The impact of human-AI collaboration types on consumer evaluation and usage intention: a perspective of responsibility attribution
Despite the widespread availability of artificial intelligence (AI) products and services, consumer evaluations and adoption intentions have not met expectations. Existing research mainly focuses on AI’s instrumental attributes from the consumer perspective, along with negative impacts of AI failure...
Saved in:
| Published in: | Frontiers in psychology Vol. 14; p. 1277861 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Frontiers Media S.A
30.10.2023
|
| Subjects: | |
| ISSN: | 1664-1078, 1664-1078 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Despite the widespread availability of artificial intelligence (AI) products and services, consumer evaluations and adoption intentions have not met expectations. Existing research mainly focuses on AI’s instrumental attributes from the consumer perspective, along with negative impacts of AI failures on evaluations and willingness to use. However, research is lacking on AI as a collaborative agent, investigating the impact of human-AI collaboration on AI acceptance under different outcome expectations. This study examines the interactive effects of human-AI collaboration types (AI-dominant vs. AI-assisted) and outcome expectations (positive vs. negative) on AI product evaluations and usage willingness, along with the underlying mechanisms, from a human-AI relationship perspective. It also investigates the moderating role of algorithm transparency in these effects. Using three online experiments with analysis of variance and bootstrap methods, the study validates these interactive mechanisms, revealing the mediating role of attribution and moderating role of algorithm transparency. Experiment 1 confirms the interactive effects of human-AI collaboration types and outcome expectations on consumer evaluations and usage willingness. Under positive outcome expectations, consumers evaluate and express willingness to use AI-dominant intelligent vehicles with autonomous driving capabilities higher than those with emergency evasion capabilities (AI-assisted). However, under negative outcome expectations, consumers rate autonomous driving capabilities lower compared to emergency evasion capabilities. Experiment 2 examines the mediating role of attribution through ChatGPT’s dominant or assisting role under different outcome expectations. Experiment 3 uses a clinical decision-making system to study algorithm transparency’s moderating role, showing higher transparency improves evaluations and willingness to use AI products and services under negative outcome expectations. Theoretically, this study advances consumer behavior research by exploring the human-AI relationship within artificial intelligence, enhancing understanding of consumer acceptance variations. Practically, it offers insights for better integrating AI products and services into the market. |
|---|---|
| AbstractList | Despite the widespread availability of artificial intelligence (AI) products and services, consumer evaluations and adoption intentions have not met expectations. Existing research mainly focuses on AI’s instrumental attributes from the consumer perspective, along with negative impacts of AI failures on evaluations and willingness to use. However, research is lacking on AI as a collaborative agent, investigating the impact of human-AI collaboration on AI acceptance under different outcome expectations. This study examines the interactive effects of human-AI collaboration types (AI-dominant vs. AI-assisted) and outcome expectations (positive vs. negative) on AI product evaluations and usage willingness, along with the underlying mechanisms, from