Integration of machine learning insights into organizational learning: A case of B2B sales forecasting

Lecture Notes in Information Systems and Organisation

Volumn 19, Issue , 2016, Pages 71-85

  Bohanec, Marko ^a   Borštnar, Mirjana Kljajić ^b   Robnik Šikonja, Marko ^c  

^a Salvirt doo   (Slovenia)

^b UNIVERSITY OF MARIBOR   (Slovenia)

^c UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84976320184     PISSN: 21954968     EISSN: 21954976     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-319-38974-5_7     Document Type: Chapter

References (30)

1. Gronhaug, K., Stone, R.: The learning organization. Compet. Rev. Int. Bus. J. 22(3) (2012)
2. Kljajić Borštnar, M., Kljajić, M., Škraba, A., Kofjač, D., Rajkovič, V.: The relevance of facilitation in group decision making supported by a simulation model. Syst. Dyn. Rev. 27(3), 270–293 (2011) ISSN 0883–7066
3. Locke, J.: An essay concerning human understanding (Slovenian translation). Slovenska šolska matica, Ljubljana (1924)
4. Argyris, C.: On Organizational Learning. Blackwell, New York (1996)
5. DiBella, A.J., Nevis, E.C.: How Organizations Learn. Jossey-Bass, San Francisco, USA (1998)
6. Gephart, M.A., Marsick, V.J., Mark, E., VanBuren, M.E., Spiro, M.S.: Learning organizations come alive. Training Development 50(12), 36–41 (1996)
7. Nonaka, I., Takeuchi, H.: The Knowledge Creating Organization. Oxford University Press, New York (1995)
8. Senge, P.: The Fifth discipline: The Art & Practice of the Learning Organization. Doubleday Currency, New York (1990)
9. Bohanec, M., Kljajić Borštnar, M., Robnik-Šikonja, M.: Modeling attributes for forecasting B2B opportunities acquisition. In: Proceedings of 34th Conference of Organizational science development, Portorož, Slovenia (2015)
10. Ngai, E.W.T., Xiu, L., Chau, D.C.K.: Application of data mining techniques in CRM: a literature review and classification. Expert Syst. Appl. 36, 2592–2602 (2009)
11. Monat, J. P.: Industrial sales lead conversion modeling. Market. Intell. Plan. 29(2), 178–194 (2011)
12. Rieg, R.: Do forecast improve over time? Int. J. Account. Inform. Manage. 18(3) (2010)
13. Alvarado-Valencia, J.A., Barrero, L.H.: Reliance, trust and heuristics in judgmental forecasting. Comput. Hum. Behav. 36, 102–113 (2014)
14. Maaß, D., Spruit, M., Waal, P.D.: Improving short-term demand forecasting for short-lifecycle consumer products with data mining techniques, pp. 1–17. Decision Analytics, Springer (2014)
15. Witten, I.H., Eibe, F., Hall, M.A.: Data mining—practical machine learning tools and techniques, 3rd edn. Elsevier (2011)
16. Liao, S.H., Chu, P.H., Hsiao, P.Y.: Data mining techniques and applications—a decade review from 2000 to 2011. Expert Syst. Appl. 39 (2012)
17. Bose, I., Mahapatra, R.K.: Business data mining—a machine learning perspective. Inf. Manag. 39(3), 211–225 (2001)
18. Robnik-Šikonja, M., Kononenko, I.: Explaining classification for individual instances. IEEE Trans. Knowl. Data Eng. 20(5), 589–600 (2008)
19. Collopy F., Adya M., Armstrong, J.S.: Expert systems for forecasting. In: Principles of Forecasting: A Handbook for Researchers and Practitioner. Kluwer (2001)
20. Sein, M.K., Henfridsson, O., Purao, S., Rossi, M., Lindgreen, R.: Action design research. MIS Q. 35, 37–56 (2011)
21. Robnik-Šikonja, M.: SemiArtificial: generator of semi-artificial data, R package version 1.2.0. http://cran.r-project.org/package=semiArtificial (2014)
22. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0 Step-by-step data mining guides, ftp://software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf (2000)
23. Shmueli, G., Patel, N.R., Bruce, P.C.: Data mining for business intelligence: concepts, techniques, and applications in microsoft office excel with XLMiner. Wiley (2007)
24. Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956)
25. Demšar, J., Zupan, B.: Orange: Data mining fruitful and fun—a historical perspective. Informatica 37, 55–60 http://orange.biolab.si (2013)
26. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
27. McCarthy Byrne, T.M., Moon, M.A., Mentzer, J.T.: Motivating the industrial sales force in the sales forecasting process. Indus. Market. Manage. 40, 128–138 (2011)
28. Breiman, L.: Random forests. Mach. Learn. J. 45, 5–32 (2001)
29. Elisseeff, A., Pontil, M.: Leave-one-out error and stability of learning algorithms with applications. In: Suykens, J.A.K. (ed.) Advances in Learning Theory: Methods, Models and Applications. IOS Press (2003)
30. Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds.): Visual Data Mining. Springer, Lecture Notes in Computer Science (2008)