{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/21597"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/21597","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Technology Implementation and the Job Demands-Resources Model: A Dynamic Consideration of Adjustment Trajectories","abstract":"Purpose: This study further develops job demands-resources (JD-R) theory using a dynamic (changing over time) consideration of resources, demands, burnout, and engagement associated with technology implementation in the workplace. Design/Methodology/Approach: A case study approach, including focus groups and content analysis, was used to explore digital job demands, digital job resources, techno-work burnout, and techno-work engagement over time within the JD-R framework. Twelve employees, who were part of a ChatGPT Enterprise pilot program, participated in focus groups discussing how they experienced ChatGPT implementation. Results: Findings illustrate the dynamic nature of employee perceptions when technology is implemented, as understood in the JD-R framework. Across participants’ retrospective accounts of the implementation process, experiences followed a pattern of initial learning demands gradually decreasing as participants integrated ChatGPT into their work and perceived increasing value of ChatGPT as a resource to support efficiency for mundane tasks, as well as support for complex work. Across these common phases, participant descriptions of their adjustment to technology also aligned with one of three profiles of change: (1) Rapid Adjustment, (2) Gradual Adjustment, and (3) Minimal Adjustment. Theoretical Implications: This research refines JD-R theory’s application to technology experiences by demonstrating how, in the context of AI implementation in the workplace, experiences are dynamic and nonuniform across employees. Additionally, techno-overload is expanded to include a monitoring burden in reviewing ChatGPT output, and techno-insecurity is expanded to include concerns about deskilling. Finally, participants provided theory-aligned descriptions of their experiences, given minimal prompting, providing support for JD-R theory. Practical Implications: Organizations can maintain awareness of employee experiences using focus groups, such as those conducted in this study, or questionnaires. They can also leverage both informal and formal learning opportunities to support employees’ adjustment to ChatGPT and other technology interventions. Originality/Value: Previous research on JD-R theory and technology has overlooked the implementation of new technologies and how perceptions of digital job demands, digital job resources, techno-work burnout, and techno-work engagement evolve over time. Understanding how these changes occur over time when new workplace technologies are implemented can help organizations successfully manage the implementation process.","abstract_html":"Purpose: This study further develops job demands-resources (JD-R) theory using a dynamic (changing over time) consideration of resources, demands, burnout, and engagement associated with technology implementation in the workplace. Design/Methodology/Approach: A case study approach, including focus groups and content analysis, was used to explore digital job demands, digital job resources, techno-work burnout, and techno-work engagement over time within the JD-R framework. Twelve employees, who were part of a ChatGPT Enterprise pilot program, participated in focus groups discussing how they experienced ChatGPT implementation. Results: Findings illustrate the dynamic nature of employee perceptions when technology is implemented, as understood in the JD-R framework. Across participants’ retrospective accounts of the implementation process, experiences followed a pattern of initial learning demands gradually decreasing as participants integrated ChatGPT into their work and perceived increasing value of ChatGPT as a resource to support efficiency for mundane tasks, as well as support for complex work. Across these common phases, participant descriptions of their adjustment to technology also aligned with one of three profiles of change: (1) Rapid Adjustment, (2) Gradual Adjustment, and (3) Minimal Adjustment. Theoretical Implications: This research refines JD-R theory’s application to technology experiences by demonstrating how, in the context of AI implementation in the workplace, experiences are dynamic and nonuniform across employees. Additionally, techno-overload is expanded to include a monitoring burden in reviewing ChatGPT output, and techno-insecurity is expanded to include concerns about deskilling. Finally, participants provided theory-aligned descriptions of their experiences, given minimal prompting, providing support for JD-R theory. Practical Implications: Organizations can maintain awareness of employee experiences using focus groups, such as those conducted in this study, or questionnaires. They can also leverage both informal and formal learning opportunities to support employees’ adjustment to ChatGPT and other technology interventions. Originality/Value: Previous research on JD-R theory and technology has overlooked the implementation of new technologies and how perceptions of digital job demands, digital job resources, techno-work burnout, and techno-work engagement evolve over time. Understanding how these changes occur over time when new workplace technologies are implemented can help organizations successfully manage the implementation process.","abstract_has_math":false,"creators":["St. Aubin, Allison Danielle 1991-"],"institution":"University of Houston","degree_name":"Doctor of Philosophy","degree_level":null,"degree_discipline":"Industrial/Organizational Psychology","degree_department":null,"school":null,"contributors":[],"advisors":["Brummel, Bradley"],"committee_chairs":[],"committee_members":["Mehta, Paras","Rodwell, Elizabeth","Litson, Kaylee"],"year":2026,"date_issued":"2026-05","date_published":"2026-05","updated_at":"2026-07-24T02:33:06Z","subjects":["job demands-resources theory","techno-work burnout","techno-work engagement","employee adjustment","ChatGPT Enterprise","technology implementation","workplace technologies","digital job demands","digital job resources","techno-insecurity","techno-invasion","techno-overload","techno-uncertainty","ChatGPT","focus groups","case study","techno-complexity","techno-vigor","training and support","system usability","techno-absorption","techno-dedication","change","techno-work","job demands-resources model","job demands","job resources"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/21597","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Brummel, Bradley"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Mehta, Paras","Rodwell, Elizabeth","Litson, Kaylee"]},{"key":"dc:creator","label":"Author","values":["St. Aubin, Allison Danielle 1991-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-07-16T21:51:51Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-05"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial/Organizational Psychology"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["job demands-resources theory","techno-work