{"id":{"repo_id":"umn","oai_identifier":"oai:conservancy.umn.edu:11299/278726"},"canonical_url":"https://search.dev.ndltd.org/etd/umn/oai:conservancy.umn.edu:11299/278726","repository":{"repo_id":"umn","name":"University of Minnesota","base_url":"https://conservancy.umn.edu/server/oai/request"},"display":{"title":"Non-judgmental AI: investigating the potential of AI agents to alleviate anxiety and reduce fear of judgment under social evaluative threat","abstract":"As artificial intelligence (AI) becomes increasingly integrated into customer service, users still generally prefer human agents over AI agents. It remains unclear in which specific contexts AI agents may be favored over human agents by customers. This research focuses on social evaluative threat, that is, social contexts that involve the risk of negative judgment, which can be explained by the experience of anxiety and fear of judgment. We hypothesize that in such high-emotional-stakes contexts, AI agents may be perceived as fairer and more objective, thereby reducing the negative psychological responses typically elicited in interactions with human agents. Grounded in the Computers Are Social Actors (CASA) paradigm, machine heuristics, and prior research, the current study conducted two studies to explore how agent type (AI vs. human) affects perceived fairness, state anxiety, and fear of judgment, and how these psychological outcomes in turn influence users’ engagement and service selection intentions. Study 1 employed a survey design to assess psychological and behavioral differences in users’ responses to AI and human agents in everyday service interactions. Study 2 used an experiment to test the same mechanism within a specifically designed social evaluative scenario. Our findings suggest that AI agents may serve as a less threatening alternative in mitigating social evaluative threats—such as anxiety and fear of judgment—especially for individuals with higher trait anxiety and lower self-perceived knowledge. This research contributes to the literature on human–AI interaction by identifying psychological mechanisms that shape customer service experience. It also offers practical implications for designing AI services that support users' emotional comfort and reduce evaluative pressure in specific high-emotional-stakes contexts.","abstract_html":"As artificial intelligence (AI) becomes increasingly integrated into customer service, users still generally prefer human agents over AI agents. It remains unclear in which specific contexts AI agents may be favored over human agents by customers. This research focuses on social evaluative threat, that is, social contexts that involve the risk of negative judgment, which can be explained by the experience of anxiety and fear of judgment. We hypothesize that in such high-emotional-stakes contexts, AI agents may be perceived as fairer and more objective, thereby reducing the negative psychological responses typically elicited in interactions with human agents. Grounded in the Computers Are Social Actors (CASA) paradigm, machine heuristics, and prior research, the current study conducted two studies to explore how agent type (AI vs. human) affects perceived fairness, state anxiety, and fear of judgment, and how these psychological outcomes in turn influence users’ engagement and service selection intentions. Study 1 employed a survey design to assess psychological and behavioral differences in users’ responses to AI and human agents in everyday service interactions. Study 2 used an experiment to test the same mechanism within a specifically designed social evaluative scenario. Our findings suggest that AI agents may serve as a less threatening alternative in mitigating social evaluative threats—such as anxiety and fear of judgment—especially for individuals with higher trait anxiety and lower self-perceived knowledge. This research contributes to the literature on human–AI interaction by identifying psychological mechanisms that shape customer service experience. It also offers practical implications for designing AI services that support users' emotional comfort and reduce evaluative pressure in specific high-emotional-stakes contexts.","abstract_has_math":false,"creators":["Zhang, Rongjin"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08","date_published":"2025-08","updated_at":"2026-07-24T05:19:56Z","subjects":["Artificial intelligence","Chatbots","Customer service","Human–AI interaction","Perceived fairness","Social evaluative threat"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/11299/278726","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Zhang, Rongjin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-02-12T17:42:00Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial intelligence","Chatbots","Customer service","Human–AI interaction","Perceived fairness","Social evaluative threat"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/11299/278726"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["University of Minnesota M.A. thesis. August 2025. Major: Mass Communication. Advisor: Claire M. Segijn. 