a human-AI relationship perspective. It also investigates the moderating role of algorithm transparency in these effects. Using three online experiments with analysis of variance and bootstrap methods, the study validates these interactive mechanisms, revealing the mediating role of attribution and moderating role of algorithm transparency. Experiment 1 confirms the interactive effects of human-AI collaboration types and outcome expectations on consumer evaluations and usage willingness. Under positive outcome expectations, consumers evaluate and express willingness to use AI-dominant intelligent vehicles with autonomous driving capabilities higher than those with emergency evasion capabilities (AI-assisted). However, under negative outcome expectations, consumers rate autonomous driving capabilities lower compared to emergency evasion capabilities. Experiment 2 examines the mediating role of attribution through ChatGPT’s dominant or assisting role under different outcome expectations. Experiment 3 uses a clinical decision-making system to study algorithm transparency’s moderating role, showing higher transparency improves evaluations and willingness to use AI products and services under negative outcome expectations. Theoretically, this study advances consumer behavior research by exploring the human-AI relationship within artificial intelligence, enhancing understanding of consumer acceptance variations. Practically, it offers insights for better integrating AI products and services into the market. Despite the widespread availability of artificial intelligence (AI) products and services, consumer evaluations and adoption intentions have not met expectations. Existing research mainly focuses on AI's instrumental attributes from the consumer perspective, along with negative impacts of AI failures on evaluations and willingness to use. However, research is lacking on AI as a collaborative agent, investigating the impact of human-AI collaboration on AI acceptance under different outcome expectations. This study examines the interactive effects of human-AI collaboration types (AI-dominant vs. AI-assisted) and outcome expectations (positive vs. negative) on AI product evaluations and usage willingness, along with the underlying mechanisms, from a human-AI relationship perspective. It also investigates the moderating role of algorithm transparency in these effects. Using three online experiments with analysis of variance and bootstrap methods, the study validates these interactive mechanisms, revealing the mediating role of attribution and moderating role of algorithm transparency. Experiment 1 confirms the interactive effects of human-AI collaboration types and outcome expectations on consumer evaluations and usage willingness. Under positive outcome expectations, consumers evaluate and express willingness to use AI-dominant intelligent vehicles with autonomous driving capabilities higher than those with emergency evasion capabilities (AI-assisted). However, under negative outcome expectations, consumers rate autonomous driving capabilities lower compared to emergency evasion capabilities. Experiment 2 examines the mediating role of attribution through ChatGPT's dominant or assisting role under different outcome expectations. Experiment 3 uses a clinical decision-making system to study algorithm transparency's moderating role, showing higher transparency improves evaluations and willingness to use AI products and services under negative outcome expectations. Theoretically, this study advances consumer behavior research by exploring the human-AI relationship within artificial intelligence, enhancing understanding of consumer acceptance variations. Practically, it offers insights for