{"id":{"repo_id":"alverno","oai_identifier":"oai:alverno.omeka.net:964"},"canonical_url":"https://search.dev.ndltd.org/etd/alverno/oai:alverno.omeka.net:964","repository":{"repo_id":"alverno","name":"Alverno College","base_url":"https://alverno.omeka.net/oai-pmh-repository/request"},"display":{"title":"Deconstructing the age-diverse workforce mosaic : bridging the multigenerational technology skills gap","abstract":"Burns, Toni M. This dissertation explores the diverse learning needs of the multigenerational workforce and the barriers to developing problem-solving skills in technology-rich environments (PS-TREs). With four primary generational cohorts—baby boomers, Generation X, millennials, and Generation Z—coexisting in the workplace at present, organizations must navigate a complex landscape of differing learning styles and levels of technological competencies. This study analyzed data from 189 survey respondents and 21 interview participants working in human resources in Arkansas, obtaining insights into how generational differences impact the perceived effectiveness of workplace learning (WPL) methods on enhancing PS-TREs, including mentorship and AI-based learning tools. A mixed-methods approach was utilized, integrating qualitative and quantitative analyses to comprehensively understand generational learning preferences, challenges, and training effectiveness related to PS-TREs. The data analysis employed the following statistical tests to determine the statistical significance, examine generational differences, and identify latent learning and adaptation profiles: Welch’s ANOVA, Kruskal-Wallis H tests, chi-square tests, Monte Carlo chi-square simulations, ordinal logistic regression, univariate ANOVAs, Dunn’s post-hoc tests with Bonferroni correction, composite score ANOVAs, and cluster analysis. The findings indicate that mentorship and AI-based learning were perceived as valuable strategies across all generations, but the statistical analysis (Kruskal-Wallis test, p > .05) revealed no significant generational differences in their perceived effectiveness. Meanwhile, the qualitative data provided insights into potential variations in how different cohorts engage with these strategies. Although the perceptions of effectiveness were similar across generations, the findings highlight differences in experiences and adaptation approaches. Based on these insights, tailored training strategies could integrate blended learning models, combining structured mentorship with self-directed, AI-enhanced tools to support diverse learning preferences. In terms of practical implications, this study provides actionable recommendations for organizations, educators, and policymakers to optimize workforce development, enhance technological adaptability, and promote long-term learning sustainability across generations.","abstract_html":"Burns, Toni M. This dissertation explores the diverse learning needs of the multigenerational workforce and the barriers to developing problem-solving skills in technology-rich environments (PS-TREs). With four primary generational cohorts—baby boomers, Generation X, millennials, and Generation Z—coexisting in the workplace at present, organizations must navigate a complex landscape of differing learning styles and levels of technological competencies. This study analyzed data from 189 survey respondents and 21 interview participants working in human resources in Arkansas, obtaining insights into how generational differences impact the perceived effectiveness of workplace learning (WPL) methods on enhancing PS-TREs, including mentorship and AI-based learning tools. A mixed-methods approach was utilized, integrating qualitative and quantitative analyses to comprehensively understand generational learning preferences, challenges, and training effectiveness related to PS-TREs. The data analysis employed the following statistical tests to determine the statistical significance, examine generational differences, and identify latent learning and adaptation profiles: Welch’s ANOVA, Kruskal-Wallis H tests, chi-square tests, Monte Carlo chi-square simulations, ordinal logistic regression, univariate ANOVAs, Dunn’s post-hoc tests with Bonferroni correction, composite score ANOVAs, and cluster analysis. The findings indicate that mentorship and AI-based learning were perceived as valuable strategies across all generations, but the statistical analysis (Kruskal-Wallis test, p > .05) revealed no significant generational differences in their perceived effectiveness. Meanwhile, the qualitative data provided insights into potential variations in how different cohorts engage with these strategies. Although the perceptions of effectiveness were similar across generations, the findings highlight differences in experiences and adaptation approaches. Based on these insights, tailored training strategies could integrate blended learning models, combining structured mentorship with self-directed, AI-enhanced tools to support diverse learning preferences. In terms of practical implications, this study provides actionable recommendations for organizations, educators, and policymakers to optimize workforce development, enhance technological adaptability, and promote long-term learning sustainability across generations.","abstract_has_math":false,"creators":["Burns, Toni M."],"institution":"Alverno College","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","date_published":"2025","updated_at":"2026-07-27T18:44:41Z","subjects":["Technological literacy","Artificial intelligence","Multigenerational workforce","Technology-rich environments","Workplace learning","Problem-solving skills","Generational differences","Mentorship","AI-based learning","Digital literacy"],"languages":["English"],"rights":["These materials may be used by individuals and libraries for personal use, research, teaching (including distribution to classes), or for any fair use as defined by U.S. Copyright Law."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://alverno.omeka.net/items/show/964","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Burns, Toni M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025"]},{"key":"dc:publisher","label":"Institution","values":["Alverno College"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Technological literacy","Artificial intelligence","Multigenerational workforce","Technology-rich environments","Workplace learning","Problem-solving skills","Generational differences","Mentorship","AI-based learning","Digital literacy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["These materials may be used by individuals and libraries for personal use, research, teaching (including distribution to classes), or for any fair use as defined by U.S. Copyright Law."