Opacity behind the wheel: The relationship between transparency of algorithmic management, justice perception, and intention to quit among truck drivers

Bujold, Antoine; Parent‐Rocheleau, Xavier; Gaudet, Marie‐Claude

doi:10.1016/j.chbr.2022.100245

Cited by 17 publications

(17 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This distinction could generate a difference in workers' perceptions of exposure to AM systems. Although a recently published study among traditional truck drivers reports high perception of exposure to algorithmic monitoring and PR (Bujold et al, 2022), more research is needed to assess the validity and applicability of the AMQ across a broad range context beyond gig work.…”

Section: Discussionmentioning

confidence: 99%

Creation of the algorithmic management questionnaire: A six‐phase scale development process

Parent‐Rocheleau

Parker

Bujold

et al. 2023

Human Resource Management

Self Cite

View full text Add to dashboard Cite

There is an increasing body of research on algorithmic management (AM), but the field lacks measurement tools to capture workers' experiences of this phenomenon. Based on existing literature, we developed and validated the algorithmic management questionnaire (AMQ) to measure the perceptions of workers regarding their level of exposure to AM. Across three samples (overall n = 1332 gig workers), we show the content, factorial, discriminant, convergent, and predictive validity of the scale. The final 20‐item scale assesses workers' perceived level of exposure to algorithmic: monitoring, goal setting, scheduling, performance rating, and compensation. These dimensions formed a higher order construct assessing overall exposure to algorithmic management, which was found to be, as expected, negatively related to the work characteristics of job autonomy and job complexity and, indirectly, to work engagement. Supplementary analyses revealed that perceptions of exposure to AM reflect the objective presence of AM dimensions beyond individual variations in exposure. Overall, the results suggest the suitability of the AMQ to assess workers' perceived exposure to algorithmic management, which paves the way for further research on the impacts of these rapidly accelerating systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Creation of the algorithmic management questionnaire: A six‐phase scale development process

Parent‐Rocheleau

Parker

Bujold

et al. 2023

Human Resource Management

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, in line with the argument that it is time to study algorithmic management beyond the platform economy, Jarrahi et al (2020) opt for a distinction between platformic management and algorithmic management. Algorithmic management also appears increasingly often in more traditional work settings outside the platform economy (Benlian et al, 2022; Bujold et al, 2022; Jarrahi et al, 2020). Using Uber as a key example here might undermine the differences to be found between platformic and non‐platformic forms of algorithmic management.…”

Section: From Assumptions To Blind Spots and Avenues For Future Researchmentioning

confidence: 99%

Blinded by “algo economicus”: Reflecting on the assumptions of algorithmic management research to move forward

Lamers,

Meijerink,

Rettagliata

2024

Human Resource Management

View full text Add to dashboard Cite

This paper reflects on the paradigmatic assumptions and ideologies that have shaped algorithmic management research. We identify two sets of assumptions: one about the “ontology of algorithms” (which holds that human resource management [HRM] algorithms are non‐human entities with material agency) and one about the “ontology of management” that HRM algorithms afford (which understands algorithmic management as a form of control for maximizing economic/shareholder value). We explain how these core assumptions underpin existing research of HRM algorithms, causing blind spots that hinder new ways of understanding and studying algorithmic management. After identifying and unpacking the assumptions and blind spots, we offer avenues to overcome these blind spots, allowing for future research based on new ideological assumption grounds that will help move algorithmic management scholarship further in significant ways.

show abstract

“…Second, justice theory theoretically connects AI credibility and consumer-AI experiences. For example, perceived AI credibility is associated with higher perceived justice with AI (Bujold et al. , 2022) which in turn can lead to positive experiences, such as feeling a bond (Kim and Kim, 2018).…”

Section: Theoretical Foundationsmentioning

confidence: 99%

“…ad recommendations) and, thus, higher distributive justice. Other dimensions of AI credibility, such as AI transparency (a sub-dimension of AI credibility), are positively related to feeling higher distributed justice with AI (Bujold et al. , 2022) in AI-based management of truck driver's performance.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

“…Our qualitative study suggests that AI's transparency can also improve procedural justice as it can provide consumers with an enhanced understanding of AI's processes. For example, transparency of AI-based surveillance of truck drivers improved their understanding of processes used in surveillance, resulting in higher procedural justice (Bujold et al. , 2022).…”

Section: Conceptual Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

AI credibility and consumer-AI experiences: a conceptual framework

Khan,

Mishra

2023

JSTP

View full text Add to dashboard Cite

PurposeThis study aims to conceptualize the relationship of perceived artificial intelligence (AI) credibility with consumer-AI experiences. With the widespread deployment of AI in marketing and services, consumer-AI experiences are common and an emerging research area in marketing. Various factors affecting consumer-AI experiences have been studied, but one crucial factor – perceived AI credibility is relatively underexplored which the authors aim to envision and conceptualize.Design/methodology/approachThis study employs a conceptual development approach to propose relationships among constructs, supported by 34 semi-structured consumer interviews.FindingsThis study defines AI credibility using source credibility theory (SCT). The conceptual framework of this study shows how perceived AI credibility positively affects four consumer-AI experiences: (1) data capture, (2) classification, (3) delegation, and (4) social interaction. Perceived justice is proposed to mediate this effect. Improved consumer-AI experiences can elicit favorable consumer outcomes toward AI-enabled offerings, such as the intention to share data, follow recommendations, delegate tasks, and interact more. Individual and contextual moderators limit the positive effect of perceived AI credibility on consumer-AI experiences.Research limitations/implicationsThis study contributes to the emerging research on AI credibility and consumer-AI experiences that may improve consumer-AI experiences. This study offers a comprehensive model with consequences, mechanism, and moderators to guide future research.Practical implicationsThe authors guide marketers with ways to improve the four consumer-AI experiences by enhancing consumers' perceived AI credibility.Originality/valueThis study uses SCT to define AI credibility and takes a justice theory perspective to develop the conceptual framework.

show abstract

Opacity behind the wheel: The relationship between transparency of algorithmic management, justice perception, and intention to quit among truck drivers

Cited by 17 publications

References 92 publications

Creation of the algorithmic management questionnaire: A six‐phase scale development process

Creation of the algorithmic management questionnaire: A six‐phase scale development process

Blinded by “algo economicus”: Reflecting on the assumptions of algorithmic management research to move forward

AI credibility and consumer-AI experiences: a conceptual framework

Contact Info

Product

Resources

About