organizational diagnosis

本文通过量化团队失调与工程顶点项目表现之间的关系，基于Lencioni的模型评估团队动态，揭示了两者间存在弱至中等程度的关联。

Process mining often yields an overwhelming number of candidate process models, creating decision paralysis for managers seeking actionable insights. Method: This paper proposes a multi-criteria decision-making (MCDM) evaluation framework that jointly incorporates quantitative metrics (e.g., fitness, precision) and qualitative factors (e.g., organizational culture alignment). It systematically integrates MCDM techniques—including the Analytic Hierarchy Process (AHP)—into process model prioritization for the first time, moving beyond purely technical, performance-driven selection criteria. Contribution/Results: The framework enables structured, interpretable trade-offs between operational performance and strategic objectives. Evaluated in a logistics case study, it significantly improves contextual sensitivity and managerial alignment in model selection, facilitating robust, transparent decision-making under competing goals.

研究探讨了敏捷开发中Scrum Master角色的学习路径及性别动态，通过定性和定量分析方法发现团队规模影响该角色需求，并指出软技能学习缺乏标准化过程。

This study addresses the lack of systematic preprocessing standards, integrated analytical workflows, and cross-method consistency checks in current computer-based assessment process data. To bridge this gap, the authors propose an end-to-end analytical framework featuring a unified preprocessing pipeline and a dual-path analysis paradigm that synergistically combines feature engineering with model-based inference. The framework incorporates large language models (LLMs) to standardize action sequences and facilitate process-data-driven differential item functioning (DIF) detection. Technically, it integrates timestamp correction, action chunking, n-gram and TF-IDF feature extraction, multidimensional scaling, hidden Markov modeling, and subtask identification. Empirical results demonstrate that n-gram–based behavioral clustering offers diagnostic value for incorrect responders, multidimensional scaling effectively reconstructs behavioral constructs, and process data can identify and mitigate construct-irrelevant group differences.

Existing process conformance checking techniques identify deviations between process executions and models but cannot assess their desirability—i.e., whether they are problematic, acceptable, or beneficial—leading to subjective, inefficient, and non-reproducible manual evaluation. To address this gap, we propose the first structured, reproducible framework for assessing deviation desirability. Grounded in a systematic literature review and semi-structured expert interviews, the framework defines three mutually exclusive desirability categories—problematic, acceptable, and beneficial—each accompanied by actionable recommendations that integrate theoretical conceptualization with frontline practical insights. We empirically validate the framework through task-oriented experiments, demonstrating significant improvements in analysts’ assessment efficiency and inter-rater consistency. Crucially, it maintains comprehensiveness while supporting concise, actionable decision-making. This work provides a methodological foundation for evidence-based process deviation governance.

This study addresses the lack of empirical guidance on tool design and composition for large language model (LLM) agents in microservice root cause analysis (RCA) by constructing the first systematic empirical benchmark dedicated to agentic RCA tool abstraction and composition. We propose a hierarchical tool architecture spanning levels L0 through L3 and conduct multi-model comparative experiments alongside trajectory analysis to quantitatively evaluate how different tool configurations affect diagnostic performance. Results demonstrate that higher-level tools (L3) halve fault localization time while improving fault type identification, revealing inherent accuracy-efficiency trade-offs across tool hierarchy levels. These findings provide data-driven decision-making foundations for agent tool selection and design in automated microservice diagnostics.

This study addresses a critical gap in software leadership research by moving beyond formal roles or theoretical models to examine how practitioners genuinely enact leadership in practice. Through a systematic content analysis of 116 self-reported articles from the Dev.to community, the authors construct the first empirical framework of software leadership grounded in social media discourse. The analysis yields 103 recommended and discouraged leadership practices, organized into five thematic categories and represented through a visual conceptual map. Findings reveal that effective software leadership centers on interpersonal and managerial competencies rather than technical expertise, thereby challenging conventional role- or technology-centric perspectives and offering a nuanced, practice-based understanding of leadership in software development contexts.

研究通过采访技术作家探讨了软件文档审查过程及其挑战，识别了五个审查阶段，并揭示了组织和技术上的难题。

研究开发了AI领导力电池，通过多维度衡量AI原生组织中的领导行为，解决了现有方法无法准确评估AI环境下领导力的问题。

Contemporary BI dashboards lack a structured, iterative optimization framework, hindering their evolution from exploratory tools to robust decision-support systems. Method: This study proposes a feedback-driven, gap-analysis–informed four-stage iterative methodology, integrating a six-element data narrative framework—encompassing goals, context, insights, evidence, actions, and impact—and implements it in Power BI via DAX metric optimization and collaborative peer review. Contribution/Results: The framework demonstrably enhances narrative coherence and explanatory power. Empirical application uncovered critical issues: significantly lower gross margin for furniture (6.94% vs. 13.99% for technology), profitability erosion beyond a 20% discount threshold, and $1.35M in unrecovered freight costs—substantially improving decision accuracy. This work makes the first contribution of embedding structured narrative design directly into the BI dashboard iteration lifecycle, yielding a reusable, methodologically grounded framework.