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This study examines the relationship between streamers' product descriptions, customer comments and online sales and focuses on the moderating effect of streamer–viewer relationship strength.

Between June 2021 and April 2022, the structured data of 965 livestreaming and unstructured text data of 42,956,147 characters from two major live-streaming platforms were collected for the study. Text analysis and regression analysis methods were employed for data analysis.

First, the authors' analysis reveals an inverted U-shaped relationship between comment length and product sales. Notably, comment volume and comment emotion positively influence product sales. Furthermore, the semantic richness, emotion and readability of streamers' product descriptions also positively influence product sales. Secondly, the authors find that the strength of streamer–viewer relationship weakens the positive effects of comment volume and comment emotion without moderating the inverted U-shaped effect of comment length. Lastly, the strength of streamer–viewer relationship also diminishes the positive effects of emotion, semantics and readability of streamers' product descriptions on product sales.

This study is the first to concurrently examine the direct and interactive effects of user-generated content (UGC) and marketer-generated content (MGC) on consumer purchase behaviors in livestreaming e-commerce, offering a novel perspective on individual decision-making and cue utilization in the social retail context.

This study aims to present a holistic synopsis of the current scientific structure of inbound tourism research and suggest further research directions.

A 30-year bibliometric analysis was conducted using the Web of Science Core Collection database through CiteSpace, covering 568 Social Sciences Citation Index articles.

This study systematically identifies the evolution of scientific structures and emerging research trends in the inbound tourism field. The findings show that: co-authorship patterns reveal a trend of international cooperation; the evolution of research themes is consistent with the development of the macro-environment and inbound tourism industry, the most recent focus being on sustainable development of destinations; varied multivariate data analysis methods dominate current empirical analysis; there exist three major research dimensions in the inbound tourism field; and unilateral political factors in destinations and bilateral linkage factors affecting inbound tourism demand have aroused research interest increasingly.

This study helps scholars and practitioners understand the current issues and needs of the inbound tourism industry and develops a future research agenda to promote the industry’s continuous development.

This study is the first attempt to provide insights into the theoretical development of inbound tourism over the past three decades from the perspective of a knowledge-based platform and further facilitate sustainable development of the industry.

本篇论文旨在全面地梳理入境旅游研究的整体知识架构, 进而提出未来研究方向。

本篇论文基于文献计量法, 运用CiteSpace对从Web of Science Core Collection数据库中检索得到的30年间568篇社会科学引文索引文献进行分析。

本篇论文系统地梳理了入境旅游领域知识架构的演化和新兴研究趋势, 填补了现有研究空白。结果揭示：（1）作者协作模式展现出国际化合作趋势; （2）入境旅游领域研究主题的演化趋势与宏观环境和国际旅游的发展阶段相吻合, 并且目的地可持续发展的研究成为热点; （3）目前实证分析主要采用各种多元数据分析方法; （4）入境旅游领域的研究可以归纳为三个主要研究方向; （5）单边政治因素和国家双边联系因素对入境旅游需求的影响逐渐成为近年研究热点。

本篇论文提出了未来研究方向, 有助于学界和业界人士掌握入境旅游产业的现状和需要, 从而推动该产业的可持续发展。

本篇论文首次从知识平台视角出发, 揭示了过去三十年间入境旅游的理论发展情况。

关键词 入境旅游, 文献计量法, 知识架构, 知识平台, 未来研究方向, CiteSpace

文章类型 ：研究型论文

El objetivo de este estudio es presentar una sinopsis holística de la actual estructura científica de la investigación turística entrante y sugerir nuevas direcciones de investigación.

Se condujo un análisis bibliométrico de 30 años utilizando la base de datos de recopilación de datos de la red de ciencias básicas a través de CiteSpace, que abarcó 568 artículos del Índice de Citación de Ciencias Sociales.

Este estudio identifica sistemáticamente la evolución de estructuras científicas y las tendencias de investigación emergentes en el campo del turismo entrante. Las conclusiones muestran que: i) las pautas de coautoría han revelado una tendencia a la cooperación internacional; ii) la evolución de los temas de investigación es coherente con el desarrollo del macro-entorno y de la industria del turismo entrante, centrándose más recientemente en el desarrollo sostenible de los destinos; iii) los diversos métodos de análisis de datos multivariados predominan en el análisis empírico actual; iv) existen tres grandes dimensiones de investigación en el ámbito del turismo entrante; y v) los factores políticos unilaterales en los destinos y los factores de vinculación bilateral que afectan a la demanda turística entrante han despertado un interés cada vez mayor a la investigación.

Este estudio ayuda a los académicos y los profesionales a entender problemas y necesidades actuales de la industria turística entrante y desarrollar una futura guía de investigación para fomentar el desarrollo continuo de la industria.

Este estudio es el primer intento de proporcionar información sobre el desarrollo teórico del turismo entrante en las últimas tres décadas, desde la perspectiva de la plataforma basada en un conocimiento y facilitar aún más el desarrollo sostenible de la industria.

