Methodological and Demographic Variation in Estimates of Economic Dependence Across Two Types of Gig Work

Brawley Newlin, Alice

doi:10.1007/s41542-023-00168-6

Cited by 5 publications

(2 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent studies have attempted to address these challenges using online surveys with nonprobability sampling methods (Brawley Newlin, 2024;Churchill & Craig, 2019;Hong et al, 2020;Maffie, 2024;Norlander et al, 2023;Wang et al, 2022;Wiener et al, 2023;Wood et al, 2019). Nevertheless, the survey method falls short in capturing workers' spontaneous perspectives because survey participants can only assess their experiences and attitudes retrospectively.…”

Section: Platform Workers' Voicesmentioning

confidence: 99%

“…While recent studies have employed online surveys with non-probability sampling methods to overcome these challenges (Brawley Newlin, 2024;Churchill & Craig, 2019;Hong et al, 2020;Maffie, 2024;Norlander et al, 2023;Wang et al, 2022;Wiener et al, 2023;Wood et al, 2019), these studies fall short of capturing workers' spontaneous perspectives as surveys are usually limited to retrospective assessments.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

What Do Platform Workers Think?: Analysis of Truck Drivers’ Perspectives on Digital Platforms Using GPT-4

Song,

Jia

2024

Preprint

View full text Add to dashboard Cite

This study examines platform workers’ experiences and perspectives about digital platforms in the Chinese freight transportation industry based on a large dataset of self-employed truck drivers’ online discussions. Utilizing the state-of-the-art large language model (LLM), GPT-4 by OpenAI, this article creates a novel topic generation framework, Sequential Abstraction, to analyze the topics of concern among truck drivers. Additionally, this study employs GPT-4 to perform sentiment analysis and topic classification tasks, which achieve accuracy rates of 78.13% and 79.17%, respectively, with manually labeled results as benchmarks. The research results highlight the significant economic insecurities Chinese truck drivers face when finding gigs on digital platforms. This study contributes to existing platform work literature by examining workers’ real-time, spontaneous voices surrounding digital platforms. This study advocates for further uncovering the potential of LLMs in facilitating descriptive and explanatory social science research.

show abstract

Section: Platform Workers' Voicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What Do Platform Workers Think?: Analysis of Truck Drivers’ Perspectives on Digital Platforms Using GPT-4

Song,

Jia

2024

Preprint

View full text Add to dashboard Cite

show abstract

Diversity and emotional labor in the gig economy

Ajaiyeoba

2024

EDI

View full text Add to dashboard Cite

PurposeThis paper aims to explore and conceptualize the emotional labor of racial minorities and women in the gig economy.Design/methodology/approachThe paper draws on current research on gig workers' engagement in emotional labor and accounts of unfair practices in the gig economy. It calls out the presence of bias in algorithmic systems used within the gig economy as a means of control and gig worker management. The negative impact of such bias on a specific gig worker demographic is theorized.FindingsThrough a review of research on the nature of work in the gig economy, the paper suggests an unspoken mandate exists for gig workers to engage in emotional labor practices. Specifically, the nature of gig work in terms of algorithmic control, coupled with gig work dependence, often creates a need for gig workers to engage in emotional labor. This need is heightened for racial and ethnic minorities and women gig workers.Research limitations/implicationsAs gig platforms increasingly rely on algorithms for management and gig workers face biases, this paper offers valuable insights that contribute to a comprehensive understanding of the gig economy's impact on worker well-being. The paper discusses the need for more profound research into the nature of work in the gig economy and the life experiences of gig workers.Practical implicationsThis paper has significant implications for organizations and gig workers. Understanding the relationships proposed in this paper can inform organizational strategies to enhance the well-being of gig workers and improve their overall work experiences, potentially leading to increased satisfaction and reduced turnover. Additionally, the paper recommends strategies gig workers can utilize to achieve positive outcomes.Originality/valueThe evolving work landscape, driven by the gig economy, necessitates a thorough understanding of new work dynamics. This paper sheds light on the often-overlooked diversity within the gig workforce, mainly focusing on the unique experiences of women and racial and ethnic minorities.

show abstract

Casually cynical or trapped? Exploring gig workers’ reactions to psychological contract violation

Saksida,

Maffie,

Mihelič

et al. 2024

JMP

View full text Add to dashboard Cite

PurposeDrawing on psychological contract (PC) theory and platform labor research, the purpose of our study was to explore gig workers’ reactions to perceived PC violation. Our study was set against the backdrop of the COVID-19 pandemic, which brought workplace health and safety issues into much sharper focus, even in nonstandard employment arrangements like gig work.Design/methodology/approachThis study employed a mixed-methods design. In Study 1, we tested a conceptual model of US-based ride-hail drivers’ (n = 202) affective and cognitive reactions to Uber’s (lack of) commitment to safe working conditions. In Study 2, we conducted interviews with 32 platform workers to further explore an unexpected finding from Study 1.FindingsIn Study 1, we found that drivers’ perceptions of PC violation were related to decreased trust in Uber and higher intentions to leave this line of work; however, cynicism toward Uber only predicted withdrawal intentions for those drivers who did not believe that they had job alternatives available outside of gig work. We explored this further in Study 2, where we found that workers with low economic dependence on gig work could afford to be casually cynical toward the platform, while high-dependence workers felt “trapped” in this line of work.Originality/valueWe contribute to the social/relational theoretical approach to gig work more broadly and to the literature on PC in platform work more specifically. We also add to the emerging literature on how economic dependence shapes workers’ experience of platform work. Our findings around low-dependence gig workers experiencing a more indifferent form of cynicism – which we have termed casual cynicism – highlight the importance of treating the context of gig work as unique, not merely an extension of traditional management research.

show abstract

Methodological and Demographic Variation in Estimates of Economic Dependence Across Two Types of Gig Work

Cited by 5 publications

References 69 publications

What Do Platform Workers Think?: Analysis of Truck Drivers’ Perspectives on Digital Platforms Using GPT-4

What Do Platform Workers Think?: Analysis of Truck Drivers’ Perspectives on Digital Platforms Using GPT-4

Diversity and emotional labor in the gig economy

Casually cynical or trapped? Exploring gig workers’ reactions to psychological contract violation

Contact Info

Product

Resources

About