better integrating AI products and services into the market.Despite the widespread availability of artificial intelligence (AI) products and services, consumer evaluations and adoption intentions have not met expectations. Existing research mainly focuses on AI's instrumental attributes from the consumer perspective, along with negative impacts of AI failures on evaluations and willingness to use. However, research is lacking on AI as a collaborative agent, investigating the impact of human-AI collaboration on AI acceptance under different outcome expectations. This study examines the interactive effects of human-AI collaboration types (AI-dominant vs. AI-assisted) and outcome expectations (positive vs. negative) on AI product evaluations and usage willingness, along with the underlying mechanisms, from a human-AI relationship perspective. It also investigates the moderating role of algorithm transparency in these effects. Using three online experiments with analysis of variance and bootstrap methods, the study validates these interactive mechanisms, revealing the mediating role of attribution and moderating role of algorithm transparency. Experiment 1 confirms the interactive effects of human-AI collaboration types and outcome expectations on consumer evaluations and usage willingness. Under positive outcome expectations, consumers evaluate and express willingness to use AI-dominant intelligent vehicles with autonomous driving capabilities higher than those with emergency evasion capabilities (AI-assisted). However, under negative outcome expectations, consumers rate autonomous driving capabilities lower compared to emergency evasion capabilities. Experiment 2 examines the mediating role of attribution through ChatGPT's dominant or assisting role under different outcome expectations. Experiment 3 uses a clinical decision-making system to study algorithm transparency's moderating role, showing higher transparency improves evaluations and willingness to use AI products and services under negative outcome expectations. Theoretically, this study advances consumer behavior research by exploring the human-AI relationship within artificial intelligence, enhancing understanding of consumer acceptance variations. Practically, it offers insights for better integrating AI products and services into the market. |
| Author | Li, Hu Yue, Beibei |
| Author_xml | – sequence: 1 givenname: Beibei surname: Yue fullname: Yue, Beibei – sequence: 2 givenname: Hu surname: Li fullname: Li, Hu |
| BookMark | eNp9kUtr3DAUhU1JoWmaP9CVlt14IskaPbILoY-BQDbpWuhxNVGwLUeSA7PtL689k0DpItrocu85H5d7PjdnYxqhab4SvOk6qa7CVA77DcW02xAqhOTkQ3NOOGctwUKe_VN_ai5LecLLY5hiTM-bPw-PgOIwGVdRCuhxHszY3uyQS31vbMqmxjSiepigoKVwaSzzABnBi-nn09CMHs3F7BfOWGFce9fIoAlymcDV-AIrOUOZFnO0sY_1gEytOdp5FX9pPgbTF7h8_S-a3z--P9z-au_uf-5ub-5a1ylcW7rtlPBM-U4GRpwAprh1mNmAFZeWE8CSWe_ZVlDBzTK0VjkbuHPUSyu7i2Z34vpknvSU42DyQScT9bGR8l6bXKPrQcsOB6mCp9IrJsGa4AUBgQNnKjBBFta3E2vK6XmGUvUQi4PlZiOkuWgq1VZgQrerVJ6kLqdSMgTtYj1ermYTe02wXlPUxxT1mqJ-TXGx0v-sb1u_Y_oL0mSmmw |
| CitedBy_id | crossref_primary_10_1080_02650487_2025_2458996 crossref_primary_10_1080_10447318_2025_2520997 crossref_primary_10_1016_j_tele_2025_102304 crossref_primary_10_1080_10447318_2025_2482742 crossref_primary_10_1108_JRIM_03_2025_0135 crossref_primary_10_1108_MD_07_2024_1635 crossref_primary_10_1108_JOSM_05_2024_0223 crossref_primary_10_1108_JSM_10_2024_0539 crossref_primary_10_1109_ACCESS_2025_3567656 crossref_primary_10_1016_j_jbusres_2025_115276 crossref_primary_10_1108_TG_01_2025_0011 crossref_primary_10_1145_3710987 crossref_primary_10_3389_fpos_2025_1560180 crossref_primary_10_1108_TG_01_2025_0004 crossref_primary_10_1002_pa_70067 crossref_primary_10_1016_j_jretconser_2024_103761 crossref_primary_10_1080_19368623_2025_2532488 crossref_primary_10_3390_bs14121216 crossref_primary_10_1057_s41599_025_05097_z crossref_primary_10_15187_adr_2025_05_38_2_433 crossref_primary_10_1080_10447318_2025_2454954 crossref_primary_10_1002_cb_70045 |
