Search results

This study aims to examine the impact of information communication technology-enabled work during non-working hours (ICT-enabled WNWHs), as a source of stress, on employee behavioral outcomes –in-role job performance, organizational citizenship behaviors (OCBs) that benefit organizations and OCBs that benefit individuals, through emotional responses – work exhaustion, nonwork exhaustion and organization-based self-esteem. As the coronavirus disease 2019 (COVID-19) lockdowns demonstrated that employees frequently engage in ICT-enabled WNWHs, studying stress induced by ICT-enabled WNWHs is essential for understanding employee adaptation to the work-from-home trend that emerged from COVID-19 lockdowns.

A quantitative survey comprising 1,178 employees in China was conducted, and the data reliability and validity were confirmed. Partial least squares structural equation modeling analysis was employed to test the hypotheses.

The study results empirically proved that, although ICT-enabled WNWHs had significant effects on employee behavioral outcomes, the related emotional responses were the mediators of the stress transmission mechanism that directly affected employee behavioral outcomes. Notably, work exhaustion and organization-based self-esteem partially mediate the stress transmission mechanism, while nonwork exhaustion exerts a full mediating effect.

This study proposes the stress transmission mechanism of ICT-enabled WNWHs and delineates emotional responses regarding the work environment attributes of ICT-enabled WNWHs, an approach rarely seen in prior IS studies. To our best knowledge, this study is the first to identify and empirically demonstrate organization-based self-esteem as one among the emotional responses to ICT-enabled WNWHs. Furthermore, it expands understanding of the holistic impacts of ICT-enabled WNWHs, which is lacking in information systems (IS) literature.

The purpose of this study is to explore the relationship between nonmarket strategy and innovation performance, as well as the boundary factors that influence this relationship in the context of the pharmaceutical industry in emerging markets.

This study analyzed matched data of 227 Chinese pharmaceutical firms and two secondary databases with SPSS to examine the hypotheses.

Nonmarket strategy promotes the innovation performance. High level of firm internal knowledge utilization ability and strategic flexibility strengthens the effect of nonmarket strategy in promoting innovation performance, while information technology (IT) environment weakens the effect of nonmarket strategy in promoting innovation performance.

The research studies the positive impact of nonmarket strategy on innovation performance in the specific context of Chinese pharmaceutical industry, and it introduces the internal capabilities and external IT environment of the firm as moderators of the relationship between nonmarket strategy and innovation performance. More importantly, this research echoes the call for research on moderator of nonmarket strategy and identifies important boundary conditions. To the best of the authors’ knowledge, it also explores the impact of the IT environment on the implementation of nonmarket strategy for the first time, which deepens the research on nonmarket strategy’s effect on innovation.

The COVID-19 pandemic has significantly raised economic risk and uncertainty worldwide. How does COVID-19 affect urban housing markets? Is there any difference when different areas encounter COVID-19? This study aims to investigate the impacts of the pandemic on housing prices by using Beijing’s housing markets data in 2020.

The authors use transaction-level data from April to September in 2020 to conduct a hedonic price analysis of the housing markets in Beijing. The data included 70,843 transactions scraped from a real estate agent’s website. The authors use the difference-in-differences approach to evaluate the impacts of the COVID-19 outbreak from the Beijing Xinfadi market (the largest and most important food wholesale market in Beijing) in 2020.

This outbreak of COVID-19 caused a 6.3% drop in housing prices in Beijing from April to September in 2020. However, the impacts of COVID-19 on housing prices in different urban neighbourhoods were spatially heterogeneous. Housing prices in neighbourhoods with industries that rely on face-to-face communication were more affected by the pandemic, while those that can work remotely were less affected.

By investigating the impacts of COVID-19 on housing prices in Beijing, this study illustrates that urban housing prices would be impacted by the pandemic, at least in the short term. While the rise and fall of housing prices were found spatially heterogeneous in Beijing, it suggests that urban neighbourhoods with specific socioeconomic characteristics and geographic locations would unfold different resilience when encountering pandemic. By using data scraping and rigorous statistical tools, the study is probably one of the first ones examining the consequences of COVID-19 in intra-urban housing markets.

To solve the problem of low sensitivity of traditional capacitive proximity sensor, this paper aims to propose a novel capacitive sensor for detection of an approaching conductor.

Five capacitive proximity sensors with different structures are designed and the performance is compared with the traditional capacitive sensor. The impacts of geometrical parameters on the performance of the proposed capacitive sensor are studied. Furthermore, the sensitivity of the proposed capacitive sensor to an approaching conductor with different sizes is discussed. Also, how the designed capacitive sensor is sensitive to the lateral placement of the approaching object is analyzed.

Several capacitive proximity sensor structures have been designed and analyzed. It is found that the capacitive sensor with the top small ring-bottom large ring structure shows stronger electric field distribution around the top electrode and higher sensitivity to the approaching conductor than other sensors. Through further analysis of the proposed sensor, the results demonstrate that proposed capacitive sensor is effective for proximity object detection.

