In the past few years, Microfinance has been usually contemplated as an effective strategy instrument in the fight against poverty. The SHG women situation in India has been particularly difficult during COVID-19. It had distressing consequences on SHG women life, their income making activities and livelihoods. Therefore, the question arises whether Microfinance credit leads to poverty reduction and improve their decision-making ability in the post COVID era. To address this question, the present study undertakes to identify the impact of Microfinance, Micro Credit and Savings on the Decision making ability of SHG women in the post COVID era. A number of non-governmental organisations (NGOs) provide micro-finance programmes to women in need in order to gain access to credit and savings services. In the current research, the NGO named ‘Peetambra Foundation’ registered in 2008 in Pink City Jaipur, Rajasthan is instrumental in providing data related to SHG women registered with them. Total 306 SHG women were surveyed in the nearest village of Jaipur city. The findings revealed positive but insignificant impact of Microfinance on Financial improvement. In addition, Financial improvement has a both negative and significant impact on the Decision Making ability.
{"title":"Impact of microfinance on enhanced wellbeing of self-help group women in post-COVID scenario","authors":"Rinku Jain, Rupali Paranjpe, Prerna Manik Mahindroo, Kirti Arekar","doi":"10.3233/mas-220407","DOIUrl":"https://doi.org/10.3233/mas-220407","url":null,"abstract":"In the past few years, Microfinance has been usually contemplated as an effective strategy instrument in the fight against poverty. The SHG women situation in India has been particularly difficult during COVID-19. It had distressing consequences on SHG women life, their income making activities and livelihoods. Therefore, the question arises whether Microfinance credit leads to poverty reduction and improve their decision-making ability in the post COVID era. To address this question, the present study undertakes to identify the impact of Microfinance, Micro Credit and Savings on the Decision making ability of SHG women in the post COVID era. A number of non-governmental organisations (NGOs) provide micro-finance programmes to women in need in order to gain access to credit and savings services. In the current research, the NGO named ‘Peetambra Foundation’ registered in 2008 in Pink City Jaipur, Rajasthan is instrumental in providing data related to SHG women registered with them. Total 306 SHG women were surveyed in the nearest village of Jaipur city. The findings revealed positive but insignificant impact of Microfinance on Financial improvement. In addition, Financial improvement has a both negative and significant impact on the Decision Making ability.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48490346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With growing concern for society as a whole business organizations have been forced to think about their contribution towards society apart from their own profits. This concept has been conceptualized as Society 5.0 in the more recent times. The organizations need to have a more human-centered approach in order to establish themselves in the market today. This is an emerging topic and therefore limited research has been done in this area in the literature. The aim of the current research is to fill up this research gap. The study tries to highlight the impediments in implementation of society 5.0 in the emerging economies. The research has been carried in two steps. In the first step the themes have been generated through NVIVO software. Subsequently the themes generated are taken as the factors to be prioritized in the cause-and-effect groups through a multi-criteria decision-making approach, namely Fuzzy-DEMATEL. The case study being considered for the Indian situation solicited experts for their involvement in developing the themes and also used their evaluation as input to categorise the components into cause – effect categories.
{"title":"What is stopping us from Implementing Society 5.0?: A mixed method study","authors":"V. Agarwal, Snigdha Malhotra, A. Kaul","doi":"10.3233/mas-220402","DOIUrl":"https://doi.org/10.3233/mas-220402","url":null,"abstract":"With growing concern for society as a whole business organizations have been forced to think about their contribution towards society apart from their own profits. This concept has been conceptualized as Society 5.0 in the more recent times. The organizations need to have a more human-centered approach in order to establish themselves in the market today. This is an emerging topic and therefore limited research has been done in this area in the literature. The aim of the current research is to fill up this research gap. The study tries to highlight the impediments in implementation of society 5.0 in the emerging economies. The research has been carried in two steps. In the first step the themes have been generated through NVIVO software. Subsequently the themes generated are taken as the factors to be prioritized in the cause-and-effect groups through a multi-criteria decision-making approach, namely Fuzzy-DEMATEL. The case study being considered for the Indian situation solicited experts for their involvement in developing the themes and also used their evaluation as input to categorise the components into cause – effect categories.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47513709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmad A. Rabaa'i, Xiaodi Zhu, J. Jayaraman, Thi Diem Nguyen, Preeta P. Jha
The popularity of mobile food delivery apps (MFDAs) and the online food delivery industry surged during the COVID-19 epidemic. Despite the explosive growth in the use of these apps, relatively limited research has been done to determine what affects their continuous use. This study predicts the continuous use of MFDAs and explores the variables that influence this utilization using a novel machine learning (ML) based approach. The machine learning models included four distinct constructs (i.e., features): perceived compatibility, convenience, online reviews, and delivery experience. These features were measured using a survey instrument. Eight different machine learning (ML) models, ranging from basic decision trees to neural networks, were deployed. All eight models achieved high prediction accuracy of above 93%, with the CatBoost model having the highest accuracy among them at 98%. Feature importance analysis revealed perceived compatibility to be the most important factor impacting the continuous usage of MFDAs followed by convenience, online reviews, and delivery experience respectively. The study’s findings have ramifications for MFDA marketing and design. Given the significance of perceived compatibility, MFDA marketing campaigns should have a strong emphasis on highlighting how well these apps fit with the users’ lifestyles.
