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This paper aims to investigate how differently the COVID-19 blockade regulations influence the prices of perishable and storable foods. The authors focus on the cases of the 2020 blockade at Hubei province and the 2021 blockade at Shijiazhuang city in China, and the authors examine how the blockade influenced the prices of Chinese cabbages (perishable) and potatoes (storable) within and around the blockade area.

The paper employs the fixed effects model, the panel VAR (PVAR) model, and the spatial dynamic panel (SPD) model to estimate the impacts of the blockade on the food prices. It constructs the unique data set of 3-day average prices of Chinese cabbages and potatoes at main wholesale markets in China during the two urban blockade periods from January 1 to April 8 in 2020 and from January 1 to March 1 in 2021.

The results from the SPD models indicate that the price of Chinese cabbages was more vulnerable and increased by 7.1–9.8% due to the two blockades while the price of potatoes increased by 1.2–6.1%. The blockades also significantly influenced the prices in the areas adjacent to the blockade area. The SPD results demonstrate that the impacts of the blockades would be overestimated if the spatial dependence is not controlled for in the fixed effects model and the PVAR model.

Because the research focuses on the cases in China, the results may lack generalizability. Further research for other countries is encouraged.

This paper demonstrates the importance of considering food types and spatial dependence in examining the impact of the COVID-19 blockades on food prices.

Crowdsourcing refers to a new business model in which enterprises or individuals publish tasks or problems, attracting freelancers or contributors to participate in solving tasks, submitting bids and allowing task seekers to choose the final solution. How to attract more quantity and quality of contributors to submit their solutions through a crowdsourcing platform has become a vital question.

In this study, the authors use web crawling to obtain 43,265 effective tasks in EPWK website (www.epwk.com) to probe how to elevate the quantity and quality of contributors via task reward design. This study uses the hierarchical linear model to probe the research questions.

Results show that, with the increase of task reward, the quantity of contributors goes up first and then goes down (inverted U shape), whereas the quality of contributors goes down first and then goes up (U sharp). Moreover, the authors investigate the moderating effects of another task design attribute, task duration. This study finds that task duration weakens the effect of task reward on the quantity of contributors while strengthening the effects of task reward on the quality of contributors.

First, this study theoretically probes two key aspects of task performance, namely, the quantity and quality of contributors, which expand the scope of task performance evaluation. Second, this study reconciles previous concern about the relationship of task reward and performance, which is different from previous studies that have paid more attention to the single perspective of their relationship. Finally, the authors investigate the moderating effects of task duration, which further uncover the mechanism behind task reward and performance, that is, the quantity and quality of task contributors.

Online influencers are increasingly used by brands around the globe to establish brand communication. This study aims to investigate the characteristics of social media content in terms of presentation style and brand communication among online influencers in China. The authors identified how characteristics of social media posts influence young consumers’ engagement with the posts.

The authors analyzed 1,779 posts from the Sina Weibo accounts of ten top-ranked online influencers by combining traditional content analysis with Web data crawling of audience engagement with social media posts.

Online influencers in China more frequently used photos than videos to communicate with their social media audience. Altogether 8% and 6% of posts carried information about promotion and event, respectively. Posts with promotional incentives as well as event information were more likely to engage audiences. Altogether 22% of the sampled social media posts mentioned brands. Posts with brand information, however, were less likely to engage audiences. Furthermore, having long text is more effective than photos/images in generating likes from social media audiences.

Combining content analysis of social media posts and engagement analytics obtained via Web data crawling, this study is, to the best of the authors’ knowledge, one of the first empirical studies to analyze influencer marketing and young consumers’ reactions to social media in China.

The purpose of this study is to investigate the effect of nano ZrC particles on the mechanical and electrochemical corrosion properties of FeCrAl alloys, providing a beneficial reference basis for the development of high-performance carbide reinforced FeCrAl alloys with good mechanical and corrosion properties in the future.

Nano ZrC reinforced FeCrAl alloys were prepared by mechanical alloying and spark plasma sintering. Phases composition, tensile fractography, corrosion morphology and chemical composition of nano ZrC reinforced FeCrAl alloys were analyzed by X-ray diffraction, scanning electron microscopy and energy dispersive X-ray spectroscopy, respectively. Microhardness and tensile properties of nano ZrC reinforced FeCrAl alloys were investigated by mechanical testing machine and Vickers hardness tester. Electrochemical corrosion properties of nano ZrC reinforced FeCrAl alloys were investigated by electrochemical workstation in 3.5 wt.% NaCl solution.