This paper proposes a novel capacitive proximity sensor with top small ring-bottom large ring structure. Compared with the traditional capacitive sensor, the proposed capacitive sensor is more sensitive to the approaching object. This would be helpful for the accurate detection of the approaching object. Also, the top and bottom electrodes are much smaller.

China’s rapid pace of urbanization has resulted in millions of rural residents migrating from rural areas to urban areas for better job opportunities. Due to economic pressures and the nature of China’s demographic policies, many of these migrants have been forced to leave their children with relatives – typically paternal grandparents – at home in the countryside. Thus, while income for most migrant families has risen, a major unintended consequence of this labor movement has been the emergence of a potentially vulnerable sub-population of left-behind children (LBCs). The purpose of this paper is to examine the impacts of parental migration on both the academic performance and mental health of LBCs.

Longitudinal data were drawn from three waves of a panel survey that followed the same students and their families – including their migration behavior (i.e. whether both parents, one parent, no parent migrated) – between 2015 and 2016. The survey covers more than 33,000 students in one province of central China. The authors apply a student fixed-effects model that controls for both observable and unobservable confounding variables to explicate the causal effects of parental migration on the academic and mental health outcomes for LBC. The authors also employ these methods to test whether these effects differ by the type of migration or by gender of the child.

The authors found no overall impact of parental migration on either academic performance or mental health of LBCs, regardless of the type of migration behavior. The authors did find, however, that when the authors examined heterogeneous effects by gender (which was possible due to the large sample size), parental migration resulted in significantly higher anxiety levels for left-behind girls. The results suggest that parental migration affects left-behind boys and girls differently and that policymakers should take a more tailored approach to addressing the problems faced by LBCs.

The main contributions of this paper come from the large and representative sample, as well as the causal effects analysis of being left-behind on both academic performance and mental health. First, the paper uses comprehensive panel data from a representative and populous province in China, and the sample size is the largest one among LBC-related papers to the authors’ knowledge. Second, the paper separately examines the causal effects on the student outcomes of different migration strategies. Third, the paper analyzes the heterogeneous effects of different migration strategies on LBC gender. The authors believe that the paper makes a key contribution to the literature.

To study and validate the relationship between the development of Chinese non‐state‐owned enterprise (Chinese non‐SOE), and cultivation of scientific and technological competence empirically.

Correlation analysis in SPSS is used to analyze the correlation between Chinese non‐SOE and cultivation of scientific and technological competence based on the statistic data released by China Statistical Bureau from 1987 to 2005. Furthermore, their relationship is validated by means of Granger causality test in Eviews 3.0.

This paper finds serial GDP of Chinese non‐SOE is a first difference stationary, while serial total funds of S&T activities of Chinese non‐SOE is a second difference stationary. After they are adjusted to be difference stationary, we find that there is not only a positive correlation, but also a bilateral Granger causality between the two variables.

From the point of view of a practitioner engaging in China trade, this study suggested that when all level governmental departments and all Chinese non‐SOE establish economic development strategies and policies for cultivating scientific and technological competence, the interaction relationship must be taken into consideration in order to avoid negative impact on the sustainable development of Chinese non‐SOE.

This study provides reference and benchmark to Chinese non‐SOE's development and will help to enhance its international competence.

In the era of the digital economy, higher demands are placed on versatile talents, and the cultivation of students with innovative and entrepreneurial abilities has become an important issue for the further development of higher education, thus leading to extensive and in-depth research by many scholars. The study summarizes the characteristics and patterns of dual-innovation education at different stages of development, hoping to provide a systematic model for the development of dual-innovation education in China and make up for the shortcomings.

This paper uses Citespace software to visualize and analyze the relevant literature in CNKI and Web of Science databases from a bibliometric perspective, focusing on quantitative analysis in terms of article trends, topic clustering, keyword co-linear networks and topic time evolution, etc., to summarize and sort out the development of innovation and entrepreneurship education research at home and abroad.

The study found that the external characteristics of the literature published in the field of bi-innovation education in China and abroad are slightly different, mainly in that foreign publishers are more closely connected and have formed a more stable ecosystem. In terms of research hotspots, China is still in a critical period of reforming its curriculum and teaching model, and research on the integration of specialization and creative education is in full swing, while foreign countries focus more on the cultivation of students' entrepreneurial awareness and the enhancement of individual effectiveness. In terms of cutting-edge analysis, the main research directions in China are “creative education”, “new engineering”, “integration of industry and education” and “rural revitalization”.

Innovation and entrepreneurship education in China is still in its infancy, and most of the studies lack an overall overview and comparison of foreign studies. Based on the econometric analysis of domestic and foreign literature, this paper proposes a path for domestic innovation and entrepreneurship education reform that can make China's future education reform more effective.

