Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review.

Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.

When abusive supervision affects creative idea sharing: Understanding the role of employee cheating and organizational justice.

Getting employees to share their creative ideas has long been recognized as a vital source of organizational effectiveness. This research uses the conservation of resources theory to investigate how employee's creative idea sharing is affected by abusive supervision. Data for this research was collected from 209 employees and their immediate supervisors of generic nurses and medical dispensers of Southern Punjab public sector hospitals working under the Ministry of national health services regulation and Coordination. Data were then analyzed with the AMOS software package for simple regression and moderated mediation. This study found that with the increase in abusive supervision, employees develop cheating behavior, diminishing probability of sharing their creative ideas with coworkers. Along these lines, organizational justice moderates this relationship and attenuates the negative indirect effect of abusive supervision on creative idea sharing. The researchers recommended that organizations should develop training programs or coaching sessions for leaders to make them equip with essential interpersonal skills that can eradicate abusive supervision. Research implications, limitations, and future research directions are also discussed.

The nexus between quality of customer relationship management systems and customers' satisfaction: Evidence from online customers' reviews.

Customer Relationship Management (CRM) is a method of management that aims to establish, develop, and improve relationships with targeted customers in order to maximize corporate profitability and customer value. There have been many CRM systems in the market. These systems are developed based on the combination of business requirements, customer needs, and industry best practices. The impact of CRM systems on the customers' satisfaction and competitive advantages as well as tangible and intangible benefits are widely investigated in the previous studies. However, there is a lack of studies to assess the quality dimensions of these systems to meet an organization's CRM strategy. This study aims to investigate customers' satisfaction with CRM systems through online reviews. We collected 5172 online customers' reviews from 8 CRM systems in the Google play store platform. The satisfaction factors were extracted using Latent Dirichlet Allocation (LDA) and grouped into three dimensions; information quality, system quality, and service quality. Data segmentation is performed using Learning Vector Quantization (LVQ). In addition, feature selection is performed by the entropy-weight approach. We then used the Adaptive Neuro Fuzzy Inference System (ANFIS), the hybrid of fuzzy logic and neural networks, to assess the relationship between these dimensions and customer satisfaction. The results are discussed and research implications are provided.

Environmental MCS package and green intellectual capital influence environmental performance: a mediated-moderated perspective.

Nowadays, business is not only a financial entity. The environmental impact of rapid business growth necessitates solutions. This study considers natural resource orchestration theory (ROT) to study the impact of the environment management control system (MCS) package and green intellectual capital (IC) on environmental performance through the mediating role of top management support (TMS) and the interaction role of green business strategies. This study employed partial least squares structural equation modeling to analyze 358 questionnaires. Simple sampling was used to collect data from Pakistan manufacturing firms. The results revealed that the environmental MCS package significantly influences environmental performance directly and through TMS mediation. Still, the green IC does not influence environmental performance directly or through mediation in selected industries of Pakistan. Furthermore, the interaction of TMS and green business strategies significantly determines environmental performance. This study suggests managers use the environmental MCS package, green IC, top management support, and green business strategies in decision-making to determine environmental performance of manufacturing firms in Pakistan even though green IC has no influence on environmental performance. This study used natural ROT to impart the managerial role along with bundling firms' resources and strategies (i.e., environmental MCS package, green IC, top management support, and green business strategies) which is ignored in the resource-based view.

Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees.

The analysis of Electroencephalography (EEG) signals has been an effective way of eye state identification. Its significance is highlighted by studies that examined the classification of eye states using machine learning techniques. In previous studies, supervised learning techniques have been widely used in EEG signals analysis for eye state classification. Their main goal has been the improvement of classification accuracy through the use of novel algorithms. The trade-off between classification accuracy and computation complexity is an important task in EEG signals analysis. In this paper, a hybrid method that can handle multivariate signals and non-linear is proposed with supervised and un-supervised learning to achieve a fast EEG eye state classification with high prediction accuracy to provide real-time decision-making applicability. We use the Learning Vector Quantization (LVQ) technique and bagged tree techniques. The method was evaluated on a real-world EEG dataset which included 14976 instances after the removal of outlier instances. Using LVQ, 8 clusters were generated from the data. The bagged tree was applied on 8 clusters and compared with other classifiers. Our experiments revealed that LVQ combined with the bagged tree provides the best results (Accuracy = 0.9431) compared with the bagged tree, CART (Classification And Regression Tree) (Accuracy = 0.8200), LDA (Linear Discriminant Analysis) (Accuracy = 0.7931), Random Trees (Accuracy = 0.8311), Naïve Bayes (Accuracy = 0.8331) and Multilayer Perceptron (Accuracy = 0.7718), which demonstrates the effectiveness of incorporating ensemble learning and clustering approaches in the analysis of EEG signals. We also provided the time complexity of the methods for prediction speed (Observation/Second). The result showed that LVQ + Bagged Tree provides the best result for prediction speed (58942 Obs/Sec) in relation to Bagged Tree (28453 Obs/Sec), CART (27784 Obs/Sec), LDA (26435 Obs/Sec), Random Trees (27921), Naïve Bayes (27217) and Multilayer Perceptron (24163).

