The development of information technology has simplified the exchange of information between different parts of the supply chain. Information quality plays an important role in enhancing supply chain performance. The aim of this research was to explore the relationships between SCM-IT inhibitors, IT enablers, information sharing, and information quality in supply chain management. Data for 183 business units operating in Vlore, Albania during 2017 were analyzed. Direct and mediating or indirect effects were also analyzed. The data were analyzed using partial least squares structural equation modeling (PLS-SEM), an advanced statistical technique with the help of Smart-PLS version 3.2.7. PLS algorithm was used to determine the factor loadings and path coefficients in the theoretical model. The study has concluded that SCM-IT inhibitors have a negative and significant effect on information quality; the mediation effect of information sharing was significant, the mediation effect of IT enablers was significant; however, the multiple mediation effects of IT enablers and information sharing was not significant. These findings are useful for top management and IT specialists of the business units, and also for information technology services and products providers.

Insurance is essential for financial risk protection, but claim management is complex and requires accurate classification and forecasting strategies. This study aimed to empirically evaluate the performance of classification algorithms, including Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes to predict high insurance claims. The research analyses the variables of claims, vehicles, and insured parties that influence the classification of high-cost claims. This investigation utilizes a dataset comprising 802 observations of bodily injury claims from the motor liability portfolio of a private insurance company in Albania, covering the period from 2018 to 2024. In order to evaluate and compare the performance of the models, we employed evaluation criteria, including classification accuracy (CA), area under the curve (AUC), confusion matrix, and error rates. We found that Random Forest performs better, achieving the highest classification accuracy (CA = 0.8867, AUC = 0.9437) with the lowest error rates, followed by the XGBoost model. At the same time, logistic regression demonstrated the weakest performance. Key predictive factors in high claim classification include claim type, deferred period, vehicle brand and age of driver. These findings highlight the potential of machine learning models in improving claim classification and risk assessment and refine underwriting policy.

Poverty is a widespread phenomenon in all over the world. Measurement and analysis of poverty is needed to identify the poor, the nature and extent of poverty and its determinants, and to assess the impact of policies and welfare programs on the poor. Based on Albania Living Standard measurement Surveys (LSMSs) data, after an overall improvement of the main indicators measuring the poverty from 2002 to 2008, the percentage of population living in poverty has increased by 15.4% from 2008 to 2012 (INSTAT and World Bank, 2013). The results of LSMS 2012 data indicated an increase of poverty in both urban and rural areas, and the increase was higher in urban area. The aim of this study is to identify the determinants of poverty in Albania, at the household level using a questionnaire. The objectives of this research were: to measure the level of poverty of the sample population, to determine the potential factors that impact poverty and to evaluate their impact on the level of the household’s welfare and poverty status of the household using econometric modeling. The data were collected during November 2013, and direct interviews were conducted with 215 households living in rural and urban area. Two regression models were estimated based on the collected data, a log-linear model with the logarithm of per capita monthly consumption as the dependent variable and a logistic model with poverty status as the dependent variable, and a set of economic and demographic variables as the explanatory variables. It was found that the variables that impacted the per capita consumption of the household and the poverty status of the household were household size and residence. Poverty alleviation efforts should be made to improve the social and demographic characteristics of the households, since the number of the poor is increased in both urban and rural areas. To reduce poverty, great attention must be paid to the manufacturing sector, agriculture and tourism. DOI: 10.5901/jesr.2014.v4n2p157

Mobile telephony has become a main factor driving the social and economic development of a country. This study examines the diffusion process of mobile telephony in Albania and Turkey. The aim of this research is to model and to forecast the diffusion rate of mobile telephony using Logistic and Gompertz models, and World Bank data. The results of estimated models indicated that the Gompertz model fits best with the actual data of mobile telephony in Albania, and the Logistic model fits best with the actual data of mobile telephony in Turkey. According to the results of the Logistic model, the best model for predicting the diffusion rate of mobile telephony in Albania, the maximum level of the mobile diffusion rate of 131.89% will be achieved in the year 2025. The results of the Gompertz model, the best model for predicting the mobile telephony in Turkey indicate that the maximum diffusion rate of 97.98% is predicted to be achieved after the year 2025. These findings are useful to telecommunication operators, policymakers, and customers.