Improving innovation and entrepreneurship education in higher institutions: the impact of "Mass Entrepreneurship and Innovation" education on undergraduates' entrepreneurial intentions

The exploration of the way"mass entrepreneurship and innovation"(MEI)education influences students'aspirations to become entrepreneurs has grown into an important area of analysis in studies related to higher education.This research intends to examine the consequences of MEI education on students'tendency towards entrepreneurship,and to put forward methods for augmenting the teaching of innovation and entrepreneurship in private higher educational establishments.To achieve this objective,questionnaires and semi-structured interviews were employed in the study,which involved a total of 197 students and five education experts.The statistical analysis of the questionnaire data revealed that MEI education was positively related to students'entrepreneurial intentions,and that both entrepreneurial experience and family entrepreneurial background played moderating roles in this relationship.The interview findings indicated that private universities could enhance educational reforms by designing talent training programs,developing diversified curricula,and developing more professional entrepreneurial platforms to encourage students'entrepreneurial intentions.This study offers fresh insights for improving and perfecting the mechanism of innovation and entrepreneurship education in private universities.

Promoting sustainable consumption and circular economy:the intention of Vietnamese youth consumers to purchase products made from recycled plastics

Recycling is viewed as a key component in a circular economy and serves as an ideal solution for promoting sustainability.During the global plastic crisis,plastic recycling practices have been adopted worldwide,leading to the production of various products made from recycled plastics(PRP).Nevertheless,a gap persists between consumption and demand for such products,which is primarily attributed to a lack of comprehension from the consumer perspective.Given the pivotal role consumers play in the adoption of these products,this study explores consumers’intentions to purchase PRP.This is particularly significant in Vietnam,which is an emerging economy aspiring to achieve the objectives of a circular economy and sustainable development.Utilizing an integrated cognitive-emotional framework comprising the Valence Theory and the Norm Activation Model,data from 564 Vietnamese students were gathered and analyzed using structural equation modeling.The results show that awareness of consequences is a major driver of consumer purchase intentions,followed by perceived ease of application and monetary incentives.The results also indicate that health concerns have the strongest effect on purchase intention and in the negative side,meaning that the health-related risk is the primary concern for consumers during the decision-making process.This research holds substantial value for academics and managers,as it aids in the theoretical exploration and the formulation of strategies to improve consumer acceptance of PRP.

The Impact of E-Commerce Live Broadcasts on Consumers’Purchase Intention

E-commerce live broadcast has an important influence on consumers’purchase intention.The three dimensions of live broadcast content in the broadcast room are the number of comments,product quality,and live content as independent variables.A theoretical model is constructed with perceived value and risk as intermediaries and the consumers’purchase intention as the dependent variable,and corresponding hypotheses are put forward.We designed the scale,collected relevant data,and tested the model hypothesis using Statistical Package for Social Sciences(SPSS)and Analysis of Moment Structure(AMOS)software.The study found that the number of comments and product quality had a significant impact on perceived value,perceived risk,and consumers’purchase intention.From this conclusion,it is suggested that businesses should control the number of comments,strengthen the product quality of comments,and distinguish the repetition degree of the content of live broadcasts.

Withdrawal Intention of Farmers from Vacant Rural Homesteads and Its Influencing Mechanism in Northeast China: A Case Study of Jilin Province

The effective use of land in Northeast China is of great significance for ensuring national food security and regional rural revitalization.In this study,based on the survey data collected from Jilin Province,Northeast China,we analyzed the vacancy rates of rural homesteads in suburban,outer suburban,and remote villages,as well as the withdrawal intention of rural-settled farmers,urbansettled farmers,and farmers with urban and rural dual residency from vacant homesteads.From the perspective of farmers’perceptions,this study constructed a theoretical framework of the influence mechanism of their withdrawal intention and analyzed such mechanism by constructing a structural equation model.The results indicated that:1)rural homestead vacancy rates increased with distance from the village to the city.2)rural-settled farmers showed a low withdrawal intention for vacant rural homesteads,followed by urban and rural dual-residence farmers,and urban-settled farmers showed the highest withdrawal intention.3)the relative importance of the factors influencing withdrawal intention is in the following order:policy awareness>interest perception>living environment perception>family characteristics.Finally,this study discussed the reformation of the homestead system and rural homestead transition in Northeast China,which can provide policy support to increase the potential of cultivated land and promote sustainable rural development and urban-rural coordination.

