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Undergraduate students are known to be a high-risk group for mental health problems. The purpose of this paper is to constitute a repeated cross-sectional study on the trend of depression over the years and factors associated with depression among undergraduates.

Cross-sectional data from five surveys between 2013 and 2020 (N = 1,578) among the undergraduates of Universiti Tunku Abdul Rahman, a private university in Kampar Malaysia, were combined. The Depression Anxiety and Stress Scale-21 was used to screen for depression. Cochran’s Armitage test was used to detect trend in depression. Logistic regression, random forest regression and extra gradient boosting regression were used to identify risk factors and classification.

The prevalence of depressive symptoms was found to be between 26.4% and 36.8% between the years with an average of 29.9%. There was no significant time trend in the prevalence. The risk of depressive symptoms was higher among female students, those who were dependent on family for financial support and those who were stressed.

Periodical screening for depression is warranted for the identification of students at risk for depression. Professional cognitive-behavioral therapies, peer support and consulting services should be made available to the students in need.

Depression among students had been studied widely, but the trend over years remains unexplored, especially in developing countries.

Crowdsourcing communities enable companies to post challenges that are completed by solvers (workers); their success depends on engagement, requiring both creativity and effort. This study explores solver engagement in online crowdsourcing communities, advancing the theory of trait engagement by investigating the mediating roles of: (a) task-related self-efficacy in linking conscientiousness, neuroticism and extraversion, with solver engagement, and (b) task complexity in influencing the mediation.

215 valid responses were obtained from solvers engaged in the popular Chinese crowdsourcing community, Epwk.com, using an online questionnaire. PLS was then used to analyze the data.

Results show that self-efficacy mediates the relationships for conscientiousness, neuroticism and extraversion, with solver engagement. Moderated mediation analysis revealed that self-efficacy mediates the relationships for: (a) conscientiousness and extraversion, for only solvers with high task complexity; and (b) neuroticism, for only solvers with low task complexity.

The authors’ findings underscore the importance of accounting for solvers' situational contexts when examining the relationships between personality, self-efficacy and solver engagement in online crowdsourcing communities.

Scientific collaboration is becoming a common pattern in the social organization of knowledge production. The paper tries to figure out the relationship between scientific collaboration team size and scientific output.

Based on ESI database from year 2009–2019, the paper describes changes of collaboration team size from one author to more than 10 authors in 22 disciplines. Kernel density estimation and multidimensional kernel density estimation method are used to calculate optimal collaboration team size and appropriate collaboration team size in 22 disciplines. As bandwidth is one of the major issues in construction of kernel density estimation, the paper uses five different algorithms to calculate bandwidth. The method with the lowest mean absolute percentage error is chosen. Robustness test is conducted based on different sets of data.

The results show that scientific collaboration becomes more widely and deeply. As time goes by, collaboration team size is becoming larger and larger. Natural science disciplines have larger collaboration team size and faster growth rate than social science disciplines. Considering both qualitative and quantitative measures, the paper proves the universality of optimal and appropriate scientific collaboration team size among 22 disciplines and calculates the specific number.

The paper tries to investigate the law of scientific collaboration team size variation and provide a full picture of evolution of collaboration team size among 22 disciplines in 10 years. The paper first applies distribution method to figure out the relationship between scientific collaboration team size and scientific output and provides optimal collaboration team size and appropriate collaboration team size.

In an online shopping environment, individual reviews and aggregated ratings are important anchors for consumers’ purchasing decisions. However, few studies have considered the influence of aggregated ratings on consumer decision-making, especially at the neural level. This study aims to bridge this gap by investigating the consumer decision-making mechanism based on aggregated ratings to uncover the underlying neural basis and psychological processing.

An event-related potential experiment was designed to acquire consumers’ electrophysiological records and behavioral data during their decision-making processes based on aggregated ratings. The authors speculate that during this process, review valence categorization (RVC) processing occurs, which is indicated by late positive potential (LPP) components.

Results show that LPP components were elicited successfully, and perceptual review valence can modulate its amplitudes (one-star [negative] and five-star [positive] ratings evoke larger LPP amplitudes than three-star [neutral] ratings). The electroencephalogram data indicate that consumer decision-making processes based on aggregated ratings include an RVC process, and behavioral data show that easier review valence perception makes the purchase decision-making easier.

This study enriches the extant literature on the impact of aggregated ratings on consumer decision-making. It helps understand how aggregated ratings affect consumers’ online shopping decisions, having significant management implications. Moreover, it shows that LPP components can be potentially used by researchers and companies to evaluate and analyze consumer emotion and categorization processing, serving as an important objective physiological indicator of consumer behavior.

The purpose of this paper is to examine the ethical investment willingness decision-making process to understand how investors evaluate corporate social responsibility (CSR) actions.

