Live streaming is reshaping retail strategies and prompting manufacturers to reconsider their channel choices. This study develops a game-theoretic model to examine how the informativeness of online reviews affects the manufacturer’s decisions regarding live streaming adoption, sales format (resale vs. agency), and pricing. We find that live streaming is adopted in both formats when the setup cost falls below a threshold. In particular, the informativeness of online reviews affects the decision differently: its impact is non-monotonic and shaped by entertainment value under the resale format, while under the agency format, it is moderated by the proportion of multi-channel consumers. The selection between sales formats is governed by different factors depending on whether live streaming is adopted. Furthermore, under the resale format, the informativeness of online reviews may increase or decrease product prices, depending on market conditions. These findings underscore how multiple elements jointly affect manufacturers’ pricing and channel choices.

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.

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.

This study examines a dual distribution channel through theatres and digital platforms to understand how a producer's investment strategy and pricing in these channels are influenced by risk aversion and the ability of Bayesian consumers to learn from online reviews. We develop a Stackelberg game model, where a risk-averse producer acts as the leader, investing in the film, which is then released sequentially in theatres and on digital platforms. Our findings show that online reviews are crucial due to the uncertainty of achieving the target movie quality. A risk-averse producer tends to invest in lower-quality movies with lower prices, while a risk-neutral producer invests in higher-quality films with higher ticket prices. Critic reviews significantly impact firms' decisions more than audience reviews. Interestingly, increasing the precision of online reviews lessens the effect of risk aversion on quality investment. These insights provide valuable contributions to film industry stakeholders, highlighting how online reviews and risk aversion shape investment strategies in the industry.

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.

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.

To reduce sellers’ manipulation of online reviews, platforms conduct audits and impose penalties on abusive practices. In this article, we study the effectiveness of self-regulation in preventing fake reviews and increasing consumer and social welfare. To this purpose, we use a Stackelberg model with the platform as the leader. Our major contribution is to explain the auditing game between the platform and the seller. These are our major findings. First, we found a self-regulation’s Achilles’ heel. Despite its noble intent, the self-regulation system often falters. Why? The platform faces a powerful incentive to accommodate fake reviews, especially when consumers heavily rely on the seller’s information and regulatory repercussions remain mild. For this reason, consumer activism and external supervision are the key factors leading the platform to audit the seller. Second, we discovered the paradox of fake reviews, establishing the conditions under which, counter-intuitively, fake reviews create social value. Finally, by considering an endogenous retail price, we found evidence of a complex relationship between pricing, auditing, and review manipulation policies, all dependent on consumer sophistication and product quality, which can only be analyzed numerically, as there is no closed-form solution.

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.

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.

The number of online textual reviews on each hotel aspect can reflect the tourist preference difference on distinct aspects. Therefore, not only online textual reviews but their numbers have a significant impact on tourists' hotel selection decisions. Motivated by this observation, this study proposes a hotel ranking model for hotel selection based on the sentiment analysis of online textual reviews by considering the differences in the number of reviews on different aspects. We explicitly model the differences in the number of reviews on aspects through the confidence interval estimation. In addition, the AS-Capsules model, which can jointly perform aspect detection and aspect-level sentiment classification with high accuracy, is employed for sentiment analysis. We conducted a case study on TripAdvisor.com, the experimental results show that our proposed model is able to effectively assist the tourists in making the desirable decision on hotel selection.

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.

The hotel industry has been facing fierce competition in recent years. It is important for hotels to conduct effective strategic planning for competitiveness improvement to achieve sustainable development. Prior studies on hotel strategic planning mainly use questionnaire data or hotel internal data, which have the problems of insufficient data or neglecting customer perspectives. The purpose of this study was to develop an integrated method for customer-oriented strategic planning for hotel competitiveness improvement based on text mining of online reviews. First, text mining of online reviews was conducted to extract customer-concerned service attributes and evaluate customer concern level and the performance of the service attributes through Latent Dirichlet Allocation (LDA) and sentiment analysis. Second, the competitive structures of the hotels were analyzed and the main competitors were identified from the competitive hotels through correspondence analysis. Third, SWOT analysis of the target hotel toward the main competitors was conducted, and the priorities of factors in each SWOT category were determined. An empirical study on a five-star hotel is given to illustrate the feasibility and effectiveness of the proposed method. The results indicate that the proposed method can help managers in strategic planning to obtain more specific strategies for hotel competitiveness improvement.

In reality, supply chain members need to determine the best suitable payment scheme when transacting with their business partners. We investigate the optimization of payment schemes with quality investment and bank credit in a decentralized supply chain consisting of one manufacturer and one retailer, both of which are capital-constrained and in need of short-term financing from the bank for their operations. First, we provide a function to describe the market demand by analyzing the impacts of price and quality on the demand. Then, we develop the profit functions of advance payment scheme (APS), normal payment scheme (NPS), and delay payment scheme (DPS) for the capital-constrained manufacturer and retailer. Furthermore, we construct the joint pricing and quality level decision models under APS, NPS, and DPS to determine the optimal profits of the supply chain, manufacturer, and retailer, respectively. On this basis, we conduct theoretical and numerical analyses on the selection of the optimal payment scheme. Our research results in manufacturer Stackelberg game indicate that, the optimal payment scheme from the perspective of supply chain, manufacturer or retailer is not uniquely determined, but generally tends to be DPS. For different perspectives, the optimal payment schemes may be different, and can be affected by the parameters. Especially, for increasing bank rate for the loan of the retailer, the supply chain, manufacturer and retailer generally tend to select DPS. In addition, for increasing bank rate for the loan of the manufacturer, the supply chain and retailer tend to select DPS, but the manufacturer tends to select APS.

Many efforts have been dedicated to the research on online reviews-based hotel selection. However, the existing methods often fail to take into account the types of potential travellers and, moreover, they rarely consider the impact of the interdependencies among hotel criteria when modelling the hotel selection process. To counter these defects, this paper proposes a multi-criteria decision-making model for hotel selection that considers the types of potential travellers and the interdependencies among criteria. To achieve this, the proposed model first converts online reviews into picture fuzzy numbers, and introduces 2-order additive fuzzy measures to capture the interdependencies among criteria by proposing a novel aggregation function based on Choquet average integral. Furthermore, it uses the similarity between each traveller type and the potential traveller type to calculate the weights of different traveller types so as to account the impact of traveller type. To validate the effectiveness of the proposed model, a case study is conducted on the reviews collected from TripAdvisor. The study shows that the proposed decision support model can effectively help potential travellers of different traveller types to make their desirable hotel choices.

随着电子商务的发展,在线评论已成为企业分析其产品竞争力的重要数据资源.通过评论文本提取消费者最关注的产品特征维度,采用情感词典法对评论文本进行情感分析得到特征-情感分数对.计算特征维度的重要度和满意度以确定特征维度的机会得分,并绘制机会景观图,分析目标产品自身的竞争力.依据特征维度的满意度,比较目标产品与其竞争产品的竞争优劣势.基于两种视角下的竞争力分析结果给出目标产品竞争力提升策略,使用Zol.com.cn网站提供的在线评论进行实验,结果表明了所提出方法的可行性和有效性.