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This study explores the determinants of public willingness to comply with COVID-19 regulations to address the research gap at the intersection of public health and law enforcement within the unique sociocultural context of Taiwan.

Utilizing survey data from New Taipei City in 2021, the analysis involved multiple linear regression models to assess the influences of psychological conditions (i.e. distress and self-efficacy), community compliance and perceptions of government (i.e. general trust in government and specific perceptions of police procedural justice) on compliance tendencies while controlling for individual demographics.

The results indicated that self-efficacy, perceived community compliance, trust in government, and police procedural justice are positively associated with public compliance with COVID-19 regulations. Among these variables, trust in government and police procedural justice were identified as the most prominent factors, followed by self-efficacy and perceived community compliance. As demographic factors such as age, gender and education did not significantly affect willingness to comply, psychological, social and governmental influences are more powerful determinants of compliance than static demographic characteristics.

This study provides empirical evidence from Taiwan on the factors shaping public compliance during an unprecedented global pandemic. It highlights the importance of fostering governmental trust and enhancing police procedural justice during periods of stability to secure compliance with public health directives in times of crisis.

The purpose of this study is to propose a research model based on the stimulus–organism–response (S–O–R) model to examine whether network externality, personalization and sociability as environmental feature antecedents to learners’ learning engagement (LE) can influence their learning persistence (LP) in massive open online courses (MOOCs).

Sample data for this study were collected from learners who had experience in taking MOOCs provided by the MOOC platform launched by a well-known university in Taiwan, and 371 usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that learners’ perceived network externality, personalization and sociability in MOOCs positively affected their cognitive LE, psychological LE and social LE elicited by MOOCs, which jointly led to their LP in MOOCs. The results support all proposed hypotheses, and the research model accounts for 76.2% of the variance in learners’ LP in MOOCs.

This study uses the S–O–R model as a theoretical base to construct learners’ LP in MOOCs as a series of the inner process, which is affected by network externality, personalization and sociability. It is worth noting that three psychological constructs including cognitive LE, psychological LE and social LE are used to represent learners’ organismic states of MOOCs usage. To date, hedonic/utilitarian concepts are more often adopted as organisms in previous studies using the S–O–R model, and psychological constructs have received lesser attention. Hence, this study’ contribution on the application of capturing psychological constructs for completely expounding three types of environmental features as antecedents to learners’ LP in MOOCs is well documented.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

Social media usage has been documented to affect the psychological well-being of its users. This study aims to examine how social media overload influences cognitive fatigue among individuals in Malaysia.

This study employed a comprehensive research framework based on the stressor-strain-outcome (SSO) model to examine how perceived overload affects social media cognitive fatigue through emotional exhaustion and anxiety. Survey data were gathered from 451 social media users in Malaysia, and data analysis was performed using PLS-SEM.

The findings revealed that information overload, communication overload and interruption overload are antecedents of emotional exhaustion. Communication overload, interruption overload and cognitive overload were identified as antecedents of anxiety, while emotional exhaustion and anxiety were confirmed as predictors of social media cognitive fatigue. However, pathway analysis indicated no relationship between emotional exhaustion and anxiety.

Our study contributes to the literature on media technology and media psychology by examining the psychological mechanisms (emotional exhaustion and anxiety). The findings offer implications for service providers, practitioners and social media users, as they facilitate measures and strategies to mitigate the adverse effects of social media while elevating psychological well-being.

This study adopts the elaboration likelihood model and configuration perspectives to explore the internal mechanisms underlying the influence of live streaming on consumer trust building and purchase intention.

This study invited 757 experienced live streaming e-commerce users from Chinese platforms such as TikTok and RED, who participated in survey by filling questionnaires collected online. The research employed a mixed-method approach using SEM and fsQCA. SEM was utilized to analyze quantitative data to determine the direct and mediated relationships within product trust, while fsQCA served as a complement to identify the combinations of conditions that enhance product trust.

The findings reveal three important insights. Firstly, in the context of live streaming e-commerce, both product characteristics and streamer characteristics significantly influence consumers' trust in products. The para-social interaction plays a partial mediating role in the relationship between streamer characteristics and product trust. Secondly, four distinct paths are identified that contribute to enhancing product trust in live streaming e-commerce. Thirdly, PSI emerging as a core condition across all four paths, underscores the importance for merchants to foster positive social interactions with consumers beyond the live streaming environment.

This study enhances understanding of the dynamic live streaming e-commerce industry, offering insights into consumer behavior and practical guidance for merchants seeking to build engaged, trustworthy customer relationships.

Clarifying the relationship between building carbon emissions and economic development can help sustainable construction in the field of construction, and this paper provides a constructive suggestion for ensuring economic development while realizing energy efficiency and emission reduction in buildings.

