This study examines the determinants of the member customer’s decision of redeeming versus accumulating loyalty program (LP) points by focusing on the effects of the different channels of transaction (online versus offline) and the demographic information of member customers. Our study is based on customer-level demographic and transaction data on a major partnership LP in Korea, the OK Cashbag (OCB) program. This study differs from the existing literature in three aspects. First, the dataset employed for this study enables us to compare member customers’ point redemption behavior between online and offline channels, whereas previous studies demonstrate coupon redemption behavior either in an online (Chiou-Wei and Inman 2008) or an offline setting (e.g., Cronovich 1997; Kwon and Kwon 2007; Mittal 1994; Reibstein and Traver 1982; Ward and Davis 1978). Second, the current study investigates not only the main effects of demographic variables, but also a series of interaction effects between the online channel and each demographic variable. Clear empirical evidence of an interaction effect would provide an LP provider with significant managerial implications. Third, rich data on customers’ transaction behavior with matching demographic information for each member customer enable us to conduct both transaction-level and individual customer-level analyses. Therefore, an individual customer’s transaction behavior can be analyzed in more detail for robust results and richer implications. We find that transactions that occur through online channels and those made by younger customers demonstrate a greater tendency of redeeming LP points as opposed to accumulating them. We also find that online channels exhibit a moderating role by mitigating the demographic effects on member customers’ point redemption behavior. These findings allow the LP provider to predict the future LP point balance by analyzing its main channel of transaction and the demographic profiles of its member customers.

The camera embedded wall climbing robot in this paper combines the suction and aerodynamic attraction to achieve good balance between strong adhesion force and high mobility and adopts embedded image processing technique to detect targets on the warehouse inspection. Experimental results showed that the robot can move upward on the wall at the speed of 2.9m/min and carry 5kg payload in addition to 2.5kg self-weight, which record the highest payload capacity among climbing robots of similar size. With two 11.1V lithium-polymer battery, the robot can operate continuously for half hours. A wireless camera system, zigbee protocol module and several sensors was adopted for detecting target objects and dangerous situation on the warehouse wall and for sending alarm signals to remote sensor node or manager.