{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"4 ","pages":"Pages 251-267"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147197080","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"4 ","pages":"Pages 302-318"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147197083","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"4 ","pages":"Pages 351-365"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147197085","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"4 ","pages":"Pages 319-333"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147197087","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}
Pub Date : 2025-01-01DOI: 10.1016/j.jdec.2025.05.007
Shengzhe Meng , Jintai Ding
The digital economy, including data trading, auditing, and trust, is an essential and rapidly growing field. A secure and committed data sampling process is necessary for those processes. We introduce a novel committed random double-blind sampling methodology for data auditing and transactions, which utilizes cryptography and blockchain technologies. This approach ensures that the sampler only has access to the sampled data. The sampling method we propose is also double-blind, meaning that neither the sampler nor the data owner can independently determine the positions of the sampled data. Instead, they are jointly decided by both parties. Additionally, the method permits the data sampler to detect if the data owner has intentionally chosen high-quality data or provided data extraneous to the data set. This innovative methodology guarantees that the data sampling process is both trustworthy and traceable. We supply a security analysis and offer solutions for various scenarios, such as multi-file and three-party sampling. We also present a sampling process designed to prevent collusion when sampling occurs among three parties.
{"title":"Committed random double blinded coalition-proofed sampling","authors":"Shengzhe Meng , Jintai Ding","doi":"10.1016/j.jdec.2025.05.007","DOIUrl":"10.1016/j.jdec.2025.05.007","url":null,"abstract":"<div><div>The digital economy, including data trading, auditing, and trust, is an essential and rapidly growing field. A secure and committed data sampling process is necessary for those processes. We introduce a novel committed random double-blind sampling methodology for data auditing and transactions, which utilizes cryptography and blockchain technologies. This approach ensures that the sampler only has access to the sampled data. The sampling method we propose is also double-blind, meaning that neither the sampler nor the data owner can independently determine the positions of the sampled data. Instead, they are jointly decided by both parties. Additionally, the method permits the data sampler to detect if the data owner has intentionally chosen high-quality data or provided data extraneous to the data set. This innovative methodology guarantees that the data sampling process is both trustworthy and traceable. We supply a security analysis and offer solutions for various scenarios, such as multi-file and three-party sampling. We also present a sampling process designed to prevent collusion when sampling occurs among three parties.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"4 ","pages":"Pages 16-28"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314341","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}
Pub Date : 2025-01-01DOI: 10.1016/j.jdec.2025.06.002
Yang Liu , Dimitra Kalaitzi , Michael Wang , Christos Papanagnou
The retail industry continues to experience frequent stockouts, driven by the rise of e-commerce and disruptive events such as the COVID-19 pandemic, which have significantly impacted both profitability and supply chain stability. As a result, developing effective models for stockout prediction has become increasingly critical for enhancing the efficiency and resilience of retail operations. The growing availability of data, challenges posed by data imbalance, and high demand uncertainty underscore the need to transition from traditional forecasting models to more intelligent, data-driven approaches that integrate multiple relevant features alongside sales data. In this study, we utilise a large dataset from a retailer comprising over 1.6 million stock keeping units (SKUs) to develop an analytical model based on classical machine learning algorithms aimed at improving stockout prediction accuracy. Our results demonstrate that the proposed approach performs well in handling large-scale, imbalanced data and significantly enhances predictive performance. Feature importance analysis reveals that current inventory levels, short-term demand forecasts (three months), and recent sales data are the most influential factors in predicting stockouts. Furthermore, the findings suggest that recent demand forecasts and sales data have greater predictive power than longer-term projections (six and nine months), highlighting the importance of near-term indicators in inventory stockout prediction accuracy. To the best of our knowledge, these insights provide valuable contributions to understanding stockout dynamics and improving inventory management strategies within the retail sector.
