Recommendation Agent Adoption: How Recommendation Presentation Influences Employees’ Perceptions, Behaviors, and Decision Quality
The purpose of this paper is to report the results of a laboratory experiment that investigated how assortment planners’ perceptions, usage behavior, and decision quality are influenced by the way recommendations of an artificial intelligence (AI)-based recommendation agent (RA) are presen...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/20/4244 |
| _version_ | 1818201034122592256 |
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| author | Émilie Bigras Pierre-Majorique Léger Sylvain Sénécal |
| author_facet | Émilie Bigras Pierre-Majorique Léger Sylvain Sénécal |
| author_sort | Émilie Bigras |
| collection | DOAJ |
| description | The purpose of this paper is to report the results of a laboratory experiment that investigated how assortment planners’ perceptions, usage behavior, and decision quality are influenced by the way recommendations of an artificial intelligence (AI)-based recommendation agent (RA) are presented. A within-subject laboratory experiment was conducted with twenty subjects. Participants perceptions and usage behavior toward an RA while making decisions were assessed using validated measurement scales and eye-tracking technology. The results of this study show the importance of a transparent RA demanding less cognitive effort to understand and access the explanations of a transparent RA on assortment planners’ perceptions (i.e., source credibility, sense of control, decision quality, and satisfaction), usage behavior, and decision quality. Results from this study suggest that designing RAs with more transparency for the users bring perceptual and attitudinal benefits that influence both the adoption and continuous use of those systems by employees. This study contributes to filling the literature gap on RAs in organizational contexts, thus advancing knowledge in the human−computer interaction literature. The findings of this study provide guidelines for RA developers and user experience (UX) designers on how to best create and present an AI-based RA to employees. |
| first_indexed | 2024-12-12T02:47:08Z |
| format | Article |
| id | doaj.art-ff849b95269e47ddbaa9883fca70cf19 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-12T02:47:08Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ff849b95269e47ddbaa9883fca70cf192022-12-22T00:41:01ZengMDPI AGApplied Sciences2076-34172019-10-01920424410.3390/app9204244app9204244Recommendation Agent Adoption: How Recommendation Presentation Influences Employees’ Perceptions, Behaviors, and Decision QualityÉmilie Bigras0Pierre-Majorique Léger1Sylvain Sénécal2Tech3Lab, HEC, Montréal, QC H3T 2A7, CanadaTech3Lab, HEC, Montréal, QC H3T 2A7, CanadaTech3Lab, HEC, Montréal, QC H3T 2A7, CanadaThe purpose of this paper is to report the results of a laboratory experiment that investigated how assortment planners’ perceptions, usage behavior, and decision quality are influenced by the way recommendations of an artificial intelligence (AI)-based recommendation agent (RA) are presented. A within-subject laboratory experiment was conducted with twenty subjects. Participants perceptions and usage behavior toward an RA while making decisions were assessed using validated measurement scales and eye-tracking technology. The results of this study show the importance of a transparent RA demanding less cognitive effort to understand and access the explanations of a transparent RA on assortment planners’ perceptions (i.e., source credibility, sense of control, decision quality, and satisfaction), usage behavior, and decision quality. Results from this study suggest that designing RAs with more transparency for the users bring perceptual and attitudinal benefits that influence both the adoption and continuous use of those systems by employees. This study contributes to filling the literature gap on RAs in organizational contexts, thus advancing knowledge in the human−computer interaction literature. The findings of this study provide guidelines for RA developers and user experience (UX) designers on how to best create and present an AI-based RA to employees.https://www.mdpi.com/2076-3417/9/20/4244recommendation agentartificial intelligencedecision-makingtransparencycognitive effortperceptionbehaviordecision qualityeye tracking |
| spellingShingle | Émilie Bigras Pierre-Majorique Léger Sylvain Sénécal Recommendation Agent Adoption: How Recommendation Presentation Influences Employees’ Perceptions, Behaviors, and Decision Quality Applied Sciences recommendation agent artificial intelligence decision-making transparency cognitive effort perception behavior decision quality eye tracking |
| title | Recommendation Agent Adoption: How Recommendation Presentation Influences Employees’ Perceptions, Behaviors, and Decision Quality |
| title_full | Recommendation Agent Adoption: How Recommendation Presentation Influences Employees’ Perceptions, Behaviors, and Decision Quality |
| title_fullStr | Recommendation Agent Adoption: How Recommendation Presentation Influences Employees’ Perceptions, Behaviors, and Decision Quality |
| title_full_unstemmed | Recommendation Agent Adoption: How Recommendation Presentation Influences Employees’ Perceptions, Behaviors, and Decision Quality |
| title_short | Recommendation Agent Adoption: How Recommendation Presentation Influences Employees’ Perceptions, Behaviors, and Decision Quality |
| title_sort | recommendation agent adoption how recommendation presentation influences employees perceptions behaviors and decision quality |
| topic | recommendation agent artificial intelligence decision-making transparency cognitive effort perception behavior decision quality eye tracking |
| url | https://www.mdpi.com/2076-3417/9/20/4244 |
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