Share:


Decision making by extracting soft information from CSR news report

    Sin-Jin Lin Affiliation
    ; Ming-Fu Hsu Affiliation

Abstract

This study examines the impact of corporate social responsibility (CSR) news reports on corporate operating performance forecasting using a large database of publicly-listed electronics firms in Taiwan. Applying text mining techniques and latent topic modelling, we construct and measure the intensity of the CSR-corpus index (ICSRI), which can compress tremendous amounts of CSR textual information content into synthesized meaningful dimensions. By doing so, we are able to break down CSR news reports into multiple dimensions and then examine which dimension(s) affects operating performance. To offer decision-makers with a comprehensive, overarching view of the corporate’s operations, this study incorporates balanced scorecards (BSC) and multiple criteria decision analysis (MCDA) to form a final performance rank. The proposed approach, supported by real samples, can assist both internal and external stakeholders in allocating scarce resources to specific CSR dimensions to enhance a corporate’s growth potential as well as to achieve a win-win situation.

Keyword : corporate social responsibility, multiple criteria decision analysis, text mining, topic modelling, decision making

How to Cite
Lin, S.-J., & Hsu, M.-F. (2018). Decision making by extracting soft information from CSR news report. Technological and Economic Development of Economy, 24(4), 1344-1361. https://doi.org/10.3846/tede.2018.3121
Published in Issue
Jun 29, 2018
Abstract Views
60
PDF Downloads
48
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23, 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x

Asmild, M., & Zhu, M. (2016). Controlling for the use of extreme weights in bank efficiency assessments during the financial crisis. European Journal of Operational Research, 251, 999-1015. https://doi.org/10.1016/j.ejor.2015.12.021

Barcos, L., Barroso, A., Surroca, J., & J. Tribó, A. (2013). Corporate social responsibility and inventory policy. International Journal of Production Economics, 143, 580-588. https://doi.org/10.1016/j.ijpe.2012.04.005

Barnard, K., Duygulu, P., de Freitas, N., Forsyth, D., Blei, D., & Jordan, M. (2003). Matching words and pictures. Journal of Machine Learning Research, 3, 1107-1135.

Beaver, W. H. (1996). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171

Buncic, D., & Piras, G. D. (2016). Heterogeneous agents, the financial crisis and exchange rate predictability. Journal of International Money and Finance, 60, 313-359. https://doi.org/10.1016/j.jimonfin.2015.09.006

Blei, D., Ng, A., & Jordan, M. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.

Bohl, M. T., Michaelis, P., & Siklos, P. L. (2016). Austerity and recovery: exchange rate regime choice, economic growth, and financial crises. Economic Modelling, 53, 195-207. https://doi.org/10.1016/j.econmod.2015.11.017

Carroll, A. B. (1979). A three-dimensional model of corporate performance. Academy of Management Review, 4, 497-505. https://doi.org/10.5465/amr.1979.4498296

Carroll, A. B. (1991). The pyramid of corporate social responsibility: toward the moral management of organizational stakeholders. Business Horizons, 34, 39-48. https://doi.org/10.1016/0007-6813(91)90005-G

Carroll, A. B. (1998). The four faces of corporate citizenship. Business and Society Review, 100, 1-7. https://doi.org/10.1111/0045-3609.00008

Cavaco, S., & Crifo, P. (2014). CSR and financial performance: complementarity between environmental, social and business behaviours. Applied Economics, 46, 3323-3338. https://doi.org/10.1080/00036846.2014.927572

Chong, Y. Q., Wang, B. T., Gladys, L. Y., & Cheong, S. A. (2014). Diversified firms on dynamical supply chain cope with financial crisis better. International Journal of Production Economics, 150, 239-245. https://doi.org/10.1016/j.ijpe.2013.12.030

Cheng, B., Ioannou, I., & Serafeim, G. (2014). Corporate social responsibility and access to finance. Strategic Management Journal, 35, 1-23. https://doi.org/10.1002/smj.2131

Chen, X., Yang, J., Ye, Q., & Liang, J. (2011). Recursive projection twin support vector machine via within-class variance minimization. Pattern Recognition, 44, 2643-2655. https://doi.org/10.1016/j.patcog.2011.03.001

Crifo, P., Diaye, M. A., & Pekovic, S. (2016). CSR related management practices and firm performance: an empirical analysis of the quantity-quality trade-off on French data. International Journal of Production Economics, 171, 405-416. https://doi.org/10.1016/j.ijpe.2014.12.019

Crouch, C. J., & Yang, B. (1992). Experiments in automatic statistical thesaurus construction. Proceeding SIGIR ‘92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 77-88). https://doi.org/10.1145/133160.133180

Costea, A., Ferrara, M., & Şerban, F. (2017). An integrated two-stage methodology for optimising the accuracy of performance classification models. Technological and Economic Development of Economy, 23, 111-139. https://doi.org/10.3846/20294913.2016.1213196

CONE (2004). Cone Corporate Citizenship Study. Retrieved from http://mycoachescorner.com/media/2004ConeCorporateCitizenshipStudy.pdf

Commission of the European Communities (2001). Green Paper: Promoting a European framework for corporate social responsibility. Retrieved from https://www.iisd.org/business/issues/eu_green_paper.aspx

Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent Data Analysis, 1, 131-156. https://doi.org/10.1016/S1088-467X(97)00008-5

Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1-30.

