This study investigates the public’s perception of Retail Central Bank Digital Currency (rCBDC) and identifies the factors influencing its adoption. Conducted in collaboration with a prominent bank in Hong Kong, this research involved a hands-on experience with a prototype e-HKD payment system. Participants’ opinions on rCBDCs were assessed through surveys conducted before and after their engagement with the e-HKD pilot. Initially, participants displayed a broadly positive attitude towards rCBDC, although no single factor emerged as a decisive influence on their adoption decision. However, the pilot experience statistically significantly altered perceptions, particularly regarding security, ease of payment, and promotional functions, thereby impacting their willingness to adopt rCBDC. This study underscores the importance of understanding consumer perceptions and suggests that these perceptions are subject to change through exposure to regulatory information campaigns, prototype experiences, and initial models. Consequently, the study recommends a cautious approach to interpreting the reliability of existing survey findings in this domain.

As a fundamental graph problem, community search is applied in various areas, e.g., social networks, the world wide web, and biology. A common requirement from real applications is to return a community with a bounded size while most existing solutions do not constrain community size. Recent studies on size-constrained community search still have some critical issues, e.g., the existence of a better cohesiveness objective, some queries returning empty results, and inefficiency on partial queries. Thus, in this paper, we study the size-constrained truss community search (STCS). Given a graph$G$, a query vertex$q$, and size constraint$[l,h]$, the STCS problem aims to find a subgraph containing$q$with the largest min-support among all connected subgraphs having at least$l$and at most$h$vertices. We prove the STCS problem is NP-hard and APX-hard unless P = NP. An effective heuristic is proposed to quickly find a high-quality initial result. Then, a branch and bound algorithm is introduced to find the exact result, with novel optimizations, e.g., budget-cost-based bounding and branching strategies. Extensive experiments verify that the community quality returned by our algorithm is better and our algorithm is faster by up to 5 orders of magnitude, compared with the state-of-the-art.