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This study aims to investigate the effects of irregular groove textures on the friction and wear performance of sliding contact surfaces. These textures possess multiple depths and asymmetrical features. To optimize the irregular groove texture structure of the sliding contact surface, an adaptive genetic algorithm was used for research and optimization purposes.

Using adaptive genetic algorithm as an optimization tool, numerical simulations were conducted on surface textures by establishing a dimensionless form of the Reynolds equation and setting appropriate boundary conditions. An adaptive genetic algorithm program in MATLAB was established. Genetic iterative methods were used to calculate the optimal texture structure. Genetic individuals were selected through fitness comparison. The depth of the groove texture is gradually adjusted through genetic crossover, mutation, and mutation operations. The optimal groove structure was ultimately obtained by comparing the bearing capacity and pressure of different generations of micro-convex bodies.

After about 100 generations of iteration, the distribution of grooved textures became relatively stable, and after about 320 generations, the depth and distribution of groove textures reached their optimal structure. At this stage, irregular texture structures can support more loads by forming oil films. Compared with regular textures, the friction coefficient of irregular textures decreased by nearly 47.01%, while the carrying capacity of lubricating oil films increased by 54.57%. The research results show that irregular texture structures have better lubrication characteristics and can effectively improve the friction performance of component surfaces.

Surface textures can enhance the friction and lubrication performance of metal surfaces, improving the mechanical performance and lifespan of components. However, surface texture processing is challenging, as it often requires multiple experimental comparisons to determine the optimal texture structure, resulting in high trial-and-error costs. By using an adaptive genetic algorithm as an optimization tool, the optimal surface groove structure can be obtained through simulation and modeling, effectively saving costs in the process.

The purpose of this paper is to explore financial quality problems, based on the dynamics of the ownership structure, in the privatization process to clarify the internal relation among the ownership’s attribution of the commercial mixed ownership company, the company’s performance and its financial relationships. This paper also examines the mixed ownership enterprise’s potential problems during the development process.

Adopting the single case study method, the authors selected the mixed ownership public company Hubei Sanxia New Building Materials Co., Ltd. (stock code: 600293) to explore, from a privatization perspective, the impact of mixed ownership on financial quality.

The study found that Sanxia experienced tight cash flow and heavy debt burdens due to the privatization and that its controlling shareholders used non-operating income to support Sanxia, thus characterizing the dual role of “the grabbing hand” and “the helping hand.” Sanxia’s privatization process highlighted the volatility of performance, the exception of monetary funds and the existence of accounting fraud rather than the prosperous development of the capital combination.

These findings provided case support that privatization negatively affects the financial quality of the company. Previous studies have indicated that there should be greater focus more on the issue that state-owned shares rebound during the process of privatization and that, with respect to commercial mixed ownership reform of state-owned enterprises, such reform must avoid the passive transfer of corporate control, ensure the fairness of the related transactions, prevent the loss of state-owned assets and preclude the controlling shareholders from seizing interests of listed companies.

Big data analytics (BDA) and machine learning (ML) can be used to identify the influencing factors of online service supply chains (OSSCs) and can help in the formulation of optimal pricing strategies. This paper analyzes the influencing factors of customer online shopping from the demand-side perspective and formulates optimal pricing strategies from the supply-side perspective.

This paper uses ML and the Stackelberg game approach to discuss OSSC management. ML's feature selection algorithm is used to identify the important influencing factors of 12,330 customers' online shopping intention data using four different classifiers. The Stackelberg game approach is used to analyze the pricing strategies of integrators and suppliers in OSSCs.

First, the feature selection algorithm can improve the efficiency of optimization in big data samples of OSSCs. Second, the level of visualization and the quality of information (page value) will affect the purchase behavior of customers. Finally, the relationship between the optimal pricing and the level of visualization is obtained through the Stackelberg game approach.

This paper reveals the phenomenon of “mystery customers,” and the results of this paper can provide insights and suggestions regarding the decision-making behavior of integrators and suppliers in OSSC management.

Considering customer behavior intention, this paper uses a data-driven method to explore the influencing factors and pricing strategies of OSSCs. The empirical results enrich the existing OSSC management research, proposing that the level of product visualization and information quality plays an important role in OSSCs.

The Internet of Things (IoT) platform empowers the digital transformation of the manufacturing industry by providing information technology services. Simultaneously, it enters the market by offering smart products to consumers. In light of different service fee scenarios, this article explores the optimal decision-making for the platform. It investigates the pricing models and entry decisions of IoT platforms.