The Relationship between Energy Consumption, CO2 Emissions, Economic Growth, and Health Indicators.

The health and wellness of people through life expectancy, mortality rate improvement, and sustaining the productivity of labor contributes a lot to national income. Infrastructure development consumes energy and releases carbon dioxide at different stages of the construction process. The current study explores the nexus between CO2 emission, energy consumption, mortality, life expectancy, and GDP in the top five carbon-emitting countries by using time series data from 1975 to 2015. The study used a cointegration technique to find the long- and short-run relationships between study variables. The study also used a structural break test to identify the break time. The results of the correlation matrix show strong positive correlation between CO2 emissions and energy consumption. It also reflects a weak correlation with mortality and life expectancy in Japan and Russia. The results of the ADF test indicated that the series are stationary at first difference and provided evidence to use Johansen cointegration test for long- and short-run relationships between independent series. Vector error correction term and ECT method are used to find long-run relationships between cointegrated series and adjustment parameters. For the structural breaks of health indicators and energy consumption study, we used the Gregory Hanson structural break. Mortality rate and life expectancy rate of China, U.S., Russia, India, and Japan show relevant policy changes with economic policies of each country.

Leaders' ambition and followers' cheating behavior: The role of performance pressure and leader identification.

Purpose: We seek to understand why and how leaders' actions that are positive from organizational perspectives, drive to engage employees in cheating behaviors. Design/methodology/approach: The proposed mediated moderation model was tested in two separate studies, study 1 and study 2, with data collected from police officers and employees of Islamic banking respectively, and then analyzed with Mplus for random coefficient models for direct effects, indirect effects, and for mediated moderation. Findings: It was found that leaders' ambitions may enhance performance pressure on the subordinates, which in turn promotes their cheating behavior. Overall, we found that the traditional view of ambition theory only emphasizes good mechanisms such as motivation. However, to integrate with a social identity perspective, ambition would also cause pressure and pressure rather than motivation. Additionally, leaders' ambitions are more strongly and positively related to the performance pressure and cheating behaviors of employees when subordinates also have high leader identification. The findings of this research suggested that leaders' positive workplace behavior could also spawn subordinates' unethical behaviors. Practical implications: Through this research, we can help policymakers understand that leaders' positive desire in general and ambition, in particular, may not be necessarily associated with subordinates' positive behaviors. Our results revealed that internalized with performance pressure, the leaders' ambition is associated with subordinates' cheating behavior. The findings of this research will help policymakers understand what might be promoting unethical behavior of employees. The cheating behavior of employees is not a singular level phenomenon of subordinates, it could also be triggered by contextual factors. Therefore, in developing policies for reducing the chance of cheating at work, the policymakers should also focus on the contextual factors that might be promoting cheating. Originality/value: Ambitious leaders tend to demonstrate high performance, also, performance pressure literature focuses efforts of the employees toward high performance. The dark side of these lines of researches is still underexplored. We shifted the conventional focus of understanding to the positive side of ambition and performance pressure by explaining the potential cost in the form of employees' enhanced cheating behavior. The interplay between the relationship between leaders' ambition and subordinates' perception of leader identification also enhanced our understating about the boundary condition of the relationship between leaders' ambition, performance pressure, and cheating behavior of subordinates.

COVID-19 and sustainable development goals: A bibliometric analysis and SWOT analysis in Malaysian context.

