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This research aims at explaining the phenomenon of the “black children” (heihaizi), a very little-known generation who lived with concealment under the one-child policy in China. The one-child policy was officially introduced to nationwide at the end of 1979 by permitting per couple to have one child only, later modified to a second child allowed if the first was a girl in rural China in 1984. It was officially replaced by a nation-wide two-child policy and most existing research focused on the parents’ sufferings and policy changes. The term “black children” has been mainly used to describe their absence from their family hukou registration and education. However, this research aims at expanding the meaning of being “black” to explain the children who were concealed more than at the level of family formal registration, but also physical freedom and emotional bond. What we do not yet know are the details of their lived experiences from a day-to-day base: where did they live? How were they raised up? Who were involved? Who benefited from it and who did not? In this way, this research challenges the existing scholarship on the one-child policy and repositions the “black children” as primary victims, and reveals the family as a key figure in co-producing their diminished status with the support of state power. It is very important to understand these children’s loss of citizenship and human freedom from the inside of the family because they were concealed in so many ways away from public view and interventions. This research focuses on illustrating how their lack of access to continued, stabilized, and reciprocally recognized family interactions framed their very idea of self-worth and identity.

The digital transformation revolution has brought outstanding changes to business organizations, especially in the digital accounting transformation domain. Consequently, the purpose of this study is to explore the important role of digital accounting transformation in improving business performance in the context of the banking industry.

Data were collected through a questionnaire from the Jordanian bank sector with a sample of 190 respondents. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the collected data and test the hypotheses.

The results have shown that the adoption of digital accounting, adoption of FinTech innovation and technological competition are the major drivers for improving business performance. All direct paths leading to improving business performance were found to be significant in the hypothesized directions, while technological savvy was found to indirectly affect the relationship between (the adoption of digital accounting and FinTech innovation) and improving business performance.

The current study is differentiated from other studies by developing a theoretical research model to incorporate the adoption of digital accounting, adoption of FinTech innovation, technological competition, technological savvy and business performance in the Jordanian context under the digital transformation revolution. For practitioners, the findings provide policymakers with meaningful insight for organizations looking to adopt these digital technologies for improved business performance.

Coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented mental health repercussions in the lives of every individual including university students. Therefore, study on students’ psychological state and its associated factors during the pandemic are of importance. This study aims to discuss the aforementioned issue.

An online survey was done on a total of 207 university students of Pakistan to collect information on socio-demographic characteristics, concerns or fears amidst COVID-19 and mental distress. Validated tools; Perceived Stress Scale (PSS), Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9)-Depression were used to assess stress, anxiety and depression, respectively.

Around 14% of the university students were experiencing severe stress and anxiety, while 8.2% had severe depression. The authors found that stress among university students was related to psychiatric illness or symptoms (OR = 5.1: 1.1, 22.9) and unpredictability due to the pandemic (OR = 3.7: 1.2, 11.2). The significant determinants of anxiety were psychiatric illness/symptoms (OR = 6.6: 3.4, 12.9), implementation of public health measures (OR = 3.7: 1.1, 11.6), employed mothers (OR = 2.4: 1.1, 5.0) and lack of support from university administration (OR = 2.2: 1.0, 5.0). While the factors associated with depression included psychiatric illness or symptoms (OR = 8.4: 3.3, 21.5), unpredictability due to pandemic (OR = 6.8: 2.2, 20.7), impaired social support system (OR = 3.7: 1.3, 10.4) and studying without a scholarship (OR = 2.1: 1.0, 4.4).

These findings call for an urgent need to develop appropriate interventions and educational programs that could address the psychological needs of students.

The study directs the role of university and faculty in dealing the mental health needs of the student in COVID-19 pandemic time.

Educational programs are important that could address the psychological needs of students in COVID-19 pandemic.

University students reported mental distress during COVID-19 pandemic which shows that younger people are at risk of COVID-19 repercussions. Moreover, several stressors (i.e. impaired social support system and lack of support from universities) were revealed that could be mitigated by implementing appropriate strategies.

This paper aims to empirically investigate the impact of the carbon intensity constraint policy (CICP) on green innovation.

This study takes the implementation of the CICP as a quasi-natural experiment and uses a quasi–difference-in-difference method to investigate the impact of the CICP on firm green innovation from a microeconomic perspective.

The CICP significantly limits the quality of firms’ green innovation. Among the range of green patents, the CICP distorts only patents related to CO₂ emissions. The inhibitory effect is more pronounced in non-state-owned enterprises and heavily polluting firms. R&D investment and green investor are identified as the main mechanism.

These findings provide evidence for the influence of the CICP on firm green innovation, which can guide policymakers in China and other emerging economies that prioritize carbon intensity constraint targets and the improvement of relevant auxiliary measures.

Governments and firms should have a comprehensive understanding of environmental policies and corporate behavior and need to mitigate the negative impact through a combination of measures.

This study contributes to the literature by providing additional empirical evidence regarding the two opposing sides of the ongoing debate on the positive or negative effects of CICP. It also provides new evidence on the policy effect of the CICP on firm green innovation, together with its mechanisms and heterogeneous influences.

The purpose of this study is to investigate family tourists’ emotional responses at world heritage sites (WHSs) by using cognitive appraisal theory (CAT).

The online survey was conducted targeting the family tourists who had travel experience at Pingyao Ancient City, a United Nations Educational, Scientific and Cultural Organization world heritage destination in Northern China.

Data analysis with 347 family tourists to the site, via structural equation modeling analysis, revealed that existential authenticity (i.e. intrapersonal and interpersonal authenticity) and family interpersonal interaction help evoke emotional experience, which generates family cohesion and storytelling behavior as responses.