burnout","techno-work engagement","employee adjustment","ChatGPT Enterprise","technology implementation","workplace technologies","digital job demands","digital job resources","techno-insecurity","techno-invasion","techno-overload","techno-uncertainty","ChatGPT","focus groups","case study","techno-complexity","techno-vigor","training and support","system usability","techno-absorption","techno-dedication","change","techno-work","job demands-resources model","job demands","job resources"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10657/21597"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Purpose: This study further develops job demands-resources (JD-R) theory using a dynamic (changing over time) consideration of resources, demands, burnout, and engagement associated with technology implementation in the workplace. Design/Methodology/Approach: A case study approach, including focus groups and content analysis, was used to explore digital job demands, digital job resources, techno-work burnout, and techno-work engagement over time within the JD-R framework. Twelve employees, who were part of a ChatGPT Enterprise pilot program, participated in focus groups discussing how they experienced ChatGPT implementation. Results: Findings illustrate the dynamic nature of employee perceptions when technology is implemented, as understood in the JD-R framework. Across participants’ retrospective accounts of the implementation process, experiences followed a pattern of initial learning demands gradually decreasing as participants integrated ChatGPT into their work and perceived increasing value of ChatGPT as a resource to support efficiency for mundane tasks, as well as support for complex work. Across these common phases, participant descriptions of their adjustment to technology also aligned with one of three profiles of change: (1) Rapid Adjustment, (2) Gradual Adjustment, and (3) Minimal Adjustment. Theoretical Implications: This research refines JD-R theory’s application to technology experiences by demonstrating how, in the context of AI implementation in the workplace, experiences are dynamic and nonuniform across employees. Additionally, techno-overload is expanded to include a monitoring burden in reviewing ChatGPT output, and techno-insecurity is expanded to include concerns about deskilling. Finally, participants provided theory-aligned descriptions of their experiences, given minimal prompting, providing support for JD-R theory. Practical Implications: Organizations can maintain awareness of employee experiences using focus groups, such as those conducted in this study, or questionnaires. They can also leverage both informal and formal learning opportunities to support employees’ adjustment to ChatGPT and other technology interventions. Originality/Value: Previous research on JD-R theory and technology has overlooked the implementation of new technologies and how perceptions of digital job demands, digital job resources, techno-work burnout, and techno-work engagement evolve over time. Understanding how these changes occur over time when new workplace technologies are implemented can help organizations successfully manage the implementation process."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Technology Implementation and the Job Demands-Resources Model: A Dynamic Consideration of Adjustment Trajectories"]}]}],"canonical_facts":{"dc:contributor.advisor":["Brummel, Bradley"],"dc:contributor.committeemember":["Mehta, Paras","Rodwell, Elizabeth","Litson, Kaylee"],"dc:creator":["St. Aubin, Allison Danielle 1991-"],"dc:date.accessioned":["2026-07-16T21:51:51Z"],"dc:date.issued":["2026-05"],"dc:description.abstract":["Purpose: This study further develops job demands-resources (JD-R) theory using a dynamic (changing over time) consideration of resources, demands, burnout, and engagement associated with technology implementation in the workplace. Design/Methodology/Approach: A case study approach, including focus groups and content analysis, was used to explore digital job demands, digital job resources, techno-work burnout, and techno-work engagement over time within the JD-R framework. Twelve employees, who were part of a ChatGPT Enterprise pilot program, participated in focus groups discussing how they experienced ChatGPT implementation. Results: Findings illustrate the dynamic nature of employee perceptions when technology is implemented, as understood in the JD-R framework. Across participants’ retrospective accounts of the implementation process, experiences followed a pattern of initial learning demands gradually decreasing as participants integrated ChatGPT into their work and perceived increasing value of ChatGPT as a resource to support efficiency for mundane tasks, as well as support for complex work. Across these common phases, participant descriptions of their adjustment to technology also aligned with one of three profiles of change: (1) Rapid Adjustment, (2) Gradual Adjustment, and (3) Minimal Adjustment. Theoretical Implications: This research refines JD-R theory’s application to technology experiences by demonstrating how, in the context of AI implementation in the workplace, experiences are dynamic and nonuniform across employees. Additionally, techno-overload is expanded to include a monitoring burden in reviewing ChatGPT output, and techno-insecurity is expanded to include concerns about deskilling. Finally, participants provided theory-aligned descriptions of their experiences, given minimal prompting, providing support for JD-R theory. Practical Implications: Organizations can maintain awareness of employee experiences using focus groups, such as those conducted in this study, or questionnaires. They can also leverage both informal and formal learning opportunities to support employees’ adjustment to ChatGPT and other technology interventions. Originality/Value: Previous research on JD-R theory and technology has overlooked the implementation of new technologies and how perceptions of digital job demands, digital job resources, techno-work burnout, and techno-work engagement evolve over time. Understanding how these changes occur over time when new workplace technologies are implemented can help organizations successfully manage the implementation process."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10657/21597"],"dc:language.iso":["English"],"dc:subject":["job demands-resources theory","techno-work burnout","techno-work engagement","employee adjustment","ChatGPT Enterprise","technology implementation","workplace technologies","digital job demands","digital job resources","techno-insecurity","techno-invasion","techno-overload","techno-uncertainty","ChatGPT","focus groups","case study","techno-complexity","techno-vigor","training and support","system usability","techno-absorption","techno-dedication","change","techno-work","job demands-resources model","job demands","job resources"],"dc:title":["Technology Implementation and the Job Demands-Resources Model: A Dynamic Consideration of Adjustment Trajectories"],"dc:type":["Thesis"],"thesis:degree_discipline":["Industrial/Organizational Psychology"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:33:06Z"}