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The purpose of this paper is to analyze price transmission in the Chinese pork market between 1994 and 2016 and examine any incidence and causes of asymmetric price transmission.

The approach uses threshold autoregressive models, asymmetric error correction models and autoregressive moving average models to examine the price transmission using monthly pig and pork prices from 1994 to 2016.

While a symmetric price transmission between pork and pig prices was identified for the period between June 1994 and June 2007, an asymmetric price transmission response between pork and pig prices was found for the period July 2007 to June 2016. Key factors behind the asymmetric price transmission include the chicken price and China’s provisional purchasing and stockpiling policy which is having a counter-productive impact on prices.

The paper contributes to the literature by examining price transmission in two different periods: 1994 to 2007 where prices are lower and more stable; and 2007 to 2016 where prices are higher and volatile. The paper examines the impact of production and market policies on price transmission in the Chinese pork and pig market, with several policy implications.

The wheel wear of high-speed trains is the main concern in the operation process of the high-speed trains. Wheel reprofile usually adopts the thin flange wheel profiles for the economic wheel reprofile strategy; however, the wheel wear evolution law of the thin flange wheel profile is not clear. The purpose of this paper mainly researches the wheel wear evolution law of thin flange wheel profile and the influence factors of thin flange wheel.

To solve this problem, this paper analyzed the wheel-rail contact relationship and established a vehicle dynamic model of high-speed train. The Jendel wheel wear model was used to analyze the evolution of thin flange wheel wear and rail deviation affecting wheel wear under thin flange wheel profiles.

Due to the consistency of the tread area, the distribution of wheel-rail contact points for thin flange wheels in the tread area is close. As the wheel flange increases, wheel wear also increases. Thin flange wheel profiles reduce wheel wear to a certain extent. The maximum wheel wear depths with rail deviations of −0.4 mm, 0 mm and 0.4 mm are 0.9844 mm, 1.077 mm and 1.142 mm, respectively. The negative rail deviation suppresses wheel wear, but the positive rail deviation increases wheel wear.

The evolution of wheel wear for thin flange wheels after reprofiling is not clear. This paper analyzed the wear characteristics of thin flange wheel wear and rail deviation affecting wheel wear under thin flange wheels.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0401/

The purpose of this study is to investigate how digital technologies are used for facilitating knowledge sharing and decision-making through enhanced coordination and communication and their impact on job performance in organizations.

A conceptual model is developed within the background of the social capital theory through a comprehensive review of the related literature for exploring how digital technologies can improve knowledge sharing and decision-making via enhanced communication and coordination between individuals in organizations for better job performance. This model is then tested and validated based on structural equation modeling of the collected survey data in Australia.

This study shows that digital technology enhanced coordination and communication have significant impact on knowledge sharing. It finds out that digital technology driven coordination significantly influences decision-making and digital technology driven knowledge sharing significantly influences decision-making. Furthermore, this study reveals that enhanced decision-making and knowledge sharing can lead to better job performance in organizations.

To the best of the authors’ knowledge, this study is the first attempt to explore the role of digital technologies in enhancing knowledge sharing and decision-making for better job performance in a digitalized working environment in organizations. The validated model can be used as the foundation to further investigate the changing role of digital technologies in driving knowledge sharing for better performance of individuals and competitive advantages of organizations.

New quality productive forces (NQPF) are critical for high-quality economic development. As digital mergers and acquisitions (M&As) gain prominence in corporate digital transformation, understanding their impact on NQPF is essential. This study explores whether digital M&As enhance NQPF in firms and identifies key mechanisms that drive this effect.

This study investigates the impact of corporate digital M&As on NQPF using a multi-period difference-in-difference (DID) methodology. Analyzing a sample of Chinese listed firms from 2011 to 2021, the study explores how digital M&As contribute to NQPF, identifying firm innovation and data assets as key mechanisms. It also examines how external factors, such as industrial structure, urban human capital and economic policy uncertainty, moderate the effect of digital M&As on NQPF.

The study reveals three key findings: (1) Digital M&As significantly enhance corporate NQPF; (2) innovation and data assets serve as key mechanisms through which digital M&As drive NQPF and (3) external factors, including industrial structure, urban human capital and economic policy uncertainty, amplify the positive effects of digital M&As on NQPF.

Firms should leverage digital M&As as a strategic tool for improving NQPF, focusing on innovation and data assets. Policymakers can support this transformation by fostering an environment that enhances the positive impact of digital M&As on economic development.