The Impact of Saudi Nurses’ Happiness on Their Turnover Intentions

Highly happy nurses are more productive, engaged and loyal. If nurses are satisfied, then their intention to leave is low. Aim: to investigate the impact of Saudi nurses’ happiness on their intention to turnover in the Kingdom of Saudi Arabia. Research Hypothesis: H0. There is no relationship between the nurses’ happiness on their intention to turnover among Saudi nurses. H1. The happy Saudi nurses will have a lower turnover intention among Saudi nurses in the Medina region. Design: Quantitative, cross-sectional, and correlational study designs have been used. Setting: Big public hospital in Medina from June to October 2022. Sample: A quota, convenience sample of 281 Saudi nurses. Tools: A. Oxford happiness inventory to measure the nurses’ happiness. B. Turnover Intention Scale to evaluate the Saudi nurses’ intention to leave their current job. Results: More than half of the nurses had moderate happiness, but more than 75% of the Saudi nurses had a desire to leave. The relationship between happiness and turnover intentions is negative. Conclusion: Magnet-like work environments in Saudi Arabia are critical to recruit and retain nurses with critical nursing shortages. Recommendations: Encourage governmental policy-makers to formulate national policies and KPIs in order to make them happy and retain them. .

Effect of Family Cohesion on Depression of Chinese College Students in the COVID-19 Pandemic: Chain Mediation Effect of Perceived Social Support and Intentional Self-Regulation

Individuals’perceptions,attitudes,and patterns of getting along with family members are important factors influencing Chinese people’s self-evaluation.The aim of this study was to investigate the effect of family cohesion on depression and the role of perceived social support and intentional self-regulation in this association.A hypothesized model of the association of family cohesion,perceived social support,intentional self-regulation,and depression was examined.A convenience sampling method was used to survey 1,180 college students in Yunnan Province using self-report.Data were collected using the Family Cohesion Scale,the Perceived Social Support Scale,the Intentional Self-Regulation Scale,and the Center for Epidemiological Studies Depression Scale.The findings revealed low to moderate correlation between the variables studied.College students’family cohesion was a negative predictor of their depression.This association was also mediated by the knock-on effect of perceived social support and intentional self-regulation.These findings show how family cohesion affects college students’depressive status.Specifically,these results help demonstrate the importance of family cohesion,perceived social support,and intentional self-regulation in optimizing students’depression,which in turn can promote better psychological states.

Workplace Wellness,Mental Health Literacy,and Usage Intention of E-Mental Health amongst Digital Workers during the COVID-19

The prevalence of mental health problems in both Malaysian and global workplaces has significantly increased due to the presence of the coronavirus disease(COVID-19)pandemic,globalization,technology advancement in Industry 4.0,and other contributing factors.The pervasiveness of the issue poses a huge challenge to improving the occupational safety and health(OSH)of workers in various industries,especially in the digital industry.The emergence of the innovative industry is evident mainly due to the rapid development of Industry 4.0 and the rele-vant demands of multiple businesses in the digital transformation.Nonetheless,limited studies and academic dis-cussions were conducted on the OSH topic of digital employees.Hence,the current study serves tofill the existing gap and provide academic contributions by scrutinising the perceptions of digital workers regarding their work-place well-being,mental health literacy,and the impression of employing e-mental health.The objectives of this study are:1)To examine the mental health literacy and workplace wellness of digital workers,2)to explore the e-mental health usage intention and actual e-mental health utilization,and 3)to identify digital workers’feedback on e-mental health.In the current context,e-mental health focuses on three dimensions,namely,1)“health in our hand(HIOH)”,2)“interacting for health(IFH)”,and 3)“data enabling health(DEH)”.The present study employed an online cross-sectional survey and received 326 digital workers’completed responses.Variables,such as“mental health literacy(MHL)”,“workplace wellness(WW)”,and e-mental health intention and usage were explicated by analysing the data through descriptive statistics.The study results indicated a moderate to a high level of the MHL and the WW.More than half of the workers possessed a high intention level to employ e-mental health,with the HIOH dimension being the most prevalent domain.Nevertheless,the actual e-mental health usage was very low,owing to the online resources being a new concept amongst digital employees.Numerous confounding factors also existed in affecting the low usage,such as privacy concerns,data security levels,and health verification issues.In addition,the mental health issue has not been openly and widely discussed in Malay-sian workplaces due to stigmatisation.As such,the currentfindings could provide additional insights into the OSH literature;it could serve as a guideline for the OSH decision-makers,employers,and eHealth developers when establishing a feasible framework for the practical adoption of e-mental health services by digital workers.

