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Progress accomplished by the disabled entrepreneurs on the fronts of profits, turnover, return on investment (ROI), employees engaged, capital employed and diversification shall be studied and prevalence of gender differences in such progress shall be assessed.

The proposed research is descriptive in nature, based on primary data, collected by personally administering a well-structured interview schedule to 201 disabled entrepreneurs in Puducherry selected using a snowball sampling technique. Data collected has been analyzed using SPSS 21, using the tools of mean, one-way ANOVA, factorial ANOVA and chi-square (χ²) analysis.

The prevalence rate of entrepreneurship among female disabled is very low. Female disabled entrepreneurs manage higher turnover than their male counterparts and manage insignificantly higher progress in terms of capital employed, while male disabled entrepreneurs have managed insignificantly higher progress in terms of profits, diversification and ROI. Illiterate disabled, both men and women, struggle to manage decent turnover while the better educated manage better turnover.

This paper has highlighted the low prevalence rate of entrepreneurship among women disabled though the fewer women disabled entrepreneurs are performing better than their male counterparts in operating their business.

The findings of this paper may be taken as base for formulation of effective government policies in empowering disabled persons in general and women disabled in particular.

This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values and irrelevant features. The least square estimator and relief algorithm have been used for imputing the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description for each class that can be used to classify subsequent instances with no known class label. The method is evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental results confirm that our approach can be a promising tool towards constructing a classifier from the databases with missing values and irrelevant attributes.

The purpose of this study is to determine the impact of community-based and driven approaches during the lockdowns and early periods of the pandemic. The study examines the impact and perceptions of the state-led intervention. This would help to discover a better approach for postpandemic interventions and policy responses.

This article used the inductive method and gathered its data from surveys. In search of global opinions on COVID-19 responses received in communities, two countries in each continent with high COVID-19 infection per 100,000 during the peak period were chosen for study. In total, 13 community workers, leaders and members per continent were sampled. The simple percentile method was chosen for analysis. The simple interpretation was used to discuss the results.

The study showed that poor publicity of community-based interventions affected awareness and fame as most were mistaken for government interventions. The study found that most respondents preferred state interventions but preferred many communities or local assessments of projects and interventions while the projects were ongoing to adjust the project and intervention as they progressed. However, many preferred community-based and driven interventions.

State secrecy and perceived opposition oppression limited data sourcing for this study in countries where state interventions are performed in secret and oppression of perceived opposition voices limited data collection in some countries. Thus, last-minute changes were made to gather data from countries on the same continent. An intercontinental study requires data from more countries, which would require more time and resources. This study was affected by access to locals in remote areas where raw data would have benefited the study.

The absence of data from the two most populous countries due to government censorship limits access to over a third of the global population, as they make up 2.8 out of 7 billion.

The choice of two countries in each continent is representational enough, yet the absence of data from the two most populous countries creates a social identity gap.

The survey collected unique and genuine data and presents novel results. Thus, this study provides an important contribution to the literature on the subject. There is a need for maximum support for community-based interventions and projects as well as global data collection on community-based or driven interventions and projects.

The economic development of Bangladesh is heavily reliant on the banking industry, yet it faces numerous hurdles, including liquidity issues, capital shortages, non-performing loans, inefficiencies and so on. Therefore, this study investigated the performance and efficiency of scheduled banks (state-owned, private commercial, foreign commercial and specialized banks) operating in Bangladesh.

The research was conducted using secondary data from annual reports of banks. The CAMELS rating system and Data Envelopment Analysis (DEA) methods were employed to measure the performance and efficiency of banks, respectively.

In the overall bank rankings, results revealed that foreign commercial Standard Chartered Bank and state-owned Sonali Bank Limited came in first and last position, respectively. Among the four categories of banks, foreign commercial banks were the best performer, while state-owned banks were the worst. Only two banks, i.e. Citibank NA and HSBC Bank, were scale efficient while the remaining banks were inefficient. In terms of performance and efficiency, state-owned and specialized banks were deemed wanting.

This study proposes recommendations to the policymakers that could lead to more effective tactics for improving the banking industry's performance and efficiency.

As far as the authors are concerned, this study presents empirical evidence on the performance and efficiency of different types of banks and explores comparisons among them, which has never been done to this extent in the country before.

This study aims to measure and further compare the countries in terms of the achievement in the degree of financial inclusion over the study period and between income groups considering 26 nations from Asia for the period 2013-2017.

While measuring the degree of financial inclusion, the study prepares an index using weighted arithmetic mean and the inverse of the Euclidean distance method. Further, comparison between the study period and between the income groups has been made using the dependent samples t-test as well as the Wilcoxon signed-rank test and independent samples t-test, respectively.

