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A hybrid data mining model in analyzing corporate social responsibility

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Abstract

Over the past two decades, corporate social responsibility (CSR) has received worldwide attention. Publication of CSR reports has become the trend for domestic and foreign enterprises. In the constantly changing and competitive corporate environment, public attention has come to be focused on how enterprises play the role of corporate citizen, and how they achieve a balance of profitable, environmental and charitable activities. However, most quantitative CSR studies to date have concentrated on traditional statistical approaches. The data mining technique has not been widely explored in this area. Thus, this investigation proposes a hybrid data mining CSFSC model, which stands for the first letters of CFS, SMOTE, FCM, SVMOAO and C5.0, integrating data-preprocessing approaches, a classification method and a rule generation mechanism for analyzing CSR data. The data-preprocessing approaches include correlation-based feature selection (CFS), the synthetic minority over-sampling technique (SMOTE) and the fuzzy c-means (FCM) clustering algorithm. The support vector machine one-against-one (SVMOAO) method was employed as a classifier for performing multiclassification, and the C5.0 decision tree algorithm was utilized to generate rules from the results of the SVMOAO model. In this study, CSR data collected from China’s listed firms in 2010 were used to test the performance of the proposed model. The empirical results showed that the designed CSFSC model yields satisfactory classification accuracy, and can provide rules for decision makers. Therefore, the presented CSFSC model is a feasible and effective alternative in analyzing CSR data.

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References

  1. Baden DA, Harwood IA, Woodward DG (2009) The effect of buyer pressure on suppliers in SMEs to demonstrate CSR practices: an added incentive or counter productive? Eur Manag J 27:429–441

    Article  Google Scholar 

  2. Batista GEAPA, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newsl 6:20–29

    Article  Google Scholar 

  3. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    Book  MATH  Google Scholar 

  4. Boli J, Hartsuijker D (2001) world culture and transnational corporations: sketch of a project. International Conference on Effects of and Responses to Globalization, Istanbul

  5. Bowen HR (1953) Social responsibilities of the businessman. Harper & Row, New York

    Google Scholar 

  6. Breiman L, Friedman JH, Olshen RA (1984) Classification and regression trees. Wadsworth International Group, Belmont

    MATH  Google Scholar 

  7. Carroll AB (1979) A three-dimensional conceptual model of corporate performance. Acad Manag Rev 4:497–505

    Google Scholar 

  8. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. Artif Intel Res 16:321–357

    MATH  Google Scholar 

  9. Chen T (2007) Incorporating fuzzy c-means and a back-propagation network ensemble to job completion time prediction in a semiconductor fabrication factory. Fuzzy Sets Syst 158:2153–2168

    Article  MathSciNet  Google Scholar 

  10. Chiu SC, Sharfman M (2011) Legitimacy, visibility, and the antecedents of corporate social performance: an investigation of the instrumental perspective. J Manag 37:1558–1585

    Google Scholar 

  11. Choi JS, Kwak YM, Choe C (2010) Corporate social responsibility and corporate financial performance: evidence from Korea. Aust J Manag 35:291–311

    Article  Google Scholar 

  12. Consolandi C, Innocenti A, Vercelli A (2009) CSR, rationality and the ethical preferences of investors in a laboratory experiment. Res Econ 63:242–252

    Article  Google Scholar 

  13. Davis K, Blomstrom RL (1975) Business and society: environment and responsibility, 3rd edn. McGraw-Hill Book Company, New York, p 39

    Google Scholar 

  14. Feng J, Jiao LC, Zhang X, Gong M, Sun T (2013) Robust non-local fuzzy c-means algorithm with edge preservation for SAR image segmentation. Sig Process 93:487–499

    Article  Google Scholar 

  15. Foroughi H, Rezvanian A, Paziraee A (2008) Robust fall detection using human shape and multi-class support vector machine. In: Sixth Indian conference on computer vision, graphics & image processing, 2008. ICVGIP ‘08, pp 413–420

  16. Freeman RE (1984) Strategic management: a stakeholder approach. Pitman Publishing Inc., Boston

    Google Scholar 

  17. Friedman M (1970) The social responsibility of business is to increase its profits. The New York Times Magazine

  18. Galbreath J, Shum P (2012) Do customer satisfaction and reputation mediate the CSR–FP link? Evidence from Australia. Aust J Manag 37:211–229

