develop market differentiation

Current large language models (LLMs) exhibit dual limitations in enterprise competitive analysis: insufficient access to real-time commercial knowledge and inadequate multidimensional competitive cognition, leading to strategic decision bias. To address this, we propose a multidimensional business-element-guided framework specifically designed for competitive analysis. Our approach innovatively integrates interpretable business dimensions—such as market positioning, product competitiveness, and technological trends—explicitly into LLM reasoning. It combines prompt-engineering-driven multi-faceted cue injection, structured domain-knowledge alignment, and a dual-track evaluation mechanism integrating quantitative metrics and qualitative assessment. Empirical evaluation on real-world tasks demonstrates that our method improves key judgment accuracy by 23.6% and analytical consistency by 31.2% over baseline models, significantly enhancing the credibility and operational feasibility of strategic recommendations.

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.

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.

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 how platform imitation of successful third-party products in its own marketplace may undermine sellers’ incentives to innovate. The authors develop a Stackelberg game-theoretic model in which the platform, as the leader, makes strategic entry decisions, while sellers, as followers, optimize their exploration–exploitation trade-offs. In the single-seller setting, the optimal policy is derived using the Gittins index; in the multi-seller case, deep reinforcement learning is employed to analyze equilibrium behavior. This work presents the first integration of the Gittins index with deep reinforcement learning, systematically uncovering the incentive compatibility mechanism between platform entry strategies and seller innovation. The theoretical findings align with empirical observations from real-world platforms such as Amazon and Google Play, offering rigorous support for policies aimed at preserving market innovation and diversity.

This study investigates how online platforms jointly leverage three strategic instruments—pricing (commissions and transaction prices), matching (recommendation and search mechanisms), and bundling (product assortment)—to simultaneously enhance platform revenue and improve overall market welfare. By developing a game-theoretic model of multi-sided interactions and integrating equilibrium analysis with mechanism design theory, the paper systematically uncovers the mechanisms through which the interplay of these levers shapes participant behavior, transaction structures, and value distribution. The findings elucidate the intrinsic coupling among key platform design dimensions and offer theoretical foundations for governance strategies that balance efficiency and fairness in digital markets.

This work addresses the inefficiency and lack of guidance faced by front-end developers when manually selecting plausible and natural attribute values for instantiating reusable UI components within a vast design space. To tackle this challenge, the paper introduces the concept of “discriminative variants,” which uniquely integrates symbolic reasoning with large language models (LLMs). Symbolic reasoning identifies visually salient attributes, while the LLM leverages real-world knowledge to generate component instances that balance fidelity to exemplars with meaningful differentiation. This approach shifts the paradigm from ad hoc manual configuration to structured exploration of the design space. A user study (n=12) demonstrates that the generated variants effectively aid developers in comprehending the design space, significantly improving both instantiation efficiency and user experience, while maintaining strong domain relevance.

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 a critical gap in existing research by systematically operationalizing the Digital Markets Act’s (DMA) core normative goals—fairness, contestability, and user choice—into concrete platform architecture design strategies. For the first time, abstract human values such as fairness and user autonomy are translated into actionable architectural guidance. Through qualitative coding and thematic analysis of DMA regulatory texts and real-world platform compliance practices, the authors derive eight high-level architectural strategies accompanied by fifteen corresponding implementation tactics. This work establishes a novel value-driven architectural framework for digital platforms, offering both theoretical grounding and practical pathways to align platform ecosystems with the DMA’s regulatory imperatives.

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.