Data were collected through a survey of 298 individual investors and analyzed using structural equation modeling.

Results reveal that perfectionist decision-making style is positively related to perceived moral intensity, substitutability of financial returns, and ethical investment willingness. In addition, perceived moral intensity and substitutability of financial returns are positively related to ethical investment willingness. Finally, perceived moral intensity is positively related to substitutability of financial returns, and a two-factor causal mediation model is supported.

The limitation of this study was that the pre-tests and sampling methods required all participants to have investing experience; however, procurement of trading information for each investor was impossible; thus, actual investment behaviors were undetermined. This study shed light on the mediating roles of perceived moral intensity and the substitutability of financial returns. Future studies can further investigate the factors influencing perceived moral intensity and the substitutability of financial returns.

Future ethical investment education can focus on cultivate the ability to distinguish ethical investments and change ethical investment willingness into actual investment behavior.

Understanding the relationship between these variables can help understand why ethical investment willingness varies among investors and how the traditional financial theory investment decision model should be revised as, internationally, more people have begun to observe CSR and sustainable development.

The purpose of this paper is to identify the factors that influence people to play socially interactive games on mobile devices. Based on network externalities and theory of uses and gratifications (U&G), it seeks to provide direction for further academic research on this timely topic.

Based on 237 valid responses collected from online questionnaires, structural equation modeling technology was employed to examine the research model.

The results reveal that both network externalities and individual gratifications significantly influence the intention to play social games on mobile devices. Time flexibility, however, which is one of the mobile device features, appears to contribute relatively little to the intention to play mobile social games.

This research successfully applies a combination of network externalities theory and U&G theory to investigate the antecedents of players’ intentions to play mobile social games. This study is able to provide a better understanding of how two dimensions – perceived number of users/peers and individual gratification – influence mobile game playing, an insight that has not been examined previously in the mobile apps literature.

We modified a basic dye, which carries a positive charge, by inducing an exchange with clay, and then analysed the results by using Fourier transform infrared (FT-IR) and ultraviolet-visible (UV-Vis) spectroscopy, X-ray diffraction (XRD) and thermogravimetric analysis (TGA). The intercalation of basic dye into clay modifies the thermal properties of the dye, and the dyes become more stable as more clay is added. The colour of the modified dyes does not change with the addition of more clay, although its absorption decreases, thereby conferring improved sunlight colour fastness on the modified dyes.

China's economic reform has brought about dramatic changes in the marketing environment. In this paper, several new features of the marketing environment in China are identified by examining the process and results of the reform. The paper concludes that the market has become a dominant factor in China's economy, but the effect of the old planned system still exists, conflicting with the new market system and making the marketing environment more complicated.

The purpose of this paper is to apply a new idea and scientific method to evaluate the enterprise's independent innovation capability accurately.

The matter‐element, extension set and independent function in extension theory are introduced briefly. Based on extension theory, the matter‐element model on comprehensive evaluation of the enterprise's independent innovation capability is established. Then, an instance which makes structural analysis on the enterprise's independent innovation capability with extension evaluation method is given.

The matter‐element model on comprehensive evaluation of the enterprise's independent innovation capability is established. Extension evaluation method can combine qualitative analysis and quantitative analysis to reflect the changing process of the objective appropriately. By means of the matter‐element model, the evaluation result shows which the grade does the enterprise's independent innovation capability belong to.

Extension evaluation method resorting to extension theory, cannot only reveal the evaluation grade and competitive advantage of an enterprise within the industry or within the scope of the evaluation, but also show comprehensive index level of the enterprise's independent innovation capability.

It is significant to monitor the enterprise's development and changes effectively, find the key factors influencing the enterprise's independent innovation capability and take effective measures in time.

New idea and scientific method are introduced into the enterprise's independent innovation activity, and it can integrate both qualitative and quantitative analysis to solve contradictory problems.

This paper explored the new features of emerging stock markets, in order to point out the most associated indicators of increasing stock return volatility, which may lead to instability of emerging markets. The study covers a sample of five geographical areas of emerging economies, including Mexico, Korea, South Africa, Turkey, and Malaysia. It used the backward multiple‐regression technique to examine the relationship between monthly changes of stock price indices as dependent variable and the associated predicting local as well as international variables, which represent possible causes of increasing price volatility and initiating crises in emerging stock markets. The study covered monthly data for a period of forty‐eight months from January 1997 to December 2000. The study revealed that stock trading volume and currency exchange rate respectively represent the highest positive correlation to the emerging stock price changes; thus represent the most predicting variables of increasing price volatility. International stock price index, deposit interest rate, and bond trading volume were moderate predicting variables for emerging stock price volatility. While changes in inflation rate showed the least positive correlation to stock price volatility, thus represents the least predicting variable.