This study investigates the online retailer's optimal live streaming strategy, focusing on the impacts of online reviews and the spillover effect of live streaming on the traditional e-commerce channel. Three scenarios are analyzed: traditional e-commerce only (Scenario N), adopting live streaming without the spillover effect (Scenario O, and adopting live streaming with the spillover effect (Scenario OS). Profit-maximization models are developed for both the retailer and the streamer to identify optimal pricing strategies. The findings show that live streaming adoption is beneficial when baseline return rates and the streamer's sales ability are high, while factors like online review informativeness and consumer acceptance of live streaming are low. With the spillover effect, live streaming remains advantageous, and the conditions for adoption become more flexible, though excessive spillover effect may diminish its competitive edge. Additionally, pricing dynamics reveal that live streaming generally lowers the traditional channel price, but the spillover effect can alter this relationship. The retailer should adjust pricing and commission structures strategically to maximize profitability.

Online reviews (ORs) constitute an important source of market information and a valuable input for supply chain decision-making. However, existing studies have primarily focused on the role of ORs in customer experience and market analysis, leaving their application and key influencing factors in supply chain management underexplored. This study aims to conduct an in-depth analysis of the research trends and hotpots in the application of ORs in supply chain decision-making and suggest a path for future research. Through a systematic and unbiased search, 172 publications were selected to examine the application of ORs to supply chain management. The articles are thoroughly reviewed and categorized in terms of annual distribution, major publication channels, types of research, and co-occurrence analysis. This comprehensive analysis reveals how ORs enhances decision-making processes, identifying the 10 most popular drivers. It further identifies notable challenges across 4 key subfields: product and service, market and consumer, supply chain operation, and risk management. This contributes to future research and enriches our understanding of current and potential applications of ORs in the sector.

This study proposes a dual-orientation framework for mining customer requirements (CRs) from online reviews to support product improvement and publicity in mobile games. Existing research often overlooks silent information embedded in unmentioned CRs, limiting the effectiveness of strategic decision-making. Our framework integrates explicit and silent information through term-level asymmetry analysis. Specifically, BERTopic and BW-CNN are used to extract CRs and sentiments, while SHAP quantifies their influence on satisfaction. A four-quadrant perception model is developed to conduct asymmetry analysis of silent information, and the Kano model is applied to perform asymmetry analysis of explicit feedback. Finally, LIME-based term contribution analysis integrates these perspectives to evaluate the priorities of CR improvement and publicity. Experiments on 72,000 reviews from eight mobile games demonstrate the effectiveness of the proposed method in providing a comprehensive understanding of customer perceptions and supporting more targeted product improvement and publicity strategies.

The rapid expansion of the video game market intensifies customers' difficulty in selecting preference-aligned games. Although online reviews offer valuable insights, effectively leveraging this information remains challenging. To address this, we propose S-Kano-TOPSIS, a personalized ranking method for video games that integrates requirement categories and public opinion. First, BERTopic is used to extract customer requirements (CRs), and their performance is evaluated via sentiment analysis using a BW-CNN model. Then, SHAP is applied to quantify the influence of each CR on customer satisfaction. The Kano model is employed to adjust CR importance based on their influence patterns. Furthermore, to reflect real-world decision-making, we incorporate preference similarity by analyzing reviews of games similar to those the customer has played. Finally, TOPSIS is used to generate rankings tailored to individual needs. Experiments on 72,000 reviews from eight video games demonstrate that the proposed method surpasses baseline approaches across multiple evaluation metrics. These results suggest that S-Kano-TOPSIS offers a structured and quantifiable approach to personalized video game ranking.

In China's dual-carbon strategy, manufacturers face increasingly stringent carbon emission constraints, necessitating a balance between cost efficiency and environmental performance in production decisions. As a core regulatory instrument, carbon pricing significantly influences outsourcing strategies and emission reduction behaviors. This study investigates manufacturers' outsourcing decisions under a carbon cap-and-trade scheme, focusing on how supplier substitutability affects production mode selection. An analytical framework is developed based on varying market structures, incorporating game-theoretic modeling and numerical simulation to evaluate the joint impact of carbon policies and supply chain configurations. The results reveal that: (1) Supplier substitutability substantially alters the manufacturer's optimal outsourcing strategy, prompting cautious evaluation of potential outsourcing partners under competitive threats; (2) Different outsourcing structures lead to heterogeneous outcomes in profitability and emission performance under carbon quota constraints; (3) Consumer acceptance of supplier products further shapes the manufacturer's outsourcing preferences and environmental outcomes. This research contributes to the theoretical advancement of green supply chain management and offers practical implications for policy-makers and manufacturers seeking sustainable and efficient production strategies.

