Friday 24 July 09:50
- 11:20
Hall: 01 - Basilica
Chair:
González-Romá Vicente
Co-Chair:
Le Blanc Pascale
Discussant:
Grote Gudela
Division: Division 1: Work and Organizational Psychology
Artificial intelligence (AI) is changing the nature of work at a high speed (Bankins et al., 2024; Bick et al.
2024). AI is expected to affect 60% of jobs in advanced economies and 40% of jobs globally in the next two
years (Reuters, 2024). Considering its capabilities, its relative ease of use and accessibility, besides its
growing application in organizations, there is broad agreement in that AI is changing people's work
(Bankins et al., 2024; Parent-Rocheleau & Parker, 2022; Pereira et al., 2023).
What is less clear is how and what work aspects are affected. Previous research on some AI-based
developments (i.e., Algorithm Management) suggests that AI might have functional and dysfunctional
consequences (Ma et al., 2024; see Parent-Rocheleau & Parker, 2022). Due to the novelty of the subject,
there is a shortage of empirical studies addressing these issues, and consequently, a scarcity of evidencebased knowledge.
To address this problem, the four studies comprised of this symposium will analyze some of the influences
that AI has on several important work aspects. First, Arriagada and colleagues examine the influence of
Generative AI (GenAI) use at work on the five work characteristics considered in the SMART model (Parker
& Knight, 2023) and its indirect effect on work meaningfulness. The preliminary results obtained suggest
both functional and dysfunctional influences. Second, Vahlkamp & González-Romá's study investigates
whether GenAI use at work is a resource for work engagement. They examine the indirect effect of GenAI
use on work engagement via employee work ability. Third, Kirchner and Jensen investigate the influence of
AI on managerial work.Theirfindingsbased on interviews with managers suggest both opportunitiesand
challenges. Fourth, Maton et al., in two empirical studies, analyze how human explanations improve AI
augmented decision-making performance.Theyproposethatbiasesbasedonintuitiveandautomatic
thinkingleadtoover-relianceorunjustified rejection of AI advice,andthatdeliberateandreflectivethinking
supports appropriate reliance.Finally,GudelaGrotewilldiscussthestudies'findingsandcontributions.
