Thriving Through People: Human-Centered Strategies for Organizational Sustainability in the Digital Age

Dear Colleagues,

Digital transformation is no longer a question of narrow technological upgrade, but a broader process of organizational renewal that reshapes structures, capabilities, business models, workflows, and decision processes (Warner & Wäger, 2019; Vial, 2019; Verhoef et al., 2021; Plekhanov et al., 2023; Kostelić & Etinger, 2025). This transformation is now being accelerated by the rapid diffusion of generative artificial intelligence (GenAI), which is expanding the speed and scale at which organizations and public administrations can generate information, automate tasks, support judgment, and redesign services. At the same time, the spread of AI into managerial and public-sector contexts raises important questions about governance, accountability, transparency, and institutional readiness, especially where decisions affect employees, citizens, or other stakeholders (Aarab et al., 2025; OECD, 2025; Regulation (EU) 2024/1689).

A growing body of research shows that digital transformation unfolds unevenly across sectors and organizations, and that digital maturity strongly conditions what kinds of decisions can be delegated, supported, or improved through digital systems (Kalender & Žilka, 2024; Kadoić et al., 2024). However, the benefits of transformation are not distributed evenly. Research on the second- and third-level digital divide reminds us that inequality in digital settings is not only about access, but also about differences in skills, use, and outcomes (Scheerder et al., 2017). As a result, digital maturity, inclusion, accessibility, and internal sustainability should not be treated as parallel topics, but as interdependent conditions shaping digital transformation. This leads us to question whether these procedures may produce better, fairer, and more resilient decisions.

These developments are especially important in the context of GenAI-enabled decision-making. While AI systems can enrich analysis and support faster choices, they may also intensify familiar behavioral risks, including overreliance, automation bias, uncritical acceptance of AI-generated outputs, and poorly calibrated trust (Romeo & Conti, 2026). In practice, this means that the central question is no longer whether organizations use AI, but how human judgment and AI-generated insights are combined, monitored, validated, and governed in real decision environments. This challenge is particularly salient in public administration and regulated sectors, where legal compliance, fairness, and accountability cease to be optional design features and are becoming essential requirements (Aarab et al., 2025; OECD, 2025; Regulation (EU) 2024/1689).

At the same time, digital transformation is also a human and socio-technical process. Its outcomes depend not only on technologies and formal governance, but also on the way employees, teams, managers, and institutions experience and interpret change. Recent work using the job demands–resources perspective highlights that digitization can simultaneously create new resources and new demands: it can improve efficiency, autonomy, and information access, while also increasing overload, uncertainty, stress, and other unintended psychological burdens (Scholze & Hecker, 2024). This makes human factors central to understanding digital transformation, including stress, negative emotions, threat perception, trust, coping, emotion regulation, and cognitive bias. If transformation is to be sustainable, organizations must understand not only whether digital tools “work,” but also how they shape well-being, motivation, confidence, and the quality of human judgment over time.

Against this background, this Special Issue invites theoretical, empirical, and interdisciplinary contributions that examine how digital maturity, inclusiveness, governance, and human factors shape decision-making in organizations and public administrations undergoing digital transformation. We are particularly interested in research that bridges leadership, operations, business processes, strategic decision-making, and HR/human capital, and that offers actionable implications for practice, such as validated instruments, decision frameworks, governance mechanisms, intervention designs, and evidence-based recommendations. We also seek contributions that connect management and public administration with psychology, sociology, communication, informatics, artificial intelligence, and related fields.

Submissions may address (but are not limited to):