Integrating the valence theory and the norm activation theory to understand consumers’ e-waste recycling intention

Electrical and electronic waste(e-waste)is a growing challenge,matching the widespread boom in the use of information and communication technology.Opposite to an alarming increasing amount of e-waste,a low rate of consumer engagement in ensuring the proper disposal of such materials intensifies the pressure on the exist‐ing e-waste crisis.To deal with this thorny problem,it is of great interest to grasp consumers’disposal and re‐cycling behavioral intentions.Therefore,this study attempts to understand complementary perspectives around consumers’e-waste recycling intention based on the integration of the valence theory and the norm activation theory.Four data mining models using classification and prediction-based algorithms,namely Chi squared automatic interaction detector(CHAID),Neural network,Discriminant analysis,and Quick,unbiased,efficient statistical tree(QUEST),were employed to analyze a set of the 398 data collected in Vietnam.The re‐sults revealed that the social support value is by far the most critical predictor,followed by the utilitarian value,task difficulty,and monetary risk.It is also noteworthy that the awareness of consequences,education background,the ascription of responsibility,and age were also ranked as critical affecting factors.The lowest influential predictors found in this study were income and gender.In addition,a comparison was made in terms of the classification performance of the four utilized data mining techniques.Based on several evalua‐tion measurements(confusion matrix,accuracy,precision,recall,specificity,F-measure,ROC curve,and AUC),the aggregated results suggested that CHAID and Neural network performed the best.The findings of this research are expected to assist policymakers and future researchers in updating all information surround‐ing consumer behavioral intention-related topics focusing on e-waste.Furthermore,the adoption of data min‐ing algorithms for prediction is another insight of this study,which may shed the light on data mining applica‐tions in such environmental studies in the future.

CBA: multi source fusion model for fast and intelligent target intention identification

How to mine valuable information from massive multisource heterogeneous data and identify the intention of aerial targets is a major research focus at present. Aiming at the longterm dependence of air target intention recognition, this paper deeply explores the potential attribute features from the spatiotemporal sequence data of the target. First, we build an intelligent dynamic intention recognition framework, including a series of specific processes such as data source, data preprocessing,target space-time, convolutional neural networks-bidirectional gated recurrent unit-atteneion (CBA) model and intention recognition. Then, we analyze and reason the designed CBA model in detail. Finally, through comparison and analysis with other recognition model experiments, our proposed method can effectively improve the accuracy of air target intention recognition,and is of significance to the commanders’ operational command and situation prediction.

Alcohol and illicit drugs:prevalence of alcohol and illicit drug use and their predictors in young people from Argentina,Bulgaria,Chile and Romania

