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The fraudulent claims for Unemployment Insurance (UI) have also risen massively in the United States especially during the onset of COVID-19 pandemic with billions of dollars that were lost. These approaches applied formerly in fraud detection and prevention have been challenged by new and advanced fraud systems. For this reason, AI, Big Data and Predictive Analytics are now crucial for improving fraud mitigation in UI programs. The aim of this research is to understand how far AI, Big Data and Predictive Analytics have been utilized, for how effective they are and the barriers they pose in tackling unemployment insurance fraud in the United States. An online quantitative self-administered questionnaire was administered where 200 participants from the unemployment insurance agencies of the U.S, covering fraud investigators, IT personnel, data analysts and policymakers, were included in the study. Data were analyzed to establish the correlation between technology adoption to application of anti-fraud measures using descriptive statistics, chi-square test and regression analysis. The survey shows that about 42.5% of agencies actively use AI and 34% of them use Big Data very often. However, difficulties like high costs, integration troubles and limited availability of specialized staff are still issues that make adoption a problem. The results of using AI and Big Data were moderate and optimistic but rather elevated, as seen through regression analysis; on the other hand, predictive analytics demonstrated large potential when applied successfully. The study’s results indicate that AI, Big Data and Predictive Analytics present enormous potential for mitigating UI fraud, although broad integration will not be possible without overcoming essential infrastructural and resource limitations.

This study aims to explore key drivers of tourism sustainability practices and their impact on the sustainable competitive performance of the tourism accommodation industry. The study adopts an exploratory research design under qualitative research, using semi-structured interviewing of owners and managers of tourism accommodation firms using the TOE framework and RBV theory. The findings reveal that technological sustainability expertise, executive sustainability support, organizational resources and capability, service intermediary sustainability practices, collaborative innovation adoption capability, tourism sustainability regulatory policy, and sustainability practices perceived by residents are the key drivers of TSP. The outcomes also show that tourism sustainability practices have a positive impact on the SCP of accommodation firms. This study makes a theoretical contribution to the literature on tourism sustainability by exploring how TOE antecedents motivate firms to adopt sustainability practices as product innovation, which in turn enhance SCP. Also, this study provides strong backing for the premises of the TOE framework, and RBV theory offers valuable practical insights for managers and owners. This will aid in identifying the pivotal factors influencing TSP and SCP, enabling the implementation of innovative strategies to enhance firm competitiveness. Also, the research can help the government and policymakers formulate sustainable tourism policies to promote tourism innovations. The adoption of sustainable practices has been a focal point for both academic researchers and professionals in the tourism industry. However, there has been insufficient exploration of the TOE drivers that facilitate the integration of tourism sustainability within the context of TAFs in a developing country. Therefore, this study aims to fill this gap in knowledge.

Prior studies in Bangladesh examined how several non-demographic factors influenced consumers' online buying behavior. However, no specific research has been conducted to examine an association between consumers' demographic factors and the online shopping behavior of Bangladeshi buyers. Therefore, this study aimed to examine the association between online shopping behavior and the demographic characteristics of Bangladeshi buyers. The association between six consumers' demographic variables, such as age, gender, income, education, occupation, and marital status, and online shopping behavior, proxied by the frequency of online shopping, was examined. Primary data through a structured questionnaire was collected from the 547 respondents in urban and rural areas of seven divisions in Bangladesh through offline and online surveys. The chi-square test was used to examine the association between consumers' demographic characteristics and online shopping behavior. The study found that male and married consumers with a higher level of education and higher-category jobs have a higher propensity to shop online. However, consumers' age and income level do not significantly impact their online shopping behavior. The study's outcomes are expected to provide online retailers and merchants with critical information about buyer behavior. Keywords: Association, online shopping behavior, demographic characteristics, chi-square test, Cramér’s V, e-commerce. JEL Classification: M31, M39, and L81