May Research Roundup

Interesting Research we found in May covering Platform Cooperativism.


Hi everyone! Hope you are doing well during these trying times.

Here is a short and sweet roundup on interesting articles and projects we recently found covering Platform Cooperativism.


Platform Cooperativism

On-Demand and on-the-edge: Ride-hailing and Delivery Workers in San Francisco

Author(s): Chris Benner with Erin Johansson, Kung Feng, and Hays Witt

Publisher: Institute for Social Transformation, University of California Santa Cruz (UC Santa Cruz)

Year: 2020

Related Organizations:

  • Chris Benner

    • UC Santa Cruz

  • Erin Johansson

    • Jobs With Justice

  • Kung Feng

    • Jobs With Justice

  • Hays Witt

    • Drivers Seat Cooperative

Area(s): Platform Cooperativism, Data Cooperatives, Ownership, Cooperativism, Digital Cooperatives, Ridesharing, Partnerships, Local-to-Global, Mutualization, Delivery workers, Transportation

Article

  • https://transform.ucsc.edu/on-demand-and-on-the-edge/

  • https://transform.ucsc.edu/wp-content/uploads/2020/05/OnDemandOntheEdge_ExecSum.pdf

  • https://transform.ucsc.edu/wp-content/uploads/2020/05/OnDemand-n-OntheEdge_MAY2020.pdf

The Politics of Platform Capitalism. A Case Study on the Regulation of Uber in New York

Author(s): Timo Seidl

Year: 2020

Related Organizations:

  • Timo Seidl

    • European University Institute

Area(s): Platform Cooperativism, Data Cooperatives, Ownership, Anti-trust, Cooperativism, Digital Cooperatives, Partnerships, Local-to-Global, Mutualization, Alternative Organizing, Political Economy, Laws and Regulations, Platform Capitalism, Institutionalism

Abstract

Platform companies like Uber not only disrupt existing markets but also contest existing regulatory regimes. This raises the question of how, when, and why such companies are regulated. This paper develops, tests and defends a theoretical framework that explains the politics of regulatory response to the rise of platform capitalism. Using discourse network analysis and a case study on the regulation of Uber in New York, it shows that the success or failure of regulations depends on the ability of actors to mobilize broad coalition; that narratives affect the composition of these coalitions; and that platform companies have both unique political strengths and vulnerabilities. The paper makes substantive contributions to our understanding of the politics of platform capitalism, and it makes theoretical contributions to the literatures on coalitional politics, ideational institutionalism, and business power.

Article

  • https://www.researchgate.net/publication/340065937_The_Politics_of_Platform_Capitalism_A_Case_Study_on_the_Regulation_of_Uber_in_New_York

Exploring the governance of platform cooperatives: A case study of a multi-stakeholder marketplace platform cooperative.

Author(s): Fredrik Andersson Bohman

Year: 2017

Related Organizations:

  • Fredrik Andersson Bohman

    • University of Gothenburg

Area(s): Platform Cooperativism, Data Cooperatives, Ownership, Internet Marketplace, Governance Models, Business Models, Commons, Cooperativism, Digital Cooperatives, Partnerships, Local-to-Global, Mutualization, Alternative Organizing, Sharing Economy, Solidarity Economy

Abstract

Platform cooperativism is a movement that criticise corporate-owned sharing economy platforms regarding their negative role in generating poor social conditions of labour and extracting huge profits simply by controlling the flows between supply and demand. The aim of this movement is to bring about a change in ownership structures in favour of the platforms workers, establish democratic governance and reinvigorating the notion of solidarity. There is however a serious need to expand our current theoretical and empirical knowledge regarding this concept. Especially concerning the governance of such initiatives which is moving away from centralized top-down decision making towards complex multi-stakeholder arrangements. The objective of this thesis is therefore to identify the implemented governance mechanisms in a platform cooperative and to define the effects that these governance mechanisms has generated in the cooperative. To achieve these objectives, an inductive case study was conducted on a multi-stakeholder marketplace platform cooperative called Fairmondo. The empirical findings revealed four categories of effects generated by Fairmondos governance mechanisms, these are navigating capitalism, facilitating democratic participation, enabling mandatory transparency, engaging the community. Having identified these four categories of effects, four interwoven components constituting a general governance system for multi-stakeholder platform cooperatives was proposed, which can guide further research and practical endeavours. The theoretical contributions to commons governance and platform cooperativism is to have shown that similarities exist between both streams of literature which enrichens the novel research on platform cooperative governance with empirically tested design principles. Additionally, the proposed governance system can act as a tool for practitioners of platform cooperativism to analyse and develop their own governance structure according to the four components presented.