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://alverno.omeka.net/items/show/964"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Burns, Toni M. This dissertation explores the diverse learning needs of the multigenerational workforce and the barriers to developing problem-solving skills in technology-rich environments (PS-TREs). With four primary generational cohorts—baby boomers, Generation X, millennials, and Generation Z—coexisting in the workplace at present, organizations must navigate a complex landscape of differing learning styles and levels of technological competencies. This study analyzed data from 189 survey respondents and 21 interview participants working in human resources in Arkansas, obtaining insights into how generational differences impact the perceived effectiveness of workplace learning (WPL) methods on enhancing PS-TREs, including mentorship and AI-based learning tools. A mixed-methods approach was utilized, integrating qualitative and quantitative analyses to comprehensively understand generational learning preferences, challenges, and training effectiveness related to PS-TREs. The data analysis employed the following statistical tests to determine the statistical significance, examine generational differences, and identify latent learning and adaptation profiles: Welch’s ANOVA, Kruskal-Wallis H tests, chi-square tests, Monte Carlo chi-square simulations, ordinal logistic regression, univariate ANOVAs, Dunn’s post-hoc tests with Bonferroni correction, composite score ANOVAs, and cluster analysis. The findings indicate that mentorship and AI-based learning were perceived as valuable strategies across all generations, but the statistical analysis (Kruskal-Wallis test, p > .05) revealed no significant generational differences in their perceived effectiveness. Meanwhile, the qualitative data provided insights into potential variations in how different cohorts engage with these strategies. Although the perceptions of effectiveness were similar across generations, the findings highlight differences in experiences and adaptation approaches. Based on these insights, tailored training strategies could integrate blended learning models, combining structured mentorship with self-directed, AI-enhanced tools to support diverse learning preferences. In terms of practical implications, this study provides actionable recommendations for organizations, educators, and policymakers to optimize workforce development, enhance technological adaptability, and promote long-term learning sustainability across generations."]},{"key":"dc:format","label":"Dc Format","values":["PDF"]},{"key":"dc:title","label":"Title","values":["Deconstructing the age-diverse workforce mosaic : bridging the multigenerational technology skills gap"]}]}],"canonical_facts":{"dc:creator":["Burns, Toni M."],"dc:date":["2025"],"dc:description":["Burns, Toni M. This dissertation explores the diverse learning needs of the multigenerational workforce and the barriers to developing problem-solving skills in technology-rich environments (PS-TREs). With four primary generational cohorts—baby boomers, Generation X, millennials, and Generation Z—coexisting in the workplace at present, organizations must navigate a complex landscape of differing learning styles and levels of technological competencies. This study analyzed data from 189 survey respondents and 21 interview participants working in human resources in Arkansas, obtaining insights into how generational differences impact the perceived effectiveness of workplace learning (WPL) methods on enhancing PS-TREs, including mentorship and AI-based learning tools. A mixed-methods approach was utilized, integrating qualitative and quantitative analyses to comprehensively understand generational learning preferences, challenges, and training effectiveness related to PS-TREs. The data analysis employed the following statistical tests to determine the statistical significance, examine generational differences, and identify latent learning and adaptation profiles: Welch’s ANOVA, Kruskal-Wallis H tests, chi-square tests, Monte Carlo chi-square simulations, ordinal logistic regression, univariate ANOVAs, Dunn’s post-hoc tests with Bonferroni correction, composite score ANOVAs, and cluster analysis. The findings indicate that mentorship and AI-based learning were perceived as valuable strategies across all generations, but the statistical analysis (Kruskal-Wallis test, p > .05) revealed no significant generational differences in their perceived effectiveness. Meanwhile, the qualitative data provided insights into potential variations in how different cohorts engage with these strategies. Although the perceptions of effectiveness were similar across generations, the findings highlight differences in experiences and adaptation approaches. Based on these insights, tailored training strategies could integrate blended learning models, combining structured mentorship with self-directed, AI-enhanced tools to support diverse learning preferences. In terms of practical implications, this study provides actionable recommendations for organizations, educators, and policymakers to optimize workforce development, enhance technological adaptability, and promote long-term learning sustainability across generations."],"dc:format":["PDF"],"dc:identifier":["https://alverno.omeka.net/items/show/964"],"dc:language":["English"],"dc:publisher":["Alverno College"],"dc:rights":["These materials may be used by individuals and libraries for personal use, research, teaching (including distribution to classes), or for any fair use as defined by U.S. Copyright Law."],"dc:subject":["Technological literacy","Artificial intelligence","Multigenerational workforce","Technology-rich environments","Workplace learning","Problem-solving skills","Generational differences","Mentorship","AI-based learning","Digital literacy"],"dc:title":["Deconstructing the age-diverse workforce mosaic : bridging the multigenerational technology skills gap"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T18:44:41Z"}