5 Platform Models for Social Businesses

Digital social innovations are novelties that use, develop, or rely on digital technologies to address social and/or environmental problems. They include a broad group of digital platforms, which facilitate peer-to-peer (or business-to-peer, peer-to-business) interactions. In our research on digital social innovations in France, we identified five platform models they rely on in governing the interactions between their users, which also form the basis of their business models. These models govern how the information, knowledge, or resources flow in the platform.

Matchmakers form pairs

These platforms match users (like a, b, c, d) with each other according to their specific needs or provisions. In most cases they give rise to interactions between pairs of users that best meet each others’ needs. Examples are plenty, like sharing platforms or volunteering platforms. These platforms work well for spot exchanges that do not require high commitments, and are short term. Digital reputation is an important component that sustains relations in the platform. However, when the matching model is used for longer term and more committed relations, that are often based on meaningful deliberations around a specific cause, and among a tightly knit community of users, they might not work well, due to issues of trust, or lack of joint objectives that sustain the collaborations in the long run.

Knowledge brokers transfer knowledge and experiences

These platforms transfer knowledge between users. While they too can be based on the rules of matching, they involve more refined knowledge exchanges, often based on personal experience, or knowledge and expertise about a specific subject. Knowledge transfer takes place usually in a directional manner, from one group of users to another. The users can be experts in a certain field, or people who need customized advice or expert opinion. In the figure, (a, b, c) form one group of users, and (1,2,3) form another. Medical research platforms are an example, where patients (or relatives of patients) explain their symptoms and problems and researchers  provide advice, and also use this knowledge in their research. These platforms are useful for cases in which a deeper knowledge transfer is required, or more trust based relations as compared to spot exchanges.

Pools collect dispersed information

These platforms form a common pool of knowledge or information. Pools are platforms in which information is collected in a central pool, and accessible to all users of the platform. This is akin to the wiki model, where many users participate (often) in an open manner, and the pool is (usually) open to anyone who wants to have access to the resources. Pools are good for problems that can be effectively solved by collecting dispersed information pieces and for problems that require collective intelligence; user commitment can range from collaborative to spot users. For example IWheelShare in France is a platform in which users can signal places that are suitable or unsuitable for people with disabilities in urban areas.

Crowdsourcing platforms give direction to distributed resources

These platforms give direction to distributed resources. They help generate support for individual projects (or causes). They can be general purpose, like the case of Kisskissbankbank, which is a platform where any project can be funded. They can also be specialized on a certain problem, like Bluebees, which is confined only to agricultural projects. This model works well for mobilising people around certain problems to increase awareness or legitimacy (like in e-petitions), or in generating monetary or other kinds of support for a certain social issue; like disability, or sustainability. In some crowdsourcing platforms, there are collaborative relations between the funders and the project owners. This may depend on the level of support provided by the funder. For example in the case of journalism, project owners can feel a  strong connection to their funders, and can have various sorts of collaborative relations. In the figure, a,b,c are shown as funders, and 1,2,3 are projects.

Alerters monitor the environment and signal problems

Alerters are platforms that signal the users when a certain threshold is passed, or when a certain event occurs, mostly through mobile devices. These platforms work well in cases where the problem is related with the users’ restricted or limited abilities to keep track of relevant information or in alerting urgencies. Alerters are often confined to alerting the users themselves, or users’ social networks. In the figure this is shown by the arrows from a (the user) to A (user or her social network).

Social entrepreneurs, nonprofits, and other actors that operate platforms for social and environmental problems should consider the match between the nature of the problems they address and their mechanisms of operation, for increased effectiveness and long term survival.

