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This paper aims to find the best method to predict the friction coefficient of textured 45# steel by comparing different machine learning algorithms and analytical calculations.

Five machine learning algorithms, including K-nearest neighbor, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT) and artificial neural network (ANN), are applied to predict friction coefficient of textured 45# steel surface under oil lubrication. The superiority of machine learning is verified by comparing it with analytical calculations and experimental results.

The results show that machine learning methods can accurately predict friction coefficient between interfaces compared to analytical calculations, in which SVM, GBDT and ANN methods show close prediction performance. When texture and working parameters both change, sliding speed plays the most important role, indicating that working parameters have more significant influence on friction coefficient than texture parameters.

This study can reduce the experimental cost and time of textured 45# steel, and provide a reference for the widespread application of machine learning in the friction field in the future.

Recognizing every worker's working status instead of only describing the existing construction activities in static images or videos as most computer vision-based approaches do; identifying workers and their activities simultaneously; establishing a connection between workers and their behaviors.

Taking a reinforcement processing area as a research case, a new method for recognizing each different worker's activity through the position relationship of objects detected by Faster R-CNN is proposed. Firstly, based on four workers and four kinds of high-frequency activities, a Faster R-CNN model is trained. Then, by inputting the video into the model, with the coordinate of the boxes at each moment, the status of each worker can be judged.

The Faster R-CNN detector shows a satisfying performance with an mAP of 0.9654; with the detected boxes, a connection between the workers and activities is established; Through this connection, the average accuracy of activity recognition reached 0.92; with the proposed method, the labor consumption of each worker can be viewed more intuitively on the visualization graphics.

With this proposed method, the visualization graphics generated will help managers to evaluate the labor consumption of each worker more intuitively. Furthermore, human resources can be allocated more efficiently according to the information obtained. It is especially suitable for some small construction scenarios, in which the recognition model can work for a long time after it is established. This is potentially beneficial for the healthy operation of the entire project, and can also have a positive indirect impact on structural health and safety.

This case study is designed to enable students to understand the reasons behind the launch of a beauty brand grounded on traditions and culture, understand the strategies adopted by Florasis to establish its presence in the C-beauty space and emerge successful, analyze the positioning of a C-beauty brand in a highly competitive beauty market, identify the issues and challenges faced by a C-beauty brand in its efforts to disrupt the C-beauty space and suggest strategies that Florasis can adopt to emerge as a market leader in the global beauty industry.

Set in 2021, the case study discusses about the emerging C-beauty brand Florasis innovative strategies to promote the brand. Florasis was founded in 2017 with a vision to become a century old national makeup brand of China. Florasis was successful in getting on board a story-telling experience that featured traditional Chinese culture, aesthetics and heritage. It sold cosmetic products with retro packaging, concepts derived from traditional Chinese style, promoting a sense of national pride and nostalgia. The case study highlights the innovative strategies Florasis adopted like influencer marketing through key opinion leaders and key opinion customers, celebrity endorsements, user co-creation programs, social content and network marketing, brand crossovers and collaborations, etc. In April 2021, Florasis became the No. 1 cosmetic company in China with a gross merchandise value of 218m yuan and further the total sales for second quarter of 2021 reached 830m yuan, endorsing its supremacy over other global and local beauty brands in China. However, with success came along a set of challenges. Some analysts pointed that the brand was slow in innovating its product line-up, it focused more on promotions and advertisements and the brand positioning with a single sales channel, the cost performance and quality of the products and excessive marketing campaigns targeting a niche segment. Going forward, what should Florasis do to conquer the global beauty space? Can Florasis aspire to become a digitally empowered global beauty brand? Has it got the momentum? Will its direct-to-consumer model and unprecedented marketing and promotion gimmicks, help it achieve the lead in the global beauty space?

This case study is suitable for students of the graduate and undergraduate programs in management.

Teaching notes are available for educators only.

CSS 8: Marketing.

Live streaming commerce, a new form of social commerce where firms integrate with broadcasters, community members and customers in live chat rooms for real-time interaction to help sell products, has become increasingly popular. However, factors that impact the success of live streaming commerce are not fully explored. The purpose of this study is to examine these factors by exploring the relationships among customer trust (i.e., trust in broadcasters, community members, and products) and customer engagement, and the mediating role of swift guanxi.

The authors surveyed 422 customers who have watched Taobao Live, one of the largest live streaming commerce sites in China, and used their questionnaires to test the research model employing partial least squares modeling.

