{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/137477"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/137477","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"The Emancipatory Role of Technology in Mental Healthcare: The Case of Conversational Artificial Intelligence and Digital Platforms","abstract":"The mental health crisis has been rapidly growing over the last few decades and was further exacerbated by the COVID-19 pandemic, turning it into a deadly global challenge that leads to the loss of 12 billion working days each year, accounts for more than 14% of all deaths, and will cost the world's economy $16 trillion by 2030. Half of the world's population will experience a mental health disorder during their lifetime. Accessibility, affordability, lack of trained personnel, and stigmatization are among the most pressing mental health issues of our time. They act as serious obstacles to comprehensive solutions for this critical global problem, a situation that is getting worse at an alarming rate given the widening gap between the supply (i.e., readily available solutions) and demand (i.e., global need for mental health services). Conversational artificial intelligence (e.g., chatbots and large language models) and digital telehealth platforms, given their unique features (e.g., low operational costs, and scalability) have what is required to address the above-mentioned obstacles and thus be regarded as practical solutions for this grand challenge of our time. In a series of theoretical, empirical, and experimental studies (nine full studies, eight pilot studies, and two supplementary empirical exercises) and using various quantitative and qualitative methods (ranging from grounded theory to unsupervised machine learning), we examine different capabilities of these technological artifacts in the context of mental healthcare and uncover previously unknown, occasionally counterintuitive, and highly important insights about these promising solutions. We also propose a novel investigation of the best approaches for empathetic AI agents based on the latest generations of generative chatbots like large language models. Our results expand prior theory, challenge previous assumptions, and inform scholars and practitioners about the IT system features that encourage people to disclose risky and stigmatized information, characteristics of chatbots and LLMs that can increase the use of these AI agents, maximize patients' information self-disclosure to them, enhance patients' willingness to follow their advice, while improving their engagement and satisfaction with these agents.","abstract_html":"The mental health crisis has been rapidly growing over the last few decades and was further exacerbated by the COVID-19 pandemic, turning it into a deadly global challenge that leads to the loss of 12 billion working days each year, accounts for more than 14% of all deaths, and will cost the world's economy $16 trillion by 2030. Half of the world's population will experience a mental health disorder during their lifetime. Accessibility, affordability, lack of trained personnel, and stigmatization are among the most pressing mental health issues of our time. They act as serious obstacles to comprehensive solutions for this critical global problem, a situation that is getting worse at an alarming rate given the widening gap between the supply (i.e., readily available solutions) and demand (i.e., global need for mental health services). Conversational artificial intelligence (e.g., chatbots and large language models) and digital telehealth platforms, given their unique features (e.g., low operational costs, and scalability) have what is required to address the above-mentioned obstacles and thus be regarded as practical solutions for this grand challenge of our time. In a series of theoretical, empirical, and experimental studies (nine full studies, eight pilot studies, and two supplementary empirical exercises) and using various quantitative and qualitative methods (ranging from grounded theory to unsupervised machine learning), we examine different capabilities of these technological artifacts in the context of mental healthcare and uncover previously unknown, occasionally counterintuitive, and highly important insights about these promising solutions. We also propose a novel investigation of the best approaches for empathetic AI agents based on the latest generations of generative chatbots like large language models. Our results expand prior theory, challenge previous assumptions, and inform scholars and practitioners about the IT system features that encourage people to disclose risky and stigmatized information, characteristics of chatbots and LLMs that can increase the use of these AI agents, maximize patients' information self-disclosure to them, enhance patients' willingness to follow their advice, while improving their engagement and satisfaction with these agents.","abstract_has_math":false,"creators":["Raimi, Ryan"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Business, Business Information Technology","degree_department":"Business, Business Information Technology","school":null,"contributors":[],"advisors":[],"committee_chairs":["Lowry, Paul Benjamin","Adjerid, Idris"],"committee_members":["Liu, Jiayi","Kumar, Subodha","Dennis, Alan R."],"year":2025,"date_issued":"2025-08-12","date_published":"2025-08-12","updated_at":"2026-07-22T22:19:19Z","subjects":["Artificial intelligence (AI)","chatbot","large language model (LLM)","digital platform","telehealth","mental health","information self-disclosure","empathy","stigma"],"languages":["en"],"rights":["Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44494"],"render_values":[{"text":"vt_gsexam:44494","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/137477","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Lowry, Paul Benjamin","Adjerid, Idris"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Liu, Jiayi","Kumar, Subodha","Dennis, Alan R."]},{"key":"dc:contributor.department","label":"Department","values":["Business, Business Information Technology"]},{"key":"dc:creator","label":"Author","values":["Raimi, Ryan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-08-13T08:01:40Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-08-13T08:01:40Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08-12"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Business, Business Information Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial intelligence (AI)","chatbot","large language model (LLM)","digital platform","telehealth","mental health","information self-disclosure","empathy","stigma"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44494"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/137477"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The mental health crisis has been rapidly growing over the last few decades and was further exacerbated by the COVID-19 pandemic, turning it into a deadly global challenge that leads to the loss of 12 billion working days each year, accounts for more than 14% of all deaths, and will cost the world's economy $16 trillion by 2030. Half of the world's population will experience a mental health disorder during their lifetime. Accessibility, affordability, lack of trained personnel, and stigmatization are among the most pressing mental health issues of our time. They act as serious obstacles to comprehensive solutions for this critical global problem, a situation that is getting worse at an alarming rate given the widening gap between the supply (i.e., readily available solutions) and demand (i.e., global need for mental health services). Conversational artificial intelligence (e.g., chatbots and large language models) and digital