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Current research in the field of behavioural finance has attempted to discover behavioural biases and their characteristics in individual investors’ irrational decision-making. This study aims to find out how biases in information based on knowledge affect decisions about investments.

In step one, through existing research and consultation with specialists, 13 relevant items covering major aspects of bias were determined. In the second step, multiple linear regression and artificial neural network were used to analyse the data of 337 retail investors.

The investment choice was heavily impacted by regret aversion, followed by loss aversion, overconfidence and the Barnum effect. It was observed that the Barnum effect has a statistically significant negative link with investing choices. The research also found that investors’ fear of making mistakes and their tendency to be too sure of themselves were the most significant factors in their decisions about where to put their money.

This research contributes to the expansion of the knowledge base in behavioural finance theory by highlighting the significance of cognitive psychological traits in how leading investors end up making irrational decisions. Portfolio managers, financial institutions and investors in developing markets may all significantly benefit from the information offered.

This research is a one-of-a-kind study, as it analyses the emotional biases along with the cognitive biases of investor decision-making. Investor decisions generally consider the shadowy side of knowledge management.

Business models (BMs) are becoming increasingly crucial for value creation in the healthcare sector. The study explores the conceptualization and application of BM concepts within the healthcare sector and investigates their evolution in emerging economies (EEs) and developed economies (DEs). This study aims to uncover these two contexts' shared characteristics and unique variances through a comparative analysis.

The paper systematically investigates and consolidates the literature on healthcare by employing the antecedents, decisions and outcomes (ADO) framework and finally examines 71 shortlisted articles published between 2003 and 2022.

The recognition of the BM within healthcare is increasing, both in EEs and DEs. EEs prioritize value creation and capture through cost efficiency, while DEs focus on innovation. Key theories employed include a resource-based view, the network theory and the theory of innovation. Case studies are commonly used as a methodology. Further research is needed to explore the decisions and outcomes of BMs.

The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

Beyond comparing and highlighting gaps in BMs between EEs and DEs, benchmarking DE's healthcare business models (HBMs) helps healthcare organizations in EEs align their practices, mitigate risks and establish efficient healthcare systems tailored to their specific contexts. The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

The study analyzes HBMs using an SLR framework perspective and provides practical implications for academicians and practitioners to enhance their decision-making.

Taking instances from extant findings from the literature, the study aims to examine the community perception toward renewable energy (RE) off-grid (mini-grid/microgrid) intervention, the underlying rationales for engagement of communities in RE off-grid projects, the different alternatives/models to engage communities in various phases of RE off-grid project deployment.

The study has followed the structured literature review to explore the identified research question of the study.

Based on findings from the review, the framework for effective community engagement in RE mini-grid projects is suggested. Furthermore, the study also draws suggestions and implications for future research and practice.

Based on such understanding the present study offers the framework which suggests the steps for the engagement of the communities in the off-grid projects. The key steps are managing the perception of the community (including generation of awareness among the community), planning for the benefits of the community, linkage the sustainable development goals (SDG), planning for the inclusion of the community and measuring performance (in the line of social and economic criteria and SDG).

This study finds the gap in the literature on the nexus of community, off-grid energy projects and SDG. Following the findings from the scholars in this field, a few gaps in the policy and practice have been highlighted which could be useful for practitioners and policymakers in this area.

With the aid of bibliometric analysis, this study aims to show the state-of-the-art of research on the digital divide and identifies new areas for further investigation.

Performance analysis and science mapping were used in the study to analyse a sample of 3,571 studies that were published between 2018 and 2022. The “Title-Keyword-Abstract” search option was used to collect the anticipated publications data from the Scopus database. The gathered data were analysed using the common bibliometric indices to evaluate the research landscape. The science mapping tactics made use of the VOSviewer and Biblioshiny software.

The performance and science mapping analysis shows that recent research on the digital divide has not been sufficiently exposed and examined. The analysis discovered emerging topics, prolific authors and nations, affiliations, a network of collaboration among authors, countries and institutions, bibliographic coupling and keyword co-occurrence.