The authors’ results indicate that trust in broadcasters has a positive effect on trust in products and community members, which positively influences trust in products. Additionally, swift guanxi has a fully mediating effect on the relationship between customers' trust in broadcasters and customer engagement.

First, from the theoretical perspective, this study comprehensively identifies three types of trust, explores the transfer relationships among trust in broadcasters, community members, and products based on Trust Transfer Theory. In addition, this study examines the mediating influence of swift guanxi on the relationship between customer trust and engagement. Second, from the managerial perspective, the study provides insights to help broadcasters and practitioners use live streaming to facilitate shopping.

This paper considers the brand awareness and anchor influence on consumers' live-streaming purchases, and explores the existence of “free-riding” behavior, the comparison of brand promotion effect and active live-streaming effect and the optimal strategic combination between the brand and the anchor. The authors investigate the evolutionary stabilization strategies of the bounded rational brand and anchor, and explore the conditions for the realization of the optimal strategy. Management suggestions for the development of live streaming commerce can be provided in this paper.

Two significant models are used in this paper. The Stackelberg model is used to study the “free-riding” behavior, the comparison of brand promotion effect and active live-streaming effect and the optimal strategic combination between the brand and the anchor. Using evolutionary game theory to get the evolutionary stable equilibrium strategies and analyze the binary equilibrium strategy of the bounded rational brand and anchor. In addition, relevant simulation analysis is conducted using realistic data to verify the conclusions and for further analysis, making the conclusions of the paper have realistic significance.

The study shows that “free-riding” behavior exists and the positive effect of brand promotion is greater than that of active live-streaming. The brand and the anchor take active actions as the optimal strategy. As the sensitivity coefficient of consumers to live-streaming effort and the sensitivity coefficient of consumers to brand promotion change, various evolutionary stabilization strategies will appear. When the two sensitivity coefficients are below a certain threshold, the game sides will reach the optimal strategic combination to obtain the maximum benefits. When they rise above this threshold, it is counterproductive instead. The system achieves the optimal strategic combination when the difference factor between effort cost and promotion cost must be higher than a certain value, but when it takes the smallest possible value, the game sides tend to take active actions. This study can provide management suggestions for the sustainable development of the live-streaming model.

This paper shows that under certain conditions, the brand and the anchor can evolve into the optimal strategy to maximize the profits of both parties, which has certain practical significance for the prosperous development of live streaming commerce. In future research, the authors will consider the regulatory role of the government and construct a more realistic game model to provide constructive suggestions for the sustainable prosperity of live streaming commerce. Meanwhile, there are also games between multiple brands and multiple anchors, as well as games among brands-anchors-the live streaming platforms, and the authors will conduct more in-depth research in the future.

So far, the co-impact of anchor influence and brand awareness has not been considered simultaneously in published articles. This paper provides theoretical guidance for the behavioral choices of the brand and the anchor under the live streaming commerce, which is conducive to the prosperous development of live streaming commerce.

In the practice of live streaming e-commerce, the consumer demand is usually uncertain, and the inventory and prices can be decided by brand owners or streamers. To this end, this study examines the inventory and pricing decisions of the brand owner and streamer in a live streaming e-commerce supply chain under demand uncertainty.

In this study, four scenarios are considered, i.e. the brand owner determines the inventory and price (Scenario BB), the brand owner determines the inventory and the streamer determines the price (Scenario BS), the streamer determines the inventory and the brand owner determines the price (Scenario SB), and the streamer determines the inventory and price (Scenario SS).

The results show that the inventory and prices, as well as the profits of the brand owner and streamer increase with the consumer sensitivity to streamer’s sales effort level under the four scenarios. The inventory (price) is the highest under Scenario SS (SB), while that is the lowest under Scenario BB (BS). In addition, when the sensitivity is low, the brand owner’s profit is the highest under Scenario BB, otherwise, the profit is the highest under Scenario SS. Regardless of the sensitivity, the streamer’s profit is always the highest under Scenario SS.

Few studies focused on the inventory and pricing decisions of brand owners and streamers in live streaming e-commerce supply chains under demand uncertainty, while this work bridges the research gap. This study can provide theoretical basis and decision support for brand owners and streamers.

Social media platforms (SMPs) are pivotal in information dissemination and molding public opinion. Various platforms exhibit differences and characteristics. It is necessary to compare and analyze the roles played by different platforms in the evolution of public events.

