How platform cooperatives deal with the size-identity tradeoff?

The first presenter was Jovana Karanovic. She is precisely the founder of Reshaping Work, and researcher at the KIN Center for Digital Innovation at VU Amsterdam. Following Carmelo Cenammo, the starting point of her talk was a trade-off that platforms face: the one of the platform size that leverages on growth of network effects (the winner-takes-all logic of Uber, Airbnb, and Deliveroo), and the other being the platform identity, which leverages on market positioning, platform quality and distinct content. The examples Jovana pointed out for the later were Grab and Careem, which beat big platforms by attending the particular preferences of Southeast Asia and Middle East users, respectively.

Her research question, along with her colleagues Hans Berends and Yuval Engel, is the following: how do platform cooperatives deal with the tradeoff between platform size and identity?

To tackle this question, they are comparing four case studies of platform cooperatives across four different industries: Wehelpen (care), Partago (car rental), Stocksy (stock photography), and Fairbnb (vacation rentals). Wehelpen and Partago look for “local” network effects (market segments); Stocksy and Fairbnb look for “global” network effects (entire market).

The key here, in my opinion, is to think if the specific strategic management of the local/global tradeoffs by platform cooperatives helps them to compete with platforms that leverage on ridiculously large financial resources to lower prices and “buy” clients to boost the network effect. These are the insights she presented:

– In terms of control mechanisms, Wehelpen and Partagon bet for an identity-driven market positioning through communication, set different rules for each community they serve, and use the cost of platform affiliation as a mean of control as well. On their “global side”, Stocksy and Fairbnb establish the following control mechanisms: quality base selection (e.g Stocky selects only top photographers) and selection based on adherence to values/principles (e.g. Fairbnb has 1 host 1 house policy).

– In terms of differentiation strategies, Wehelpen and Partago enforce a strong identity and adapts the offer to local particularities. If I understand this correctly, the alternative organization flavor (and its potential impact in terms of purpose and sustainability) can be a distinctive factor in terms of identity. They also stress (of course) the importance of local adaptation and market-segment specialization (which can leverage in their connections and social ties with existing local communities). Stocksy and Fairbnb, restrict market access on the supply side, which leads to offering more consistency. Also, platform architectures can support the identity, attracting a specific type of user (again, e.g., sustainability-driven).

I think that these insights support something that I wrote elsewhere: the fact that they can design a business model not-investor-centered can suppose a greater value proposition to patrons (and other stakeholders). Also, there is the fact that being alternative forms of organization helps them to differentiate their identity in terms of competitive advantage, which is something I was not sure it would happen.

What couriers think about platform cooperatives in Barcelona?

Ricard Espelt, from Dimmons research group at Open University of Catalonia, showed preliminary results of their research on platform couriers working in Barcelona: they are isolated from the perspective of law and they had to rely on emergent or alternative unions. Nor them nor the stakeholders have reached an agreement on how to solve their problems. They are themselves divided in between those that favor the creation of alternative- more coop-oriented-platforms, while others rather prefer to fight for labor rights in the current platforms.

The good news is, therefore, that there are couriers open to alternative forms of organization such as platform cooperatives. I do not think that it is crucial to know how many are they, but their existence, for that fact changes completely the feasibility of their existence. That is important, particularly in those countries in which legislation is leaning towards profit-oriented platforms.

Do algorithms contribute to shape the legal status of platform workers?

Anna Ginès i Fabrellas, professor and researcher at ESADE Business School, took a fascinating look at platform algorithms in terms of how they actually intervene/shape the legal status of workers:

• In terms of the debate “platforms as technological firms that just mediate between offer and demand”, vs “platforms as service providers” (algorithmic management), Anna convincingly argued that the role of the algorithm is so crucial in managing the delivery of services that this platforms cannot escape from the fact that they are service providers. And by the same token, platforms are a relevant productive infrastructure.
• When looking at algorithms as subordination, she showed that the massive data collected by geolocalization systems turns out to be a very effective form of control/management.
• Finally, the nature of platform algorithms (or at least the current ones) kills any dimension of workers entrepreneurship, for they adopt the most relevant decisions.

