Organizational Learning Supported by Machine Learning Models Coupled with General Explanation Methods: A Case of B2B Sales Forecasting

Argyris, C. & Schön, D. (1996). Organizational Learning II: Theory, Method and Practice. Addison Wesley.Search in Google Scholar

Armstrong, J. S., Green, K. C. & Graefe, A. (2015). Golden Rule of Forecasting: Be conservative. Journal of Business Research, 68 (8), 1717-1731, http://dx.doi.org/10.1016/j.jbusres.2015.03.03110.1016/j.jbusres.2015.03.031Search in Google Scholar

Avison, D., & Fitzgerald, G. (2006). Methodologies for Developing Information Systems : A Historical Perspective. The Past and Future of Information Systems, 27–38, https://doi.org/10.1007/978-0-387-34732-5_310.1007/978-0-387-34732-5_3Search in Google Scholar

Bohanec, M. (2016). Anonimized data set for B2B sales history. Retrieved 15.07.2017 from http://www.salvirt.com/research/b2bdatasetSearch in Google Scholar

Bohanec, M., Kljajić Borštnar, M. & Robnik-Šikonja, M. (2015a). Feature subset selection for B2B sales forecasting. In: 13th International Symposium on Operational Research, Bled, Slovenia, 285-290.Search in Google Scholar

Bohanec, M., Kljajić Borštnar, M. & Robnik-Šikonja, M. (2015b). Machine learning data set analysis with visual simulation. In: InterSymp 2015, Baden-Baden, Germany, 16-20.Search in Google Scholar

Bohanec, M., Kljajić Borštnar, M. & Robnik-Šikonja, M. (2016a). Nabor atributov za opisovanje medorganizacijske prodaje [A collection of attributes describing business to business (B2B) sales]. Uporabna informatika, XXIV (2), 74-80.Search in Google Scholar

Bohanec, M., Kljajić Borštnar, M. & Robnik-Šikonja, M. (2016b). Sample size for identification of important attributes in B2B sales. In: 16th International Conference on Operational Research, Osijek, Croatia, 133.Search in Google Scholar

Bohanec, M., Kljajić Borštnar, M. & Robnik-Šikonja, M. (2017a). Explaining Machine Learning Predictions. Expert Systems with Applications, 71, 416-428.10.1016/j.eswa.2016.11.010Search in Google Scholar

Bohanec, M., Robnik-Šikonja, M., & Kljajić Borštnar, M. (2017). Decision-making framework with double-loop learning through interpretable black-box machine learning models. Industrial Management & Data Systems, 117(7), in print (July, 2017b), http://dx.doi.org/10.1108/IMDS-09-2016-040910.1108/IMDS-09-2016-0409Search in Google Scholar

Breiman L. (2001), Random Forests, Machine Learning Journal, 45, 5–32.10.1023/A:1010933404324Search in Google Scholar

Brynjolfsson, E., Hitt, L. M. & Kim, H. H. (2011). Strength in numbers: How does data-driven decision-making affect firm performance? Retrieved 11 May 2017 from http://ebusiness.mit.edu/research/papers/2011.12_Brynjolfsson_Hitt_Kim_Strength%20in%20Numbers_302.pdfSearch in Google Scholar

Caruana, R. and Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd International Conference on Machine Learning (pp. 161–168). New York, NY, USA: ACM.Search in Google Scholar

Davis, D. F. & Mentzer, J. T. (2007). Organizational factors in sales forecasting management. International Journal of Forecasting, 23 (3), 475-495, http://dx.doi.org/10.1016/j.ijforecast.2007.02.00510.1016/j.ijforecast.2007.02.005Search in Google Scholar

D’Haen, J., & Van der Poel, D. (2013), Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework, Industrial Marketing Management 42, 544–551, http://dx.doi.org/10.1016/j.indmarman.2013.03.00610.1016/j.indmarman.2013.03.006Search in Google Scholar

Duran, R. E. (2008). Probabilistic sales forecasting for small and medium-size business operations. In B. Prasad (Ed.), Soft Computing Applications in Business, 129–146, Springer Berlin Heidelberg, http://dx.doi.org/10.1007/978-3-540-79005-1_810.1007/978-3-540-79005-1_8Search in Google Scholar