The purpose of this paper is to develop a methodology for knowledge discovery in emergency response service databases based on police occurrence reports, generating information to help law enforcement agencies plan actions to investigate and combat criminal activities.

The developed model employs a methodology for knowledge discovery involving text mining techniques and uses latent Dirichlet allocation (LDA) with collapsed Gibbs sampling to obtain topics related to crime.

The method used in this study enabled identification of the most common crimes that occurred in the period from 1 January to 31 December of 2016. An analysis of the identified topics reaffirmed that crimes do not occur in a linear manner in a given locality. In this study, 40 per cent of the crimes identified in integrated public safety area 5, or AISP 5 (the historic centre of the city of RJ), had no correlation with AISP 19 (Copacabana – RJ), and 33 per cent of the crimes in AISP 19 were not identified in AISP 5.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary manner to the identification of criminal practices and their characteristics based on police occurrence reports stored in emergency response databases. The generated knowledge enables law enforcement experts to assess, reformulate and construct differentiated strategies for combating crimes in a given locality.

The production of knowledge from the emergency service database contributes to the government integrating information with other databases, thus enabling the improvement of strategies to combat local crime. The proposed model contributes to research on big data, on the innovation aspect and on decision support, for it breaks with a paradigm of analysis of criminal information.

The originality of the study lies in the integration of text mining techniques and LDA to detect crimes in a given locality on the basis of the criminal occurrence reports stored in emergency response service databases.

The purpose of this paper is to develop a method for the discovery of knowledge in emergency response databases based on police incident reports, generating information that identifies local criminal demands that allow the selection of the appropriate policing strategies portfolio to solve the problem.

The developed model uses a methodology for the discovery of knowledge involving text mining techniques using Latent Dirichlet Allocation (LDA) integrated with the ELECTRE I multicriteria method.

The developed method allowed the identification of the most common criminal demands that occurred from January 1 to December 31, 2016, in the policing areas studied. One of the crimes does not occur homogeneously in a particular locality. In this study, it was initially observed that 40 per cent of the crimes identified in the Integrated Public Safety Area 5, or AISP-5, (historical city center of RJ) had no correlation with AISP-19 (Copacabana - RJ), and 33 per cent of crimes crimes in AISP-19 were not identified in AISP-5. This finding guided the second part of the method that sought to identify which portfolio of policing strategies would be most appropriate for the identified demands. In this sense, using the ELECTRE I method, eight different scenarios were constructed where it can be identified that for each specific criminal demand set there is a set of policing strategies to be applied.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary way to the identification of criminal practices and their characteristics based on reports of police occurrences stored in emergency response databases. The knowledge generated through the identification of criminal demands allows law enforcement decision makers to evaluate and choose among the available policing strategies, which best suit the reality they study, and produce the reduction of criminal indices.

It is possible to infer that by choosing appropriate strategies to combat local crime, the proposed model will increase the population’s sense of safety through an effective reduction in crime.

The originality of the study lies in the integration of text mining techniques, LDA and the ELECTRE I method for detecting crime in a given location based on crime reports stored in emergency response databases, enabling identification and choice, from customized policing strategies to particular criminal demands.

With the upgrade of natural language interaction technology, the simulation extension of intelligent voice assistants (IVAs) and the uncertainty of products and services have received more and more attention. However, most of the existing research focuses on investigating the application of theories to explain consumer behavior related to intention to use and adopt IVAs, while ignoring the impact of its privacy issues on consumer resistance. This article especially examines the negative impact of artificial intelligence-based IVAs’ privacy concerns on consumer resistance, and studies the mediating effect of perceived creepiness in the context of privacy cynicism and privacy paradox and the moderating effect of anthropomorphized roles of IVAs and perceived corporate social responsibility (CSR) of IVAs’ companies. The demographic variables are also included.

Based on the theory of human–computer interaction (HCI), this study addresses the consumer privacy concerns of IVAs, builds a model of the influence mechanism on consumer resistance, and then verifies the mediating effect of perceived creepiness and the moderating effect of anthropomorphized roles of IVAs and perceived CSR of IVAs companies. This research explores underlying mechanism with three experiments.

It turns out that consumers’ privacy concerns are related to their resistance to IVAs through perceived creepiness. The servant (vs. partner) anthropomorphized role of IVAs is likely to induce more privacy concerns and in turn higher resistance. At the same time, when the company’s CSR is perceived high, the impact of the concerns of IVAs’ privacy issues on consumer resistance will be weakened, and the intermediary mechanism of perceiving creepiness in HCI and anthropomorphism of new technology are further explained and verified. The differences between different age and gender are also revealed in the study.

The research conclusions have strategic reference significance for enterprises to build the design framework of IVAs and formulate the response strategy of IVAs’ privacy concerns. And it offers implications for researchers and closes the research gap of IVAs from the perspective of innovation resistance.