• Digital maturity, inclusion, and governance
  • Digital maturity models and their decision-making implications;
  • Digital divide, inclusiveness, accessibility, and equity in digital workplaces;
  • Responsible/ethical AI governance, accountability, transparency, and oversight;
  • AI policy and governance in public administration and regulated industries.
• GenAI in managerial decision-making
  • Human judgment vs. AI-generated insights: validation, monitoring, and quality assessment;
  • Trust calibration, overreliance/automation bias, and explainability in practice;
  • Ethical and behavioral implications of GenAI use in teams and organizations.
• Emotions, stress, and well-being in digital transformation
  • Negative emotions, threat perception, and stress responses to GenAI adoption;
  • Emotional intelligence and emotion regulation in AI-enabled decision contexts;
  • Coping strategies and organizational interventions (e.g., in healthcare settings).
• Leadership, HR, and human capital
  • Leadership styles and motivation in digitally transforming organizations;
  • Skills, competencies, and training needs for AI-enabled transformation;
  • Sustainable, resilient, and inclusive human capital strategies;
  • Differences in behavior perception among employees, teams, and supervisors.
• Operations, processes, and decision support
  • Business process change, decision workflows, and socio-technical design;
  • AI-assisted decision support systems: evaluation, bias mitigation, and ethics-by-design;
  • Simulation, experimentation, and decision analytics for ethically informed choices.

We welcome diverse methods, including the following:

• Systematic or scoping reviews; theory development;
• Case studies and mixed-methods research;
• Surveys and validation studies (scale development, measurement invariance, and cross-cultural validation);
• Controlled experiments and behavioral studies;
• Simulation experiments (e.g., agent-based, system dynamics, Monte Carlo) and decision analytics;
• Interviews and qualitative comparative analysis.

References

1. Warner, K. S.R. and Wäger, M. (2019) Building dynamic capabilities for digital transformation: an ongoing process of strategic renewal. Long Range Planning, 52(3), pp. 326-349. (doi: 10.1016/j.lrp.2018.12.001)
2. Vial, G. (2019) Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118-144. https://doi.org/10.1016/j.jsis.2019.01.003
3. Verhoef, P. C. et al. (2021) Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, pp. 889-901. https://doi.org/10.1016/j.jbusres.2019.09.022
4. Plekhanov, D., Franke, H. and Netland T. H. (2023) Digital transformation: A review and research agenda. European Management Journal, 41(6), pp. 821-844. https://doi.org/10.1016/j.emj.2022.09.007
5. Aarab, A., El Marzouki, A., Boubker, O., & El Moutaqi, B. (2025). Integrating AI in Public Governance: A Systematic Review. Digital, 5(4), 59. https://doi.org/10.3390/digital5040059
6. Kalender, Z., T. and Zilka, M. (2024). A Comparative Analysis of Digital Maturity Models to Determine Future Steps in the Way of Digital Transformation. Procedia Computer Science, 232, pp. 903-912. https://doi.org/10.1016/j.procs.2024.01.090
7. Kadoić, N., Monllau Jaques, T., & Lacueva Rueda, J. (2024). Measuring the Digital Maturity of Organisations in the People Care Sector. Sustainability, 16(20), 9053. https://doi.org/10.3390/su16209053
8. Romeo, G. and Conti, D. (2026). Exploring automation bias in human–AI collaboration: a review and implications for explainable AI. AI & Society, 41, pp. 259–278. https://doi.org/10.1007/s00146-025-02422-7
9. Scholze, A. and Hecker, A. (2024). The job demands-resources model as a theoretical lens for the bright and dark side of digitization. Computers in Human Behavior, 155, 108177. https://doi.org/10.1016/j.chb.2024.108177
10. Scheerder, A., van Deursen, A. and van Dijk, J. (2017). Determinants of Internet Skills, Uses and Outcomes. A Systematic Review of the Second- and Third-Level Digital Divide. Telematics and Informatics, 34(8), pp. 1607-1624. https://doi.org/10.1016/j.tele.2017.07.007
11. Kostelić, K. and Etinger, D. (2025). Metaverse Business Models and Framework: A Systematic Search with Narrative Synthesis. Systems, 13(11) (968), pp. 1-45. https://doi.org/10.3390/systems13110968
12. REGULATION (EU) 2024/1689 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 13 June 2024. Official Journal of the European Union, 2024/1689. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1689
13. OECD (2025), Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions, OECD Publishing, Paris, https://doi.org/10.1787/795de142-en

Prof. Dr. Marli Gonan Božac
Dr. Katarina Kostelić
Guest Editors

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Publisher's Notice

The Special Issue has been assigned to Section Strategic Management on 24 August 2026. At the time of the move, there were no publications in this Special Issue.