A New Model for Predicting the Probability of Product Return in Online Shopping

Ehram Safari, Seyedeh OmSalameh Pourhashemi, Mohsen Gharahkhani

Abstract


One of the current important issues for online stores regarding online shopping is customer satisfaction from online shopping. Many of the customers who purchase their products online from electronic stores are not satisfied with the products they receive. Some of the reliable online stores follow the policy of "product returns" to increase their customers' satisfaction. According to this policy, if the customers are not satisfied with the products they have received, they can return them to the store under predetermined conditions. Therefore, the purpose of this paper is to present a model based on the factors influencing online customers' satisfaction during online shopping. The presented model in this study is based on the data obtained from eBay Store using data mining and SPSS Modeler. Using this method and well-known algorithms such as CHAID and C&R Tree, and C5.0, a model is created that can predict the order returns at high accuracy. The investigations showed that the values of cross-validation for the accuracy of the models created by CHAID, C&R Tree and C5.0 algorithms on the data set of the three algorithms were 78.6 – 80.1 percent, which confirmed the accuracy of the final model. Research results showed that prediction of the probability of product returns is, in fact, in line with the estimation of the maximum logical costs which determine the logical number of messages and call duration for any product with specific profit and cost so that product returns can be prevented.


Keywords


Online shopping, Data mining, Satisfaction, Product return

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References


Abumalloh, R., Ibrahim, O., & Nilashi, M. (2020). Loyalty of young female Arabic customers towards recommendation agents: A new model for B2C E-commerce. Technology in Society, 101253.

Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management, 80, 52-77.

Anderson, E. T., Hansen, K., & Simester, D. (2009). The option value of returns: Theory and empirical evidence. Marketing Science, 28(3), 405-423.

Asadi, S., Abdullah, R., Safaei, M., & Nazir, S. (2019). An integrated SEM-Neural Network approach for predicting determinants of adoption of wearable healthcare devices. Mobile Information Systems, 2019.

Asadi, S., Hussin, A. R. C., & Dahlan, H. M. (2018). Toward Green IT adoption: from managerial perspective. International Journal of Business Information Systems, 29(1), 106-125.

Asadi, S., Nilashi, M., Husin, A. R. C., & Yadegaridehkordi, E. (2017). Customers perspectives on adoption of cloud computing in banking sector. Information Technology and Management, 18(4), 305-330.

Asadi, S., Nilashi, M., Safaei, M., Abdullah, R., Saeed, F., Yadegaridehkordi, E., & Samad, S. (2019). Investigating factors influencing decision-makers’ intention to adopt Green IT in Malaysian manufacturing industry. Resources, Conservation and Recycling, 148, 36-54.

Asadi, S., Pourhashemi, S. O., Nilashi, M., Abdullah, R., Samad, S., Yadegaridehkordi, E., . . . Razali, N. S. (2020). Investigating influence of green innovation on sustainability performance: A case on Malaysian hotel industry. Journal of cleaner production, 120860.

Bagherifard, K., Rahmani, M., Rafe, V., & Nilashi, M. (2018). A Recommendation Method Based on Semantic Similarity and Complementarity Using Weighted Taxonomy: A Case on Construction Materials Dataset. Journal of Information & Knowledge Management, 17(01), 1850010.

Chu, W., Gerstner, E., & Hess, J. D. (1998). Managing dissatisfaction: How to decrease customer opportunism by partial refunds. Journal of Service Research, 1(2), 140-155.

De, P., Hu, Y., & Rahman, M. S. (2013). Product-oriented web technologies and product returns: An exploratory study. Information Systems Research, 24(4), 998-1010.

Heiman, A., Just, D. R., McWilliams, B. P., & Zilberman, D. (2015). A prospect theory approach to assessing changes in parameters of insurance contracts with an application to money-back guarantees. Journal of Behavioral and Experimental Economics, 54, 105-117.

Hsiao, L., & Chen, Y. J. (2012). Returns policy and quality risk in e‐business. Production and Operations Management, 21(3), 489-503.

Ng, S., & Stevens, L. (2015). Where your unwanted christmas gifts get a second life. Wall Street Journal(December 27).

Nilashi, M. (2016). An overview of data mining techniques in recommender systems. Journal of Soft Computing and Decision Support Systems, 3(6), 16-44.

Nilashi, M., bin Ibrahim, O., & Ithnin, N. (2014). Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, 41(8), 3879-3900.

Nilashi, M., bin Ibrahim, O., Ithnin, N., & Sarmin, N. H. (2015). A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS. Electronic Commerce Research and Applications, 14(6), 542-562.

Nilashi, M., Ibrahim, O., Yadegaridehkordi, E., Samad, S., Akbari, E., & Alizadeh, A. (2018). Travelers decision making using online review in social network sites: A case on TripAdvisor. Journal of computational science, 28, 168-179.

Nilashi, M., Jannach, D., bin Ibrahim, O., Esfahani, M. D., & Ahmadi, H. (2016). Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electronic Commerce Research and Applications, 19, 70-84.

Nilashi, M., Samad, S., Ahmadi, N., Ahani, A., Abumalloh, R. A., Asadi, S., . . . Yadegaridehkordi, E. (2020). Neuromarketing: A Review of Research and Implications for Marketing. Journal of Soft Computing and Decision Support Systems, 7(2), 23-31.

Sahoo, N., Dellarocas, C., & Srinivasan, S. (2018). The impact of online product reviews on product returns. Information Systems Research, 29(3), 723-738.

Sahoo, N., Srinivasan, S., & Dellarocas, C. (2013). The impact of online product reviews on product returns and net sales. Paper presented at the 2013 Workshop on Information Systems Economics, Milan, Italy.

Samad, S., Asadi, S., Nilashi, M., Ibrahim, O., Abumalloh, R. A., & Abdullah, R. (2020). Organizational Performance and Adoption of Green IT from the Lens of Resource Based View. Journal of Soft Computing and Decision Support Systems, 7(2), 1-6.

Shang, G., Pekgün, P., Ferguson, M., & Galbreth, M. (2017). How much do online consumers really value free product returns? Evidence from eBay. Journal of Operations Management, 53, 45-62.

Shulman, J. D., Coughlan, A. T., & Savaskan, R. C. (2009). Optimal restocking fees and information provision in an integrated demand-supply model of product returns. Manufacturing & Service Operations Management, 11(4), 577-594.

Yadegaridehkordi, E., Iahad, N. A., & Asadi, S. (2015). Cloud computing adoption behaviour: an application of the technology acceptance model. Journal of Soft Computing and Decision Support Systems, 2(2), 11-16.


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