maturity modeling

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.

This study addresses the absence of an objective and reproducible evaluation framework for artificial general intelligence (AGI), which has led to subjective assessments of progress and challenges in governance. Drawing on foundations from psychology, neuroscience, and cognitive science, this work proposes a systematic cognitive taxonomy comprising ten core capabilities grounded in human cognition. It introduces targeted retention tasks designed to evaluate system performance across these dimensions, thereby generating multidimensional cognitive profiles. The approach enables an operational decomposition and empirical measurement of AGI progress, offering an initial benchmark to identify system strengths and weaknesses and to advance the standardization and transparency of AGI research.

Systematic Literature Reviews (SLRs) in software engineering frequently suffer from validity threats due to omitted or inadequately executed steps, and lack an actionable, quality-improvement framework. Method: This paper introduces, for the first time, the Capability Maturity Model Integration (CMMI) maturity paradigm into SLR process modeling, proposing MM4SLR—a five-level, incremental maturity model grounded in 39 key practices, 9 goals, and 5 process areas. The model was designed via literature-driven identification, clustering analysis, and level mapping, and empirically validated across four published SLRs. Contribution/Results: MM4SLR enables effective diagnosis of SLR quality deficiencies, supports researchers in selecting context-appropriate practices, and facilitates continuous process improvement. It constitutes the first structured, assessable, and evolutionary maturity framework for enhancing the rigor, standardization, and credibility of SLRs in software engineering.

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 a critical gap in human-computer interaction (HCI) research: the lack of systematic understanding regarding how “frameworks” are practically used and conceptually constructed. Through a systematic review of 615 CHI papers from 2015 to 2024 that center on frameworks, this work proposes six distinct types of framework engagement and develops a functional taxonomy analyzing framework practices along four dimensions—role, structure, validation, and reuse. The analysis reveals that proposals of novel frameworks significantly outnumber iterative refinements of existing ones, and that many frameworks suffer from ambiguous functional scope and insufficient validation. These findings highlight a non-cumulative tendency in HCI’s approach to framework development and call for more rigorous, reflective, and sustainable paradigms in both the construction and application of frameworks within the field.

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.

Traditional compliance assessments rely on point-in-time audits and self-attestation, which struggle to enable continuous, cross-organizational, and traceable verification of security controls in multi-vendor environments. This work proposes a permissioned blockchain-based Third-Party Risk Assessment (TPRA) framework that transforms static compliance into a dynamic, repeatable, and verifiable continuous governance mechanism through smart contract–automated evaluation workflows, multi-party governance protocols, and longitudinal state tracking. The study contributes an actionable TPRA architecture, along with complementary compliance maturity metrics and a qualitative model, enabling quantification and long-term validation of security control implementation maturity across organizational boundaries and time periods.

Although agile software development has been widely adopted, there remains a lack of a systematic, integrated framework identifying the critical success factors that consistently contribute to project success. Addressing this gap, this study conducts a systematic literature review of 53 empirical studies, employing thematic synthesis and content analysis to identify 21 critical success factors. These factors are systematically categorized into five dimensions—organizational, human, technical, process, and project—yielding a novel multidimensional theoretical framework. This framework represents the first comprehensive integration and classification of agile success factors, emphasizing the pivotal roles of team effectiveness and project management. It provides a solid theoretical foundation for future quantitative validation and practical application in agile contexts.

This study addresses the frequent failure of enterprise technology modernization initiatives due to the absence of structured governance mechanisms. Building on 24 years of practical experience, the authors propose the EMRGF framework—an end-to-end integrated model encompassing governance of cloud and legacy systems, data platform reliability, AI-driven automation governance, and root-cause analysis for mission-critical operations. EMRGF pioneers a standardized approach to cross-domain governance spanning migration, data platforms, and AI, aligning with NIST CSF 2.0, NIST AI RMF, and U.S. Executive Orders 14028 and 14110. The framework integrates four interlocking modules, five implementation tool categories, and a trainer development mechanism. Empirical validation demonstrates that its large-scale adoption reduces development effort by 30%, shortens testing cycles by 35%, enables zero-downtime high-load data migration, and achieves 99.9% reliability in critical analytics pipelines.

Software engineering research has long lacked a structured methodology to guide researchers in formulating industrially relevant research questions. Method: This paper proposes and empirically validates a seven-dimensional problem modeling framework—comprising Actual Problem, Context, Impact, Practitioners, Evidence, Goal, and Research Question—and innovatively incorporates financial dimensions (e.g., ROI) and feasibility constraints to enhance industrial applicability. We conducted an empirical evaluation with 42 senior SE researchers via participatory workshops using Problem Vision boards, structured questionnaires, and qualitative analysis. Contribution/Results: The framework significantly improves the practical relevance and operationalizability of research questions. It yields actionable guidelines for refining problem formulation, thereby effectively bridging the gap between academic research and industrial needs.