{"title":"The use of machine learning to predict the main factors that influence the continuous usage of mobile food delivery apps","authors":"Ahmad A. Rabaa'i, Xiaodi Zhu, J. Jayaraman, Thi Diem Nguyen, Preeta P. Jha","doi":"10.3233/mas-220405","DOIUrl":"https://doi.org/10.3233/mas-220405","url":null,"abstract":"The popularity of mobile food delivery apps (MFDAs) and the online food delivery industry surged during the COVID-19 epidemic. Despite the explosive growth in the use of these apps, relatively limited research has been done to determine what affects their continuous use. This study predicts the continuous use of MFDAs and explores the variables that influence this utilization using a novel machine learning (ML) based approach. The machine learning models included four distinct constructs (i.e., features): perceived compatibility, convenience, online reviews, and delivery experience. These features were measured using a survey instrument. Eight different machine learning (ML) models, ranging from basic decision trees to neural networks, were deployed. All eight models achieved high prediction accuracy of above 93%, with the CatBoost model having the highest accuracy among them at 98%. Feature importance analysis revealed perceived compatibility to be the most important factor impacting the continuous usage of MFDAs followed by convenience, online reviews, and delivery experience respectively. The study’s findings have ramifications for MFDA marketing and design. Given the significance of perceived compatibility, MFDA marketing campaigns should have a strong emphasis on highlighting how well these apps fit with the users’ lifestyles.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46988193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sanket Dangra, Nimisha Pandey, Suvechcha Sengupta, Shweta Dixit Kadam
{"title":"Application of cluster analysis for customer segmentation: Study on menstrual cups","authors":"Sanket Dangra, Nimisha Pandey, Suvechcha Sengupta, Shweta Dixit Kadam","doi":"10.3233/mas-220408","DOIUrl":"https://doi.org/10.3233/mas-220408","url":null,"abstract":"","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44923653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
“Amazon Big Data”, conducts a thorough analysis on the e-commerce industry using big data and how certain trends can affect the functioning of the organizations delving in the field. With the growth of e-commerce, there has been a significant rise of the online consumers’ footprint. Companies such as Amazon, Flipkart and other e-commercial platforms have accrued huge chunks of consumer information, especially since the start of the pandemic. In this industry, reviews and ratings given to a product play a crucial role in determining the sentiments of the customers associated towards making the final purchase. Such factors account for the brand’s sales and image. In today’s landscape, a careful customer goes through the ratings of the product, its reviews which serve as a medium of screening. In a tie between two similar products, customers purchase a product with higher ratings and better reviews. Therefore, this leads us to the development of an ideal rating metric that is significant for the sales of the product. Moreover, become a tool for product differentiation. This manuscript is a method to standardize the ratings of customers and preserve the sanctity of the data. We discuss models which are an amalgamation of customer ratings, their respective reviews and a sentiment scored derived from the same review. These models also help us define customer clusters with different personalities based on their reviews and ratings. In addition to this, customer segmentation is a future scope to deep dive into the sales data and understand the financial behavior of a customer.
