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The purpose of this study is to explore the critical factors that affect users’ acceptance and usage intention toward blockchain-based smart lockers.

The research is designed in two phases. In the first phase, the key stakeholders are interviewed. Participants include managers from technology providers and logistics companies. In the second phase, a questionnaire survey is used to validate the proposed model.

Based on the final results, this study makes the following three suggestions. First, perceived usefulness and perceived ease of use are the critical factors. In other words, it is important to emphasize the function and convenience of a new service when introducing it to a potential user. Second, safety is not the major concern when using a blockchain-based smart locker. This means that users will trust the service provider for providing a secure service. Users do not worry about the security problem. Finally, the network externality of smart locker is also insignificant.

This study has three major contributions. First, this study identifies the critical factors that will affect user acceptance of blockchain-based smart lockers. Next, this study combines the opinions from service providers and users to understand the gap between different stakeholders. Finally, this study can enrich our understanding on the applications of blockchain from a managerial perspective and not only from a technical perspective, as in most previous studies.

Group buying (GB) is a shopping strategy through which customers obtain volume discounts on the products they purchase, whereas retailers obtain quick turnover. In the scenario of GB, the optimal discount strategy is a key issue because it affects the profit of sellers. Previous research has focused on exploring the price discount and order quantity with a fixed selling price of the product assuming that customer demand is uncertain (but follows a known distribution). This study aims to look at the same problem but goes further to examine the case where not only customer demand is certain but also the demand distribution is unknown.

In this study, optimal price discount and order quantity of a GB problem cast as a price-setting newsvendor problem were obtained assuming that the distribution of customer demand is unknown. The price–demand relationship is considered in addition form and product form, respectively. The bootstrap sampling technique is used to develop a solution procedure for the problem. To validate the usefulness of the proposed method, a simulated comparison of the proposed model and the existing one was conducted. The effects of sample size, demand form and parameters of the demand form on the performance of the proposed model are presented and discussed.

It is revealed from the numerical results that the proposed model is appropriate to the problem at hand, and it becomes more effective as sample size increases. Because the two forms of demand indicate restrictive assumptions about the effect of price on the variance of demand, it is found that the proposed model seems to be more suitable for addition form of demand.

This study contributes to the growing literature on GB models by developing a bootstrap-based newsvendor model to determine an optimal discount price and order quantity for a fixed-price GB website. This model can assist the sellers in making decisions on optimal discount price and order quantity without knowing the form of customer demand distribution.