Anna paid attention as well to the new forms of worker’s precarity, and the different approaches to battle them. Being platform cooperatives one of them, she also pointed to the French regulation of platform worker’s rights, or the proposal of an entirely new legal regime for them.

As I see it, platform cooperatives are the straight-forward solution, because it not requires legal changes on their side.

Would a UBI help a transition to platform cooperatives?

Finally, Melisa Renau, also from Dimmons at UOC, presented her analytical model for conflict social relationships, applied to the courier’s case. Her research question is “How and if UBI could affect power relations between employers and workers by increasing and improving workers’ exit and voice options in the platform economy. Her elegant model, that draws from the Hirschman’s triangle and the Birnbaum and Wispelaere exit options models, showed that UBI is not a silver bullet:

• the empowering potential of a UBI depends on endogenous and exogenous variables.
• Providing economic independence does not mean ensuring equality,

While there is a hype around UBI, I see much more desirable the platform cooperative option, based on workers ownership and multistakeholder governance, (or open value networks, for that matter).

Finally, some of the best outcomes of the event came from the intervention of platform workers. I participated in a walk with two women that founded a union for cleaning ladies like them that deserved a dissertation at UAB. They showed outstanding intelligence, courage, and dignity in front of the abuses of the platform business model. And I could not help to tell them that I will contact them to talk about cooperative platforms.

New Reshaping Work regional events are on the way at Amsterdam, Novi Sad and Stockholm. They will equally stress the importance of research-based knowledge. Keep your eye on the growing list… or organize one in your city!

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Ambika Kandasamy: Cognitive neuroscientist Christopher Madan says open-access data or data that is freely shared among researchers to use in their studies can not only save time and money, it can enable scientists to “skip straight to doing analysis and then drawing conclusions from it,” if the datasets they need already exist. Madan works as an assistant professor at the University of Nottingham in England, where he studies the impact of aging in the brain, focusing specifically on memory. He started using open-access data in his work about three years ago.

Given the stiff competition for funding, scientists like Madan are turning to open-access data as a way to expedite their own research process as well as the work of others in the field. Madan says there are various benefits to using open-access data in research — namely, it provides researchers with large and diverse datasets that might otherwise be difficult to obtain independently. This pre-existing data could help them make inferences about generalizing the results of their studies to larger populations, he says. Making research data freely available, however, isn’t such a straightforward process. In some cases, especially when researchers use patient data in studies, they must take steps to anonymize it, he says, adding that “we also need to have balance, so we don’t become too dependent on specific open datasets.”

We spoke with Madan about how he uses open-access data in his research.

Ambika Kandasamy, Shareable: I attended a talk you gave at the MIT Media Lab in August about some of the benefits of sharing research material such as MRI datasets with other researchers. What compelled you to move towards this open and shared approach?

Christopher Madan: To some degree, I’m more a consumer of open data than adding to it. The main plus is that the data is already there. Instead of, I have an idea and then I have to acquire the data — both applying for grants or somehow getting the money side sorted and then having a research assistant to put in the actual time to get them — people to come in and be scanned. Scanners are kind of expensive. All of this would take, on the optimistic side, I’d say several months or more into years, if I wanted to get a sample size of like three, four hundred people.

But for the sake of just looking at age, datasets exist. It can take a few minutes to download, maybe into hours depending on which one and how much other data I have to sort through to organize it into a way that is more how I want the data organized to be analyzed. It’s still in the scale of hours and maybe days versus months to years. Then the analysis on that going forward is the same at that point.

In an article in the Frontiers in Human Neuroscience journal, you wrote that “open-access data can allow for access to populations that may otherwise be unfeasible to recruit — such as middle-age adults, patients, and individuals from other geographic regions.” Could you elaborate on that?

