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The purpose of this paper is to construct a multi-relational network for an online sharing platform in the age of the sharing economy, to identify the factors impacting users’ product adoption behavior and to predict consumers’ purchases of user-generated products on the platform.

The study conducted multi-relational network analyses of five different sub-networks in identifying influential factors for e-book adoption. Meanwhile, the study adopted machine learning methods with different classification algorithms and feature sets to predict users’ purchasing behaviors.

The authors found that an individual’s adoption of a product was correlated with his or her purchasing habits and collaboration with others on the online sharing platform. Through the inclusion of network features, the authors were able to build a predictive model that forecasted consumers’ purchases of user-generated e-books with reasonable accuracy.

The interdisciplinary approach used in the study can serve as a good reference for identifying factors impacting the product adoption behavior of users in the online sharing platform, through employing different sociological and computational methods.

The outcome of the study has provided important managerial implications, especially for the design of social commerce platform in the age of the sharing economy.

The authors verified the social influence impacting consumers’ product adoption behavior and shed light on the value of collaboration in the age of the sharing economy.

The study was the first to identify user-generated e-book adoption on an online sharing platform from a multi-relational network perspective. The idea and the approach supplied a new method of behavioral analysis in the context of a sharing economy.

The purpose of this paper is to investigate the potential influence of the big five personality traits − extraversion, neuroticism, agreeableness, openness and conscientiousness − on the time taken by travelers to submit online reviews after their hotel stay.

The study analyzed 83,235 TripAdvisor reviews from 415 hotels in six major US tourism cities using random forest algorithms and Poisson regression. The research investigated the influence of the big five personality traits on the time taken by travelers to submit online reviews post-hotel stay, merging personality psychology with consumer behavior research through a combination of machine learning and statistical analysis.

The findings reveal significant correlations between certain personality traits and the time taken to post online hotel reviews. Extraversion, neuroticism and agreeableness were found to be negatively correlated with response time, suggesting that individuals scoring higher in these traits tend to submit their reviews more quickly. Conversely, openness exhibited a positive correlation, indicating that those with higher levels of openness tend to delay their feedback. Conscientiousness showed no significant correlation with response time.

This study represents a novel approach to understanding the relationship between personality traits and online review behavior in the hospitality industry. By leveraging advanced machine learning techniques, such as random forest algorithms, alongside traditional statistical methods like Poisson regression, this research offers a unique perspective on the influence of personality on consumer behavior. The innovative application of these technologies to a large data set of TripAdvisor reviews provides fresh insights that can inform the development of personalized customer engagement strategies. The findings contribute to the growing body of literature on the intersection of personality psychology, consumer behavior and hospitality management in the digital age.

本研究旨在探讨五大人格特质（外向性、神经质、宜人性、开放性和尽责性）对旅行者在入住酒店后提交在线评论时间的潜在影响.

本研究分析了来自美国六大旅游城市 415 家酒店的 83,235 条TripAdvisor评论, 采用随机森林算法和泊松回归方法, 探讨了五大人格特质对旅行者在入住酒店后提交在线评论时间的影响。研究将人格心理学与消费者行为研究相结合, 运用了机器学习和统计分析的组合方法。

研究结果表明, 某些人格特质与发布在线酒店评论的时间存在显著相关性。外向性、神经质和宜人性与响应时间呈负相关, 表明具有较高这些特质的人倾向于更快速地提交评论。相反, 开放性与响应时间呈正相关, 暗示开放性较高的人往往延迟反馈。尽责性与响应时间无显著相关性。

本研究通过利用随机森林等先进的机器学习技术和泊松回归等传统统计方法, 为理解人格特质对消费者行为的影响提供了新颖视角。这种创新性技术应用于包含大量TripAdvisor评论的数据集, 提供了新的见解, 可用于开发个性化客户互动策略。本研究成果为人格心理学、消费者行为和酒店管理领域在数字时代的交叉研究增添了新的学术价值。

Because natural resource utilization is a predictor of sustainable development, an evaluation of the efficiency of resource utilization is critical for assessing developmental potentiality. The purpose of this paper is to apply three-dimensional (3D) ecological footprint theory to assess the effects of production and consumption on ecological systems in Hefei, China.

