Author(s)

Jiamei Qiao ¹, Xiangyang Cheng ¹

Affiliation(s)

¹ School of Business, Fuyang Normal University, Fuyang, 236037, China

Corresponding Author

Xiangyang Cheng

ABSTRACT

As a digital labour management practice under the gig economy, whether algorithmic control can stimulate gig workers' continuance intention is the key to testing the success of algorithm management. However, few studies have focused on the mechanism of the influence of gig workers' perceived algorithmic control on their persistence willingness. Based on an organisational behavioural perspective, this study empirically examines the mechanism and boundary conditions through which algorithmic rule affects the continuance intention of gig workers. By analysing data from 309 samples, we found that algorithmic control had a significant positive effect on the continuance intention of gig workers by positively influencing work meaning; algorithmic transparency positively moderated the relationship between algorithmic control and work telling. The results of this study highlight the importance of algorithmic control in promoting long-term organisational benefits.

KEYWORDS

Algorithmic control; Work meaning; Continuance intention; Algorithmic transparency

CITE THIS PAPER

Jiamei Qiao, Xiangyang Cheng, An empirical study of gig workers' continuance intention. Applied & Educational Psychology (2023) Vol. 4: 45-55. DOI: http://dx.doi.org/10.23977/appep.2023.040807.

REFERENCES

[1] Kalleberg A. L., Vallas S. P. (2018). Precarious work: Causes, characteristics, and consequences. Research in the Sociology of Work, 31, 1-31.
[2] Duggan J., Sherman U., Carbery R., McDonnell A. (2020). Algorithmic management and app‐work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114-132.
[3] Pignot E. (2023). Who is pulling the strings in the platform economy? Accounting for the dark and unexpected sides of algorithmic control. Organization, 30(1): 140-167.
[4] Lehdonvirta V. (2018). Flexibility in the gig economy: managing time on three online piecework platforms. New Technology, Work and Employment, 33(1): 13-29.
[5] Anderson D. N. (2016). Wheels in the head: Ride sharing as monitored performance. Surveillance & Society, 14(2), 240-258.
[6] Wiener M., Cram W., Benlian A. (2021). Algorithmic control and gig workers: a legitimacy perspective of Uber drivers. European Journal of Information Systems, 32(3), 485-507.
[7] Liu Y. (2019). The effect of workers' justice perception on continuance participation intention in the crowdsourcing market. Internet Res., 29, 1485-1508.
[8] Beukes I., Botha E. (2013). Organisational commitment, work engagement and meaning of work of nursing staff in hospitals. SA Journal of Industrial Psychology, 39(2), 1-10.
[9] Pieters W. (2011). Explanation and trust: What to tell the user in security and AI? Ethics and Information Technology, 13(1), 53-64.
[10] Cardinal L. B., Sitkin S. B., Long C. P. (2017). Balancing and rebalancing in the creation and evolution of organizational control. Organization science, 15(4), 411-431.
[11] Rosenblat A., Stark L. (2018). Algorithmic labor and information asymmetries: A case study of Uber's drivers. International journal of communication, 10, 27.
[12] Pei J.N., Liu S.S., Cui X., Zhang Z. P., Ge C.M. (2022). Dose Algorithmic Control Motivates Gig Workers to Offer the Proactive Services?——Based on the Perspective of Work Motivation. Nankai Business Review.
[13] Pratt M. G., Ashforth B. E. (2003). Fostering meaningfulness in working and at work. Positive organizational scholarship: Foundations of a new discipline, 309, 327.
[14] Goldbach T., Benlian, A., Buxmann P. (2018). Differential effects of formal and self-control in mobile platform ecosystems: Multi-method findings on third-party developers’ continuance intentions and application quality. Information & Management, 55(3), 271-284.
[15] Cartwright S., Holmes N. (2006). The meaning of work: The challenge of regaining employee engagement and reducing cynicism. Human resource management review, 16(2), 199-208.
[16] Shin D., Park Y. J. (2019). Role of fairness, accountability, and transparency in algorithmic affordance. Computers in Human Behavior, 98, 277-284.
[17] Gedikli F., Jannach D., Ge M. (2014). How should I explain? A comparison of different explanation types for recommender systems. International Journal of Human-Computer Studies, 72(4), 367-382.
[18] May D. R., Gilson R. L., Harter L. M. (2004). The psychological conditions of meaningfulness, safety and availability and the engagement of the human spirit at work. Journal of occupational and organizational psychology, 77(1), 11-37.
[19] Durcikova A., Gray P. (2009). How knowledge validation processes affect knowledge contribution. Journal of Management Information Systems, 25(4), 81-108. 

Sponsors, Associates, and Links

---

• Advances in Educational Technology and Psychology
  
  
• Curriculum and Teaching Methodology
  
  
• Transactions on Comparative Education
  
  
• Advances in Vocational and Technical Education
  
  
• Adult and Higher Education