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This paper aims to analyse how financial literacy (FL) is conceptualised and operationalised in the Indian context.

A systematic literature review (SLR) was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) protocol. Thirty-six articles published between 2010 and 2020 were considered for analysis. The FL conceptualisation was examined based on knowledge, ability, skill, attitude and confidence elements. The FL operationalisation was analysed using the modified version of the Organisation for Economic Co-operation and Development’s (OECD) Programme for International Student Assessment (PISA) 2012 model for organising the domain for an assessment framework.

The findings indicate that, despite offering operationalisation details of the FL, 13 out of 36 studies did not include a conceptual definition of FL. Of the 23 studies that mentioned a conceptual definition, 87% are primarily focused on the “knowledge” element and only 39% have combined knowledge, ability/skill and attitude elements in defining FL. As in the developed countries, the Indian studies also preferred investment/saving-related contents in their FL measures. The volume of content focusing on the financial landscape is meagre amongst the FL measures used in India and developed countries. The survey instruments of most studies have been designed in the individuals’ context but have failed to measure the extent to which individuals apply the knowledge in performing their day-to-day financial transactions. Further, it was found that 20 out of 36 studies did not convert the FL level of their target groups into a single indicator or operational value.

To the best of our knowledge, this is the first study that explores the FL’s assessment practices in India. Further, this study offers new insights by comparing the contents of FL measures used in Indian studies with those used in developed countries.

The study aims to explore the savings behaviour of Indian farmers. An attempt is also made to inspect the effect of financial literacy (FL) and financial confidence (FC) on the savings behaviour of the farmers in India.

This study used secondary data on 10,263 Indian farmers from Financial Inclusion Insights, 2017 database. Relevant statistical techniques and ordered probit regression were used to unfold the effect of FL and FC on the savings behaviour of farmers.

The outcome of the study revealed that the majority of the Indian farmers exhibited poor levels of FL and FC. Of the total, 42.99% were found to save regularly. FL and FC were observed to play instrumental roles in steering the savings behaviour of the Indian farmers. Household size, financial shocks, gender, farm ownership, income, household financial decision-making process, religion and educational attainment have emerged to be significant predictors of the savings behaviour of Indian farmers.

The present study makes an original contribution to the extant literature by unfolding the savings behaviour of Indian farmers and the effect of FL and FC on such behaviour using a rich sample of 10,263 farmers for the first time.

The purpose of this study is to examine the role of personality traits (PTs) of individual investors on their investment intention (II). Further, to study the mediating role of overconfidence (OC) bias and financial literacy (FL) on the relationship between PTs and II.

The present study uses the quantitative approach for the data collection from the sample of 327 Indian investors investing in the stock market. The questionnaire was divided into segments to assess the investor’s PTs, OC, FL and II. The PT has been measured using the Big Five Personality Traits. Confirmatory factor analysis was used to test the reliability and validity of the constructs. The hypothesis was tested using structural equation modeling.

Findings of the study show that the PTs of an individual investor are associated with FL and II but insignificant with OC bias. Further, the FL and OC bias have a positive and significant influence on II. In addition, the mediation analysis showed that FL partly mediates the relationship between PTs and II.

The present study is helpful for financial companies, government, personal finance advisors and individual investors; they can keep in mind the behavior-related traits that can influence the investment decisions and design the portfolio accordingly. The policy-makers can implement programs on FL to enhance investment decisions in India.

This paper is unique that covers the mediating role of psychological bias, i.e. OC bias and FL, between the PTs and II of an Indian investor.

This study aims to propose novel edge device-tailored federated learning algorithms of local classifiers (stochastic gradient descent, support vector machines), namely, FL-lSGD and FL-lSVM. These algorithms are designed to address the challenge of large-scale ImageNet classification.

The authors’ FL-lSGD and FL-lSVM trains in a parallel and incremental manner to build an ensemble local classifier on Raspberry Pis without requiring data exchange. The algorithms load small data blocks of the local training subset stored on the Raspberry Pi sequentially to train the local classifiers. The data block is split into k partitions using the k-means algorithm, and models are trained in parallel on each data partition to enable local data classification.

