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The purposes of this paper are to (1) explore how personality traits pertaining to the dominance influence steadiness compliance model manifest themselves in terms of user interaction behavior on social media and (2) examine whether social interaction data on social media platforms can predict user personality.

Social interaction data was collected from 198 users of Sina Weibo, a popular social media platform in China. Their personality traits were also measured via questionnaire. Machine learning techniques were applied to predict the personality traits based on the social interaction data.

The results demonstrated that the proposed classifiers had high prediction accuracy, indicating that our approach is reliable and can be used with social interaction data on social media platforms to predict user personality. “Reposting,” “being reposted,” “commenting” and “being commented on” were found to be the key interaction features that reflected Weibo users' personalities, whereas “liking” was not found to be a key feature.

The findings of this study are expected to enrich personality prediction research based on social media data and to provide insights into the potential of employing social media data for the purpose of personality prediction in the context of the Weibo social media platform in China.

This study aims to empirically examine the relationships among perceived environmental uncertainty (EV), the level of knowledge distance (KD) and the impact of value network on firm performance.

The quantitative analysis is based on data from 243 Chinese companies with engineering, procurement and construction (EPC) business in the context of the COVID-19 pandemic.

The two dimensions of value network [network centrality (NC) and network openness (NO)] have a different impact on firm performance [financial performance (FP) and market performance (MP)]. NC has a positive impact on FP, but not on MP. NO has a positive effect on MP, but not on FP. A reduced KD mediates the relationship between value network and firm performance. Moreover, it fully mediates the relationship between NC and MP, NO and FP. Finally, during the COVID-19 pandemic, only EV has a moderating effect on KD and MP.

This study is limited in terms of data set because it relies on a limited amount of cross-sectional data from one specific country. Therefore, researchers are encouraged to test the proposed propositions further.

The present findings suggest that EPC professionals should pay more attention to the EV, which may be impacted by policy, technology and the economy. This research has actionable implications for the reform of EPC in the construction industry, and practical recommendations for EPC firms to improve their corporate performance.

The results measure the complementary effects of both dimensions of value network (NC and NO) on two distinct aspects of firm performance (MP and FP) and assess the moderating effect of EV and KD in the context of the COVID-19 pandemics.

This study aims to explore the complex relationship between leadership and organizational knowledge sharing by investigating the moderating role of exchange ideology on the relation between transformational leadership in attributed charisma and knowledge sharing and the influence of attributed charisma and knowledge sharing on task performance. The influence of leadership in organizational knowledge sharing process has been gradually highlighted.

Based on the review of relevant literature and survey, a structural equation model considering four factors in the model together is now constructed and provides four hypotheses which can be verified. Self-completed questionnaires were collected from 163 students in the context of a graduate class in China.

The findings illustrate the relationship between leadership theory and knowledge sharing from a perspective of social exchange theory. In particular, results show that both transformational leadership and knowledge sharing have positive impacts to task performance and for individuals with low exchange ideology the positive influence from attributed charisma to knowledge sharing is stronger.

This research introduces exchange ideology as a moderator and explains the complex relationship between transformational leadership and knowledge sharing with sufficient proof. Transformational leadership in attributed charisma is more effective to those individuals with low exchange ideology in facilitating their knowledge effort. This paper can be theoretically and practically helpful to researchers and enterprise leaders in organizational knowledge management.

This study aims to explore the underlying mechanisms that support the resilience of the Chinese hospitality industry during and after the COVID-19 epidemic.

Content analysis was applied to 133 manually collected text articles about COVID-19 responses and strategies.

A two-step learning model (emergency reaction, precautions and prevention stages) was identified in the study. In the emergency reaction step, the primary strategies were related to customers, employees, suppliers and facility/food. In the precautions and prevention step, the strategies were related to customers, employees, suppliers and society/public relations. Multiple stakeholders are discussed in the two circles over a continual process in the learning, reacting and adapting stages.

A gap in the literature is filled by this study, providing a learning model and synthesizing various strategies applied in the hotel sector for multiple stakeholders.

The brand logo, as a fundamental element of marketing communications, serves as a crucial visual representation of a brand. In the current era of mobile Internet, logo flatness has become a new trend in practice. However, there remains a scarcity of research that explores the effects of logo flatness on consumer perceptions and brand attitudes.

Across four studies, using both observational analyses of real brands and experimental manipulations of fictitious brands, the authors examined the impact of logo flatness on consumer perceptions and brand attitudes.

Results show that logo flatness promotes the perception of modernity due to the simplicity it presents. Consumers will evaluate the brand more positively when their perception of the logo association is congruent with the brand image. Notably, traditional brands using skeuomorphic logos and modern brands employing flat logos can effectively enhance consumers' brand attitudes.

The findings of this study have significant implications for businesses seeking to enhance consumers' brand attitude and foster brand renewal through the strategic selection and design of logos that align with their brand image.

This study provides a theoretical and empirical test of the influence of logo flatness on consumers' perception of brand image, thereby enriching the existing research on brand management.

Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.

This paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.

The research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.

Unlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

With rapid urbanization, cities are facing various ecological and environmental problems. Living in harmony with nature is more important than ever. This paper aims to evaluate the ecosystem and ecological features of Azheke village, a key component of the Hani Rice Terraces World Cultural Heritage in China. The focus is on exploring effective ways to improve the relationship between humans and the natural environment through urban design in order to create a livable and sustainable city that can promote the development of sustainable smart urban ecology design.

This study conducted a systematic literature review to answer the following research questions: (1) How does Azheke design achieve harmony between humans and nature? (2) What are the effective approaches to improve the relationship between humans and nature within urban ecosystems? (3) How can urban design learn and integrate from Azheke’s ecological features to improve the relationship between humans and nature?

Azheke sustains long-term human-nature harmony through traditional ecological knowledge (TEK) and efficient natural resource use. By incorporating biophilic design and nature-based solutions from Azheke, along with biodiversity-friendly urban planning, we can boost urban ecosystem health and create unique Azheke-inspired urban designs.

This research primarily focuses on the human-nature relationship, exploring design strategies based on biodiversity without delving into the interactions between other components of urban ecosystems, such as social-cultural and economic components.

This paper provides a new perspective and strategies for developing sustainable and smart urban ecology design. These findings can provide theoretical references for urban planners, designers and decision-makers.

Information distortion affects the perception of quality, which, in turn, influences investment decisions and determines the pledge results of fundraising. This study combines signalling theory with persuasion theory to empirically study the effects of linguistic information distortion from fraudulent cues on a crowdfunding campaign's fundraising outcomes using text analytics, with implications for entrepreneurs, platforms and investors.

This study empirically analyzes 328,974 crowdfunding projects from the Kickstarter platform. Information distortion is detected using four indicators, based on text mining analytics. An econometric model is built to estimate the impact of information distortion, while the predictive power of the information distortion is detected through machine learning.

The results inform that distortion in the blurb, detailed description and reward statement dampen a campaign's success, but embellishing the entrepreneur's biography enhances the success of financing. Furthermore, information distortion exhibits a significant inverted U-shaped influence. The effect of the interaction terms suggests that campaigns with high pledge goals are more sensitive to information distortion, and that native-speaking entrepreneurs are adept at applying linguistic skills to promote the campaign.

This study provides a linguistic method to detect the influence of information distortion on crowdfunding campaigns. Further, the study offers some practical suggestions for entrepreneurs on how to generate attractive narratives, and contributes to the investor's decision-making and informs the platform's promotion strategy.

This study aims to better understand the morphological characteristics of single particle and the health risk characteristics of heavy metals in PM_2.5 in different functional areas of Handan City.

High resolution transmission electron microscopy was used to observe the aerosol samples collected from different functional areas of Handan City. The morphology and size distribution of the particles collected on hazy and clear days were compared. The health risk evaluation model was applied to evaluate the hazardous effects of particles on human health in different functional areas on hazy days.

The results show that the particulate matter in different functional areas is dominated by spherical particles in different weather conditions. In particular, the proportion of spherical particles exceeds 70% on the haze day, and the percentage of soot aggregates increases significantly on the clear day. The percentage of each type of particle in the teaching and living areas varied less under different weather conditions. Except for the industrial area, the size distribution of each type of particle in haze samples is larger than that on the clear day. Spherical particles contribute more to the small particle size segment. Soot aggregate and other shaped particles contribute more to the large size segment. The mass concentrations of hazardous elements (HEs) in PM_2.5 in different functional areas on consecutive haze pollution days were illustrated as industrial area > traffic area > living area > teaching area. Compared with the other functional areas, the teaching area had the lowest noncarcinogenic risk of HEs. The lifetime carcinogenic risk values of Cr and As elements in each functional area have exceeded residents’ threshold levels and are at high risk of carcinogenicity. Among the four functional areas, the industrial area has the highest carcinogenic and noncarcinogenic risks. But the effects of HEs on human health in the other functional areas should also be taken seriously and continuously controlled.

The significance of the study is to further understand the morphological characteristics of single particles and the health risks of heavy metals in different functional areas of Handan City. the authors hope to provide a reference for other coal-burning industrial cities to develop plans to improve air quality and human respiratory health.

The purpose of this study is to solve the issues of time-consuming and complicated computation of traditional measures, as well as the underutilization of two-dimensional (2D) phase-trajectory projection matrix, so a new set of features were proposed based on the projection of attractors trajectory to characterize the friction-induced attractors and to reveal the tribological behavior during the running-in process.

The frictional running-in experiments were conducted by sliding a ball against a static disk, and the friction coefficient was collected to reconstruct the friction-induced attractors. The projection of the attractors in 2D subspace was then mapped and the distribution of phase points was adapted to conduct the feature extraction.

The evolution of the proposed moment measures could be described as “initial rapid decrease/increase- midterm gradual decrease/increase- finally stable,” which could effectively reveal the convergence degree of the friction-induced attractors. Moreover, the measures could also describe the relative position of the attractors in phase–space domain, which reveal the amplitude evolution of signals to some extent.

The proposed measures could reveal the evolution of tribological behaviors during the running-in process and meet the more precise real-time running-in status identification.