Association rule mining approach: evaluating pre-purchase risk intentions in the online second-hand goods market
Research background: A considerable amount of research has been conducted on the riskiness associated with online transactions in general. However, few studies have paid particular attention to the risk of online second-hand goods transactions. We, therefore, argue in this paper that, while online transactions pose several risks to consumers, the addition of second-hand goods intensifies the risks to the user. As the risk factors brought about by the online second-hand goods transactions persist, the magnitude of such risk inherent in the customer in question has not clearly emerged.
Purpose of the article: This paper aims at eliciting the magnitude of risky components aligned with the tendency to connect online in search of second-hand goods. Again, providing insight into demographic variables in relation to the pre-purchasing risk factors; averting customers to connect online in search of second-hand goods stands as one of the key reasons for the present study.
Methods: The research adopts a data mining algorithm, notably the Association rule mining to glean relevant patterns in the data accrued from the Czech Republic, premised on risk components governing the online buying behaviour of second-hand goods. To this end, a simple random technique was adopted to gauge the views of e-shoppers in the Czech Republic on online second-hand goods transactions; with 329 out of 411 respondents eligible for our analysis.
Findings & Value added: The results of the association rule technique have revealed that respondents within the gender frame are both adamant to hook-up online, in spite of the fact that they have shopped online, yet do not think of looking at second-hand goods sites because of some risky influence inherent in them, even if the respondent is an ordinary personal-user of online transactions. In all these developments, the research concludes that the second-hand industry needs to redesign the websites with much attention to reinforce stringent measures that will give better assurance of the risk factors, which will tend to avert the customer from connecting via the Internet in pursuit of second-hand goods.
Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference. Santiago, Chile.
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data. Acm sigmod record. doi: 10.1145/170035.170072.
Alturkestani, H. (2004). E-marketing application on Saudi business sector. In E-commerce Symposium. King Khaled University, Kingdom of Saudi Arabia.
Bryan, D. (2016). The Internet; empowering us to sell second hand goods online profitably. Bdaily Business News. Retrieved form https://bdaily.co.uk/articles/ 2016/06/28/the-internet-empowering-us-to-sell-second-hand-goods-online-prof itably (26.10.2018).
CBC (2018). Used goods market worth $36B last year, Kijiji-sponsored survey finds. Retrieved from http://www.cbc.ca/news/business/kijiji-second-hand-1.3470695 (30.04.2018).
Charbonneau, J. S. (2008). Social responsibility and women's acquisition of secondhand clothing. Doctoral dissertation, Colorado State University.
Chen, J., Feng, W., & Luo, M., (2016). A reliability evaluation system of association rules. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 3(2).
Chipambwa, W., Sithole, L., & Chisosa, D. F. (2016). Consumer perceptions towards second-hand undergarments in Zimbabwe: a case of Harare urban dwellers. International Journal of Fashion Design, Technology and Education, 9(3). doi: 10.1080/17543266.2016.1151555.
Corbitt, B. J., Thanasankit, T., & Yi, H. (2003). Trust and e-commerce: a study of consumer perceptions. Electronic Commerce Research and Applications, 2(3). doi: 10.1016/S1567-4223(03)00024-3.
Czech Statistical Service (2018). Retail trade in the Czech Republic. Retrieved form https://www.czso.cz (05.05.2018).
Dehaspe, L., & Toivonen, H. (2001). Discovery of relational association rules. In Relational data mining. Berlin, Heidelberg: Springer. doi: 10.1007/978-3-662-04599-2_8.
Domadiya, N., & Rao, U. P. (2019). Privacy preserving distributed association rule mining approach on vertically partitioned healthcare data. Procedia Computer Science, 148. doi: 10.1016/j.procs.2019.01.023.
Donthu, N., & Garcia, A. (1999). The internet shopper. Journal of Advertising Research, 39(3).
Edbring, E. G., Lehner, M., & Mont, O. (2016). Exploring consumer attitudes to alternative models of consumption: motivations and barriers. Journal of Cleaner Production, 123. doi: 10.1016/j.jclepro.2015.10.107.