1 computer file (PDF); vii, 75 pages."]},{"key":"dc:description.abstract","label":"Abstract","values":["As artificial intelligence (AI) becomes increasingly integrated into customer service, users still generally prefer human agents over AI agents. It remains unclear in which specific contexts AI agents may be favored over human agents by customers. This research focuses on social evaluative threat, that is, social contexts that involve the risk of negative judgment, which can be explained by the experience of anxiety and fear of judgment. We hypothesize that in such high-emotional-stakes contexts, AI agents may be perceived as fairer and more objective, thereby reducing the negative psychological responses typically elicited in interactions with human agents. Grounded in the Computers Are Social Actors (CASA) paradigm, machine heuristics, and prior research, the current study conducted two studies to explore how agent type (AI vs. human) affects perceived fairness, state anxiety, and fear of judgment, and how these psychological outcomes in turn influence users’ engagement and service selection intentions. Study 1 employed a survey design to assess psychological and behavioral differences in users’ responses to AI and human agents in everyday service interactions. Study 2 used an experiment to test the same mechanism within a specifically designed social evaluative scenario. Our findings suggest that AI agents may serve as a less threatening alternative in mitigating social evaluative threats—such as anxiety and fear of judgment—especially for individuals with higher trait anxiety and lower self-perceived knowledge. This research contributes to the literature on human–AI interaction by identifying psychological mechanisms that shape customer service experience. It also offers practical implications for designing AI services that support users' emotional comfort and reduce evaluative pressure in specific high-emotional-stakes contexts."]},{"key":"dc:title","label":"Title","values":["Non-judgmental AI: investigating the potential of AI agents to alleviate anxiety and reduce fear of judgment under social evaluative threat"]}]}],"canonical_facts":{"dc:creator":["Zhang, Rongjin"],"dc:date.accessioned":["2026-02-12T17:42:00Z"],"dc:date.issued":["2025-08"],"dc:description":["University of Minnesota M.A. thesis. August 2025. Major: Mass Communication. Advisor: Claire M. Segijn. 1 computer file (PDF); vii, 75 pages."],"dc:description.abstract":["As artificial intelligence (AI) becomes increasingly integrated into customer service, users still generally prefer human agents over AI agents. It remains unclear in which specific contexts AI agents may be favored over human agents by customers. This research focuses on social evaluative threat, that is, social contexts that involve the risk of negative judgment, which can be explained by the experience of anxiety and fear of judgment. We hypothesize that in such high-emotional-stakes contexts, AI agents may be perceived as fairer and more objective, thereby reducing the negative psychological responses typically elicited in interactions with human agents. Grounded in the Computers Are Social Actors (CASA) paradigm, machine heuristics, and prior research, the current study conducted two studies to explore how agent type (AI vs. human) affects perceived fairness, state anxiety, and fear of judgment, and how these psychological outcomes in turn influence users’ engagement and service selection intentions. Study 1 employed a survey design to assess psychological and behavioral differences in users’ responses to AI and human agents in everyday service interactions. Study 2 used an experiment to test the same mechanism within a specifically designed social evaluative scenario. Our findings suggest that AI agents may serve as a less threatening alternative in mitigating social evaluative threats—such as anxiety and fear of judgment—especially for individuals with higher trait anxiety and lower self-perceived knowledge. This research contributes to the literature on human–AI interaction by identifying psychological mechanisms that shape customer service experience. It also offers practical implications for designing AI services that support users' emotional comfort and reduce evaluative pressure in specific high-emotional-stakes contexts."],"dc:identifier.uri":["https://hdl.handle.net/11299/278726"],"dc:language.iso":["en"],"dc:subject":["Artificial intelligence","Chatbots","Customer service","Human–AI interaction","Perceived fairness","Social evaluative threat"],"dc:title":["Non-judgmental AI: investigating the potential of AI agents to alleviate anxiety and reduce fear of judgment under social evaluative threat"],"dc:type":["Thesis or Dissertation"]},"updated_at":"2026-07-24T05:19:56Z"}