Haodong Chang’s research while affiliated with Chengdu University of Technology and other places

The swift incorporation of artificial intelligence (AI) into higher education has significantly propelled the digital transformation of education. This advancement is crucial for educators aiming to augment teaching quality through AI technologies, such as ChatGPT. However, the acceptance of ChatGPT among college students remains underexplored. This paper aims to clarify the determinants influencing college students’ acceptance of ChatGPT and to facilitate its widespread adoption in higher education. To achieve this, we integrate the Technology Readiness Index (TRI), Technology Acceptance Model (TAM), and Theory of Planned Behavior (TPB) to develop a novel research framework. Employing a mixed-method approach that includes PLS-SEM and fsQCA, we analyze data from 298 Chinese college students. Our findings indicate that discomfort and insecurity adversely affect Perceived Ease of Use (PEU) and Perceived Usefulness (PU) in the context of ChatGPT adoption. Additionally, both PEU and PU positively impact attitudes, which, in conjunction with Subjective Norms (SN) and Perceived Behavioral Control (PBC), bolster the intention to accept ChatGPT. Insights from fsQCA reveal that the acceptance of ChatGPT among students is not driven by a singular factor but by an amalgamation of these elements, underscoring the complex nature of technology adoption. The paper concludes with practical recommendations for educators and designers to refine curriculum design and teaching methodologies, boost student engagement and learning efficacy, and promote the broader adoption of educational technology.

Along with the digital transformation of the administrative environment and the end of the COVID-19 pandemic, official endorsers have nurtured a new channel for tourism destination marketing, which is of great significance to local economic recovery. However, less attention has been paid to the different effects of endorsement between ordinary endorsers and official endorsers, mainly due to their contrasting social statuses. To bridge the research gap, the source credibility model and social identity theory are integrated to construct the distinctive attributes of officials, as well as structural equation model is utilized to explore the underlying mechanism of official endorsement. Findings indicate that trustworthiness, the sense of authority, expertise, and attractiveness have direct positive effects on official identification, while also indirectly influencing tourists’ attitudes toward the destination through official identification. These findings provide theoretical and managerial implications for the local government managers involved in tourism destination marketing.

To study the multi-factor linkage effect of carbon trading on green technology innovation, this paper employs the dynamic QCA analysis method and uses panel data from China's carbon trading pilot areas. The aim is to explore the causal path considering the time effect. Additionally, the Kruskal-Wallis rank sum test is applied to investigate the provincial coverage difference of the configuration and reveal the variation in configuration preferences between regions from a spatial dimension. The results indicate that a single factor alone does not constitute the necessary conditions for the “quantity” and “quality” of high-green technology innovation. However, the necessity of carbon trading price exhibits a declining trend over the years, demonstrating the presence of a time effect. Regarding the sufficiency analysis of conditional configuration, it mainly includes a “price-market scale” dual effect model and a single market scale effect model, with three configuration paths for each model. Among them, the “price-market scale” dual effect model can drive the increase in the quantity of green technology innovation through carbon trading price, market scale, government intervention degree, and other factors. The single market scale effect model can promote the high-quality development of green technology innovation, but the impact of carbon trading price on the quality of green technology innovation is relatively insignificant. In terms of the time dimension, the three configurations still maintain good applicability to green technology innovation under normal conditions. However, when considering the spatial dimension, the coverage distribution of the three configurations exhibits evident regional differences. This study introduces the dynamic panel QCA method into the research field for the first time. It addresses the limitations of the traditional QCA method, which is constrained by cross-section data and lacks the ability to explore the linkage effect between factors over time. Additionally, the study analyzes the effects of carbon trading price and market size on the “quantity” and “quality” of green technology innovation, considering both time and space dimensions, from a configuration perspective.

Introduction: Industrial green and low-carbon transformation is the key to improve economic development and necessary process to achieve the goal of the carbon peaking and carbon neutrality. Few studies have been done on the decomposition of carbon emission factors in industries and sub-industries and the impact of green and low-carbon transformation about carbon emission in each industry quantitatively. However, the study of industries and sub-industries can comprehensively analyze the development path of green and low-carbon transformation from a more detailed perspective, and provide scientific reasons for the optimization of industrial structure and energy structure. Methods: The extended Kaya identity for industrial carbon emission is constructed to obtain four factors influencing industrial carbon emission: economic output effect, industrial structure effect, energy intensity effect, carbon consumption intensity in this paper. Then, the LMDI decomposition method is combined with the above identity to innovatively obtain the contribution value of carbon emissions from the perspective of overall, industrial sector and tertiary industry. Then, based on the results of factor decomposition, a multi-index scenario prediction model is constructed. On this basis, the extreme learning machine model optimized by particle swarm optimization (PSO-ELM) was used to predict the influence of the changes in the driving factors on the reduction of industrial carbon emissions. By setting the baseline and industrial green and low-carbon transformation scenarios, it is predicted that industrial carbon emission in Sichuan Province. Results and discussion: (1) Economic output effect always promotes the growth of industrial carbon emissions, and with the adjustment of industrial structure and energy structure, the other three factors begin to restrain the growth of carbon emissions. (2) Scenario prediction shows that without considering the economic costs of transformation, improving carbon emission reduction efficiency can be obtained through accelerating the rate of change of industrial structure of the secondary and tertiary industries, increasing the proportion of energy intensity reduction, and strengthening the proportion of non-fossil energy use.