Background:Alcohol and illicit drugs(AID)continue to be a major global health concern.Although preventable,AID is linked to millions of deaths annually worldwide.The situation is particularly grave for young people,with AID being a major direct risk factor for disability-adjusted youth life-years lost and death.It further contributes to assaults,road crashes,accidental poisoning,and suicide,leading to long-term issues and public health concerns.Objective:This study aimed at disclosing current AID prevalence data for Argentinian,Bulgarian,Chilean and Romanian youth.It shed light on the predictors of AID in young people from those countries.Method:The study used an online survey to gather data from people aged 18 to 25(n=1,297).The survey was underpinned by the theory of planned behaviour(TPB).Predictors were investigated separately for drinking alcohol and using illicit drugs.Results:Our data revealed that across the four target countries,49%to 90%of the participants drank alcohol,and 8%to 35%used illicit drugs in the past three months.Between 20%and 91%of them intended to drink,and between 8%and 31%intended to use illicit drugs in the following three months.Our TPB model predicted statistically significant(P<0.001)amounts of variance in drinking alcohol(between 61%and 72%)and using illicit drugs(between 20.3%and 74.4%).Intention was consistent in significantly predicting both behaviours.Evidence around the predictive validity of self-efficacy,age and gender was mixed across the investigated countries.Conclusion:This research provided an update on the scarce AID epidemiological data.It also supplied evidence about what theoretically-informed measures might be useful targets of interventions in the case of Argentina,Bulgaria,Chile and Romania.This new knowledge of understanding substance abuse determinants and prevalence may help researchers and practitioners better meet young people's health prevention needs.

Factors Affecting the Entrepreneurial Intention of Students at Tan Trao University

Entrepreneurship is one of the popular choices for students after graduation from higher education.The study focuses on proposing a model to identify the factors affecting the entrepreneurial intention of students at Tan Trao University by researching some typical models in the world.By quantitative research methods based on a survey of 391 students,the study has shown that there are 5 factors affecting the entrepreneurial intention of students at Tan Trao University,including opportunity,motivation,perception of behavioral control,skills and resources for entrepreneurship.Thereby,the study proposes a number of solutions for the students,the university and other related parties to promote students’entrepreneurial intentions.

KGTLIR:An Air Target Intention Recognition Model Based on Knowledge Graph and Deep Learning

As a core part of battlefield situational awareness,air target intention recognition plays an important role in modern air operations.Aiming at the problems of insufficient feature extraction and misclassification in intention recognition,this paper designs an air target intention recognition method(KGTLIR)based on Knowledge Graph and Deep Learning.Firstly,the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism.Meanwhile,the accuracy,recall,and F1-score after iteration are introduced to dynamically adjust the sample weights to reduce the probability of misclassification.After that,an intention recognition model based on Knowledge Graph is constructed to predict the probability of the occurrence of different intentions of the target.Finally,the results of the two models are fused by evidence theory to obtain the target’s operational intention.Experiments show that the intention recognition accuracy of the KGTLIRmodel can reach 98.48%,which is not only better than most of the air target intention recognition methods,but also demonstrates better interpretability and trustworthiness.

Cardiovascular health awareness,risk perception,behavioural intention and INTERHEART risk stratification among middle-aged adults in Malaysia

Objective:To investigate the interrelationship between cardiovascular health awareness,risk perception,behavioural intention,and INTERHEART risk stratification in a middle-aged adult population in Malaysia.Methods:A cross-sectional survey with convenience sampling was conducted during November 2022 and January 2023.Participants completed validated questionnaires assessing cardiovascular health awareness,risk perception of cardiovascular diseases,behavioural intention towards adopting healthy habits,and INTERHEART risk stratification score(IHRS)based on established risk factors.A total of 602 respondents were included in the final analysis.Data were analysed with independent t-test/one-way ANOVA or Mann-Whitney/Kruskal-Wallis to test the differences,Pearson correlation or linear regression test to analyze the association of independent and dependent variables.Results:There was a significant positive correlation between medical knowledge related to cardiovascular disease(CVD)and knowledge related to CVD risk prevention,risk perception,behavioural intention and IHRS(P<0.05,Pearson correlation).Notably,individuals with higher IHRS tended to have lower knowledge related to CVD and CVD risk prevention,risk perception,and behavioural intention.Males,laborers,active/former smokers,individuals with lower household income and educational levels,those involved in occupations not related to the healthcare sector,and those who did not receive the CVD health brochure or are unaware of health self-assessment tools are likely to have lower levels of knowledge,risk perception,and poorer behavioural intention regarding cardiovascular health(P<0.05,one-way ANOVA).While educational level,smoking status,awareness about CVD poster,self-assessment tools were repeatedly significantly associated with knowledge related to CVD and CVD risk prevention,risk perception,behavioral intention and/or IHRS(P<0.05,linear regression).Conclusions:These findings underscore the importance of promoting cardiovascular health awareness and risk perception among middle-aged adults to foster positive BI and reduce CVD risk.Tailored interventions targeting specific risk factors identified by INTERHEART may enhance risk stratification accuracy and facilitate targeted preventive strategies.