The maybe more surprising one of those is the middle-age adults. People in their 30s to 50s could generally have jobs and families and are busy, so it’s harder to get them to be in research studies. If we’re interested in aging, getting young adults that are effectively university student age, they’re relatively easy to be recruited in university studies because they’re walking down the halls of the same places that the research is done. Older adults, to some degree, can be easier to recruit. … But middle age adults have a lot less flexibility of their time. Even if they’re interested, they have a lot of other commitments that they have to balance. It’s just harder to get them into research studies. Now, it’s not that they’re impossible to get. It’s just effectively lower odds for that demographic. If people have already spent the effort of trying to get them in, then we should take advantage of that data and not just use it for one study and that’s it, but answer multiple research questions and try to get more out of the same data that’s already been collected.

In the article, you also mentioned that you keep a list of open-access datasets of structural MRIs on GitHub. Have other researchers contributed to this list?

Yes, they have. I initially made a list of basically just stuff that I knew. One morning, I was like, “maybe I should do this.” I was keeping track of things, but every so often, new datasets get shared. How much can you keep in your head or keep the PDFs related to these in a folder? It’s not that great of an organization. So I thought, maybe I’ll make a list where I’ll say the name — some of them have shorter abbreviations, so a spelled out version, a link to where that data actually is, a link to the paper that kind of describes it, some notes about what kind of MRIs are with it or how many individuals are included in it, the demographics — is it all young adults or old adults — that sort of information. I basically just made a list of it and put that online. Other people found it useful. Some people needed parts of that but not others, or generally didn’t think about open-access data as much until that point. Here’s a list of them. You can look up what’s there and what might be useful to you and take advantage of it.

Since then, some that I basically didn’t include, that I didn’t know of or didn’t think of or whichever, that other people are involved in, they requested to add themselves to the list, and I approved that. Other ones, people that aren’t just involved in the data collection of it, but knew of that weren’t in the list, contributed to it. It’s grown a bit since then, particularly I’ll say from other people’s additions, which also shows other people are looking at it and making a note of it. At least you can have people favorite it for later. I think it’s about 2,000 or so people have. I think maybe eight, nine people have actively added new things to it, so it’s growing a bit. Again, it is a bit of a specialized topic and resource, but other people have found it useful, so that does kind of show that it’s not just a list that I made for myself, but other people have found some benefit in this as well.

How could this kind of open-access data accelerate the process of scientific discovery?

I think the main thing is just after having some idea about what datasets exist — as soon as you have some sort of research idea and you can match it onto something of that sort — you can just download the data. In some cases you have to do an application, so maybe there’s a week or something when someone needs to approve that you’re using this for valid purposes, but you can skip straight to doing analysis and then drawing conclusions from it and writing up a research paper if it went somewhere, rather than having things be drawn out for probably several years.

From your own experience, have you noticed any trends over the years in data sharing among researchers?

There’s definitely more open data now than there used to be. That’s great, both in terms of more people using it, but also just more people sharing whatever data they’ve been collecting anyway. From more personal analysis, talks with researchers that have not shared data yet, but have been thinking about it for data they’ve already been collecting — can they share it because in terms of consent of what the initial participants gave? Would that include sharing of their data when that wasn’t explicitly asked? Even if that doesn’t and they’re working with more medical kind of patient data, then you can still plan forward and say, “okay, what do we need to add?” A couple of extra sentences to the consent form to allow for this at this point forward even if we can’t do it retrospectively. People are thinking about it even beyond just what’s kind of more apparent in terms of what data is actually available today — little more behind the scenes. The field is shifting in that direction. It’ll just continue along that trajectory.

This Q&A has been edited for length and clarity. Photo of Madan [top] by Dan Lurie and [left] by Yang Liu. This is part of Shareable’s series on the open science movement. Further reading:

How the Mozilla Science Lab is improving access to research and data

The post Could Sharing Research Data Propel Scientific Discovery? appeared first on P2P Foundation.