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This study aims to examine investors' reactions to bad financial news (IRBFN) based on complex financial accounting disclosures (CFAD) as well as how investors' herding behavior influences investor reactions in United Arab Emirates (UAE) project-based organizations (PBOs).

The primary data collection was furnished via online questionnaires, and 310 completed questionnaires were analyzed using structural equation modelling (SEM), moderation analysis, multiple regression simulations and path analysis.

The study shows that four out of the five CFAD dimensions observed – investors’ relations (IR), board and management structure, transparency disclosure and other disclosure channels – have a direct influence on investor's reactions to bad financial news, with the exception of “external auditing and audit service”. In addition, investor herding has a moderation impact on the relationship between CFAD and IRBFN.

There is a possibility that the broad view of the results may be limited by the size of the research sample. The paper's findings should therefore be authenticated at an intercontinental level with the same conceptual framework in other nations.

The purpose of modeling stakeholders' decision-making process is to improve their decisions and to control their reactions that may negatively affect PBOs in the UAE.

This research contributes to planned behavior theory and agency theory in the UAE context, both of which are empirically tested.

The Purpose of the study was to investigate the construct validity and internal consistency of the LaMI among staff in the context of elderly care in Sweden.

Questionnaire data from a longitudinal study of staff working in elderly care were used. Data were collected using the Leadership and Management Inventory. First data collection was for explorative factor analysis (n = 1,149), and the second collection, one year later, was for confirmatory factor analysis (n = 1,061).

The explorative factor analysis resulted in a two-factor solution that explained 70.2% of the total variance. Different models were tested in the confirmatory factor analysis. The final model, a two-factor solution where three items were omitted, showed acceptable results.

The instrument measures both leadership and management performance and can be used to continually measure managers’ performances as perceived by staff to identify areas for development.

This paper aims to identify the kind of internal climate leaders should offer health-care professionals to promote a patient safety culture in public hospitals managed by social health organizations in Brazil.

Two surveys were applied to health-care professionals working at three Brazilian public hospitals. The internal climate survey reached 1,013 respondents, and the patient safety culture survey reached 1,302 participants. Both factor and regression analyses were used to analyze the study model and determine how internal climate influences patient safety culture.

Results indicate that to promote a patient safety culture among health-care professionals, leaders should generate an internal climate based on trust to foster pride in working in the hospital. Possibly, the trust dimension is the most important one and must be developed to achieve job satisfaction and provide better services to patients.

All the hospitals studied were managed by the same Organização Social de Saúde. Due to the limited responses concerning the respondents’ profiles, demographic variables were not analyzed.

This research reveals that the trust and pride dimensions can most strongly influence a positive patient safety culture, helping hospital leaders face this huge managerial challenge of consistently delivering high standards of patient safety.

This research studies the promotion of a patient safety culture in public hospitals managed by social health organizations, characterized by greater flexibility and autonomy in health-care management and by a greater need for accountability.

This study aims to measure the status of rural household food security across regions using multidimensional indicators. It also aims to identify the determinants of rural household food security in Ethiopia.

The study adopted descriptive and explanatory designs. It used data from the fourth wave of the Ethiopian socioeconomic survey that has 3,115 respondents. The authors constructed household food security index using variables that capture availability, access, utilization and stability dimensions of food security. The authors categorized households into relative food security groups, namely, alarming and moderately food insecure, as well as moderately and highly food secure. Beta regression model, which is widely used to analyze response variables that assume values between 0 and 1, is used to estimate the determinants of food security.

The study finds that 77.7% of rural households are food insecure. Of this, 90% are moderately food insecure. Regional variations in magnitude of food security showed that Harari, Gambella and Benshanguel Gumuz regional states are relatively better-off than other regions in Ethiopia. The study identified sex, education level, marital status, location and wealth status of households as significant determinants of food security.

This study sheds light on regional variations in multidimensional food security in Ethiopia. It thus challenged previous estimates of food security using uni-dimensional indicator. It highlighted the need for region-specific analysis of determinants and a follow up of tailored regional interventions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0139

The purpose of this paper is to calculate the financial inclusion index and analyze its dynamics in developing countries.

The authors use the two-stage principal component analysis (PCA) method and consider financial technology innovations to improve the accuracy of the financial inclusion index.

The authors found a downward trend in the financial inclusion index in most developing countries over the study period. The authors also found that a high financial inclusion index is linked to high scores in the Doing Business and high business climate regulation ranking. In addition, the authors observed that the rates of low financial inclusion in developing countries are due to low utilization of and unequal access to financial services.

The analysis suggests that policymakers in developing countries could invest in digital infrastructure to extend access to financial services in remote areas. They could also encourage financial innovation, particularly in financial technologies, by adopting flexible regulatory frameworks. Promoting the financial inclusion of marginalized groups through targeted initiatives tailored to their needs is another solution. They could also encourage the use of financial services by raising awareness and educating populations through training programs. Finally, to improve the business climate, governments could simplify administrative procedures and promote transparency and legal stability.

