packaging and pricing strategy

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 paper studies the combinatorial allocation problem for multiple-unit, indivisible complementary goods, aiming to reconcile sellers’ packaging cost preferences with fairness, transparency, and linearity of equilibrium prices. Methodologically, it introduces a novel graph-structured incremental packaging cost model, where packaging cost is defined as the marginal increment of a good bundle over a graph—enabling, for the first time, the unified existence of anonymous and package-linear Walrasian equilibria. Theoretically, it establishes necessary and sufficient conditions for Walrasian equilibrium existence, along with several verifiable sufficient conditions. Algorithmically, it develops a computationally tractable, transparent equilibrium computation framework grounded in linear programming and dual pricing analysis, which simultaneously satisfies two fairness criteria: (i) allocation concentration preferences (reflecting packaging cost structure) and (ii) inter-buyer pricing fairness. The framework ensures both economic interpretability and implementability in practical multi-unit complementary markets.

This study addresses the growing challenges traditional failure analysis methods face in the era of advanced packaging technologies—such as chiplets, hybrid bonding, and 3D stacking—by conducting an anonymous global survey of over 100 semiconductor design, packaging, and failure analysis organizations. The findings reveal that 69% of respondents prioritize heterogeneous integration products (mean importance score: 7.92/10), while 54% identify hybrid bonding as the most analytically challenging technique. A strong consensus emerges around the need for standardized data formats, with 83% of participants advocating for unified protocols, and high-resolution non-destructive imaging garners substantial support (mean score: 8.18/10). The research systematically identifies critical pain points in sample preparation and 3D structural inspection, offering empirical insights to guide industry standardization and technological innovation.

Small and medium-sized enterprises (SMEs) lack the resources and technical expertise to deploy conventional media-mix modeling (MMM), particularly amid tightening privacy regulations that exacerbate attribution challenges. Method: This paper introduces Robyn—a novel, open-source m/MMM framework designed specifically for SME advertisers. It features a modular, “plug-and-play” architecture integrating Bayesian time-series modeling (via PyMC/Stan), automated hyperparameter optimization, Adstock response modeling, and a scalable Python implementation—ensuring both interpretability and organizational deployability. Contribution/Results: Robyn systematically addresses data sparsity, prior bias, and cross-functional collaboration barriers. It reduces modeling turnaround from weeks to hours, enabling multi-channel attribution and budget allocation optimization. The framework has been deployed at scale across over 1,000 SMEs and within Meta’s advertising ecosystem, undergoing continuous iteration and real-world validation.

Increasing complexity in software supply chains is compounded by low adoption rates and inconsistent implementation of software signing practices, undermining trust in software provenance and integrity. Method: We conducted semi-structured interviews with 18 security practitioners across 13 organizations and applied thematic coding alongside cross-organizational comparative analysis. Contribution/Results: We systematically identify technical, organizational, and human barriers to signing adoption; propose the first practice-oriented “Software Supply Chain Factory Signing Model”; reveal industry-wide divergences in perceived necessity of signing; and demonstrate how internal/external security incidents and evolving compliance requirements dynamically shape adoption decisions. Our four core findings provide empirical grounding for optimizing standards, guiding tool development, and strengthening enterprise governance—advancing software signing from superficial compliance toward substantively effective assurance.

This work addresses the challenges of low quality and poor transparency in build-or-buy decisions within enterprise software development, which often stem from reliance on unstructured experiential knowledge. To overcome these limitations—particularly in cold-start scenarios lacking historical data—the authors propose a structured approach that integrates a decision-factor ontology, rule-based reasoning, and reference-class matching. This method enables transparent, auditable evaluation of alternatives and represents the first application of combined ontology modeling and rule reasoning to build-or-buy decision-making. By revealing critical decision thresholds and supporting traceability, the approach enhances the rationality, transparency, and auditability of choices. Its practical efficacy is demonstrated through a lightweight tool validated in a financial industry case study, showing significant improvements in decision quality.

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

This study addresses the lack of systematic guidance for enterprise software teams in choosing between monolithic and microservices architectures. The work proposes a decision-making framework that integrates technical and organizational factors, evaluating the trade-offs of each architecture across dimensions such as scalability, reliability, deployment efficiency, and organizational complexity. The assessment is grounded in system scale, business requirements, operational maturity, and long-term maintainability. Through architectural pattern analysis, a structured evaluation model, and multiple case studies, the authors develop a practical selection methodology tailored to real-world engineering contexts. This approach offers enterprises clear architectural evolution pathways and actionable guidelines aligned with their developmental stages, thereby significantly enhancing the rationality and sustainability of system design decisions.