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This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.

Multiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.

The study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.

This study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

COVID-19 has alerted all stakeholders to the serious challenges of biothreats to humanity and its progress. Tourism as a business has been one of the worst sufferers from the COVID-19 impacts and has been held responsible to an extent for the spread of the virus. The ‘containment, isolation, and restrictions’ of biosafety and biosecurity measures exposed the vulnerability of the tourism industry. The densely populated urban areas in India can always be hot spots for the spread of biovectors from similar biothreats in the future, with a more significant threat to employment from mitigation measures. This theoretical paper combines research from medical science, biology, tourism, and other related areas to develop a thesis that social scientists can add value in designing socially acceptable measures for scientific solutions recommended by experts from medical and other related fields. The general environment of health and hygiene in India needs to be made safer for its population and tourists as a first-level strategy to reduce the risks of biothreats. The next level strategy shall be dominated by science to suggest mitigation measures with parallel action by tourism stakeholders for a socially acceptable face of safety measures.

Masstige (mass-produced and affordable luxury) goods are receiving increasing literature-based attention. However, despite existing advances, insight into how different cultural backgrounds shape consumer perceptions, attitudes and behaviors toward masstige goods remains tenuous. Correspondingly, this study aims to examine the association of masstige luxury with customers’ love for and brand engagement with masstige products across cultures.

This study used a sample comprising 342 Indian and 354 Canadian masstige customers. Partial least squares structural equation modeling was used to analyze the data.

The results corroborate brand prestige and identification as key antecedents to customers’ love for masstige brands, which in turn impact their brand engagement. Surprisingly, the authors find that the effects of brand prestige and brand identification on brand love and customer brand engagement do not significantly differ between Indian and Canadian customers. However, the positive effect of brand identification and brand love on customer brand engagement is stronger for Indian customers than for Canadian customers.

This study addresses an important literature-based gap in understanding how cultural backgrounds shape consumer perceptions of masstige brands. It offers key theoretical and practical implications for masstige marketing.

Identifying differential effects among Indian and Canadian customers provides a foundation for tailoring marketing approaches in the masstige sector.

This study addresses a critical literature-based gap in understanding how cultural backgrounds shape consumer perceptions of masstige brands, offering key theoretical and practical implications for masstige marketing.

For close to two decades, the West African Monetary Zone (WAMZ) has been preparing to launch a second monetary union within the ECOWAS region. This study aims to determine the impact such a unionised monetary regime will have on financial stability as represented by the nonperforming loan ratios of Ghana in a counterfactual framework.

This study models nonperforming loan ratios as dependent on the monetary policy rate and the business cycle. The study then used historical data to estimate the parameters of the nonperforming loan ratio response function using an Autoregressive Distributed Lag (ARDL) approach. The estimated parameters are further used to estimate the impact of several counterfactual unionised monetary policy rates on the nonperforming loan ratios and its volatility of Ghana. As robustness check, the Least Absolute Shrinkage Selection Operator (LASSO) regression is also used to estimate the nonperforming loan ratios response function and to predict nonperforming loans under the counterfactual unionised monetary policy rates.

The results of the counterfactual study reveals that the apparent cost of monetary unification is much less than supposed with a monetary union likely to dampen volatility in non-performing loans in Ghana. As such, the WAMZ members should increase the pace towards monetary unification.

The paper contributes to the existing literature by explicitly modelling nonperforming loan ratios as dependent on monetary policy and the business cycle. The study also settles the debate on the financial stability cost of a monetary union due to the nonalignment of business cycles and economic structures.

Recognising the significance of international trade in economic growth, this research explores the drivers of exports in South Asian Association for Regional Cooperation countries from 2008 to 2021. The study employs the export demand model and the augmented exports supply model and utilises pooled time-series data. This study questions whether export supply decisions are based on traditional trade model factors, emerging trading realities or macroeconomic variables. The model based on fixed effects evaluates the connection between exports and their possible drivers. Traditional export supply models suggest determinants like production capacity, variable cost and relative pricing influencing South Asian export supply performance substantially. Changes in trade, for example, have a substantial impact on export supply, demonstrating that the trade liberalisation procedure promotes growth in exports, compression in imports and technological advancement. The worsening state of the energy industry and growing levels of corruption have proved to be significant deterrents to export supply decisions. The results verify foreign direct investment's positive and medium influence on the expansion of exports. Other variables, however, such as GDP and its growth, Official Development Assistance (ODA), development expenditure, indirect taxation, labour supply and the exchange rate of currencies, have a positive impact on the flow of exports. Furthermore, the data corroborate the notion that increased savings have a significant beneficial influence on the flow of exports. The study proposes that concerned governments examine their export policies and adopt new policies adapted in accordance with changing circumstances with the goal of increasing and enhancing the performance of exports.

The study explored various dimensions of overconfidence bias (OB) among retail investors in Indian financial markets. Further, these dimensions were validated through formative assessments for OB.

The study applied exploratory factor analysis (EFA) to 764 respondents to explore dimensions of OB. These were validated with formative assessments on 489 respondents by the partial least square path modeling (PLS-PM) approach in SmartPLS 4.0 software.

The major findings of EFA explored four dimensions for OB, i.e. accuracy, perceived control, positive illusions and past investment success. The formative assessments revealed that positive illusions followed by past investment success among retail investors played an instrumental role in orchestrating the OBs that affect investment decisions in financial markets.

The formative index of OB has several practical implications for registered financial and investment advisors, bank advisors, business media companies and portfolio managers, besides individual investors in the domain of behavioral finance.