The “14th Five-Year Plan” period is a crucial phase for China to achieve the goal of carbon peaking and carbon neutrality (referred to as the “double carbon”). Thus, it is very important to analyze the main factors affecting carbon emissions and accurately predict the change of carbon emissions to achieve the goal of double carbon. For the slow data updates and the low accuracy of traditional prediction models about the carbon emissions, the key factors of carbon emissions change selected by gray correlation method and the consumption of coal, oil, and natural gas were input into four single prediction models: gray prediction model GM(1,1), ridge regression, BP neural network, and WOA-BP neural network to obtain the fitted and predicted values of carbon emissions, which serve as input to the particle swarm optimization–extreme learning machine (PSO-ELM) model together. Based on the PSO-ELM combined prediction method above and the scenario prediction indicators constructed according to relevant policy documents of Chongqing Municipality, the carbon emission values of Chongqing Municipality during the 14th Five-Year Plan period are predicted in this paper. The empirical results show that carbon emissions of Chongqing Municipality still maintain an upward trend, but the growth rate slow down compared with 1998 to 2018. In general, the carbon emission and GDP of Chongqing Municipality showed a weak decoupling state during 1998 to 2025. By calculation, the PSO-ELM combined prediction model is superior to the above four single prediction models in carbon emission prediction and has good property by the robust testing. The research results can enrich the combined prediction method about the carbon emissions and provide policy suggestions for Chongqing’s low-carbon development during the 14th Five-Year Plan period.

Background Both enterprise resilience and HRM practices can have a positive impact on enterprise performance. The impact of enterprise resilience or human resource management (HRM) practices on enterprise performance independently has been studied widely. But few studies have combined the above two aspects to discuss their impact on enterprise performance. Objective In order to provide positive conclusions for improving enterprise performance, the theoretical model is established to expound the relationship between enterprise resilience, HRM practices including their internal influencing factors and enterprise performance. According to this model, a series of hypotheses about the influence of the combination from these internal factors on enterprise performance are presented. Method Based on the statistical data of the questionnaire survey with managers and general employees at different levels in enterprises as respondents, the correctness of these hypotheses is proved by the fuzzy set qualitative comparative analysis (fsQCA) method. Results and discussion The impact of the combination of enterprise resilience for high enterprise performance is shown in Table 3. The positive impact on the configuration of HRM practices for enterprise performance is shown in Table 4. The influences of the various combinations of internal factors about enterprise resilience and HRM practices on enterprise performance are shown in Table 5. From Table 4, it is discovered that performance appraisal and training have a significant positive effect on high enterprise performance. From Table 5, it is found that information sharing capabilities play a critical role, and enterprise resilience capabilities have a relatively positive impact on enterprise performance. Therefore, managers need to seek the development of enterprise resilience and HRM practices simultaneously and choose the most suitable combination configuration according to the actual situation of the enterprise itself. Moreover, a meeting system should be set up to ensure the transmission of internal information efficiently and accurately.

Since the carbon peaking and carbon neutrality goals was included into the ecological civilization construction system, every province and city in China have been actively released their local the carbon peaking and carbon neutrality plans for the “14th Five-Year Plan”. To address the problems of slow updating of carbon emission data and low accuracy of traditional forecasting models, this paper used data from Chongqing, China, to conduct a study on the subject. this paper measured carbon emissions according to the IPCC method，and assessing the development process of resources and environment by means of decoupling analysis. The important factors influencing carbon emissions are selected by the grey correlation method, and the scenario forecast indicators are constructed according to the relevant policy documents of Chongqing, and the important factors and the consumption of coal, oil and natural gas are taken as the inputs of a single forecast model. The following conclusions were obtained: by comparison, the PSO-ELM model is the best model for predicting carbon emissions in Chongqing. The following conclusions were obtained: the combined PSO-ELM prediction model has lower prediction error and higher accuracy, and is more suitable for carbon emission research. The prediction results show that the carbon emissions in Chongqing during the “14th Five-Year Plan” still maintain upward trend, but the growth rate has slowed down compared with 1998-2018, and the carbon emissions tend to stabilize. Overall, there is a weak decoupling between carbon emissions and GDP in Chongqing from 1998 to 2025.