[
Aguinis, H., Forcum, L. E., & Joo, H. (2013). Using market basket analysis in management research. Journal of Management, 39(7), 1799-1824. doi: 10.1177/014920 6312466147
]Search in Google Scholar
[
Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: A systematic review and agenda for future research. Electronic Markets, 26(2), 173-194. doi: 10.1007/s12525-016-0219-010.1007/s12525-016-0219-0
]Search in Google Scholar
[
Bucklin, R. E., & Sismeiro, C. (2009). Click here for Internet insight: Advances in click-stream data analysis in marketing. Journal of Interactive Marketing, 23(1), 35-48.10.1016/j.intmar.2008.10.004
]Search in Google Scholar
[
Calder, B. J., Malthouse, E. C., & Maslowska, E. (2016). Brand marketing, big data and social innovation as future research directions for engagement. Journal of Marketing Management, 32(5-6), 579-585. doi: 10.1080/0267257X.2016.114432610.1080/0267257X.2016.1144326
]Search in Google Scholar
[
Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big data. Information Sciences, 275, 314-347. doi: 10.1016/j.ins.2014.01.01510.1016/j.ins.2014.01.015
]Search in Google Scholar
[
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209. doi: 10.1007/s11036-013-0489-010.1007/s11036-013-0489-0
]Search in Google Scholar
[
Elgendy, N., & Elragal, A. (2014). Big data analytics: A literature review paper. Industrial Conference on Data Mining (pp. 214-227). Berlin: Springer-Cham, doi: 10.1007/978-3-319-08976-8_1610.1007/978-3-319-08976-8_16
]Search in Google Scholar
[
Fan, S., Lau, R. Y., & Zhao, J. L. (2015). Demystifying big data analytics for Business Intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32. doi: 10.1016/j.bdr.2015.02.00610.1016/j.bdr.2015.02.006
]Search in Google Scholar
[
Gordon, S. Linoff, M., & Berry, J.A. (2011). Data Mining Techniques: For marketing, sales, and customer relationship. New York: John Wiley & Sons.
]Search in Google Scholar
[
Grandhi, B., Patwa, N., & Saleem, K. (2017). Data-driven marketing for growth and profitability. 10th Annual Conference of the EuroMed Academy of Business, 675-694.
]Search in Google Scholar
[
Han, J., Pei, J., & Kamber, M. (2012). Data Mining: Concepts and techniques. Amsterdam: Elsevier.
]Search in Google Scholar
[
Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, 652-687. doi: 10.1109/ACCESS. 2014.2332453.
]Search in Google Scholar
[
Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: An introduction. Cambridge: Cambridge University Press.10.1017/CBO9780511763113
]Search in Google Scholar
[
Kitchenham, B. (2004). Procedures for performing systematic reviews. Joint Technical Report, Keele: Keele University TR/SE-0401 and NICTA 0400011T.1, July.
]Search in Google Scholar
[
Kitchenham, B., & Charters, C. (2007). Guidelines for performing systematic literature reviews in software engineering. Keele University and Durham University Joint Report–EBSE 2007-001.
]Search in Google Scholar
[
Kitchenham, B., Pretorius, R., Budgen, D., Brereton, O. P., Turner, M., Niazi, M., & Linkman, S. (2010). Systematic literature reviews in software engineering – a tertiary study. Journal Information and Software Technology, 52, 792-805. doi: 10.1016/j.infsof.2008.09.00910.1016/j.infsof.2008.09.009
]Search in Google Scholar
[
Konstan, J.A., & Adomavicius, G. (2013). Toward identification and adoption of best practices in algorithmic recommender systems research [in] Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation (pp. 23-28). New York: ACM.