Fildes, R., Goodwin, P., & Lawrence, M. (2006). The design features of forecasting support systems and their effectiveness. Decision Support Systems, 42(1), 351–361, https://doi.org/10.1016/j.dss.2005.01.00310.1016/j.dss.2005.01.003Search in Google Scholar

Fleischmann, K. R., & Wallace, W. A. (2005). A covenant with transparency. Communications of the ACM, 48(5), 93–97, https://doi.org/10.1145/1060710.106071510.1145/1060710.1060715Search in Google Scholar

Goodwin, P., Fildes, R., Lawrence, M., & Stephens, G. (2011). Restrictiveness and guidance in support systems. Omega, 39(3), 242–253, https://doi.org/10.1016/j.omega.2010.07.00110.1016/j.omega.2010.07.001Search in Google Scholar

Grossler, A., Maier, F. H., & Milling, P. M. (2000). Enhancing Learning Capabilities by Providing Transparency in Business Simulators. Simulation & Gaming, 31(2), 257–278, https://doi.org/10.1177/10468781000310020910.1177/104687810003100209Search in Google Scholar

Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design Science in Information Systems Research. Management Information Systems Quarterly, 28(1), 75–105. https://doi.org/10.2307/2514862510.2307/25148625Search in Google Scholar

Huber, G.P. (1991). Organizational Learning: The Contributing Processes and the Literatures. Organization Science, 2(1), Special Issue: Organizational Learning: Papers in Honor of (and by) James G. March, 88-115, https://doi.org/10.1287/orsc.2.1.8810.1287/orsc.2.1.88Search in Google Scholar

Ingram, T. N., LaForge, R. W., Avila, R. A., Schwepker Jr, C. H. & Williams, M. R. (2012). Sales Management: Analysis and Decision Making. ME Sharpe.Search in Google Scholar

Kerkkänen, A. & Huiskonen, J. (2007). Analysing inaccurate judgmental sales forecasts. European J. Industrial Engineering, 1 (4), 355-369, https://doi.org/10.1504/EJIE.2007.01538710.1504/EJIE.2007.015387Search in Google Scholar

Kljajić, M., & Farr, J. V. (2010). The Role of Systems Engineering in the Development of Information Systems, In M. Hunter (ed.), Strategic Information Systems: Concepts, methodologies, tools, and applications, Hershey, PA, Information Science Reference. 369–381.Search in Google Scholar

Kljajić Borštnar, M., Kljajić, M., Škraba, A., Kofjač, D., & Rajkovič, V. (2011). The relevance of facilitation in group decision making supported by a simulation model. System Dynamics Review, 27(3), 270–293, https://doi.org/10.1002/sdr.46010.1002/sdr.460Search in Google Scholar

Kuchinke, K. P. (2000). The role of feedback in management training settings. Human Resource Development Quarterly, 11 (4), 381–401, http://dx.doi.org/10.1002/1532-1096(200024)11:4%3C381::AID-HRDQ5%3E3.0.CO;2-3Search in Google Scholar

Lawrence, M., Goodwin, P., O’Connor, M. & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22 (3), 493-518, http://dx.doi.org/10.1016/j.ijforecast.2006.03.00710.1016/j.ijforecast.2006.03.007Search in Google Scholar

Lilien, G. L. (2016). The B2B knowledge gap. International Journal of Research in Marketing, 33, 543–556, http://dx.doi.org/10.1016/j.ijresmar.2016.01.00310.1016/j.ijresmar.2016.01.003Search in Google Scholar

Lodato, M. W. (2006). Integrated sales process management: A methodology for improving sales effectiveness in the 21st century. AuthorHouse.Search in Google Scholar

McAfee, A. & Brynjolfsson, E. (2012). Big data. The management revolution. Harvard Business Review, 90 (10), 61–67.Search in Google Scholar

McCarthy, T. M., Davis, D. F., Golicic, S. L. & Mentzer, J. T. (2006). The evolution of sales forecasting management: a 20-year longitudinal study of forecasting practices. Journal of Forecasting, 25 (5), 303-324, http://dx.doi.org/10.1002/for.98910.1002/for.989Search in Google Scholar