telehealth platforms, given their unique features (e.g., low operational costs, and scalability) have what is required to address the above-mentioned obstacles and thus be regarded as practical solutions for this grand challenge of our time. In a series of theoretical, empirical, and experimental studies (nine full studies, eight pilot studies, and two supplementary empirical exercises) and using various quantitative and qualitative methods (ranging from grounded theory to unsupervised machine learning), we examine different capabilities of these technological artifacts in the context of mental healthcare and uncover previously unknown, occasionally counterintuitive, and highly important insights about these promising solutions. We also propose a novel investigation of the best approaches for empathetic AI agents based on the latest generations of generative chatbots like large language models. Our results expand prior theory, challenge previous assumptions, and inform scholars and practitioners about the IT system features that encourage people to disclose risky and stigmatized information, characteristics of chatbots and LLMs that can increase the use of these AI agents, maximize patients' information self-disclosure to them, enhance patients' willingness to follow their advice, while improving their engagement and satisfaction with these agents."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Mental health challenges are rising worldwide—and the COVID-19 pandemic made things even worse. Today, these issues cause the loss of billions of workdays, contribute to a large share of deaths, and are projected to cost the global economy $16 trillion by 2030. Sadly, half of all people will face a mental health condition during their lives, but many will not get the help they need due to high costs, lack of access, too few trained professionals, and social stigma. This research explores how technologies like chatbots, AI-powered conversation tools, and digital platforms could help. These tools are affordable, scalable, and can reach people in ways traditional systems often cannot. Through a set of studies using different methods, the work reveals new and sometimes surprising insights about how these technologies can support mental health care. It also introduces new ways to design AI that can respond more empathetically—like a good listener who helps people feel safe opening up. The findings offer guidance for researchers, healthcare professionals, and technology designers, showing what features in digital tools make people more comfortable sharing sensitive information and more likely to trust and follow advice from AI. Ultimately, this work sheds light on how emerging technologies could play a big role in improving mental health support around the world."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["The Emancipatory Role of Technology in Mental Healthcare: The Case of Conversational Artificial Intelligence and Digital Platforms"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Lowry, Paul Benjamin","Adjerid, Idris"],"dc:contributor.committeemember":["Liu, Jiayi","Kumar, Subodha","Dennis, Alan R."],"dc:contributor.department":["Business, Business Information Technology"],"dc:creator":["Raimi, Ryan"],"dc:date.accessioned":["2025-08-13T08:01:40Z"],"dc:date.available":["2025-08-13T08:01:40Z"],"dc:date.issued":["2025-08-12"],"dc:description.abstract":["The mental health crisis has been rapidly growing over the last few decades and was further exacerbated by the COVID-19 pandemic, turning it into a deadly global challenge that leads to the loss of 12 billion working days each year, accounts for more than 14% of all deaths, and will cost the world's economy $16 trillion by 2030. Half of the world's population will experience a mental health disorder during their lifetime. Accessibility, affordability, lack of trained personnel, and stigmatization are among the most pressing mental health issues of our time. They act as serious obstacles to comprehensive solutions for this critical global problem, a situation that is getting worse at an alarming rate given the widening gap between the supply (i.e., readily available solutions) and demand (i.e., global need for mental health services). Conversational artificial intelligence (e.g., chatbots and large language models) and digital telehealth platforms, given their unique features (e.g., low operational costs, and scalability) have what is required to address the above-mentioned obstacles and thus be regarded as practical solutions for this grand challenge of our time. In a series of theoretical, empirical, and experimental studies (nine full studies, eight pilot studies, and two supplementary empirical exercises) and using various quantitative and qualitative methods (ranging from grounded theory to unsupervised machine learning), we examine different capabilities of these technological artifacts in the context of mental healthcare and uncover previously unknown, occasionally counterintuitive, and highly important insights about these promising solutions. We also propose a novel investigation of the best approaches for empathetic AI agents based on the latest generations of generative chatbots like large language models. Our results expand prior theory, challenge previous assumptions, and inform scholars and practitioners about the IT system features that encourage people to disclose risky and stigmatized information, characteristics of chatbots and LLMs that can increase the use of these AI agents, maximize patients' information self-disclosure to them, enhance patients' willingness to follow their advice, while improving their engagement and satisfaction with these agents."],"dc:description.abstractgeneral":["Mental health challenges are rising worldwide—and the COVID-19 pandemic made things even worse. Today, these issues cause the loss of billions of workdays, contribute to a large share of deaths, and are projected to cost the global economy $16 trillion by 2030. Sadly, half of all people will face a mental health condition during their lives, but many will not get the help they need due to high costs, lack of access, too few trained professionals, and social stigma. This research explores how technologies like chatbots, AI-powered conversation tools, and digital platforms could help. These tools are affordable, scalable, and can reach people in ways traditional systems often cannot. Through a set of studies using different methods, the work reveals new and sometimes surprising insights about how these technologies can support mental health care. It also introduces new ways to design AI that can respond more empathetically—like a good listener who helps people feel safe opening up. The findings offer guidance for researchers, healthcare professionals, and technology designers, showing what features in digital tools make people more comfortable sharing sensitive information and more likely to trust and follow advice from AI. Ultimately, this work sheds light on how emerging technologies could play a big role in improving mental health support around the world."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:44494"],"dc:identifier.uri":["https://hdl.handle.net/10919/137477"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:subject":["Artificial intelligence (AI)","chatbot","large language model (LLM)","digital platform","telehealth","mental health","information self-disclosure","empathy","stigma"],"dc:title":["The Emancipatory Role of Technology in Mental Healthcare: The Case of Conversational Artificial Intelligence and Digital Platforms"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Business, Business Information Technology"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:19Z"}