Müge Ozman and Cédric Gossart

The Reality and Virtuality of Collaborations

I recently read an article titled “from city space to cyber space”, written by Jennifer Light in 1999. It is written in a period where the internet had recently taken off, while many skeptics were concerned about its effects on urban spaces because they claimed, “internet makes people isolated”. Light explains how this skepticism resonated with historical cases: Middletown studies in 1956 found that with the use of telephones “visiting” each other in neighborhoods decreased. In other sources we read how telephones, televisions, and VCRs  were tied to the closing of neighbourhood establishments. Based on these, Light explains how the meanings and interpretations of reality change as societies and urban spaces evolve. Take the example of shopping malls, which were regarded as the death of real public spaces in US: not too far from today, Light explains.

“Reality and virtuality” dialectic have taken different forms throughout the relatively short history of the internet. Only 20 years later, we are no longer questioning the internet, we are now questioning the ways in which we are engaged with smart devices and how we incorporate them into the reality of our lives. Watch a few videos on Youtube, that evoke nostalgia for authentic old days, showing how smart devices have killed our human side, as we take selfies in front of burning buildings. Or, consider sharing platforms. Schor’s concept of “strange sharing” is a vivid example of how the “real and virtual” are reinterpreted as societies and spaces evolve: it is not like sharing with kins and neighbours, it is a new type of sharing that is enabled virtually as our digital reputation helps us connect with strangers, she explains.

We maybe questioning the authenticity of our spaces today, but the picture is not so bleak. The real power of innovations, be the internet, smart phones, or plastic surgery, depends on how people use them. Back in the 80s, electronic communication was already helping citizens in urban spaces to connect with others, to find solutions to the problems of neighbourhoods by fostering collaborations between locals. Santa Monica’s Public Electronic Network (PEN) in 1989 is an example.  Or, various communities in Chicago emerged to help citizens gain digital literacy and access the internet for free. Such stories are countless in the history of the internet.

Its not too different today. As individuals who have always been the “real” agents of change, it is time that we understand the real meaning of smart: smart devices are smart not only because of their information processing capacities, they are smart because they enable a form of virtual collaboration that can help solve many societal and environmental problems. Cities have been transforming into virtual spaces of collaborations among citizens, and urban problems are increasingly addressed through enabling the participation of people, the mobilization of resources, skills, and capabilities of individuals.

Moral of the story? Before jumping on the nostalgia bandwagon and question the authenticity of our virtual spaces, be smart, and use your smart device for a real purpose. How? You can join a crowdsourcing platform and donate to a project, or vote, or spend a few extra hours online to volunteer for an association… instead of that last selfie. Opportunities are endless.

Müge Ozman

 

De l’ambigüité des innovations sociales numériques

Qu’y a-t-il de commun entre une plateforme collaborative permettant de louer l’appartement d’un particulier, un objet connecté mesurant la pollution de l’air, et une application cartographiant les zones urbaines dangereuses ? Outre le fait qu’elles reposent sur un équipement ou service numérique, elles impliquent un certain degré de collaboration entre acteurs et justifient leur existence par la poursuite d’objectifs sociaux ou environnementaux. Pourtant, comme le souligne par exemple Juliet Schor dans “Debating the Sharing Economy” (Great Transition Initiative, October 2014), les effets néfastes de certaines d’entre elles pourraient bien l’emporter sur leurs avantages, d’où l’ambigüité de ces « innovations sociales numériques » (ISN, ou digital social innovation –cf. NESTA).

Dès lors, ces innovations ne sont-elles qu’un pâle reflet d’une mode passagère ?

Pour en savoir plus, lisez notre article : De l’ambigüité des innovations sociales numériques.

Pour le citer : Gossart, C. & Ozman, M. (2017). De l’ambigüité des innovations sociales numériques. INESS Blog, 24 mai, https://digitalsocinno.wp.imt.fr/?p=170.

On the meaning of digital social innovation

Authors: Müge Ozman and Cédric Gossart

One of the problems that we encounter in our research on digital social innovation is related with defining it. Is it a catch-all phrase? A combination of three trendy words in our era? Digital social innovations (DSI) are often associated with positive meanings, like openness, collaboration or inclusion, as opposed to more commercially-oriented innovations. In trying to define such a contested concept as digital social innovation, we should strive to disentangle it from its positive aura. The following figure is helpful for a start.

