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The goal of this research is to look at how urban microfinance affects livelihood transformation in terms of poverty reduction, living standards, social well-being, empowerment and entrepreneurship.

This paper analyses the role of urban microfinance towards livelihood with special reference to Western Uttar Pradesh. Primary data were collected from 321 respondents who are users of a microfinance programme using a standardised questionnaire. The data were collected using a stratified random sampling technique, and the data were analysed using structural equation modelling.

Urban microfinance has a considerable impact on poverty reduction, the standard of living, social well-being, empowerment and entrepreneurship in the urban poor, according to the findings.

The fact that the majority of the borrowers were uneducated was the most significant barrier to them filling out the questionnaire. Their anxiety was the most significant psychological obstacle to successfully answering the questions, and it took time. As a result, it is urged that proper counselling be conducted before the poor borrowers fill out the questionnaire.

The current study highlights the factors that lead to the utilisation of microfinance services. This research will aid MFIs in selecting the appropriate products and services for the urban poor. The results of this study will aid them in understanding and meeting the expectations of microfinance CEOs.

This is a first study conducted in Northern zone of India measuring the roles urban microfinance institutions (MFIs) in uplifting the livelihood of urban poor.

There are two theoretical concepts that can be taught in this case.The new approach to teaching entrepreneurship is termed “lean start-up” and “hypothesis-driven entrepreneurship.” The business model canvas is a core tool of this approach. This framework defines nine key components of a successful business strategy. These components include defining value propositions; identifying customer segments; identifying channels; maintaining customer relationships; defining key activities, key resources and key partners; understanding the revenue model of the business; and the organization’s cost structure. This is considered to be a rigorous approach to learning about and developing a new venture.The other theoretical approach that can be discussed through this case is the link between uncertainty and entrepreneurial growth. These theories associate the willingness of entrepreneurs to bear the perceived uncertainty associated with entrepreneurial acts as representative of the belief-desire model. There is a need for entrepreneurs to experiment and search for alternative paths forward in order to counter this uncertainty. Systematic search processes to discover relevant information will strengthen this process.

This case is based on primary data collected through interviews with company personnel. The company consented freely to the use of their data in the case. The authors have no connection with the company. The four student coauthors had previously pursued an internship with the company and had worked on the machine learning analysis part.The two faculty coauthors in the case contacted the company after the internship and discussed the opportunity to write the case on the company. One of the faculty then interviewed key personnel in the company, including one of the co-founders.

Xoxoday is a technology company that provides employee rewards and corporate gifting to its customers. The company was started by Sumit Khandelwal, Manoj Agarwal, Abhishek Kumar and Kushal Agarwal. In 2018, the company reinvented itself as an experiential gifting company.The company faced some challenges during the lockdowns imposed due to COVID-19. Khandelwal knew that they had to try something new to achieve higher growth in the future. He wondered if higher usage of technology was the solution. It was necessary for them to carve a new path in these times.

This case study can be used at the undergraduate level in courses relating to entrepreneurship strategy and business models for entrepreneurs.The case can be used to highlight the dilemmas faced by entrepreneurs due to unforeseen crises. This case is relevant for classes that will discuss growth crises and out-of-the-box solutions for unprecedented crisis situations.

Entrepreneurship.

The case is suitable for analysis in an undergraduate/graduate entry level course on entrepreneurship. It may also be taught in a course for non-business majors who are unfamiliar with basic business concepts.

Shrey Gupta and Manoj Agarwal were the co-owners of “After 12”, a food facility in the Dhanakwadi area of Pune, India. In only nine months, their food enterprise had become quite popular among the local college going crowd. They were doing brisk business and sales had picked up quite a bit in the last two months. They both were quite pleased with the progress of their business. However, in spite of increasing sales figures in the last few months they hardly generated any profits. The revenue they generated was completely spent on buying raw materials like vegetables and other food materials and the rest was used in the payment of utility bills and wages of the cook and the helper. They only had enough money to continue for another month or so. Both pondered about what is the way to proceed. What can be done to make it sustainable? How would they arrange the extra money to get them going? Should they close down “After 12”?

The case will aid students to understand: how small businesses are started and may fail; the importance of researching your business idea; and SWOT analysis.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

This study aims to understand the relative intensity of the challenges and problems faced by small-scale entrepreneurs in Uttarakhand.