Perspectives and Experiences of Education Stakeholders: A Quantitative Study on the Adoption of Artificial Intelligence in Executive Training Using Structural Equation Modeling

The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).

A Cognitive Study on Politeness Intention Processing and Its Association with Pragmatic Failure in Cross-Cultural Communication

Although a large number of studies have focused on various aspects of politeness,very little is known about how politeness intention is activated cognitively during verbal communication.The present study aims to explore the cognitive mechanism of politeness intention processing,and how it is related to pragmatic failure during cross-cultural communication.Using 30 Chinese EFL university students who were instructed to finish a probe word judgment task with 96 virtual scenarios,the results indicate that within both mono-and cross-cultural contexts,the response time in the experimental scenarios was significantly slower than that of the filler scenarios.This suggests that politeness intention was activated while understanding the surface meaning of the conversation;however,the EFL learners could not completely avoid the negative transfer of their native politeness conventions when they were comprehending the conversational intention of the target language.Furthermore,no significant differences in response time were found between the groups with high and low English pragmatic competence,illustrating that transferring the pragmatic rules and principles into cross-cultural communication skills was more cognitively demanding.Overall,this study adds to the literature on politeness research and provides some implications for foreign language pragmatic instructions.

Parental Psychological Control and Internet Gaming Disorder Tendency:A Moderated Mediation Model of Core Self-Evaluation and Intentional Self-Regulation

Internet gaming disorder(IGD)among junior high school students is an increasingly prominent mental health concern.It is important to look for influences behind internet gaming disorder tendency(IGDT)in the junior high school student population.The present study aimed to reveal the explanatory mechanisms underlying the association between parental psychological control(PPC)and internet gaming disorder tendency among junior high school students by testing the mediating role of core self-evaluation(CSE)and the moderating role of intentional self-regulation(ISR).Participants in present study were 735 Chinese junior high school students who completed offline self-report questionnaires on parental psychological control,core self-evaluation,intentional self-regulation,and Internet gaming disorder tendency.Analyses were conducted via mediation and moderated mediation.The results showed that:(1)Parental psychological control was positively related to junior high school students’Internet gaming disorder tendency.Core self-evaluation,and intentional self-regulation were negatively related to junior high school students’Internet gaming disorder tendency,respectively.(2)Core self-evaluation partially mediated the relationship between parental psychological control and junior high school students’Internet gaming disorder tendency.(3)Intentional self-regulation moderated the association between parental psychological control and Internet gaming disorder tendency,as well as the relationships between parental psychological control and core self-evaluation and core self-evaluation and Internet gaming disorder tendency in the mediated model.Based on these findings,we believe that there is a need to weaken parental psychological control,strengthen junior high school students’core self-evaluation and intentional self-regulation,and to recognize the important role of parents as well as their children’s personal positive traits in the healthy development of junior high school students.

Trust beyond Technology Algorithms: A Theoretical Exploration of Consumer Trust and Behavior in Technological Consumption and AI Projects

