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The world has been forced to implement movement restriction strategy because of the COVID-19 pandemic, and industries have to embrace online technologies and social media marketing activities (SMMAs) to continue their business operations. Considering the aftermath of COVID-19 on the business world, this study aims to explore the determining elements of SMMAs and analyse how these factors affect brand equity (BE), relationship equity (RE) and purchase intention (PI) among smartphone consumers in China. Five constructs of SMMAs, namely, entertainment (EN), interactivity (IN), trendiness (TR), customisation (CU) and electronic word-of-mouth (WM), were examined to examine the effects of SMMAs on BE and RE. Subsequently, the mediation effects of BE and RE on the relationships of all constructs of SMMAs with PI were analysed.

An online survey was conducted with the participation of 347 Chinese consumers who used social media platform managed by the smartphone brands as their marketing activities during COVID-19. The data were analysed via structural equation modelling using SmartPLS.

This study’s result showed the significant and positive influence of CU, TR and WM on BE and the strong and positive influence of CU, IN and TR on RE. Furthermore, BE was found to fully mediate the relationships of CU, TR and WM with PI, while RE was found to mediate the relationships of TR and CU with PI. The performance and impact factor analysis revealed RE as the most important factor for PI, followed by BE, CU and TR.

This study extended the authors’ knowledge and understanding about social media marketing, BE and smart phone PI during COVID-19. Findings of this study can assist smartphone brands in China to develop the most effective strategies for SMMAs, which can be tailored for consumers to maximise profits, even during any crisis when physical business activities are deemed difficult. Moreover, this study’s findings can benefit the government and policymakers in developing and regulating rules and regulations for e-commerce and social media commerce for all industries and areas.

This study aims to determine whether the stock holdings of equity mutual funds are informative for predicting future stock performance in the Chinese market. It is a puzzle that actively managed mutual funds underperform passive benchmarks, whereas retail investors still delegate investment decisions to the fund managers. The present study sheds light on whether mutual fund managers possess security selection skills in their top ten holdings.

By regression analysis and portfolio sorting, this study focuses on 830 Chinese A-share stocks in the industry research reports from the Guotai Junan Securities Company. It collects mutual fund's top ten holdings data from the Wind Financial Terminal between 2019Q1 and 2021Q1. As robustness checks, the result holds for the fixed-effect model, an additional measure of ranks in the top ten holdings, the predictability test based on the confusion matrix and two stage least square (2SLS) regression.

The authors find that the top ten holdings by equity mutual funds are informative for predicting stock performance and can provide valuable information for investors to support their decision-making.

The findings of this study provide insightful guidance for retail investors in making investment decisions and support the hypothesis that active fund management adds value.

Firstly, the authors find that the top ten holdings of Chinese mutual funds show significantly positive signals for future stock excess returns, indicating the selection skills of fund managers. Secondly, the above positive relationship exhibits a diminishing marginal effect with more funds holding this stock. Thirdly, the authors find that the predictability horizon of the number of overweighing funds is up to three quarters and then diminishes in the fourth quarter. Finally, investors have a 59% prediction accuracy for the whole stock sample and an 85% precision conditional on the predicted positive subsample to outperform the market. The authors also address the endogeneity and reverse causality issues.

Soil nutrient management and fertilizer use by farmers are important for sustainable grain production. The authors examined the effect of an experimental agricultural extension program, the science and technology backyard, in promoting sustainable soil nutrient management in the North China Plain (NCP). The science and technology backyard integrates farmer field schools, field demonstrations, and case-to-case counselling to promote sustainable farming practices among rural smallholders.

The authors conducted a large-scale household survey of more than 2,000 rural smallholders. The authors used a multivariate regression analysis as the benchmark to assess the effect of the science-and-technology backyard on smallholder soil nutrient management. Furthermore, the authors used coarse exact matching (CEM) methods to control for potential bias due to self-selection and the (endogenous) switching regression approach as the main empirical analysis.

The results show that the science-and-technology backyard program increased smallholders' wheat yield by approximately 0.23 standard deviation; however, no significant increase in maize yield was observed. Regarding soil nutrient use efficiency, the authors found a significant improvement in smallholders' phosphorus and potassium use efficiencies for both wheat and maize production, and a significant improvement in nitrogen use efficiency for wheat production, but no significant improvement of nitrogen use efficiency for maize production.

This study evaluated a novel participatory agricultural extension model to improve soil nutrient management practices among smallholders. The integration of agronomists' scientific knowledge and smallholders' local contextual experiences could be an effective way to improve farmers' soil nutrient management. This study provides the first quantitative estimates based on rigorous impact assessment methods of this novel extension approach in rural China.

The geomagnetic field vector is a function of the satellite’s position. The position and speed of the satellite can be determined by comparing the geomagnetic field vector measured by on board three-axis magnetometer with the standard value of the international geomagnetic field. The geomagnetic model has the disadvantages of uncertainty, low precision and long-term variability. Therefore, accuracy of autonomous navigation using the magnetometer is low. The purpose of this paper is to use the geomagnetic and sunlight information fusion algorithm to improve the orbit accuracy.

In this paper, an autonomous navigation method for low earth orbit satellite is studied by fusing geomagnetic and solar energy information. The algorithm selects the cosine value of the angle between the solar light vector and the geomagnetic vector, and the geomagnetic field intensity as observation. The Adaptive Unscented Kalman Filter (AUKF) filter is used to estimate the speed and position of the satellite, and the simulation research is carried out. This paper also made the same study using the UKF filter for comparison with the AUKF filter.

