Negative externalities produced by sharing economy ecosystems in Mobility

Publication Type:

Conference Paper

Source:

Gerpisa colloquium, Detroit (2022)

Abstract:

Since the launch of Uber in 2008, succeeded by similar entrepreneurships in other sectors (Geissinger et al., 2021), the way people and goods transited inside large cities changed significantly. Some individuals changed their habits and decided not to own a vehicle, trusting they could opt for a ride-hailing service at a reasonable price anytime (Markman et al., 2021). With time other conveniences, not financially achievable before, started to be accessed frequently, like ordering meals from a food delivery service from a mobile application, shopping groceries from home or buying medicine during an emergency and receiving it all in few minutes. Nevertheless, this was only a perspective of the consumer of the benefits of sharing economy services and not a generalized view. Negative externalities could be perceived by a passenger of a public transportation, complaining about a chaotic traffic caused by delivery riders, by a citizen, suffering from an asthmatic crisis because of higher CO2 emissions, or even by the government, not empowered to collect some expected revenues and liabilities from the platforms. All of them are stakeholders of the mentioned services (Laczko et al., 2019), often also called ecosystem ‘species’ (Xu et al., 2021) or platform actors (Leung et al., 2019; Wirtz et al., 2019). Since all of the services roughly described above are but examples of the reproduction of Uber multisided platform business model in other sectors (Geissinger et al., 2021), they follow the same economic business logic equally depending on providers equipped with vehicles to keep it running (Markman et al., 2021).
When a sharing economy platform like these ones is launched it primarily invests in having strong links with members of the local community in order to recruit enough providers and consumers to catalyze initial economic dynamics (Rong et al., 2021). The same way as a new technology developed and ready to be launched (Arthur, 1996), it benefits from positive feedbacks of the increasing returns of its adoption. Thus in this initial phase it is essentially driven by “on-demand resource adaptation” (Zeng et al., 2021), “watching for the next wave that is coming, figuring out what shape it will take, and positioning the company to take advantage of it” (Arthur, 1996). During this period the platform focus on generating “cognitive legitimacy” among its users (Garud et al., 2020). In a subsequent phase it promotes the massive entering of providers and consumers in order to achieve a supply-demand enhanced distribution enabling efficient matching, what is also known as “liquidity” (Wirtz et al., 2019). This is a moment when the platform is entirely focused on scaling-up, distancing from social values it was in contact, while keeping its main stakeholders closer (investors and other platform complementors) (Rong et al., 2021). Here price and financing wars against competitors increase the pressure over the platform which reacts by promoting “big-data-driven [indirect network] effects” between providers and consumers through surge pricing regulated by algorisms (Zeng et al., 2021). It is only when the business leverages a relevant scale achieving economic maturity that it starts to take conscious on the effects of its negative externalities. According to Rong et al. (2021) in this [legitimation] stage the business is large enough to be perceived by reactive peripheral stakeholders and the platform is forced to change its focus to achieve a better balance between social and economic values. It is exactly when, on the one hand, it looks more seriously for the “sociopolitical legitimacy” of the service (Garud et al., 2020) and, on the other one, its realizes the capabilities it developed to orchestrate peripheral ecosystem resources beyond its core competence. That is when it takes actions to mitigate negative externalities eventually transforming these challenges into opportunities, by increasing the value created for the consumers (Zeng et al., 2021).
Although sharing economy ecosystems involving mobility resources are ubiquitous in the large cities around the world (Geissinger et al., 2021), very typical (Garud et al., 2020; Markman et al., 2021; Rong et al., 2021; Xu et al., 2021; Zeng et al., 2021) and contextually specific (Garud et al., 2020) no study has been found in the literature describing how they are managed to mitigate negative externalities or to transform them in business opportunities. According to Snihur & Bocken (2022) there is a lack disclosure of the value destruction impact of business model innovation and its dynamics over time. Same authors also called for studies that show exemplar ecosystem sustainable innovation. Based on that we propose the following research questions:
“What are the main negative externalities produced by sharing economy ecosystems involving mobility resources and how are the platforms managing to internalize them?”
Inspired by Buhalis et al. (2020), who investigated the dynamics of positive and negative externalities co-creation by the Airbnb ecosystem in Barcelona (Spain), we intent to produce a multi case study (Eisenhardt & Graebner, 2007) based on a qualitative exploratory longitudinal investigation that must involve a total of four sharing economy platforms operating in cities situated in Brazil (two) and in France (other two), which are facing the legitimation stage (Rong et al., 2021) struggling to get (or after they have already gotten) the “sociopolitical legitimacy” of their service (Garud et al., 2020). The data collection will consider documents (from the platform, the city councils, communities and NGOs), press and social media data and interviews, allowing the triangulation of data sources (Yin, 2010). The analysis will be handled through a codification of sentences and words perceived as salient according to a literature review, following techniques described in Saldaña (2009). The process of analysis will be the abductive procedure – i.e. varying from inductive to deductive, and vice versa, in a iterative and recursive way, according to findings and the insights produced by the data on the scholars (Suddaby, 2006). The analysis objective will be to find common evidences emerging from “within and between case analysis” (Eisenhardt & Graebner, 2007).
The study will contribute with the development of Sustainable Business Model Innovation literature (Snihur & Bocken, 2022) as well as with the Sharing Economy literature improvement (Rong et al., 2021; Zeng et al., 2021) by introducing a framework relating emergent negative externalities in different contexts and the strategic actions that either mitigate or transform these challenges into value creation opportunities enhancing sustainability.
The study will also provide managerial contribution for the platforms executives in charge of managing this type of ecosystem (Gomes et al., 2022) and for policy makers regulating the sector during socio-technical transitions.

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