Turismo entrante, Bibliométrica, Estructura científica, Plataforma basada en el conocimiento, Agenda de investigación futura, CiteSpace

Revisión bibliográfica

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results can also provide references for railway departments to plan railway operation lines reasonably and efficiently.

This paper intends to establish a seasonal cycle first order univariate grey model (GM(1,1) model) combing with a seasonal index. GM (1,1) is termed as the trend equation to fit the railway passenger volume in China from 2014 to 2018. The railway passenger volume in 2019 is used as the experimental data to verify the forecasting effect of the proposed model. The forecasting results of the seasonal cycle GM (1,1) model are compared with the traditional GM (1,1) model, seasonal grey model (SGM(1,1)), Seasonal Autoregressive Integrated Moving Average (SARIMA) model, moving average method and exponential smoothing method. Finally, the authors forecast the railway passenger volume from 2020 to 2022.

The quarterly data of national railway passenger volume have a clear tendency of cyclical fluctuations and show an annual growth trend. According to the comparison of the modeling results, the authors know that the seasonal cycle GM (1,1) model has the best prediction effect with the mean absolute percentage error of 1.32%. It is much better than the other models, reflecting the feasibility of the proposed model.

As the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study. Besides, considering the impact of the epidemic on passenger volume, the authors introduce a disturbance factor to deal with the forecasting results in 2020, making the modeling results more scientific, practical and referential.

This paper aims to explore the relationship between environmental regulation, technological innovation and manufacturing quality competitiveness to provide some references for emission reduction activities and improvements in manufacturing quality competitiveness to achieve environmental protection targets and economic development as part of a win–win situation.

Based on the structure-behavior-performance paradigm and Grabowski’s research, a new empirical model was provided. The software, EViews 6.0, was used for econometric analysis. Regression analysis was adopted to explore the three indicators’ relationships.

First, environmental regulation can promote technological innovation effectively. Second, compared with wasted gas and wasted solids, investment in wasted water control promotes Chinese technological innovation most. Third, the impact of research and development investment, induced by environmental regulation, on manufacturing quality competitiveness is greater than that induced by non-environmental regulation. Fourth, the impact of lagged two-phase environmental regulation on manufacturing quality competitiveness is similar to that of lagged one-phase regulation.

The issue that Chinese manufacturing is facing is how to manage the trade-off between pollution control investment and improved quality competitiveness. This study enables managers to understand how to better implement environmental regulation initiatives while achieving environmental protection and quality competitiveness as part of a win–win situation.

This paper analyzes the relationships between environmental regulation, technological innovation and manufacturing quality competitiveness for the first time and provides the basic argument for integrating Chinese environmental regulation with quality competitiveness to reveal the uniqueness of the circumstances determining China’s economic development.

The purpose of this paper is to make an evaluation and early warning prediction using grey prediction models and the index of process ability.

An early warning prediction for food security risk is proposed in this paper. A quality index is constructed and an improved grey prediction model is also presented. The quality index model is applied to measure the level of food quality; then the grey prediction model is applied to predict the trend of quality index for food in the future. A comparison between the predicted trends and standard limit proposed by experts is made to judge the food security risk.

The results in this paper indicate that more attention should be paid to the food security situation and steps should also should be taken to prevent harm to people's health from food.

The method presented in the paper could be used to make predictions for those systems which have the characteristic of small sample and poor information. The combination of grey prediction model and process ability index could also be applied to evaluation and early warning for those systems.

Food quality index is constructed for evaluating food security and an improved grey prediction model is also presented in this paper. The combination of these two models could be applied to make evaluation and warning prediction for food security risk with poor information.

Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch medical resources on time. Based on the background of standard hospital operation and Coronavirus disease (COVID-19) periods, this paper constructs a hybrid grey model to forecast the outpatient volume to provide foresight decision support for hospital decision-makers.

This paper proposes an improved hybrid grey model for two stages. In the non-COVID-19 stage, the Aquila Optimizer (AO) is selected to optimize the modeling parameters. Fourier correction is applied to revise the stochastic disturbance. In the COVID-19 stage, this model adds the COVID-19 impact factor to improve the grey model forecasting results based on the dummy variables. The cycle of the dummy variables modifies the COVID-19 factor.

This paper tests the hybrid grey model on a large Chinese hospital in Jiangsu. The fitting MAPE is 2.48%, and the RMSE is 16463.69 in the training group. The test MAPE is 1.91%, and the RMSE is 9354.93 in the test group. The results of both groups are better than those of the comparative models.

The two-stage hybrid grey model can solve traditional hospitals' seasonal outpatient volume forecasting and provide future policy formulation references for sudden large-scale epidemics.

The purpose of this research on the control of three-axis aero-dynamic pendulum with disturbance is to facilitate the applications of equipment with similar pendulum structure in intelligent manufacturing and robot.