Article

  • https://gupea.ub.gu.se/bitstream/2077/53721/1/gupea_2077_53721_1.pdf

#CoopTech: Platform Cooperativism as the Engine of Solidary Growth

Author(s): Anna Burnicka and Jan J. Zygmuntowski

Year: 2019

Publisher: Fundacja Inicjatyw Strategicznych (Instrat)

Related Organizations:

  • Jan J. Zygmuntowski

    • Fundacja Inicjatyw Strategicznych (Instrat)

Area(s): Platform Cooperativism, Data Cooperatives, Ownership, Internet Marketplace, Governance Models, Business Models, Cooperativism, Digital Cooperatives, Partnerships, Local-to-Global, Mutualization, Alternative Organizing, Sharing Economy, Solidarity Economy, European Cooperatives

Article

  • www.instrat.pl/cooptech

Potentials and Challenges of the Health Data Cooperative Model

Author(s): Ilse van Roessel, Matthias Reumann and Angela Brand

Year: 2018

Publisher: S. Karger AG, Basel

Journal: Public Health Genomics

Related Organizations:

  • Ilse van Roessel,

    • Faculty of Health Medicine and Life Sciences (FHML), Maastricht University, Maastricht, The Netherlands

  • Matthias Reumann

    • The United Nations University – Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht University, Maastricht, The Netherlands

  • Angela Brand

    • IBM – Research, Zurich, Switzerland

Area(s): Platform Cooperativism, Data Cooperatives, Health Data Cooperatives, Data Sovereignty, Ownership, Data Trusts, Data Collaboratives, Data Privacy, Cooperativism, Digital Cooperatives, Partnerships, Local-to-Global, Mutualization, Alternative Organizing, Data Democratization, Data Analytics, Data Economy

Abstract

Introduction: Currently, abundances of highly relevant health data are locked up in data silos due to decentralized storage and data protection laws. The health data cooperative (HDC) model is established to make this valuable data available for societal purposes. The aim of this study is to analyse the HDC model and its potentials and challenges. Results: An HDC is a health data bank. The HDC model has as core principles a cooperative approach, citizen-centredness, not-for-profit structure, data enquiry procedure, worldwide accessibility, cloud computing data storage, open source, and transparency about governance policy. HDC members have access to the HDC platform, which consists of the “core,” the “app store,” and the “big data.” This, respectively, enables the users to collect, store, manage, and share health information, to analyse personal health data, and to conduct big data analytics. Identified potentials of the HDC model are digitization of healthcare information, citizen empowerment, knowledge benefit, patient empowerment, cloud computing data storage, and reduction in healthcare expenses. Nevertheless, there are also challenges linked with this approach, including privacy and data security, citizens’ restraint, disclosure of clinical results, big data, and commercial interest. Limitations and Outlook: The results of this article are not generalizable because multiple studies with a limited number of study participants are included. Therefore, it is recommended to undertake further elaborate research on these topics among larger and various groups of individuals. Additionally, more pilots on the HDC model are required before it can be fully implemented. Moreover, when the HDC model becomes operational, further research on its performances should be undertaken.

Article

  • https://www.karger.com/Article/FullText/489994

Democratizing Health Research Through Data Cooperatives

Author(s): Alessandro Blasimme, Effy Vayena and Ernst Hafen

Year: 2018

Publisher: Springer Link

Journal: Philosophy & Technology

Related Organizations:

  • Alessandro Blasimme

    • Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland

  • Effy Vayena

    • Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland

  • Ernst Hafen

    • Department of Biology, ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland

Area(s): Platform Cooperativism, Data Cooperatives, Health Data Cooperatives, Data Sovereignty, Ownership, Data Trusts, Data Collaboratives, Data Privacy, Cooperativism, Digital Cooperatives, Partnerships, Local-to-Global, Mutualization, Alternative Organizing, Health Research, Data Democractization, Data Analytics, Citizen Empowerment, Policy Change, Data Economy

Abstract

Massive amounts of data are collected and stored on a routine basis in virtually all domains of human activities. Such data are potentially useful to biomedicine. Yet, access to data for research purposes is hindered by the fact that different kinds of individual-patient data reside in disparate, unlinked silos. We propose that data cooperatives can promote much needed data aggregation and consequently accelerate research and its clinical translation. Data cooperatives enable direct control over personal data, as well as more democratic governance of data pools. This model can realize a specific kind of data economy whereby citizens and communities are empowered to steer data use according to their motivations, preferences, and concerns. Policy makers can promote this model by recognizing citizens’ rights to access and to obtain a copy of their own data, and by funding distributed data infrastructures piloting new data aggregation models.

Innovative data mining capabilities, such as natural language processing and machine learning, can detect clinically relevant patterns in an ever-expanding sea of health data. Emerging paradigms like precision medicine (Collins and Varmus 2015; Blasimme and Vayena 2017) and digital health (Vayena et al. 2018) are premised on such advanced capabilities (Hawgood et al. 2015). However, before deploying such tools, phenotypic, clinical, lifestyle, and multi-omic data—including data generated directly by patients and healthy individuals—must become available for analysis. This is not happening at the desired pace, as different kinds of individual-patient data reside in disparate, unlinked silos (Tenopir et al. 2011; Blasimme et al. 2018).

We propose that data cooperatives can promote much needed data aggregation and consequently accelerate research and its clinical translation. The rationale for adopting data cooperatives is that people (healthy and sick) are the legitimate controllers of their personal data. Data cooperatives offer tools for exerting direct control over personal data, and for participating in the democratic governance of data pools. This model can realize a specific kind of data economy whereby citizens and communities are empowered to pull multifarious types of data in one place, and steer data use according to their motivations, preferences, and concerns. Policy makers can promote the creation of data cooperatives by recognizing citizens’ right to access and to obtain a copy of their own data, and by funding the creation of distributed data infrastructures piloting new data aggregation models.

Article

https://link.springer.com/article/10.1007/s13347-018-0320-8


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