The results showed that addition of nano ZrC can effectively improve the mechanical and corrosion properties. However, excessive nano ZrC could decrease the mechanical properties and reduce the corrosion resistance. In all the FeCrAl alloys, FeCrAl–0.6 wt.% ZrC alloy exhibits the optimum mechanical properties with an ultimate tensile strength, elongation and hardness of 990.7 MPa, 24.1% and 335.8 HV1, respectively, and FeCrAl–0.2 wt.% ZrC alloy has a lower corrosion potential (−0.179 V) and corrosion current density (2.099 µA/cm2) and larger pitting potential (0.497 V) than other FeCrAl–ZrC alloys, showing a better corrosion resistance.

Adding proper nano ZrC particles can effectively improve the mechanical and corrosion properties, while the excessive nano ZrC is harmful to the mechanical and corrosion properties of FeCrAl alloys, which provides an instruction to develop high-performance FeCrAl cladding materials.

The set-union knapsack problem is one of the most significant generalizations of the Non-deterministic Polynomial (NP)-hard 0-1 knapsack problem in combinatorial optimization, which has rich application scenarios. Although some researchers performed effective algorithms on normal-sized instances, the authors found these methods deteriorated rapidly as the scale became larger. Therefore, the authors design an efficient yet effective algorithm to solve this large-scale optimization problem, making it applicable to real-world cases under the era of big data.

The authors develop three targeted strategies and adjust them into the adaptive tabu search framework. Specifically, the dynamic item scoring tries to select proper items into the knapsack dynamically to enhance the intensification, while the age-guided perturbation places more emphasis on the diversification of the algorithm. The lightweight neighborhood updating simplifies the neighborhood operators to reduce the algorithm complexity distinctly as well as maintains potential solutions. The authors conduct comparative experiments against currently best solvers to show the performance of the proposed algorithm.

Statistical experiments show that the proposed algorithm can find 18 out of 24 better solutions than other algorithms. For the remaining six instances on which the competitor also achieves the same solutions, ours performs more stably due to its narrow gap between best and mean value. Besides, the convergence time is also verified efficiency against other algorithms.

The authors present the first implementation of heuristic algorithm for solving large-scale set-union knapsack problem and achieve the best results. Also, the authors provide the benchmarks on the website for the first time.

The purpose of this study is to discover the uniqueness of China's online livestreaming industry and to make preliminary predictions about its future.

Providing a panoramic description of China's online livestreaming industry and a comparative analysis with other countries' online livestreaming industries.

China's online livestreaming industry is world leader in general. In terms of user size and age structure, although China has a high proportion of online livestreaming users, the proportion of anchors is not high, while the age structure of users in both China and other countries shows the characteristics of younger users. In terms of policy, the difference between China and foreign support in online livestreaming is small, but developed countries have stricter regulations.

The research in this paper has great significance in gaining an in-depth understanding of the development status and trends of China's online livestreaming industry and in designing accurate support policies.

Addressing users’ migration is a prerequisite for the sustainable development of the sharing economy. Ethical concerns that may lead to users’ migration are frequent in sharing economy platforms. Therefore, this study explores whether the long-term governance of sharing economy platforms can effectively mitigate users’ migration caused by ethical concerns.

Using a questionnaire survey of 549 participants, this study investigated the mechanism of users’ migration and governance strategies in the platform ecosystem based on trust theory.

The results indicate that users’ ethical concerns regarding the platform ecosystem significantly and positively influence their migration. Furthermore, users’ continued trust played a significant mediating role in the relationship between ethical concerns and users’ migration. The results also showed that future orientation and resilience significantly moderated the impact of users’ ethical concerns on their continued trust, thereby weakening this effect.

The author clarified the relationship between ethical concerns and users’ migration, identified the underlying mechanisms and provided guidance on how to mitigate migration behavior. However, users’ migration is influenced by various factors beyond ethical concerns. In addition to some factors that lead to migration, other factors make users stay on the platform. Future research should integrate multiple factors.

This study reveals the mechanism of action between users’ migration and ethical concerns in the platform ecosystem and sheds light on the output of long-term orientation practices of the platform.