The controller proposed in this paper is mainly implemented in the following ways. First, the kinematic model of the three-axis aero-dynamic pendulum is derived in state space form to construct the predictive model. Then, according to the predictive model and objective function, the control problem can be expressed a quadratic programming (QP) problem. The optimal solution of the QP problem at each sampling time is the value of control variable.

The trajectory tracking and point stability tests performed on the 3D space with different disturbances are validated and the results show the effectiveness of the proposed control strategy.

This paper proposes a nonlinear unstable three-axis aero-dynamic pendulum with less power devices. Meanwhile, the trajectory tracking and point stability problem of the pendulum system is investigated with the model predictive control strategy.

The purpose of this study is to build a consensus model of social network group decision-making (SNGDM) based on improved PageRank algorithm. By objectively and fairly measuring the evaluation ability of participants in the decision-making process, the authors can improve the fairness and authenticity of the weight solution of decision-makers (DM) in the decision-making process. This ensures the reliability of the final group consensus results.

This study mainly includes six parts: preference expression, calculation of DM's weight, preference aggregation, consensus measurement, opinion adjustment and alternative selection. First, Pythagorean fuzzy expression is introduced to express the preference of DMs, which expands the scope of preference expression of DMs. Second, based on the social network structure among DMs, the process of “mutual judgment” among DMs is increased to measure the evaluation ability of DMs. On this basis, the PageRank algorithm is improved to calculate the weight of DMs. This makes the process of reaching consensus more objective and fair. Third, in order to minimize the evaluation difference between groups and individuals, a preference aggregation model based on plant growth simulation algorithm (PGSA) is proposed to aggregate group preferences. Fourth, the consensus index of DMs is calculated from three levels to judge whether the consensus degree reaches the preset value. Fifth, considering the interaction of DMs in the social network, the evaluation value to achieve the required consensus degree is adjusted according to the DeGroot model to obtain the overall consensus. Finally, taking the group preference as the reference, the ranking of alternatives is determined by using the Pythagorean fuzzy score function.

This paper proposes a consensus model of SNGDM based on improved PageRank algorithm to aggregate expert preference information. A numerical case of product evaluation is introduced, and the feasibility and effectiveness of the model are explained through sensitivity analysis and comparative analysis. The results show that this method can solve the problem of reaching consensus in SNGDM.

Different DMs may have different judgment criteria for the same decision-making problem, and the angle and depth of considering the problem will also be different. By increasing the process of mutual evaluation of DMs, the evaluation ability of each DM is judged only from the decision-making problem itself. In this way, the evaluation opinions recognized by most DMs will form the mainstream of opinions, and the influence of corresponding DMs will increase. Therefore, in order to improve the fairness and reliability of the consensus process, this study measures the real evaluation ability of DMs by increasing the “mutual judgment” process. On this basis, the defect of equal treatment of PageRank algorithm in calculating the weight of DMs is improved. This ensures the authenticity and objectivity of the weight of DMs. That is to improve the effectiveness of the whole evaluation mechanism. This method considers both the influence of DMs in the social network and their own evaluation level. The weight of DMs is calculated from two aspects: sociality and professionalism. It provides a new method and perspective for the calculation of DM’s weight in SNGDM.

Cloud manufacturing platform has a high degree of openness, with a large variety of users having different needs. Designers on such platforms exhibit great differences in their knowledge abilities and knowledge needs, necessitating the cloud platform to provide personalized knowledge recommendation. To satisfy the personalized knowledge needs of the designers in product design tasks and other manufacturing tasks on a cloud manufacturing platform and provide them with high-quality knowledge resources, a knowledge recommendation method based on designers’ knowledge ability is proposed. The proposed method, with appropriate adjustments, can also be used for personalized knowledge recommendation to other personnel or institutions in cloud manufacturing platforms.

A knowledge recommendation method model is developed. The method consists of three stages. First, a designer knowledge system is constructed based on customer reviews in historical tasks, and designer knowledge ability and knowledge demand degree are quantitatively evaluated by synthesizing customer reviews and expert evaluations. Subsequently, the design knowledge domain ontology is constructed, and knowledge resources and tasks are modeled based on the ontology. Finally, the semantic similarity between tasks and knowledge resources and the knowledge demand degree of designers are integrated to calculate the knowledge recommendation coefficient, which realizes the personalized knowledge recommendation of designers.

Two design tasks of a 3D printing cloud platform are taken as examples to verify the feasibility and effectiveness of the proposed method. Compared with other methods, it is proved that the method proposed in this paper can obtain more knowledge resources that meet the needs of designers and tasks.

The method proposed in this paper is important for the expansion of data applications of the cloud manufacturing platform and for enriching the knowledge recommendation method. The proposed method has two innovations. First, both designer needs and task needs are considered in knowledge recommendation. Compared with most of the existing methods, which only consider one factor, this method is more comprehensive. Second, the designer’s knowledge ability model is constructed by using customer reviews on the cloud manufacturing platform. This overcomes the defect of low accuracy of the interest model in existing methods and makes full use of the big data of the cloud manufacturing platform.