Mining interest in online shoppers’ data: An association rule mining approach

Kwarteng, Michael Adu; Pilík, Michal; Juřičková, Eva

dc.title	Mining interest in online shoppers’ data: An association rule mining approach	en
dc.contributor.author	Kwarteng, Michael Adu
dc.contributor.author	Pilík, Michal
dc.contributor.author	Juřičková, Eva
dc.relation.ispartof	Acta Polytechnica Hungarica
dc.identifier.issn	1785-8860 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued	2017
utb.relation.volume	14
utb.relation.issue	7
dc.citation.spage	143
dc.citation.epage	160
dc.type	article
dc.language.iso	en
dc.publisher	Budapest Tech
dc.identifier.doi	10.12700/APH.14.7.2017.7.9
dc.relation.uri	https://www.uni-obuda.hu/journal/Issue78.htm
dc.subject	E-commerce	en
dc.subject	online shopping	en
dc.subject	consumer buying behavior	en
dc.subject	association rule mining	en
dc.description.abstract	Online shopping, as a form of e-commerce, is not nearing extinction anytime soon. As the interplay between shoppers and vendors continues to grow in the midst of complex transactional data, extracting knowledge from the data has become imperative. In view of this, this paper explores the use of the association rule mining technique to glean relevant information from such shopper-vendor interactions. In particular, this paper looks at some of the unusual, frequent relationships existing between online shoppers on one hand, and vendors on the other hand in the Czech Republic. The results revealed with higher confidence values the following: (1) there is a strong association between criteria for buying items on the Internet and information gathered before initiating an online transaction; (2) a sizable number of online customers engage in online shopping because of the price attached to the product in question; and (3) a greater proportion of online customers engage in online transactions through specialized e-shops. The work provides general insights into how shopper-vendor transactional data can be explored.	en
utb.faculty	Faculty of Management and Economics
dc.identifier.uri	http://hdl.handle.net/10563/1007775
utb.identifier.obdid	43877280
utb.identifier.scopus	2-s2.0-85042306592
utb.identifier.wok	000423414600009
utb.source	j-wok
dc.date.accessioned	2018-02-26T10:20:07Z
dc.date.available	2018-02-26T10:20:07Z
dc.description.sponsorship	Internal Grant Agency of FaME TBU [IGA/FaME/2016/006]
utb.contributor.internalauthor	Kwarteng, Michael Adu
utb.contributor.internalauthor	Pilík, Michal
utb.contributor.internalauthor	Juřičková, Eva
utb.fulltext.affiliation	Michael Adu Kwarteng, Michal Pilik, Eva Jurickova Tomas Bata University in Zlin, Faculty of Management and Economics, nam. T. G. Masaryka 5555, 760 01 Zlin, Czech Republic kwarteng@fame.utb.cz, pilik@fame.utb.cz, jurickova@fame.utb.cz
utb.fulltext.dates	-
utb.fulltext.references	[1] D. Grewal, GR. Iyer, M. Levy: Internet retailing: enablers, limiters and market consequences, Journal of Business Research, 2004, 57 (7), pp. 703-713 [2] C. Steinfield, A. Mahler, J. Bauer: Electronic commerce and the local merchant, Electronic Markets, 1999, 9 (1-2), pp. 51-57 [3] A. Hamidi, M. Safabakhsh: The impact of information technology on E. marketing, Procedia Computer Science, 2011, 3, pp. 365-368 [4] S. Min, M. Wolfinbarger: Market share, profit margin, and marketing efficiency of early movers, bricks and clicks, and specialists in ecommerce, Journal of Business Research, 2005, 58(8), pp. 1030-1039 [5] D. Brown: Searching for online gold, American Journalism Review, 2003, 25(5), pp. 54-61 [6] P. Ahluwalia, J. Hughes, V. Midha: Drivers of e-retailer peak sales period price behavior: an empirical analysis, International Journal of Accounting & Information Management, 2013, 21(1), pp. 72-90 [7] SJ. Lennon, KK. Johnson, KK, J. Lee: A perfect storm for consumer misbehavior: shopping on Black Friday, Clothing and Textiles Research Journal, 2011, 29 (2), pp. 119-134 [8] C. Topaloğlu: Consumer motivation and concern factors for online shopping in Turkey, Asian Academy of Management Journal, 2012, 17(2), pp. 1-19 [9] CH. Park, YG. Kim: Identifying key factors affecting consumer purchase behavior in an online shopping context, International Journal of Retail & Distribution Management, 2003, 31(1), pp. 16-29 [10] DJ. Reibstein: What attracts customers to online stores, and what keeps them coming back? Journal of the academy of Marketing Science, 2002, 30(4), pp 465-473 [11] M. Khalifa, V. Liu: Online consumer retention: Contingent effects of online shopping habit and online shopping experience, European Journal of Information Systems, 2007, 16(6), pp. 780-792 [12] J. C. Jiang, C. A. Chen, CA, C. C. Wang: Knowledge and trust in Econsumers' online shopping behavior. International Symposium on Electronic Commerce and Security, Guangzhou City, 2008, IEEE, pp. 652-656 [13] J. Kim, S. Forsythe: Factors affecting adoption of product virtualization technology for online consumer electronics shopping, International Journal of Retail & Distribution Management, 2010, 38(3), pp. 190-204 [14] S. Elliot, S. Fowell: Expectations versus reality: a snapshot of consumer experiences with Internet retailing, International Journal of Information Management, 2000, 20(5), pp. 323-336 [15] T. Bao, TLS. Chang: Why Amazon uses both the New York Times Best Seller List and customer reviews: an empirical study of multiplier effects on product sales from multiple earned media, Decision Support Systems, 2014, 67, pp. 1-8 [16] K. Pousttchi, Y. Hufenbach: Engineering the value network of the customer interface and marketing in the data-rich retail environment, International Journal of Electronic Commerce, 2014, 18(4), pp. 17-42 [17] Eurost: Ecommerce statistics explained, available at http://ec.europa.eu/eurostat/statistics-explained/index.php/E-commerce_statistics_for_individuals, download time: 22. 12. 2016 [18] A.T Kearney: Global Retail E-Commerce Keeps On Clicking, available from www.atkearney.com, download time 06. 01. 2017 [19] eMarketer: Worldwide retail ecommerce, available at www.emarketer.com/Article/ / Worldwide Retail Ecommerce Sales Will Reach $1.915 Trillion -by-2020/1011694, download time: 22. 12. 2016 [20] T. Yang, R.T. Peterson: Customer Perceived Value, Satisfaction, and Loyalty: The Role of Switching Costs, Psychology & Marketing, 2004, 21, 10, pp. 799-822, ISSN: 07426046 [21] Redakce: Mall, Alza.cz a ti druzí. Proč se z Česka stal ráj e-shopů. Deník Insider. available at http://www.denikinsider.cz/mall-alza-cz-a-ti-druzi-proc-se-z-ceska-stal-raj-e-shopu/?eid=630, download time: 28. 08. 2013 [22] Czech Statistical Office: Individuals buying on the Internet in the EU countries, Iceland, Croatia and Norway, available at http://www.czso.cz/csu/redakce.nsf/i/nakupy_pres_Internet_v_zemich_eu/$File/nakupy_pres_Internet.xls, download time: 20. 07. 2010 [23] Czech News Agency. Třetina elektroniky se v ČR prodá přes Internet, je to nejvíc v Evropě, Denik, available at http://www.denik.cz/ekonomika/tretina-elektroniky-se-v-cr-proda-pres-internet-je-to-nejvic-v-evrope-20130810.html, download time: 15. 09. 