A survey methodology was used for this study. The judgement sampling method was used to select the sample for this study. The data were collected from 240 small-scale entrepreneurs using a self-structured questionnaire. Descriptive statistics, principal component analysis and confirmatory factor analysis were used to analyse the data.

The survey found that marketing, finance, taxation, human resource and government support–related problems are the major problems of small-scale entrepreneurs in the state.

This study was conducted in both rural and urban areas, but due to the unreachability of rural entrepreneurs, the representation of rural entrepreneurs is less, so the findings are more inclined towards urban entrepreneurs.

The research has highlighted the intensity of the major problems faced by small-scale entrepreneurs in Uttarakhand. Although many support schemes are operational in the state, small–scale entrepreneurs face many challenges, so this study provides solutions for those challenges.

This study is unique in that it measures the intensity of problems and challenges of small-scale entrepreneurs and provides insight into more serious issues prevalent in the state.

With the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.

This paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.

Experimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.

The Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.

The present study examines the foremost determinants of teachers' perception, i.e. teachers' satisfaction, attitude and continuance intention towards adopting e-learning in Higher Educational Institutions (HEIs) in India during the COVID-19 pandemic.

Data were collected through online Google forms from 1,111 (1,060 considered useable) teachers of different HEIs in India using the purposive sampling technique and was analyzed by PLS-SEM (performing partial least squares-structural equation modeling).

Results of this study show that perceived usefulness (PU) followed by institutional support, perceived ease of use (PEOU), and teacher-student interaction positively and significantly impact teachers' satisfaction. Results also revealed that perceived usefulness (PU), institutional support, and satisfaction significantly affect teachers' attitude. Finally and most notably, teachers' continuance intention towards using online teaching in HEIs is most significantly influenced by teachers' satisfaction than perceived usefulness (PU), perceived ease of use (PEOU), and attitude.

The authors anticipate that this study brings a significant and valuable input to the existing literature by providing inclusive research in a more harmonizing understanding of the teachers' satisfaction, attitude, and continuance intention with online teaching-learning practices in diverse educational institutions.

Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

This chapter elaborates the concept of Structural Equation Modelling (SEM) and further validates the model for the sustainability of financial innovation using factors of e-payment system, adoption intention, trust and sustainability, and finally derives a model for the sustainability of the financial innovation using SEM. The chapter also presents Sustainability of E-payment System on Customer Perspective (SEPSCP) through segmentation approach.

This chapter focusses on the identification of sustainability factors. Out of the 77 variables used in the questionnaire for collection of the information, on sustainability of financial innovation in e-payment system (EPS), the important factors are identified and derived using exploratory factor analysis (EFA). This chapter further presents validation of the identified factors through confirmatory factor analysis (CFA). Based on the identified factors, a model for EPS is proposed. The chapter also presents a scale developed based on identified factors.

The purpose of this paper is to efficiently assist the decision makers in evaluating and selecting the most appropriate third-party logistics (3PL) provider from environmental sustainability perspective using a two-phase model based on data envelopment analysis (DEA) and analytic network process (ANP).

The study uses an integrated approach of DEA and ANP as an evaluation and selection methodology to select an efficient and requisite 3PL. The integrated model is a sound technique for 3PL evaluation and selection. DEA effectively screens the maximally efficient 3PLs, whereas ANP easily performs the cumbersome process of weighting diverse criteria and ranks various alternatives according to their performance on the basis of these criteria. This study lists the prominent evaluation and selection criteria taking into consideration the environmental sustainability. Finally, a real-life industrial application is presented to demonstrate the proposed method.

The current research work is devoted to the emerging research topic of environmental sustainability in logistics industry and supply chain management. The results of study indicate that eco-efficient transportation and 3PLs lead better transportation planning, better inventory and warehouse management, lower inventory cost and sustainable supply chain operations. Also, the applied approach enables the decision makers to better understand the complex relationships of the relevant criteria in the decision-making process.

This study does not investigate the relationships between environmental performances and differentiation advantage from the perspective of 3PLs. Also, sensitivity analysis is not performed.

The developed integrated model enables decision analysts to better understand the complete evaluation process of 3PL evaluation and selection considering environmental sustainability perspective. Furthermore, the developed approach provides a more flexible, simple, accurate, effective, and systematic decision support tool for 3PL evaluation and selection.

The current study is first of its kind to use the integrated approach in evaluation and selection of 3PLs from environmental sustainability perspective considering the inter-dependencies among the evaluation and selection criteria.