Algorithmic control and resistance in the gig economy: A case of Uber drivers in Dhaka

被引:0
|
作者
Lata, Lutfun Nahar [1 ]
机构
[1] Univ Melbourne, Sch Social & Polit Sci, Dept Sociol, Melbourne, Vic 3010, Australia
关键词
algorithmic management; Bangladesh; digital labour platform; gig economy; labour movement; Uber; SHARING ECONOMY;
D O I
10.1177/00380261251335371
中图分类号
C91 [社会学];
学科分类号
030301 ; 1204 ;
摘要
This article explores how Uber drivers in Dhaka exercise agency to earn and sustain their livelihoods. Uber drivers not only experience extortion by Uber, but also face various challenges, such as precarious working conditions and algorithmic control of their activities. In most Global South countries, the regulatory practices are not in favour of Uber drivers either. Within this context, drawing on in-depth interviews with 27 Uber drivers and one focus group discussion with members of the Dhaka Ride-Sharing Drivers' Union, this article makes an original contribution to the discussion of the algorithmic governance of labour and how gig workers in the Global South subvert algorithms utilising everyday tactics to earn their livelihoods. While existing literature mostly demonstrates that Uber drivers use bot apps or switch off their app to protest Uber's algorithmic control, this article shows that Uber drivers in Bangladesh utilise a different strategy known as khep (contractual ride) to subvert the algorithmic governance of labour and increase their income. Through the presentation of Uber drivers' overt and covert resistance strategies in Dhaka, this article also advances the theories of the labour movement and industrial relations and the gig economy literature by demonstrating that overt and covert resistance strategies can complement each other if workers are unable to legally form unions to bargain with platforms and claim their rights for fair pay and fair working conditions.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy
    Wood, Alex J.
    Graham, Mark
    Lehdonvirta, Vili
    Hjorth, Isis
    WORK EMPLOYMENT AND SOCIETY, 2019, 33 (01) : 56 - 75
  • [2] India's "Uberwallah": Profiling Uber Drivers in the Gig Economy
    Prabhat, Shantanu
    Nanavati, Sneha
    Rangaswamy, Nimmi
    PROCEEDINGS OF THE TENTH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES AND DEVELOPMENT (ICTD), 2019,
  • [3] Welcome to the Gig Economy: neoliberal industrial relations and the case of Uber
    Zwick, Austin
    GEOJOURNAL, 2018, 83 (04) : 679 - 691
  • [4] Algorithmic HRM control in the gig economy: The app-worker perspective
    Duggan, James
    Carbery, Ronan
    McDonnell, Anthony
    Sherman, Ultan
    HUMAN RESOURCE MANAGEMENT, 2023, 62 (06) : 883 - 899
  • [5] Algorithmic Domination in the Gig Economy
    Muldoon, James
    Raekstad, Paul
    EUROPEAN JOURNAL OF POLITICAL THEORY, 2023, 22 (04) : 587 - 607
  • [6] Algorithmic management in the gig economy: A systematic review and research integration
    Kadolkar, Imran
    Kepes, Sven
    Subramony, Mahesh
    JOURNAL OF ORGANIZATIONAL BEHAVIOR, 2024,
  • [7] Boundaryless careers and algorithmic constraints in the gig economy
    Duggan, James
    Sherman, Ultan
    Carbery, Ronan
    McDonnell, Anthony
    INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT, 2022, 33 (22) : 4468 - 4498
  • [8] The effects of technological supervision on gig workers: organizational control and motivation of Uber, taxi, and limousine drivers
    Norlander, Peter
    Jukic, Nenad
    Varma, Arup
    Nestorov, Svetlozar
    INTERNATIONAL JOURNAL OF HUMAN RESOURCE MANAGEMENT, 2021, 32 (19) : 4053 - 4077
  • [9] New tech, old exploitation: Gig economy, algorithmic control and migrant labour
    Lata, Lutfun Nahar
    Burdon, Jasmine
    Reddel, Tim
    SOCIOLOGY COMPASS, 2023, 17 (01):
  • [10] Implications of algorithmic management on careers and employment relationships in the gig economy - a developing country perspective
    Adekoya, Olatunji David
    Mordi, Chima
    Ajonbadi, Hakeem Adeniyi
    Chen, Weifeng
    INFORMATION TECHNOLOGY & PEOPLE, 2025, 38 (02) : 686 - 713