The COVID-19 crisis has been a core threat to the lives of billions of individuals over the world. The COVID-19 crisis has influenced governments' aims to meet UN Sustainable Development Goals (SDGs); leading to exceptional conditions of fragility, poverty, job loss, and hunger all over the world. This study aims to investigate the current studies that concentrate on the COVID-19 crisis and its implications on SDGs using a bibliometric analysis approach. The study also deployed the Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach to perform a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact on Malaysia. The results of the study indicated the unprecedented obstacles faced by countries to meet the UN's SDGs in terms of implementation, coordination, trade-off decisions, and regional issues. The study also stressed the impact of COVID-19 on the implementation of the SDGs focusing on the income, education, and health aspects. The outcomes highlighted the emerging opportunities of the crisis that include an improvement in the health sector, the adoption of online modes in education, the swift digital transformation, and the global focus on environmental issues. Our study demonstrated that, in the post-crisis time, the ratio of citizens in poverty could grow up more than the current national stated values. We stressed the need to design an international agreement to reconsider the implementation of SDGs, among which, are strategic schemes to identify vital and appropriate policies.

Corporate Social Responsibility and Energy-Related Pro-Environmental Behaviour of Employees in Hospitality Industry.

Tourism and hospitality are at a crossroads. The growth and developmental potential of these industries indicate the economic benefits for an associated nation at one end. However, the environmental issues related to tourism and hospitality create challenges for the administration at another end. In most cases, a sheer amount of carbon emission in hospitality lies with energy consumption, especially electrical energy. However, past studies on environmental management have mainly focused on the supply side of energy (production) and left the terrain of the demand side (consumption by individuals) unattended. Recently, behavioral scientists have indicated that corporate social responsibility (CSR) actions of a firm can promote sustainable behavior among individuals, including employees. We tend to spark this discussion from an energy consumption perspective by investigating the relationship between CSR and energy-related pro-environmental behavior of employees (EPB) in the hospitality sector of a developing country (Pakistan). To understand the underlying mechanism of this relationship, this study proposes the mediating role of green intrinsic motivation (GIM) and the moderating role of human emotions, e.g., employee admiration (ADM). We developed a theoretical model for which the data were gathered from different hotel employees with the help of a questionnaire. We used structural equation modeling for hypotheses testing. The empirical evidence indicated that CSR significantly predicts EPB, and there is a mediating role of GIM. The study also confirmed that ADM moderates this relationship. The findings of this study will be helpful for hotel administration to understand the profound importance of CSR-based actions to promote energy-related sustainable behavior among employees, e.g., EPB. Other implications for theory and practice have been highlighted in the main text of this draft.

What is the impact of eWOM in social network sites on travel decision-making during the COVID-19 outbreak? A two-stage methodology.

Social media users share a variety of information and experiences and create Electronic Word of Mouth (eWOM) in the form of positive or negative opinions to communicate with others. In the context of the COVID-19 outbreak, eWOM has been an effective tool for knowledge sharing and decision making. This research aims to reveal what factors of eWOM can influence travelers' trust in their decision-making to travel during the COVID-19 outbreak. In addition, we aim to find the relationships between trust in eWOM and perceived risk, and perceived risk and the decision to travel. These relationships are investigated based on online customers' reviews in TripAdvisor's COVID-19 forums. We use a two-stage data analysis which includes cluster analysis and structural equation modeling. In the first stage, a questionnaire survey was designed and the data was collected from 1546 respondents by referring to the COVID-19 forums on TripAdvisor. Specifically, we use k-means to segment the users' data into different groups. In the second stage, Structural Equation Modeling (SEM) was performed to inspect the relations between the variables in the hypothesized research model using a subsample of 679 respondents. The results of the first stage of the analysis showed that three segments could be discovered from the collected data for trust based on eWOM source and eWOM message attributes. These segments clearly showed that there are significant relationships between trust and perceived risk, and between perceived risk and the decision to travel. The results in all segments showed that users with a low level of trust have a high level of perceived risk and a low level of intention to travel during the COVID-19 outbreak. In addition, it was found that users with a high level of e-trust have a low level of perceived risk and a high level of intention to travel. These results were confirmed in all segments and these relationships were confirmed by SEM. The results of SEM revealed that visual and external information moderated the relationship between eWOM length and trust, and experience moderated the relationship between trust and perceived risk. For the moderating role of gender, it was found that the perceived risk has a higher impact on the decision to travel in the female sample.

