Barriers to entry of gig workers in the gig platforms: exploring the dark side of the gig economy

Abstract

PurposeThe alternative arrangements to traditional employment have become a promising area in the gig economy with the technological advancements dominating every work. The purpose of this paper is to explore the barriers to the entry of gig workers in gig platforms pertaining to the food delivery sector. It proposes a framework using interpretive structural modelling (ISM) for which systematic literature review is done to extract the variables. This analysis helps to examine the relationship between the entry barriers to gig platforms. The study further proposes strategies to reduce the entry barriers in gig sector which would help to enhance productivity and generate employment opportunities.Design/methodology/approachThe study uses interpretive structural model (ISM) to ascertain the relationship between various entry barriers of the gig workers to the gig platforms. It also validates the relationship and understand the reasons of their association along with MICMAC analysis. The model was designed by consulting the gig workers and the experts allied to food delivery gig platforms namely Zomato and Swiggy.FindingsIt was observed that high competition, longer login hours and late-night deliveries are the significant barriers with high driving power and low dependence power. Poor payment structures and strict terms and conditions for receiving the incentives are interdependent on each other and have moderate driving and dependence power. The expenses borne by the gig workers, such as Internet, fuel and vehicle maintenance expenses have high dependence power and low driving power. Hence, they are relatively less significant than other barriers.Research limitations/implicationsThe study is confined to food delivery sector of India, without considering other important sectors of gig economy for generalizing the framework. As the study is based on forming an ISM framework through literature review only, it does not consider other research methods for analysing the entry barriers to the gig platforms.Practical implicationsThe study attempts to dig out the low entry barriers for gig workers in food delivery platforms as there is a dearth of analysis of these factors. This study would weave them using ISM framework to help the gig platforms overcome these barriers at various levels, thus adding to the body of literature.Originality/valueThe study discusses the need for understanding relationship between the entry barriers in the form of ISM model to identify the dependent and driving factors of the same.

Socio‐technical issues in the platform‐mediated gig economy: A systematic literature review: An Annual Review of Information Science and Technology (ARIST) paper

“…The third subcode under power asymmetry, social isolation, only mentioned in 24% of the papers, has more been studied through a psychological lens, and these papers were excluded from this SLR, so there is work to be done examining social isolation from a socio‐technical perspective. As most of the studies in industrial and organizational psychology were conducted in a traditional workplace, it would be interesting to investigate job satisfaction, motivation (Pogorevici & Serobe, 2020), burnout, and well‐being in the context of digital workplaces (Parent‐Rocheleau & Parker, 2022), and how those issues could interact with issues of technical design (Behl, Rajagopal, et al, 2022).…”

“…This kind of surveillance also happened during the COVID-19 pandemic, while the platforms imposed strict hygiene protocols for workers, for example, a daily temperature update and rigorous sanitization measures during the task performance (Anjali Anwar et al, 2022). Algorithmic control (B13) refers to the case in which gig economy platforms leverage their user interfaces, experience designs, and data about gig workers to exercise control over their work (Behl, Rajagopal, et al, 2022). For example, workers must maintain a persistent connection to the platform, running up data costs, working under conditions of continuous monitoring (Newlands, 2022), having less control over the jobs for which they can compete (Polkowska & Mika, 2022), and submitting to human resource activities that control their work, such as accepting sanctions based on the rating system and constraints on their decision-making (Ens et al, 2018).…”

Sustainable successes in third-party food delivery operations in the digital platform era

“…Besides, riders will base on the autonomy level, job market vulnerability and economic attachment of the food delivery platform to select the employment status and work schedule. Behl et al (2022) concluded that the major obstacles of being a rider include high competition as well as long login hours and late-night delivery services provision, followed by poor remuneration and unfavorable conditions for getting incentives. Moreover, Puram et al (2021) found that rider faces different work pressures and difficulties especially during the COVID-19 pandemic, including operational, customer-related, organizational, and technological issues.…”

Workplace learning and information exchange among gig workers: crowdsourcing and the social media advantage

“…Considering that research on gig workers has predominantly been about unfair treatment the riders received due to their informal worker status (e.g. Behl et al , 2022; Kost et al , 2020; Parth et al , 2023), understanding the nature of learning and information exchange among gig workers on social media platforms advances the role of workplace learning amidst the new contingent work landscape. This missing piece of the gig economy literature will also provide insight to government, gig work platforms and organizations with contingent employment to improve quality of service and potentially lead to clearer upskilling pathways to springboard these workers to a higher skill segment of the labor force.…”