Validation Sample Research Articles

Impact of boundary-spanning search on firm innovation performance: a strategic knowledge integration perspective

PurposeThis study aims to explore the mechanism of boundary-spanning search on firm’s innovation performance under environmental dynamics from the perspective of strategic knowledge integration.Design/methodology/approachA survey was conducted among Chinese firm managers and R&D personnel, resulting in the collection of 315 valid samples. Hierarchical regression analysis was mainly adopted to demonstrate the hypothesized relationships, while the Sobel test and bootstrap method were used to further validate the mediating effects.FindingsThe results demonstrate that boundary-spanning search in different dimensions is a critical factor in the improvement of firm innovation performance (FIP). Two types of strategic knowledge integration are the main factors causing FIP and mediate the influence of boundary-spanning search on FIP. Furthermore, environmental dynamics moderate the relationship among boundary-spanning search, strategic knowledge integration and FIP.Practical implicationsManagers need to strengthen the boundary-spanning search for market and technical knowledge, which will promote firm innovative performance. Managers also need to implement strategic knowledge integration, which specifically includes using planned strategic knowledge integration to compensate for knowledge deficiencies, thereby achieving predetermined objectives; and using emergent strategic knowledge integration to update their understanding of internal and external environments, and to reset strategic objectives. In dynamic environments, managers should emphasize strategic knowledge management activities more.Originality/valueFrom a strategic management perspective, this study categorizes strategic knowledge integration into planned and emergent forms. By applying the logic of knowledge acquisition, integration and creation, it explores how boundary-spanning search affects FIP through strategic knowledge integration as the intermediary and the boundary conditions of environmental dynamics. This not only provides a deeper understanding of the nature and effects of boundary-spanning research but also enhances the theory of strategic knowledge management.

Protocol for tropical bed bug use as forensic tools

As blood-feeding insects that feed on human hosts, bed bugs could be used in forensic investigations if they are present at a crime scene with no apparent evidence. This study describes how tropical bed bugs (Cimex hemipterus) can be used as forensic tools to collect valid human DNA samples. Short Tandem Repeat (STR) analysis was performed on collected bed bug samples, whereby the results indicate that the obtained quantities of human DNA are mostly substantial to facilitate a comprehensive genetic profiling process.

Development and application of a low-cost and portable multi-channel spectral detection system for mutton adulteration

It is important to develop low-cost, fast and portable meat adulteration detection systems to ensure the meat authenticity and safety in complex market environments. A multi-channel spectral detection system for meat adulteration was developed in this study. The core hardware of the system mainly includes a designed spectral module and a Raspberry pi controller. The spectral module consists of three multi-channel spectral sensors and LED lamps with specific wavelengths, containing 18 channels covering a range of 410–940 nm. The software was developed based on PyQt5. After completing the construction of the system, the detection distance was discussed and determined to be 4 mm. Based on the spectral data collected by the developed system, the models for classifying pure mutton, pure pork, mutton flavour essence adulteration, colourant adulteration and adulterated mutton with pork were established and compared. Four intelligent optimisation algorithms were further used to improve the model performance. The results of the test set showed that the support vector classification (SVC) model optimised by a sparrow search algorithm (SSA) obtained the best classification performance, with an accuracy of 97.59% and a Kappa coefficient of 0.9696. After the SSA-SVC was incorporated into the sensor software, the system performance was evaluated using external validation samples. The overall accuracy of the system was 94.29%. The system took about 5.31 s to detect a sample, and the total weight of the system was 1.55 kg. Overall, the developed portable spectral system has considerable potential to rapidly and accurately discriminate adulterated mutton in the field.

Advancing CO2 Solubility Prediction in Brine Solutions with Explainable Artificial Intelligence for Sustainable Subsurface Storage

