Skip to main content

Effects of innovation management system standardization on firms: evidence from text mining annual reports


Using a management formula to standardize innovation management can be thought of as deeply contradictory, however, several successful firms in Spain have been certified under the pioneer innovation management standard UNE 166002. This paper analyzes the effects that standardization has in the attitudes and values as regard to innovation for a sample of firms by text-mining their corporate disclosures. Changes in the relevance of the concepts, co-word networks and emotion analysis have been employed to conclude that the effects of certification on the corporate behavior about innovation are coincident with the open innovation and transversalization concepts that UNE 166002 promotes.

This is a preview of subscription content, access via your institution.

Fig. 1

Source Adapted from AENOR (2014)

Fig. 2
Fig. 3
Fig. 4

Source Wikimedia Commons

Fig. 5
Fig. 6
Fig. 7


  1. AENOR. (2014). Gestión de la I+D+i: Requisitos del Sistema de Gestión de la I+D+i. UNE 166002:2014.

  2. AENOR. (2016). UNE 166002 R&D&I management systems. Retrieved from

  3. Anderson, S. (1999). Why firms seek ISO 9000 certification: Regulatory compliance or competitive advantage? Production and Operations Management, 8(1), 28–43.

    Article  Google Scholar 

  4. Back, B., Toivonen, J., Vanharanta, H., & Visa, A. (2001). Comparing numerical data and text information from annual reports using self-organizing maps. International Journal of Accounting Information Systems, 2(4), 249–269. doi:10.1016/S1467-0895(01)00018-5.

    Article  Google Scholar 

  5. Balakrishnan, R., Qiu, X. Y., & Srinivasan, P. (2010). On the predictive ability of narrative disclosures in annual reports. European Journal of Operational Research, 202(3), 789–801. doi:10.1016/j.ejor.2009.06.023.

    Article  MATH  Google Scholar 

  6. Butler, M., & Kešelj, V. (2009). Financial forecasting using character N-Gram analysis and readability scores of annual reports. In Y. Gao & N. Japkowicz (Eds.), Advances in Artificial Intelligence (pp. 39–51). Berlin: Springer. doi:10.1007/978-3-642-01818-3_7.

    Chapter  Google Scholar 

  7. Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010). Making words work: Using financial text as a predictor of financial events. Decision Support Systems, 50(1), 164–175. doi:10.1016/j.dss.2010.07.012.

    Article  Google Scholar 

  8. Glynn, M. A. (1996). Innovative genius: a framework for relating individual and organizational intelligences to innovation. Academy of Management Review, 21(4), 1081–1111. doi:10.5465/AMR.1996.9704071864.

    Google Scholar 

  9. Goel, S., & Gangolly, J. (2012). Beyond the numbers: mining the annual reports for hidden cues indicative of financial statement fraud. Intelligent Systems in Accounting, Finance and Management, 19(2), 75–89. doi:10.1002/isaf.1326.

    Article  Google Scholar 

  10. Gök, A., Waterworth, A., & Shapira, P. (2015). Use of web mining in studying innovation. Scientometrics, 102(1), 653–671. doi:10.1007/s11192-014-1434-0.

    Article  Google Scholar 

  11. Kenneth, J. M. (2011). More than numbers: R&D-related disclosure and firm performance. Dissertation, University of Michigan.

  12. Kim, D.-Y., Kumar, V., & Kumar, U. (2012). Relationship between quality management practices and innovation. Journal of Operations Management, 30(4), 295–315. doi:10.1016/j.jom.2012.02.003.

    Article  Google Scholar 

  13. Kloptchenko, A., Eklund, T., Karlsson, J., Back, B., Vanharanta, H., & Visa, A. (2004). Combining data and text mining techniques for analysing financial reports. Intelligent Systems in Accounting, Finance & Management, 12(1), 29–41. doi:10.1002/isaf.239.

    Article  Google Scholar 

  14. Li, F. (2006). Do stock market investors understand the risk sentiment of corporate annual reports? SSRN Electronic Journal. doi:10.2139/ssrn.898181.

    Google Scholar 

  15. Li, F. (2010). Textual analysis of corporate disclosures: A survey of the literature. Journal of Accounting Literature, 29, 143–165.

