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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 attempts to critically assess one of the financial inclusion policy “Pradhan Mantri Jan Dhan Yojna” introduced by the government of India in 2014.

Number of bank accounts opened (rural, urban and overall) under the policy, total balance in such account and total number of debit cards issued till October, 2017 were taken as the criterion variables. The macroeconomic indicators, infrastructure, literacy, regional dummy and percentage labour participation were taken as predictors. Finally, a State index for financial inclusion under the policy was developed through Normalized Inverse Euclidean Distance using per capita number of accounts, total balance and number of debit cards issued as the parameters.

Andaman and Nicobar, Puducherry and Chandigarh came out to be the top three State indexes for Financial Inclusion under the policy. Status of infrastructure (such as number of roads) was found to be the most significant determining factor. Other factors were labour force participation, poverty and regional disparity.

This paper is unique in the sense that financial inclusion policy has been assessed both through its reachability and assessment of its predictors.

The purpose of this study is to measure the availability, accessibility and usability of financial products and services in both rural and urban India from 1991 to 2014.

This paper uses principal component analysis (PCA) method to construct financial inclusion index that serves as a proxy variable for indicating the inclusiveness of financial products and services among the rural and urban people. To fulfill this objective, the study proposes separate indexes of financial inclusion for both rural and urban India from 1991 to 2014. The paper uses annual time series data from 1991 to 2014 to construct the rural-urban financial inclusion index. The used data have been collected from the basic statistical returns of Reserve Bank of India and Economic Political Weekly research foundation.

The study inferences that though there is a remarkable increase in financial inclusion in India from 1991 onwards, it does not result in sizeable growth of financial access to rural masses in comparison to urban masses. The rural India does not substantiate an equivalent growth to that of urban India, contrasting a perceptible increase in financial inclusion. The finding of this study will help the researchers and policymakers to understand the status of financial inclusion in the context of both rural and urban India. Furthermore, policymakers can take appropriate policy initiatives to fulfill the financial inclusion gap that exists between rural and urban people. Additionally, the proposed index is easy to compute and can be used to make comparison across countries for further studies.

The present paper attempts to include all possible dimensions (and indicators within a dimension) that have been considered so far by various authors. Therefore, the authors hope that this index will be more indicative and accurate than previous index. Again, the authors propose to use PCA for the first time to assign the weight of factors in the financial inclusion index for rural and urban India separately.

Reaching at the true development state is one of the important policy agenda of any country or its provinces under it. The true development state can further be ensured if the country or province does inclusive development. Financial inclusion is one of the important agenda through which a country or a province's inclusive growth and development can be ensured. The present study aims to compute the magnitudes of financial inclusion and its associated income link across the 18 districts of the state of West Bengal in India, with the help of the four banking indicators or dimensions – number of branches, number of accounts, amounts of deposits, and amounts of credit of the scheduled commercial banks, for the period 1997–2018. It finds that except Kolkata, all the other districts have low IFI values. Kolkata is at the top with near 100 percent financial inclusion. Mostly the districts from the North Bengal region are having very low magnitudes whereas the districts from the South Bengal region are having relative high magnitude of financial inclusion. The ultimate effects of these levels of financial inclusion have led to very low level of net domestic products of the districts.

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.

With the aim of analysing the growth and developmental aspects of bank credit allocations in the selected countries, the primary requirements are to see the trends of the lead variables, credit, gross domestic product (GDP) and Human Development Index (HDI), and get ideas on the descriptive statistics. The present chapter has attempted to do all these primary analyses across the countries for the period of 1990–2019. The study observes that the levels of GDP have increased for all the countries throughout the entire period with some downhill breaks during the global financial crisis of 2007–2009. There are also similar types of upward trends in the credit delivery to the private sectors of the countries over time with some exceptions in the second phase (2001–2010) for Germany and the first phase (1990–2000) for Brazil. On the other hand, the HDI values for all the countries have improved over time in the entire period of time and the developing countries in the list have progressed more in all three indicators, GDP, credit and HDI, compared to that of the developed countries in the list. The correlation analysis of credit with GDP and HDI shows positive coefficients in many of the developed and developing countries which primarily justify the existence of strong linkages of their financial sectors with their real sector and overall development.

Organizations create their environments by constructing interpretations and then acting on them as if they were true. This study examines the cognitive spatial boundaries that managers of Manhattan hotels impose on their competitive environment. We derive and estimate a model that specifies how the attributes of managers’ own hotels and potential rival hotels influence their categorization of competing and non-competing hotels. We show that similarity in geographic location, price, and size are central to managers’ beliefs about the identity of their competitors, but that the weights they assign to these dimensions when categorizing competitors diverge from their influence on competitive outcomes, and indicate an overemphasis on geographic proximity. Although such categorization is commonly conceived as a rational process based on the assessment of similarities and differences, we suggest that significant distortions can occur in the categorization process and examine empirically how factors including managers’ attribution errors, cognitive limitations, and (in)experience lead them to make type I and type II competitor categorization errors and to frame competitive environments that are incomplete, erroneous, or even superstitious. Our findings suggest that understanding inter-firm competition may require greater attention being given to the cognitive foundations of competition.

The purpose of this paper is to focus on measuring financial inclusion (FI) level for the developing countries.

By using a two-stage principal component analysis method, we construct a composite FI index to measure the degree of FI. Data are collected through secondary sources including World Bank and IMF reports for the period 2012–2018.

We have built an overall FI index which is considered as a comprehensive measure of FI, a useful tool for policymaking and policy evaluation. Comparison with other studies shows that our FI index corroborates with them.

Building a good FI measurement method is important for developing countries. It helps to assess and compare the level of FI of each country and between countries together, made easily and accurately.

This study emphasizes the important role of FI in the economy. From there, an FI solution is integrated into the construction and calculation of its impact on other factors. This will help policymakers to take effective measures to increase FI levels to achieve sustainable economic growth.

Using data on Indian banks for 1997‐2007, the purpose of this paper is to develop an index of banking fragility and subsequently examine the factors affecting the index.

The author employs basic distributional assumptions to develop the index and subsequently, employs panel data techniques to examine the factors which affect the index.

Based on the statistical properties of the index, banks are classified as exhibiting high, moderate, and low stability. The multivariate regressions indicate an important role for banking industry variables in influencing the index.

The paper complements the strand of literature which has been focusing on developing indicators of banking stability and examining the factors affecting them.

To the author's knowledge, this is perhaps the first study for an emerging economy and more certainly for India, to examine this issue.

This current research tries to answer the widespread debate about the role of derivatives in propagating the last financial crisis. So, this work aims to examine the effect of derivatives on bank stability in emerging countries by using the bank stability index (BSI) as developed by Ghosh (2011) from three major dimensions of banking operations: stability, soundness, and profitability. We use the generalized method of moments (GMM) estimator technique developed by Blundell and Bond (1998) to estimate regressions during the normal, the turbulent, and the whole period, following the guidance given by Chiaramonte, Poli, and Oriani (2013).

The major conclusion of this study reveals that except to futures the other derivative instruments cannot be considered as troubling factors. The main implication of the research shows that derivatives – in general – are not responsible for the propagation of the recent financial crisis. Hence, the common debate accusing derivatives as being responsible for the aggravation of the recent financial crisis should be rejected.