The rapid integration of Artificial Intelligence (AI) into Human Resource Management (HRM) creates a human-centric paradox, promising enhanced operational efficiency and data-driven decision-making while simultaneously introducing novel stressors that may diminish employee performance and well-being. This study investigates the complex interplay between AI adoption, technostress, and digital literacy in shaping employee productivity within smart work environments. Utilizing an explanatory sequential mixed-methods design, quantitative data from a survey of 300 employees in technology-oriented firms was analysed using regression and mediation models in SPSS and SmartPLS. This was followed by qualitative thematic analysis of 20 in-depth interviews to contextualize the statistical findings. Results confirmed that AI integration significantly predicts higher productivity, but this relationship is negatively impacted by the multifaceted dimensions of technostress, such as techno-overload and techno-insecurity. Crucially, digital literacy was found to be a powerful mediator and buffer, mitigating these adverse effects and enabling employees to leverage AI as an augmenting tool rather than a perceived threat to their roles. Qualitative findings further revealed that technostress stems from constant algorithmic monitoring and the pace of technological change, while digital literacy acts as an empowering mechanism that fosters confidence and control. The study concludes that realizing AI's full productivity benefits requires a balanced, human-centric approach, contributing to technostress theory by empirically validating digital literacy's pivotal role. Therefore, organizations must complement technological implementation with robust digital upskilling initiatives, participatory design of AI tools, and supportive organizational practices to mitigate technostress and foster a resilient, productive, and sustainable workforce. References Abdelrahman, M., & Elshennawy, A. (2023). Digital competence and workforce readiness in the era of artificial intelligence. Computers in Human Behavior Reports, 9(4), 100319. https://doi.org/10.1016/j.chbr.2023.100319 Agarwal, R., & Karahanna, E. (2023). Technostress and adaptation in digitalized workplaces: A review and research agenda. Information Systems Research, 34(2), 255–273. https://doi.org/10.1287/isre.2022.1234 Almazán, D., Olarte-Pascual, C., & Reinares-Lara, E. (2023). Technostress and resistance to digital transformation: The moderating effect of digital competence. Computers in Human Behavior, 144, 107726. https://doi.org/10.1016/j.chb.2023.107726 Babashahi, L., Barbosa, C. E., Lima, Y., Lyra, A., Salazar, H., Argôlo, M., Almeida, M. A. d., & Souza, J. M. d. (2024). AI in the workplace: A systematic review of skill transformation in the industry. Administrative Sciences, 14(6), Article 127. https://doi.org/10.3390/admsci14060127 Bailey, K., & De Witte, H. (2024). Digitalization, job insecurity, and well-being: The mediating role of organizational support. European Journal of Work and Organizational Psychology, 33(2), 158–172. https://doi.org/10.1080/1359432X.2023.2284210 Bawack, R. E., & Wamba, S. F. (2023). Artificial intelligence in HRM: Examining employee attitudes toward automation. International Journal of Human Resource Studies, 13(1), 55–72. https://doi.org/10.5296/ijhrs.v13i1.20540 Brougham, D., & Haar, J. M. (2022). Smart technologies, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 28(3), 403–418. https://doi.org/10.1017/jmo.2021.14 Califf, C. B., & Brooks, S. (2023). The role of technostress in shaping digital productivity: Evidence from AI-enabled organizations. Information & Management, 60(2), 103719. https://doi.org/10.1016/j.im.2022.103719 Cao, X., & Sun, J. (2022). How AI-induced uncertainty influences employee stress and adaptation: Evidence from hybrid workplaces. Journal of Business Research, 146, 112–124. https://doi.org/10.1016/j.jbusres.2022.03.016 Chatterjee, S., Rana, N. P., & Dwivedi, Y. K. (2022). Exploring the factors influencing employees’ adoption of AI-based HR systems: A unified perspective. Technological Forecasting and Social Change, 181, 121789. https://doi.org/10.1016/j.techfore.2022.121789 Costa, C., Ferreira, A., & Santos, J. (2024). Digital literacy and employee resilience in AI-driven organizations: A structural equation modeling approach. Technological Forecasting and Social Change, 197, 122986. https://doi.org/10.1016/j.techfore.2024.122986 Dima, J., Gilbert, M.