Motivated by the practice of reward-based crowdfunding on the Indiegogo platform, this paper studies the optimal financing mechanism of the entrepreneur from a perspective on information disclosure. To do this, we develop a two-period crowdfunding model, in which a monopoly entrepreneur sells the new product to a group of strategic consumers by adopting all-or-nothing (AON) or keep-it-all (KIA) mechanisms and decides whether to disclose the partial or full product information in the first period. Our analysis yields several vital insights. First, under the AON mechanism, the pricing strategy under partial information disclosure depends on the entrepreneur's type, while full information disclosure supports a lower pricing strategy to maximize crowdfunding potential. Moreover, the high-type entrepreneur has no incentive for partial information disclosure, whereas the low-type entrepreneur avoids full information disclosure. Second, under the KIA mechanism, it is beneficial for the entrepreneur to disclose partial information only when the success rate is low, consumers' acceptance of the substitute product is high, and signal strength is relatively low. Otherwise, it is more desirable to disclose full information. Finally, we identify the optimal financing mechanism and discuss the applicable conditions of each financing mechanism. Our research results have certain guiding significance for the entrepreneur in the pricing and financing of reward-based crowdfunding.

Faced with financial dilemmas, firms have long relied on bank financing as a prevalent and trusted solution. Recently, however, crowdfunding has emerged as an innovative alternative, enabling capital-constrained firms to raise funds from diverse investors. This study explores the optimal financing strategy for such firms under market uncertainty and evaluates pricing decisions across different financing strategies, including bank financing and crowdfunding. Our results reveal that under crowdfunding, market uncertainty may lead to either overfunding or underfunding, which in turn necessitates different pricing strategies. Crowdfunding is not entirely barrier-free for firms, as its accessibility often hinges on setup costs. In contrast, under bank financing, market uncertainty may hinder firms from generating enough revenue to cover the principal and interest on the bank loan, potentially leading to bankruptcy. Similarly, bank financing is also constrained by setup costs. Finally, by comparing crowdfunding and bank financing, our findings suggest that there is no one-size-fits-all solution. Firms must consider dynamic factors, including platform commission, interest rate, and setup costs, when selecting the most appropriate financing strategy. This approach ensures that the choice aligns with both the market conditions and the unique situation of firms. Additionally, in the extensions, we modify the crowdfunding financing mechanism and introduce a hybrid financing strategy designed to further alleviate financing difficulties for firms. These findings provide actionable insights for firms navigating uncertain financial environments.

In the mobile game industry, modelling customer requirements (CRs) is becoming indispensable for developers before improving games. Online reviews, as a common way of customer feedback, can reflect customers’ real experiences and preferences. Several studies have extracted important CRs from online reviews of mobile games, but they haven’t thoroughly analysed the correlation with customer satisfaction. In this study, we propose a CR modelling framework to extract and classify CRs from online reviews and analyse the influence of each CR on customer satisfaction to assist developers in improving games. First, we extract important CRs based on word embedding from online reviews. Then, we construct a BERT-Word2Vec-Convolutional Neural Network (BW-CNN) model to analyse the sentiment of online reviews by considering semantic features at Chinese character and word granularity. Finally, based on the Shapley Additive Explanation (SHAP) method, the S-Kano model is further proposed to classify the CRs considering customer attention, and the product improvement suggestions are provided for the corresponding mobile game developers based on the CR classification results. To verify the effectiveness and advantages of the proposed method, we crawled the online reviews of four Gacha games from taptap (www.taptap.cn) for case study and compared with the existing methods. The results show that our proposed framework is effective in modelling CRs from online reviews of mobile games. Meanwhile, the results comparison illustrate that customer attention has a significant influence on the CRs classification. In addition, we find that the S-Kano categories of CRs vary across games, and there is strong heterogeneity among games.

Fresh product traceability plays an integral role in ensuring food safety. Recently, many firms have introduced blockchain technology (BCT) for product traceability to guarantee product authenticity and consumer rights. However, introduction of the new technology has also intensified the competition among fresh products. Meanwhile, previous studies on traceability strategies only considered the impact of traceability cost and ignored the issue of increased product competition in blockchain era. Driven by these problems, we build a game-theoretical model to analyze the optimization of traceability schemes for both low-end fresh products (LEFP) and high-end fresh products (HEFP) in a fresh product supply chain (FPSC) in a competitive environment. Moreover, this paper investigates the impact of power structure on traceability schemes and identifies the conditions under which one scheme outperforms the others. We find that the cost per unit of product input has no effect on the traceability decision of the FPSC. Nonetheless, the introduction of BCT increases the number of consumers who choose LEFP and raises the selling price and wholesale price of corresponding products. Finally, from the supply chain perspective, it is best to adopt the same traceability scheme for both products. This study sheds light on the use of BCT in traceability can provide enhanced product authenticity and consumer rights while increasing product competition and demonstrates the significance of traceability schemes in the FPSC.