    Google Scholar 

  16. Libaers, D., Hicks, D., & Porter, A. L. (2016). A taxonomy of small firm technology commercialization. Industrial and Corporate Change, 25(3), 371–405. doi:10.1093/icc/dtq039.

    Article  Google Scholar 

  17. Liew, W. Te, Adhitya, A., & Srinivasan, R. (2014). Sustainability trends in the process industries: A text mining-based analysis. Computers in Industry, 65(3), 393–400. doi:10.1016/j.compind.2014.01.004.

    Article  Google Scholar 

  18. Liu, Z., & Jiang, Y. (2014). Analysis of multi-dimensional characteristics of corporate social responsibility report using China’s listed companies in 2011 as the case study. Journal of Nanjing University of Finance and Economics, 5, 10–18.

    Google Scholar 

  19. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35–65. doi:10.1111/j.1540-6261.2010.01625.x.

    Article  Google Scholar 

  20. Mir-Mauri, M., & Casadesús, M. (2011). Standardised innovation management systems: A case study of the Spanish Standard UNE 166002:2006. Innovar, 21(40), 171–188.

    Google Scholar 

  21. Mir-Mauri, M., & Fa, M. (2008). Une 166002: 2006: Estandarizar y sistematizar la i+d+i la norma y la importancia de las tic en su implementación. DYNA-Ingeniería E Industria, 83(6), 325–331.

    Google Scholar 

  22. Modapothala, J. R., & Issac, B. (2009). Study of economic, environmental and social factors in sustainability reports using text mining and Bayesian analysis. In 2009 IEEE symposium on industrial electronics & applications (pp. 209–214). doi:10.1109/ISIEA.2009.5356467.

  23. Mohammad, S., & Turney, P. (2013). Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3), 436–465.

    MathSciNet  Article  Google Scholar 

  24. Naveh, E., & Erez, M. (2004). Innovation and attention to detail in the quality improvement paradigm. Management Science, 50(11), 1576–1586.

    Article  Google Scholar 

  25. Pekovic, S., & Galia, F. (2009). From quality to innovation: Evidence from two French employer surveys. Technovation, 29(12), 829–842. doi:10.1016/j.technovation.2009.08.002.

    Article  Google Scholar 

  26. Pineda, Á. (2015). Sistematizar con éxito la innovación: UNE 166002 Integrated Circuits Málaga. AENOR: Revista de la normalización y la certificación, 309, 44–47.

    Google Scholar 

  27. Prajogo, D. I., & Hong, S. W. (2008). The effect of TQM on performance in R&D environments: A perspective from South Korean firms. Technovation, 28(12), 855–863. doi:10.1016/j.technovation.2008.06.001.

    Article  Google Scholar 

  28. Prajogo, D. I., & Sohal, A. S. (2004). The multidimensionality of TQM practices in determining quality and innovation performance—An empirical examination. Technovation, 24(6), 443–453. doi:10.1016/S0166-4972(02)00122-0.

    Article  Google Scholar 

  29. Qiu, X. Y., Srinivasan, P., & Hu, Y. (2014). Supervised learning models to predict firm performance with annual reports: An empirical study. Journal of the Association for Information Science and Technology, 65(2), 400–413. doi:10.1002/asi.22983.

    Article  Google Scholar 

  30. Saha, A., & Nabareseh, S. (2015). Communicating corporate social responsibilities: Using text mining for a comparative analysis of banks in India and Ghana. Mediterranean Journal of Social Sciences, 6(3), 11–20. doi:10.5901/mjss.2015.v6n3s1p11.

    Google Scholar 

  31. Shirata, C. 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(1), 31–44. doi:10.2308/jeta-10182.

    Article  Google Scholar 

  32. Terziovski, M., & Guerrero, J.-L. (2014). ISO 9000 quality system certification and its impact on product and process innovation performance. International Journal of Production Economics, 158, 197–207. doi:10.1016/j.ijpe.2014.08.011.

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Rosa Río-Belver.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 31 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Garechana, G., Río-Belver, R., Bildosola, I. et al. Effects of innovation management system standardization on firms: evidence from text mining annual reports. Scientometrics 111, 1987–1999 (2017).

Download citation


  • UNE 166002
  • Innovation management
  • Text mining
  • Sentiment analysis
  • Management standards