Paper Type
ERF
Abstract
In the context of digital labour platforms, algorithm sensemaking refers to the process in which platform workers interpret and understand the algorithms that manage them. Building on this, we introduce two ongoing studies in this paper. In Study 1, we develop and validate a new scale to measure platform workers’ algorithm sensemaking efforts, including enactment, selection, and retention. We suggest an item pool and introduce our plan to validate this scale. In Study 2, we examine how technological self-efficacy and perceived opacity of the platform’s algorithmic management jointly impact workers’ algorithm sensemaking motivation and their actual efforts. We expect this research to contribute to IS literature by offering a new measurement tool for people’s efforts to make sense of algorithmic management and by shedding light on factors that impact such efforts.
Paper Number
1927
Recommended Citation
Xu, Ji and Grant, Gerald G., "The impact of platform workers’ technological self-efficacy on their algorithm sensemaking efforts" (2025). AMCIS 2025 Proceedings. 17.
https://aisel.aisnet.org/amcis2025/sig_cnow/sig_cnow/17
The impact of platform workers’ technological self-efficacy on their algorithm sensemaking efforts
In the context of digital labour platforms, algorithm sensemaking refers to the process in which platform workers interpret and understand the algorithms that manage them. Building on this, we introduce two ongoing studies in this paper. In Study 1, we develop and validate a new scale to measure platform workers’ algorithm sensemaking efforts, including enactment, selection, and retention. We suggest an item pool and introduce our plan to validate this scale. In Study 2, we examine how technological self-efficacy and perceived opacity of the platform’s algorithmic management jointly impact workers’ algorithm sensemaking motivation and their actual efforts. We expect this research to contribute to IS literature by offering a new measurement tool for people’s efforts to make sense of algorithmic management and by shedding light on factors that impact such efforts.
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