| Cites_doi | 10.1177/00187208221113448 10.1016/j.jengtecman.2018.04.006 10.1177/1461444818773059 10.1007/s11747-009-0179-4 10.1016/j.newideapsych.2017.11.001 10.1177/00222429211045687 10.1016/j.engappai.2016.05.009 10.1177/0146167292185006 10.1093/jcmc/zmz026 10.1111/jedm.12050 10.1007/BF01173577 10.1080/15332861.2020.1832817 10.1177/1094670516675416 10.1111/poms.13770 10.2307/30036540 10.1080/10447318.2021.2004139 10.1038/s41598-022-18751-2 10.1016/j.chb.2023.107714 10.1002/mar.21721 10.1016/j.jbusres.2007.09.008 10.1016/j.ijindorg.2017.09.003 10.1145/3361118 10.1016/j.intcom.2006.07.005 10.1007/s11002-019-09485-9 10.1037/0022-3514.66.4.742 10.1109/MIS.2007.21 10.1080/10447318.2018.1456150 10.1108/INTR-08-2021-0600 10.21307/ijssis-2017-283 10.1108/NBRI-05-2022-0051 10.1080/1369118X.2019.1568515 10.1016/j.compedu.2018.09.009 10.1109/ACCESS.2018.2870052 10.1037/10628-000 10.1016/j.jbusres.2006.05.006 10.1038/s41586-019-1138-y 10.1007/s10458-019-09408-y 10.1016/j.jii.2021.100257 10.1145/3233231 10.1093/jcmc/zmac010 10.1518/hfes.46.1.50.30392 10.1037/1089-2680.5.4.323 10.1016/j.biopsycho.2010.07.001 10.1177/00222437211050351 10.1109/THMS.2017.2648849 10.1016/j.jbusvent.2012 10.1007/s12559-018-9619-0 10.1016/S0065-2601(07)00002-0 10.1016/j.jretconser.2021.102900 |
| ContentType | Journal Article |
| Copyright | Copyright © 2023 Yue and Li. |
| Copyright_xml | – notice: Copyright © 2023 Yue and Li. |
| DBID | AAYXX CITATION 7X8 DOA |
| DOI | 10.3389/fpsyg.2023.1277861 |
| DatabaseName | CrossRef MEDLINE - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE - Academic |
| DatabaseTitleList | CrossRef MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Psychology |
| EISSN | 1664-1078 |
| ExternalDocumentID | oai_doaj_org_article_830f89fd28d948ebafd71e70f649f471 10_3389_fpsyg_2023_1277861 |
| GroupedDBID | 53G 5VS 9T4 AAFWJ AAKDD AAYXX ABIVO ACGFO ACGFS ACHQT ADBBV ADRAZ AEGXH AFPKN AIAGR ALMA_UNASSIGNED_HOLDINGS AOIJS BAWUL BCNDV CITATION DIK EBS EJD EMOBN F5P GROUPED_DOAJ GX1 HYE KQ8 M48 M~E O5R O5S OK1 P2P PGMZT RNS RPM 7X8 |
| ID | FETCH-LOGICAL-c390t-25397d49d38f41c7e496bc04bf0968b61e084bdd457276a96bbb9cbf6cc2d8b83 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 28 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001101677200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1664-1078 |
| IngestDate | Fri Oct 03 12:43:32 EDT 2025 Thu Oct 02 12:13:13 EDT 2025 Sat Nov 29 03:06:12 EST 2025 Tue Nov 18 22:31:00 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c390t-25397d49d38f41c7e496bc04bf0968b61e084bdd457276a96bbb9cbf6cc2d8b83 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://doaj.org/article/830f89fd28d948ebafd71e70f649f471 |
| PQID | 2895701251 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_830f89fd28d948ebafd71e70f649f471 proquest_miscellaneous_2895701251 crossref_citationtrail_10_3389_fpsyg_2023_1277861 crossref_primary_10_3389_fpsyg_2023_1277861 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-10-30 |
| PublicationDateYYYYMMDD | 2023-10-30 |
| PublicationDate_xml | – month: 10 year: 2023 text: 2023-10-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationTitle | Frontiers in psychology |
| PublicationYear | 2023 |
| Publisher | Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Media S.A |