In our opinion, digital social innovation lies at the intersection of three spheres; innovation, social and environmental problems, and digital technologies. The first sphere is innovation. It refers to the development and diffusion of a (technological, social, …) novelty that is not used yet in the market or sector or country where it is being introduced. The second sphere concerns the solutions put in place to address social and environmental problems, for example through public policies, research projects, new practices, civil society actions, business activities, or by decentralising the distribution of power and resources through social movements. For example, social inclusion measures facilitate, enable and open up channels for people to participate in social life, regardless of their age, sex, disability, race, ethnicity, origin, religion or socioeconomic status (cf. e.g. the positive discrimination measures which enable students from minorities to enter universities). Finally, the third sphere relates to digital technologies, which concerns hardware and software technologies used to collect, process, and diffuse information.

Many digital technologies are no longer considered innovations in 2017, at least in Europe where they have become mainstream. For example, according to Eurostat only 14 % of the EU population have never used the internet. On the other hand, some digital technologies are novel ones (area C in the figure), such as the service Victor & Charles which enables hotel managers to access the social media profile of their clients in order to best meet their needs.

As regards the yellow sphere, many of its solutions to social and environmental problems are neither digital nor innovative. They relate to more traditional ways of fighting social exclusion or pollution for example. To name but a few, in order to solve housing problems in France, the HLM system (Habitations à Loyer Modéré) was introduced after the Second World War to provide subsidised housing to modest households. When introduced, it was an innovative solution, but it has now institutionalised.

At the intersection between the solutions and digital technologies spheres we find the area B which does not intersect with the blue innovation sphere. There we find digital solutions to social and environmental problems which are not innovative, such as the monthly electronic newsletter ATOUTS from OPH (Fédération nationale des Offices Publics de l’Habitat), a federation of institutions in charge of the HLM system, which uses the newsletter to foster best practices among HLM agencies in France. As for non-digital innovations aiming to solve social and environmental problems, they can be found in area A. For example, the French startup Baluchon offers affordable wooden and DIY micro-houses that enable modest people to become independent dwelling-wise. As for area C, it concerns innovative digital technologies which do not aim to solve a social or environmental problem, such as a 3D tablet.

In the area where the three spheres intersect lie digital social innovations. DSI can thus be defined as novelties that use, develop, or rely on digital technologies to address social and/or environmental problems. They include a broad group of digital platforms which facilitate peer-to-peer interactions and the mobilisation of people in order to solve social and/or environmental problems. For example, the Ushahidi application, designed to map violent acts following the 2008 elections in Kenya, aggregates and diffuses information collected by citizens about urban violence, which enables citizens and local authorities to take precautionary measures. Along similar lines, the Egyptian application Harassmap collects cases of sexual harassment reported on its platform, which allows women to avoid dangerous areas (Source: Banque Mondiale (2014), Rapport sur le développement dans le monde, p. 153). A similar application was developed in France under the name of App-Elles. It enables girls and women who are victims of an assault to alert the police and other contacts. As for the I Wheel Share application, it facilitates the collection and diffusion of information about urban (positive and negative) experiences that may be useful to disabled people. Two last examples involve the use of a digital hardware (other than a smartphone). First the KoomBook, created by the NGO Librarians Without Borders, is an electronic box using a wifi hotspot to provide key educational resources to people deprived of Internet access. Second, the portable sensor developed by the Plume Labs company, which can be used as a key holder, measures local air pollution in real time and communicates the data to the community.

But as it always happens with categorisations, boundaries are not as clear-cut as it may seem on a figure. In our case, there is a grey area (the fuzzy line in the figure) surrounding digital social innovations. For example, if a technology makes it easier for a lot of people to access certain goods or services (short term recreational housing, individual urban mobility, …), does it solve a social problem? The answer is clouded with the positive meaning attached to digital innovations, which can conflict with their actual negative social and environmental impacts (e.g. they might generate unfair competition or strong rebound effects).

