Paper Type
Short
Paper Number
1493
Description
Algorithmic systems in digital workplaces enhance business performance and efficiency but may also impede human autonomy, creating a paradox of algorithm appreciation and algorithm aversion. This paradox, which we term 'algorithmic duality,' challenges organisations in balancing autonomy effectively. To explore this duality, we propose a algorithmic duality research model based on previous research into human responses to algorithmic systems and psychological needs theories. We tested the algorithmic duality model with data from users of an AI-enabled conversational assistant, confirming a quadratic relationship between autonomy and satisfaction, moderated by users' competence with these systems. Our findings highlight implications for designing algorithmic systems and for organisations in choosing appropriate autonomy configurations and training their users. Our next research phase will extend the algorithmic duality model to include job outcomes. This research contributes to understanding the complex dynamics of algorithmic interaction in the workplace.
Recommended Citation
Nguyen, Lemai; Saundage, Dilal; Xiong, Bingqing; Ngo, Leanne; and Sharma, Rajeev, "Algorithmic Duality and its Effects on User Satisfaction" (2024). PACIS 2024 Proceedings. 4.
https://aisel.aisnet.org/pacis2024/track13_hcinteract/track13_hcinteract/4
Algorithmic Duality and its Effects on User Satisfaction
Algorithmic systems in digital workplaces enhance business performance and efficiency but may also impede human autonomy, creating a paradox of algorithm appreciation and algorithm aversion. This paradox, which we term 'algorithmic duality,' challenges organisations in balancing autonomy effectively. To explore this duality, we propose a algorithmic duality research model based on previous research into human responses to algorithmic systems and psychological needs theories. We tested the algorithmic duality model with data from users of an AI-enabled conversational assistant, confirming a quadratic relationship between autonomy and satisfaction, moderated by users' competence with these systems. Our findings highlight implications for designing algorithmic systems and for organisations in choosing appropriate autonomy configurations and training their users. Our next research phase will extend the algorithmic duality model to include job outcomes. This research contributes to understanding the complex dynamics of algorithmic interaction in the workplace.
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