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This paper takes the Shanghai-Shenzhen-Hong Kong Stock Connect as a quasi-natural experiment and investigates the impact of capital market liberalization on the corporate debt maturity structure. It also aims to provide some policy implications for corporate debt financing and further liberalization of the capital market in China.

Employing the exogenous event of Shanghai-Shenzhen-Hong Kong Stock Connect and using the data of Chinese A-share firms from 2010 to 2020, this study constructs a difference-in-differences model to examine the relationship between capital market liberalization and corporate debt maturity structure. To validate the results, this study performed several robustness tests, including the parallel test, the placebo test, the Heckman two-stage regression and the propensity score matching.

This paper finds that capital market liberalization has significantly increased the proportion of long-term debt of target firms. Further analyses suggest that the impact of capital market liberalization on the debt maturity structure is more pronounced for firms with lower management ownership and non-Big 4 audit. Channel tests show that capital market liberalization improves firms’ information environment and curbs self-interested management behavior.

This research provides empirical evidence for the consequences of capital market liberalization and enriches the literature on the determinants of corporate debt maturity structure. Further this study makes a reference for regulators and financial institutions to improve corporate financing through the governance role of capital market liberalization.

This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues.

The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques.

The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction.

This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.

This paper looks at how stakeholders co-create value at mega-events from a service ecosystem perspective. Despite the growing interest, little is known about how value is co-created through such initiatives for individual stakeholders and the community.

Drawing on institutional and stakeholder theory, the study focuses on Cortina 2021, the World Ski Championships held in Italy in February 2021. It investigates how multiple actors co-create value within a service ecosystem through qualitative interviews with key stakeholders combined with the analysis of official documents and reports.

The research established that key stakeholders were willing to get involved with Cortina 2021 if they recognised the value which could be co-created. Such an ecosystem requires a focal organisation with a clear regulative and normative framework and a common cultural basis. The latter helped resilience in the extraordinary circumstances of Cortina 2021 and safeguarded long-term impacts, even though the expected short-term ones were compromised.

From a managerial point of view, the evidence from Cortina 2021 shows how a clear strategy with well-defined stakeholder engagement mechanisms can facilitate value co-creation in service ecosystems. Moreover, when regulative and normative elements are blurred because of an extraordinary circumstance, resource integration and value creation processes need to be entrusted to those cultural elements that characterise an ecosystem.

The study takes an ecosystemic approach to mega-events to explore value creation for the whole community at the macro level, not only at the individual or organisational level, even during a crisis, which greatly impaired the preparation and running of the event.

The pedestrian dead reckoning (PDR) based on smartphones has been widely applied in continuous indoor positioning. However, when the position of the mobile phone and the walking patterns of the pedestrian are mixed, traditional PDR tends to become confused and thus degrade performance. To address this issue, this paper aims to propose an improved PDR scheme by focusing on gait pattern recognition and the impact of short-period but negative transitions on tracking.

The overall solution uses the inertial sensor integrated within the phone for positioning. A binary classifier-based change point detection algorithm is used to identify the transition points in pedestrian gait. Additionally, to enhance the accuracy of gait recognition, this paper presents a combined CNN-attention-based bi-directional long short-term memory(ABiLSTM) model, integrating convolutional neural networks (CNN), bi-directional long short-term memory (Bi-LSTM) and an attention mechanism, to recognize the current gait pattern. The outcomes of this gait pattern recognition are then applied to PDR. Based on distinct gait patterns, corresponding PDR strategies are devised to enable continuous tracking and positioning of pedestrians.

Through experimental verification, the CNN-ABiLSTM model achieves a gait recognition accuracy of 99.52% on the self-constructed data set. The pedestrian navigation estimation method proposed in this paper, which is based on gait recognition assistance, demonstrates a 32.56% improvement in accuracy over traditional positioning algorithms in multi-gait scenarios.

The improved PDR scheme algorithm significantly enhances the robustness and smoothness of pedestrian tracking, particularly during multiple gait transitions. This, in turn, provides strong support for the utilization of low-cost inertial sensors integrated within mobile phones for indoor positioning applications.

This study aims to investigate the relationship between academic motivations and the risk of Not in Employment, Education or Training (NEET) among university undergraduates and Vocational Education and Training (VET) undergraduates.

The sample included 402 Vietnamese university undergraduates and 250 VET undergraduates in the southern region of Vietnam. Students took part in a survey, with all participants being informed about the study’s purpose and assured that their involvement was entirely voluntary. In addition to descriptive statistics, the study employed linear regression in SPSS to examine hypotheses.

The findings indicate that, for university students, intrinsic motivation and mastery approach motivation are associated with reduced NEET risk, while performance avoidance motivation is positively linked to this tendency. In contrast, for VET students, extrinsic motivation and performance approach motivation are negatively associated with NEET risk, but mastery approach motivation may exacerbate the risk.

Grounded in the principles of Self-Determination Theory (SDT) and Achievement Goal Theory (AGT), the study proposes that university students may prioritize competence improvement, knowledge acquisition and the satisfaction of their learning interests, which they believe will help them acquire valuable knowledge beneficial for their future careers. Conversely, VET students emphasize performance and external achievement, which may enhance their outcome and reduce NEET risk. These findings offer significant theoretical and practical insights into the adoption of SDT and AGT and also provide educators or policymakers with more detailed information regarding university and VET students’ learning and development.

