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Based on the textual-analyzed data covering 2148 IPO firms in China’s stock market during the 2007–2018 period, the authors’ purpose is to examine the influence of anti-takeover provision (ATP) adoption on initial public offerings (IPO) underpricing and identify the reducing effect of the former.

The authors examine the sample consisting of Chinese A-share listed IPO firms between 2007 and 2018 from China Stock Market Accounting Research and Chinese Research Data Services, with ATP data collected from the IPO firm chapters. Specifically, the authors use text analysis to identify whether there are ATPs in the IPO firm chapters, as well as the number of ATPs. H1: IPO underpricing is less severe for firms adopting ATPs. H2: The effect of ATP adoption on IPO underpricing is more salient for firms in worse information environments.

The authors examine the influence of ATP adoption on IPO underpricing and identify the reducing effect of the former. This effect can be explained by the fact that adopting ATPs in IPO firm chapters can reduce information asymmetry to a large extent by helping external investors obtain more private information, which alleviates IPO underpricing. The authors also find that the reducing effect is more significant in the worsened information environment. Furthermore, the authors explore the influence of adopting ATPs on other IPO characteristics and find positive effects on IPO over-subscription, funds raised and trading activity and negative effects on listing fees.

This study mainly contributes to the literature from the following two aspects. First, the study enriches the literature about the influencing factors of IPO underpricing. Second, the study also enriches the literature about the economic consequences of ATP adoption. This study also has important policy implications. With the coming of the era of decentralized ownership in China’s capital market, ATP adoption has become more important and attracted more attention. Also, investors focus more on pricing efficiency. The findings in this paper provide a more comprehensive understanding of the relationship between ATP adoption and IPO underpricing.

As of December 2022, there were 119,000 gas stations, 10,800 gas stations and 4,488,000 charging piles nationwide, while the number of vehicles reached 312 million, including 11.49 million new energy vehicles. The imbalance between transportation energy supply and energy replenishment demand leads to crowded queues of vehicles at some stations and idle resources in others. How to reduce the phenomenon of large queues and improve the utilization rate of idle resources is the key to alleviating the imbalance between supply and demand.

Therefore, from the perspective of spatio-temporal equilibrium of urban transportation energy supply stations, multi-energy supply station cooperation is established in view of the phenomenon of large spatio-temporal differences among different energy supply stations, and corresponding inducing strategies are adopted for energy supplement vehicles in the road network, so that part of queued users go to energy supply stations with fewer vehicles, so as to balance the supply and demand of transportation energy in the region. On this basis, the income distribution of urban transportation energy supply station is discussed.

The total revenue after the cooperation was 13,095, an increase of 22.9%. Secondly, in terms of distribution rationality, three impact factors are selected and Shapley correction value is used to distribute the total income. Compared with independent operation, both sites have a certain degree of increase.

Traffic congestion at energy supply stations is closely related to the number, location and number of vehicles at energy supply stations. Therefore, using a cooperative approach of energy trading cannot solve the queuing problem. In addition, there are a few research results on the equalization of energy supply station services considering time-of-use pricing. However, these studies do not consider the vehicular grooming at congested stations. As far as the authors know, there are no relevant research results in the research on the service equilibrium of energy supply stations based on cooperative games.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

This study aims to analyze and discuss the impact of corporate social responsibility (CSR) on firms’ performance, as well as to examine the interplay between CSR and the economy, society and innovation.

This paper collects data from 420 manufacturing firms across various geographical regions in China. By using a structural equation model, the paper investigates the impact of CSR on enterprise innovation, customer management capability, market competitiveness (MC) and firm financial performance.

The findings demonstrate that CSR performance positively contributes to enhancing the level of enterprise innovation, as well as customer management capability and market competitiveness. Furthermore, it assists enterprises in improving market competitiveness and elevating customer management capabilities. Thus, CSR can have a positive effect on the firm financial performance.

The outcomes presented in this paper offer valuable evidence regarding the influence of implementing CSR on different aspects of enterprise performance and innovation. Moreover, it provides practical recommendations for enterprises seeking to transition towards low-carbon practices and upgrade their manufacturing industry.

This paper aims to reveal how different types of events and top management teams' (TMTs’) cognitive frames affect the generation of breakthrough innovations.