Ding, S., & Hua, X. (2014). Recursive least squares projection twin support vector machines for nonlinear classification. Neurocomputing, 130, 3-9. https://doi.org/10.1016/j.neucom.2013.02.046

Eccles, R., Ioannou, I., & Serafeim, G. (2012). The impact of a corporate culture of sustainability on corporate behavior and performance. Harvard Business School Working Paper.

Fatma, M., Rahman, Z., & Khan, I. (2016). Measuring consumer perception of CSR in tourism industry: scale development and validation. Journal of Hospitality and Tourism Management, 27, 39-48. https://doi.org/10.1016/j.jhtm.2016.03.002

Fatma, M., & Rahman, Z. (2016). The CSR’s influence on customer responses in Indian banking sector. Journal of Retailing and Consumer Services, 29, 49-57. https://doi.org/10.1016/j.jretconser.2015.11.008

Freeman, R. (1984). Strategic management: a stakeholder perspective. Piman: Boston, MA.

Geng, R., Bose, I., & Chen, X. (2015). Prediction of financial distress: an empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241, 236-247. https://doi.org/10.1016/j.ejor.2014.08.016

Grigoroudis, E., Orfanoudaki, E., & Zopounidis, C. (2012). Strategic performance measurement in a healthcare organisation: a multiple criteria approach based on balanced scorecard. Omega, 40, 104-119. https://doi.org/10.1016/j.omega.2011.04.001

Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Science, 101, 5228-5235. https://doi.org/10.1073/pnas.0307752101

Hillman, A. J., & Keim, G. D. (2001). Shareholder value, stakeholder management, and social issues: what’s the bottom line?. Strategic Management Journal, 22, 125-139. https://doi.org/10.1002/1097-0266(200101)22:2<125::AID-SMJ150>3.0.CO;2-H

Huang, H., Amy Zang Y., & Zheng, R. (2014). Evidence on the information content of text in analyst reports. Accounting Review, 89, 2151-2180. https://doi.org/10.2308/accr-50833

Hull, C. E., & Rothenberg, S. (2008). Firm performance: the interactions of corporate social performance with innovation and industry differentiation. Strategic Management Journal, 29, 781-789. https://doi.org/10.1002/smj.675

Hyun, J. (2016). Financial crises and the evolution of credit reallocation: evidence from Korea. Economic Modelling, 56, 25-34. https://doi.org/10.1016/j.econmod.2016.03.019

Isaksson, R., & Steimle, U. (2009). What does GRI – reporting tell us about corporate sustainability?. The TQM Journal, 21, 168-181. https://doi.org/10.1108/17542730910938155

International Organization for Standardization. (2010). ISO 26000 – Social responsibility. Retrieved from https://www.iso.org/iso-26000-social-responsibility.html

Jayadeva, Khemchandani, R., & Chandra S. (2007). Twin support vector machine for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 905-910. https://doi.org/10.1109/TPAMI.2007.1068

Jensen, R., & Parthaláin, N. M. (2015). Towards scalable fuzzy–rough feature selection. Information Sciences, 323, 1-15. https://doi.org/10.1016/j.ins.2015.06.025

Jones, F. L. (1987). Current techniques in bankruptcy prediction. Journal of Accounting Literature, 6, 131-164.

Kapstein, E. (2001). The corporate ethics crusade. Foreign Affairs, 80, 105-119. https://doi.org/10.2307/20050254

Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard measures that drive performance. Harvard Business Review, 70, 71-79.

Kaplan, R. S., & Norton, D. P. (1993). Putting the balanced scorecard to work. Harvard Business Review, 71, 134-147.

