Credit card companies provide payment services between merchants and members (i.e., cardholders) and charge merchant discount rates and annual membership fees, respectively. There is a discriminatory price structure in which merchants pay a higher commission rate than members because of the recognition that the utility of merchants is relatively greater than that of members and they have an obligation to accept cards. To prevent aggravating the burden on merchants due to the discriminatory price structure, regulations on the merchant discount rate were adopted in Korea. This study suggests a direction to improve the understanding of the credit card markets and to solve structural problems through a cost sensitivity analysis of merchant discount rates determined in the qualifying cost system introduced by the Financial Services Commission. First, we found empirical evidence that the effect of changes in each cost ratio on the merchant discount rate (price) differs depending on the intensity of the cost regulation (regulation effect). In addition, the merchant discount rate was lowered by government policy, regardless of cost. Combining the analysis results, we demonstrated that some expenses could not be recovered as merchant discount revenue when the related expenses rose due to the increase in card usage amount. To fix the structural problem of the card payment business, we suggest changing the preferential commission rates according to market conditions, reflecting eligible costs in member recruitment costs, reflecting adjustment costs in the loss rate due to the application of preferential merchant discount rates, and excluding the decrease in cost ratio due to management efficiency when the merchant discount rate is re-adjusted.

This paper develops a model to analyze inter-organizational technology adoption in a supply chain. While the basic model is general, this study is motivated by several cases of inter-organizational technology adoption in supply chains. The proposed model in this study considers firms on both levels of the supply chain, namely, supplier firms and buyer firms. These individual firms’ thresholds for adoption should be considered by other firms’ decisions within a network, together with their own organizational attributes. The heterogeneity across the population should be allowed. That is, individual firms make a decision for adopting the technology at different times due to their different network sizes, prior beliefs, and amounts of information observed. The main finding is that this uncertainty decreases as other suppliers adopt the technology, and information about their experiences becomes available. In addition, at any given time, an estimate of the benefit to a supplier depends on the number of supplier firms and on the number of buyer firms that have already adopted the technology. Thus, we seek to capture this dependence and analyze its effect on the adoption of a new inter-organizational technology. The next step is to embed the firm-level adoption model into a population model. The model includes various types of heterogeneity in the population model to capture the factors affecting the speed of diffusion. This allows us to derive an adoption curve that is specified by the accumulated fraction of firms that have adopted the technology in or before any given period. The population model allows us to consider the effect of several strategies observed in practice and numerical experiments yielding many managerial implications in this area.