Underground CO2 storage is crucial for sustainability as it reduces greenhouse gas (GHG) emissions, helping mitigate climate change and protect the environment. This research explores the use of Explainable Artificial Intelligence (XAI) to enhance the predictive modelling of CO2 solubility in brine solutions. Employing Random Forest (RF) models, the study integrates Shapley Additive exPlanations (SHAP) analysis to uncover the complex relationships between key variables, including pressure (P), temperature (T), salinity, and ionic composition. Our findings indicate that while P and T are primary factors, the contributions of salinity and specific ions, notably chloride ions (Cl−), are essential for accurate predictions. The RF model exhibited high accuracy, precision, and stability, effectively predicting CO2 solubility even for brines not included during the model training as evidenced by R2 values greater than 0.96 for the validation and testing samples. Additionally, the stability assessment showed that the Root Mean Squared Error (RMSE) spans between 8.4 and 9.0 for 100 different randomness, which shows good stability. SHAP analysis provided valuable insights into feature contributions and interactions, revealing complex dependencies, particularly between P and ionic strength. These insights offer practical guidelines for optimising CO2 storage and mitigating associated risks. By improving the accuracy and transparency of CO2 solubility predictions, this research supports more effective and sustainable CO2 storage strategies, contributing to the overall goal of reducing greenhouse gas emissions and combating climate change.

Interlinkage between Integration Management, Risk Factors, and Project Management Performance in the Construction Industry of UAE

Introduction: The key purpose of the present study was to investigate the role of integration management, technical risk, social risk, and project management risk in determining the performance of United Arab Emirates’ (UAE) construction sector, grounded in the theoretical foundations of agency theory and resource-based view (RBV).Methods: Data was collected through a survey questionnaire from various respondents linked with the UAE construction industry. A final valid sample of 288 respondents was utilized for both descriptive and inferential statistics. Measurement model and structural model assessments were conducted using Smart PLS.Results: The study findings showed a significant positive impact of integration management on project management performance within the construction sector. Conversely, technical risk and project management risk were found to be adversely and significantly linked with project performance.Conclusions: For achieving peak performance in the construction industry, project managers and related individuals need to mitigate the adverse impacts of the identified risk factors. Additionally, promoting integration across different functional departments and project-related activities can enhance project management performance. The study also discusses meaningful policy implications, future research directions, and limitations.

Limited predictive value of the gut microbiome and metabolome for response to biological therapy in inflammatory bowel disease

ABSTRACT Emerging evidence suggests the gut microbiome’s potential in predicting response to biologic treatments in patients with inflammatory bowel disease (IBD). In this prospective study, we aimed to predict treatment response to vedolizumab and ustekinumab, integrating clinical data, gut microbiome profiles based on metagenomic sequencing, and untargeted fecal metabolomics. We aimed to identify predictive biomarkers and attempted to replicate microbiome-based signals from previous studies. We found that the predictive utility of the gut microbiome and fecal metabolites for treatment response was marginal compared to clinical features alone. Testing our identified microbial ratios in an external cohort reinforced the lack of predictive power of the microbiome. Additionally, we could not confirm previously published predictive signals observed in similar sized cohorts. Overall, these findings highlight the importance of external validation and larger sample sizes, to better understand the microbiome’s impact on therapy outcomes in the setting of biologicals in IBD before potential clinical implementation.

What determinants of COVID-19 vaccine hesitancy among Chinese nursing students? A cross-sectional study.

The coronavirus disease 2019 (COVID-19) continues to threaten human health, and health professionals, including nursing students, usually work in healthcare frontiers with a high risk of infection. Vaccination is currently one of the most effective preventive measures. This study aimed to explore the determinants of COVID-19 vaccine hesitancy in nursing students. In November 2022, a sample of undergraduate nursing students was recruited from several medical schools in Anhui Province, China, and an online cross-sectional survey was conducted using the questionnaire star platform (Wenjuanxin). A Chi-square test was used to explore vaccine hesitancy among nursing students with different social demographic characteristics and vaccine attitudes. Binary logistic regression analysis was then used to determine the influence factors of vaccine hesitancy among nursing students. A total of 1,090 valid samples were collected in this study. Of these, 27.06% (295) of nursing students reported COVID-19 vaccine hesitancy. The results showed "the need to go out of town recently" (OR = 0.670), "very confident that the outbreak could be controlled sustainably" (OR = 0.393), "feeling at risk of infection" (OR = 0.658), "not being worried/being generally worried about the vaccine's safety" (OR = 0.226 and OR = 0.686, respectively), and "not being worried about the vaccine's effectiveness" (OR = 0.411). These five factors are protective factors associated with COVID-19 vaccine hesitancy in nursing students. The factors "considering the country completely safe from an outbreak" (OR = 3.436), "considering themselves safe because others are vaccinated" (OR = 2.239), and "Agreeing that other protective measures can be relaxed after vaccination with the COVID-19 vaccine" (OR = 2.007) are risk factors associated with COVID-19 vaccine hesitancy among nursing students (P < 0.05). Overall, relatively few nursing students had COVID-19 vaccine hesitancy. Schools and relevant institutions still need to actively guide them to improve their confidence in the COVID-19 vaccine, strengthen the prevention and control measures of the epidemic, and improve their awareness of the crisis to improve the vaccination rate to reduce the COVID-19 vaccine hesitancy in nursing students.

