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The mental health issues faced by young people can significantly hinder a nation’s development. The purpose of this study is to integrate the Self-Determination Theory and the Theory of Planned Behavior to examine the factors influencing the utilization of mental health days among young individuals.

An online survey was conducted to collect data from young people.

The results indicated that more positive attitudes, favorable subjective norms and a stronger sense of perceived behavioral control regarding mental health days are all associated with a higher intention to use them. Young individuals who have used counseling resources are more likely to endorse the use of mental health days. Satisfaction of the three basic psychological needs of autonomy, competence and relatedness has a diminishing effect on both motivation and intention to request mental health days. However, there is a positive relationship between motivation and intention to apply for mental health days. When competence needs are less well satisfied, the motivation and, in turn, the intention to request mental health days are enhanced.

Shifting the leave framework from a disease-centered approach to a psychological leave model focused on mood adjustment can maximize the positive impact of mental health leave.

This study contributes to the theoretical understanding of the relationships between needs and motivation within the framework of self-determination theory. Furthermore, it integrates components from the theory of planned behavior to examine the influence of social pressures and self-efficacy on the intention to use mental health days. Mental health days can be seen as a cry for help from young adults. The utilization of mental health days serves as an effective means of managing one's mental state.

Knowledge sharing usually happens in a work group context, but it is rarely know how group leaders influence their members’ knowledge-sharing performance. Based on social exchange theory (SET) and the perspective of positive organizational behavior (POB), this study aims to argue that a group leader’s positive leadership (e.g. empowering leadership) can help group members develop positive psychological capital which can increase their knowledge sharing.

The authors conduct a multilevel analysis to explore the interrelationship among empowering leadership, psychological capital and knowledge sharing. The sample includes 64 work groups consisting of 537 group members, and empirical testing is carried out by hierarchical linear modeling.

The results show that empowering leadership in a work group has a direct cross-level impact on members’ knowledge sharing and that psychological capital partially mediates the relationship between empowering leadership and knowledge sharing. As a result, this study shows that group leaders with positive leadership can help their members develop better positive psychological resources, which should lead to better knowledge sharing.

Based on the multilevel perspective and SET, this is the first study to explore how group leaders’ empowering leadership influences members’knowledge sharing. Depending on integrating the POB perspective into SET, this study is also the first one that connects two emerging and important research issues – POB and knowledge sharing.

Despite the prevalence of destructive leadership in today’s workplace, the authors know little about its influence on knowledge sharing among employees. Using the conservation of resources (COR) theory, the authors examine how abusive supervision influences psychological capital and affects knowledge sharing. Further, the authors take a context variable (group trust) to explore its cross-level influence on the above causal relationship. The paper aims to discuss these issues.

This study conducts multi-level analyses of knowledge sharing. Abusive supervision and psychological capital are the determinants of knowledge sharing at the individual level. Group trust is considered a group-level variable with cross-level influences. The final sample for an empirical test conducted using hierarchical linear modeling includes 449 group members of 55 working groups.

Empirical results show that abusive supervision is negatively related to knowledge sharing. The results also indicate that psychological capital mediates the relationship between abusive supervision and knowledge sharing. At the group level, group trust has a direct cross-level impact on employees’ knowledge sharing and mitigates the relationship between abusive supervision and psychological capital.

Applying the COR theory, this is the first research to discuss how destructive leadership (i.e. abusive supervision) influences knowledge sharing. Based on the multi-level perspective, the authors also examine how group trust can have a cross-level impact on knowledge sharing and the relationship between abusive supervision and psychological capital.

Although the work group is the main context for knowledge exchange and combination in today’s organizations, few knowledge-sharing studies have been conducted at the group level. The purpose of this paper is to apply the concept of group social capital to determine how to promote knowledge sharing at the group level. The authors divided group social capital into two segments, conduits and resources, and argue that different group social capital conduits (i.e. work design in this study) lead to varied resources, which subsequently influence group knowledge sharing.

In this study, group social capital conduits included social interaction and task interdependence, and group social capital resources included group trust and a supportive climate for knowledge sharing. The authors conducted a survey on work groups in the high-tech industry using a sample of 86 work groups.

The results indicated that social interaction in a work group was positively related to group trust and that task interdependence was positively related to group trust and a supportive climate for knowledge sharing. Furthermore, group trust and a supportive climate for knowledge sharing were both found to have an influence on knowledge sharing.

Applying the concept of group social capital, this paper is the first research to discuss how group social capital conduits and resources influence knowledge sharing. The results of this study lead us to a better understand the relationship between group social capital and knowledge sharing.

Many bike rental organizations permit customers to pick-up bikes from one bike station and return them at a different one. However, this service may result in bike imbalance, as bikes may accumulate in stations with low demand. To overcome the imbalance problem, this paper aims to develop a decision model to minimize the total costs of unmet demand and empty bike transport by determining bike fleet size, deployments and the vehicle routing schedule for bike transports.

This paper developed a constrained mixed-integer programming model to deal with this bike imbalance problem. The proposed model belongs to the non-deterministic polynomial-time (NP)-hard problem. This paper developed a two-phase heuristic approach to solve the model. In Phase 1, the approach determines fleet size, deployment level and the number of satisfied demands. In Phase 2, the approach determines the routing schedule for bike transfers.

Computational results show the following results that the proposed approach performs better than General Algebraic Modeling System (GAMS) in terms of solution quality, regardless of problem size. The objective values and the fleet size of rental bikes allocated increase as the number of rental stations increases. The cost of transportation is not directly proportional to the number of bike stations.

