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Abstract The rapid rise of live-streaming selling and the increasing diversity of e-commerce channels have sparked ongoing debates regarding product pricing power. This price competition behaviour of online dual channels triggered by the supply-side pricing dilemma is a topic of significant research interest and forms the focal point of this paper. Therefore, we leverage the principles of game theory to construct a three-party evolutionary game model consisting of a brand, a steamer, and a traditional e-commerce platform. Within this framework, the brand holds the primary pricing authority. Our analysis examines the equilibrium strategies adopted by the various players under diverse scenarios and explores how the evolving behaviours of these parties impact the overall game system. Additionally, we employ the “people-goods-scene” perspective to deconstruct the intricate dynamics within this complex supply chain ecosystem. Our findings reveal two distinct evolutionary stable strategies: one in which brands maintain a stance against low-price negotiation, streamers avoid bargaining, and platforms avoid providing subsidies; the other in which brands are open to negotiating low prices, streamers engage in bargaining, and platforms provide subsidies. Notably, in most cases, brands tend to avoid explicitly stating their opposition to low-price negotiation. However, if a brand’s strategy shifts towards rejecting such negotiation, the negative implications of price wars across channels become a critical consideration. Conversely, when a brand opts for a more concessive pricing strategy, we need to consider the potential positive impacts of low-price competition.

IntroductionWith the growing awareness of sustainable development, organic food has been favored by consumers due to its advantages in both human health and environmental sustainability. However, the economic and ecological values of organic food have been two factors that consumers weigh. Online reviews, as an important source of data for capturing consumers’ perceived value, especially the temporal information of reviews, provide new opportunities for revealing the dynamic impact of consumers’ perceived value on satisfaction.MethodsBased on 63,674 online reviews, this study utilizes structural topic modeling to identify the specific dimensions of consumers’ perceptions of economic and ecological values of organic food, and incorporates multiple linear regression modeling to explore the specific effects of these perceived values on consumer satisfaction and their dynamic trends.ResultsThe results of the study indicate that consumers’ perceptions of both economic and ecological values significantly enhance their satisfaction with organic food. Further, this study found that the positive effect of perceived ecological value on consumer satisfaction increased over time, while the positive effect of perceived economic value on consumer satisfaction gradually weakened.DiscussionThis study theoretically provides a new research idea to identify consumer perceived value based on online review data, and reveals the dynamic impact of perceived value on satisfaction, adding a new perspective for sustainable consumption research. Practically, this study provides an important reference for the marketing and product optimization of the organic food industry, especially in terms of dynamic consumer behavior analysis and market positioning.

ObjectiveDrawing on the conservation of resources theory (COR), the research aims to reveal the influence of artificial intelligence (AI) awareness on employees’ mental health and behaviors, particularly examining whether and how employees’ AI awareness impacts their counterproductive work behaviors (CWB) in human-intelligence collaborations.MethodsData was collected from 327 Chinese employees who collaborated with AI in sales, manufacturing, logistics, and other industries. The measurement instruments included scales for AI awareness, psychological contract (PC), emotional exhaustion (EE), and counterproductive work behavior (CWB). We used Hayes’s PROCESS macro to analyze the data.FindingsAI awareness had a significant positive impact on CWB (β = 0.448, p

In the post-pandemic era, medical resources are uneven, and access to healthcare is complicated. Online medical platforms have become a solution to bridge the information gap and reduce hospital pressure. This study uses the stereotype content model and signaling theory to explore the impact of patient perception of patient decision making (PDM) on online medical service platforms. Also, it tests the moderating effect of physician image. We collected information on 12,890 physicians and 746,981 patient reviews from online medical platforms in China. Unsupervised machine learning was used to construct a topic model to extract patients' perceptions of physicians' competence and warmth. Meanwhile, the facial features of physicians, such as age, smile, and glasses, are recognized by convolutional neural networks. Finally, the influence of PDM concern on decision-making and the moderating effect of physician image were analyzed by multiple linear regression. The results of the study showed that (1) patients' perceptions of physicians' competence and warmth had a positive effect on decision-making; (2) physicians' age and wearing glasses enhanced the positive effect of perception on decision-making; and (3) however, physicians' smiles weakened the positive effect of perception on decision-making. This study provides new insights into patients' online physician selection, guides the construction and promotion of medical service platforms, and provides an effective avenue of exploration to alleviate the problem of uneven distribution of offline medical resources.

ObjectiveWith consumers’ concerns about food safety and the environment growing, the interest in organic food has increased. However, due to the late start of the organic food market in China, the market size of the Chinese organic food industry is still relatively small. This study aims to examine whether organic food credence attributes have an impact on consumers’ attitudes and willingness to pay a premium (WTPP), in order to provide valuable information to facilitate the development of the organic food market in China.MethodsA questionnaire survey was conducted with 647 respondents in China. Structural equation modeling (SEM) was utilized to verify the model and test the relationships among the constructs.ResultsSEM analyses showed that credence attributes stimulate consumers’ attitudes and increase consumers’ WTPP. Utilitarian attitudes and hedonistic attitudes play a partially mediating role in the relationship between credence attributes and WTPP. Uncertainty negatively moderates the role between utilitarian attitudes and WTPP, while it positively moderates the role between hedonistic attitudes and WTPP.DiscussionThe findings reveal the motivations and barriers for Chinese consumers to purchase organic food at a premium, providing a theoretical basis for companies to gain a deeper understanding of consumer groups and develop organic food marketing strategies.

During the normalization stage of the COVID-19 epidemic prevention and control, the safety threats caused by improper epidemic prevention measures of airlines have become the primary concern for air passengers. Negative e-WOM related to safety perception obtained based on online multimodal reviews of travel websites has become an important decision-making basis for potential air passengers when making airline choices. This study aims to examine the relationship between potential air passengers’ negative safety perception and the usefulness of online reviews, as well as to test the moderating effect of review modality and airline type. It also further explores the effectiveness and feasibility of applying big data sentiment analysis to e-WOM management. To this end, the theoretical model of negative safety perception, review modality, and airline type affecting review usefulness was constructed. Then we select 10 low-cost airlines and 10 full-service airlines, respectively, according to the number of reviews sorted by the TripAdvisor website, and use crawling techniques to obtain 10,485 reviews related to COVID-19 safety of the above companies from December 2019 to date, and conduct safety perception sentiment analysis based on Python’s Textblob library. Finally, to avoid data overdispersion, the model is empirically analyzed by negative binomial regression using R software. The results indicate that (1) Negative safety perception significantly and negatively affects review usefulness, that is, extreme negative safety perception can provide higher review usefulness for potential air passengers. (2) Review modality and airline type have a significant moderating effect on the relationship between negative safety perception and review usefulness, in which multimodal reviews and full-service airlines both weakened the negative impact of negative safety perception on review usefulness. The theoretical model in this paper is both an extension of the application of big data sentiment analysis techniques and a beneficial supplement to current research findings of e-WOM, providing an important reference for potential air passengers to identify useful reviews accurately and thus reduce safety risks in online decision-making.