| References | Peng (ref35) 2022 Rosenfeld (ref41) 2019; 33 Hong (ref15) 2022; 38 Hayes (ref13) 2013; 51 Choi (ref6) 2008; 61 Mao (ref30) 2019; 3 McAuley (ref31) 1992; 18 Venkatesh (ref52) 2003; 27 Sundar (ref50) 2015 Heider (ref14) 1958 Wang (ref53) 2023; 14 Jenkins (ref16) 2014; 29 Kaur (ref19) 2018; 48 Oh (ref33) 2018 Reverberi (ref38) 2022; 12 Grunewald (ref11) 2017; 55 Lipton (ref27) 2018; 61 Lee (ref25) 2004; 46 Collier (ref7) 2010; 38 Kim (ref20) 2022; 59 Baumeister (ref5) 2001; 5 Stubbs (ref48) 2007; 22 Yang (ref56) 2020 Park (ref34) 2018 Crolic (ref8) 2022; 86 Peterson (ref36) 1982; 6 Karray (ref18) 2008; 1 Kim (ref21) 2019; 30 Cuddy (ref9) 2008 Maddikunta (ref29) 2022; 26 Song (ref46) 2022; 66 Sundar (ref49) 2020; 25 Gu (ref12) 2010; 85 Scherer (ref43) 2019; 128 Zarifis (ref57) 2021; 20 Rahwan (ref37) 2019; 568 Serenko (ref44) 2007; 19 West (ref54) 2018; 20 Adadi (ref2) 2018; 6 Louie (ref28) 2020 Shank (ref45) 2019; 22 Lehmann (ref26) 2022; 31 Laato (ref22) 2022; 32 Rudin (ref42) 2019 Basso (ref4) 2016; 55 van der Woerdt (ref51) 2019; 54 Robinette (ref40) 2017; 47 Molina (ref32) 2022; 27 Lai (ref23) 2022 Zhang (ref58) 2022; 39 Albrecht (ref3) 2017; 20 Ribeiro (ref39) 2016 Westphal (ref55) 2023; 144 Lee (ref24) 2012 Kalamas (ref17) 2008; 61 Franke (ref10) 2019; 35 Strathman (ref47) 1994; 66 Abbass (ref1) 2019; 11 |
| References_xml | – year: 2018 ident: ref34 – year: 2022 ident: ref35 article-title: Drivers' evaluation of different automated driving styles: is it both comfortable and natural? publication-title: Hum. Factors doi: 10.1177/00187208221113448 – volume: 48 start-page: 87 year: 2018 ident: ref19 article-title: Trust in driverless cars: investigating key factors influencing the adoption of driverless cars publication-title: J. Eng. Technol. Manag. doi: 10.1016/j.jengtecman.2018.04.006 – volume: 20 start-page: 4366 year: 2018 ident: ref54 article-title: Censored, suspended, shadowbanned: user interpretations of content moderation on social media platforms publication-title: New Media Soc. doi: 10.1177/1461444818773059 – volume-title: The secrets of machine learning: Ten things you wish you had known earlier to be more effective at data analysis year: 2019 ident: ref42 – volume: 38 start-page: 490 year: 2010 ident: ref7 article-title: Examining the influence of control and convenience in a self-service setting publication-title: J. Acad. Mark. Sci. doi: 10.1007/s11747-009-0179-4 – volume: 54 start-page: 93 year: 2019 ident: ref51 article-title: When robots appear to have a mind: the human perception of machine agency and responsibility publication-title: New Ideas Psychol. doi: 10.1016/j.newideapsych.2017.11.001 – volume: 86 start-page: 132 year: 2022 ident: ref8 article-title: Blame the bot: anthropomorphism and anger in customer-Chatbot interactions publication-title: J. Mark. doi: 10.1177/00222429211045687 – volume: 55 start-page: 14 year: 2016 ident: ref4 article-title: Engineering multi-agent systems using feedback loops and holarchies publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2016.05.009 – volume: 18 start-page: 566 year: 1992 ident: ref31 article-title: Measuring causal attributions: the revised causal dimension scale (CDSII) publication-title: Personal. Soc. Psychol. Bull. doi: 10.1177/0146167292185006 – volume: 25 start-page: 74 year: 2020 ident: ref49 article-title: Rise of machine agency: a framework for studying the psychology of human-AI interaction (HAII) publication-title: J. Comput.