The authors provide an integrated model to simultaneously deal with the problems of fleet sizing, empty-resource repositioning and vehicle routing for bike transfer in multiple-station systems, and they also present an algorithm that can be applied to large-scale problems which cannot be solved by the well-known commercial software, GAMS/CPLEX.

The historical data usually consist of overlapping reports, and these reports may contain inconsistent data, which may return incorrect results of a query search. Moreover, users who issue the query may not learn of this inconsistency even after a data cleaning process (e.g. schema matching or data screening). The inconsistency can exist in different types of data, such as temporal or spatial data. Therefore, this paper aims to introduce an information fusion method that can detect data inconsistency in the early stages of data fusion.

This paper introduces an information fusion method for multi-robot operations, for which fusion is conducted continuously. When the environment is explored by multiple robots, the robot logs can provide more information about the number and coordination of targets or victims. The information fusion method proposed in this paper generates an underdetermined linear system of overlapping spatial reports and estimates the case values. Then, the least squares method is used for the underdetermined linear system. By using these two methods, the conflicts between reports can be detected and the values of the intervals at specific times or locations can be estimated.

The proposed information fusion method was tested for inconsistency detection and target projection of spatial fusion in sensor networks. The proposed approach examined the values of sensor data from simulation that robots perform search tasks. This system can be expanded to data warehouses with heterogeneous data sources to achieve completeness, robustness and conciseness.

Little research has been devoted to the linear systems for information fusion of tasks of mobile robots. The proposed information fusion method minimizes the cost of time and comparison for data fusion and also minimizes the probability of errors from incorrect results.

The purpose of this study is to identify critical demographic variables that would significant influence each dimension of patient safety culture. Understanding nurses' attitudes toward patient safety is important for healthcare organizations to relentlessly improve medical quality and services for patients.

The internal survey data sets in 2015 and 2016 from nurses' viewpoints are used. Linear regression with forward selection is applied where nine demographic variables are the input variables, while each dimension of the Chinese version of safety attitudes questionnaire (SAQ) is the dependent variable.

Supervisor/manager is the most essential demographic variable that has significant impacts on six dimensions. Experience in organization is the other critical demographic variable.

Nurses who are in charge of supervisors/managers are more satisfied in six of eight dimensions. Nurses who have much experience in an organization tend to have less satisfaction in three dimensions. Therefore, hospital management should enhance the leader's effectiveness in engaging their subordinates' commitment.

The results enable the hospital management to pay much attention to two major demographic variables, namely supervisor/manager and experience in organization, in order to improve the patient safety culture based on the Chinese version of SAQ in this hospital. Moreover, supervisor/manager is a more critical demographic variable for nurses due to larger absolute values of standardized coefficients by linear regression with forward selection.

This paper aims to investigate bank credit policies and uncover yacht building finance assessment factors from bank credit policies toward the yacht industry.

This study’s questionnaire attempts to identify survey respondents’ degrees of awareness through difference analysis, and then uses entropy weighting and gray relational analysis to discover priority ranking order of bank credit assessment considerations from the perspective of Taiwan’s banking sector.

The research findings show that yacht builders have to review their ship financing application methods and improve shortcomings to meet banks’ credit granting requirements.

Banks emphasize yacht builders’ repayment ability to protect their depositors and shareholders.

This study empirically evaluates the effect of China’s 2016 Green Financial System (GFS) framework on corporate green development, focusing on the role of green investment in achieving sustainability.

This study uses a quasinatural experiment design to combine difference-in-difference and propensity score matching methods for analysis. It examines 799 polluting and 1,130 nonpolluting firms from 2013 to 2020, enabling a comprehensive assessment of the GFS framework’s influence.

This study affirms a statistically significant positive influence of the GFS framework on escalating green investment levels in polluting firms. Robust sensitivity analyses, encompassing parallel trend assessment, entropy balancing test, and alternative proxies, corroborate these findings. A mediation analysis identifies the implementation of an environmental management system as the potential underlying mechanism. A cross-sectional analysis identifies high financial slack, high profitability, mandatory CSR regulations, and marketization level as the influencing factors.

The study’s findings have critical implications for policymakers, regulators, and companies. Demonstrating the effectiveness of the GFS framework in driving green investment underscores the importance of aligning financial systems with sustainability goals.

This study contributes novel empirical evidence on the positive effect of China’s GFS framework on corporate green development. The quasinatural experiment design, coupled with comprehensive sensitivity analyses, strengthens the robustness of the findings.

Recent literature has proposed many theoretical methods to help decision makers choose an appropriate project delivery system (PDS) in a rational manner. None of these articles however systematically compare and systematize the available PDS selection methods and guide decision makers in choosing a method that best meets their PDS decision‐making circumstances. This paper aims to bridge this gap.

Four groups of PDS selection methods, namely, guidance (e.g. decision charts and guidelines), multi‐attribute analysis (e.g. multi‐attribute utility theory and analytical hierarchical process), knowledge‐ and experience‐based (e.g. case‐based reasoning), and mix‐method approaches are reviewed, compared and systematized.

The discussed methods vary in their underlying concepts, complexities of implementation and levels of required information. They also differ in the ways how decision makers' preferences are elucidated, expressed and measured. A conceptual framework is proposed to help decision makers match a PDS selection method with their decision‐making circumstances.

The paper highlights limitations of the discussed methods, and presents areas for future research.

This paper helps decision makers develop a fundamental understanding of the available PDS selection methods, and match a PDS selection method with their unique decision‐making circumstances. Using a suitable method will improve the decision‐making efficiency.