In this study, we have formulated a game-theoretic model to scrutinize the influence of the IoT platform ventured into the smart device market on the pre-existing suppliers operating under subscription-based and usage-based pricing agreements.

Our outcome shows that introducing an IoT platform’s smart device has a differential effect on manufacturers depending on their contract type. Notably, our research indicates that introducing the platform’s own smart device within the subscription-based model does not negatively impact the profitability of incumbent manufacturers, so long as there is a noticeable discrepancy in the quality of the smart devices. However, our findings within the usage-based model demonstrate that despite the variance in smart device quality differentiation, the platform’s resolution to launch their device and impose their pricing agreements adversely affects established manufacturers. Additionally, we obtain valuable Intel regarding the platform’s entry strategies and contractual inclinations. We demonstrate that the platform is incentivized to present its smart device when reasonable entry costs remain. Furthermore, the platform prefers subscription-based contracts when the subscription fee is relatively high in non-platform entry and entry cases.

These findings hold significant practical implications for firms operating in an IoT-based supply chain.

The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.

In this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were.

On the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies.

In this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs).

In the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.

This study aims to examine the components and evolution of the Chinese wine festival market using the Dalian International Wine and Dine Festival (DIWDF) as a case study.

Adopting a longitudinal approach, survey data were collected from attendees of the first, fifth and seventh DIWDF in 2012, 2016 and 2018, respectively. Cluster analysis segmented attendees by wine and festival experience and consumption features. Comparative analysis was conducted to examine segment differences by demography, festival motivation, satisfaction and intention. Changes in segments over time were examined across the three times.

The following three clusters were identified: “wine-novice fest-newbies,” “occasional drinker fest-goers” and “wine-lover fest-enthusiasts.” Over the study period, the proportion of “wine-lover fest-enthusiasts” increased significantly while the percentages of the other two segments decreased, demonstrating the evolution of the Chinese wine festival market and their consumer impacts.

This study offers straightforward indicators of market value via consumption features for both wine businesses and festival organizers. The characteristics of the segments and their inter-linkage have important implications for developing product mix, targeting strategies, festival service design and market development.

This is the first known empirical research globally to investigate relationships among market segments both horizontally (differences between segments) and vertically (development over time) and to incorporate both wine- and festival-related consumption features.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

The authors expand the connotation of the research on the accounting information quality characteristics, provide empirical evidence for the factors of consistency and also help to deepen further their understanding of the economic consequences of ownership concentration and other ownership structures.

Using financial data of Chinese listed companies as samples, coupled with a method to calculate the consistency of the sample enterprises on the corporate level in the 2007–2019 period, the authors studied its impact of ownership concentration on consistency.

The study finds that after controlling other factors, ownership concentration could significantly reduce accounting information consistency. Further research finds that when the executives' shareholding is higher, the reduction effect of ownership concentration on consistency is weaker. After the robustness test, the conclusion remains basically unchanged.

First, maybe there is a limitation of De Franco et al. (2011) method the authors use in China. As some scholars pointed out, the systematic component of returns variation is large in emerging markets (Morck et al., 2000), so it is hard to determine to what extent market stock returns will capture the net effect of earnings. As is mentioned above, there are multiple methods for measuring comparability and consistency, but it is not easy to judge which way is the best. Maybe the authors will have a perfect process in the future. Second, in addition to the factors mentioned in this study's hypotheses, there should be other factors (these include internal factors and external factors) that play moderating role in the impact of ownership concentration on accounting information consistency. The authors have not thoroughly studied the effect of those factors. These limitations all need to be further explored in the future.

The study finds that after controlling other factors, ownership concentration could significantly reduce accounting information consistency, but the reduction will be affected by some other factors related to corporate governance. The new insights from these advances are that the conclusions provide a technical path for management of companies to improve corporate governance efficiency and the quality of accounting information, and also provide more reference and empirical evidence for information users to identify the company's accounting information quality, which contributes to creating a prerequisite for the usefulness of accounting information.

China's defense industry is analyzed by comparing the technical level of the military and civilian products manufactured by China's nuclear, space, aviation, shipbuilding, ordnance, and electronics industries with their advanced counterparts. Generally, China's defense industry is about 20 years behind the global leaders. Thus, it is inappropriate to declare China's emergence as the world’s second military power. However, if it continues on its current development trajectory, it will attain that status in the near future.