competitive intelligence

This study addresses the challenge of extracting business-interpretable item association rules from retail transaction data to support precision marketing, shelf-space optimization, and inventory management. To bridge the gap between statistical discoverability and operational actionability, we propose a novel rule filtering and prioritization framework that jointly considers statistical significance (via support, confidence, and lift) and managerial feasibility (through domain-specific semantic mapping). Our method integrates Apriori and FP-Growth algorithms, incorporates a three-dimensional rule evaluation scheme, and enables interactive rule visualization. Evaluated on a real-world supermarket dataset, the framework identified 327 high-value, actionable association rules. Deployment yielded an 18.6% increase in cross-buying rate and a 22.3% improvement in promotional response rate, empirically validating its practical effectiveness and scalability for retail analytics.

Current what-if analysis lacks a unified conceptual framework, leading to terminological inconsistency across domains, structural ambiguity, and divergent interpretations. To address this, we conduct a systematic review of 141 papers in visual analytics and human-computer interaction, proposing Praxa—the first integrative framework that unifies scenario modeling, sensitivity analysis, and counterfactual analysis under a coherent paradigm. Praxa formally defines the underlying motivations, core components (hypothesis generation, intervention modeling, outcome evaluation), and a taxonomy of analytical types. It establishes a standardized terminology and structured model, exposing critical challenges including interpretability, causal modeling fidelity, and alignment with user intent. By clarifying conceptual boundaries and operational relationships among methods, Praxa significantly enhances cross-domain conceptual consistency and application clarity. The framework provides a rigorous foundation for theoretical advancement and the design of next-generation interactive analytical tools.

Antitrust market definition lacks consensus, undermining the robustness of merger assessments. This paper proposes a systematic sensitivity analysis framework grounded in partially ordered sets (posets) and Hasse diagrams—the first application of Hasse diagrams to market definition research. Integrating Shapley values and the Shapley–Shubik power index, the method quantifies firms’ marginal contributions and power distributions across alternative market definitions. It enables interpretable, structured cross-definition evaluation, substantially enhancing analytical transparency and reproducibility. Applied to the 2015 Albertsons/Safeway merger case, the framework successfully identifies pivotal firms and assesses the robustness of market definitions under varying assumptions. The approach establishes a novel paradigm for antitrust economic analysis and delivers a practical, implementable tool for competition authorities and practitioners.

Business professionals—non-technical domain experts—lack appropriate tools and methodologies for effective what-if analysis (WIA), hindering data-informed decision-making. Method: We conducted a two-phase mixed-methods user study—comprising contextual interviews and in-situ task-based evaluations—to systematically characterize their analytical behaviors for the first time. Contribution/Results: Based on empirical findings, we propose three domain-grounded design principles: business-contextual data preparation, risk-aware assessment, and domain-knowledge integration. We implemented and validated these principles in an interactive visual analytics prototype. The study identifies three critical support gaps, empirically confirms that six classes of what-if techniques significantly improve decision efficiency and confidence, and yields eight actionable design guidelines for commercial business intelligence systems. This work bridges a key theoretical and practical gap in WIA research concerning non-technical users.

Financial cluster analysis faces three key challenges: difficulty in modeling dynamic temporal dependencies, high ambiguity in heterogeneous business knowledge sources, and poor interpretability due to exhaustive pairwise comparisons. To address these in the context of thematic stock investment, this paper proposes a three-stage collaborative clustering paradigm—“dynamic generation, knowledge exploration, and correlation validation.” It integrates time-series similarity metrics with domain-specific knowledge graph embeddings to construct a multi-view interactive clustering framework that jointly quantifies performance and qualifies semantic relationships. The framework incorporates heatmap-, relational-graph-, and trajectory-based visualizations and supports user-driven iterative refinement. Experiments demonstrate significant improvements in clustering validity and interpretability; domain experts highly endorse its effectiveness in thematic stock construction, risk-hedging portfolio identification, and emerging investment theme discovery.

本文提出一种混合代理AI框架，通过协调代理解析用户意图并分配任务给专门代理，解决供应链分析中的决策难题，提高效率和成本效益。

This work addresses the limited capacity of existing online recruitment platforms to analyze multidimensional attribute relationships and hierarchical market structures. We propose an interactive visual analytics system designed for both job seekers and HR professionals, which, through a coordinated multiple-view design, enables the first integrated exploration of cross-hierarchical, multidimensional hiring data—spanning from macro-level industry trends to micro-level emerging roles. The system integrates data aggregation, pattern recognition, and multi-scale visualization techniques to support comprehensive labor market analysis. Case studies demonstrate its effectiveness in revealing regional salary distributions, characterizing industry evolution trajectories, and accurately identifying high-demand emerging positions.

This study investigates how goal-aligned and goal-agnostic reward mechanisms influence decision-making behavior and design diversity in creative tasks. Using a 3D parametric chair design task as the experimental setting, the design process is formalized as a Markov decision process, and a mixed-methods approach—integrating user behavior tracking, experimental psychology paradigms, and hybrid analytical techniques—is employed to systematically examine participants’ exploration strategies and subjective experiences under different reward conditions. The findings reveal that goal-aligned rewards not only enhance goal attainment but also foster more thorough exploration of the design space while preserving diversity. Moreover, the nature of the design goal significantly moderates users’ perceived usefulness of the rewards. These results elucidate the synergistic mechanism between rewards and goals and offer actionable guidelines for designing effective feedback systems in creative design contexts.

This study addresses the limitations of traditional B2B customer segmentation approaches, such as the RFM model, which rely on singular metrics and struggle to capture the complexity and dynamics of business interactions. To overcome this, the authors propose a dynamic, multi-criteria segmentation framework that extends RFM by incorporating stability and growth dimensions. The framework aligns with strategic business objectives through an adaptive Analytic Hierarchy Process (AHP) and integrates multivariate time series clustering with a graph consensus model to enable temporal segmentation. Evaluated on data from over 3,000 manufacturing enterprises, the approach demonstrates strong temporal robustness and significantly enhances the precision of customer strategy formulation through preference-driven dynamic clustering.

Organizations struggle to quantify the commercial value of data assets due to fragmented, siloed valuation approaches—divided across economic, governance, and strategic perspectives—and the absence of actionable mechanisms. This paper proposes an integrated data valuation framework that unifies these three perspectives using a Balanced Scorecard–inspired hybrid model. The framework combines qualitative scoring, cost-utility estimation, data quality indexing, and Analytic Network Process (ANP)-based multi-criteria weighting to enhance transparency and strategic alignment. Adopting a design science research methodology, it is iteratively refined through embedded industrial case studies. Empirical evaluation demonstrates that the framework significantly reduces subjectivity in valuation, improves the precision of mapping data assets to organizational strategic objectives, and supports diverse monetization pathways—including Data-as-a-Service (DaaS). It exhibits cross-industry applicability and robustness.