]Search in Google Scholar
[
Kridel, D., & Dolk, D. (2013). Automated self-service modelling: Predictive analytics as a service. Information Systems and e-Business Management, 11(1), 119-140. doi: 10.1007/s10257-011-0185-110.1007/s10257-011-0185-1
]Search in Google Scholar
[
Leeflang, P. S., Verhoef, P. C., Dahlström, P., & Freundt, T. (2014). Challenges and solutions for marketing in a digital era. European Management Journal, 32(1), 1-12. doi: 10.1016/j.emj.2013.12.00110.1016/j.emj.2013.12.001
]Search in Google Scholar
[
Niazi, M. (2015). Do systematic literature reviews outperform informal literature reviews in the software engineering domain? An initial case study. Arabian Journal for Science and Engineering, 40(3), 845-855. doi: 10.1007/s13369-015-1586-010.1007/s13369-015-1586-0
]Search in Google Scholar
[
Pabedinskaitė, A., Davidavičienė, V., & Milišauskas, P. (2014). Big data driven e-commerce marketing. In Proceedings of 8th International Scientific Conference Business and Management-Spausdinta (pp. 645-654). Vilnus, Lietuva. doi: 10. 3846/bm.2014.07910.3846/bm.2014.079
]Search in Google Scholar
[
Pavlo, A., Paulson, E., Rasin, A., Abadi, D. J., DeWitt, D. J., Madden, S., & Stonebraker, M. (2009). A comparison of approaches to large-scale data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data (pp. 165-178). Providence, RI: ACM. doi: 10.1145/1559845.155986510.1145/1559845.1559865
]Search in Google Scholar
[
Pawełoszek, I., & Korczak, J. (2017). From data exploration to semantic model of customer. In Intelligent Systems Conference (IntelliSys) (pp. 382-388). London, 7-8 September. doi: 10.1109/IntelliSys.2017.832432210.1109/IntelliSys.2017.8324322
]Search in Google Scholar
[
Pierański, B., & Strykowski, S. (2017). Towards a personalized virtual customer experience. In Asian Conference on Intelligent Information and Database Systems (pp. 185-195). Berlin: Springer. doi: 10.1007/978-3-319-56660-3_1710.1007/978-3-319-56660-3_17
]Search in Google Scholar
[
Pondel, M., & Korczak, J. (2017). A view on the methodology of analysis and exploration of marketing data. In Proceedings of the 2017 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems (Vol. 9, pp. 1135-1143). Prague, September 3-6. doi: 10.15439/2017F44210.15439/2017F442
]Search in Google Scholar
[
Quiñones, D., & Rusu, C. (2017). How to develop usability heuristics: A systematic literature review. Computer Standards & Interfaces, 53, 89-122. doi: 10.1016/j.csi. 2017.03.009
]Search in Google Scholar
[
Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems: Introduction and challenges. In Recommender systems handbook (pp. 1-34). Boston, MA: Springer, doi: 10.1007/978-0-387-85820-310.1007/978-0-387-85820-3
]Search in Google Scholar
[
Sackett, D. L., Straus, S. E., Richardson, W. S., Rosenberg, W., & Haynes, R. B. (2000). Evidence-based medicine: How to practice and teach EBM. 2nd ed., Edinburgh: Churchill Livingstone. doi: 10.1177/08850666010160030710.1177/088506660101600307
]Search in Google Scholar
[
Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26-48.doi: 10.1525/cmr.2016.58.3.2610.1525/cmr.2016.58.3.26
]Search in Google Scholar
[
Setia, S., & Jyoti, D. (2013). Multi-level association rule mining: A review. International Journal of Computer Trends and Technology (IJCTT), 6(3), 166-170.
]Search in Google Scholar
[
Sharda, R., Delen, D., & Turban, E. (2014). Business intelligence and analytics: Systems for decision support. Harlow: Pearson Educational.
]Search in Google Scholar
[
Slavakis, K., Giannakis, G. B., & Mateos, G. (2014). Modelling and optimization for big data analytics: (statistical) learning tools for our era of data deluge. IEEE Signal Processing Magazine, 31(5), 18-31. doi: 10.1109/MSP.2014.232723810.1109/MSP.2014.2327238
]Search in Google Scholar
[
Vahid, G., & Mäntyläc, M. V. (2016). A systematic literature review of literature reviews in software testing. Journal Information and Software Technology, 80, 195-216. doi: 10.1016/j.infsof.2008.09.00910.1016/j.infsof.2008.09.009
]Search in Google Scholar
[
Vera-Baquero, A., Colomo-Palacios, R., & Molloy, O. (2013). Business process analytics using a big data approach. IT Professional, 15(6), 29-35. doi: 10.1109/MITP. 2013.60
]Search in Google Scholar
[
Witten, I., Frank, E., Hall, M., & Pal, C. (2017). Data mining: Practical machine learning tools and techniques. Burlington, MA: Morgan Kaufman. doi: 10.1016/C2015-0-02071-810.1016/C2015-0-02071-8
]Search in Google Scholar
[
Wohlin, C., & Prikladniki, R. (2013). Systematic literature reviews in software engineering. Information and Software Technology, 55(6), 919-920. doi: 10.1016/j.infsof. 2008.09.009
]Search in Google Scholar
[
Yeung, R., & Yee, W. (2015). Application of cluster analysis and discriminant analysis in market segmentation and prediction. In J. Mendy & S. G. Geringer (Eds.), Leading Issues in Business Research Methods (Vol. 2, pp. 63-79). Sonning Common, RG: ASPI.
]Search in Google Scholar
[
Zhao, D. (2013). Frontiers of big data business analytics: Patterns and cases in online marketing. In J. Liebowitz (Ed.), Big data and business analytics (pp. 43-67). Boston, MA: Auerbach Publications.
]Search in Google Scholar
[
Zhao, J. L., Fan, S., & Hu, D. (2014). Business challenges and research directions of management analytics in the big data era. Journal of Management Analytics, 1(3), 169-174. doi: 10.1080/23270012.2014.96864310.1080/23270012.2014.968643
]Search in Google Scholar