Ghose, A. (2009). Internet exchanges for used goods: an empirical analysis of trade patterns and adverse selection. Mis Quarterly, 33(2). doi: doi.org/10.2307/2065 0292.
Gregson, N., & Crewe, L. (2003). Second-hand cultures. Berg Publishers.
Gupta, A., Su, B. C., & Walter, Z. (2004). Risk profile and consumer shopping behavior in electronic and traditional channels. Decision Support Systems, 38(3). doi: 10.1016/j.dss.2003.08.002.
Hansen, K. T. (2004). Helping or hindering? Controversies around the international second‐hand clothing trade. Anthropology Today, 20(4). doi: 10.1111/j.0268-540X.2004.00280.x.
Houtsma, M., & Swami, A. (1995). Set-oriented mining for association rules in relational databases. In Proceedings of the eleventh international conference on data engineering. IEEE. doi: 10.1109/ICDE.1995.380413.
Khan, S., & Parkinson, S. (2018). Eliciting and utilising knowledge for security event log analysis: an association rule mining and automated planning approach. Expert Systems with Applications, 113. doi: 10.1016/j.eswa. 2018.07.006.
Kukar-Kinney, M., Scheinbaum, A. C., & Schaefers, T. (2016). Compulsive buying in online daily deal settings: an investigation of motivations and contextual elements. Journal of Business Research, 69(2). doi: 10.1016/j.jbusres.2015 .08.021.
Lane, R., Horne, R., & Bicknell, J. (2009). Routes of reuse of second-hand goods in Melbourne households. Australian Geographer, 40(2). doi: 10.1080/0004918 0902964918.
Liang, T. P., & Lai, H. J. (2002). Effect of store design on consumer purchases: an empirical study of on-line bookstores. Information & Management, 39(6). doi: 10.1016/S0378-7206(01)00129-X.
Mhango, M. W., & Niehm, L. S. (2005). The second-hand clothing distribution channel: Opportunities for retail entrepreneurs in Malawi. Journal of Fashion Marketing and Management: An International Journal, 9(3). doi: 10.1108/13612020510610462.
Park, J. S., Chen, M. S., & Yu, P. S. (1997). Using a hash-based method with transaction trimming for mining association rules. IEEE Transactions on Knowledge and Data Engineering, 9(5). doi: 10.1109/69.634757.
Shmueli, G., Patel, N. R., & Bruce, P. C. (2007). Data mining for business intelligence: concepts, techniques, and applications in Microsoft Office Excel with XLMiner. John Wiley & Sons.
Statista (2018). E-commerce revenue: second-hand goods 2013-2016. Spain. Retrieved form https://www.statista.com/statistics/442802/second-hand-goods-quarterly-e-commerce-revenue-in-spain/ (26.10.2018).
Lin, I. C., & Chang, K. F. (2013). A study to explore how disposing old-goods factors influence consumer’s behavior. Journal of Advanced Management Science, 1(4). doi: 10.12720/joams.1.4.372-377.
Swinyard, W. R., & Smith, S. M. (2003). Why people (don't) shop online: a lifestyle study of the internet consumer. Psychology & Marketing, 20(7). doi: 10.1002/mar.10087.
Waight, E. (2015). Buying for baby: how middle-class mothers negotiate risk with second-hand goods. In Intimacies, critical consumption and diverse economies. London: Palgrave Macmillan. doi: 10.1057/9781137429087_10.
Williams, C. C., & Paddock, C. (2003). The meanings of informal and second-hand retail channels: some evidence from Leicester. International Review of Retail, Distribution and Consumer Research, 13(3). doi: 10.1080/0959396032000101372.
Zaki, M. J., Parthasarathy, S., Ogihara, M., & Li, W. (1997). New algorithms for fast discovery of association rules. In KDD-97 Proceedings. Association for the Advancement of Artificial Intelligence.
This work is licensed under a Creative Commons Attribution 4.0 International License.