-H., Dextras-Gauthier, J., & Giraud, L. (2024). The effects of artificial intelligence on human resource activities and the roles of the human resource triad: Opportunities and challenges. Frontiers in Psychology, 15, 1360401. https://doi.org/10.3389/fpsyg.2024.1360401 Fatima, N., Rafiq-uz-Zaman, M., Arshad, I., Rasheed, I., & Fatima, A. (2025). The role of human resource management in teacher training for inclusive education: A phenomenological study. Indus Journal of Social Sciences, 3(3), 551–564. https://doi.org/10.59075/ijss.v3i3.1921 Fountaine, T., McCarthy, B., & Saleh, T. (2023). The augmented HR function: Redefining human resource management through AI. MIT Sloan Management Review, 64(4), 54–63. https://doi.org/10.5555/mit.smr.2023.64.4.54 Hertel, G., & Leka, S. (2022). Human-centered AI and employee well-being: The balancing act of automation. Journal of Occupational Health Psychology, 27(5), 530–542. https://doi.org/10.1037/ocp0000324 Jaiswal, A., Arun, C. J., & Varma, A. (2022). Rebooting employees: Upskilling for artificial intelligence in multinational corporations. International Journal of Human Resource Management, 33(6), 1179–1208. https://doi.org/10.1080/09585192.2021.1891114 Joo, B. K., & Lee, I. (2023). Digital literacy and adaptive performance in AI-integrated HR systems. Asia Pacific Journal of Human Resources, 61(2), 410–429. https://doi.org/10.1111/1744-7941.12315 Kakar, A. K., & Shehzad, S. (2023). Emotional exhaustion and technostress in smart workplaces: The moderating role of organizational support. Employee Relations, 45(3), 756–773. https://doi.org/10.1108/ER-02-2022-0054 Kushwah, S., & Kaushik, M. (2023). Digital literacy and self-efficacy as determinants of techno-adaptation: Implications for human capital development. Information Systems Frontiers, 25(5), 1149–1163. https://doi.org/10.1007/s10796-023-10311-7 Li, M., & Wang, J. (2023). The interplay between AI technologies, digital literacy, and human productivity: A systematic review. Frontiers in Psychology, 14, 1125407. https://doi.org/10.3389/fpsyg.2023.1125407 Mahapatra, M., & Pati, S. (2023). Work intensification and mental fatigue in AI-driven workplaces: A moderated mediation model. Human Relations, 76(7), 1042–1065. https://doi.org/10.1177/00187267221138492 Nguyen, T., Vo, T., & Dang, P. (2023). Artificial intelligence adoption and employee performance: The mediating role of digital engagement. Computers in Industry, 151, 103930. https://doi.org/10.1016/j.compind.2023.103930 Niazi, M., & Qamar, A. (2022). AI-driven HR analytics: Implications for employee engagement and performance management. International Journal of Human Resource Management, 33(10), 2041–2063. https://doi.org/10.1080/09585192.2021.1934502 Ötting, S., & Maier, G. W. (2023). Algorithmic management and employee autonomy: The paradox of digital supervision. Frontiers in Psychology, 14, 1128412. https://doi.org/10.3389/fpsyg.2023.1128412 Park, E., & Lim, J. (2023). Organizational trust and the mitigation of technostress in digital transformation. Information & Management, 60(4), 103823. https://doi.org/10.1016/j.im.2023.103823 Rafiq-uz-Zaman, M. (2025). Use of artificial intelligence in school management: A contemporary need of school education system in Punjab (Pakistan). Journal of Asian Development Studies, 14(2), 1984–2009. https://doi.org/10.62345/jads.2025.14.2.56 Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2022). The consequences of technostress for individuals and organizations. Information Systems Journal, 32(5), 940–964. https://doi.org/10.1111/isj.12334 Ratten, V., & Usmanij, P. (2023). Human capital and AI: A new framework for smart workforce development. International Journal of Manpower, 44(4), 711–727. https://doi.org/10.1108/IJM-06-2022-0228 Riedl, R., Fischer, T., & Banker, R. (2023). Techno-eustress in digital transformation: When technology challenges become opportunities. Information Systems Journal, 33(1), 78–104. https://doi.org/10.1111/isj.12371 Saleem, F., Malik, M. I., Qureshi, S. S., Farid, M. F., & Qamar, S. (2021). Technostress and employee performance nexus during COVID-19: Training and creative self-efficacy as moderators. Frontiers in Psychology, 12, 595119. https://doi.org/10.3389/fpsyg.2021.595119 Tarafdar, M., & Stich, J. F. (2023). Digital stress in the workplace: Challenges and coping mechanisms. European Journal of Information Systems, 32(1), 38–56. https://doi.org/10.1080/0960085X.2022.2071139 Wamba, S. F., Bawack, R. E., Guthrie, C., Queiroz, M. M., & Carillo, K. D. A. (2023). Can artificial intelligence mitigate technostress? Evidence from digital transformation practices. Information Systems Frontiers, 25(3), 981–999. https://doi.org/10.1007/s10796-022-10291-8 Wang, S., & Zhang, Y. (2023). The moderating effect of digital literacy on AI adoption and work outcomes. Computers in Human Behavior, 144, 107769. https://doi.org/10.1016/j.chb.2023.107769 Wang, W., & Siau, K. (2023). Emotional disconnect in digitalized HRM: Implications of AI for organizational empathy. International Journal of Information Management, 71, 102652. https://doi.org/10.1016/j.ijinfomgt.2023.102652 Yoon, S., & Choi, B. (2022). Digital transformation readiness and employee adaptability: The role of continuous learning culture. Sustainability, 14(24), 16520. https://doi.org/10.3390/su142416520 Zhou, Y., & Li, Q. (2022). Smart HRM systems and human-centric leadership in digital enterprises. Journal of Business Research, 147, 442–455. https://doi.org/10.1016/j.jbusres.2022.04.041
