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10.1007/s11192-020-03856-0 http://scihub22266oqcxt.onion/10.1007/s11192-020-03856-0 33531720!7844786!33531720     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Scientometrics 2021 ; 126 (3): 2141-2188 Nephropedia Template TP {{tp|p=33531720|t=2021. Rethinking SME default prediction: a systematic literature review and future perspectives |pdf=|usr=}}{{33531720}} gab.com Text Rethinking SME default prediction: a systematic literature review and future perspectives JO: Scientometrics http://www.kidney.de/pget.php?&tw=1&pmid=33531720 #FOAvip Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007-2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results. Twit Text FOAVip Rethinking SME default prediction: a systematic literature review and future perspectives ????Scientometrics http://www.kidney.de/pget.php?&tw=1&pmid=33531720 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33531720 #FOAvip English Wikipedia <ref>{{Cite pmid|33531720}}</ref> Rethinking SME default prediction: a systematic literature review and future perspectives #MMPMID33531720 Ciampi F; Giannozzi A; Marzi G; Altman EI Scientometrics 2021[]; 126 (3): 2141-2188 PMID33531720show ga Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007-2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results. äRethinking SME default prediction: a systematic literature review and (epmc).pdf DeepDyve Pubget Overpricing