Influencer marketing involves brands partnering with influencers to promote products, often through combined text and image posts on social media platforms. This multimodal data is critical for identifying suitable products for effective marketing practice. In this study, we presented MMInfluencer, a novel framework that addressed the critical upstream challenge of knowledge extraction from unstructured multimodal social media data. Our framework operationalized communication theories, the heuristic-systematic model (HSM) and dual coding theory (DCT), to guide large language models (LLMs) in constructing a high-quality knowledge graph (KG) from the multimodal posts and biographies of 1000 Instagram influencers. Human assessment of the KG confirmed its relevance and completeness with accuracy scores of 0.89 and 0.75, respectively. We then examined the KG’s efficacy in various influencer marketing tasks using graph-based learning methods and retrieval-augmented generation (RAG) technology. The KG derived from multimodal data significantly improved product category prediction and recommendation tasks, with the Node2Vec model showing improvements of 20.47% (AUROC, p$<$0.0001) and 1142.15% (NDCG@20, p$<$0.0001), respectively. In contrast, the RAG method was ineffective, yielding an accuracy of just 0.1195 ($\pm$0.0162). Furthermore, the LLM-extracted KG significantly improved performance in challenging scenarios, where the best model achieved improvements of 155.51% (NDCG@20, p$=$0.0170) for emerging influencers (zero-shot) and 1144.51% (NDCG@20, p$<$0.0001) for influencers with scarce data (cold-start). Our research underscores the potential of leveraging multimodal data, LLMs, and graph-based learning methods for effective influencer marketing.

The newsvendor problem is a fundamental building block for inventory management with a stochastic demand. The classical newsvendor problem focuses on a sole objective of either minimizing the expected cost or maximizing the expected profit. However, the performance measure with expected value alone is insufficient, and it ignores the risk preferences of the decision makers. As a result, we carry out a mean-variance analysis of the newsvendor problem. We construct analytical models and reveal the problem's structural properties. We propose the solution schemes which help to identify the optimal solutions. Interesting findings regarding the efficient frontier, the case with a stockout penalty cost, and the safety-first objective are discussed.

This paper considers a multiperiod newsvendor problem with partially observed supply capacity information that evolves as a Markovian process. The supply capacity is fully observed by the buyer when the capacity is smaller than the buyer's ordering quantity. Otherwise, the buyer knows that the current-period supply capacity is greater than its ordering quantity. Based on these two observations, the buyer updates the future supply-capacity forecasting accordingly. With a dynamic programming formulation, we prove the existence of a unique optimal ordering policy.

This paper considers a horizontal market of multiple firms that face stochastic price-dependent demand. The firms make joint pricing/inventory decisions and use price to compete for market demand. With fairly general demand models that are price-dependent, stochastic, and substitutable among firms, we prove the existence and uniqueness of the pure-strategy Nash equilibrium. The market at the equilibrium exhibits a bias toward under-pricing caused by competition; specifically, raising prices at any equilibrium of the game increases the total system profit, and at any joint-optimal set of pricing levels each self-interested firm has an incentive to lower its price. This result closely parallels that obtained in the inventory competition games in which prices are fixed and the bias is toward overstocking.