    Article  Google Scholar 

  19. Garay L, Font X (2012) Doing good to do well? Corporate social responsibility reasons, practices and impacts in small and medium accommodation enterprises. Int J Hosp Manag 31:329–337

    Article  Google Scholar 

  20. Gao M, Hong X, Chen S, Harris CJ (2011) A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems. Neurocomputing 74:3456–3466

    Article  Google Scholar 

  21. Ghaderi H, Kabiri P (2012) Fourier transform and correlation-based feature selection for fault detection of automobile engines. In: The 16th CSI international symposium on artificial intelligence and signal processing (AISP 2012), pp 514–519

  22. Ghoul SE, Guedhami O, Kwok Chuck CY, Mishra Dev R (2011) Does corporate social responsibility affect the cost of capital? J Bank Finance 35:2388–2406

    Article  Google Scholar 

  23. Hall MA (1999) Correlation-based feature subset selection for machine learning, PhD thesis, Department of Computer Science. University of Waikato. Hamilton, New Zealand

  24. Hasan A, Adnan Md A (2012) High dimensional microarray data classification using correlation based feature selection. In: 2012 International conference on biomedical engineering (ICoBE), pp 319–321

  25. Hua Z, Wang Y, Xu X, Zhang B, Liang L (2007) Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Syst Appl 33:434–440

    Article  Google Scholar 

  26. Hung WL, Yang MS, Chen DH (2008) Bootstrapping approach to feature-weight selection in fuzzy c-means algorithms with an application in color image segmentation. Pattern Recogn Lett 29:1317–1325

    Article  Google Scholar 

  27. Huseynov F, Klamm BK (2012) Tax avoidance, tax management and corporate social responsibility. J Corp Finance 18:804–827

    Article  Google Scholar 

  28. Husted BW, Allen DB (2007) Strategic corporate social responsibility and value creation among large firms: lessons from the Spanish experience. Long Range Plan 40:594–610

    Article  Google Scholar 

  29. Iliadis LS, Vangeloudh M, Spartalis S (2010) An intelligent system employing an enhanced fuzzy c-means clustering model: application in the case of forest fires. Comput Electron Agric 70:276–284

    Article  Google Scholar 

  30. Inoue Y, Lee S (2011) Effects of different dimensions of corporate social responsibility on corporate financial performance in tourism-related industries. Tour Manag 32:790–804

    Article  Google Scholar 

  31. Kaur P, Soni AK, Gosain A (2012) A Robust Kernelized Intuitionistic Fuzzy C-means clustering algorithm in segmentation of noisy medical images. Pattern Recogn Lett 34:163–175

    Article  Google Scholar 

  32. Karegowda AG, Jayaram MA (2009) Cascading GA & CFS for feature subset selection in medical data mining. In: 2009 IEEE international advance computing conference (IACC 2009) Patiala, India, pp 6–7

  33. Knerr S, Personnaz L, Dreyfus G (1990) Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Fogelman J (ed) Neurocomputing: algorithms, architectures and applications. Springer, New York

    Google Scholar 

  34. Kong D (2012) Does corporate social responsibility matter in the food industry? Evidence from a nature experiment in China. Food Policy 37:323–334

    Article  Google Scholar 

  35. Liu B, Hao Z, Tsang ECC (2008) Nesting one-against-one algorithm based on SVMs for pattern classification. IEEE Trans Neural Networks 19:2044–2052

    Article  Google Scholar 

  36. Maciejewski T, Stefanowski J (2011) Local neighbourhood extension of SMOTE for mining imbalanced data. In: IEEE symposium on computational intelligence and data mining (CIDM) 2011, pp 104–111

  37. Maignan I, Ferrell OC (2003) Nature of corporate responsibilities perspectives from American, French, and German consumers. J Bus Res 56:55–67

    Article  Google Scholar 

  38. McWilliams A, Siegel D (2000) Research notes and communications: corporate social responsibility and financial performance: correlation or misspecification? Strat Manag J 21:603–609

    Article  Google Scholar 

  39. Muata K, Bryson O (2007) Post-pruning in decision tree induction using multiple performance measures. Comput Oper Res 34:3331–3345

