{"id":{"repo_id":"temple","oai_identifier":"oai:scholarshare.temple.edu:20.500.12613/11123"},"canonical_url":"https://search.dev.ndltd.org/etd/temple/oai:scholarshare.temple.edu:20.500.12613/11123","repository":{"repo_id":"temple","name":"Temple University","base_url":"https://scholarshare.temple.edu/server/oai/request"},"display":{"title":"HOW TECHNOLOGY ADOPTION, HUMAN BEHAVIOR, AND COST EFFICIENCY INFLUENCE HOSPITAL TASK ALLOCATION: A RESOURCE-BASED VIEW AND TECHNOLOGICAL UNEMPLOYMENT PERSPECTIVE","abstract":"Policy mandates and cost constraints, including reduced Medicare and Medicaid reimbursement levels, pressure hospitals to balance financial sustainability with delivering high-quality patient care. In response, hospitals are increasingly investing in Artificial Labor (AL) technologies to optimize operations. This study investigates factors influencing hospital management’s preference for allocating tasks to AL rather than Human Labor (HL), focusing on cost efficiency, negative human behaviors, and the irreversible \"lock-in\" effect of technology adoption. Adopting Autor’s (2013) definition of tasks and building on Acemoglu and Restrepo’s (2018) framework, this study distinguishes between Capital—represented by AL—and Labor, encompassing HL contributions. This distinction highlights growing competition between AL and HL as strategic resources within hospital environments. Grounded in Resource-Based View (RBV) and Technological Unemployment Theory (TUT), the findings demonstrate that hospital managers overwhelmingly prefer AL over HL. Managers cited cost efficiency, labor shortages, productivity, and improved patient outcomes as key drivers, while negative human behaviors such as absenteeism, insubordination, and workplace hostility further motivated AL adoption. Once tasks are allocated to AL, the study confirms a technological \"lock-in\" effect, reducing the likelihood of task reallocation to HL. These findings contribute to the discourse on Technological Unemployment in hospitals and offer practical insights for policymakers and healthcare leaders tasked with addressing AL’s looming ethical and operational implications.","abstract_html":"Policy mandates and cost constraints, including reduced Medicare and Medicaid reimbursement levels, pressure hospitals to balance financial sustainability with delivering high-quality patient care. In response, hospitals are increasingly investing in Artificial Labor (AL) technologies to optimize operations. This study investigates factors influencing hospital management’s preference for allocating tasks to AL rather than Human Labor (HL), focusing on cost efficiency, negative human behaviors, and the irreversible "lock-in" effect of technology adoption. Adopting Autor’s (2013) definition of tasks and building on Acemoglu and Restrepo’s (2018) framework, this study distinguishes between Capital—represented by AL—and Labor, encompassing HL contributions. This distinction highlights growing competition between AL and HL as strategic resources within hospital environments. Grounded in Resource-Based View (RBV) and Technological Unemployment Theory (TUT), the findings demonstrate that hospital managers overwhelmingly prefer AL over HL. Managers cited cost efficiency, labor shortages, productivity, and improved patient outcomes as key drivers, while negative human behaviors such as absenteeism, insubordination, and workplace hostility further motivated AL adoption. Once tasks are allocated to AL, the study confirms a technological "lock-in" effect, reducing the likelihood of task reallocation to HL. These findings contribute to the discourse on Technological Unemployment in hospitals and offer practical insights for policymakers and healthcare leaders tasked with addressing AL’s looming ethical and operational implications.","abstract_has_math":false,"creators":["Avael, Julio"],"institution":"Temple University. Libraries","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Di Benedetto, C. Anthony"],"committee_chairs":[],"committee_members":["Wattal, Sunil","Seok Pang, Min","Wilk, David J."],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-27T21:20:51Z","subjects":["Management","Health care management","Artificial intelligence","Artificial labor","Cost efficiency in healthcare","Healthcare management","Hospital task allocation theory","Negative human behavior in hospitals","Technological unemployment in healthcare","Business administration/strategic management"],"languages":["eng"],"rights":["IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available."],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholarshare.temple.edu/handle/20.500.12613/11123","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Di Benedetto, C. 