Reducing Healthcare Employees' Burnout through Ethical Leadership: The Role of Altruism and Motivation.

Globally, employee burnout (EBO) is a black swan in healthcare management. Previous organizational management literature shows that EBO was often misunderstood by assuming it as a personal issue. However, the new definition by the World Health Organization (WHO) clearly indicates that EBO is an occupational phenomenon that places responsibility on organizations to manage it. Although recent evidence suggests ethical leadership (ELP) style may be important to mitigate EBO, shockingly, such relationships were not tested in healthcare systems, especially in low- and middle-income countries. Filling this knowledge gap in the existing body of knowledge, this study aimed to investigate the ELP-EBO relationship. To explain the underlying mechanism of how ELP reduces EBO, this study included two psychological factors as a mediator and a moderator: altruism (AL) and intrinsic motivation (IM). The data were obtained from hospital employees via a self-administered questionnaire (n = 289, paper-pencil method). A hypothetical framework was designed and tested for empirical validation through structural equation modeling (SEM). Empirical evidence confirmed that ELP reduces the risk of burnout among hospital employees, and AL mediates this relationship. The results also confirmed the conditional indirect role of IM in the above proposed mediated relationship. This study's outcomes can help hospital administration deal with EBO's epidemic in an ELP framework. Other, different implications have also been discussed in detail.

Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology.

Online reviews have been used effectively to understand customers' satisfaction and preferences. COVID-19 crisis has significantly impacted customers' satisfaction in several sectors such as tourism and hospitality. Although several research studies have been carried out to analyze consumers' satisfaction using survey-based methodologies, consumers' satisfaction has not been well explored in the event of the COVID-19 crisis, especially using available data in social network sites. In this research, we aim to explore consumers' satisfaction and preferences of restaurants' services during the COVID-19 crisis. Furthermore, we investigate the moderating impact of COVID-19 safety precautions on restaurants' quality dimensions and satisfaction. We applied a new approach to achieve the objectives of this research. We first developed a hybrid approach using clustering, supervised learning, and text mining techniques. Learning Vector Quantization (LVQ) was used to cluster customers' preferences. To predict travelers' preferences, decision trees were applied to each segment of LVQ. We used a text mining technique; Latent Dirichlet Allocation (LDA), for textual data analysis to discover the satisfaction criteria from online customers' reviews. After analyzing the data using machine learning techniques, a theoretical model was developed to inspect the relationships between the restaurants' quality factors and customers' satisfaction. In this stage, Partial Least Squares (PLS) technique was employed. We evaluated the proposed approach using a dataset collected from the TripAdvisor platform. The outcomes of the two-stage methodology were discussed and future research directions were suggested according to the limitations of this study.

Advancing the debate on hotel employees' environmental psychology by promoting energy-saving behavior in a corporate social responsibility framework.

Considering the vulnerable climatic conditions in most parts of the planet, a successful transition toward a carbon-free future is a critical challenge worldwide. In this respect, around 35% of the world's total greenhouse gas emission (GHG) is associated with the power sector (especially electrical energy). To this end, a vast of electrical energy has been used by the people in buildings. Specifically, a significant amount of energy in buildings is used for heating, cooling, and ventilation. While the available literature highlights the importance of neat, clean, and green electrical energy for the decarbonization of society, a critical gap exists in such literature. That is, most of the literature under this stream deals with the supply side (production) of electrical energy, while the demand side (consumption at an individual level) was neglected. To bridge this critical knowledge gap, this study investigates how the CSR engagement of a hotel organization can promote the energy-related pro-environmental behavior (ERPEB) among the employees with the intervening effect of employees' environmental commitment (EMEC) and Green intrinsic motivation (GRIM). Further, the conditional indirect role of altruistic values was also tested in this study. The data were collected from different hotel employees in Pakistan with the help of a self-administered questionnaire. We tested the hypothesized relationship through structural equation modeling (SEM). The results confirmed that CSR can be a potential motivator to impact the ERPEB of employees, while EMEC and GRIM mediated this relationship significantly. The findings of this study also confirmed the conditional indirect role of altruistic values. These findings offer various theoretical and practical contributions which are conversed in detail.