Recognizing the Role of ChatGPT in Decision-Making and Recognition of Mental Health Disorders among Entrepreneurs

Nowadays, mental health disorders have become a significant concern for everyone. There are diverse reasons emanating from the workplace, business, and everyday environment. Therefore, there is a current need to use technology to detect and review their symptoms and causes. Accordingly, in this study, the researcher attempted to recognize ChatGPT’s role in decision-making and the recognition of mental health disorders among Egyptian entrepreneurs. The researcher used a quantitative approach in this study based its findings on 332 valid samples. The study's results through path analysis using Analysis of Moment Structures (AMOS) confirmed a positive effect of user perception of ChatGPT and trust in ChatGPT on the decision-making process and recognition of mental disorders. On the other hand, the study found a negative effect of the decision-making process in recognizing mental disorders. On the other hand, this study’s findings demonstrate that the decision-making process hurts recognizing mental health disorders. This study's results assist the development of policies to improve available digital services, such as ChatGPT or AI, to increase societal awareness. Moreover, by providing empirical evidence from Egyptian entrepreneurs in a developing country context, this study’s findings contribute to the existing psychology, digital technology, and management literature.

Research on the motivation for viewer addiction to live streaming: a cross-cultural investigation of China and the USA

PurposeWith the increasing popularity of live streaming, the exploration of online behaviors on live streaming platforms has become a research hot spot in academia. However, the literature lacks cross-cultural investigations into live streaming addictive (LSA) behaviors among viewers. To address this gap, based on stimulus-organism-response (SOR) theory and motivation theory, this study investigates how information seeking, sensation seeking and community commitment (stimulus) affect hedonic and utilitarian motivation (organism), which in turn influence viewers’ LSA behaviors (response). This study also compares American and Chinese viewers to understand how LSA behaviors differ across national cultures.Design/methodology/approachWe surveyed live stream viewers in both nations, collecting 758 valid samples including 377 Chinese respondents and 381 American respondents for use with the research model. A partial least squares (PLS) approach is employed for data analysis.FindingsThe results show that information seeking increases utilitarian motivation, sensation seeking increases hedonic motivation, and community commitment strengthens both hedonic and utilitarian motivation. Both hedonic motivation and utilitarian motivation positively affect LSA behaviors. In addition, based on cross-cultural analysis, we found that there was a significant mediating path common to the respondents in China and the USA (i.e. sensation seeking ? Hedonic motivation ? LSA).Originality/valueThe findings of this study make theoretical contributions to live streaming research and offer practical guidance for live streaming firms to formulate proper marketing strategies. Last, this study outlines several limitations and possible directions for future research.

Human papillomavirus-driven head and neck cancers in Japan during 2008-2009 and 2018-2019: The BROADEN study.

There is limited understanding of epidemiology and time trends of human papilloma virus (HPV)-driven head and neck cancers (HNC) in Japan, especially outside of the oropharynx. To assess HPV-driven HNC, a non-interventional study (BROADEN) of HNC patients diagnosed in 2008-2009 and 2018-2019 was conducted in Japan. Adult patients with oropharyngeal, nasopharyngeal, laryngeal, hypopharyngeal or oral cavity cancers were included in this study. HPV was centrally tested using p16INK4a immunohistochemistry, HPV-DNA PCR and HPV E6*I mRNA. HPV attributability required positivity in at least two tests (p16INK4a immunohistochemistry, HPV-DNA PCR, HPV E6*I mRNA) in the oropharynx, and HPV-DNA and HPV E6*I mRNA positivity for non-oropharynx sites. Nineteen hospitals included a total of 1108 patients, of whom 981 had valid samples. Men accounted for 82% of HNC diagnoses. Patients in the earlier cohort were younger and included a higher percentage of smokers. There was an increasing trend of HPV-driven oropharyngeal cancer over the last decade, from 44.2% to 51.7%. HPV attribution in nasopharyngeal cancers was 3.2% in 2008-2009 and 7.5% in 2018-2019; and 4.4% and 0% for larynx respectively. In total, 95.2% of HPV-driven HNC were attributed to HPV genotypes included in the 9-valent HPV vaccine being HPV16 the most prominent genotype. These results suggest that an epidemiologic shift is happening in Japan, with a decrease in smoking and alcohol use and an increase in HPV-driven HNC. The increasing trend of HPV-driven HNC in Japan highlights the need for preventive strategies to mitigate the rise of HPV-driven HNC.