-Mediat. Commun. doi: 10.1093/jcmc/zmz026 – volume: 51 start-page: 335 year: 2013 ident: ref13 article-title: Introduction to mediation, moderation, and conditional process analysis: a regression-based approach publication-title: J. Educ. Meas. doi: 10.1111/jedm.12050 – volume: 6 start-page: 287 year: 1982 ident: ref36 article-title: The attributional Style Questionnaire publication-title: Cogn. Ther. Res. doi: 10.1007/BF01173577 – volume: 20 start-page: 66 year: 2021 ident: ref57 article-title: Evaluating if trust and personal information privacy concerns are barriers to using health insurance that explicitly utilizes AI publication-title: J. Internet Commer. doi: 10.1080/15332861.2020.1832817 – volume: 20 start-page: 188 year: 2017 ident: ref3 article-title: Perceptions of group versus individual service failures and their effects on customer outcomes: the role of attributions and customer entitlement publication-title: J. Serv. Res. doi: 10.1177/1094670516675416 – volume: 31 start-page: 3419 year: 2022 ident: ref26 article-title: The risk of algorithm transparency: how algorithm complexity drives the effects on the use of advice publication-title: Prod. Oper. Manag. doi: 10.1111/poms.13770 – volume: 27 start-page: 425 year: 2003 ident: ref52 article-title: User acceptance of information technology: toward a unified view publication-title: MIS Q. doi: 10.2307/30036540 – volume: 38 start-page: 102 year: 2022 ident: ref15 article-title: Why is artificial intelligence blamed more? Analysis of faulting artificial intelligence for self-driving Car accidents in experimental settings publication-title: Int. J. Human-Computer Interact. doi: 10.1080/10447318.2021.2004139 – year: 2012 ident: ref24 – volume: 12 start-page: 14952 year: 2022 ident: ref38 article-title: Experimental evidence of effective human-Al collaboration in medical decision-making publication-title: Sci. Rep. doi: 10.1038/s41598-022-18751-2 – year: 2020 ident: ref28 – volume: 144 start-page: 107714 year: 2023 ident: ref55 article-title: Decision control and explanations in human-AI collaboration: improving user perceptions and compliance publication-title: Comput. Hum. Behav. doi: 10.1016/j.chb.2023.107714 – volume: 39 start-page: 2171 year: 2022 ident: ref58 article-title: Consumer reactions to AI design: exploring consumer willingness to pay for AI-designed products publication-title: Psychol. Mark. doi: 10.1002/mar.21721 – volume: 61 start-page: 813 year: 2008 ident: ref17 article-title: Reaching the boiling point: Consumers' negative affective reactions to firm-attributed service failures publication-title: J. Bus. Res. doi: 10.1016/j.jbusres.2007.09.008 – year: 2020 ident: ref56 – volume: 55 start-page: 91 year: 2017 ident: ref11 article-title: Advertising as signal jamming publication-title: Int. J. Ind. Organ. doi: 10.1016/j.ijindorg.2017.09.003 – volume: 3 start-page: 1 year: 2019 ident: ref30 article-title: How data ScientistsWork together with domain experts in scientific collaborations: to find the right answer or to ask the right question? publication-title: Proc ACM Hum Comput Interact doi: 10.1145/3361118 – volume: 19 start-page: 293 year: 2007 ident: ref44 article-title: Are interface agents scapegoats? Attributions of responsibility in human-agent interaction publication-title: Interact. Comput. doi: 10.1016/j.intcom.2006.07.005 – volume: 30 start-page: 1 year: 2019 ident: ref21 article-title: Eliza in the uncanny valley: anthropomorphizing consumer robots increases their perceived warmth but decreases liking publication-title: Mark. Lett. doi: 10.1007/s11002-019-09485-9 – volume: 66 start-page: 742 year: 1994 ident: ref47 article-title: The onsideration of future consequences:weighingimmediate and distant outcomes of behavior publication-title: J. Personality Social Psychol. doi: 10.1037/0022-3514.66.4.742 – volume: 22 start-page: 42 year: 2007 ident: ref48 article-title: Autonomy and common ground in human-robot interaction: a field study publication-title: IEEE Intell. Syst. doi: 10.1109/MIS.2007.21 – volume: 35 start-page: 456 year: 2019 ident: ref10 article-title: A personal resource for technology interaction: development and validation of the affinity for technology