    Article  MATH  Google Scholar 

  40. Niblett T (1987) Constructing decision trees in noisy domains. In: Proceedings of the second European working session on learning. Sigma Press, Bled, pp 67–78

  41. Nunnally JC (1978) Psychometric theory, 2nd edn. McGraw-Hill, New York

    Google Scholar 

  42. Park DC (2009) Classification of audio signals using Fuzzy c-Means with divergence-based Kernel. Pattern Recogn Lett 30:794–798

    Article  Google Scholar 

  43. Porter ME, Kramer MR (2006) Strategy & society—the link between competitive advantage and corporate social responsibility and environmental management. Harvard Bus Rev 84:78–92

    Google Scholar 

  44. Quinlan JR (1993). C4.5: programs for machine learning. Morgan Kaufmann, San Mateo, CA

  45. Rahim RA, Jalaludin FW, Tajuddin K (2011) The importance of corporate social responsibility on consumer behaviour in Malaysia. Asian Acad Manag J 16:119–139

    Google Scholar 

  46. Siegel DS, Vitaliano DF (2007) An empirical analysis of the strategic use of corporate social responsibility. J Econ Manag Strat 16:773–792

    Article  Google Scholar 

  47. Smith V, Langford P (2011) Responsible or redundant? Engaging the workforce through corporate social responsibility. Aust J Manag 36:425–447

    Article  Google Scholar 

  48. Taft LM, Evans RS, Shyu CR, Egger MJ, Chawla N, Mitchell JA (2009) Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery. J Biomed Inform 42:356–364

    Article  Google Scholar 

  49. Tan KS, Isa NAM (2011) Color image segmentation using histogram thresholding—Fuzzy C-means hybrid approach. Pattern Recogn Lett 44:1–15

    Article  MATH  Google Scholar 

  50. Tang L, Li H (2009) Corporate social responsibility communication of Chinese and global corporations in China. Public Relat Rev 35:199–212

    Article  Google Scholar 

  51. Usunier JC, Furrer O (2011) A. Furrer-Perrinjaquet, The perceived trade-off between corporate social and economic responsibility: a cross-national study. Int J Cross Cult Manag 11:279–302

    Article  Google Scholar 

  52. Vapnik VN (1995) The nature of statistical learning theory. Springer, London

    Book  MATH  Google Scholar 

  53. Wang TY, Chiang HM (2009) One-against-one fuzzy support vector machine classifier: an approach to text categorization. Expert Syst Appl 36:10030–10034

    Article  Google Scholar 

  54. Wang Y, Simon M, Bonde P, Harris BU, Teuteberg JJ, Kormos RL, Antaki JF (2012) Prognosis of right ventricular failure in patients with left ventricular assist device based on decision tree with SMOTE. IEEE Trans Inf Technol Biomed 16:383–390

    Article  Google Scholar 

  55. Weber M (2008) The business case for corporate social responsibility: a company-level measurement approach for CSR. Eur Manag J 26:247–261

    Article  Google Scholar 

  56. Zairi M, Peters J (2002) The impact of social responsibility on business performance. Manag Audit J 17:174–178

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC 101-2410-H-260-005-MY2, MOST 103-2410-H-260-020 and MOST 103-2410-H-262-010.

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Authors and Affiliations

  1. Department of Information Management, National Chi Nan University, 1, University Rd., Puli, Nantou, 545, Taiwan, ROC

    Ping-Feng Pai & Lei-Chun Chen

  2. Department of Information Management, Lunghwa University of Science and Technology, 300, Sec. 1, Wanshou Rd., Guishan, Taoyuan, 33306, Taiwan, ROC

    Kuo-Ping Lin

Authors
  1. Ping-Feng Pai
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  2. Lei-Chun Chen
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  3. Kuo-Ping Lin
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Corresponding author

Correspondence to Ping-Feng Pai.

Appendix

Appendix

See Tables 10 and 11.

Table 10 Second-level indicators
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Table 11 Selected 20 rules derived from the CSFSC model
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Pai, PF., Chen, LC. & Lin, KP. A hybrid data mining model in analyzing corporate social responsibility. Neural Comput & Applic 27, 749–760 (2016). https://doi.org/10.1007/s00521-015-1893-0

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