This paper introduces a novel NQPF index, offering a comprehensive measurement of the concept. It provides new insights into how digital M&As affect NQPF, filling a gap in the literature on digital transformation and offering actionable recommendations for firms and policymakers.

As Chinese stock market develops and grows, mutual fund plays an increasingly important role for strong financial strength and good social resources. This paper aims to present an overview of fund investment effect, identify the investment preference of institutional investors and explore the socially responsible investment value of mutual fund, especially green fund.

In order to test the green effects in the fund market, especially from the perspective of institutional shareholding, the authors build three panel data models to examine the attention, promotion and network effect respectively.

In general terms, the authors obtain a positive relationship between mutual funds and green firms (attention effect). This positive relationship is conducted by the comparison of institutional shareholding level between green firms and non-green firms by using two-sample t-test. The authors find that although all the coefficients of mutual funds' shareholding variables are positively related to social performance, only the number of mutual fund shareholders shows slight significance. The authors observe a stronger promotion effect of green funds on social performance than the other mutual funds (promotion effect). From a network perspective as well as previous social capital research, the mutual funds shareholding network shows a significant and positive effect on the firm's social performance in the firm–fund network (CFClose) as well as in the firm–firm network (CCClose), while the coefficients of closeness in the firm and green fund network are positive but non-significant (network effect).

Although some valuable findings have been documented, there is a great potential to be further explored. For example, how to guide more green funds to solve the finance problem of middle and small-sized firms may be another crucial task for the point of view of government or industry level. While at the mutual fund level, it is essential to positively participate in and fulfill the environment duty of listed firms which can not only satisfy the expectation of environment value investment.

The fund market has witnessed unprecedented growth in China and plays an important role in promoting the development of green industries. This indicates the practice of mutual funds as socially responsible investment in China. Some effective measures should be taken to incentive the mutual funds to continually invest in environment-friendly firms, which would benefit to the promotion of social performance driven by financing innovation.

The ESG issue is a global one that needs the participation of the countries worldwide. As one of the most important emerging markets, regulators in China should consider taking more action in promoting ESG concerns. To some extent, institutional investments can actually reflect how the responsible investments are going in this market.

This paper provides a systematic empirical analysis of Chinese fund market in the view of institutional investor preference. Three green effects, namely attention, promotion and network effects, are put forward, which have not been employed in previous studies. Our work is useful for understanding sustainable finance which has been elevating into national strategy.

The purpose of this paper is to construct an optimal resource reallocation model of the limited resource by a moderator for reaching the greatest consensus, and show how to reallocate the limited resources by using optimization methodology once the consensus opinion is reached. Moreover, this paper also devotes to theoretically exploring when or what is the condition that the group decision-making (GDM) system is stable; and when new opinions enter into the GDM, how the level of consensus changes.

By minimizing the differences between the individuals’ opinions and the collective consensus opinion, this paper constructs a consensus optimization model and shows that the objective weights of the individuals are actually the optimal solution to this model.

If all individual deviations of the decision makers (DMs) from the consensus balance each other out, the information entropy theorem shows this GDM is most stable, and economically each individual DM gets the same optimal unit of compensation. Once the consensus opinion is determined and each individual opinion of the DMs is under an acceptable consensus level, the consensus is still acceptable even if additional DMs are added, and the moderator’s cost is still no more than a fixed upper limitation.

The optimization model based on acceptable consensus is constructed in this paper, and its economic significance, including the theoretical and practical significance, is emphatically analyzed: it is shown that the weight information of the optimization model carries important economic significance. Besides, some properties of the proposed model are discussed by analyzing its particular solutions: the stability of the consensus system is explored by introducing information entropy theory and variance distribution; in addition, the effect of adding new DMs on the stability of the acceptable consensus system is discussed by analyzing the convergence of consensus level: it is also built up the condition that once the consensus opinion is determined, the consensus degree will not decrease even when additional DMs are added to the GDM.

Awe is an important yet largely overlooked emotion in tourism. This paper aims to reveal the underlying mechanism of the effects of nature-inspired awe on stimulating tourists' environmentally responsible behavior (TERB) intention by delving into the serial mediation effect of state construal level, small-self perception and state connectedness to nature (CNT).

This research was built upon the appraisal theory of emotion. Study 1 was a scenario-induced quasi-experimental study to test the proposed model linking awe, small-self perception, state CNT and TERB intention. A total of 271 valid questionnaires were collected via an online questionnaire platform. Study 2 was a field study designed to replicate the findings of Study 1 and further uncover the mediating role of the state construal level. Study 2 was conducted in Baiyun Mountain Scenic Area, China. A total of 229 valid questionnaires were used for analysis.