Safety and immunogenicity of a next-generation live-attenuated yellow fever vaccine produced in a Vero cell line in the USA: a phase 1 randomised, observer-blind, active-controlled, dose-ranging clinical trial

Recent outbreaks between 2015-17 and production delays have led to a yellow fever vaccine shortage. Therefore, there is an urgent need for new yellow fever vaccines with improved production scalability. A next-generation live-attenuated yellow fever vaccine candidate (vYF), produced in a Vero cell line has shown similar immunogenicity to licensed yellow fever vaccines in preclinical studies. In this study, we aimed to report the safety and immunogenicity of vYF in human clinical trial participants. In this first in-human, phase 1 randomised, observer-blind, active-controlled, dose-ranging clinical trial conducted at a single centre in the USA (Walter Reed Army Institute of Research, Silver Spring, MD, USA), 72 healthy adults (aged 18-60 years), without a known history of flavivirus infection or vaccination were randomly assigned (1:1:1:1) using interactive response technology to receive one dose of either vYF at 4, 5 or 6 Log CCID50 or the licensed YF-VAX (18 individuals per group). The primary outcomes were safety, neutralising antibody (NAb) titres through D180 post-vaccination in the per-protocol analysis set (comprised of yellow fever-naive participants who received their intended vaccine and provided a valid post-vaccination blood sample), and occurrence, and level of yellow fever viraemia in each vaccine group through D14 post-vaccination. All vYF doses had a safety and tolerability profile similar to YF-VAX. The most frequently reported solicited injection site reactions (vYF groups vs YF-VAX group) were pain (22% [12 of 54 participants, 95% CI 12-36] vs 28% [five of 18 participants, 10-54]), and erythema (13% [seven of 54 participants, 5-25] vs 39% [seven of 18 participants, 17-64]), with headache (32% [17 of 54 participants, 20-46] vs 44% [eight of 18 participants, 22-69]) and malaise (26% [14 of 54 participants, 15-40] vs 33% [six of 18 participants, 13-59]) as the most frequently reported solicited systemic reactions. One grade 3 solicited reaction (erythema) reported in the YF-VAX group resolved spontaneously. No serious unsolicited adverse events or deaths were reported. Viraemia was transiently detected in 50 participants between D4 and D10 in all groups and was observed in more participants or for a longer time in the vYF 6 Log CCID50 and YF-VAX groups. All yellow fever-naive vaccine recipients across the study groups seroconverted yielding four-fold increase from baseline in yellow fever NAb titres measured by yellow fever microneutralisation assay by D28 and were seroprotected with yellow fever NAb titres of at least 10 [1/dil]). Overall, 100% (18 of 18 participants, 95% CI 82-100), 89% (16 participants, 65-99), 100% (18 participants, 82-100), and 94% (17 participants, 73-100) of participants in the vYF 4 Log, vYF 5 Log, vYF 6 Log CCID50 groups, and YF-VAX group, respectively, remained seroprotected through D180. vYF has a similar safety and immunogenicity profile to YF-VAX. In general, the vYF 5 Log CCID50 dose appeared to show optimal viraemia, safety, and immunogenicity, and was chosen for subsequent development. Sanofi.

