The potential of artificial intelligence (AI) to improve decision making : investigating the reliance on AI advice in a business context
Main Author: | |
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Publication Date: | 2022 |
Format: | Master thesis |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | http://hdl.handle.net/10400.14/45875 |
Summary: | With the intention of overcoming human decision making biases, organizations are increasingly using AI as decision support. However, to unlock the full potential of AI-based advice to improve decision making, users must be willing to rely on it in the first place. To better understand people’s readiness to accept advice from AI, two experimental studies were conducted in the scope of this research. Study 1 examined whether people rely more on advice coming from AI or a human. People showed algorithm appreciation in both tasks – the performance evaluation of an employee and the closing price prediction of a stock. The effect was fully mediated by people’s trust in the source and varied across different levels of confidence in one’s own decision. Study 2 examined whether people also choose AI advice over human advice when presented with both options and whether they choose equally for themselves and for others. In this setting, algorithm appreciation persisted only for the stock price prediction task, irrespectively of who the decision was made for. Furthermore, several influencing factors were identified that point to domains where AI is most likely to be accepted and ways in which its benefits can be maximized. The results from these studies have clear implications for organizations that turn to Big Data and AI-generated advice to improve decision making, suggesting that AI might be a good addition to their daily operations. |
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The potential of artificial intelligence (AI) to improve decision making : investigating the reliance on AI advice in a business contextO potencial da inteligência artificial (IA) para melhorar a tomada de decisões : investigar a confiança no aconselhamento sobre IA num contexto empresarialArtificial intelligenceAI adviceStrategic decision makingReliance on adviceJAS paradigmTrustSelf-confidenceInteligência artificialAconselhamento artificialTomada de decisão estratégicaConfiança de aconselhamentoParadigma JASConfiançaAutoconfiançaWith the intention of overcoming human decision making biases, organizations are increasingly using AI as decision support. However, to unlock the full potential of AI-based advice to improve decision making, users must be willing to rely on it in the first place. To better understand people’s readiness to accept advice from AI, two experimental studies were conducted in the scope of this research. Study 1 examined whether people rely more on advice coming from AI or a human. People showed algorithm appreciation in both tasks – the performance evaluation of an employee and the closing price prediction of a stock. The effect was fully mediated by people’s trust in the source and varied across different levels of confidence in one’s own decision. Study 2 examined whether people also choose AI advice over human advice when presented with both options and whether they choose equally for themselves and for others. In this setting, algorithm appreciation persisted only for the stock price prediction task, irrespectively of who the decision was made for. Furthermore, several influencing factors were identified that point to domains where AI is most likely to be accepted and ways in which its benefits can be maximized. The results from these studies have clear implications for organizations that turn to Big Data and AI-generated advice to improve decision making, suggesting that AI might be a good addition to their daily operations.Almeida, Filipa deVeritatiFortuin, Eva Kim2023-01-272022-012025-07-25T00:00:00Z2023-01-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.14/45875urn:tid:203252918enginfo:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-13T14:04:10Zoai:repositorio.ucp.pt:10400.14/45875Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T02:01:53.126557Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
dc.title.none.fl_str_mv |
The potential of artificial intelligence (AI) to improve decision making : investigating the reliance on AI advice in a business context O potencial da inteligência artificial (IA) para melhorar a tomada de decisões : investigar a confiança no aconselhamento sobre IA num contexto empresarial |
title |
The potential of artificial intelligence (AI) to improve decision making : investigating the reliance on AI advice in a business context |
spellingShingle |
The potential of artificial intelligence (AI) to improve decision making : investigating the reliance on AI advice in a business context Fortuin, Eva Kim Artificial intelligence AI advice Strategic decision making Reliance on advice JAS paradigm Trust Self-confidence Inteligência artificial Aconselhamento artificial Tomada de decisão estratégica Confiança de aconselhamento Paradigma JAS Confiança Autoconfiança |
title_short |
The potential of artificial intelligence (AI) to improve decision making : investigating the reliance on AI advice in a business context |
title_full |
The potential of artificial intelligence (AI) to improve decision making : investigating the reliance on AI advice in a business context |
title_fullStr |
The potential of artificial intelligence (AI) to improve decision making : investigating the reliance on AI advice in a business context |
title_full_unstemmed |
The potential of artificial intelligence (AI) to improve decision making : investigating the reliance on AI advice in a business context |
title_sort |
The potential of artificial intelligence (AI) to improve decision making : investigating the reliance on AI advice in a business context |
author |
Fortuin, Eva Kim |
author_facet |
Fortuin, Eva Kim |
author_role |
author |
dc.contributor.none.fl_str_mv |
Almeida, Filipa de Veritati |
dc.contributor.author.fl_str_mv |
Fortuin, Eva Kim |
dc.subject.por.fl_str_mv |
Artificial intelligence AI advice Strategic decision making Reliance on advice JAS paradigm Trust Self-confidence Inteligência artificial Aconselhamento artificial Tomada de decisão estratégica Confiança de aconselhamento Paradigma JAS Confiança Autoconfiança |
topic |
Artificial intelligence AI advice Strategic decision making Reliance on advice JAS paradigm Trust Self-confidence Inteligência artificial Aconselhamento artificial Tomada de decisão estratégica Confiança de aconselhamento Paradigma JAS Confiança Autoconfiança |
description |
With the intention of overcoming human decision making biases, organizations are increasingly using AI as decision support. However, to unlock the full potential of AI-based advice to improve decision making, users must be willing to rely on it in the first place. To better understand people’s readiness to accept advice from AI, two experimental studies were conducted in the scope of this research. Study 1 examined whether people rely more on advice coming from AI or a human. People showed algorithm appreciation in both tasks – the performance evaluation of an employee and the closing price prediction of a stock. The effect was fully mediated by people’s trust in the source and varied across different levels of confidence in one’s own decision. Study 2 examined whether people also choose AI advice over human advice when presented with both options and whether they choose equally for themselves and for others. In this setting, algorithm appreciation persisted only for the stock price prediction task, irrespectively of who the decision was made for. Furthermore, several influencing factors were identified that point to domains where AI is most likely to be accepted and ways in which its benefits can be maximized. The results from these studies have clear implications for organizations that turn to Big Data and AI-generated advice to improve decision making, suggesting that AI might be a good addition to their daily operations. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01 2023-01-27 2023-01-27T00:00:00Z 2025-07-25T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.14/45875 urn:tid:203252918 |
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http://hdl.handle.net/10400.14/45875 |
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urn:tid:203252918 |
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eng |
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eng |
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embargoedAccess |
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application/pdf |
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