship. Journal of Organizational Behavior, 24, 473-490. https://doi.org/10.1002/job.203
Mittelstadt, B., Russell, C., & Wachter, S. (2019). Explaining Explanations in AI. In Pr o ceedings of the Conference on Fairness, Accountability, and Transparency (pp. 279-288). Association for Computing Machinery. https://doi.org/10.1145/3287560.3287574
Nasirpouri Shadbad, F., & Biros, D. (2020). Technostress and Its Influence on Employee Information Security Policy Compliance. Information Technology & People, 35, 119-141. https://doi.org/10.1108/ITP-09-2020-0610
Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information . Harvard University Press. https://doi.org/10.4159/harvard.9780674736061
Price, W. N. (2018). Big Data and Black-Box Medical Algorithms. Science Translational Medicine, 10, eaao5333. https://doi.org/10.1126/scitranslmed.aao5333
Rader, E., Cotter, K., & Cho, J. (2018). Explanations as Mechanisms for Supporting Algorithmic Transparency. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-13). ACM. https://doi.org/10.1145/3173574.3173677
Ramesh, R., Ananthram, S., Vijayalakshmi, V., & Sharma, P. (2021). Technostressors—A Boon or Bane? Toward an Integrative Conceptual Model. Journal of Indian Business Research, 14, 278-300. https://doi.org/10.1108/JIBR-10-2021-0348
Rani, U., & Furrer, M. (2021). Digital Labour Platforms and New Forms of Flexible Work in Developing Countries: Algorithmic Management of Work and Workers. Compet i tion & Change, 25, 212-236. https://doi.org/10.1177/1024529420905187
Rank, J., Carsten, J. M., Unger, J. M., & Spector, P. E. (2007). Proactive Customer Service Performance: Relationships with Individual, Task, and Leadership Variables. Human Performance, 20, 363-390.
Restubog, S. L. D., Hornsey, M. J., Bordia, P., & Esposo, S. R. (2008). Effects of Psychological Contract Breach on Organizational Citizenship Behaviour: Insights from the Group Value Model. Journal of Management Studies, 45, 1377-1400. https://doi.org/10.1111/j.1467-6486.2008.00792.x
Rousseau, D. M. (1990). New Hire Perceptions of Their Own and Their Employer’s Obligations: A Study of Psychological Contracts. Journal of Organizational Behavior, 11, 389-400. https://doi.org/10.1002/job.4030110506
Sandvig, C., Hamilton, K., Karahalios, K., Langbort, & C. (2014) Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms. In 64th Annual Meeting of the International Communication Association .
Shah, M. U., Rehman, U., Parmar, B., & Ismail, I. (2023). Effects of Moral Violation on Algorithmic Transparency: An Empirical Investigation. Journal of Business Ethics . https://doi.org/10.1007/s10551-023-05472-3
Shahriari, K., & Shahriari, M. (2017). IEEE Standard Review—Ethically Aligned Design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems. In 2017 IEEE Canada International Humanitarian Technology Conf e rence (IHTC) (pp. 197-201). IEEE. https://doi.org/10.1109/IHTC.2017.8058187
She, S., Xu, H., Wu, Z., Tian, Y., & Tong, Z. (2020). Dimension, Content, and Role of Platform Psychological Contract: Based on Online Ride-Hailing Users. Frontiers in Psychology , 11 , Article 2097. https://doi.org/10.3389/fpsyg.2020.02097
Shin, D., Zhong, B., & Biocca, F. A. (2020). Beyond User Experience: What Constitutes Algorithmic Experiences? International Journal of Information Management, 52 , Article 102061. https://doi.org/10.1016/j.ijinfomgt.2019.102061
Springer, A., & Whittaker, S. (2020). Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? ACM Transactions on Interactive Intelligent Systems, 10, 1-32. https://doi.org/10.1145/3374218
Stamper, C. L., Masterson, S. S., & Knapp, J. (2009). A Typology of Organizational Membership: Understanding Different Membership Relationships through the Lens of Social Exchange. Management and Organization Review , 5, 303-328. https://doi.org/10.1111/j.1740-8784.2009.00147.x
Stohl, C., Stohl, M., & Leonardi, P. M. (2016). Digital Age | Managing Opacity: Information Visibility and the Paradox of Transparency in the Digital Age. International Jou r nal of Communication, 10, 123-137.
