• Culinary science and hospitality research
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• v.17 no.5
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• pp.92-107
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• 2011

The purpose of this study was to understand the effect of the characteristics of coupons purchased through a social shopping site upon customer satisfaction and future behavior intention. Based on total 332 samples who had bought franchise restaurant coupons and used them before, this study reviewed reliability and fitness of the research model and verified total 4 hypotheses with AMOS and SPSS program. The hypothesized relationships among the models were tested simultaneously by using a structure equation model(SEM). The proposed model provided an adequate fit to the data, ${\chi}^2$=309.795(df=103, p<.001), CMIN/DF=3.008, RMR=.103, GFI=.912, NFI=.927, CFI=.950, RMSEA=.074. The result showed that the coupon proneness(${\beta}$=.645) and price sensitivity(${\beta}$=-.315) had a significant influence on restaurant satisfaction(p<.001) and only coupon proneness(${\beta}$=1.040) had a significant influence on coupon satisfaction(p<.001). Also, restaurant satisfaction had a positive significant influence on restaurant customers' revisit intention(${\beta}$=.603, p<.001) and coupon users' repurchase intention(${\beta}$=.335, p<.001). Furthermore, coupon satisfaction had a positive significant influence on coupon repurchase intention(${\beta}$=.353, p<.001) but had a negative significant influence on restaurant revisit intention(${\beta}$=-.263, p<.001). Limitations and future research directions were also discussed.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.