Unlike previous studies, the use of the two-stage PCA method and the consideration of financial technology (Fintech) innovations such as mobile money in the determinants of the financial inclusion index improve the accuracy of the index.

This study empirically demonstrates a contradiction between pillar 3 of Basel norms III and the designation of Systemically Important Banks (SIBs), also known as Too Big to Fail (TBTF). The objective of this study is threefold, which has been approached in a phased manner. The first is to determine the systemic importance of the banks under study; second, to examine if market discipline exists at different levels of systemic importance of banks and lastly, to examine if the strength of market discipline varies at different levels of systemic importance.

This study is based on all the public and private sector banks operating in the Indian banking sector. The Gaussian Mixture Model algorithm has been utilized to classify banks into distinct levels of systemic importance. Thereafter, market discipline has been observed by analyzing depositors' sentiments toward banks' risk (CAMEL indicators). The analysis has been performed by employing the system Generalized Method of Moments (GMM) to estimate models with different dependent variables.

The findings affirm the existence of market discipline across all levels of systemic importance. However, the strength of market discipline varies with the systemic importance of the banks, with weak market discipline being a negative externality of the SIBs designation.

By employing the Gaussian Mixture Model algorithm to develop a framework for categorizing banks on the basis of their systemic importance, this study is the first to go beyond the conventional method as outlined by the Reserve Bank of India (RBI).

This paper aims to present a set of processes for obtaining the global spraying trajectory of a cold spraying robot on a complex surface.

The complex workpiece surfaces in the project are first divided by triangular meshing. Then, the geodesic curve method is applied for local path planning. Finally, the subsurface trajectory combination optimization problem is modeled as a GTSP problem and solved by the ant colony algorithm, where the evaluation scores and the uniform design method are used to determine the optimal parameter combination of the algorithm. A global optimized spraying trajectory is thus obtained.

The simulation results show that the proposed processes can achieve the shortest global spraying trajectory. Moreover, the cold spraying experiment on the IRB4600 six-joint robot verifies that the spraying trajectory obtained by the processes can ensure a uniform coating thickness.

The proposed processes address the issue of different parameter combinations, leading to different results when using the ant colony algorithm. The two methods for obtaining the optimal parameter combinations can solve this problem quickly and effectively, and guarantee that the processes obtain the optimal global spraying trajectory.

Despite the significant interest from researchers and practitioners, the literature on project value measurement from the perspective of the customer is non-existent. This study aims to address this gap by developing and validating a scale to measure project value through a customer lens called the customer perception of project value scale.

A list of items was initially generated based on 762 sample responses through a systematic review of the literature and with the participation of specialists. Exploratory and confirmatory factorial analyses and structural equation modelling were used to develop and validate the scale.

The authors formulated a four-dimension scale. The dimensions used to measure the second-order construct are customer centrality, process, delivery and cost-benefit. This was validated using a nomological structure.

The non-consensual nature of what is value in projects restricts the results of this study to the context of a specific group of stakeholders only, that is, the consumers of the projects. The authors also see limitations in the absence of competing scales, which do not allow the comparison of the instrument with alternative measures.

This study allows project managers and other professionals to measure a project’s perceived value from the customer’s point of view and manage the improvement of this perception.

To the best of the authors’ knowledge, this is the first study to propose a scale to measure project value, which advances the literature on project management and value and contributes to academic knowledge and practice by measuring project value from the customer standpoint.

This study aims to construct a mental health service system for middle school students in the post-COVID-19 era with the framework of Six Sigma DMAIC (define, measure, analyze, improve and control) and analyze the influencing factors of the mental health service system to study the implementation strategies of quality-oriented mental health services in middle schools.

This study was conducted in Tianjin, China, from September to November 2022, and 350 middle school students from Tianjin Public Middle School were selected as subjects. A questionnaire survey was used to collect data. In this study, the Six Sigma DMAIC method, sensitivity analysis method, exploratory factor analysis and principal component analysis were used to analyze the mental health services provided to middle school students.

Based on the Six Sigma DMAIC framework, this study indicates that the contribution rate of the mental health service process factor is the largest in the post-COVID-19 era. The mental health cultivation factor ranks second in terms of its contribution. Mental health quality and policy factors are also important in the construction of middle school students’ mental health service system. In addition, the study highlights the importance of parental involvement and social support in student mental health services during the post-COVID-19 era.

To the best of the authors’ knowledge, a study on middle school students’ mental health in the post-Covid-19 era has not yet been conducted. This study developed a quality-oriented mental health system and analyzed the influencing factors of mental health for middle school students based on data analysis and the Six Sigma DMAIC method.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.