This research provides a novel approach to provide a formative index of OB with four dimensions. This formative index can acts as an overview for upcoming researchers to investigate the OB of retail individual investors.

1. Overconfidence bias is an important predictor of retail investors' behavior

2. Formative dimensions of the overconfidence bias index.

3. Accuracy, perceived control, positive illusions and past investment success are important dimensions of overconfidence bias.

4. Modern portfolio theory and illusion of control theory support this study.

Overconfidence bias is an important predictor of retail investors' behavior

Formative dimensions of the overconfidence bias index.

Accuracy, perceived control, positive illusions and past investment success are important dimensions of overconfidence bias.

Modern portfolio theory and illusion of control theory support this study.

In the dynamic landscape of the modern business world, the pursuit of employee well-being and satisfaction with life (SWL) has gained prominence. Yet, few studies exist on SWL among Indian working professionals. This study aims to investigate the impact of inclusive leadership (IL) on the employees’ SWL. Moreover, employing a mediation approach, the study also reveals the mediating role of workplace inclusion (WI) and career satisfaction (CS) in the relationship between IL and SWL.

The study involves a cross-sectional quantitative approach, drawing upon a sample of 279 professionals belonging to the Indian IT sector by administering standardized questionnaires. Data were analyzed using PLS through SmartPLS4.0 software.

The findings reveal a fully complementary mediating role of WI and CS in the IL–SWL relationship, supporting the hypothesized mediation model.

This finding implies that leaders, with their inclusive behavior, can significantly influence employees’ SWL when accompanied by a sense of inclusiveness and CS among employees. The study offers valuable insights for organizations and leaders seeking to enhance employee satisfaction in the IT sector, emphasizing the significance of career-focused and inclusive practices in the workplace through leadership to promote a fulfilling workplace.

This study represents a pioneering effort to integrate the relationship between IL and SWL within the Indian context, going beyond the existing literature on WI and SWL. Additionally, the research model explores the influence of WI and CS in the IL–SWL relationship, a novel approach that has not been previously investigated.

Talent management is conducted to maximize an organization's overall performance and efficiency which helps to serve as a competitive advantage. Human resource management is a concept which includes human-related activities, but talent management is a strategy which helps to get new talent, develop their skill sets and provide better employee engagement and experience to retain the top potential employees in an organization. Improvement in recruiting and retention of a workforce results from a well-executed talent acquisition approach. In the 21st century, employee retention has become a primary concern for the organizations specially with work from home and hybrid models.

The workforce for tomorrow is going to be very different from what it has been. Technology is transforming the way people work within organizations. The workplace is rapidly evolving in terms of people and processes and is going through a lot of technological changes. The terminologies such as automation, artificial intelligence, augmented reality and block chain technologies are slowly becoming part of the workplace and everyday activities of the organization.

The challenges are many and especially post-pandemic organizations are going through some major changes such as a mindset shift of employees to take up more remote working opportunities, building virtual teams, increase in the gig economy workers (contractual workers) and a diverse workforce which makes it even more challenging for the organization to manage and retain talent.

Ukraine hosts thousands of international students for educational tourism, of which more than 18,000 Indian medical students were compelled to escape Ukraine under emergency conditions of war. This paper aims to examine their intention to return to Ukraine to complete their education based on an integrated theory of planned behaviour (TPB) framework with added constructs, i.e. risk perception, career anxiety, rescue and relief memory.

The data were collected from 26 February 2022 to 30 June 2022 in two phases and two modes. It was ensured that the respondents were strictly confined to Indian medical students who had travelled to Ukraine for educational tourism. SPSS 25 and AMOS 23.0 were used to analyse the data. The hypotheses proposed were statistically tested.

The analysis reveals that the extended TPB model resulted in a strong model and the empirical findings corroborate that the students’ attitude, subjective norms, perceived behavioural control and career anxiety significantly and positively influence the students’ revisit intention (RI) while risk perception and rescue and relief memory have a negative influence on the RI.

The study provides timely insights and implications to the Ukrainian tourism industry, particularly educational tourism business and medical institutions under the present turmoil, which can also act as blueprint research for destinations with a similar unstable political background.

The primary value of this research work is that it provides an understanding of the intention of medical students (educational tourists) towards revisiting the war-hit destination of Ukraine.

The purpose of this study is to investigate the impact of Inclusive Leadership (IL) and Organizational Justice (OJ) on employees’ Happiness at Work (HAW). Utilizing a mediation mechanism, the study additionally uncovers the mediating impact of Workplace Inclusion (WI).

The research involved a cross-sectional study with a quantitative methodology, collecting data from 311 employees working in IT sector firms in India by administering standardized questionnaires. Statistical analyses, including Partial Least Square Structural Equation Modelling using SmartPLS4.0, were conducted to examine the relationship between constructs.

The hypothesized mediation model was supported. WI mediated the relationship partially between OJ and HAW, whereas there is a full mediating effect of WI on the IL–HAW relationship. Overall, the study shows that by providing fair treatment, inclusive leaders promote inclusivity among employees, further enhancing HAW.

The study’s implications suggest that leaders, with their inclusive behaviour and fair practices, can have a significant positive impact on employees’ workplace happiness when accompanied by a sense of inclusivity among employees.

Organizations and leaders can utilize this study’s findings to promote inclusiveness and HAW, which can be a key to organizational growth and development in a post-pandemic era.

This study contributes to the research literature by addressing the unexplored relationship between IL, OJ and HAW. The exclusive as well as inclusive focus on the mediating role of WI adds new insights and enriches the understanding of the intricate conceptualization of the variables under study.