Managing Reputation in the Workplatform: How Freelancers Interpret Algorithmic Scores in OLM
Academy of Management Annual Meeting Proceedings
Although considered independent from the platforms they work from, freelancers in online labor markets need to develop an ‘algorithmic competence’ to become and stay competitive. To increase the likelihood of being hired, in particular, they need to deal with algorithmically calculated reputation, which is a standardized score associated to their quality as workers. By drawing on signaling theory, this research aims to explore how freelancers working on online platforms interpret algorithmic calculated reputation and with what consequence for their work. The grounded model we developed through interviews and documents collected with freelancers from a major platform reveals two phases through which freelances manage their reputation. First, freelancers interpret algorithmic scores as barriers and strive to build their initial reputation with emotional consequences in terms of feelings of hardship and loneliness. In a second phase, freelancers develop three different strategies to manage reputation, that we labelled as instrumental, relational, and indifferent. The interpretations and behaviors associated to the different strategies lead to different, although mainly negative, emotional responses, i.e. emotion regulation, anxiety, and frustration. We believe our model offers implications for theories on imposed reputation signals, gig work, and emotions in new work contexts.
Francesca Bellesia, Elisa Mattarelli, and Fabiola Bertolotti. "Managing Reputation in the Workplatform: How Freelancers Interpret Algorithmic Scores in OLM" Academy of Management Annual Meeting Proceedings (2020). https://doi.org/10.5465/AMBPP.2020.16777abstract