{"title":"A machine learning approach to inventory stockout prediction","authors":"Yang Liu , Dimitra Kalaitzi , Michael Wang , Christos Papanagnou","doi":"10.1016/j.jdec.2025.06.002","DOIUrl":"10.1016/j.jdec.2025.06.002","url":null,"abstract":"<div><div>The retail industry continues to experience frequent stockouts, driven by the rise of e-commerce and disruptive events such as the COVID-19 pandemic, which have significantly impacted both profitability and supply chain stability. As a result, developing effective models for stockout prediction has become increasingly critical for enhancing the efficiency and resilience of retail operations. The growing availability of data, challenges posed by data imbalance, and high demand uncertainty underscore the need to transition from traditional forecasting models to more intelligent, data-driven approaches that integrate multiple relevant features alongside sales data. In this study, we utilise a large dataset from a retailer comprising over 1.6 million stock keeping units (SKUs) to develop an analytical model based on classical machine learning algorithms aimed at improving stockout prediction accuracy. Our results demonstrate that the proposed approach performs well in handling large-scale, imbalanced data and significantly enhances predictive performance. Feature importance analysis reveals that current inventory levels, short-term demand forecasts (three months), and recent sales data are the most influential factors in predicting stockouts. Furthermore, the findings suggest that recent demand forecasts and sales data have greater predictive power than longer-term projections (six and nine months), highlighting the importance of near-term indicators in inventory stockout prediction accuracy. To the best of our knowledge, these insights provide valuable contributions to understanding stockout dynamics and improving inventory management strategies within the retail sector.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"4 ","pages":"Pages 144-155"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465631","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}
Pub Date : 2025-01-01DOI: 10.1016/j.jdec.2025.11.006
Tianna Yang , Tianxi Yang
Does digital financial inclusion alleviate poverty? This study investigates this question by integrating the Digital Financial Inclusion Index of Peking University with microdata from the China Family Panel Studies (CFPS) to examine how the expansion of digital financial inclusion affects household multidimensional poverty in China. Anchored in Amartya Sen's capability approach and operationalized through the Alkire–Foster (A–F) framework, the study identifies multidimensional poverty across five key dimensions: income, health, education, insurance, and living standards. Probit models are employed to estimate how digital financial inclusion influences both the likelihood and structure of multidimensional poverty, while instrumental variable techniques are used to address potential endogeneity. Beyond the average effects, the study further explores the mechanisms through which digital financial inclusion contributes to poverty alleviation, focusing on three channels—promoting household consumption, increasing financial investment, and enhancing access to credit. The results reveal that digital financial inclusion significantly mitigates multidimensional poverty, particularly by improving income, living standards, and health outcomes, though its effects on education and insurance are limited. These findings underscore the transformative role of digital finance in fostering inclusive growth, suggesting that policies expanding digital financial infrastructure and literacy can amplify its poverty-reducing effects and advance equitable development.
{"title":"The impact of China's digital financial inclusion on multidimensional poverty of households","authors":"Tianna Yang , Tianxi Yang","doi":"10.1016/j.jdec.2025.11.006","DOIUrl":"10.1016/j.jdec.2025.11.006","url":null,"abstract":"<div><div>Does digital financial inclusion alleviate poverty? This study investigates this question by integrating the Digital Financial Inclusion Index of Peking University with microdata from the China Family Panel Studies (CFPS) to examine how the expansion of digital financial inclusion affects household multidimensional poverty in China. Anchored in Amartya Sen's capability approach and operationalized through the Alkire–Foster (A–F) framework, the study identifies multidimensional poverty across five key dimensions: income, health, education, insurance, and living standards. Probit models are employed to estimate how digital financial inclusion influences both the likelihood and structure of multidimensional poverty, while instrumental variable techniques are used to address potential endogeneity. Beyond the average effects, the study further explores the mechanisms through which digital financial inclusion contributes to poverty alleviation, focusing on three channels—promoting household consumption, increasing financial investment, and enhancing access to credit. The results reveal that digital financial inclusion significantly mitigates multidimensional poverty, particularly by improving income, living standards, and health outcomes, though its effects on education and insurance are limited. These findings underscore the transformative role of digital finance in fostering inclusive growth, suggesting that policies expanding digital financial infrastructure and literacy can amplify its poverty-reducing effects and advance equitable development.</div></div>","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"4 ","pages":"Pages 289-301"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145622931","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"4 ","pages":"Pages 29-51"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147197072","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"4 ","pages":"Pages 237-250"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147197081","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}
{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":100773,"journal":{"name":"Journal of Digital Economy","volume":"4 ","pages":"Pages 289-301"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147197082","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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