Technology-Mediated Control Legitimacy in the Gig Economy: Conceptualization and Nomological Network

Wiener, Martin; Cram, W. Alec; Benlian, Alexander

doi:10.1007/978-3-030-45819-5_16

Cited by 11 publications

(18 citation statements)

References 65 publications

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“…Consistent with previous studies in IS and strategic management research we refer to digital platforms as infrastructure that mediates interactions between complementors and end-users [17,18,37]. Complementors, as the focus of our research, are external parties that supply complements to the platform ecosystem, but are not directly related to the platform provider [55]. End-users, on the other hand, are individuals that use complements available in the platform ecosystem [41].…”

Section: Theoretical Background 21 Digital Platformsmentioning

confidence: 82%

The Effects of Control Mechanisms on Complementors’ Behavioral Intentions: An Empirical Study of Reward-Based Crowdfunding Platforms

Croitor¹,

Werner²,

Benlian³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Although Information Systems research has been increasingly exploring the role of control mechanisms on digital platforms, empirical research on the effects of control mechanisms on complementors' behavioral intentions in platform ecosystems is sparse. Control mechanisms refer to measures employed by platform providers to influence desirable behaviors of complementors and thus to manage dynamics, growth, and evolution of their digital platforms. Drawing on IS control literature and goal attainment theory, we conducted an online survey with 116 complementors from two major reward-based crowdfunding platforms, Kickstarter and Indiegogo. Our findings reveal that input control (self control) decreases (increases) complementors' intention to stay on their respective digital platform. Furthermore, we shed light on the role of complementors' perceived effort, perceived usefulness and satisfaction in shaping these relationships. Thus, our findings contribute to the literature on digital platforms in general and control mechanisms in particular.

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Section: Theoretical Background 21 Digital Platformsmentioning

confidence: 82%

The Effects of Control Mechanisms on Complementors’ Behavioral Intentions: An Empirical Study of Reward-Based Crowdfunding Platforms

Croitor¹,

Werner²,

Benlian³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…However, the role of algorithms and digital technology within managerial control processes has been largely neglected in prior control research (Cram & Wiener, 2020;Saunders et al, 2020;Schafheitle et al, 2020). This is in stark contrast to recent developments in practice, where organisations are increasingly leveraging technological advances to support managerial control (e.g., by acting as a monitoring tool for human controllers), or to fully automate control processes by acting as a proxy for human controllers (Cram & Wiener, 2020); see Figure 1.…”

Section: Algorithmic Controlmentioning

confidence: 99%

“…Control has been portrayed as "management's most fundamental problem" (Cardinal et al, 2017, p. 559). In this regard, algorithmic control (AC) -broadly defined as the managerial use of intelligent algorithms and advanced digital technology as a means to align worker behaviours with organisational objectives (Cram & Wiener, 2020;Kellogg et al, 2020) -represents an emerging phenomenon. For example, a recent New York Times article predicts that using "algorithms to manage workers will not simply be a niche phenomenon [but] may become one of the most common ways of managing the American labor force" (Scheiber, 2017).…”

Section: Introductionmentioning

confidence: 99%

Algorithmic control and gig workers: a legitimacy perspective of Uber drivers

Wiener

Cram

Benlian

2021

European Journal of Information Systems

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Organisations increasingly rely on algorithms to exert automated managerial control over workers, referred to as algorithmic control (AC). The use of AC is already commonplace with platform-based work in the gig economy, where independent workers are paid for completing a given task (or "gig"). The combination of independent work alongside intensive managerial monitoring and guidance via AC raises questions about how gig workers perceive AC practices and judge their legitimacy, which could help explain critical worker behaviours such as turnover and non-compliance. Based on a three-dimensional conceptualisation of micro-level legitimacy tailored to the gig work context (autonomy, fairness, and privacy), we develop a research model that links workers' perceptions of two predominant forms of AC (gatekeeping and guiding) to their legitimacy judgements and behavioural reactions. Using survey data from 621 Uber drivers, we find empirical support for the central role of micro-level legitimacy judgements in mediating the relationships between gig workers' perceptions of different AC forms and their continuance intention and workaround use. Contrasting prior work, our study results show that workers do not perceive AC as a universally "bad thing" and that guiding AC is in fact positively related to micro-level legitimacy judgements. Theoretical and practical implications are discussed.

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“…To understand more about how the platforms work in the gig economy, it is essential to look at the platform model by Constantiou characterized by the control exerted by platform owners and the rivalry among platform participants (Constantiou et al, 2017;Wiener, Cram, & Benlian, 2019). Based on these two dimensions, Constantiou et al (2017) differentiates among four types of platforms as seen in Figure 2: 1) Gardeners, which focus on offering an infrastructure for a community to use while setting only minimum standards for platform participation and fostering low rivalry among participants; 2) Chaperones, characterized by a low standard created by platform owners and intense rivalry among its participants; 3) Franchisers, which features ride-hailing platforms such as Uber, which exercise tight control and promote intense rivalry among drivers; 4) Principals, which exercise tight control and low rivalry among its participants to mitigate risk and maintain costs.…”

Section: Figure 1 Narrow and Broader Characteristics Of Thementioning

confidence: 99%

“…Figure 2 Typology of Gig Economy Platforms (Constantiou et al, 2017;Wiener et al, 2019) Another different but similar model by Kalleberg & Dunn (2016) is used to control and wages for the primary dimension to determine job quality types. The control dimension is different from the Constantine model.…”

Section: Figure 1 Narrow and Broader Characteristics Of Thementioning

confidence: 99%

Regulatory Developments in the Gig Economy: A Literature Review

Wardhana

Herlina²,

Bangsawan³

et al. 2020

The Winners

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The emergence of the gig economy and its rapid growth was anticipated to play a big part in its economy. Despite the enormous benefits, the gig economy business model had also attracted numerous issues in many countries and regions. The research utilized a Systematic Literature Review (SLR) methodology by Snyder for analyzing regulation issues in the gig economy, which was divided into six steps, those were defining the central question, determining databases, using search string to find relevant keywords, extracting data, filtering data, and analyzing the findings to answer to the main question. The SLR results show licensing and misclassifications are the most dominant factors in gig economy, while regulatory issues such as safety, tax, externalities, wage, benefit, privacy, and discrimination area are other factors in it. The most popular platform types that attract regulators are work on demand, asset rental, and crowd-work.

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Technology-Mediated Control Legitimacy in the Gig Economy: Conceptualization and Nomological Network

Cited by 11 publications

References 65 publications

The Effects of Control Mechanisms on Complementors’ Behavioral Intentions: An Empirical Study of Reward-Based Crowdfunding Platforms

The Effects of Control Mechanisms on Complementors’ Behavioral Intentions: An Empirical Study of Reward-Based Crowdfunding Platforms

Algorithmic control and gig workers: a legitimacy perspective of Uber drivers

Regulatory Developments in the Gig Economy: A Literature Review

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