The unethical use of natural resources is contributing to the increasing environmental degradation. The depleting environment poses a threat to the sustainability of present and future generations. This paper aims to investigate the impact of social media on the green purchase intention of consumers. The research adopts the theory of reciprocal determinism to integrate the variables of social media, green product knowledge, green consumption values and drive for environmental responsibility, assessing their collective impact on green purchase intention.

Data were gathered from a sample of 310 young consumers using a structured close-ended questionnaire. The proposed hypothesis was tested by employing PLS-SEM.

The study validates that social media (SM) has the ability to shape consumers' intention to choose more eco-friendly products. In addition to social media, green consumption values and the drive for environmental responsibility exert a significant influence on green purchase intention. However, green product knowledge did not have a significant impact on green purchase intention nor did mediate the relationship between social media and green purchase intention.

The existing scholarly literature indicates that researchers have employed a variety of theories as the basis for their studies aimed at predicting intentions and behaviors related to environmentally conscious purchases. To our knowledge, this is the first study to incorporate social media in the theory of reciprocal determinism. Notably, the paper represents the inaugural investigation in the context of an emerging economy to incorporate green product knowledge as a mediating variable.

Green finance aims to promote sustainable financial activities, environmental conservation and ecological balance. This study examines how renewable energy consumption (REN), technological innovation (TEC) and green finance (GRF) influence CO2 emissions in Vietnam from 2000 to 2022.

We utilize a novel three-stage methodology including quantile-on-quantile regression, wavelet coherence and wavelet-quantile regression to explore the relationship in the structure of intercorrelation in terms of quantile, time and frequency.

The findings show that Vietnam will increase environmental quality for higher green development. Specifically, there is a negative influence of TEC, REN and GRF on CO2 emissions across different quantiles and timescales.

The study recommends policies that support green development and reduce carbon emissions, such as increasing the use of renewable energy and conducting well-planned research to achieve a carbon-free, sustainable environment.

This article looks into the effects of GRF, TEC and REN on CO2 emissions in Vietnam. Some studies argue that green development in underdeveloped nations is insufficient to reduce CO2 emissions, thereby limiting the sample to a few advanced economies. Adopting diverse methodologies demonstrates the varied and intricate nature of understanding CO2 drivers. Additionally, our work makes detailed policy implications for Vietnam to meet its net-zero emission target and achieve sustainable development by 2050.

Considering the influence of deformation error, the target poses must be corrected when compensating for positioning error but the efficiency of existing positioning error compensation algorithms needs to be improved. Therefore, the purpose of this study is to propose a high-efficiency positioning error compensation method to reduce the calculation time.

The corrected target poses are calculated. An improved back propagation (BP) neural network is used to establish the mapping relationship between the original and corrected target poses. After the BP neural network is trained, the corrected target poses can be calculated with short notice on the basis of the pose correction similarity.

Under given conditions, the calculation time when the trained BP neural network is used to predict the corrected target poses is only 1.15 s. Compared with the existing algorithm, this method reduces the calculation time of the target poses from the order of minutes to the order of seconds.

The proposed algorithm is more efficient while maintaining the accuracy of the error compensation.

This method can be used to quickly position the error compensation of a large parallel mechanism.

This paper aims to examine the role of local/provincial government in influencing corporate social and environmental reporting (CSER) in China, and more specifically, how the underlying economic and political factors associated with local government have influenced the quality of CSER.

The authors used 234 environmentally sensitive companies listed on the Shanghai and Shenzhen Stock Exchanges during 2013 and 2015 as the research sample to test the relationship between CSER and local government’s political connection and economic prioritisation and the potential mediating effect of local economic prioritisation.

The analysis provides evidence that local/provincial government’s political geographical connectedness with the central government has directly and positively influenced the level of CSER, while local prioritisation of economic development has a direct but negative effect on CSER in China. In addition, local/provincial prioritisation of economic development has mediated the relationship between local–central political geographical connectedness and CSER.

While local/provincial governments are heavily influenced by the coercive pressure from the central government, they also act in their own political and economic interests in overseeing CSER at the local level. This study raises the question about the effectiveness of the top-down approach to improving CSER in China and suggests that the central government may need to focus more on coordinating and harmonising different local/provincial governments’ interests to enable achieving a common sustainability goal.

The authors provide evidence revealing how the economic and political contexts of local government have played a significant role in shaping CSER in China. More specifically, this paper addresses a gap in the literature by highlighting the importance of local government oversight power for CSER development and how such oversight is determined by local prioritisation of economic development and political geographical connectedness of local and central governments.

This study aims to examine the co-creation of customer experiences at different levels in service ecosystems, analyzing the case of a tourist destination.

A questionnaire was designed based on previously validated scales. The questionnaire was distributed through the social media platforms Facebook and Instagram. The survey yielded 1,476 valid responses for three types of destinations. Structural equation modeling and multigroup analysis were performed to test the hypotheses.

Aggregate service experience and memorable customer experience (MCE) in service ecosystems are determined by customer experiences at a dyadic level. Service experience at the ecosystem level is formed from ordinary experiences at the actor level, while MCE is formed from extraordinary experiences at the dyadic level. The type of ecosystem moderates the relationships between the variables but does not alter the importance of each of them.

The relationship between the co-creation of customer experiences at different levels of service ecosystems (dyadic vs aggregate) is addressed. A relationship is established between the ordinary and extraordinary character of experiences and their memorability at the ecosystem level.