{"title":"Amazon customer service: Big data analytics","authors":"Suyash Sharma, Mansha Kalra, Ashu Sharma","doi":"10.3233/mas-220403","DOIUrl":"https://doi.org/10.3233/mas-220403","url":null,"abstract":"“Amazon Big Data”, conducts a thorough analysis on the e-commerce industry using big data and how certain trends can affect the functioning of the organizations delving in the field. With the growth of e-commerce, there has been a significant rise of the online consumers’ footprint. Companies such as Amazon, Flipkart and other e-commercial platforms have accrued huge chunks of consumer information, especially since the start of the pandemic. In this industry, reviews and ratings given to a product play a crucial role in determining the sentiments of the customers associated towards making the final purchase. Such factors account for the brand’s sales and image. In today’s landscape, a careful customer goes through the ratings of the product, its reviews which serve as a medium of screening. In a tie between two similar products, customers purchase a product with higher ratings and better reviews. Therefore, this leads us to the development of an ideal rating metric that is significant for the sales of the product. Moreover, become a tool for product differentiation. This manuscript is a method to standardize the ratings of customers and preserve the sanctity of the data. We discuss models which are an amalgamation of customer ratings, their respective reviews and a sentiment scored derived from the same review. These models also help us define customer clusters with different personalities based on their reviews and ratings. In addition to this, customer segmentation is a future scope to deep dive into the sales data and understand the financial behavior of a customer.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44940165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Financial analytics has been highly crucial in forecasting possible future economic scenarios. The relationship between a country’s macroeconomic indicators and its stock market has been extensively studied in the literature. Stock prices should be used as leading indications of future economic activity if they accurately reflect the underlying fundamentals. On the contrary, if economic activity follows stock price movement, the outcomes should be the opposite, i.e., economic activity should lead stock price movement. The paper attempts to make use of financial descriptive analytics to explore the interconnection between prominent macroeconomic indicators and stock market activity post ten years of financial crisis 2008. The study’s range is constrained to explore the aforementioned interconnection for the period from September’ 2008 to August’ 2018. The following factors have been found to be related over the long term: GDP, Production Index, Inflation, Exchange Rate, Money Supply, Imports, Exports, FDI, and Stock Market Returns. Shockingly FII has not shown any cointegrating equation. Also causality was observed between stock market and economic indicators. Impulse Response Function (IRF) and Variance Decomposition (VDC) techniques of VAR model are applied to decompose or fractionalize the variability caused by macroeconomic indicators on the BSE Sensex returns which has given some interesting results.
{"title":"Financial analytics for interlinking stock market and macroeconomic performance- post financial crisis 2008","authors":"Anjali Bhute","doi":"10.3233/mas-220404","DOIUrl":"https://doi.org/10.3233/mas-220404","url":null,"abstract":"Financial analytics has been highly crucial in forecasting possible future economic scenarios. The relationship between a country’s macroeconomic indicators and its stock market has been extensively studied in the literature. Stock prices should be used as leading indications of future economic activity if they accurately reflect the underlying fundamentals. On the contrary, if economic activity follows stock price movement, the outcomes should be the opposite, i.e., economic activity should lead stock price movement. The paper attempts to make use of financial descriptive analytics to explore the interconnection between prominent macroeconomic indicators and stock market activity post ten years of financial crisis 2008. The study’s range is constrained to explore the aforementioned interconnection for the period from September’ 2008 to August’ 2018. The following factors have been found to be related over the long term: GDP, Production Index, Inflation, Exchange Rate, Money Supply, Imports, Exports, FDI, and Stock Market Returns. Shockingly FII has not shown any cointegrating equation. Also causality was observed between stock market and economic indicators. Impulse Response Function (IRF) and Variance Decomposition (VDC) techniques of VAR model are applied to decompose or fractionalize the variability caused by macroeconomic indicators on the BSE Sensex returns which has given some interesting results.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46282008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shweta Dixit Kadam, Prerna Manik Mahindroo, J. Jayaraman, S. Kumar, Rinku Jain, Suvechcha Sengupta
{"title":"Special issue: 2nd International Business Analytics Conference","authors":"Shweta Dixit Kadam, Prerna Manik Mahindroo, J. Jayaraman, S. Kumar, Rinku Jain, Suvechcha Sengupta","doi":"10.3233/mas-220401","DOIUrl":"https://doi.org/10.3233/mas-220401","url":null,"abstract":"","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48063534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We provide four case studies that use Bayesian machinery to making inductive reasoning. Our main motivation relies in offering several instances where the Bayesian approach to data analysis is exploited at its best to perform complex tasks, such as description, testing, estimation, and prediction. This work is not meant to be either a reference text or a survey in Bayesian statistical inference. Our goal is simply to provide several examples that use Bayesian methodology to solve data-driven problems. The topics we cover here include analysis of times series and analysis of spatial data.