Motivated by marketing practices in advance selling, this paper studies the optimal pricing decisions and advance selling strategy with online reviews. The firm may charge a full or partial price in advance selling and then determine whether to continue selling products in the spot market. We characterize the equilibrium pricing decisions, selling scheme, and advance selling strategy in the monopoly market. We find that no matter what kind of advance selling strategy is implemented, the firm should choose the two-period selling scheme instead of the one-period one to market the product. Notably, when the two-period selling scheme is chosen, either the skimming pricing or the penetration pricing may be optimal for the firm. Specifically, when the review valence is significantly lower, the skimming pricing is always favored by the firm; otherwise, the penetration pricing is more profitable. Moreover, the disclosure quality and review valence jointly affect the choice of firm's optimal advance selling strategy. In detail, advance selling with a full-price strategy is always dominant if the disclosure quality is high; otherwise, the firm's optimal advance selling strategy is determined by the review valence. Meanwhile, we also analyze several extensions to explore meaningful insights and provide decision-making support for the firm facing a complex marketing environment.

The frequent occurrence of natural disasters and public health and safety events bring considerable risk and uncertainty to the supply of materials, especially for perishable fresh food. Currently, there is limited scholarly research addressing the procurement decisions related to perishable fresh food during emergencies. In this paper, the emergency food procurement problem is addressed by considering cost, supply risk, and other factors, and a procurement decision optimisation model is constructed with the dual objectives of minimizing both total cost and supply risk. Additionally, the urban emergency is used as the case study, which provides background description and the actual data that enable better understanding of what actually occurs during emergencies, as well as validating the applicability of the model. The findings could support the decision of supply chain participants engaged in emergency fresh food procurement and enrich the body of theory in the field of emergency fresh food procurement decisions.

We investigate strategy choice of sales format considering online reviews and the spillover effect in a dual-channel supply chain. Generally, online and offline consumers read online reviews before purchasing, and thus offline retailers will be subjected to the spillover effect of online reviews. We develop joint product quality and service level decision models in reselling and agency selling with online reviews and the spillover effect. There are some interesting findings: first, the optimal service level and product quality increase with online reviews and spillover effect. Second, counterintuitively, the spillover effect of online reviews benefits not only the offline retailer and manufacturer but also the online retailer in some circumstances. Moreover, the win–win sales format for the manufacturer and online and offline retailers exists in some circumstances. Finally, it is interesting to find that consumers’ channel preference in the agency sales format is more important than in the resales.

Aiming at the financing mode selection problem of a closed-loop supply chain consisting of a retailer,a recycler,and a capital constrainted manufacturer,the revenue functions of the manufacturer,retailer,recycler,and closed-loop supply chain are constructed when the capital constrained manufacturer can get financing from the retailer(internal financing)or the bank(external financing),respectively,and based on the Stackelberg game,the optimal pricing and return rate strategies under two financing modes are given.On this basis,by comparing the optimal decisions and revenue of the recycler,retailer,manufacturer,and closed-loop supply chain under different financing modes,the financing mode selection strategies are given from different perspectives.The research results show that under certain conditions,the retailer providing financing services are beneficial for the manufacturer,retailer,recycler and closed-loop supply chain.

In launching new green product or responding to market environment change, supply chain firms need to pour attention to best retailer's channel structure policy and coordination conditions. We consider a retailer-led green supply chain, and investigate optimal retailer's channel structure policy and supply chain coordination considering the impact of channel purchasing cost. First, we analyze three potential channel structures of green supply chain: offline channel structure (F), online channel structure (N), and dual-channel structure (D), and provide three functions to describe market demands for potential channel structures by analyzing the impacts of price, channel purchasing cost, and product greening level. Then, we develop profit functions and construct three joint pricing and product greening level optimization models to determine optimal joint optimization policies for structures F, N, and D, respectively. Furthermore, we conduct the theoretical analysis of retailer's channel structure selection, and provide numerical studies to illustrate and testify the theoretical results. Finally, we use a two-part tariff (TPT) contract to realize green supply chain coordination for structures F, N, and D, and show coordination conditions. Our research results indicate that, the optimal retailer's channel structure from each perspective is generally not uniquely determined, and optimal retailer's channel structures from three perspectives of manufacturer, retailer, and supply chain can be affected by channel purchasing cost, greening cost sensitivity, and preference degree of consumer to online channel.