Environmental Uncertainty, Environmental Regulation and Enterprises' Green Technological Innovation.

This paper examines the impact of environmental uncertainty and environmental regulation on enterprises' green technological innovation, using a panel data of Chinese A-share listed companies in Shanghai and Shenzhen from 2005 to 2019 to conduct an empirical study using an OLS model and Poisson regression model. We employ environmental complexity and environmental dynamism to measure environmental uncertainty, and we have the following findings: first, both environmental uncertainty and environmental regulation promote enterprises' green technological innovation, while environmental regulation has positive moderating effects on the relationship between environmental uncertainty and enterprises' green technological innovation; second, environmental complexity positively affects enterprises' green technological innovation, while environmental dynamism has negative effects on enterprises' green technological innovation; third, environmental regulation accentuates the relationship between environmental complexity and green technological innovation, while it weakens the relationship between environmental dynamism and green technological innovation.

Proposing a Robust Model to Reduce Employees' Turnover Intentions in an Ethical Leadership Framework: Empirical Evidence from the Healthcare Sector.

Employee turnover is expensive and disruptive for an organization. Studies have already mentioned that the economic cost of turnover is huge, ranging from 90% to 200% of the existing employee's salary. With an increase in turnover rate, the social fabric of an enterprise may be disrupted. Additionally, organizations with an increasing turnover are expected to lose intangible knowledge and skills, operational effectiveness, customer satisfaction, and product or service quality. In a healthcare context, an increasing turnover rate has more consequences than other sectors because the healthcare sector worldwide is already identified as a sector facing resource scarcity. Exacerbating the situation, current evidence suggests that employee turnover has been increasing globally in the healthcare sector. The literature suggests that an ethical leadership style may reduce employees' likelihood of quitting an organization. However, such literature is sparse in healthcare, especially from the perspective of a developing economy in the Global South, which is more resource-deficient than the Global North. To fill this knowledge gap, this study investigates the relationship between ethical leadership style and turnover intentions in the healthcare context of the Global South. This study also tests the mediating effect of intrinsic motivation and psychological contract fulfillment in the above-proposed relationship. Furthermore, the conditional indirect effect of resilience is also tested. The data are collected from the hospital employees through a self-administered questionnaire. The hypothesized relationships are tested through structural equation modeling. The empirical evidence indicates that ethical leadership reduces employees' turnover intentions significantly. The results further confirm the mediating and moderating effects of intrinsic motivation, psychological contract fulfillment, and resilience. These results have different theoretical and practical implications for the healthcare sector. The results especially highlight the role of ethical leaders in a hospital to deal with the challenge of turnover, which has been rising worldwide.

Environmentally Specific Servant Leadership and Employees' Energy-Specific Pro-Environmental Behavior: Evidence from Healthcare Sector of a Developing Economy.

Environmental issues are significantly rising worldwide. Addressing the environmental issues and preserving the biosphere is a critical matter of concern in this era. The sheer amount of total greenhouse gas (GHG) emissions in the world is related to the energy sector, especially electrical energy. A bulk of electrical energy is consumed by individuals in buildings for cooling and heating purposes. Prior researchers have emphasized employing clean and green energy sources to deal with environmental issues. The role of green energy from a decarbonization aspect is unchallengeable. However, a critical gap in most energy-related studies exists in the available literature. That is, most of the literature focuses on the supply side (the production) of energy, neglecting the critical issue lies with the demand side (consumption side). Energy data show that a sheer amount of electrical energy is wasted by individuals due to their inadequate energy consumption behavior. In this respect, a country's healthcare system uses a significant amount of electrical energy. In particular, hospital staff uses a bulk of electricity during patient treatment, care, and other service delivery operations. The critical aim of this study is to improve the energy-specific pro-environmental behavior (EPEB) of hospital employees in an environmentally specific servant leadership (ESL) framework. Specifically, the study was conducted in Pakistan, which is a developing country. This study also tests the mediating effect of green self-efficacy (GSE) and green perceived organizational support (GPOS) in the above-proposed relationship. The data for the current work were collected from hospital employees by employing a survey strategy (n = 316) from a developing country. Structural equation modeling was considered to analyze the data, which confirmed that a servant leader with environmental preferences could significantly drive the EPEB of employees (ß = 0.699), while GSE (ß = 0.138) and GPOS (ß = 0.102) mediated this relationship. The findings of this study can help the healthcare sector to improve its efforts toward de-carbonization by improving the energy consumption behavior of employees through ESL, GSE, and GPOS.