This study results contribute to an existing body of literature on the ability of CAT to illustrate how emotional experience forms in the context of family tourism at WHSs. The study also provides a clear understanding on how to elicit emotions among family tourists at heritage destinations.

旨在运用认知评价理论 (CAT) 探讨家庭游客在世界遗产地 (WHSs) 的情感反应。

这项线上调查的对象为游览过平遥古城的家庭旅游者, 平遥古城是一个位于中国北方的联合国教科文名录中的世界文化遗产地。

本研究通过结构方程模型对由347名家庭旅游者提供的数据进行分析显示, 存在的真实性（即个人内部和人际真实性）和家庭人际互动有助于唤起情感体验, 从而产生家庭凝聚力和故事讲述行为的情感反应。

本研究结果为有关认知评价理论（CAT）的现有文献做出贡献, 以说明在世界遗产旅游地家庭旅游的背景下情感体验是如何形成的。该研究也为在文化遗产目的地如何引发家庭游客的情绪提供了清晰的解释。

El propósito de este estudio es investigar las respuestas emocionales de los turistas familiares en los sitios del Patrimonio Mundial (WHS) mediante el uso de la teoría de evaluación cognitiva (CAT).

Se realizó una encuesta en línea dirigida a los turistas familiares que habían tenido una experiencia de viaje en la Ciudad Antigua de Pingyao, un destino de patrimonio mundial de la UNESCO en el norte de China.

El análisis de los datos con 347 turistas familiares, a través del análisis SEM, reveló que la autenticidad existencial (es decir, la autenticidad intrapersonal e interpersonal) y la interacción interpersonal familiar ayudan a evocar la experiencia emocional, lo que genera cohesión familiar y comportamiento narrativo como respuestas.

Los resultados de este estudio contribuyen a un cuerpo existente de literatura sobre la capacidad de CAT para ilustrar cómo se forma la experiencia emocional en el contexto del turismo familiar en las CHM. El estudio también proporciona una comprensión clara sobre cómo provocar emociones entre los turistas familiares en el destino de patrimonio.

We seek to quantify the relationship between urbanization and economic growth in China using recent advances in econometric techniques.

This study adopts a smoothed instrumental variables quantile regression (SIVQR) estimator to obtain consistent estimates of the effects of urbanization on economic growth in China. Our approach accounts for the differential impacts of urbanization across the conditional distribution of economic growth while allowing for an identification strategy that addresses the endogeneity of urbanization. Our main findings reveal that ignoring urbanization endogeneity leads to inconsistent estimates of urbanization effects. Further, we find a positive relationship between urbanization and growth resembling an inverted U-shape. This supports the hypothesis that the beneficial effects of urbanization intensify at initial stages while diminishing beyond a certain threshold, due perhaps to weakening scale economies.

Our main results indicate that the individual productivity gains brought by urbanization outweigh the negative effects thereof that impede productivity, thus contributing to the economic growth in China. Further, we find that ignoring differential impacts of urbanization underestimates the beneficial effects of urbanization for provinces whose quality of governance is in the vicinity of the center of quality distribution. Ignoring the endogeneity of urbanization generates inconsistent estimates of the elasticity of economic growth with respect to urbanization. Finally, we estimate an inverted U-shape resembling relationship between urbanization and growth.

First, future studies would benefit from incorporating more data as provinces further east on the mainland become more urbanized and urbanization runs its course. Second, controlling the barriers to rural-urban mobility would contribute to the robustness of the estimated relationship between urbanization and growth once such data became available. Unveiling the impact of government-imposed barriers is key to designing optimal policies that help fuel economic growth in the country. Finally, future research could benefit from information on urbanization sources not considered here such as inter-provincial migration, as such data become publicly available.

Quantifying the beneficial effects of urbanization on economic growth can help guide the government in China to further fuel the growth through a set of relevant policy tools that promote urbanization.

Rural-urban migration in China lays the groundwork for economic advancement in recipient cities and economies, as it may induce scale economies. This can benefit both the economy at large and the migrants.

The SIVQR estimator accounts for potential heterogeneous effects of urbanization across the entire conditional distribution of growth while allowing for an identification strategy that addresses the endogeneity of urbanization. An additional distinguishing feature of the current study is our use of the most recent novel, provincial-level data obtained from the National Bureau of Statistics of China. Our focus on a single country allows sidestepping issues arising from the inconsistency of the definition of urban across different countries while accounting for intra-country urbanization drivers intrinsic to China, such as natural features and geographic characteristics. Therefore, our approach has the potential to sidestep the bias resulting from the differences in mechanisms behind urbanization-growth relationships across different countries.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

On the basis of social identity theory, the purpose of this study is to simultaneously examine how social-based identity (i.e. organizational identification and supervisor support) and personal-based identity (i.e. work-related characteristics and job embeddedness) influence employees’ service sabotage.

By using a sample of 685 employee–customer dyads, this study investigated whether the cross-level moderating roles of organizational identification and supervisor support can activate linkage between work-related characteristics and job embeddedness.

The results of this study indicated that job embeddedness mediates the multiple cross-level effects of organizational identification, supervisor support and work-related characteristics on service sabotage. Moreover, work-related characteristics influence job embeddedness more positively in higher than lower levels of organizational identification and supervisor support.

This study provides a valuable approach to effective management practices, helps to clarify identification at work and expands perceived external prestige for hospitality companies.

These findings support that identity in organizations can be recognized as one of the fundamental concepts that influence individual psychological traits, capabilities, bodily attributes, group classifications and organizational effectiveness.