This study develops a framework to evaluate the role of SMPs at different stages of public events. To calculate some of these indicators, the GPT-AP-TextRank topic model is constructed. The study further analyzes the correlation between indicators at different stages and SMP’s role and compares SMP’s different roles among the four stages.

The results reveal significant disparities in the role of different SMPs during public events. Weibo demonstrates notable performance during the outbreak, spread and recession stages of the event, exhibiting a strong influence on public event evolution. Bilibili, Douban, Zhihu and Baidu Tieba show relatively ordinary roles. In addition, compared to the spread stage, SMPs exhibit a stronger ability to influence event redirection in the initial stage, which is different from the original assumption of the study.

The findings expose the powerful roles of SMPs in event evolution, providing valuable insights for enhancing public event governance.

This study proposes an evaluation method for SMPs’ role and introduces a novel GPT-AP-TextRank topic generation model for the indicator calculation.

The rapid development of e-commerce live streaming is inseparable from the important role of live-streamer. However, research exploring the impacts of live-streamers’ characteristics on customer engagement in different types of live streaming rooms remains limited. This study aims to examine the impacts of live-streamers’ characteristics (i.e. expertise, moral reputation, popularity, responsiveness) on consumer engagement in e-commerce live streaming, and verify whether there are differences in the impact of live-streamers’ characteristics on consumer experience for different types of live-streamers.

Data were gathered from 266 Chinese adults who have experience watching e-commerce live streaming. Partial least squares techniques were used to verify the model.

The results show that expertise, moral reputation, popularity, and responsiveness of live-streamers have positive impacts on consumer engagement through perceived trust and perceived pleasure. Moreover, through multi-group analysis, this study found that compared with enterprise live-streamers, the moral reputation of individual live-streamers exerts a stronger effect on consumers’ perceived trust, and the popularity of individual live-streamers exerts a stronger effect on consumers’ perceived pleasure. Compared with individual live-streamers, the responsiveness of enterprise live-streamers exerts a stronger effect on consumers’ perceived pleasure.

This study contributes to e-commerce live streaming research by categorizing live-streamers into two types: individual live-streamers and enterprise live-streamers, and verifying the specific difference in the impacts of live-streamers’ characteristics on consumer experience for the two types of live-streamers.

This study investigates whether the visualization of agricultural products influences consumers’ purchase intentions in the context of farmer-assisted livestreaming in China. Moreover, it explores the moderating effect of packaging functionality and the mediating effect of consumer trust.

Consumers in China from multiple social media platforms participated in this survey, which yielded 333 valid responses for analysis.

The results revealed a positive relationship between the video presentation about the agricultural production process and consumers’ purchase intention, which is mediated by consumers’ trust. Meanwhile, packaging functionality moderates the relationship between agricultural product visualization and consumers’ purchase intentions as well as the indirect effect of consumers’ trust.

This study extends the application of the stimulus-organism-response (SOR) model to the field of farmer-assisted livestreaming. By building a more detailed model, this study adds to knowledge on the influencing mechanisms of consumers’ purchase intentions in farmer-assisted livestreaming.

This study aims to understand how different levels of streamer channels influence luxury brand sales in live streaming commerce. This study also seeks to understand the conditions under which luxury brands may benefit more from different level streamer channels.

Panel data were collected from 17 international luxury brands on the Douyin live streaming platform in an 18 week period from August to December 2020 and analyzed by using a two-way fixed effects model.

The authors compared different mega-, macro- and micro-streamer channels within live streaming commerce and found that the densities of mega- and macro-streamer channels had significant positive impacts on luxury brand sales in live streaming commerce. Moreover, the effects of the density of streamer channel on luxury brand sales were moderated by such variables as product line breadth, product line depth, product type (star/non-star) and product price (high/low). The authors found that product line breadth and depth could reduce the positive impact of the densities of mega- and macro-streamer channels on luxury brand sales. For star products and high-priced products, the relationship between the density of mega-streamer channel and luxury brand sales was more likely to be observed than for non-star products and low-priced products. The relationship between the density of macro-streamer channel and luxury brand sales was more likely to be observed in low-priced products than in high-priced products.

The findings make important contributions to the literature in that the authors expand the influencer-brand fit theory by proposing a new model of effects of the densities of mega-, macro- and micro-streamer channels on sales performance across different luxury products to improve our understanding of the fit among influencers, brands and products. This helps luxury brands make basic decisions of “who sells” and “sells what” when engaging in live streaming commerce.