The study extends empirical insights by laying out the ranks for the countries considered for each of the study periods individually as well as in terms of mean financial inclusion scores for the study period. Further, comparison in terms of mean financial inclusion scores shows significant differences between the income groups, whereas the differences between the study periods turn out to be non-significant.

Less availability of intended variables over time restricts the predictive capability of sketching the phenomena in a true sense and claims further an exhaustive research to pursue in the future.

With the declining trend except for 2016-2017 in the achievement of financial inclusion scores over time, the study suggests emphasizing the initiatives targeted to include the excluded within the ambit of the formal financial system, which somehow seems unstable.

The novelty of the study lies in the portrayal of a measure that seems representative of the scale for development with deeper insight.

This study aims to investigate the feasibility of employing a multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation. The formulated objectives are the minimisation of the total allocation cost of the anti-fraud capacities and the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots.

From the literature survey conducted and primary qualitative data gathered from the 17 licenced banks in South Africa on fraud investigators, the suggested fraud investigators are the organisation’s finance department, the internal audit committee, the external risk manager, accountants and forensic accountants. These five human resource capacities were considered for the formulation of the multi-objectives integer programming (MOIP) model. The MOIP model is employed for the optimisation of the employed capacities for cyberfraud mitigation to ensure the effective allocation and utilisation of human resources. Thus, the MOIP model is validated by a genetic algorithm (GA) solver to obtain the Pareto-optimum solution without the violation of the identified constraints.

The formulated objective functions are optimised simultaneously. The Pareto front for the two objectives of the MOIP model comprises the set of optimal solutions, which are not dominated by any other feasible solution. These are the feasible choices, which indicate the suitability of the MOIP to achieve the set objectives.

The results obtained indicate the feasibility of simultaneously achieving the minimisation of the total allocation cost of the anti-fraud capacities, or the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots – or the trade-off between them, if they cannot be reached simultaneously. This study recommends the use of an iterative MOIP framework for decision-makers which may aid decision-making with respect to the allocation and utilisation of human resources.

The originality of this work lies in the development of multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

The study aims to map the links between Industry 4.0 (I-4.0) technologies and circular economy (CE) for sustainable operations and their role to achieving the selected number of sustainable development goals (SDGs).

The study adopts a systematic literature review method to identify 76 primary studies that were published between January 2010 and December 2020. The authors synthesized the existing literature using Scopus database to investigate I-4.0 technologies and CE to select SDGs.

The findings of the study bridge the gap in the literature at the intersection between I-4.0 and sustainable operations in line with the regenerate, share, optimize, loop, virtualize and exchange (ReSOLVE) framework leading to CE practices. Further, the study also depicts the CE practices leading to the select SDGs (“SDG 6: Clean Water and Sanitation,” “SDG 7: Affordable and Clean Energy,” “SDG 9: Industry, Innovation and Infrastructure,” “SDG 12: Responsible Consumption and Production” and “SDG 13: Climate Action”). The study proposes a conceptual framework based on the linkages above, which can help organizations to realign their management practices, thereby achieving specific SDGs.

The originality of the study is substantiated by a unique I-4.0-sustainable operations-CE-SDGs (ISOCES) framework that integrates I-4.0 and CE for sustainable development. The framework is unique, as it is based on an in-depth and systematic review of the literature that maps the links between I-4.0, CE and sustainability.

This study presents the development of a supply chain (SC) observatory, which is a benchmarking solution to support companies within the same industry in understanding their positioning in terms of SC performance.

A case study is used to demonstrate the set-up of the observatory. Twelve experts on automatic equipment for the wrapping and packaging industry were asked to select a set of performance criteria taken from the literature and evaluate their importance for the chosen industry using multi-criteria decision-making (MCDM) techniques. To handle the high number of criteria without requiring a high amount of time-consuming effort from decision-makers (DMs), five subjective, parsimonious methods for criteria weighting are applied and compared.

A benchmarking methodology is presented and discussed, aimed at DMs in the considered industry. Ten companies were ranked with regard to SC performance. The ranking solution of the companies was on average robust since the general structure of the ranking was very similar for all five weighting methodologies, though simplified-analytic hierarchy process (AHP) was the method with the greatest ability to discriminate between the criteria of importance and was considered faster to carry out and more quickly understood by the decision-makers.

Developing an SC observatory usually requires managing a large number of alternatives and criteria. The developed methodology uses parsimonious weighting methods, providing DMs with an easy-to-use and time-saving tool. A future research step will be to complete the methodology by defining the minimum variation required for one or more criteria to reach a specific position in the ranking through the implementation of a post-fact analysis.