The study focuses on the building sector and firstly analyzes the complex relationship between economic agglomeration (EA) and carbon emission intensity (CEI) of commercial buildings at the city level through the spatial Durbin model and the threshold effect model, and then discusses the regional heterogeneity of this complex relationship from the dimensions of economic density and climate zones, respectively, and finally analyzes in depth the intrinsic influencing mechanism of EA on the CEI of commercial buildings.

The authors found that (1) there is an inverted U-shaped nonlinear relationship between EA and CEI of commercial buildings, and the inflection point of the EA level is 2.42, i.e. 1.125 bn RMB/km². (2) Significant regional differences exist in the inverted U-shaped relationship for cities with different economic densities and cities in different climate zones. (3) EA mainly affects the CEI of commercial buildings through externalities such as commercial building size and tertiary industry share, of which commercial building size is the most important factor hindering the decoupling of urban economic development from the CEI of commercial buildings.

This paper discusses for the first time the relationship between economic development and carbon emissions at the city level and clarifies the spatial differences and influencing mechanism of this relationship, providing a fuller reference for policymakers to develop differentiated building energy efficiency and emission reduction strategies.

To address the various irregularities that occurred during the development of China’s electricity market, particularly the issue of collusive pricing between upstream and downstream firms.

This study constructs a tripartite evolutionary game model involving government regulators, grid operators and power producers to address electricity market pricing chaos. By analyzing the stable strategies within each subject’s evolutionary game, adjustments to the relevant parameters are made to achieve a stable state of strategy selection.

The findings of this study indicate the following: (1) Enhancing the government’s rewards and punishments, increasing speculation and rent-seeking costs for grid operators and modifying tariff sales revenue can promote the integrity of grid operators. (2) Establishing reasonable incentives and penalties can effectively mitigate rent-seeking behaviors resulting from collusive pricing in the power industry. (3) Strengthening the accountability of higher authorities to government regulators and adjusting incentives for grid operators to comply and generators to refrain from rent-seeking behavior can increase the likelihood of rigorous inspections by government regulators.

This study elucidates the impact of factors such as the cost of speculation and sales revenue of grid operators, the cost of rent-seeking by power producers and the strength of rewards and punishments by government departments on the power sector. Adjusting these factors can significantly influence the stability of the three-party evolutionary game, providing valuable insights into the regulatory mechanisms of the power industry.

The purpose of this paper is to introduce cyclic electrochemical impedance spectroscopy (EIS) method to understand the corrosion evolution behavior of structural materials in secondary circuit water environments of pressurized water reactor (PWR) system.

The cyclic EIS has been used to understand the corrosion evolution of 304 stainless steel (SS) in simulated secondary circuit water environment. Scanning electron microscopy and X-ray photoelectron spectroscopy have been used to characterize the microstructure and corrosion morphology of 304 SS sample.

Cyclic EIS measurement is applicable in gaining information on the corrosion evolution of 304 SS in high-temperature and high-pressure (HTHP) water environments. Based on analyses of the cyclic EIS data, it is considered that the measured EIS response of 304 SS sample under HTHP water environment mainly comes from the compact inner part of the newly formed oxide layer, which gradually inhibits the progress of electrochemical reactions at the oxide layer/substrate interface.

The cyclic EIS has been introduced into HTHP water environment, and its reliability has been evaluated. It may find a wide application in corrosion studies of materials under HTHP water environments, which is critical for a safe operation in nuclear power plants and beneficial for the development of corrosion-resistant materials in PWR system.

Purpose: This chapter identifies performance factors that have the strongest impact on companies’ sustainable outcomes and compares the obtained results across different sectors.

Methodology: About 3,384 observations were gathered from 2015 to 2022 from companies in communication services, energy, financials, real estate, and utilities sectors that comprise the ‘STOXX Global ESG Leaders Select 50’ index. The multiple regression model is constructed with companies’ ESG scores as dependent variables and independent variables representing operational, financial, and market performance.

Findings: Companies that tend to have higher operational and financial performance in the financial sector are more likely to have higher ESG performance. The financial performance results of companies showed the strongest statistically significant relationship with environmental and the weakest with governance scores.

Implications: Results benefit private and institutional investors aiming to create more sustainable portfolios. The obtained results indicate that these investors should focus on companies operating in the financial and energy sectors with higher performance results. Better ROE, ROA, and Tobin’s Q may have a negative impact on sustainable outcomes for companies operating in the real estate and utility sectors.

Limitations: Firstly, not all ESG index providers disclose information about their index constituents. Secondly, within the chosen ‘STOXX Global ESG Leaders Select 50’ index, not all constituents had complete ESG data available on the Bloomberg platform. When selecting the analysis period, it was observed that the accessible ESG data on Bloomberg covers a relatively short time span, only from 2015 onwards.

Future research: A larger number of companies by choosing a more comprehensive available ESG index.