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Purpose The purpose of this paper is to investigate how the context of open innovation offered by accelerators can affect the successful growth of start-ups. The authors explore accelerators practices and tools in sustaining start-ups and increasing survival probability in their innovation process, with the aim of addressing the following research question: how can start-ups benefit from participation in an accelerator programme from an open innovation perspective? Design/methodology/approach A review of the literature on start-up successes and failures and on major practices in the open innovation paradigm was carried out, delineating them in the context of accelerators. Given the absence of literature on accelerator practices for supporting start-ups, and aiming at a comprehensive understanding of how the open environment within the accelerator influences a start-up’s survival (or even success) by mitigating the probability of failure, the authors conducted an exploratory case study in an English accelerator. Findings The open innovation practices mediated by an accelerator and the ones that are not covered, but that can benefit a start-up’s survival, are shown. On the one hand, main effective practices, such as dyadic co-creation with accelerator network partners and crowdsourcing, are revealed to address mostly the lack of, or wrong direction in, product, marketing and relative managerial abilities, which are not usually owned by a start-up due to its “newness”. On the other hand, some causes of failures, such as the intrinsic characteristics of founder teams, do not seem to be addressed by an open approach and neither does participation in an accelerator programme. Originality/value This paper is the first to study and link the literature on accelerators, start-ups and open innovation.

RESUMEN: This study is motivated by the importance of accurately predicting insolvency before it happens. The paper aims to develop an insolvency prediction model for Colombian firms with one, two and three years of anticipation through financial ratios, keeping sample structures and taking into account insolvency-related regulation. This research contributes to the literature because unlike many studies, it takes legislation into account, explains the different types of financial ratios, and uses boosting algorithms without biasing the sample. Data from 11,812 Colombian companies covering the period 2012-2016 was used. The results show accuracy above 70% for insolvency prediction with one, two and three years of anticipation. RESUMEN: Esta investigación es motivada por la importancia de tener una buena predicción de la insolvencia con anticipación. El objetivo de este artículo es desarrollar un modelo predictivo para las empresas colombianas con uno, dos y tres años de anticipación usando indicadores financieros, conservando la estructura de la muestra original y teniendo en cuenta la regulación sobre insolvencia. Este artículo contribuye a la literatura ya que, a diferencia de los estudios tradicionales, se tienen en cuenta aspectos como la legislación, se explican los diferentes tipos de indicadores financieros y se utiliza el algoritmo boosting sin sesgar la muestra inicial. Para el desarrollo de este estudio se consideró una muestra de 11.812 empresas colombianas durante el periodo 2012.

Kurumsal iflas tahmini finans literaturunde yaygin olarak incelenmistir. Bu tez 2000 ve 2015 yillari arasinda finansal sektorde olmayan Turk sirketlerinin iflas tahminlerini 7 farkli modelin: coklu degiskenli dogrusal diskriminant, ikinci derece diskriminant, logit, probit, karar agaci, yapay sinir aglari ve destek vektor makinesi modelleri dogruluklarini karsilastirarak inceler. Bu calsma,finansal oranlarn iflastan 1 ila 2 yl oncesine kadar guclu belirleyici olduklarini gostermektedir. Ayrca, uc finansal oranin: doner sermayenin toplam aktiflere orani, net gelirin toplam aktiflere orani ve net gelirin toplam pasiflere oraninin iflas tahminini ongormede onemli oldugunu gostermektedir. Butun veri seti kullanildiginda, karar agaci modeli %75 ile en yuksek dogruluk oranina sahiptir. Ikincil veri seti kullanildignda ise yapay sinir aglari diger modellere gore en yuksek dogruluk oranina sahiptir.Anahtar sözcükler : çoklu değişkenli doğrusal diskriminant modeli, ikinci derecediskriminant modeli, logit modeli, probit modeli, karar ağacı modeli, yapay sinirağları modeli, destek vektör makineleri, iflas tahmini, finansal oranlar . The prediction of corporate bankruptcies has been widely studied in the finance literature. This paper investigates business failures in non-financial Turkish companies between the years 2000 and 2015. I compare the accuracies of different prediction models such as multivariate linear discriminant, quadratic discriminant, logit, probit, decision tree, neural networks and support vector machine models. This study shows that accounting variables are powerful predictors of business failures one to two years prior to the bankruptcy. The results show that three financial ratios: working capital to total assets, net income to total assets, net income to total liabilities are significant in predicting business failures in non-financial Turkish companies. When the whole sample is used, all five models predict the business failures with at least 75% total accuracy, where the decision tree model has the best accuracy. When the hold-out samples are used, neural networks model has the best prediction power among all models used in this study.Keywords: multivariate linear discriminant model, quadratic discriminant model,logit model, probit model, decision tree model, neural networks model, support vector machines, business failures, bankruptcy prediction, financial ratios . 45