market opportunity identification

This study addresses the challenge of early-stage technology opportunity identification, where ambiguous user needs and the absence of systematic integration of end-user values often lead to misalignment between technological potential and market demands. To bridge this gap, the authors propose a novel decision-support framework that integrates Technology Readiness Levels (TRL) with Schwartz’s theory of basic human values—introducing, for the first time, human values into the process of technology opportunity recognition. The framework defines two key metrics: “value breadth” and “vision gap.” Through qualitative analysis combining expert and consumer workshops in a case study at Sony CSL, the research demonstrates that successful technologies resonate across a broader spectrum of human values, and that experts articulate richer value dimensions than consumers. These findings validate the framework’s capacity to enhance technology–market fit through a value-driven approach.

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.

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.

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.

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.

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.

This study addresses the persistent challenge of translating academic frameworks into actionable practices during the pre-production phase of AAA game development, where theoretical models often falter due to misalignment with industrial constraints, production realities, and cross-functional collaboration demands. Through in-depth interviews with 15 AAA game UX leads and subsequent qualitative analysis, the research elucidates how design decisions integrate theory, experiential knowledge, and evidence-informed intuition to balance player needs, technical feasibility, and creative vision. The work proposes a flexible theoretical toolkit that operationalizes academic concepts into context-sensitive insights, systematizes tacit expertise, and adapts to dynamic development workflows. Key contributions include a shared language for cross-team alignment, reusable design systems, and adaptive strategies that offer a practical pathway to bridge the gap between academic research and industry practice.

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 work addresses the challenge of transitioning AI systems from executing predefined tasks to autonomously planning business actions aligned with high-level strategic objectives. It introduces, for the first time, a systematic application of world models to commercial settings by constructing an executable business simulator that integrates semantic representations, deterministic business rules, and probabilistic machine learning. This framework explicitly models business states, dynamics, constraints, objectives, and action spaces, enabling agents to perform counterfactual reasoning, predict outcomes, and evaluate trade-offs under uncertainty. By supporting goal-driven autonomous decision-making, the proposed approach establishes both conceptual and technical foundations for autonomous business agents, marking a significant step toward advancing AI from mere instruction execution to strategic planning.