Using data for Hefei for the period 2005-2014, an ecological footprint model (EFM) was developed to calculate the area’s ecological footprint (EF), ecological carrying (EC) capacity and obtain two indices, namely, footprint depth and size. The relationship between economic development and natural resource utilization was subsequently evaluated based on the calculated ecological deficit and the EF demand per Renminbi 10,000 of gross domestic product (GDP).

Over the last decade, Hefei’s EF per capita evidenced a 9.87 percent growth rate, increasing from 1.16 hm²/person in 2005 to 2.70 hm²/person in 2014. EC capacity per capita increased from 0.21 hm²/person in 2005 to 0.36 hm²/person in 2014, evidencing a gradually increasing trend at an average annual growth rate of 6.24 percent. Thus, between 2005 and 2014, the ecological deficit increased annually by three times. The amplification of footprint depth significantly exceeded that of footprint size. Between 2005 and 2014, Hefei’s EF per capita Renminbi 10,000 of GDP decreased annually by 4.68 percent. Thus, energy consumption in Hefei exceeded the natural regeneration capacity of energy resources, with excessive development and resource utilization impacting on the regional ecological system.

The application of a 3D EFM sheds light on natural resource utilization within regional development. Moreover, footprint depth and size are significant predictors of the impacts of natural resource utilization. These findings will also benefit other countries or cities.

This is one of the first empirical studies to apply a 3D EFM to evaluate the relationship between natural resource utilization and economic development. Adopting a sustainable development framework, it provides insights into the effects of natural resource utilization in relation to the balance between the natural ecological system and economic development. This has far-reaching implications beyond Hefei and China.

Based on social information processing theory, this paper aims to explore how and when leader self-deprecating humor may spark subordinate learning from failure. The authors cast perspective taking as a novel explanatory mechanism for this indirect effect, and further consider leader–member exchange as a boundary condition of the relationship.

The authors tested the hypotheses by conducting a multiwave and multisource survey of 604 members from 152 teams in a Chinese high-technology company.

Results of multilevel path analyses demonstrate that leader self-deprecating humor positively influences subordinate learning from failure via perspective taking. Further, this mediation effect is stronger at higher levels of leader–member exchange.

This study contributes to the theoretical understanding of the relationship between leader self-deprecating humor and subordinate learning from failure. However, the research design was not longitudinal or experimental, and thus the authors were unable to make strong inferences about absolute causality.

The work yields useful insights for practitioners aiming to encourage subordinates to learn from failure.

This study provides evidence that leader self-deprecating humor can stimulate subordinate learning from failure via perspective taking, and the indirect effect is further strengthened by leader–member exchange. The findings offer new directions for research on leader self-deprecating humor and learning from failure.

Since the opening of China (aka, reform and opening-up), a great number of rural residents have migrated to large cities in the past 40 years. Such a one-way population inflow to urban areas introduces nontrivial social conflicts between urban natives and migrant workers. This study aims to investigate the most discussed topics about migrant workers on Sina Weibo along with the corresponding sentiment divergence.

An exploratory-descriptive-explanatory research methodology is employed. The study explores the main topics on migrant workers discussed in social media via manual annotation. Subsequently, guided LDA, a semi-supervised topic modeling approach, is applied to describe the overall topical landscape. Finally, the authors verify their theoretical predictions with respect to the sentiment divergence pattern for each topic, using regression analysis.

The study identifies three most discussed topics on migrant workers, namely wage default, employment support and urban/rural development. The regression analysis reveals different diffusion patterns contingent on the nature of each topic. In particular, this study finds a positive association between urban/rural development and the sentiment divergence, while wage default exhibits an opposite relationship with sentiment divergence.

The authors combine unique characteristics of social media with well-established theories of social identity and framing, which are applied more to off-line contexts, to study a unique phenomenon of migrant workers in China. From a practical perspective, the results provide implications for the governance of urbanization-related social conflicts.

