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The goal of the paper is to present the development of social service system in Lithuania. The first period is from 1990 to 1998. It is stated that the first period is characterized by the rapid increase in the amount of social services. The second period started in 1998 and its distinguishing characteristic is an attention to the quality of social services. The issues and difficulties of quality measurement are discussed in the last part of the paper.

As the number and economic impact of disasters rise annually, additional information is required to understand the recovery process. Clearly, any theory of disaster recovery will require the identification of factors that cause vulnerability and resilience, as these can enable or impede successful recovery. The authors performed a content analysis of journal articles from 2000 to 2010 in four disaster-focused journals to identify the factors posited to influence vulnerability and resilience from multiple disciplinary perspectives. Factors were identified in the areas of infrastructural, social, economic, institutional and post-disaster recovery strategy. A panel of experts then validated these factors through a multi-round Delphi survey by rating the level of importance of each factor and providing reasoning on their ratings. All causal factors identified received median ratings of at least important, but not all came to consensus. This paper synthesizes findings related to resilience and vulnerability from many researchers in a variety of fields. This multi-disciplinary perspective may help propel future research to consider the interactions between these multiple factors and empirically examine the link between these factors and community recovery.

PurposeThe research documented in this paper aims to examine multiple factors related to borrowers' default in peer-to-peer (P2P) lending in the USA. This study is motivated by the hypothesis that both P2P loan characteristics and macroeconomic variables have influence on loan performance. The authors define a set of loan characteristics, borrower characteristics and macroeconomic variables that are significant in determining the probability of default and should be taken into consideration when assessing credit risk.Design/methodology/approachThe research question in this study is to find the significant explanatory variables that are essential in determining the probability of default for LendingClub loans. The empirical study is based on a total number of 1,863,491 loan records issued through LendingClub from 2007 to 2020Q3 and a logistic regression model is developed to predict loan defaults.FindingsThe results, in line with prior research, show that a number of borrower and contractual loan characteristics predict loan defaults. The innovation of this study is the introduction of specific macroeconomic indicators. The study indicates that macroeconomic variables assessed alongside loan data can significantly improve the forecasting performance of default model. The general finding demonstrates that higher percentage change in House Price Index, Consumer Sentiment Index and S&P500 Index is associated with a lower probability of delinquency. The empirical results also exhibit significant positive effect of unemployment rate and GDP growth rate on P2P loan default rates.Practical implicationsThe results have important implications for investors for whom it is of great importance to know the determinants of borrowers' creditworthiness and loan performance when estimating the investment in a certain P2P loan. In addition, the forecasting performance of the model could be applied by authorities in order to deal with the credit risk in P2P lending and to prevent the effects of increasing defaults on the economy.Originality/valueThis paper fulfills an identified need to shed light on the association between specific macroeconomic indicators and the default risk from P2P lending within an economy, while the majority of the existing literature investigate loan and borrower information to evaluate credit risk of P2P loans and predict the likelihood of default.

To understand what organizations do which brings about successful digital transformation (DX), a review of representative literature was performed. From a total of 89 articles, 16 single-case articles which sufficiently documented end-to-end DX success stories of 10 exemplar organizations we selected. Within case content analysis and asking the questions analytical procedure were employed to code each exemplar case, where a total of 174 attributes were found. Making the comparisons procedure was employed to build thematic higher-level categories by grouping similar attributes. Cross case content analysis was employed to determine presence strength of attributes in the category groupings. A list of 7 success factors and 23 subfactors emerged from the thematic groupings. These success factors constitute the initial steps towards building a DX framework which organizations seeking to embark on successful digital transformation journeys can adopt.