Drawing on the event system theory and upper echelon theory, this study chose a Chinese manufacturing enterprise as the case firm and conducted an exploratory single-case study to unpack how breakthrough innovation generates over time.

By conducting the in-depth case analysis, the study revealed that firms do not produce breakthrough innovation in the catch-up stage and parallel-running stage but achieve it in the leading stage. It also indicated that when facing proactive events in the catch-up stage, TMTs often adopt a contracted lens, being manifested as consistency orientation, less elastic organizational identity and narrower competitive boundaries. In addition, they tend to adopt a contracted lens when facing reactive and proactive events in the parallel-running stage. In the face of reactive and proactive events in the leading stage, they are more inclined to adopt an expanded lens, being manifested as a coexistence orientation, more elastic organizational identity and wider competitive boundaries.

First, by untangling how TMT's cognitive frame functions in breakthrough innovations, this paper provides a micro-foundation for producing breakthrough innovations and deepens the understanding of upper echelon theory by considering the cognitive dimension of TMTs. Second, by teasing out several typical events experienced by the firm, this paper is the first attempt to reveal how events affect the generation of breakthrough innovation. Third, the work extends the application of the event system theory in technological innovation. It also provides insightful implications for promoting breakthrough innovations by considering the role of proactive and reactive events a firm experiences and TMT's perceptions.

This study aims to explore the essential factors of the reason for (RF) and reason against (RA) and their impact on tourist attitudes and intentions to use e-bike sharing services by using a novel approach based on behavioral reasoning theory (BRT).

A quantitative method was used to collect data from 292 tourists and analyze the data using the partial least squares structural equation modeling (PLS-SEM) technique.

The outcomes indicate that both “RF” and “RA” are crucial in affecting tourist attitudes and intentions to use e-bike-sharing services. In addition, factors such as convenience, price saving and time saving were identified as “RF” while “RA” weather conditions, safety concerns and image barriers were identified that affect tourist attitudes and intention to use e-bike sharing services. These findings also indicate that tourists’ values of openness to change significantly influence “RF” and have no impact on “RA” and attitude toward e-bike-sharing services.

This study provides valuable insights that complement the existing literature on electric bike sharing and BRT in the context of promoting sustainable and eco-friendly consumption.

This research takes a significant step forward in investigating the tourist attitudes and intentions toward using e-bike sharing services in a tourist destination growing economy by using the BRT theory and proposing a model that considers both the “RF” and “RA” using e-bikes for urban transportation.

本研究旨在利用基于 BRT 的新方法, 探讨使用电动自行车共享服务的支持原因（RF）和反对原因（RA）的基本因素及其对游客使用电动自行车共享服务的态度和意图的影响。

采用定量方法收集 292 名游客的数据, 并使用 PLS-SEM 技术分析数据。

结果表明“RF”和“RA”对于影响游客使用电动自行车共享服务的态度和意图至关重要。此外, 方便、节省价格和节省时间等因素被确定为“RF”, 而“RA”则被确定为影响游客态度和使用电动自行车共享服务意愿的天气条件、安全问题和形象障碍。这些发现还表明, 游客对变革的开放价值观显着影响“RF”, 但对“RA”和对电动自行车共享服务的态度没有影响。

这项研究提供了宝贵的见解, 在促进可持续和环保消费的背景下补充了有关电动自行车共享和 BRT 的现有文献。

本研究利用 BRT 理论, 在调查经济增长的旅游目的地中游客对使用电动自行车共享服务的态度和意图方面迈出了重要一步, 并提出了一个同时考虑“RF”和“RA”的模型用于城市交通的电动自行车。”

Este estudio pretende explorar los factores esenciales de las razones a favor (RF) y en contra (RA) y su impacto en las actitudes e intenciones de los turistas de utilizar los servicios de bicicletas eléctricas compartidas (e-bike sharing) utilizando un enfoque novedoso basado en la BRT.

Se utilizó un método cuantitativo para recopilar datos de 292 turistas y analizarlos mediante la técnica PLS-SEM.

