Abstract
The use of algorithmic control is commonplace in gig economy platforms. Algorithms may direct, evaluate, or discipline gig workers to enforce their compliance, or conversely, trigger their reactance. This paper explores the impact of algorithmic control on ride-hailing drivers’ coping behavior. Based on the legitimacy process model, we develop a research model that links perceived algorithmic control to gig drivers’ compliance and workaround behaviors. Using survey data from 197 ride-hailing drivers, we find that perceived algorithmic control positively affects fairness and privacy judgment. Fairness judgment has a positive impact on compliance behavior, and privacy judgment has a negative impact on workaround behavior. The paper discusses theoretical and practical implications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alter, S.: Theory of workarounds. Commun. Assoc. Inf. Syst. 34, 1041–1066 (2014). Article 55
Bijlsma-Frankema, K.M., Costa, A.C.: Consequences and antecedents of managerial and employee legitimacy interpretations of control: a natural, open system approach. Organ. Control, 396–434 (2010)
Bitektine, A., Haack, P.: The “macro” and the “micro” of legitimacy: toward a multilevel theory of the legitimacy process. Acad. Manag. Rev. 40(1), 49–75 (2015)
Cram, W.A., Wiener, M., Tarafdar, M., et al.: Examining the impact of algorithmic control on Uber drivers’ technostress. J. Manag. Inf. Syst. 39, 426–453 (2022)
Edwards, R.: Contested terrain: the transformation of the workplace in the twentieth century. Sci. Soc. 46(2), 237–240 (1982)
Hair, J.F., Ringle, C.M., Sarstedt, M.: PLS-SEM: indeed a silver bullet. J. Market. Theory Pract. 19(2), 39–152 (2011)
Hair, J., Hair, J.F. Jr., Sarstedt, M., Gudergan, S.P., et al.: Advanced Issues in Partial Least Squares Structural Equation Modeling. Sage (2018)
Laumer, S., Maier, C., Weitzel, T.: Information quality, user satisfaction, and the manifestation of workarounds: a qualitative and quantitative study of enterprise content management system users. Eur. J. Inf. Syst. 26(4), 333–360 (2017)
Lee, M.K., Kusbit, D., Metsky, E., et al.: Working with machines: the impact of algorithmic and data-driven management on human workers. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 1603–1612 (2015)
Liang, H., Xue, Y., Liansheng, W.: Ensuring employees’ IT compliance: carrot or stick? Inf. Syst. Res. 24(2), 279–294 (2013)
Kellogg, K.C., Valentine, M.A., et al.: Algorithms at work: the new contested terrain of control. Acad. Manag. Ann. 14(1), 366–410 (2020)
Pei, J., Liu, S., Cui, X., Qu, J.: Perceived algorithmic control of gig workers: conceptualization, measurement and verification the impact on service performance. Nankai Bus. Rev. 24(06), 14–27 (2021)
Möhlmann, M., Zalmanson, L., Henfridsson, O., Gregory, R.W.: Algorithmic management of work on online labor platforms: when matching meets contorl. MIS Q. 45(4), 1999–2022 (2021)
Möhlmann, M., Zalmanson, L.: 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 (2017)
Wiener, M., Alec Cram, W., Benlian, A.: Algorithmic control and gig workers: a legitimacy perspective of Uber drivers. Eur. J. Inf. Syst. 32(3), 485–507 (2023)
Acknowledgment
This work is supported by the Social Science Fund Research Base Project of Beijing (19JDGLB029).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Y., Tang, J., Li, X., Zhou, X. (2024). The Influence of Algorithmic Control on Gig Drivers’ Coping Behavior in Ride-Hailing Platforms. In: Tu, Y.P., Chi, M. (eds) E-Business. New Challenges and Opportunities for Digital-Enabled Intelligent Future. WHICEB 2024. Lecture Notes in Business Information Processing, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-031-60324-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-60324-2_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-60326-6
Online ISBN: 978-3-031-60324-2
eBook Packages: Computer ScienceComputer Science (R0)