Integrating dynamic Malmquist DEA and social network computing for advanced management decisions

[1]  Yiwen Bian,et al.  A Gram-Schmidt process based approach for improving DEA discrimination in the presence of large dimensionality of data set , 2012, Expert Syst. Appl..

[2]  Amine Lahiani,et al.  Financial distress prediction: The case of French small and medium-sized firms , 2017 .

[3]  Moses Acquaah,et al.  Utilization and value of social networking relationships in family and nonfamily firms in an African transition economy , 2011 .

[4]  Olivia R. Liu Sheng,et al.  Mining competitor relationships from online news: A network-based approach , 2011, Electron. Commer. Res. Appl..

[5]  Hokey Min,et al.  A data envelopment analysis‐based balanced scorecard for measuring the comparative efficiency of Korean luxury hotels , 2008 .

[6]  E. Henry.  Are Investors Influenced By How Earnings Press Releases Are Written? , 2008 .

[7]  Agha Iqbal Ali,et al.  DEA Malmquist productivity measure: New insights with an application to computer industry , 2004, Eur. J. Oper. Res..

[8]  Rui Li,et al.  Competitor Mining with the Web , 2008, IEEE Transactions on Knowledge and Data Engineering.

[9]  Meng Guo,et al.  Research on the financial risk evaluation of listed companies with intuitionistic fuzzy information , 2017, J. Intell. Fuzzy Syst..

[10]  M. Oral,et al.  An empirical study on measuring operating efficiency and profitability of bank branches , 1990 .

[11]  Emad Roghanian,et al.  Customer relationship management performance measurement using balanced scorecard and fuzzy analytic network process: The case of MAPNA group , 2014, J. Intell. Fuzzy Syst..

[12]  Indranil Bose,et al.  Deciding the financial health of dot-coms using rough sets , 2006, Inf. Manag..

[13]  R. Kaplan,et al.  Using the balanced scorecard as a strategic management system , 1996 .

[14]  So Young Sohn,et al.  Support vector machines for default prediction of SMEs based on technology credit , 2010, Eur. J. Oper. Res..

[15]  R. Kaplan,et al.  The balanced scorecard--measures that drive performance. , 2015, Harvard business review.

[16]  Hui Li,et al.  Data mining method for listed companies' financial distress prediction , 2008, Knowl. Based Syst..

[17]  Tyler Shumway.  Forecasting Bankruptcy More Accurately: A Simple Hazard Model , 1999 .

[18]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[19]  Kun Chen,et al.  Measuring social influence for firm-level financial performance , 2016, Electron. Commer. Res. Appl..

[20]  Sudip Bhattacharjee,et al.  Economic sustainability of closed loop supply chains: A holistic model for decision and policy analysis , 2015, Decis. Support Syst..

[21]  Dong-Young Kim,et al.  Understanding supplier structural embeddedness: A social network perspective , 2014 .

[22]  Quey-Jen Yeh.  The Application of Data Envelopment Analysis in Conjunction with Financial Ratios for Bank Performance Evaluation , 1996 .

[23]  Larry Jenkins,et al.  A multivariate statistical approach to reducing the number of variables in data envelopment analysis , 2003, Eur. J. Oper. Res..

[24]  Jie Sun,et al.  Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble , 2017, Knowl. Based Syst..

[25]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[26]  Cindy Yoshiko Shirata,et al.  Extracting Key Phrases as Predictors of Corporate Bankruptcy: Empirical Analysis of Annual Reports by Text Mining , 2011 .

[27]  Yu Wang,et al.  Financial failure prediction using efficiency as a predictor , 2009, Expert Syst. Appl..

[28]  James A. Ohlson.  FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[29]  Zdzis?aw Pawlak,et al.  Rough sets , 2005, International Journal of Computer & Information Sciences.

[30]  Lan Bai,et al.  Weighted Lagrange ε-twin support vector regression , 2016, Neurocomputing.

[31]  Mu-Yen Chen,et al.  A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering , 2013, Inf. Sci..

[32]  R. Färe,et al.  Productivity changes in Swedish pharamacies 1980–1989: A non-parametric Malmquist approach , 1992 .

[33]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[34]  Evaluation of emergency risk management capability based on hesitant fuzzy Einstein operator , 2016, J. Intell. Fuzzy Syst..

[35]  Avraham Shtub,et al.  R&D project evaluation: An integrated DEA and balanced scorecard approach ☆ , 2008 .

[36]  Alexandre Veronese Bentes,et al.  Multidimensional assessment of organizational performance: Integrating BSC and AHP☆ , 2010 .

[37]  W. Beaver.  Financial Ratios As Predictors Of Failure , 1966 .

[38]  Xinjun Peng,et al.  Primal twin support vector regression and its sparse approximation , 2010, Neurocomputing.

[39]  Yi Yang,et al.  Data envelopment analysis application in sustainability: The origins, development and future directions , 2018, Eur. J. Oper. Res..

[40]  Jonathan Crook,et al.  Dynamic Prediction of Financial Distress Using Malmquist DEA , 2017, Expert Syst. Appl..