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This study aims to examine the psycho-emotional and social experiences of caregivers of children with autism spectrum disorder. Various facets of the caregiving experience are explored, including the feelings and thoughts of the parents/caregivers, such as the resilience experienced in their journey, how they coped with the challenges and also their positive experiences.

In this study, these aspects of the caregiving experience are broadly probed using semi-structured interviews subjected to narrative analysis. Lastly, there is a focus on the role of therapist-led intervention, specifically, the Eye to I^© intervention model and its contributions to the parent/caregiver experience.

Findings from this study indicate that parents benefit from interventions that bridge gaps in skills and interpersonal communication which parents/caregivers feel they encounter in their day-to-day activities. Additionally, support groups for parents and caregivers could further address these issues.

This exploration reveals insights about the roles of societal structures and the caregiving journey.

The behavioural decision-making process of individuals highlights the importance of investors’ sentiment and their correlation with the real economy. This paper aims to contribute to the literature of behavioural finance by examining the influence of contextual factors on investment decision-making.

Using a questionnaire, a total of 445 valid responses were collected from March to May 2021 through online sources. The current study uses a technique of Fuzzy-analytical hierarchical process (AHP) to assign relative weights to various contextual factors influencing investment decision-making. Harman’s single factor test was used to check common method bias.

Results of the study reveal that accounting information, self-image/firm-image coincidence, and neutral information as the top-ranked factors in influencing investment decisions, whereas advocate recommendation and personal financial needs emerged as less important factors in influencing investment decisions.

The current study collects data from Indian stock market investors, which may limit the generalization of the study to India only. Moreover, this study is cross-sectional in nature, and there are numerous factors that are not part of the study but might significantly influence the investors’ decision-making process.

The research has implications for both academicians working in the area of behavioural finance and practitioners’ who are active in stock markets, more specifically dealing with retail investors and in the domain of personal finance. Also, the current study will accommodate different groups, i.e. policy makers, financial advisors, investors, investment professionals, etc. in carrying out their professional work.

The current study will provide a comprehensive overview of individual investor behaviour. To the best of the authors’ knowledge, the present study is one of its kind to use the Fuzzy-AHP technique for evaluating the relative ranks of contextual factors influencing investment decision-making.

In recent years, significant research has focused on the question of whether severe market periods are accompanied by herding behavior. As herding behavior is a considerable cause of the speculative bubble and leads to stock market deviations from their basic values it is necessary to examine the motivators which led to herding behavior among investors. The paper aims to discuss this issue.

In this study, the authors performed a two-phase analysis to address the research questions of the study. In the first phase, for text analysis NVivo software was used to identify the factors driving herding behavior among Indian stock investors. The analysis of a text was performed using word frequency analysis. While in the second phase, the Fuzzy-AHP analysis techniques were employed to examine the relative importance of all the factors determined and assign priorities to the factors extracted.

Results of the study depicted Investor Cognitive Psychology (ICP), Market Information (MI), Stock Characteristics (SC) as the top-ranked factors driving herding behavior, while Socio-Economic Factors (SEF) emerged as the least important factor driving herding behavior.

The current study was undertaken among stock investors from North India only. Moreover, numerous factors are not part of the study but might significantly influence the investors' herding behaviors.

Comprehending the influences of the different factors discussed in the study would enable stock investors to be more aware of their investment choices and not resort to herd behavior. This research enables decision-makers to understand the reasons for herd activity and helps them act accordingly to improve the stock market's performance.

The current study will provide an inclusive overview of herding behavior motivators among Indian stock investors. This study's results can be extremely useful for both academics and policymakers to gain some insight into the functioning of the Indian stock market.

Although India is seen as a key player in the global economy, it is still below its potential level of growth. In this age of globalism, integration with the global economy through trade and foreign investments fosters domestic growth. For India, although this integration has strengthened over the years, there are certain gaps that remain to be addressed. Though numerous studies in the literature have tried to find answers to these questions, an important aspect that has not been considered by these studies relates to India’s federal structure and the role of states in determining the aggregate economic outcome. As Foreign Direct Investment (FDI) inflows to India are concentrated in a few states, this paper aims to provide an assessment of the reasons behind this trend.

This paper aims to investigate the reasons behind the interstate differences with respect to FDI inflows in India. The analytical work undertaken for this paper is based on secondary data, collected and collated from various sources. The approach adopted for this paper includes a heat graph analysis to examine whether there is a clear pattern in terms of the state-specific factors for high FDI states versus the low FDI states. This data analysis is followed by an econometric estimation to gauge the impact of state-specific factors in determining the FDI inflows.

As per the secondary data–driven heat graph and econometric analysis, factors like industrial output, social sector expenditure, judicial quality, connectivity indicators, labor cost and availability of credit, act as differentiators between high and low FDI-receiving states. It then becomes imperative to bridge the gap between the two sets of states in terms of these specific factors. Implementation and success of policy interventions can only be derived at the state level and therefore needs more decentralized approach.

This paper tries to identify the reasons that are responsible for FDI inflows being concentrated in a few Indian states. This involves a comprehensive analysis of several variables to understand whether there is a clear pattern where high-FDI states are also in a better position with respect to these attributes. This effort to factor in the federal aspect of a macroeconomic indicator like FDI provides new dynamic to this area of work.

Introduction: Data play a very significant role in solving the problem faced at micro and macro levels. Financial inclusion and insurance penetration have been a major problem of developing economies. These two economic indicators can be strengthened with the emergence of data alchemy.