An Inclusive Leadership Framework to Foster Employee Creativity in the Healthcare Sector: The Role of Psychological Safety and Polychronicity.

Creativity at the level of employees is of utmost importance for every sector of an economy, with no exception to a healthcare system. The reason why employee creativity is important lies in the fact that employees have profound knowledge of their job and thus can serve as a source of meaningful innovation in an organization. Research shows that employee creativity is largely dependent on leadership. Corporate leaders significantly influence subordinates' behavior. However, with the economic development, globalization, and changing business environment, a traditional authoritative leadership style can no longer be effective in understanding employees' psychological needs to foster their creative behavior. In this regard, the role of inclusive leadership as an effective organizational management strategy was recently discussed in literature at different levels. It was also stated that an inclusive leader could foster employee creativity. However, such relationships in healthcare systems of developing economies have largely remained under-explored previously. We explored employee creativity in a healthcare context of a developing economy in an inclusive leadership framework to bridge such knowledge gaps. We also investigated the mediating roles of psychological safety and polychronicity in the above-stated relationship. We collected the data from hospital employees through a questionnaire (paper-pencil method). A hypothetical model was developed, which was tested through structural equation modeling in AMOS. Based upon the statistical outcomes, we found that an inclusive leadership style in a hospital can significantly foster employee creativity, whereas psychological safety and polychronicity mediate this relationship. This study offers different theoretical and practical insights, especially to a healthcare system. An important finding was that an inclusive leader can motivate the followers to be more creative. This finding is significant for a hospital because creative employees provide a hospital with a solid competitive base.

Impact of Sleep Deprivation on Job Performance of Working Mothers: Mediating Effect of Workplace Deviance.

The current study takes its philosophical roots from organizational behavior and psychology domains to investigate the impact of sleep deprivation on the job performance of mothers working in primary, secondary, and higher education institutions (HEIs) of Pakistan. It also examines the mediating role of workplace deviance in the relationship between sleep deprivation and the job performance of working mothers. The authors followed the non-probability convenience sampling technique to study the relationship between sleep deprivation, workplace deviance, and job performance. The structural analyses indicated that sleep deprivation has a significant negative impact on the job performance of working mothers and sleep-deprived individuals often tend to perform poorly at the workplace. Such workers are also more likely to engage in workplace deviant behaviors. Moreover, workplace deviance is also found to act as a mediating variable in the relationship between sleep deprivation and job performance. The present research bridges the literature gap on the rarely investigated factors, namely sleep deprivation and workplace deviance, and provide a detailed understanding of how these factors can influence the performance of working mothers, specifically in Pakistan.

Conceptualizing the Role of Target-Specific Environmental Transformational Leadership between Corporate Social Responsibility and Pro-Environmental Behaviors of Hospital Employees.

The healthcare sector throughout the world is identified for its outsized carbon footprint. Despite the mounting importance of employees' pro-environmental behavior (PEB) for decarbonization, the role of PEB in a healthcare context was less emphasized previously, especially in a developing country context. To address this knowledge gap, the current work was carried out to examine the relationship between a hospital's corporate social responsibility (CSR) initiatives and PEB with the mediating effect of environmental-specific transformational leadership (ESTL). At the same time, the conditional indirect effect of altruistic values (AV) was also considered in the above relationship. The data were collected through a questionnaire by employing a paper-pencil method from the hospital employees (n = 293). By considering the structural equation modeling, the hypothesized relationships were validated. The results indicated that CSR directly (ß1 = 0.411) and indirectly, via ESTL, (ß4 = 0.194) influenced the PEB of employees. It was also realized that A.V produced a conditional indirect effect in this relationship (ß5 = 0.268). This work tends to help a hospital to improve its environmental footprint through CSR and ESTL. Moreover, the current work also highlights the role of employees' values (e.g., A.V) to guide the environment-specific behavior of employees.

Predicting Parkinson's Disease Progression: Evaluation of Ensemble Methods in Machine Learning.

Parkinson's disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.