This retrospective observational study evaluated the clinical use and treatment outcomes in virologically suppressed people with HIV (PWH) switching to either bictegravir/emtricitabine/tenofovir alafenamide (B/F/TAF) or dolutegravir (DTG)-based regimens (single- and multi-tablet formulations (STR and MTR)). We analyzed electronic medical and dispensing records from Trio Health HIV Research Network for treatment-experienced PWH ≥ 18 years suppressed (viral load [VL] < 200 copies/mL) at switch to B/F/TAF, DTG STR (DTG/3TC, DTG/RPV, DTG/3TC/ABC) or most common DTG MTRs with VL at 12 months (+/-3) since switch between April 2019 and December 2024. Univariate comparisons: chi-square or t-test; characteristics associated with virologic suppression at 12 months: multivariable logistic regression [LR], controlling for age, gender, race, baseline CD4 count, regimen, and adherence (proportion days covered [PDC] ≥ 80
ADVERTISEMENT RETURN TO ISSUEEditorialNEXTFlow Chemistry and Continuous Processing: More Mainstream than Ever!Kevin P. Cole*Kevin P. ColeSynthetic Molecule Design and Development, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States*Email: [email protected]More by Kevin P. Colehttps://orcid.org/0000-0002-2333-6557, Jonathan N. JaworskiJonathan N. JaworskiProcess Chemistry Development, Takeda Pharmaceuticals International Co., 35 Landsdowne Street, Cambridge, Massachusetts 02139, United StatesMore by Jonathan N. Jaworskihttps://orcid.org/0000-0002-9509-3769, C. Oliver KappeC. Oliver KappeInstitute of Chemistry, University of Graz, Heinrichstrasse 28, A-8010 Graz, AustriaMore by C. Oliver Kappehttps://orcid.org/0000-0003-2983-6007, Shu KobayashiShu KobayashiDepartment of Chemistry, School of Science, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-0033, JapanMore by Shu Kobayashihttps://orcid.org/0000-0002-8235-4368, Anita R. MaguireAnita R. MaguireSchool of Chemistry and School of Pharmacy, Analytical and Biological Chemistry Research Facility, SSPC, the SFI Research Centre for Pharmaceuticals, University College Cork, Cork T12 K8AF, IrelandMore by Anita R. Maguirehttps://orcid.org/0000-0001-8306-1893, Anne O'Kearney-McMullanAnne O'Kearney-McMullanChemical Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Macclesfield SK10 2NA, United KingdomMore by Anne O'Kearney-McMullan, and Jaan A. PestiJaan A. PestiPharma Resource Group Inc., 880 Enterprise Drive, Royersford, Pennsylvania 19468, United StatesMore by Jaan A. PestiCite this: Org. Process Res. Dev. 2024, 28, 5, 1269–1271Publication Date (Web):May 17, 2024Publication History Received11 December 2023Published online17 May 2024Published inissue 17 May 2024https://pubs.acs.org/doi/10.1021/acs.oprd.3c00483https://doi.org/10.1021/acs.oprd.3c00483editorialACS PublicationsCopyright © Published 2024 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views-Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-AlertscloseSUBJECTS:Catalysts,Crystallization,Industrial manufacturing,Pharmaceuticals,Separation science Get e-Alerts
Algebra is, arguably, one of the pillar topics in mathematical education, and is, as such, one of the competencies necessary for furthering mathematical education, and a compulsory requirement in the majority of higher education and career paths. One of the main public discourses is whether Algebra 3-4 LS should be a compulsory subject in high schools, including for children with learning disabilities. While some believe that exposure and competency in algebra facilitate problem-solving, logical reasoning, and analytical skills, a good number feel that the high level of abstractness in algebra puts an undue burden on many students with learning disabilities [1]. This problem necessitates careful deliberations on the importance of algebraic literacy, the challenges faced by students with learning disabilities, and possible accommodations and alternative approaches that could bridge the gap between accessibility and rigor, to guarantee equal opportunities for success for all children.
Barbara Piskor, MPH, BSN, RN, NEA-BC, BKP HealthCare Resources, Home Health, Hospice and Home Care Consulting, Pittsburgh, Pennsylvania. The author declares no conflicts of interest. Address for correspondence: Barbara Piskor, MPH, BSN, RN, NEA-BC, BKP HealthCare Resources, LLC. 112 Poplar Drive, Pittsburgh, PA 15238 ([email protected]).
