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This study investigates whether the visualization of agricultural products influences consumers’ purchase intentions in the context of farmer-assisted livestreaming in China. Moreover, it explores the moderating effect of packaging functionality and the mediating effect of consumer trust.

Consumers in China from multiple social media platforms participated in this survey, which yielded 333 valid responses for analysis.

The results revealed a positive relationship between the video presentation about the agricultural production process and consumers’ purchase intention, which is mediated by consumers’ trust. Meanwhile, packaging functionality moderates the relationship between agricultural product visualization and consumers’ purchase intentions as well as the indirect effect of consumers’ trust.

This study extends the application of the stimulus-organism-response (SOR) model to the field of farmer-assisted livestreaming. By building a more detailed model, this study adds to knowledge on the influencing mechanisms of consumers’ purchase intentions in farmer-assisted livestreaming.

This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

The main aim of this paper is to establish a reasonable and scientific evaluation index system to assess the high quality and full employment (HQaFE).

This paper uses a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria framework to evaluate the quality and quantity of employment, wherein the integrated weights of attributes are determined by the combined the Criteria Importance Through Inter-criteria Correlation (CRITIC) and entropy approaches.

Firstly, the gap in the Yangtze River Delta in employment quality is narrowing year by year; secondly, employment skills as well as employment supply and demand are the primary indicators that determine the HQaFE; finally, the evaluation scores are clearly hierarchical, in the order of Shanghai, Jiangsu, Zhejiang and Anhui.

A scientific and reasonable evaluation index system is constructed. A novel CRITIC-entropy-TOPSIS evaluation is proposed to make the results more objective. Some policy recommendations that can promote the achievement of HQaFE are proposed.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

This study examines the relationship between internal control and corporate environmental responsibility.

Unlike US studies that concentrate solely on internal control over financial reporting, this study uses a comprehensive index that encompasses internal control over financial reporting, operations, and compliance. Corporate environmental responsibility is measured by environmental investments. Our research sample comprises Chinese listed firms from 2010 to 2018.

The results demonstrate a positive correlation between internal control and corporate environmental investments. Furthermore, we find that firms with high-quality internal control can improve their financial and environmental performance through environmental investments. After decomposing internal control into its five components, we show that the control environment, control activities, and information and communication components exhibit stronger effects on environmental investments than the risk assessment and monitoring components. Finally, the cross-sectional analyses reveal that the positive effect of internal control is more pronounced in private firms and in firms that are subject to weaker environmental regulation.

By focusing on the effect of a comprehensive internal mechanism on corporate environmental responsibility in China, this study contributes to the literature in developed-country settings that overwhelmingly focuses on the impact of external stakeholders and regulations.

With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.

Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.

The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.

The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

The ballooning health misinformation on social media raises grave concerns. Drawing upon the S-O-R (Stimulus-Organism-Response) model and the information processing literature, this study aims to explore (1) how social media health information seeking (S) affects health misinformation sharing intention (R) through the channel of health misperceptions (O) and (2) whether the mediation process would be contingent upon different information processing predispositions.

Data were collected from a survey comprising 388 respondents from the Chinese middle-aged or above group, one of China's most susceptible populations to health misinformation. Standard multiple linear regression models and the PROCESS Macro were adopted to examine the direct effect and the moderated mediation model.

Results bolstered the S-O-R-based mechanism, in which health misperceptions mediated social media health information seeking's effect on health misinformation sharing intention. As an indicator of analytical information processing, need for cognition (NFC) failed to moderate the mediation process. Contrarily, faith in intuition (FI), an indicator reflecting intuitive information processing, served as a significant moderator. The positive association between social media health information seeking and misperceptions was stronger among respondents with low FI.

This study sheds light on health misinformation sharing research by bridging health information seeking, information internalization and information sharing. Moreover, the authors extended the S-O-R model by integrating information processing predispositions, which differs this study from previous literature and advances the extant understanding of how information processing styles work in the face of online health misinformation. The particular age group and the Chinese context further inform context-specific implications regarding online health misinformation regulation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0157.

This study aims to offer an overview of hospitality and tourism research on artificial intelligence (AI) and its impact on the industry. More specifically, this study examines hospitality and tourism AI research trends in hospitality and tourism customer service experience creation and delivery, service failure and recovery, human resources and organizational behavior. Based on the review, this study identifies the challenges and opportunities and provides directions for future studies.

A narrative synthesis approach was used to review the hospitality and tourism research on AI and its impact on various aspects of the industry.

AI and AI applications in customer service experience creation and delivery and its possible effects on employees and organizations are viewed as a double-edged sword. Although the use of AI and AI applications offers various benefits, there are also serious concerns over the ethical use of AI, the replacement of human employees by AI-powered devices, discomfort among customers and employees and trust toward AI.

The paper offers an updated holistic overview of AI and its implications in different facets of the hospitality and tourism industry. Challenges and opportunities are discussed to foster future discussions on the use of AI among scholars and industry professionals.

In recent decades, emerging market multinational enterprises (EMNEs) have predominantly adopted a big step internationalization strategy to expand their business overseas. This study aims to examine the effect of big step internationalization on the speed of subsequent foreign direct investment (FDI) expansion for EMNEs. The authors also investigate the potential boundary conditions.

The authors use the random effects generalized least squares (GLS) regression following a hierarchical approach to analyze the panel data set conducted by a sample of publicly listed Chinese firms from 2001 to 2012.

The findings indicate that implementing big step internationalization in the initial stages accelerates the speed of subsequent FDI expansion. Notably, the authors find that this effect is more pronounced for firms that opt for acquisitions as the entry mode in their first big step internationalization and possess a board of directors with strong political connections to their home country’s government. In contrast, the board of director’s international experience negatively moderates this effect.

This study provides insights into our scholarly and practical understanding of EMNEs’ big step internationalization and subsequent FDI expansion speed, which offers important implications for firms’ decision-makers and policymakers.

This study extends the internationalization theory, broadens the international business literature on the consequences of big step internationalization and deepens the theoretical and practical understanding of foreign expansion strategies in EMNEs.

Culture is considered a critical aspect of social media usage. The purpose of this paper is to explore how cultures and languages influence multilingual users' cross-cultural information sharing patterns.

This study used a crowdsourcing survey with Amazon Mechanical Turk to collect qualitative and quantitative data from 355 multilingual users who utilize two or more languages daily. A mixed-method approach combined statistical, and cluster analysis with thematic analysis was employed to analyze information sharing patterns among multilingual users in the Chinese cultural context.

It was found that most multilingual users surveyed preferred to share in their first and second language mainly because that is what others around them speak or use. Multilingual users have more diverse sharing characteristics and are more actively engaged in social media. The results also provide insights into what incentives make multilingual users engage in social media to share information related to Chinese culture with the MOA model. Finally, the ten motivation factors include learning, entertainment, empathy, personal gain, social engagement, altruism, self-expression, information, trust and sharing culture. One opportunity factor is identified, which is convenience. Three ability factors are recognized consist of self-efficacy, habit and personality.

The findings are conducive to promoting the active participation of multilingual users in online communities, increasing global resource sharing and information flow and promoting the consumption of digital cultural content.