This work presents a state-of-the-art that has significant theoretical and practical ramifications for the existing digital divide literature. The methodologies and database used in the current study are more extensive.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

Increasing workforce diversity requires leadership to ensure employees retain their well-being. This study aims to examine how employees’ and managers’ co-creation of diversity in the workplace influences positive leadership (PL) style, which in turn affects employee well-being (EWB) positively.

Employees and managers of Australian businesses participated in a cross-sectional survey. EWB was regressed on PL style and diversity dimensions (DDs). Also, the mediation effect of PL style between DDs and EWB was tested.

Results suggest that Australian organisations appear to have more employee diversity with its partial impact on managers’ PL style, which then positively affects on employee well-being (EWB). Furthermore, the PL style partially mediated the relationship between DDs and EWB.

Managers will better understand workplace diversities and the key role that PL style can play in enhancing EWB.

This study will help improve employees' and managers' personal and social lives by developing a better understanding of health and well-being. It will have further economic impacts, such as higher organisational productivity.

This study fills the gap in the literature where PL style will positively affect EWB. Investigating the relationship between DDs, PL style and EWB using PERMA-profiler is a unique contribution.

The purpose of the study is to analytically as well as numerically investigate the weight of throughflow on the onset of Casson nanofluid layer in a permeable matrix. This study examines both the marginal and over stable kind of convective movement in the system.

A double-phase model is used for Casson nanofluid, which integrates the impacts of thermophoresis and Brownian wave, whereas for flow in the porous matrix the altered Darcy model is occupied under the statement that nanoparticle flux is disappear on the boundaries. The resultant eigenvalue problem is resolved analytically as well as numerically with the help of Galerkin process with the Casson nanofluid Rayleigh–Darcy number as the eigenvalue.

The findings revealed that the throughflow factor postpones the arrival of convective flow and reduces the extent of convective cells, whereas the Casson factor, the Casson nanoparticle Rayleigh–Darcy number and the reformed diffusivity ratio promote convective motion and also decrease the extent of convective cells.

Controlling the convective movement in heat transfer systems that generate high heat flux is a real mechanical challenge. The proposed framework proved that the use of throughflow is one of the most important ways to control the convective movement in Casson nanofluid. To the best of the authors’ knowledge, no inspection has been established in the literature that studies the outcome of throughflow on the Casson nanofluid convective flow in a porous medium layer. However, the convective flow of Casson nanofluid finds many applications in improving heat transmission and energy efficiency in a range of thermal systems, such as the cooling of heat-generating elements in electronic devices, heat exchangers, pharmaceutical practices and hybrid-powered engines, where throughflow can play a significant role in controlling the convective motion.

This paper aims to analyze the nature, magnitude and determinants of access to food among rural households in India during the COVID-19 pandemic.

The study is based on the World Bank’s Rural Impact Survey, which has documented the shocks of COVID-19 among 2,787 rural households across six states in India. The chi-square test and binary logistics regression have been used to analyze the data.

About 49.7 % of rural households have reported the incidence of food inaccessibility and shortage, and the majority of them reported a reduction in food intake during the COVID-19 outbreak. However, the magnitude of food accessibility varied across the states and the sociodemographic characteristics of the households. Furthermore, regression analysis indicates that family size, social category and occupation as sociodemographic variables and membership in self-help groups, wage employment under Mahatma Gandhi National Rural Employment Guarantee Act and Cash transfer under Pradhan Mantri Kisan Samman Nidhi and Pradhan Mantri Garib Kalyan Yojana as institutional support factors have a significant effect on access to food among rural households.

The findings of the study have far-reaching policy implications for developing an effective food distribution system in crisis situations like the COVID-19 outbreak. The study also provides directions for extending the research on determinants of access to food during crisis.

The study is based on a large survey data from the rural households in India and provides empirical evidence on access to food faced by rural communities during the COVID-19 lockdown.