Quite simply, to the skilled, the prepared, the connected, in the well-resourced, go the spoils. Those with capital and skills are better positioned to prosper, differentially and over time, relative to those who start with less, if they can start at all. In platform livelihoods, reputation ratings serve as powerful signals to each individual new buyer about whether to choose a more or less experienced freelancer, driver, innkeeper, musician, etc. these ratings quickly amplify differences between the newbies and the superstars an application effect where good ratings and good work are self-fulfilling and compounding.
Find references for this section at the bottom of this page or see the PDF for in-text citations.
Finally, it’s worth comparing livelihood experiences, but not simply between individuals, or between livelihoods, or even between countries, but instead between pathways of progression and advancement. If patterns of adoption, impact, and use of these technologies (marketplace platforms and social commerce) follow those of other technologies, we should be attuned to ways in which the benefits and harms of such technologies do not accrue to all individuals equally. Several studies in this corpus of 75 show patterns of diversions and amplification, as outlined elsewhere in the technology literature (Toyama 2011; Tichenor, Donohue, and Olien 1970). Quite simply, to the skilled, the prepared, the connected, and the well-resourced, go the spoils.
Skills and capital are well-understood from observations of earlier technologies. Those educated and skilled do better in retail (Zollmann and Wanjala 2020; Kabanda and Matsinhe 2019); and perhaps do better in microtasking and freelancing as well (Anwar and Graham 2020). Those with resources to buy more cars, or open more properties for home sharing, may do better than those who are struggling to amass the capital to get involved (Ruiz-Correa et al. 2019). To be clear, this is not simply a case of barriers to access, but in changes to the velocity with which one can advance via platform labor or platform sales. Those with capital and skills are better positioned to prosper, differentially and over time, relative to those who start with less, if they can start at all.
But the new effect here is ratings. Reputation ratings, and even the counts on amount of work acquired in the past (D’Cruz and Noronha 2016), serve as powerful signals (Partnership for Finance in a Digital Africa 2019; Rani and Furrer 2019; Nastiti 2017; Selabe 2017; Garcia et al. 2020; Wood et al. 2019a; 2019b; Jack, Chen, and Jackson 2017; Anwar and Graham 2019) to each individual new buyer about whether to choose a more or less experienced freelancer, driver, innkeeper, musician, etc. These ratings quickly amplify differences between the newbies and the superstars, between those designated as trusted brands, and those that are portrayed as a greater risk. This is an application effect where good ratings and good work are self-fulfilling and compounding. Granted, professional reputations and track records have been a part of doing business for as long as there’s been business. Platform models foreground these reputations, requiring hypervigilance of all (Gray and Suri 2019), and codifying precarity for more users than not. To those with the scores go the new work, and new entrants must struggle and scratch to even get in the game (Malik, Nicholson, and Heeks 2018).
References
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