Search

The gig economy and gig work have grown quickly in recent years and have drawn much attention from researchers in different fields. Because the platform mediated gig economy is a relatively new phenomenon, studies have produced a range of interesting findings; of interest here are the socio‐technical issues that this work has surfaced. This systematic literature review (SLR) provides a snapshot of a range of socio‐technical issues raised in the last 12 years of literature focused on the platform mediated gig economy. Based on a sample of 515 papers gathered from nine databases in multiple disciplines, 132 were coded that specifically studied the gig economy, gig work, and gig workers. Three main socio‐technical themes were identified: (1) the digital workplace, which includes information infrastructure and digital labor that are related to the nature of gig work and the user agency; (2) algorithmic management, which includes platform governance, performance management, information asymmetry, power asymmetry, and system manipulation, relying on a diverse set of technological tools including algorithms and big data analytics; (3) ethical design, as a relevant value set that gig workers expect from the platform, which includes trust, fairness, equality, privacy, and transparency. A social informatics perspective is used to rethink the relationship between gig workers and platforms, extract the socio‐technical issues noted in prior research, and discuss the underexplored aspects of the platform mediated gig economy. The results draw attention to understudied yet critically important socio‐technical issues in the gig economy that suggest short‐ and long‐term opportunities for future research directions. [ABSTRACT FROM AUTHOR]

Like information science more broadly, search engine research has largely been fragmented into two factions: system‐oriented and user‐oriented studies. This limits our capacity for answering some fundamental questions surrounding an integral—often invisible—part of modern life. Given the "search‐ification" of this life, given an oligopolous global market and an information‐wealthy but attention‐poor audience, methods capable of studying search engines, as well as their relationship with users and society are increasingly necessary. This paper proposes critical discourse analysis (CDA) as an effectual, oft‐overlooked method for search engine research, one capable of interrogating both search engines and their use. The paper outlines CDA, provides examples of its application, and highlights its capacity for progressing our critical understanding of search engines. This developing understanding, evidenced by a review of the literature, suggests that challenges brought by search cannot be resolved by critiquing the power of systems alone. Rather, a reclaiming of today's information infrastructure requires we also illuminate the socio‐political environments of search systems, and the metacognitive, invisible processes pivotal to our communication with them. While power‐analyses of search continue, and some have begun to employ CDA, little recognition exists of this theoretical perspective's capacity for supporting both system‐oriented and user‐oriented studies. [ABSTRACT FROM AUTHOR]

With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps. [ABSTRACT FROM AUTHOR]