interaction (ATI) scale publication-title: Int. J. Human-Computer Interact. doi: 10.1080/10447318.2018.1456150 – volume: 32 start-page: 1 year: 2022 ident: ref22 article-title: How to explain AI systems to end users: a systematic literature review and research agenda publication-title: Internet Res. doi: 10.1108/INTR-08-2021-0600 – volume: 1 start-page: 137 year: 2008 ident: ref18 article-title: Human-computer interaction publication-title: Int. J. Smart Sensing Intelligent Systems doi: 10.21307/ijssis-2017-283 – year: 2016 ident: ref39 – volume: 14 start-page: 177 year: 2023 ident: ref53 article-title: "facilitators" vs "substitutes": the influence of artificial intelligence products' image on consumer evaluation publication-title: Nankai Bus. Rev. Int. doi: 10.1108/NBRI-05-2022-0051 – volume: 22 start-page: 648 year: 2019 ident: ref45 article-title: When are artificial intelligence versus human agents faulted for wrongdoing? Moral attributions after individual and joint decisions publication-title: Inf. Commun. Soc. doi: 10.1080/1369118X.2019.1568515 – volume: 128 start-page: 13 year: 2019 ident: ref43 article-title: The technology acceptance model (TAM): a meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education publication-title: Comput. Educ. doi: 10.1016/j.compedu.2018.09.009 – year: 2018 ident: ref33 – volume: 6 start-page: 52138 year: 2018 ident: ref2 article-title: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI) publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2870052 – start-page: 47 volume-title: Toward a theory of interactive media effects (TIME) four models for explaining how interface features affect user psychology. year: 2015 ident: ref50 – volume-title: The psychology of interpersonal relations/Fritz Heider year: 1958 ident: ref14 doi: 10.1037/10628-000 – volume: 61 start-page: 24 year: 2008 ident: ref6 article-title: Perceived controllability and service expectations: influences on customer reactions following service failure publication-title: J. Bus. Res. doi: 10.1016/j.jbusres.2006.05.006 – volume: 568 start-page: 477 year: 2019 ident: ref37 article-title: Machine behaviour publication-title: Nature doi: 10.1038/s41586-019-1138-y – year: 2022 ident: ref23 – volume: 33 start-page: 673 year: 2019 ident: ref41 article-title: Explainability in human-agent systems publication-title: Auton. Agent. Multi-Agent Syst. doi: 10.1007/s10458-019-09408-y – volume: 26 start-page: 100257 year: 2022 ident: ref29 article-title: Industry 5.0: a survey on enabling technologies and potential applications. Journal of industrial information publication-title: J. Ind. Inf. Integr. doi: 10.1016/j.jii.2021.100257 – volume: 61 start-page: 36 year: 2018 ident: ref27 article-title: The mythos of model interpretability publication-title: Commun. ACM doi: 10.1145/3233231 – volume: 27 start-page: zac010 year: 2022 ident: ref32 article-title: When AI moderates online content: effects of human collaboration and interactive transparency on user trust publication-title: J. Comput.Mediat. Commun. doi: 10.1093/jcmc/zmac010 – volume: 46 start-page: 50 year: 2004 ident: ref25 article-title: Trust in automation: designing for appropriate reliance publication-title: Hum. Factors doi: 10.1518/hfes.46.1.50.30392 – volume: 5 start-page: 323 year: 2001 ident: ref5 article-title: Bad is Stronger than Good publication-title: Rev. Gen. Psychol. doi: 10.1037/1089-2680.5.4.323 – volume: 85 start-page: 200 year: 2010 ident: ref12 article-title: Anxiety and outcome evaluation: the good, the bad and the ambiguous publication-title: Biol. Psychol. doi: 10.1016/j.biopsycho.2010.07.001 – volume: 59 start-page: 79 year: 2022 ident: ref20 article-title: Home-tutoring services assisted with