The algorithm of adding the sun direction vector information improves the positioning accuracy compared with the simple geomagnetic navigation, and the convergence and stability of the filter are better. The navigation error does not accumulate with time and has engineering application value. It also can be seen that AUKF filtering accuracy is better than UKF filtering accuracy.

Geomagnetic navigation is greatly affected by the accuracy of magnetometer. This paper does not consider the spacecraft’s environmental interference with magnetic sensors.

Magnetometers and solar sensors are common sensors for micro-satellites. Near-Earth satellite orbit has abundant geomagnetic field resources. Therefore, the algorithm will have higher engineering significance in the practical application of low orbit micro-satellites orbit determination.

This paper introduces a satellite autonomous navigation algorithm. The AUKF geomagnetic filter algorithm using sunlight information can obviously improve the navigation accuracy and meet the basic requirements of low orbit small satellite orbit determination.

Cross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors.

The concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established.

The application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors.

This is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.

The purpose of this study is to investigate whether the corporate environmental, social and governance (ESG) performance of enterprise is influenced by the enterprise digital transformation. In addition, this study explains how enterprise digital transformation affects ESG performance.

The sample covers 4,646 nonfinancial companies listed on China’s A-share market from 2009 to 2021. The study adopts the fixed-effects multiple linear regression to perform the data analysis.

The study finds that enterprise digital transformation has a significant inverted U-shaped impact on ESG performance. Moderate digital transformation can improve enterprise ESG performance, whereas excessive digital transformation will bring new organizational conflicts and increase enterprise costs, which is detrimental to ESG performance. This inverted U-shaped effect is more pronounced in industrial cities, manufacturing industries and enterprises with less financing constraints and executives with financial backgrounds. Enterprise digital transformation mainly affects ESG performance by affecting the level of internal information communication and disclosure, the level of internal control and the principal-agent cost.

The government should take multiple measures to encourage enterprises to choose appropriate digital transformation based on their own production behaviors and development strategies, encourage them to innovate and upgrade their organizational management and development models in conjunction with digital transformation and guide them to use digital technology to improve ESG performance.

This study shows that irrational digital transformation cannot effectively improve the ESG performance of enterprises and promote the sustainable development of the country. Enterprises should carry out reasonable digital transformation according to their own development needs and finally improve the green and sustainable development ability of enterprises and promote the sustainable development of society.

This study examines the relationship between enterprise digital transformation and ESG performance. Different from the linear relationship between the two in previous major studies, this study proves the inverse U-shaped relationship between enterprise digital transformation and ESG performance through mathematical theoretical model derivation and empirical test. This study also explores in detail how corporate digital transformation affects ESG performance, as well as discusses heterogeneity at the city, industry and firm levels. It is proposed that enterprises should take into account their own characteristics and carry out reasonable digital transformation according to their development needs.

Collaboration in agricultural value chains (AVCs) has the potential to increase smallholders’ participation in international value chains and increase their benefits from participation. This scoping review explores existing collaboration models among stakeholders of AVCs in developing countries, examines enablers and constraints of collaboration and identifies policy gaps.

We systematically searched three databases, CAB Abstracts, Econlit (EBSCO) and Agricola, for studies published between 2005 and 2023 and included 59 relevant studies on AVC collaboration.

The primary motivations for collaboration are to enhance market access and improve product quality. Key outcomes of collaboration include improvements in farmers’ welfare, market participation and increased production; only a few studies consider improved risk management as an important outcome. Robust support from government and non-governmental entities is a primary enabler of collaboration. Conversely, conflicts of interest among stakeholders and resource limitations constrain collaboration possibilities. Collaboration involving high-value crops prioritizes income increases, whereas collaboration involving staple crops focuses on improving household food security.

This study may have publication bias as unsuccessful instances of collaboration are less likely to be published.

This study is unique in highlighting collaboration models’ characteristics and identifying AVC policy and programmatic areas where private firms, farmers’ groups, local governments and donor agencies can contribute.

The purpose of this paper is to examine the factors that lead to the controversy within the existing empirical literature on the relationship between land tenure and agricultural investments in China.

This study conducts a meta-regression analysis (MRA) based on 265 samples from 29 selected studies. These studies are collected from different regions and populations; therefore, this study utilizes a random-effects meta-regression model to control between-study heterogeneity.

The empirical results show that the variable “land-related long-term investments” significantly affects the relationship between land tenure and agricultural investments. The variables: “ration land,” “land titling” and “land transfer rights” all have significant effects on this relationship, but at different levels. The study area (e.g. “western China,” “central/inland China,” “two regions contained”), “plot level,” “panel data,” “sample size,” “considered endogeneity” and “off-farm employment” variables all significantly influence the relationship. Additionally, the results show that the relationship is significantly affected by the survey time.

Policymakers should treat the existing research conclusions with caution and pay more attention to defining land tenure. The relationship between land tenure and agricultural investments also depends on regional resource availability. Therefore, land property rights policies should be region-specific in order to successfully encourage agricultural investments.

In this study, the author collectively examines existing empirical studies to investigate whether their inconsistent results are affected by research characteristics. To the author’s knowledge, this is the first study that analyzes land tenure and agricultural investments in China using MRA. Future research should refine the definition of land tenure, the selection of agricultural investment types, the research method and the method of data collection.