HRM models of online labor platforms : Strategies of market and corporate logics
Immonen, J. (2023). HRM models of online labor platforms : Strategies of market and corporate logics. Frontiers in Sociology, 7, Article 980301. https://doi.org/10.3389/fsoc.2022.980301
Published in
Frontiers in SociologyAuthors
Date
2023Copyright
© 2023 Immonen
Studies on online labor platforms (OLPs) have revealed that OLPs can have extensive managerial control over independent workers, which affects their autonomy and precariousness. The permeability of the management makes some OLPs' roles as neutral intermediaries in labor exchanges questionable. While there are several platform work studies on the effects of human resource management (HRM) activities, earlier studies have focused more on certain types of OLP companies. Earlier OLP classifications did not make systematic distinctions between HRM activities either. This paper offers a classification to view how HRM activities manifest in OLPs. The study utilizes terms of service and webpage data from 46 multinational and Finland-based OLPs. Based on these data, OLPs have been classified into six models with five governance principles and institutional logic. The study uses the idea of institutional complexity and claims that OLPs balance their operations between the complexity of two institutional logics, market, and corporation, by using varying strategies with their HRM activities. Differently managed OLPs are also often marketed to different worker groups. This indicates that workers' levels and quality of autonomy differ between OLPs. Hence, could be expected that platform workers' expectations toward OLPs, perceptions of fairness, and experiences of wellbeing may be influenced by the HRM activities in which they engage. The results contribute to the ongoing discussions of power asymmetries between OLPs and platform workers, and thus OLPs' roles as either marketplaces or hierarchical corporations. Formed models can be utilized to enrich studies on key issues of platform workers' autonomy, precariousness, and experiences in different types of OLPs.
...
Publisher
Frontiers Media SAISSN Search the Publication Forum
2297-7775Keywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/164947305
Metadata
Show full item recordCollections
Additional information about funding
The original research is based on evidence collected and analyzed within the Fair Work on Platforms, project funded by Finnish Institute of Occupational Health.License
Related items
Showing items with similar title or keywords.
-
Updating strategies for distance based classification model with recursive least squares
Raita-Hakola, Anna-Maria; Pölönen, Ilkka (Copernicus Publications, 2022)The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the ... -
HR scenario game : Learning human resource management in a virtual environment
Riivari, Elina; Auvinen, Tommi; Merilehto, Juhani (Universitat Politècnica de València, 2021)This paper introduces a computer-based online scenario game that was developed to enhance the learning of human resource management (HRM) in an undergraduate course at a business school in Finland. What makes this game ... -
The Truth is Out There : Focusing on Smaller to Guess Bigger in Image Classification
Terziyan, Vagan; Kaikova, Olena; Malyk, Diana; Branytskyi, Vladyslav (Elsevier, 2023)In Artificial Intelligence (AI) in general and in Machine Learning (ML) in particular, which are important and integral components of modern Industry 4.0, we often deal with uncertainty, e.g., lack of complete information ... -
A Computational Approach to Bio-optical Functional Group Classification of Phytoplankton in Inland Waters
Naik, Pritish; Pölönen, Ilkka; Salmi, Pauliina (Aalto-yliopisto, 2024) -
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
DUNE Collaboration (American Physical Society, 2020)The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on ...