{"title":"Illustrating advantages and challenges of Bayesian statistical modelling: An empirical perspective","authors":"Juan Sosa, Lina Buitrago","doi":"10.3233/mas-221342","DOIUrl":"https://doi.org/10.3233/mas-221342","url":null,"abstract":"We provide four case studies that use Bayesian machinery to making inductive reasoning. Our main motivation relies in offering several instances where the Bayesian approach to data analysis is exploited at its best to perform complex tasks, such as description, testing, estimation, and prediction. This work is not meant to be either a reference text or a survey in Bayesian statistical inference. Our goal is simply to provide several examples that use Bayesian methodology to solve data-driven problems. The topics we cover here include analysis of times series and analysis of spatial data.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46901025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ranked Set Sampling (RSS) is a method of sampling that can be advantageous when quantification of all sampling units is costly but when small sets of units can be ranked according to the character under investigation by means of the methods not requiring actual measurements. The units corresponding to each rank are used in RSS and it performs better than simple random sampling (SRS) while estimating the population mean and other population parameters. In this paper, a new RSS procedure (RSSVO) for estimating the population mean of skew distributions is suggested. RSSVO measures only one or two order statistics depending upon the set size. The proposed estimator under RSSVO is then compared with the estimators based on SRS and RSS with equal allocation and Neyman’s optimal allocations. It is shown that the relative precisions of the estimators based on RSSVO are higher than those of the estimators based on SRS and RSS (both equal and Neyman’s optimal allocation) when the distributions under consideration are highly positive skew. Further, it is shown that, the performance of the proposed estimator increases as the skewness increases by using the example of lognormal distribution.
{"title":"Ranked set sampling with varied order statistics for skew distributions","authors":"D. S. Bhoj, Girish Chandra","doi":"10.3233/mas-211334","DOIUrl":"https://doi.org/10.3233/mas-211334","url":null,"abstract":"Ranked Set Sampling (RSS) is a method of sampling that can be advantageous when quantification of all sampling units is costly but when small sets of units can be ranked according to the character under investigation by means of the methods not requiring actual measurements. The units corresponding to each rank are used in RSS and it performs better than simple random sampling (SRS) while estimating the population mean and other population parameters. In this paper, a new RSS procedure (RSSVO) for estimating the population mean of skew distributions is suggested. RSSVO measures only one or two order statistics depending upon the set size. The proposed estimator under RSSVO is then compared with the estimators based on SRS and RSS with equal allocation and Neyman’s optimal allocations. It is shown that the relative precisions of the estimators based on RSSVO are higher than those of the estimators based on SRS and RSS (both equal and Neyman’s optimal allocation) when the distributions under consideration are highly positive skew. Further, it is shown that, the performance of the proposed estimator increases as the skewness increases by using the example of lognormal distribution.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49501528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study attempts to explore the influence of observations in a time series or a discrete time signal. The goal is to detect abnormal observations from a frequency domain point of view, while the most of relevant studies have been done from a time domain point of view. The concept of the influence function in the field of robust statistics is borrowed to identify influential observations in a time series. An empirical version of the influence function on the discrete Fourier transform of a time series is designed and subsequently a statistic is proposed to identify influential observations of a time series from the frequency domain point of view. Though the proposed statistic is simple enough to be calculated with simple arithmetic operations, case studies show that the proposed method is capable of identifying influential or abnormal observations of a time series. By identifying influential or abnormal observations, we would be able to gain a better understanding of the nature of a time series and to control possible future influential observations.
{"title":"Identifying influential observations in a time series from the frequency domain point of view","authors":"R. Pak","doi":"10.3233/mas-201353","DOIUrl":"https://doi.org/10.3233/mas-201353","url":null,"abstract":"This study attempts to explore the influence of observations in a time series or a discrete time signal. The goal is to detect abnormal observations from a frequency domain point of view, while the most of relevant studies have been done from a time domain point of view. The concept of the influence function in the field of robust statistics is borrowed to identify influential observations in a time series. An empirical version of the influence function on the discrete Fourier transform of a time series is designed and subsequently a statistic is proposed to identify influential observations of a time series from the frequency domain point of view. Though the proposed statistic is simple enough to be calculated with simple arithmetic operations, case studies show that the proposed method is capable of identifying influential or abnormal observations of a time series. By identifying influential or abnormal observations, we would be able to gain a better understanding of the nature of a time series and to control possible future influential observations.","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47913517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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