technology: investigating the role of artificial intelligence using a randomized field experiment publication-title: J. Mark. Res. doi: 10.1177/00222437211050351 – volume: 47 start-page: 425 year: 2017 ident: ref40 article-title: Effect of robot performance on human-robot Trust in Time-Critical Situations publication-title: Ieee Transactions on Human-Machine Systems doi: 10.1109/THMS.2017.2648849 – volume: 29 start-page: 17 year: 2014 ident: ref16 article-title: Individual responses to firm failure: appraisals, grief, and the influence of prior failure experience publication-title: J. Bus. Ventur. doi: 10.1016/j.jbusvent.2012 – volume: 11 start-page: 159 year: 2019 ident: ref1 article-title: Social integration of artificial intelligence: functions, automation allocation logic and human-autonomy trust publication-title: Cogn. Comput. doi: 10.1007/s12559-018-9619-0 – volume-title: Advances in experimental social psychology-book year: 2008 ident: ref9 article-title: Warmth and competence as universal dimensions of social perception: the stereotype content model and the BIAS map doi: 10.1016/S0065-2601(07)00002-0 – volume: 66 start-page: 102900 year: 2022 ident: ref46 article-title: Will artificial intelligence replace human customer service? The impact of communication quality and privacy risks on adoption intention publication-title: J. Retail. Consum. Serv. doi: 10.1016/j.jretconser.2021.102900 |
| SSID | ssj0000402002 |
| Score | 2.5022929 |
| Snippet | Despite the widespread availability of artificial intelligence (AI) products and services, consumer evaluations and adoption intentions have not met... |
| SourceID | doaj proquest crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 1277861 |
| SubjectTerms | artificial intelligence evaluation human-AI collaboration outcome expectation responsibility attribution usage intention |
| Title | The impact of human-AI collaboration types on consumer evaluation and usage intention: a perspective of responsibility attribution |
| URI | https://www.proquest.com/docview/2895701251 https://doaj.org/article/830f89fd28d948ebafd71e70f649f471 |
| Volume | 14 |
| WOSCitedRecordID | wos001101677200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1664-1078 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000402002 issn: 1664-1078 databaseCode: DOA dateStart: 20100101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1664-1078 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000402002 issn: 1664-1078 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8QwEA66ePAiPnF9EcGbVNM2bRJvKoqCigeFvYU8RZDusg9hLx785WbS7kMEvXgppU3bkG-azLTffIPQkXXMe0aA1E5tEozCJ8JSlThXOkYpLQoTq5bcsYcH3umIx7lSX8AJq-WB64E75TnxXHibcSsod1p5y1LHiC-p8DRmj2fB65kLpuIcDGERUHcgSyZEYeLU9wbjlxMoFn6SZiCaln5biaJg_4_5OC4y16topfEO8XndqzW04Kp1tDydpMYb6DPgiuvURtz1OJbYS85v8Tc8MXxYHeCwY5oMSzxT9caqsngEfDIMWhGR7XiGFe7N0i7hzv158uwYq-G0NNYmer6-erq8SZo6ConJBRkmWRGcDkuFzbmnqWGOilIbQrUP8QvXZeoIp9paWgRnplThpNbCaA-Uass1z7dQq-pWbhthyER1GcmV8oIaKrjSXvuCZ6ZwgnDXRulkTKVpRMah1sWbDMEG4CAjDhJwkA0ObXQ8vaZXS2z82voCoJq2BHnseCAYjWyMRv5lNG10OAFahtcJ_pGoynVHAxniz4IR8Pp2_uNBu2gZOh8XO7KHWsP-yO2jJfM-fB30D9Ai6_CDaLlhe_9x9QUhpvb8 |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=The+impact+of+human-AI+collaboration+types+on+consumer+evaluation+and+usage+intention%3A+a+perspective+of+responsibility+attribution&rft.jtitle=Frontiers+in+psychology&rft.au=Yue%2C+Beibei&rft.au=Li%2C+Hu&rft.date=2023-10-30&rft.issn=1664-1078&rft.eissn=1664-1078&rft.volume=14&rft.spage=1277861&rft_id=info:doi/10.3389%2Ffpsyg.2023.1277861&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1664-1078&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1664-1078&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1664-1078&client=summon |