Merkert, J., Mueller, M., & Hubl, M. (2015), A Survey of the Application of Machine Learning in Decision Support Systems, 23rd European Conference on Information Systems (ECIS) 2015 Completed Research Papers, Münster, Germany, 26.-29.05.2015.Search in Google Scholar

Monat, J. (2011). Industrial sales lead conversion modeling. Marketing Intelligence and Planning, 29, 178-194, http://dx.doi.org/10.1108/0263450111111761010.1108/02634501111117610Search in Google Scholar

Provost, F. & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1 (1), 51–59, http://dx.doi.org/10.1089/big.2013.150810.1089/big.2013.1508Search in Google Scholar

Robnik-Šikonja, M. & Kononenko, I. (2008). Explaining classifications for individual instances. IEEE Transactions on Knowledge and Data Engineering, 20 (5), 589-600, http://dx.doi.org/10.1109/TKDE.2007.19073410.1109/TKDE.2007.190734Search in Google Scholar

Sein, M., Henfridsson, O., Purao, S., Rossi, M., & Lindgren, R. (2011). Action Design Research. Management Information Systems Quarterly, 35(1). Retrieved 12.01.2017 from http://aisel.aisnet.org/misq/vol35/iss1/510.2307/23043488Search in Google Scholar

Senge P.M & Sterman J.D (1992). Systems thinking and organizational learning—acting locally and thinking globally in the organization of the future. European Journal of Operational Research, 59(1), 137–150, http://dx.doi.org/10.1016/0377-2217(92)90011-W10.1016/0377-2217(92)90011-WSearch in Google Scholar

Simon, H. (1960). The new science of management decision. Prentice-Hall.10.1037/13978-000Search in Google Scholar

Simon, H. A. (1991). Organizations and Markets. Journal of Economic Perspectives, 5(2), 25–44. Retrieved 31.7.2017 from http://people.ds.cam.ac.uk/mb65/mst-ir/documents/simon-1991.pdf10.1257/jep.5.2.25Search in Google Scholar

Sterman, J. D. (1994). Learning in and about complex systems. System Dynamics Review, 10(2–3 (Special Issue: Systems thinkers, systems thinking), 291–330. https://doi.org/10.1002/sdr.426010021410.1002/sdr.4260100214Search in Google Scholar

Söhnchen, F. & Albers, S. (2010). Pipeline management for the acquisition of industrial projects. Industrial Marketing Management, 39 (8), 1356-1364.10.1016/j.indmarman.2010.04.001Search in Google Scholar

Škraba A, Kljajić M, & Kljajić Borštnar M. (2007). The role of information feedback in the management group decision-making process applying system dynamics models. Group Decision and Negotiation, 16, 77–95.10.1007/s10726-006-9035-9Search in Google Scholar

Štrumbelj, E., Kononenko, I. & Robnik-Šikonja, M. (2009). Explaining instance classifications with interactions of subsets of feature values. Data & Knowledge Engineering, 68(10), 886–904, https://doi.org/10.1016/j.datak.2009.01.00410.1016/j.datak.2009.01.004Search in Google Scholar

Verikas, A., Gelzinis, A., & Bacauskiene, M. (2011). Mining data with random forests: A survey and results of new tests, Pattern Recognition, 44(2), 330–349, http://dx.doi.org/10.1016/j.patcog.2010.08.01110.1016/j.patcog.2010.08.011Search in Google Scholar

Wirth, R., & Hipp, J. (n.d.). CRISP-DM: Towards a Standard Process Model for Data Mining. Retrieved 12.2.20117 from https://www.scribd.com/document/76859286/CRISP-DM-Towards-a-Standard-Process-Model-for-DataSearch in Google Scholar

Yan, J., Zhang, C., Zha, H., Gong, M., Sun, C., Huang, J., Chu, S., & Yang, X. (2015), “On machine learning towards predictive sales pipeline analytics”, In: Twenty-Ninth AAAI Conference on Artificial Intelligence, 1945–1951.10.1609/aaai.v29i1.9455Search in Google Scholar