Take the case of AirBnB: according to our definition, it could be considered as a digital social innovation. It relies on a digital platform through which a traveller can find  accommodation while possibly discovering local people and lifestyles, and otherwise idle resources are put into use. Besides avoiding the anonymity of hotels, tailored services are now offered to clients of the platform. Do you want to take a koto course while having your matcha tea in a Japanese culture house? This AirBnB ‘experience’ will cost you 63€.

On the one hand, AirBnB enables (some) people to earn extra income, on the other it helps travellers access a wider array of local experiences. But the system also facilitates replacing established, institutionalised capabilities and knowledge, and excludes locals deprived of digital literacy (as well as of extra dwelling space located in hyper central urban areas). While AirBnB customers might enjoy the profusion of offers available on the platform as well as local cultural highlights sold in a 2-hour pack, an ignored local culture lies on the poor side of the digital (and economic) divide.

Without having robust indicators of the social impact of DSI, it is difficult to clarify this grey area and to solve the problem of definition. But constructing ex-ante and ex-post indicators of social impact is not easy from a scientific point of view. Moreover it is difficult to obtain user data as innovators keep them proprietary, which impedes research to a great extent. In addition, innovators and other ecosystem members can engage in “share-washing” and conceal commercial activities behind a smokescreen of sharing activities. An important step towards overcoming these difficulties is to foster an open debate about how profits obtained from DSI are distributed, about who is prevented (or excluded) from using DSI and why, and about the contextual factors that ultimately
shape DSI social impacts. Troublesome as definitional issues maybe, researchers should not reject the term altogether for being too vague, since there is indeed a category of DSIs having a strong transformative power regarding empowerment and sustainability. But neither should they impose a restrictive categorisation of DSI, in which Uber and AirBnB have no place. The involvement of a broad variety of actors (users and non-users, for profit and non for profit, …) in the definition of this public construct would do justice to the positive reputation of DSIs.

Research (and debate) questions on DSI

While digital social innovations are developing rapidly, and notwithstanding their potential power in  bringing effective solutions to many of the world’s problems, there are many issues and areas of dispute that needs to be tackled, with the participation of diverse actors. Here are some of the issues that we think are important to address in future research and debates:

  1. Who joins? While digital social economy is growing rapidly, the users are confined mostly to people with digital literacy. Moreover, research by sociology Professor Juliet Schor on the sharing economy highlights a paradox of openness: sometimes users can be confined to people coming from privileged social classes, forming cohesive groups (find a link to her article here) at the expense of diversity. How can digital social economy be more inclusive, and its actors more diverse?
  2. Replicability and openness: Scaling potential of DSI is an important issue to address in future research. How can the scaling potential of DSIs be strengthened? While DSI discourse prioritises openness as one of its founding principles, in our research on digital social innovations in France, we find that most innovations are protected and difficult to replicate over distant contexts and geographies.
  3. Synergies with incumbents: How do the incumbents integrate into the digital social economy? There are a range of strategies that existing firms implement to tackle the competitive threats. On this issue, technology journalist Alexandra Samuels offers some insights in a Harvard Business Review article, where her focus is on the collaborative economy; here is a link to her article: Established Companies, Get Ready for the Collaborative Economy.
  4. Research on users: One of the problems in carrying out research on the DSI is that we have few insights about user behaviour in platforms, due to the fact that data on users are not (and cannot be) revealed by platform owners. This forms a barrier to address many questions related with user behaviour, attitudes, and profiles.
  5. Indicators: Stakeholder involvement and cross sectoral partnerships are a very important aspect of digital social innovations. However, one of the problems in cross sectoral partnerships is related with the difficulty in finding a common language in collaborations. For example, different sectors can value/ or measure success in different ways, which can be a barrier to effective partnerships. How can we develop indicators of success to take into account the expectations of a diverse range of interests?