In an era dominated by artificial intelligence (AI), establishing customer confidence is crucial for the integration and acceptance of AI technologies. This interdisciplinary study examines factors influencing customer trust in AI systems through a mixed-methods approach, blending quantitative analysis with qualitative insights to create a comprehensive conceptual framework. Quantitatively, the study analyzes responses from 1248 participants using structural equation modeling (SEM), exploring interactions between technological factors like perceived usefulness and transparency, psychological factors including perceived risk and domain expertise, and organizational factors such as leadership support and ethical accountability. The results confirm the model, showing significant impacts of these factors on consumer trust and AI adoption attitudes. Qualitatively, the study includes 35 semi-structured interviews and five case studies, providing deeper insight into the dynamics shaping trust. Key themes identified include the necessity of explainability, domain competence, corporate culture, and stakeholder engagement in fostering trust. The qualitative findings complement the quantitative data, highlighting the complex interplay between technology capabilities, human perceptions, and organizational practices in establishing trust in AI. By integrating these findings, the study proposes a novel conceptual model that elucidates how various elements collectively influence consumer trust in AI. This model not only advances theoretical understanding but also offers practical implications for businesses and policymakers. The research contributes to the discourse on trust creation and decision-making in technology, emphasizing the need for interdisciplinary efforts to address societal challenges associated with technological advancements. It lays the groundwork for future research, including longitudinal, cross-cultural, and industry-specific studies, to further explore consumer trust in AI.

The impact of work support and organizational career growth on nurse turnover intention in China

Objective:The paper aimed to explore the impact of work support and potential for career advancement on the nurse turnover intention in order to find an effective approach to curb high turnover.Methods:A cross-sectional survey of 526 nurses from eight teaching hospitals in Tianjin,China,was conducted to test the hypothesized multilevel model.The organizational career growth scale,work support scale,and the nurse turnover intention scale were used to measure the effect of organizational career growth and work support on nurse turnover intention.Finally,SPSS 17.0 and AMOS 17.0 software were used to analyse the relationship of these variables.Results:The score of the three scales nurse turnover,organizational career growth,and work support were 1.98±0.60,2.40±0.50,and 4.06±0.98,respectively.The total and direct effects of work support on turnover intention were
0.361(p<0.01)and
0.147(p<0.01),respectively.The indirect effect was
0.169 with a 95%bootstrap confidence interval of
0.257 to
0.102.Conclusion:The study showed a lack of work support negatively and directly associated with nurse turnover intention.Additional opportunities for career growth within the organization may strengthen the effect of work support and consequently increase the retention of qualified nursing staff.

The relationships among perceived organizational support,intention to remain,career success and self-esteem in Chinese male nurses

Aim:The purpose of this study was to investigate the relationships among perceived organizational support,intention to remain,career success and self-esteem in male nurses in China.Background:Recently,turnover is considered increasingly more normal.Male nurses could be stabilized by improving their perceived organizational support,career success and selfesteem.Design:A cross-sectional design was used in the present study.Method:405 full-time male Registered Nurses,from Grade A hospitals in Tianjin,were surveyed.Pearson correlation,Cronbach's alpha coefficient and structural equation modelling were conducted in the study.Results:The results illustrated that self-esteem mediated the relationship between perceived organizational support and career success.Career success mediated the relationship between self-esteem and intention to remain.Conclusion:Hospitals should pay more attention to the benefits of positive personality trait on working motivation.It is meaningful to enhance perceived organizational support,career success and self-esteem in the working process so that nurses are willing to stay in the current employment.

Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model

Intersections are quite important and complex traffic scenarios,where the future motion of surrounding vehicles is an indispensable reference factor for the decision-making or path planning of autonomous vehicles.Considering that the motion trajectory of a vehicle at an intersection partly obeys the statistical law of historical data once its driving intention is determined,this paper proposes a long short-term memory based(LSTM-based)framework that combines intention prediction and trajectory prediction together.First,we build an intersection prior trajectories model(IPTM)by clustering and statistically analyzing a large number of prior traffic flow trajectories.The prior trajectories model with fitted probabilistic density is used to approximate the distribution of the predicted trajectory,and also serves as a reference for credibility evaluation.Second,we conduct the intention prediction through another LSTM model and regard it as a crucial cue for a trajectory forecast at the early stage.Furthermore,the predicted intention is also a key that is associated with the prior trajectories model.The proposed framework is validated on two publically released datasets,next generation simulation(NGSIM)and INTERACTION.Compared with other prediction methods,our framework is able to sample a trajectory from the estimated distribution,with its accuracy improved by about 20%.Finally,the credibility evaluation,which is based on the prior trajectories model,makes the framework more practical in the real-world applications.