Trust in the machine: How contextual factors and personality traits shape algorithm aversion and collaboration
This paper studies how contextual factors and personal variables influence the delegation of decisions to an algorithm. Using a multi-armed bandit task, we conducted an experiment with four treatments – baseline, explanation, payment, and automation – where participants repeatedly chose between maki...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Computers in Human Behavior Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2451958824002112 |
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| author | Vinícius Ferraz Leon Houf Thomas Pitz Christiane Schwieren Jörn Sickmann |
| author_facet | Vinícius Ferraz Leon Houf Thomas Pitz Christiane Schwieren Jörn Sickmann |
| author_sort | Vinícius Ferraz |
| collection | DOAJ |
| description | This paper studies how contextual factors and personal variables influence the delegation of decisions to an algorithm. Using a multi-armed bandit task, we conducted an experiment with four treatments – baseline, explanation, payment, and automation – where participants repeatedly chose between making decisions themselves or delegating to an algorithm under uncertainty. We evaluated the impact of Big Five personality traits, locus of control, generalized trust, and demographics alongside the treatment effects using statistical analyses and machine learning models, including Random Forest Classifiers for delegation behavior and Uplift Random Forests for causal effects. Results show that payment reduces delegation, whereas full automation increases it. Age, extraversion, neuroticism, generalized trust, and internal locus of control significantly and consistently influenced delegation decisions across both predictive and causal analyses. Additionally, female participants reacted more strongly to algorithm errors. Increased delegation rates improved algorithm accuracy. These findings provide new insights into the roles of contextual conditions, personal variables, and gender in shaping algorithm aversion and utilization, offering practical implications for designing user-centric AI systems. |
| format | Article |
| id | doaj-art-aa5ade23f11b447d9fbcbbfd9b58c84c |
| institution | OA Journals |
| issn | 2451-9588 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers in Human Behavior Reports |
| spelling | doaj-art-aa5ade23f11b447d9fbcbbfd9b58c84c2025-08-20T02:04:18ZengElsevierComputers in Human Behavior Reports2451-95882025-03-011710057810.1016/j.chbr.2024.100578Trust in the machine: How contextual factors and personality traits shape algorithm aversion and collaborationVinícius Ferraz0Leon Houf1Thomas Pitz2Christiane Schwieren3Jörn Sickmann4Alfred Weber Institute for Economics, Heidelberg University, Bergheimer Str. 58, Heidelberg, 69115, Germany; Corresponding author.Alfred Weber Institute for Economics, Heidelberg University, Bergheimer Str. 58, Heidelberg, 69115, GermanyFaculty of Society and Economics, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, Kleve, 47533, GermanyAlfred Weber Institute for Economics, Heidelberg University, Bergheimer Str. 58, Heidelberg, 69115, GermanyFaculty of Society and Economics, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, Kleve, 47533, GermanyThis paper studies how contextual factors and personal variables influence the delegation of decisions to an algorithm. Using a multi-armed bandit task, we conducted an experiment with four treatments – baseline, explanation, payment, and automation – where participants repeatedly chose between making decisions themselves or delegating to an algorithm under uncertainty. We evaluated the impact of Big Five personality traits, locus of control, generalized trust, and demographics alongside the treatment effects using statistical analyses and machine learning models, including Random Forest Classifiers for delegation behavior and Uplift Random Forests for causal effects. Results show that payment reduces delegation, whereas full automation increases it. Age, extraversion, neuroticism, generalized trust, and internal locus of control significantly and consistently influenced delegation decisions across both predictive and causal analyses. Additionally, female participants reacted more strongly to algorithm errors. Increased delegation rates improved algorithm accuracy. These findings provide new insights into the roles of contextual conditions, personal variables, and gender in shaping algorithm aversion and utilization, offering practical implications for designing user-centric AI systems.http://www.sciencedirect.com/science/article/pii/S2451958824002112Algorithm aversionHuman–computer interactionDecision behaviorMachine learningCausal inference |
| spellingShingle | Vinícius Ferraz Leon Houf Thomas Pitz Christiane Schwieren Jörn Sickmann Trust in the machine: How contextual factors and personality traits shape algorithm aversion and collaboration Computers in Human Behavior Reports Algorithm aversion Human–computer interaction Decision behavior Machine learning Causal inference |
| title | Trust in the machine: How contextual factors and personality traits shape algorithm aversion and collaboration |
| title_full | Trust in the machine: How contextual factors and personality traits shape algorithm aversion and collaboration |
| title_fullStr | Trust in the machine: How contextual factors and personality traits shape algorithm aversion and collaboration |
| title_full_unstemmed | Trust in the machine: How contextual factors and personality traits shape algorithm aversion and collaboration |
| title_short | Trust in the machine: How contextual factors and personality traits shape algorithm aversion and collaboration |
| title_sort | trust in the machine how contextual factors and personality traits shape algorithm aversion and collaboration |
| topic | Algorithm aversion Human–computer interaction Decision behavior Machine learning Causal inference |
| url | http://www.sciencedirect.com/science/article/pii/S2451958824002112 |
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