organizational strategy development

This study addresses critical challenges confronting higher education institutions in AI adoption—namely, strategic misalignment, ethical risks, and insufficient capacity development. Employing policy analysis, stakeholder mapping, and multi-scenario case-based simulation, the research develops the original CASD framework (Challenge–Action–Stakeholder–Deployment), structured across five analytical dimensions. The framework specifies five strategic action types and tiered implementation pathways for key actors—including institutional leaders, faculty, and students. Grounded in UNESCO’s international guidelines and empirically validated through localized practice, CASD constitutes the first theoretically rigorous yet operationally feasible paradigm for AI integration in higher education. It delivers replicable, institution-level governance models and course-level pedagogical templates, thereby enhancing the systemic coherence, cross-stakeholder collaboration, and long-term sustainability of AI-enabled educational transformation.

This study addresses the persistent challenge faced by California Community Colleges (CCCs) in aligning financial planning with their Diversity, Equity, and Inclusion (DEI) mission amid ongoing state budget reforms. Using publicly available data from 1993 to 2023, we employ quantitative correlation analysis and statistical modeling to assess the predictive power of macroeconomic indicators on state education appropriations. Results indicate that GDP growth rate and the Consumer Price Index (CPI) exert statistically significant positive effects on state-level community college funding (p < 0.01). Building on these findings, we propose an institutional innovation: integrating a dynamic economic forecasting system into colleges’ strategic decision-making frameworks. This approach enhances budgetary resource allocation efficiency and strengthens sustainable support for historically underserved student populations. The study contributes empirically grounded, actionable policy and administrative design recommendations for advancing fiscal resilience and equity-centered resource stewardship in public higher education.

This study addresses the persistent challenges faced by User Experience Research (UXR) teams—namely, stakeholder bias, reactive engagement, and fragmented insights—that hinder their ability to exert strategic influence. To overcome these limitations, the authors innovatively integrate structured strategic thinking into UXR function development, proposing an organizational maturity model grounded in a UXR Point-of-View (POV) framework. Complementing this model is a practical playbook that combines “offensive” and “defensive” strategies to guide implementation. This integrated approach systematically enables UXR teams to transition from tactical execution to strategic impact, significantly enhancing their capacity to forge strategic partnerships, generate actionable insights, and contribute meaningfully to long-term corporate strategy formulation.

This study addresses the multi-stage coordination challenges in managing capstone course projects at higher education institutions, particularly concerning student interest alignment, external project solicitation, and team formation. The authors propose and implement a web-based management platform designed to facilitate academic–industry collaboration, marking the first application of a systematic digital tool across the entire capstone project lifecycle. The platform emphasizes automated project solicitation and algorithm-driven intelligent team formation, integrating student profiles, project data, and matching logic to support multi-stakeholder collaboration. Deployed successfully at Insper, the system has significantly improved both the efficiency of project allocation and the quality of team composition, offering a reproducible operational model and foundational dataset for similar educational contexts.

Existing performance measurement frameworks struggle to simultaneously satisfy customizability, interpretability, and mathematical tractability in interdisciplinary contexts. Method: This paper proposes a goal-oriented, customizable metric construction framework featuring a novel “base metric–auxiliary metric” dichotomy. Integrating utility theory and multi-criteria decision analysis, it introduces an uncertainty-aware utility function and establishes a systematic metric decomposition–synthesis workflow. Contributions: (1) It reduces reliance on complex mathematical formalisms, enhancing applicability under resource constraints or high uncertainty; (2) it ensures metric transparency, traceability, and domain adaptability; and (3) it enables quantitative assessment of goal attainment, real-time progress monitoring, and downstream statistical modeling and decision optimization. The framework has been empirically validated across diverse disciplines, demonstrating generality and extensibility.

In multi-stakeholder platforms, software architecture decisions often implicitly entrench conflicting requirements without systematic support for mapping governance principles to technical design. This work proposes the first governance-architecture alignment framework, explicitly linking five core governance principles to the space of architectural decisions, thereby rendering implicit governance stances identifiable and contestable. The framework also exposes how default technical choices can obscure underlying value commitments. Feasibility is preliminarily demonstrated through a constructive case study of a pig-farming knowledge platform in Rwanda. Future work will employ pre- and post-intervention user judgment studies to evaluate the framework’s impact on actual governance outcomes.

This study addresses the absence of a formal modeling framework in strategic crisis analysis that operates without requiring complete payoff or probabilistic information, integrates expert qualitative judgments, explicitly captures dependencies, and supports auditable update rules. To bridge this gap, the authors propose a two-layer formal framework that decouples a static scenario database from a dynamic scenario tree system. They introduce, for the first time, a formally defined extended scenario bundle analysis model, incorporating a domain-modifier layer, a topological structure over scenario space, a typed state-update mechanism, and a multi-criteria evaluation method. This architecture enables context-sensitive modeling of multi-agent attitudes—including beliefs, desires, intentions, fears, and coalition commitments—while maintaining mathematical rigor and computational tractability, thereby significantly enhancing the transparency, traceability, and expressive power of complex crisis analysis.

This work proposes a systematic approach to derive task effectiveness requirements in the absence of explicit user needs. The method deconstructs task intent into context, functionality, constraints, critical dimensions, performance attributes, and architectural solutions, and introduces a task complexity factor to quantify the impact of external challenges and technology maturity. By integrating Best-Worst Scaling, it prioritizes critical dimensions based on stakeholder judgments. Through task decomposition modeling and quantitative complexity analysis, the framework supports integration with UAF/SysML artifacts and establishes a traceable mechanism for generating Tier 1 and Tier 2 requirements. The approach is validated using a close air support mission case study, effectively addressing a critical gap in requirements engineering when clear initial inputs are unavailable.

This study addresses the persistent challenge of translating European academic research into industrial impact, particularly in light of Industry 5.0’s demands for technical depth, sustainability, and human-centric design—requirements inadequately met by traditional doctoral training. To bridge this gap, the project proposes a dual-layer competence framework guided by four design principles: modularity, practical relevance, robust mentorship, and cross-domain applicability. Through expert interviews, co-design workshops, and a multi-method analytical framework, the approach systematically integrates academic rigor with industrial needs, yielding a scalable and modular developmental pathway for early-career researchers. This model effectively narrows the translational divide between scholarly output and real-world industrial application, offering an innovative paradigm for cultivating research talent aligned with the ethos and exigencies of Industry 5.0.

This study addresses interpretive biases in higher education analytics dashboards arising from data limitations and contextual omissions by proposing the FACTRIA framework. This framework pioneers the deep integration of structured bias factors with generative AI, employing a chatbot interface to guide users in reflecting on potential biases and thereby enabling context-aware interactive analytical support. Through qualitative research combined with network analysis methods, empirical findings demonstrate that this approach effectively helps users identify overlooked influencing factors and significantly enhances the objectivity and accountability of data interpretation. Ultimately, this work provides an innovative paradigm for responsible educational data analytics.