Development and External Validation of a Motor Intention–Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-Driven Robotic Hand Training for Chronic Stroke

ObjectiveTo derive and validate a prediction model for minimal clinically important differences (MCIDs) in upper extremity (UE) motor function after intention-driven robotic hand training using residual voluntary electromyography (EMG) signals from affected UE. DesignA prospective longitudinal multicenter cohort study. We collected preintervention candidate predictors: demographics, clinical characteristics, Fugl-Meyer assessment of UE (FMAUE), Action Research Arm Test scores, and motor intention of flexor digitorum and extensor digitorum (ED) measured by EMG during maximal voluntary contraction (MVC). For EMG measures, recognizing challenges for stroke survivors to move paralyzed hand, peak signals were extracted from 8 time windows during MVC-EMG (0.1-5s) to identify subjects’ motor intention. Classification and regression tree algorithm was employed to predict survivors with MCID of FMAUE. Relationship between predictors and motor improvements was further investigated. SettingNine rehabilitation centers. ParticipantsChronic stroke survivors (N=131), including 87 for derivation sample, and 44 for validation sample. InterventionsAll participants underwent 20-session robotic hand training (40min/session, 3-5sessions/wk). Main Outcome MeasuresPrediction efficacies of models were assessed by area under the receiver operating characteristic curve (AUC). The best effective model was final model and validated using AUC and overall accuracy. ResultsThe best model comprised FMAUE (cutoff score, 46) and peak activity of ED from 1-second MVC-EMG (MVC-EMG 4.604 times higher than resting EMG), which demonstrated significantly higher prediction accuracy (AUC, 0.807) than other time windows or solely using clinical scores (AUC, 0.595). In external validation, this model displayed robust prediction (AUC, 0.916). Significant quadratic relationship was observed between ED-EMG and FMAUE increases. ConclusionsThis study presents a prediction model for intention-driven robotic hand training in chronic stroke survivors. It highlights significance of capturing motor intention through 1-second EMG window as a predictor for MCID improvement in UE motor function after 20-session robotic training. Survivors in 2 conditions showed high percentage of clinical motor improvement: moderate-to-high motor intention and low-to-moderate function; as well as high intention and high function.

Understanding the high-risk human papillomavirus prevalence and associated factors in the European country with a high incidence of cervical cancer.

High-risk human papillomavirus (HR-HPV) is a known cause of cervical cancer (CC). Latvia has a high incidence of CC compared with the average incidence in the European Union. This study aims to fill the data gap on the HR-HPV burden in Latvia, providing information on its prevalence and associated factors. The cross-sectional study was conducted from February 2021 to April 2022. Participants 25-70 years old visiting a general practitioner (general population) or those referred to a colposcopy clinic with changes in their cervical cytology (colposcopy population) collected vaginal self-sample and completed a paper-based questionnaire. Samples were analyzed with Cobas 6800 System (Roche) for HPV16, HPV18 and other HR-HPV (HPV31/33/35/39/45/51/52/56/58/59/66/68). Descriptive statistics for categorical variables were performed. The Chi-square test was used to determine for the statistical significance of differences in the proportions of the dependent variable between subgroups of the independent variable. Univariate and multivariate binary logistic regression were used to identify factors associated with positive HR-HPV status. Results were considered statistically significant at P < 0.05. A total of 1274 participants provided a valid sample. The prevalence of any HR-HPV infection was 66.8% in the colposcopy group and 11.0% in the general population. Factors associated with positive HR-HPV status were marital status single/divorced/widowed (vs. married/cohabiting) [adjusted OR (aOR) 2.6; P = 0.003], higher number of lifetime sex partners [aOR 5.1 (P < 0.001) and 4.0 (P = 0.001)] for six or more and three to five partners in the general population; in the colposcopy group, the statistical significance remained only for Latvian ethnicity (vs. other) (aOR 1.8; P = 0.008) and current smoking (vs. never) (aOR 1.9; P = 0.01). We documented a comparison to European Union HR-HPV infection burden in Latvia. Any HR-HPV positivity was significantly associated with sexual and other health behavior.