The continuous impact of the pandemic and the downturn of the global economy have brought new challenges to the tourism industry. In this context, effectively attracting consumers and improving user stickiness are the top priorities of tourism platform companies. This study explores the impact of ethical concerns raised by new issues under the multi-governance environment on user stickiness. Based on the trust theory, the authors provide solutions for tourism platforms.

This study adopted a quantitative approach, gathering survey data via an online platform. A total of 400 participants were investigated, and 356 valid questionnaires were returned, with a recovery rate of 89%. Questionnaires that did not meet the inclusion criteria were excluded, leaving 298 valid responses.

Studies have found that consumers' ethical concerns about platform companies are key factors affecting user stickiness, and among these, consumer trust plays a mediating role. They have found that corporate social responsibility (CSR) behaviours help alleviate ethical concerns and improve trust in enterprises. At the same time, enterprises should properly control the number of platform collaborators, and excessive platform cooperation negatively moderates the impact of consumer ethical concerns on competence-based trust.

This study complements the deficiency of previous research with regard to ethical concerns in a multi-governance environment. These findings indicate that subject diversity exacerbates the negative impact of ethical concerns on consumer trust; however, CSR alleviates the impact of ethical concerns on consumer trust.

The maturity of artificial intelligence technology and the emergence of AI-generated content (AIGC) tools have endowed college students with a human-AIGC tools collaboration learning mode. However, there is still a great controversy about its impact on learning effect. This paper is aimed at investigating the impact of the human-AIGC tools collaboration on the learning effect of college students.

In this paper, a hypothesized model was constructed to investigate the effects of dependence, usage purpose, trust level, frequency, and proficiency of using AIGC tools on the learning effect, respectively. This paper distributed questionnaires through random sampling. Then, the improved Analytic Hierarchy Process (AHP) was used to assign weights and normalize data. Lastly, one-way ANOVA and multiple linear regression analyses were used to measure and analyze variables, revealing the mechanism of influence.

The usage purpose, frequency, and proficiency of using AIGC tools have a significant positive effect on learning. Being clear about the usage purpose of AIGC tools and matching the specific study tasks will enhance the learning effect. College students should organically integrate AIGC tools into each learning process, which is conducive to building a learning flow applicable to oneself, improving efficiency, and then enhancing learning effects. The trust level in AIGC tools is significant, but positively and weakly correlated, indicating that college students need to screen the generated content based on their knowledge system framework and view it dialectically. The dependence on AIGC tools has a negative and significant effect on learning effect. College students are supposed to systematically combine self-reflection and the use of AIGC tools to avoid overdependence on them.

Based on the findings, the learning suggestions for college students in human-machine collaboration in the AIGC era are proposed to provide ideas for the future information-based education system. For further research, scholars can expand on different groups, professions, and fields of study.

Previous studies have focused more on the impact of AIGC on the education system. This paper analyzed the impact of the various factors of using AIGC tools in the learning process on the learning effect from the perspective of college students.

We adopt an information risk view and argue that higher levels of pledge risk incurred by insiders incentivize opportunistic financial disclosure and impair the quality of information available to analysts to forecast firm performance.

We sample Chinese listed companies from 2010 to 2022. Following the literature, we apply established models to measure and test analysts’ forecasting accuracy/dispersion related to controlling shareholders pledging equity and the amount of margin call pressure. Analyst characteristics and nonfinancial disclosures proxied by CSR reports are also examined as factors likely to influence the relationship between pledge risk and analysts’ forecast quality.

We find that analysts’ earnings predictions are less accurate and more dispersed as the proportion of shares pledged (pledge ratio) increases and in combination with greater margin call pressure. Pledge ratios are significantly associated with several information risk proxies (i.e. earnings permanence, accruals quality, audit quality, financial restatements, related party transactions and internal control weaknesses), validating the channel through which equity pledges undermine analysts’ forecast quality. The results also demonstrate that forecast quality declines for a wide variety of analysts’ attributes, including high- and low-quality analysts and analysts from small and large brokerage firms. Importantly, nonfinancial disclosures, as proxied by CSR reporting, improve analysts’ forecasts.

We extend the literature by demonstrating that incremental pledge risk increases non-diversifiable information risk; all non-pledging shareholders pay a premium through more diverse and less accurate earnings forecasts. Our study provides important policy implications with economically significant costs to investors associated with insider equity pledges. Our results highlight the benefits of nonfinancial disclosures in China, which has implications for the current debate on the global convergence of CSR reporting.