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Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarshare.temple.edu/handle/20.500.12613/11123"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Policy mandates and cost constraints, including reduced Medicare and Medicaid reimbursement levels, pressure hospitals to balance financial sustainability with delivering high-quality patient care. In response, hospitals are increasingly investing in Artificial Labor (AL) technologies to optimize operations. This study investigates factors influencing hospital management’s preference for allocating tasks to AL rather than Human Labor (HL), focusing on cost efficiency, negative human behaviors, and the irreversible \"lock-in\" effect of technology adoption. Adopting Autor’s (2013) definition of tasks and building on Acemoglu and Restrepo’s (2018) framework, this study distinguishes between Capital—represented by AL—and Labor, encompassing HL contributions. This distinction highlights growing competition between AL and HL as strategic resources within hospital environments. Grounded in Resource-Based View (RBV) and Technological Unemployment Theory (TUT), the findings demonstrate that hospital managers overwhelmingly prefer AL over HL. Managers cited cost efficiency, labor shortages, productivity, and improved patient outcomes as key drivers, while negative human behaviors such as absenteeism, insubordination, and workplace hostility further motivated AL adoption. Once tasks are allocated to AL, the study confirms a technological \"lock-in\" effect, reducing the likelihood of task reallocation to HL. These findings contribute to the discourse on Technological Unemployment in hospitals and offer practical insights for policymakers and healthcare leaders tasked with addressing AL’s looming ethical and operational implications."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["D.B.A."]},{"key":"dc:title","label":"Title","values":["HOW TECHNOLOGY ADOPTION, HUMAN BEHAVIOR, AND COST EFFICIENCY INFLUENCE HOSPITAL TASK ALLOCATION: A RESOURCE-BASED VIEW AND TECHNOLOGICAL UNEMPLOYMENT PERSPECTIVE"]}]}],"canonical_facts":{"dc:contributor.advisor":["Di Benedetto, C. Anthony"],"dc:contributor.committeemember":["Wattal, Sunil","Seok Pang, Min","Wilk, David J."],"dc:creator":["Avael, Julio"],"dc:date.accessioned":["2025-07-14T20:19:23Z"],"dc:date.available":["2025-07-14T20:19:23Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["Policy mandates and cost constraints, including reduced Medicare and Medicaid reimbursement levels, pressure hospitals to balance financial sustainability with delivering high-quality patient care. In response, hospitals are increasingly investing in Artificial Labor (AL) technologies to optimize operations. This study investigates factors influencing hospital management’s preference for allocating tasks to AL rather than Human Labor (HL), focusing on cost efficiency, negative human behaviors, and the irreversible \"lock-in\" effect of technology adoption. Adopting Autor’s (2013) definition of tasks and building on Acemoglu and Restrepo’s (2018) framework, this study distinguishes between Capital—represented by AL—and Labor, encompassing HL contributions. This distinction highlights growing competition between AL and HL as strategic resources within hospital environments. Grounded in Resource-Based View (RBV) and Technological Unemployment Theory (TUT), the findings demonstrate that hospital managers overwhelmingly prefer AL over HL. Managers cited cost efficiency, labor shortages, productivity, and improved patient outcomes as key drivers, while negative human behaviors such as absenteeism, insubordination, and workplace hostility further motivated AL adoption. Once tasks are allocated to AL, the study confirms a technological \"lock-in\" effect, reducing the likelihood of task reallocation to HL. These findings contribute to the discourse on Technological Unemployment in hospitals and offer practical insights for policymakers and healthcare leaders tasked with addressing AL’s looming ethical and operational implications."],"dc:description.degree":["D.B.A."],"dc:identifier.uri":["https://scholarshare.temple.edu/handle/20.500.12613/11123"],"dc:language.iso":["eng"],"dc:publisher":["Temple University. Libraries"],"dc:rights":["IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available."],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Management","Health care management","Artificial intelligence","Artificial labor","Cost efficiency in healthcare","Healthcare management","Hospital task allocation theory","Negative human behavior in hospitals","Technological unemployment in healthcare","Business administration/strategic management"],"dc:title":["HOW TECHNOLOGY ADOPTION, HUMAN BEHAVIOR, AND COST EFFICIENCY INFLUENCE HOSPITAL TASK ALLOCATION: A RESOURCE-BASED VIEW AND TECHNOLOGICAL UNEMPLOYMENT PERSPECTIVE"],"dc:type":["Text"]},"updated_at":"2026-07-27T21:20:51Z"}