Kumar, M. A., & Gopal, M. (2009). Least squares twin support vector machines for pattern classification. Expert Systems with Applications, 36, 7535-7543. https://doi.org/10.1016/j.eswa.2008.09.066

Kumar, M. A., Khemchandani, R., Gopal, M., & Chandra, S. (2010). Knowledge based least squares twin support vector machines. Information Sciences, 180, 4606-4618. https://doi.org/10.1016/j.ins.2010.07.034

Kou, G., Peng, Y., Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1-12. https://doi.org/10.1016/j.ins.2014.02.137

Li, F. (2010). The information content of forward-looking statements in corporate filings – a naïve Bayesian machine learning approach. Journal of Accounting Research, 48, 1049-1102. https://doi.org/10.1111/j.1475-679X.2010.00382.x

Magni, C. A. (2015). Investment, financing and the role of ROA and WACC in value creation. European Journal of Operational Research, 244, 855-866. https://doi.org/10.1016/j.ejor.2015.02.010

Malmi, T. (2001). Balanced scorecards in Finnish companies: a research note. Management Accounting Research, 12, 207-220. https://doi.org/10.1006/mare.2000.0154

Mollah, S., Quoreshi, A. M. M. S., & Zafirov, G. (2016). Equity market contagion during global financial and Eurozone crises: evidence from a dynamic correlation analysis. Journal of International Financial Markets, Institutions and Money, 41, 151-167. https://doi.org/10.1016/j.intfin.2015.12.010

Mohr, L. A., Webb, D. J., & Harris, K. E. (2001). Do consumers expect companies to be socially responsible? The impact of corporate social responsibility on buying behavior. Journal of Consumer Affairs, 35, 45-72. https://doi.org/10.1111/j.1745-6606.2001.tb00102.x

Navarro, P. (1988). Why do corporations give to charity?. Journal of Business, 61, 65-93. https://doi.org/10.1086/296420

Pantalone, C. C., & Platt, M. B. (1987). Predicting failure of savings and loan associations. Real Estate Economics, 15, 46-64. https://doi.org/10.1111/1540-6229.00418

Peng, Y., Wang, G., Kou, G., & Shi, Y. (2011). An empirical study of classification algorithm evaluation for financial risk prediction. Applied Soft Computing, 11, 2906-2915. https://doi.org/10.1016/j.asoc.2010.11.028

Peng, H., & Fan, Y. (2017). Feature selection by optimizing a lower bound of conditional mutual information. Information Sciences, 418-419, 652-667. https://doi.org/10.1016/j.ins.2017.08.036

Rodriguez-Fernandez, M. (2016). Social responsibility and financial performance: the role of good corporate governance. BRQ Business Research Quarterly, 19, 137-151. https://doi.org/10.1016/j.brq.2015.08.001

Shirata, Cindy Y., Takeuchi, H., Ogino, S., & Watanabe, H. (2011). Extracting key phrases as predictors of corporate bankruptcy: empirical analysis of annual reports by text mining. Journal of Emerging Technologies in Accounting, 8, 31-44. https://doi.org/10.2308/jeta-10182

Sen, S., & Bhattacharya, C. B. (2001). Does doing good always lead to doing better? Consumer reactions to corporate social responsibility. Journal of Marketing Research, 38, 225-243. https://doi.org/10.1509/jmkr.38.2.225.18838

Shao, Y. H., Zhang, C. H., Wang, X. B., & Deng, N. Y. (2011). Improvements on Twin Support Vector Machines. IEEE Transactions on Neural Networks, 22, 962-968. https://doi.org/10.1109/TNN.2011.2130540

Shao, Y. H., Wang, Z., Chen, W. J., & Deng, N. Y. (2013). A regularization for the projection twin support vector machine. Knowledge-Based Systems, 37, 203-210. https://doi.org/10.1016/j.knosys.2012.08.001

Shao, Y. H., Chen, W. J., Wang, Z., Li, C. N., & Deng, N. Y. (2015). Weighted linear loss twin support vector machine for large-scale classification. Knowledge-Based Systems, 73, 276-288. https://doi.org/10.1016/j.knosys.2014.10.011

Shao, Y. H., & Deng, N. Y. (2012). A coordinate descent margin based-twin support vector machine for classification. Neural Networks, 25, 114-121. https://doi.org/10.1016/j.neunet.2011.08.003

Surroca, J., Tribo, J. A., & Waddock, S. (2010). Corporate responsibility and financial performance: the role of intangible resources. Strategic Management Journal, 31, 463-490. https://doi.org/10.1002/smj.820

Xu, X., & Wang, Y. (2009). Financial failure prediction using efficiency as a predictor. Expert Systems with Applications, 36, 366-373. https://doi.org/10.1016/j.eswa.2007.09.040

Zhu, Q., Liu, J., & Lai, K. (2016). Corporate social responsibility practices and performance improvement among Chinese national state-owned enterprises. International Journal of Production Economics, 171, 417-426. https://doi.org/10.1016/j.ijpe.2015.08.005

Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82. https://doi.org/10.2307/2490859

Zhang, Y., Song, X., & Gong, D. (2017). A return-cost-based binary firefly algorithm for feature selection. Information Sciences, 418-419, 561-574. https://doi.org/10.1016/j.ins.2017.08.047