Empirical test results on the ImageNet data set show that the authors’ FL-lSGD and FL-lSVM algorithms with 4 Raspberry Pis (Quad core Cortex-A72, ARM v8, 64-bit SoC @ 1.5GHz, 4GB RAM) are faster than the state-of-the-art LIBLINEAR algorithm run on a PC (Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32GB RAM).

Efficiently addressing the challenge of large-scale ImageNet classification, the authors’ novel federated learning algorithms of local classifiers have been tailored to work on the Raspberry Pi. These algorithms can handle 1,281,167 images and 1,000 classes effectively.

The low level of financial literacy among young adults is a pressing concern at both individual and country levels. Therefore, there is a dire need to understand the best-worst antecedents of financial literacy and how they influence each other.

A two-phased multicriteria decision-making (MCDM) technique consisting of best-worst method and interpretive structural modeling (BWM-ISM) was employed for pair-wise comparison, assigning weights, ranking and establishing the relationship among antecedents of financial literacy.

Results suggest that use of Internet (SF1), role of financial advisors (SF3) and education level of individuals (DS7) are top ranked antecedents, whereas masculinity/feminity, language and power distance in society are the least ranked antecedents of financial literacy. Findings will help both academicians and practitioners focus on the key factors and make efforts to increase financial literacy by minimizing resource usage.

The current study provides clarity among antecedents of financial literacy by following BWM-ISM approach for the first time in the financial literacy context.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0746

Incorporating flipped learning (FL) into teaching English as a foreign language may improve student learning outcomes. This study gathered information on Saudi EFL teachers' readiness and willingness to apply FL. So, it aims to describe Saudi EFL teachers' readiness and willingness to apply FL in language classrooms and to find suitable guidelines for Saudi EFL professional development (PD) designers to follow.

This descriptive study involved 153 male and female Saudi EFL teachers as participants, investigating the perspectives and perceptions of these teachers within the context of foreign language teaching in Saudi Arabia. Surveys in Qualtrics were employed as the primary data collection tool for the study.

Results showed that teachers' self-efficacy of their current teaching was high. Most participants had positive attitudes and abilities related to FL, although they also identified potential challenges related to its engagement and assessment. Teachers expressed a strong willingness to participate in PD in this area, with a preference for online videos and group workshops.

The study emphasizes the importance of PD for Saudi EFL teachers. In addition, it offers guidelines for planning effective PD.

This study aims to examine the association between risk tolerance and risky investment intention with financial literacy as a moderating variable. The proposed relationship was explored specifically for millennials.

The questionnaire was divided into three segments to assess millennials' financial literacy, risk tolerance and risky investment intention. This study uses survey data from 402 millennial investors residing in Delhi-NCR region. The authors exploited PLS-SEM for the analysis because the model involved higher-order constructs.

The findings revealed that financial literacy has a negative impact on risky investment intention. Further, risk tolerance had a positive and significant influence on risky investment intention; however, when financial literacy was added as a moderating variable in this relationship, it had a negative impact on risky investment intention.

Every generation has its quirks, and millennials are no exception. Given their age and sheer number, leading to their dominance in the global workforce, millennials will bring about a generational shift. Awareness of Gen Y's financial literacy and risk behavior enhances their ability to make informed financial decisions, thus proving beneficial not only to them, but also to the whole economy. This will also help policymakers and institutions to introduce financial literacy programs and financial products in alignment with their needs and preferences.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

The authors investigate the role of financial literacy in influencing the relationship between human capital and social capital, with access to finance of micro, small and medium enterprises (MSMEs).

Data were gathered from 337 MSMEs in Brunei Darussalam, and analysis on the data was carried out using a number of statistical methods. The relationships between human capital, social capital, financial literacy and access to finance were analyzed using PLS-SEM.

The results show that human capital does influence access to finance but contrary to previous studies, the influence is negative. Financial literacy is an important element in the relationship between human capital, social capital and access to finance, although it plays a greater role in the relationship between social capital and access to finance. Further analysis shows that financial knowledge is significant in moderating the relationships between human and social capital with access to finance. Financial skills is found to only moderate the relationship between social capital and access to finance.

To the authors' knowledge, this study is the first that integrates the human capital, social capital, financial literacy and access to finance in a single model. The authors also highlight the importance of enhancing the financial literacy of MSMEs so that the problem of access to finance can be alleviated, especially in developing countries.

For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.

This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.

While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.

By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.