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This paper investigates the relationship between institutional ownership and idiosyncratic volatility in Chinese and the USA stock markets and explores the potential explanations.

In this paper, the authors use the panel data regressions and the dynamic tests of two-way Granger causality in the panel VAR model to examine the relationship between institutional ownership and idiosyncratic volatility in Chinese and the USA stock markets.

The authors find that the institutional ownership in the Chinese (the USA) stock market is significantly and positively (negatively) related to idiosyncratic volatility through various tests. This paper indicates that institutional investors in the USA are more prudent and risk-averse, while the Chinese institutional investors are not because of high risk-bearing capacity.

This paper deepens the authors’ understanding on the relationship between institutional ownership and idiosyncratic volatility and in the USA and the Chinese stock markets. This paper explains the opposite relationships between institutional ownership and idiosyncratic volatility in the stock markets in China and USA.

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

This paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.

1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.

The methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.

Purpose: The public health burden caused by tobacco is heavy among first-generation Chinese immigrant men whose home country has significantly higher smoking rates than the United States. The current study is part of a larger effort to pilot an mHealth tobacco cessation intervention using MMS (graphic) mobile phone technologies to target East Asian immigrant populations. Grounded in the Extended Parallel Process Model (EPPM), our specific aims were to determine what message themes, level of graphic intensity, and types of efficacy information are most appropriate and useful for mHealth interventions targeting this population.

Methodology/Approach: A qualitative study utilizing a series of focus groups (k = 5) was conducted with male adult smokers who were born in China and currently reside in the United States. The primary aim of the focus groups was to solicit reactions to a series of preliminary messages developed by the research team. A secondary aim was to gauge receptivity to the use of MMS as a vehicle for smoking cessation intervention. Participants (n = 32) were recruited from local Chinese communities in a large Mid-Atlantic metropolitan area.

Findings: Opinions about different message strategies were mixed. However, participants tended to rate messages more positively when they focused on the impact of smoking on family and loved ones, particularly children. Messages with fear-arousing images were also perceived to be effective at low frequency of exposure, but there were concerns that they may backfire at high exposure. Awareness of and interest in Quitline were low, and concrete quitting tips were perceived as more effective. Participants reported a preference for receiving messages a few times a week, and an MMS message platform was generally preferred to WeChat (a Chinese social media platform).

Implications: Our results suggest that graphic MMS messaging holds promise as an effective intervention method for this population and that EPPM is an appropriate framework to develop, test, and analyze mHealth intervention messages. While messages that focused primarily on impact on children, health, and specific quitting tips were generally found to be more effective, a mix of different types of messages that address a wide range of issues may be most appropriate for this population.

Originality/Value: This study is the first to explore the utility of graphic text messaging as an intervention method to promote smoking cessation among male Chinese immigrants. Findings from the study provide important insights for future intervention work targeting this underserved population.

The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods.

First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data.

The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R² is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R² are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties.

This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.

The purpose of this study is to explore the antecedents of consumers’ online review intention in e-commerce platforms from a unique perspective of consumer commitment and platform design. Meanwhile, for the dual-platform strategy, i.e. providing both the web and mobile platforms simultaneously, which is widely adopted in the industry but lacks theoretical concerns, this study aims to examine the differences that platform design influences consumer commitment, consequently contributing to online review intention, between the web and mobile contexts.

A cross-sectional online survey is employed, and a structural equation model-based approach is utilized to analyze the data collected from both the website-preferred consumers (N = 167) and the mobile app-preferred consumers (N = 247).

The results indicate that instrumental support design factors and socio-emotional support factors positively influence consumer commitment, which further affect online review intention positively. Furthermore, design factors in different use contexts generate different impacts, and consumer commitment generates a greater effect on online review intention in the mobile than in the web context. Empathy is found to be an important motivator of consumer commitment in both contexts.

To the best of the authors’ knowledge, as one of the first attempts to capture the differences in the relationship between platform design on consumer commitment and online review intention in different use contexts within the dual-platform e-commerce, this study provides insights for e-commerce platform managers and designers to promote consumer commitment and online review engagement by prioritizing the platform design.

The purpose of this study is to comprehend and determine the impact of the COVID-19 pandemic on business organizations and society, together with its relationship to entrepreneurship, innovation, digitization or digital transformation, by means of analysis of the Spanish Internet domains registration data set.

Following existing literature regarding time series analysis, the authors have designed a SARIMA methodology involving the forecasting of a non-COVID-19 data set from the available data and compared it to the existing COVID-19 data set in order to validate the formulated hypothesis.

The COVID-19 pandemic was found to be an unfortunate accelerator, regarding entrepreneurship and innovation as a digitization and digital transformation lever, with the results of the Internet domain registration analysis as a reliable indicator.

This research confirms the existence of new non-invasive approaches to complementary information, such as Internet domain registration analysis, that could serve as an early and quick indicator of innovation and entrepreneurship initiatives within business activities.