考虑旅行社在选择合作酒店时会关注备选酒店的需求信息和电子口碑的发展趋势,提出一种考虑这两类信息的酒店合作伙伴选择方法.首先基于备选酒店需求和电子口碑信息构建有向加权图,具体地,依据备选酒店的需求信息确定有向加权图节点权重,并给出一种考虑备选酒店电子口碑横向、纵向发展趋势的权重修正系数,用来计算时间权重,在此基础上,采用TOWGA算子计算备选酒店的电子口碑评价值,并依据评价值进行两两酒店比较来确定有向边及其权重;然后基于PageRank算法求解各备选酒店的排序值;最后通过一个实例分析说明本文提出方法的可行性和有效性.

This paper investigates the strategies for adopting blockchain technology in the fresh product supply chain (FPSC) consisting of a supplier, a third-party logistics service provider (3PL) and an e-tailer. We analyse the optimal strategies of FPSC members under the benchmark scenario where the FPSC does not adopt blockchain technology and those under the three scenarios where the supplier, 3PL and e-tailer lead the construction of the blockchain-based traceability system (BTS), respectively. We find that adopting blockchain technology is not always the optimal decision for the FPSC, which is related to the consumers' acceptance degree for the product without blockchain technology, the deterioration rate of the fresh product and the allocation proportion of traceability cost of FPSC members when adopting blockchain technology. Regardless of the power and status of each member in the FPSC, it can lead the construction of the BTS. From the perspective of whole FPSC's profit maximisation, the leader of the FPSC should lead the construction of the BTS under the coordination of a two-part tariff contract. This study provides valuable insights for FPSCs to adopt blockchain technology.

Introduction: In reality, due to the low credit rating of small and medium-sized enterprises (SMEs), it is difficult for them to obtain sufficient financing from a single financier. This paper considers a dual-channel supply chain consisting of a capital-constrained manufacturer, an e-commerce platform (ECP), a third-party logistics company (3PL) and consumers. There are two innovations in this paper: the manufacturer obtains sufficient production funds through hybrid financing of the ECP and 3PL, and consumers want to know product information and compare prices. The contributions of this paper are to investigate new applications of blockchain in both hybrid financing and meeting consumer information search needs.Methodology: We discuss the operation and pricing decisions of supply chain in two scenarios. These two scenarios are without adopting blockchain (N) and with adopting blockchain (B). Then, we compare the equilibrium decisions in two scenarios.Results: The results show that the supply chain will adopt blockchain when certain conditions are met. The initial adoption of blockchain is bad for the ECP and 3PL. Further, we find that with the increase of financing ratio, the optimal financing interest rate of the ECP decreases, while the optimal financing interest rate of the 3PL increases.Discussion: The numerical analysis shows that the adoption of blockchain can be more profitable when the cost of information search is high.Management insights: In order to achieve supply chain coordination, the manufacturer should give subsidies the ECP and 3PL.

The quality of fresh products through cross-regional sales has been uncertain to consumers. To improve the quality of fresh products, some fresh product supply chains have implemented blockchain technology to provide traceable information for their products, and some regional governments have subsidized their local firms to incentivize them to implement blockchain-driven traceability systems. However, with regional government subsidy heterogeneity, cross-regional fresh product supply chain firms lack theoretical guidance on their operation decisions. Based on the research gap, we investigate optimal operation policies in a fresh product supply chain consisting of a manufacturer and a retailer located in different regions. The local governments may subsidize the manufacturer or the retailer located in their own regions, which construct four subsidy strategies (SS, SN, NS, and NN) along the supply chain. We find that the optimal operation policies under four subsidy strategies can be affected by the sensitivity to traceability level, cost-sharing rate of the manufacturer, rate of products left after corrosion, and subsidy rate to the manufacturer. Moreover, the government subsidy to the retailer is always beneficial to the retailer and the supply chain but does not affect the manufacturer’s operation policies and profits. The government subsidy to the manufacturer is always beneficial to the manufacturer but not always beneficial to the retailer and the supply chain. Hence the desired subsidy strategy for the manufacturer is SS and SN, and the one for the retailer and the supply chain is either NS or SS with different conditions.