Tajfel, H. (Ed.) (1978). Differentiation between Social Groups: Studies in the Social Ps y chology of Intergroup Relations . Academic Press.
Tarafdar, M., Pullins, E., Bolman, & Ragu-Nathan, T. S. (2015). Technostress: Negative Effect on Performance and Possible Mitigations. Information Systems Journal, 25, 103-132. https://doi.org/10.1111/isj.12042
Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The Impact of Technostress on Role Stress and Productivity. Journal of Management Information Systems, 24, 301-328. https://doi.org/10.2753/MIS0742-1222240109
Tomprou, M., & Lee, M. K. (2022). Employment Relationships in Algorithmic Management: A Psychological Contract Perspective. Computers in Human Behavior, 126, Article 106997. https://doi.org/10.1016/j.chb.2021.106997
Turnley, W. H., Bolino, M. C., Lester, S. W., & Bloodgood, J. M. (2003). The Impact of Psychological Contract Fulfillment on the Performance of In-Role and Organizational Citizenship Behaviors. Journal of Management, 29, 187-206. https://doi.org/10.1177/014920630302900204
Wang, C., Chen, J., & Xie, P. (2022). Observation or Interaction? Impact Mechanisms of Gig Platform Monitoring on Gig Workers’ Cognitive Work Engagement. International Journal of Information Management, 67, Article 102548. https://doi.org/10.1016/j.ijinfomgt.2022.102548
Wiener, M., Cram, W. A., & Benlian, A. (2023). Algorithmic Control and Gig Workers: A Legitimacy Perspective of Uber Drivers. European Journal of Information Systems, 32, 485-507. https://doi.org/10.1080/0960085X.2021.1977729
Wu, C.-M., & Chen, T.-J. (2015). Psychological Contract Fulfillment in the Hotel Workplace: Empowering Leadership, Knowledge Exchange, and Service Performance. International Journal of Hospitality Management, 48, 27-38. https://doi.org/10.1016/j.ijhm.2015.04.008
Yang, Q., & Pitafi, A. H. (2023). A Moderated Mediation Investigation of the Influence of Enterprise Social Media Visibility on Work Stress. Acta Psychologica, 241, Article 104084. https://doi.org/10.1016/j.actpsy.2023.104084
You, S., Yang, C. L., & Li, X. (2022). Algorithmic VErsus Human Advice: Does Presenting Prediction Performance Matter for Algorithm Appreciation? Journal of Management Information Systems, 39, 336-365. https://doi.org/10.1080/07421222.2022.2063553
Young, M. M., Bullock, J. B., & Lecy, J. D. (2019). Artificial Discretion as a Tool of Governance: A Framework for Understanding the Impact of Artificial Intelligence on Public Administration. Perspectives on Public Management and Governance , 2, 301-313. https://doi.org/10.1093/ppmgov/gvz014
Zagenczyk, T. J., Gibney, R., Few, W. T., & Scott, K. L. (2011). Psychological Contracts and Organizational Identification: The Mediating Effect of Perceived Organizational Support. Journal of Labor Research, 32, 254-281. https://doi.org/10.1007/s12122-011-9111-z
Zarsky, T. (2016). The Trouble with Algorithmic Decisions: An Analytic Road Map to Examine Efficiency and Fairness in Automated and Opaque Decision Making. Science, Technology, & Human Values, 41, 118-132. https://doi.org/10.1177/0162243915605575
Zhang, Z., Ye, B., Qiu, Z., Zhang, H., & Yu, C. (2022). Does Technostress Increase R&D Employees’ Knowledge Hiding in the Digital Era? Frontiers in Psychology , 13 , Article 873846. https://doi.org/10.3389/fpsyg.2022.873846