Development and validation of a COVID-19 vaccination prediction model based on self-reporting results in Chinese older adults from September 2022 to November 2022: A nationwide cross-sectional study

ABSTRACT It was common to see that older adults were reluctant to be vaccinated for coronavirus disease 2019 (COVID-19) in China. There is a lack of practical prediction models to guide COVID-19 vaccination program. A nationwide, self-reported, cross-sectional survey was conducted from September 2022 to November 2022, including people aged 60 years or older. Stratified random sampling was used to divide the dataset into derivation, validation, and test datasets at a ratio of 6:2:2. Least absolute shrinkage and selection operator and multivariable logistic regression were used for variable screening and model construction. Discrimination and calibration were assessed primarily by area under the receiver operating characteristic curve (AUC) and calibration curve. A total of 35057 samples (53.65% males and mean age of 69.64 ± 7.24 years) were finally selected, which constitutes 93.73% of the valid samples. From 33 potential predictors, 19 variables were screened and included in the multivariable logistic regression model. The mean AUC in the validation dataset was 0.802, with sensitivity, specificity, and accuracy of 0.732, 0.718 and 0.729 respectively, which were similar to the parameters in the test dataset of 0.755, 0.715 and 0.720, respectively, and the mean AUC in the test dataset was 0.815. There were no significant differences between the model predicted values and the actual observed values for calibration in these groups. The prediction model based on self-reported characteristics of older adults was developed that could be useful for predicting the willingness for COVID-19 vaccines, as well as providing recommendations in improving vaccine acceptance.

Peer or tutor? The congruity effects of service robot role and service type on usage intention

The invention of service robots has reduced the labor cost and improved enterprises' efficiency and service quality. However, it is still difficult to enhance consumers' intention to use robot-by-robot design efficiently. Based on social roles of anthropomorphic cues, service robots can be divided into peer (e.g., kind and amiable friends) or tutor (e.g., authoritative and professional experts) robots. From a matching perspective, this paper investigates (1) whether robot role and service type have an impact on consumers' intention to employ service robots in different ways, and (2) how cognitive trust and affective trust can play a mediating role during this process. In this paper, the authors conducted an online a scenario-based experiment and collected a valid sample of 332 consumers. The results show that the participants are more willing to apply the tutor robot in the scenario of utilitarian service, and the peer robot in the scenario of hedonic service. In addition, cognitive trust and affective trust have a matching mediation effect. Specifically, for the utilitarian service, cognitive trust mediates the effect of robot role on consumers' intention to adopt the robots, while the mediating effect of affective trust is not significant. As for the hedonic service, affective trust mediated the effect of robot role on the intention to use, whereas the mediating effect of cognitive trust is not significant.

Comparison of machine learning and conventional statistical modeling for predicting readmission following acute heart failure hospitalization

IntroductionDeveloping accurate models for predicting the risk of 30-day readmission is a major healthcare interest. Evidence suggests that models developed using machine learning (ML) may have better discrimination than conventional statistical models (CSM), but the calibration of such models is unclear. ObjectivesTo compare models developed using ML with those developed using CSM to predict 30-day readmission for cardiovascular and non-cardiovascular causes in HF patients. MethodsWe retrospectively enrolled 10,919 patients with HF (> 18 years) discharged alive from a hospital or emergency department (2004-2007) in Ontario, Canada. The study sample was randomly divided into training and validation sets in a 2:1 ratio. CSMs to predict 30-day readmission were developed using Fine-Gray subdistribution hazards regression (treating death as a competing risk), and the ML algorithm employed random survival forests for competing risks (RSF-CR). Models were evaluated in the validation set using both discrimination and calibration metrics. ResultsIn the validation sample of 3602 patients, RSF-CR (c-statistic=0.620) showed similar discrimination to the Fine-Gray competing risk model (c-statistic=0.621) for 30-day cardiovascular readmission. In contrast, for 30-day non-cardiovascular readmission, the Fine-Gray model (c-statistic=0.641) slightly outperformed the RSF-CR model (c-statistic=0.632). For both outcomes, The Fine-Gray model displayed better calibration than RSF-CR using calibration plots of observed vs. predicted risks across the deciles of predicted risk. ConclusionsFine-Gray models had similar discrimination but superior calibration to the RSF-CR model, highlighting the importance of reporting calibration metrics for ML-based prediction models. The discrimination was modest in all readmission prediction models regardless of the methods used.

