product positioning

Enterprises lack systematic, context-sensitive evaluation methods for selecting low-code development platforms (LCDPs) during digital transformation. Method: This paper proposes a strategic needs-oriented five-dimensional evaluation framework—encompassing business process orchestration, UI/UX customization, integration and interoperability, governance and security, and AI-enhanced automation—integrating multi-criteria decision analysis (MCDA) with a configurable weighted scoring model. Contribution/Results: The framework bridges the gap between marketing-driven generic comparisons and rigorous contextual evaluation, enabling organizations to quantitatively compare alternatives and mitigate vendor lock-in risks. Empirically validated in enterprise settings, it significantly improves selection decision quality, reduces implementation failure rates, and minimizes resource waste. To our knowledge, it is the first structured decision-support tool for LCDP selection that simultaneously ensures scalability, interpretability, and practical deployability.

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 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.

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.

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 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 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 proposes a systematic approach to derive task effectiveness requirements in the absence of explicit user needs. The method deconstructs task intent into context, functionality, constraints, critical dimensions, performance attributes, and architectural solutions, and introduces a task complexity factor to quantify the impact of external challenges and technology maturity. By integrating Best-Worst Scaling, it prioritizes critical dimensions based on stakeholder judgments. Through task decomposition modeling and quantitative complexity analysis, the framework supports integration with UAF/SysML artifacts and establishes a traceable mechanism for generating Tier 1 and Tier 2 requirements. The approach is validated using a close air support mission case study, effectively addressing a critical gap in requirements engineering when clear initial inputs are unavailable.

研究通过分析Shopify应用生态系统，探讨了规模、集中度和进入时机对应用生存的影响，使用了2019至2026年的数据集。

This study addresses the absence of a formal modeling framework in strategic crisis analysis that operates without requiring complete payoff or probabilistic information, integrates expert qualitative judgments, explicitly captures dependencies, and supports auditable update rules. To bridge this gap, the authors propose a two-layer formal framework that decouples a static scenario database from a dynamic scenario tree system. They introduce, for the first time, a formally defined extended scenario bundle analysis model, incorporating a domain-modifier layer, a topological structure over scenario space, a typed state-update mechanism, and a multi-criteria evaluation method. This architecture enables context-sensitive modeling of multi-agent attitudes—including beliefs, desires, intentions, fears, and coalition commitments—while maintaining mathematical rigor and computational tractability, thereby significantly enhancing the transparency, traceability, and expressive power of complex crisis analysis.