Los resultados indican que tanto la “RF” como la “RA” son cruciales para afectar a las actitudes e intenciones de los turistas de utilizar los servicios de e-bike-sharing. Además, se identificaron como “RF” factores como la comodidad, el ahorro de precio y el ahorro de tiempo, mientras que como “RA” se identificaron las condiciones meteorológicas, las preocupaciones por la seguridad y las barreras de imagen que afectan a las actitudes de los turistas y a su intención de utilizar los servicios de e-bike sharing. Estos resultados también indican que los valores de apertura al cambio de los turistas influyen significativamente en “RF” y no tienen ningún impacto en “RA” ni en la actitud hacia los servicios de e-bike sharing.

Este estudio aporta valiosas ideas que complementan la bibliografía existente sobre el uso compartido de bicicletas eléctricas (e-bike sharing) y el BRT en el contexto de la promoción de un consumo sostenible y respetuoso con el medio ambiente.”

Esta investigación da un importante paso adelante en la investigación de las actitudes e intenciones de los turistas hacia la utilización de los servicios de e-bike sharing en un destino turístico de economía en crecimiento utilizando la teoría del BRT y proponiendo un modelo que tiene en cuenta tanto la “RF” como la “RA” que utilizan las e-bikes para el transporte urbano.”

The growing focus on socially responsible supply chain management (SRSCM) has made it crucial to extend corporate social responsibility (CSR) to upstream suppliers. Drawing on resource dependence theory, this study aims to examine how supplier dependence upon socially responsible buyers impacts suppliers' CSR performance and how this relationship is moderated by network prominence and demand uncertainty.

The proposed hypotheses are tested using regression analysis with Heckman's two-stage model and a dyadic supply chain dataset constructed based on publicly traded Chinese firms between 2008 and 2016. This time window is selected due to a one-year lag of the dependent variable and the change in evaluation methods of the database providing CSR performance in 2018.

The empirical results indicate that supplier dependence upon socially responsible buyers is positively associated with suppliers' CSR performance. However, this positive relationship is attenuated when suppliers occupy a prominent position in the network or when they face high demand uncertainty.

This study extends knowledge about the role of relationship dependence in implementing SRSCM by highlighting its positive impact on suppliers' CSR. Thus, this study contributes to the buyer–supplier relationship literature and the power and relationship dependence literature. This study further advances the understanding of the factors that influence suppliers' behavior by exploring the moderating roles of network prominence and demand uncertainty. The results have several practical implications for managers and policymakers.

This study aims to investigate the impact of supply chain concentration (SCC) on corporate research and development (R&D) investment and determine the moderating roles of industry concentration and financing constraints on the relationship between SCC and R&D investment.

The study collected data from Chinese listed companies, used the fixed effects model to test the research hypotheses and further used the two-stage Heckman test and propensity score matching (PSM) to address potential endogeneity issues.

The result reveals a negative impact of SCC on corporate R&D investment. In addition, industry concentration mitigates the negative impact of SCC on corporate R&D investment, but financing constraints strengthen the negative impact.

This study introduces the concept of SCC and empirically tests its effect on R&D investment, further explaining the lack of corporate innovation. This study inspires companies to strengthen SC management and weigh the level of SCC with environmental factors.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.

Recent trends have shown the integration of Chinese word segmentation (CWS) and part-of-speech (POS) tagging to enhance syntactic and semantic parsing. However, the potential utility of hierarchical and structural information in these tasks remains underexplored. This study aims to leverage multiple external knowledge sources (e.g. syntactic and semantic features, lexicons) through various modules for the joint task.

We introduce a novel learning framework for the joint CWS and POS tagging task, utilizing graph convolutional networks (GCNs) to encode syntactic structure and semantic features. The framework also incorporates a pre-defined lexicon through a lexicon attention module. We evaluate our model on a range of public corpora, including CTB5, PKU and UD, the novel ZX dataset and the comprehensive CTB9 dataset.

Experimental results on these benchmark corpora demonstrate the effectiveness of our model in improving the performance of the joint task. Notably, we find that syntax information significantly enhances performance, while lexicon information helps mitigate the issue of out-of-vocabulary (OOV) words.

This study introduces a comprehensive approach to the joint CWS and POS tagging task by combining multiple features. Moreover, the proposed framework offers potential adaptability to other sequence labeling tasks, such as named entity recognition (NER).