Purpose: The present research study is conducted with the objective of measuring the impact of technological infrastructure, data alchemist techniques, and regulatory environment on insurance penetration and financial inclusion.

Methodology: To meet the research objectives, data were collected through a random sampling technique from the insurance agents in Mumbai, which can be considered the heart of insurance in India. On the data collected, the partial least squares (PLS) algorithm was applied using smart PLS software. PLS is a statistical method used for predictive modeling and analysis of complex data with multiple variables.

Findings: The final results revealed a significant relationship between data alchemy techniques and financial inclusion. Also, a significant impact on the financial inclusion level of the regulatory environment is also recorded. However, in a developing country like India, currently data alchemy techniques are not significantly impacting insurance penetration.

Stock markets are considered as the largest and most important units for the development and growth of the economy. The present study attempts to provide a comprehensive view of factors influencing investment decision making process of stock market investors. A multi group analysis of gender is also carried out on the proposed model.

The data of 402 valid responses are collected through structured questionnaires from individual investors of North India. SPSS 23 is used to do the descriptive analysis and AMOS 22 is used to establish the validity of the constructs and for hypotheses testing. For performing multi group analysis, several invariance tests have also been conducted to check the robustness of the model.

The results reveal that all the factors such as firm image, accounting information, neutral information, advocate recommendation and personal financial needs significantly influence investment decision making concluding image of the firm being the most influential factor and advocate recommendation being the least influential factor for investment decisions. No significant differences between males and females were found.

The current study suffers from the limitation of restricted geographical area of North India. Moreover, there is also a scope to incorporate more demographic factors for predicting investment decisions.

This study incorporates a range of factors which covers all the aspects of investment decision making. This study also highlights the notion of signaling theory, thus contributing to the limited literature in Indian context.

This study compared approaches to school closures in four Latin American countries (Bolivia, Colombia, Ecuador, Peru), describing the impact on the health and educational wellbeing of school-age children and youth, and evaluating their approaches in regard to continuing education through the pandemic.

We collected 75 publicly available documents including scientific and gray literature (government documents and news releases), that referred to school closures and their impact on children’s health and wellbeing. We did thematic analyses using open, axial, and selective coding and applied the latest Health Promoting Schools standards and indicators to the findings.

Results showed that countries followed epidemiological reasons for prioritizing school closures while adopting some policies that abide by Health Promoting School principles. While they emphasized the need to reopen schools so that instruction could continue, school closures were among the longest in the world. The most significant impacts on wellbeing identified in the four countries were related to food security and mental health.

This study focused on a particular set of documents, and it may not capture the full spectrum of relevant information in different contexts or regions.

By comparing school closures approaches among four Latin American countries, this study highlights the importance of context-specific interventions. In a post-pandemic era, lessons learned from these experiences should help foster more resilient and inclusive educational systems and explore the paths forward for following the new Health Promoting Schools framework in the region.

Cross-country qualitative analyses on this topic are rare. This study adds to the knowledge base by eliciting lessons for future health education research and policy efforts.

Behavioral finance proposes that psychology of the individual plays a vital role in investment decisions. Therefore, this study aims to examine the influence of one of the important disciplines of psychology, i.e. personality on investment decision-making by incorporating financial satisfaction as an intervening variable and gender as a moderator.

The data of 406 valid responses were collected through structured questionnaires from individual investors of Indian stock market and analyzed using structural equation modeling. Several invariance tests were also conducted to perform the multigroup analysis of gender on the mediated model.

The results revealed that extraversion, agreeableness, conscientiousness and neuroticism significantly influence investment decision-making through financial satisfaction. While financial satisfaction significantly mediates the indirect relationships between personality traits and investment decision-making for both males and females, no significant differences among males and females were found in the mediated model.

The current study covers a limited geographical area of North India. In addition to this, it is cross-sectional in nature and incorporates only limited factors for predicting investment decisions.

The study possesses numerous significant implications for financial practitioners, advisors, investors, academicians and researchers in the field of behavioral finance.

This study suggests a moderated mediation approach, which incorporates financial satisfaction as a mediator and gender as a moderator. To the best of the authors’ knowledge, so far, no study has been conducted in this context, and it will enhance the understanding of investment decisions of individual investors.

This paper aims to cognizance consumers' intention to participate in collaborative consumption (CC). Also, the gender difference regarding the above is examined.

To quantify the consumers’ intention to participate in CC cross-sectional survey method has been used. In total, 333 potential consumers selected through convenience sampling participated in the survey. The study used the capabilities of the structured equation modelling technique to validate the proposed research model.

Except for hedonic motives, all other drivers such as reputation, economic benefits, sustainable motives and trust have a significant influence on the intention to participate in CC. The effect of gender was found on the relationship between Trust and Intentions only.

This study can be used as a guiding path in the domain of CC for practitioners, marketers, startups and policymakers as the opinion of potential users has been reported. The results of the study highlight that the consumers’ interest in CC participation and social reputation are the most influential drivers of intention to participate in CC. Marketers should design their strategies in such a way that the individual should feel like a social hero rather than just a responsible consumer while participating in CC.

The present study contributes to the literature by examining the intention to participate in CC through the lens of self-determination theory (SDT), specifically in the Indian context. The authors have also extended the SDT by adding a trust factor that is best to their knowledge not integrated till now. The present study integrated cognitive, economic, psychological and relational aspects to understand CC behavior.