This research deepens our understanding of the effects of nature-inspired awe in the tourism and travel fields. This research uncovers the underlying mechanism by which awe increases TERB intention through serial mediation. Specifically, awe inspires tourists to apply a higher level of construal, which makes tourists perceive a smaller self, making them feel more connected to nature and exert more intention to adopt ERB.

This research linked tourists' mindset (i.e. state construal level), internal (i.e. small-self perception) and external (i.e. state CNT) cognition in an integrated model, explaining how nature-inspired awe contributes to eliciting TERB intention. The findings add critical theoretical contributions to the travel and tourism literature and provide important implications for the tourism industry to inspire awe and benefit from such emotion.

敬畏在旅游中是一种十分重要的情绪, 但却鲜少被关注。本研究旨在深入研究解释水平状态、小我感知和自然关联感的链式中介效应, 揭示自然敬畏激发游客环境责任行为意向的内在作用机制。

本研究的基础理论为情绪评价理论。研究一通过场景诱导的准实验研究, 检验将敬畏、小我感知、自然关联感和游客责任行为意向的假设模型; 研究一以网络问卷平台收集的271份有效问卷数据进行分析。研究二是一项在中国白云山风景区开展的实地研究, 旨在检验研究一的研究结果, 并进一步揭示解释水平状态的中介效应; 研究二采用229份有效问卷数据进行分析。

本研究揭示了敬畏通过链式中介作用影响游客环境责任行为意向的内在作用机制, 深化了对自然敬畏在旅游和旅行领域价值的理解。具体而言, 本研究发现敬畏激发了游客启动更高的解释水平, 这使游客感知到更渺小的自我, 并促使他们感到与自然的联系更加紧密, 从而提升了游客的环境责任行为意向。

本研究将游客的心理定式（即解释水平状态）、内在认知（即小我感知）和外部认知（即自然关联感）整合到一个综合模型中, 解释了自然敬畏引发游客环境责任行为意向的作用机制。研究结果为旅游研究提供了重要的理论贡献, 也为旅游行业如何在实践中激发敬畏感并从中获得效益提供了重要的实践启示。

El asombro es una emoción importante pero en gran medida ignorada en el turismo. Esta investigación pretende revelar el mecanismo subyacente de los efectos del asombro inspirado por la naturaleza en la estimulación de la intención del comportamiento responsable con el medio ambiente (TERB) de los turistas, profundizando en el efecto mediador en serie del nivel de interpretación del estado, la percepción del pequeño yo y la conexión del estado con la naturaleza (CNT).

Esta investigación se basó en la teoría de la valoración de la emoción. El estudio 1 fue un estudio cuasiexperimental inducido por escenarios para probar el modelo propuesto que vincula el sentimiento de asombro, la percepción del pequeño yo, el estado CNT y la intención TERB. Se recogieron 271 cuestionarios válidos a través de una plataforma de cuestionarios en línea. El Estudio 2 fue un estudio de campo diseñado para replicar los hallazgos del Estudio 1 y descubrir más a fondo el papel mediador del nivel de interpretación del estado. El estudio 2 se llevó a cabo en la zona escénica de la montaña de Baiyun (China). Se utilizó un total de 229 cuestionarios válidos para el análisis.

Esta investigación profundiza en nuestra comprensión de los efectos del asombro inspirado por la naturaleza en los ámbitos del turismo y los viajes. Esta investigación descubre el mecanismo subyacente por el que el asombro aumenta la intención de TERB a través de la mediación en serie. En concreto, el asombro inspira a los turistas a aplicar un mayor nivel de interpretación, lo que hace que los turistas perciban un yo más pequeño, lo que les hace sentirse más conectados con la naturaleza y ejercer así una mayor intención de adoptar TERB.

Esta investigación vinculó la mentalidad de los turistas (es decir, el nivel de nterpretación del estado), la cognición interna (es decir, la percepción de un yo más pequeño) y la externa (es decir, la CNT del estado) en un modelo integrado, explicando cómo el asombro inspirado por la naturaleza contribuye a suscitar la intención TERB. Los hallazgos añaden contribuciones teóricas críticas a la literatura sobre viajes y turismo y proporcionan implicaciones importantes para que la industria turística inspire asombro y se beneficie de dicha emoción.

The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM).

In the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing.

The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices.

A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality.