business strategy

Strategic planning is vulnerable in adversarial environments due to its inability to proactively model and withstand deliberate interference. Method: This paper proposes a goal-tree-oriented adversarial game framework that integrates goal decomposition modeling with adversarial game tree search, establishing a minimax robust path search mechanism and designing a goal-directed policy evaluation function centered on “interference resilience.” Contribution/Results: The approach shifts strategic resilience from passive reaction to active modeling, enabling provably robust responses against worst-case adversarial disruptions. Experiments across multiple scenarios demonstrate a 37% improvement in high-level strategic goal achievement rate and a 52% reduction in the probability of successful opponent disruption of critical sub-goals—significantly enhancing the proactive resilience and robustness of strategic execution.

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.

Existing decision support systems treat analytical frameworks (e.g., 6C) and heuristic strategies (e.g., Thirty-Six Stratagems) as disjoint entities, lacking semantic-level integration. Method: We propose a semantics-driven strategy recommendation system that (i) establishes the first semantic alignment between analytical frameworks and heuristics via a cross-paradigm semantic mapping mechanism; (ii) designs a multimodal language representation to uniformly encode heterogeneous strategic knowledge—including text, matrices, and diagrams; and (iii) adopts an LLM-constrained computational architecture to ensure interpretable and controllable reasoning. Our approach integrates deep semantic NLP, vector-space modeling, cross-framework similarity computation, and lightweight collaborative inference. Contribution/Results: Experiments on multiple corporate strategy cases demonstrate its effectiveness. The system supports plug-and-play recommendation for arbitrary framework–heuristic combinations and generates strategy proposals that balance theoretical rigor with practical feasibility.

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.

This study addresses the tendency in existing literature to reduce AI value alignment to a purely technical or normative issue, thereby overlooking its structural and governance dimensions. Drawing on principal–agent theory, the paper proposes a triaxial analytical framework encompassing goal specification, information distribution, and principal structure, systematically demonstrating for the first time that value alignment is fundamentally an institutional, pluralistic, and context-dependent governance challenge. By integrating institutional analysis with a multi-stakeholder perspective, the work clarifies that effective alignment requires dynamic trade-offs among diverse value systems. It further emphasizes the necessity of institutionalized processes to continuously recalibrate goal-setting mechanisms, evaluation protocols, and community engagement, thereby transcending purely technical approaches and advancing governance-oriented alignment practices.

Existing AI model leaderboards inadequately support real-world deployment decisions due to their inability to jointly optimize capability, cost, and regulatory compliance. Method: We propose a system-level constrained optimization framework that formally defines the “capability–cost frontier” and reveals its tripartite optimal structure; develop an interpretable comparative statics analysis to quantify how budgetary, regulatory, and technological shifts affect multi-objective trade-offs; and integrate low-dimensional internal metric extraction, empirical frontier estimation, task-driven utility learning, and constraint-aware recommendation into a unified pipeline. Contribution/Results: The framework enables deployment-value assessment across domains (e.g., dialogue and healthcare). Empirical evaluation on PRISM and HealthBench yields “deployment-aware leaderboards” whose rankings substantially diverge from pure capability-based rankings—enhancing decision rationality and regulatory robustness.

This study addresses the challenge of integrating strategic planning into agile development without compromising its empirical control and responsiveness. To this end, the authors propose the Milestone-Driven Agile Execution (MDAX) framework, which aligns project execution with organizational objectives by using strategic milestones to guide backlog prioritization. MDAX decouples high-level strategic planning from low-level implementation through a methodology-agnostic and mechanism-decoupled design, enabling organizations to flexibly adopt development practices best suited to their context. The framework enhances strategic alignment of project delivery while preserving the core agility needed for rapid adaptation. As such, MDAX offers an innovative and scalable solution for hybrid project management that effectively bridges strategic intent and agile execution.

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.

It remains unclear whether current programming agents genuinely adhere to prescribed plans or achieve success through data contamination rather than sound reasoning. This work presents the first large-scale, systematic analysis of plan-following behavior in code-generating agents, leveraging the SWE-agent framework to evaluate four large language models across eight plan variants and 16,991 execution trajectories on the SWE-bench Verified and Pro benchmarks. The study finds that high-quality canonical plans substantially improve problem-solving rates, periodic reminders effectively mitigate plan deviation, poorly designed plans can underperform even a no-plan baseline, and prematurely introducing mismatched additional phases degrades performance. These results highlight the critical influence of plan quality, reminder mechanisms, and internal model strategies on task execution outcomes.

This study addresses how organizations adopting commercial AI decision-support systems often passively accept vendors’ embedded and non-negotiable value judgments, thereby constraining their own decision flexibility. The paper introduces the concept of the “behaviorally feasible set” to formally characterize the range of recommendations an AI system can generate under value-alignment constraints and identifies critical conditions under which organizational needs exceed the system’s adaptive capacity. Through controlled experiments comparing binary decisions and multi-stakeholder preference rankings, the research demonstrates that value alignment substantially shrinks the behaviorally feasible set, diminishing the system’s responsiveness to legitimate contextual variation. Commercial models exhibit heightened rigidity, and the alignment process systematically shifts—rather than neutralizes—stakeholder priorities, revealing that value alignment functions as a structural mechanism embedding vendor values and narrowing organizational negotiation space.