By applying the American Customer Satisfaction Index (ACSI) model to virtual art exhibitions, this research aims to reveal insights into the factors impacting visitor satisfaction and electronic word-of-mouth (e-WOM). Furthermore, the investigation of exhibition promotion seeks to understand how external factors contribute to the overall visitor experience in virtual art exhibitions.

With advancements in virtual communication technology and the transformative impact of the COVID-19 pandemic in recent years, there has been a notable surge in the popularity of virtual art exhibitions based on the Internet. This study uses the ACSI model to examine visitor satisfaction and e-WOM in virtual art exhibitions. Additionally, it explores the influence of exhibition promotion on the ACSI model.

Key findings revealed that 1) both promotion efforts and e-WOM exhibited significant relationships with the ACSI model, and 2) most of the relationships within the ACSI model were consistent with previous research outcomes.

This study extends the ACSI model’s application to virtual art exhibitions, enhancing its relevance. Additionally, it addresses the knowledge gap concerning the direct impact of promotion on audience expectations and its relationship with the ACSI model in virtual art exhibitions. Furthermore, the research explores the influence of customer satisfaction on electronic word-of-mouth in exhibitions, offering valuable insights for exhibition evaluation systems. The study serves as a guide, providing data and models for researchers investigating virtual art exhibitions.

The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory.

This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry.

This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased.

This paper has research limitations in variable measurement and data selection.

This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration.

The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

The metaverse enables users to create their own avatars in a shared virtual space, giving rise to a new avatar personality that differs from their real-self personality. The aim of this research is to explore how users' real-self and avatar personalities may affect their behavioral engagement and satisfaction in the metaverse context.

This research applies self-discrepancy theory to investigate how the big five traits of both real-self and avatar personalities influence users' engagement and satisfaction in the metaverse. The present research employed a mixed-methods approach, beginning with a qualitative study to identify prevalent personality cues among users on metaverse social media platforms. Subsequently, a quantitative study was conducted to further validate the findings of the qualitative study.

The results indicated that avatar personality scored higher than the real-self personality in the dimensions of openness, conscientiousness and extraversion, while scored lower in the dimensions of agreeableness and neuroticism. Both real-self and avatar personality traits positively influenced metaverse satisfaction via behavioral engagement in the metaverse. Notably, avatar personality traits had a stronger impact on behavioral engagement compared to real-self personality traits, which further influence metaverse satisfaction.

The present study offers practical insights for metaverse developers and managers to enhance user satisfaction by focusing on users’ big five traits of both real-self and avatar personality. It suggests implementing personalized tools, organizing personality-based social activities and other initiatives to encourage user’s behavioral engagement and ultimately enhance metaverse satisfaction.

Unlike existing research that concentrates on a single facet of personality traits, this research employs a mixed-methods approach to conceptualize users' real-self personality and avatar personality, further exploring their impacts on metaverse satisfaction.

The hygiene factor is always imperative when customers consider a certain restaurant, and the information contained in customer reviews can be an efficient approach to gauge a restaurant's hygiene during gaps in the official inspection. Therefore, the purpose of this study was to investigate whether information obtained from online reviews could predict the upcoming hygiene rating, specifically, evaluating the impact of both qualitative and quantitative content of reviews on the restaurant hygiene rating.

The quantitative research method with big data analytic techniques was applied in this study. In total, 127,656 pieces of data collected from 1,710 restaurants in four major cities in the USA were used in the analysis. Both quantitative factors (i.e. reviewer's numerical rating, days to review, readability, useful/funny/cool) and qualitative factors (i.e. eight emotional dimensions of textual reviews) were analyzed from the online customer reviews and considered in predicting the restaurant's hygiene rating.

Six out of eight emotional dimensions including anger, disgust, fear, sadness, surprise and trust were identified as having significant influences on the restaurant hygiene ratings. While three quantitative variables including days to review, readability and usefulness were identified with significant impacts on the dependent variable of restaurant hygiene rating.

This study opens an avenue for innovative research that establishes a connection between customers' reviews and restaurants' inspection systems. The results allow restaurants to predict an impending hygiene inspection rating upon dynamic assessment of review content and aid in adjusting hygiene measures accordingly.