Automated construction scheduling using deep reinforcement learning with valid action sampling

The growing demand for buildings and infrastructures requires optimal construction schedules under real-world complexities. This paper presents an automated scheduling optimization method for large construction projects, considering real-world constraints and the need for rescheduling. A Deep Reinforcement Learning (DRL) model with a Valid Action Sampling (VAS) mechanism is proposed to optimize schedules. The method integrates a Graph Convolutional Network (GCN) for feature extraction and includes a reward shaping mechanism to expedite convergence. The proposed method outperforms traditional methods with reduced project duration and runtime in both scheduling and rescheduling cases. This advancement benefits construction managers seeking efficient and flexible project management. The findings inspire future research into broader and more practical construction scheduling solutions utilizing DRL.

Effect of Social Support on Job Satisfaction and Organization Commitment: Employee Engagement as a Mediating Factor

Background- The issues of job engagement, satisfaction and social support are very crucial factors in service sector as employee being a major aspect for such organization. This study aimed to examine the mediating influence of employee engagement in between social support factors and job satisfaction relationship in IT companies of Kathmandu Valley. Methods- This study followed post positivist epistemology which consists of unbiased axiology, and numerical rhetoric assumption. In this study, descriptive and casual research design has been used. Two hundred and four valid samples were used for data analysis. Hierarchical regression was used to test the mediation hypotheses. Results- Correlation analysis indicated that the relationships among perceived supervisor support, peer support, perceived organization support, job engagement, organization engagement, job satisfaction and organization commitment are statistically significant (p&lt;.01) and the nature of the relationships are positive. Furthermore, hierarchical regression revealed that employee engagement is the significant mediator in between social support and employee outcomes; however, all the hypotheses were not neatly reproduced. Conclusion- This study suggested that support offered by colleagues, supervisor and organizational system to the employees are seems to be engaged in their work and organization that resulted commitment towards their organization and feel satisfied. Further, IT sectors could enhance organizational commitment and job satisfaction of employees by managing the support system through supervisor and peers which make them fully engaged and dedicated in their work as well. Novelty- The newness in this study is that the social support is the root cause of organizational engagement consisting of job engagement and organization engagement and it plays the major role for determining the job satisfaction and organization commitment.

Mechanisms of Media Persuasion and Positive Internet Word-of-Mouth Driving Green Purchasing Behavior: Evidence from China

As environmental issues intensify, sustainability development is becoming mainstream, with environmental topics gaining increasing attention in the media and online. Shifting consumer behavior in China toward green purchasing is crucial for mitigating environmental pollution and achieving sustainable, low-carbon consumption. This study constructed a theoretical model combining media persuasion (MP) and positive internet word-of-mouth (PIM) with green purchasing behavior (GPB), based on the Stimulus–Organism–Response (SOR) and persuasion theories, to explore consumer responses to environmental information campaigns. A total of 357 valid samples were collected through an online questionnaire survey and subjected to analysis using the structural equation model (SEM). The results indicate that MP, PIM, and environmental attitude (EA) significantly influence GPB. Specifically, EA partially mediates the relationship between MP, PIM, and GPB, while environmental knowledge (EK) negatively moderates the relationship between independent variables and EA. Additionally, EK moderates the mediating effect of EA. The findings highlight that the effective implementation of MPs and PIMs can facilitate the creation of positive EA, which stimulates consumer GPB. This is essential for promoting sustainable consumption. This research contributes to sustainability by providing insights and practical suggestions for developing green marketing strategies that support environmental goals.

The Willingness of New Power Industry to Accept Green Credit: Case of Guizhou Jinyuan Company

The objectives of this study / study are: (1) to explore the impact of information disclosure on the willingness to accept green credit; (2) to explore the impact of policy environment on the willingness to accept green credit; (3) to study the impact of perceived trust on the willingness to accept green credit; and (4) to provide more targeted and effective policies and measures for financial institutions and the new energy power industry. This study used a quantitative research method, the questionnaire data was collected as a quantitative method, and a total of 400 valid samples were collected from within Guizhou Jinyuan Group. Quantitative data were interpreted using descriptive data analysis, reliability test, validity test, exploratory factor analysis, confirmatory factor analysis, and structural equation model analysis. The results are as follows: (1) the positive correlation between information disclosure and perceived trust; (2) the positive correlation between information disclosure and the willingness to accept green credit; (3) the positive correlation between policy environment and perceived trust; (4) the positive correlation between policy environment and the willingness to accept green credit; (5) the positive correlation between perceived trust and the willingness to accept green credit.