2013 [24] D. Hutchings, N. Best, M. Mahmuti: Global Perspective on Retail: Online Retailing, available at http://annualreview.cushwake.com/downloads/01_Global_Perspective_on_Retail.pdf, download time: 22. 12. 2016 [25] Ecommerce news: Ecommerce in the Czech Republic, available at https://ecommercenews.eu/ecommerce-in-the-czech-republic-grew-over-20-last-year/, download time: 22. 12. 2016 [26] S. Brin, R. Motwani, C. Silverstein: Beyond market baskets: generalizing association rules to correlations, ACM SIGMOD Record, 1997, Vol. 26, No. 2, pp. 265-276 [27] J. M. Adamo: Data mining for association rules and sequential patterns: sequential and parallel algorithms, Springer Science & Business Media, 2012 [28] F. Schwenkreis: International Business Machines Corporation, Data mining for association rules and sequential patterns within data of inhomogeneous type, 2003, U.S. Patent 6,553,359 [29] R. Agrawal, T. Imielinski, A. Swami: Database mining: a performance perspective, Transactions on Knowledge and Data Engineering, 1993, IEEE, 5 (6), pp. 914-925 [30] R. Agrawal, R. Srikant: Fast algorithms for mining association rules, 20th Very Large Data Bases, 1994, Vol. 1215, pp. 487-499 [31] C. Hidber: Online association rule mining, 1999, Vol. 28, No. 2, pp. 145-156 [32] J. Vaidya, C. Clifton: Privacy preserving association rule mining in vertically partitioned data. 8th International Conference on Knowledge Discovery and Data Mining, 2002, pp. 639-644 [33] C. Becquet, S. Blachon, B. Jeudy, J.F. Boulicaut, O. Gandrillon: Strongassociation-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data, Genome Biology, 2002, 3(12), research0067–1 [34] C. Borgelt, M. R. Berthold: Mining molecular fragments: finding relevant substructures of molecules, International Conference on Data Mining, 2002, IEEE, pp. 51-58 [35] M. J. Zaki, S. Parthasarathy, M. Ogihara, W. Li: New Algorithms for Fast Discovery of Association Rules, 3rd International Conference on Knowledge, Discovery and data Mining, 1997, Vol. 97, pp. 283-286 [36] J. Han, J. Pei, M. Kamber: Data mining: concepts and techniques. Elsevier, 2011 [37] M. Song, I.Y. Song, X. Hu, R.B. Allen: Integration of association rules and ontologies for semantic query expansion, Data & Knowledge Engineering, 2007, 63(1), pp. 63-75 [38] Y. Chen, Q. Wang, J. Xie: Online social interactions: a natural experiment on word of mouth versus observational learning, Journal of Marketing Research, 2011, 48(2), pp. 238-254 [39] A.G. Parsons: Non-functional motives for online shoppers: why we click, Journal of Consumer Marketing, 2002, 19(5), pp. 380-392 [40] C. Zhang, S. Zhang: Association rule mining: models and algorithms, Springer-Verlag, 2002 [41] X. Zhang, V. R. Prybutok, D. Strutton: Modeling influences on impulse purchasing behaviors during online marketing transactions, Journal of Marketing Theory and Practice, 2007, 15(1), pp. 79-89 [42] G. Punj: Effect of consumer beliefs on online purchase behavior: the influence of demographic characteristics and consumption values, Journal of Interactive Marketing, 2011, 25(3), pp. 134-144 [43] M. North: Data mining for the masses, Global Text Project, 2012, pp. 91-100
utb.fulltext.sponsorship	This paper was supported by the Internal Grant Agency of FaME TBU No. IGA/FaME/2016/006, “Enterprise’s Competitiveness Influenced by Consumer Behavior on Traditional and Online Markets.”
utb.wos.affiliation	[Kwarteng, Michael Adu; Pilik, Michal; Jurickova, Eva] Tomas Bata Univ Zlin, Fac Management & Econ, Nam TG Masaryka 5555, Zlin 76001, Czech Republic
utb.fulltext.projects	IGA/FaME/2016/006