摘要
Loyalty program (LP) is a popular marketing activity of enterprises. As a result of firms’ effort to increase customers’ loyalty, point exchange or redemption services are now available worldwide. These services attract not only customers but also attackers. In pioneering research, which first focused on this LP security problem, an empirical analysis based on Japanese data is shown to see the effects of LP-point liquidity on damages caused by security incidents. We revisit the empirical models in which the choice of variables is inspired by the Gordon-Loeb formulation of security investment: damage, investment, vulnerability, and threat. The liquidity of LP points corresponds to the threat in the formulation and plays an important role in the empirical study because it particularly captures the feature of LP networks. However, the actual proxy used in the former study is artificial. In this paper, we reconsider the liquidity definition based on a further observation of LP security incidents. By using newly defined proxies corresponding to the threat as well as other refined proxies, we test hypotheses to derive more implications that help LP operators to manage partnerships;the implications are consistent with recent changes in the LP network. Thus we can see the impacts of security investment models include a wider range of empirical studies.
Loyalty program (LP) is a popular marketing activity of enterprises. As a result of firms’ effort to increase customers’ loyalty, point exchange or redemption services are now available worldwide. These services attract not only customers but also attackers. In pioneering research, which first focused on this LP security problem, an empirical analysis based on Japanese data is shown to see the effects of LP-point liquidity on damages caused by security incidents. We revisit the empirical models in which the choice of variables is inspired by the Gordon-Loeb formulation of security investment: damage, investment, vulnerability, and threat. The liquidity of LP points corresponds to the threat in the formulation and plays an important role in the empirical study because it particularly captures the feature of LP networks. However, the actual proxy used in the former study is artificial. In this paper, we reconsider the liquidity definition based on a further observation of LP security incidents. By using newly defined proxies corresponding to the threat as well as other refined proxies, we test hypotheses to derive more implications that help LP operators to manage partnerships;the implications are consistent with recent changes in the LP network. Thus we can see the impacts of security investment models include a wider range of empirical studies.
